social health research paper

REVIEW article

Social media use and mental health and well-being among adolescents – a scoping review.

• 1 Department of Health Promotion, Norwegian Institute of Public Health, Bergen, Norway
• 2 Alcohol and Drug Research Western Norway, Stavanger University Hospital, Stavanger, Norway
• 3 Faculty of Health Sciences, University of Stavanger, Stavanger, Norway

Introduction: Social media has become an integrated part of daily life, with an estimated 3 billion social media users worldwide. Adolescents and young adults are the most active users of social media. Research on social media has grown rapidly, with the potential association of social media use and mental health and well-being becoming a polarized and much-studied subject. The current body of knowledge on this theme is complex and difficult-to-follow. The current paper presents a scoping review of the published literature in the research field of social media use and its association with mental health and well-being among adolescents.

Methods and Analysis: First, relevant databases were searched for eligible studies with a vast range of relevant search terms for social media use and mental health and well-being over the past five years. Identified studies were screened thoroughly and included or excluded based on prior established criteria. Data from the included studies were extracted and summarized according to the previously published study protocol.

Results: Among the 79 studies that met our inclusion criteria, the vast majority (94%) were quantitative, with a cross-sectional design (57%) being the most common study design. Several studies focused on different aspects of mental health, with depression (29%) being the most studied aspect. Almost half of the included studies focused on use of non-specified social network sites (43%). Of specified social media, Facebook (39%) was the most studied social network site. The most used approach to measuring social media use was frequency and duration (56%). Participants of both genders were included in most studies (92%) but seldom examined as an explanatory variable. 77% of the included studies had social media use as the independent variable.

Conclusion: The findings from the current scoping review revealed that about 3/4 of the included studies focused on social media and some aspect of pathology. Focus on the potential association between social media use and positive outcomes seems to be rarer in the current literature. Amongst the included studies, few separated between different forms of (inter)actions on social media, which are likely to be differentially associated with mental health and well-being outcomes.

In just a few decades, the use of social media have permeated most areas of our society. For adolescents, social media play a particularly large part in their lives as indicated by their extensive use of several different social media platforms ( Ofcom, 2018 ). Furthermore, the use of social media and types of platforms offered have increased at such a speed that there is reason to believe that scientific knowledge about social media in relation to adolescents’ health and well-being is scattered and incomplete ( Orben, 2020 ). Nevertheless, research findings indicating the potential negative effects of social media on mental health and well-being are frequently reported in traditional media (newspapers, radio, TV) ( Bell et al., 2015 ). Within the scientific community, however, there are ongoing debates regarding the impact and relevance of social media in relation to mental health and well-being. For instance, Twenge and Campbell (2019) stated that use of digital technology and social media have a negative impact on well-being, while Orben and Przybylski (2019) argued that the association between digital technology use and adolescent well-being is so small that it is more or less inconsequential. Research on social media use is a new focus area, and it is therefore important to get an overview of the studies performed to date, and describe the subject matter studies have investigated in relation to the effect of social media use on adolescents mental health and well-being. Also, research gaps in this emerging research field is important to highlight as it may guide future research in new and meritorious directions. A scoping review is therefore deemed necessary to provide a foundation for further research, which in time will provide a knowledge base for policymaking and service delivery.

This scoping review will help provide an overall understanding of the main foci of research within the field of social media and mental health and well-being among adolescents, as well as the type of data sources and research instruments used so far. Furthermore, we aim to highlight potential gaps in the research literature ( Arksey and O’Malley, 2005 ). Even though a large number of studies on social media use and mental health with different vantage points has been conducted over the last decade, we are not aware of any broad-sweeping scoping review covering this area.

This scoping review aims to give an overview of the main research questions that have been focused on with regard to use of social media among adolescents in relation to mental health and well-being. Both quantitative and qualitative studies are of interest. Three specific secondary research questions will be addressed and together with the main research question serve as a template for organizing the results:

• Which aspects of mental health and well-being have been the focus or foci of research so far?

• Has the research focused on different research aims across gender, ethnicity, socio-economic status, geographic location? What kind of findings are reported across these groups?

• Organize and describe the main sources of evidence related to social media that have been used in the studies identified.

Defining Adolescence and Social Media

In the present review, adolescents are defined as those between 13 and 19 years of age. We chose the mean age of 13 as our lower limit as nearly all social media services require users to be at least 13 years of age to access and use their services ( Childnet International, 2018 ). All pertinent studies which present results relevant for this age range is within the scope of this review. For social media we used the following definition by Kietzmann et al. (2011 , p. 1): “Social media employ mobile and web-based technologies to create highly interactive platforms via which individuals and communities share, co-create, discuss, and modify user-generated content.” We also employed the typology described by Kaplan and Haenlein’s classification scheme across two axes: level of self-presentation and social presence/media richness ( Kaplan and Haenlein, 2010 ). The current scoping review adheres to guidelines and recommendations stated by Tricco et al. (2018) .

See protocol for further details about the definitions used ( Schønning et al., 2020 ).

Data Sources and Search Strategy

A literature search was performed in OVID Medline, OVID Embase, OVID PsycINFO, Sociological Abstracts (proquest), Social Services Abstracts (proquest), ERIC (proquest), and CINAHL. The search strategy combined search terms for adolescents, social media and mental health or wellbeing. The database-controlled vocabulary was used for searching subject headings, and a large spectrum of synonyms with appropriate truncations was used for searching title, abstract, and author keywords. A filter for observational studies was applied to limit the results. The search was also limited to publications from 2014 to current. The search strategy was translated between each database. An example of full strategy for Embase is attached as Supplementary Material .

Study Selection: Exclusion and Inclusion Criteria

The exclusion and inclusion criteria are detailed in the protocol ( Schønning et al., 2020 ). Briefly, we included English language peer-reviewed quantitative- or qualitative papers or systematic reviews published within the last 5 years with an explicit focus on mental health/well-being and social media. Non-empirical studies, intervention studies, clinical studies and publications not peer-reviewed were excluded. Intervention studies and clinical studies were excluded as we sought to not introduce too much heterogeneity in design and our focus was on observational studies. The criteria used for study selection was part of an iterative process which was described in detail in the protocol ( Schønning et al., 2020 ). As per the study protocol ( Schønning et al., 2020 ), and in line with scoping review guidelines ( Peters et al., 2015 , 2017 ; Tricco et al., 2018 ), we did not assess methodological quality or risk of bias of the included studies.

The selection process is illustrated by a flow-chart indicating the stages from unsorted search results to the number of included studies (see Figure 1 ). Study selection was accomplished and organized using the Rayyan QCRI software 1 . The inclusion and exclusion process was performed independently by VS and JCS. The interrater agreement was κ = 0.87, indicating satisfactory agreement.

Figure 1. Flowchart of exclusion process from unsorted results to included studies.

Data Extraction and Organization

Details of the data extracted is described in the protocol. Three types of information were extracted, bibliographic information, information about study design and subject matter information. Subject matter information included aim of study, how social media and mental health/well-being was measured, and main findings of the study.

Visualization of Words From the Titles of the Included Studies

The most frequently occurring words and bigrams in the titles of the included studies are presented in Figures 2 , 3 . The following procedure was used to generate Figure 1 : First, a text file containing all titles were imported into R as a data frame ( R Core Team, 2014 ). The data frame was processed using the “tidy text”-package with required additional packages ( Silge and Robinson, 2016 ). Second, numbers and commonly used words with little inherent meaning (so called “stop words,” such as “and,” “of,” and “in”), were removed from the data frame using the three available lexicons in the “tidy-text”-package ( Silge and Robinson, 2016 ). Furthermore, variations of “adolescents” (e.g., “adolescent,” “adolescence,” and “adolescents”) and “social media” (e.g., “social media,” “social networking,” “online social networks”) were removed from the data frame. Third, the resulting data frame was sorted based on frequency of unique words, and words occurring only once were removed. The final data frame is presented as a word cloud in Figure 1 ( N = 113). The same procedure as described above was employed to generate commonly occurring bigrams (two words occurring adjacent to each other), but without removing bigrams occurring only once ( N = 231). The word clouds were generated using the “wordcloud2”-package in R ( Lang and Chien, 2018 ). For Figure 1 , shades of blue indicate word frequencies >2 and green a frequency of 2. For Figure 2 , shades of blue indicate bigram frequencies of >1 and green a frequency of 1.

Figure 2. Word cloud from the titles of the included studies. Most frequent words, excluding variations of “adolescence” and “social media.” N = 113. Shades of blue indicate word frequencies >2 and green a frequency of 2. The size of each word is indicative of its relative frequency of occurrence.

Figure 3. Word cloud from the titles of the included studies. Bigrams from the titles of the included studies, excluding variations of “adolescence” and “social media.” N = 231. Shades of blue indicate bigram frequencies of >1 and green a frequency of 1. The size of each bigram is indicative of its relative frequency of occurrence.

Characteristics of the Included Studies

Of 7927 unique studies, 79 (1%) met our inclusion criteria ( Aboujaoude et al., 2015 ; Banjanin et al., 2015 ; Banyai et al., 2017 ; Barry et al., 2017 ; Best et al., 2014 , 2015 ; Booker et al., 2018 ; Bourgeois et al., 2014 ; Boyle et al., 2016 ; Brunborg et al., 2017 ; Burnette et al., 2017 ; Colder Carras et al., 2017 ; Critchlow et al., 2019 ; Cross et al., 2015 ; Curtis et al., 2018 ; de Lenne et al., 2018 ; de Vries et al., 2016 ; Erfani and Abedin, 2018 ; Erreygers et al., 2018 ; Fahy et al., 2016 ; Ferguson et al., 2014 ; Fisher et al., 2016 ; Foerster and Roosli, 2017 ; Foody et al., 2017 ; Fredrick and Demaray, 2018 ; Frison and Eggermont, 2016 , 2017 ; Geusens and Beullens, 2017 , 2018 ; Hamm et al., 2015 ; Hanprathet et al., 2015 ; Harbard et al., 2016 ; Hase et al., 2015 ; Holfeld and Mishna, 2019 ; Houghton et al., 2018 ; Jafarpour et al., 2017 ; John et al., 2018 ; Kim et al., 2019 ; Kim, 2017 ; Koo et al., 2015 ; Lai et al., 2018 ; Larm et al., 2017 , 2019 ; Marchant et al., 2017 ; Marengo et al., 2018 ; Marques et al., 2018 ; Meier and Gray, 2014 ; Memon et al., 2018 ; Merelle et al., 2017 ; Neira and Barber, 2014 ; Nesi et al., 2017a , b ; Niu et al., 2018 ; Nursalam et al., 2018 ; Oberst et al., 2017 ; O’Connor et al., 2014 ; O’Reilly et al., 2018 ; Przybylski and Bowes, 2017 ; Przybylski and Weinstein, 2017 ; Richards et al., 2015 ; Rousseau et al., 2017 ; Salmela-Aro et al., 2017 ; Sampasa-Kanyinga and Chaput, 2016 ; Sampasa-Kanyinga and Lewis, 2015 ; Sampasa-Kanyinga et al., 2018 ; Scott and Woods, 2018 ; Settanni et al., 2018 ; Spears et al., 2015 ; Throuvala et al., 2019 ; Tiggemann and Slater, 2017 ; Tseng and Yang, 2015 ; Twenge and Campbell, 2019 ; Twenge et al., 2018 ; van den Eijnden et al., 2018 ; Wang et al., 2018 ; Wartberg et al., 2018 ; Wolke et al., 2017 ; Woods and Scott, 2016 ; Yan et al., 2017 ). Among the included studies, 74 (94%) are quantitative ( Aboujaoude et al., 2015 ; Banjanin et al., 2015 ; Banyai et al., 2017 ; Barry et al., 2017 ; Best et al., 2014 ; Booker et al., 2018 ; Bourgeois et al., 2014 ; Boyle et al., 2016 ; Brunborg et al., 2017 ; Colder Carras et al., 2017 ; Critchlow et al., 2019 ; Cross et al., 2015 ; Curtis et al., 2018 ; de Lenne et al., 2018 ; de Vries et al., 2016 ; Erfani and Abedin, 2018 ; Erreygers et al., 2018 ; Fahy et al., 2016 ; Ferguson et al., 2014 ; Fisher et al., 2016 ; Foerster and Roosli, 2017 ; Foody et al., 2017 ; Fredrick and Demaray, 2018 ; Frison and Eggermont, 2016 , 2017 ; Geusens and Beullens, 2017 , 2018 ; Hamm et al., 2015 ; Hanprathet et al., 2015 ; Harbard et al., 2016 ; Hase et al., 2015 ; Houghton et al., 2018 ; Jafarpour et al., 2017 ; John et al., 2018 ; Kim et al., 2019 ; Kim, 2017 ; Koo et al., 2015 ; Lai et al., 2018 ; Larm et al., 2017 , 2019 ; Marchant et al., 2017 ; Marengo et al., 2018 ; Marques et al., 2018 ; Meier and Gray, 2014 ; Memon et al., 2018 ; Merelle et al., 2017 ; Neira and Barber, 2014 ; Nesi et al., 2017a , b ; Niu et al., 2018 ; Nursalam et al., 2018 ; Oberst et al., 2017 ; O’Connor et al., 2014 ; Przybylski and Bowes, 2017 ; Przybylski and Weinstein, 2017 ; Richards et al., 2015 ; Rousseau et al., 2017 ; Salmela-Aro et al., 2017 ; Sampasa-Kanyinga and Chaput, 2016 ; Sampasa-Kanyinga and Lewis, 2015 ; Sampasa-Kanyinga et al., 2018 ; Scott and Woods, 2018 ; Settanni et al., 2018 ; Spears et al., 2015 ; Tiggemann and Slater, 2017 ; Tseng and Yang, 2015 ; Twenge and Campbell, 2019 ; Twenge et al., 2018 ; van den Eijnden et al., 2018 ; Wang et al., 2018 ; Wartberg et al., 2018 ; Wolke et al., 2017 ; Woods and Scott, 2016 ; Yan et al., 2017 ), three are qualitative ( O’Reilly et al., 2018 ; Burnette et al., 2017 ; Throuvala et al., 2019 ), and two use mixed methods ( Best et al., 2015 ; Holfeld and Mishna, 2019 ) (see Supplementary Tables 1 , 2 in the Supplementary Material for additional details extracted from all included studies). In relation to study design, 45 (57%) used a cross-sectional design ( Bourgeois et al., 2014 ; Ferguson et al., 2014 ; Meier and Gray, 2014 ; Neira and Barber, 2014 ; O’Connor et al., 2014 ; Banjanin et al., 2015 ; Hanprathet et al., 2015 ; Hase et al., 2015 ; Koo et al., 2015 ; Sampasa-Kanyinga and Lewis, 2015 ; Spears et al., 2015 ; Tseng and Yang, 2015 ; Frison and Eggermont, 2016 ; Sampasa-Kanyinga and Chaput, 2016 ; Woods and Scott, 2016 ; Banyai et al., 2017 ; Barry et al., 2017 ; Brunborg et al., 2017 ; Colder Carras et al., 2017 ; Larm et al., 2017 , 2019 ; Merelle et al., 2017 ; Oberst et al., 2017 ; Przybylski and Bowes, 2017 ; Przybylski and Weinstein, 2017 ; Tiggemann and Slater, 2017 ; Wolke et al., 2017 ; Yan et al., 2017 ; de Lenne et al., 2018 ; Erreygers et al., 2018 ; Fredrick and Demaray, 2018 ; Geusens and Beullens, 2018 ; Lai et al., 2018 ; Marengo et al., 2018 ; Marques et al., 2018 ; Niu et al., 2018 ; Nursalam et al., 2018 ; Sampasa-Kanyinga et al., 2018 ; Scott and Woods, 2018 ; Settanni et al., 2018 ; Wang et al., 2018 ; Wartberg et al., 2018 ; Critchlow et al., 2019 ; Kim et al., 2019 ; Twenge and Campbell, 2019 ), 17 used a longitudinal design ( Cross et al., 2015 ; Boyle et al., 2016 ; de Vries et al., 2016 ; Fahy et al., 2016 ; Frison and Eggermont, 2016 ; Harbard et al., 2016 ; Foerster and Roosli, 2017 ; Geusens and Beullens, 2017 ; Kim, 2017 ; Nesi et al., 2017a , b ; Rousseau et al., 2017 ; Salmela-Aro et al., 2017 ; Booker et al., 2018 ; Houghton et al., 2018 ; van den Eijnden et al., 2018 ; Holfeld and Mishna, 2019 ), seven were systematic reviews ( Aboujaoude et al., 2015 ; Best et al., 2015 ; Fisher et al., 2016 ; Marchant et al., 2017 ; Erfani and Abedin, 2018 ; John et al., 2018 ; Memon et al., 2018 ), two were meta-analyses ( Foody et al., 2017 : Curtis et al., 2018 ), one was a causal-comparative study ( Jafarpour et al., 2017 ), one was a review article ( Richards et al., 2015 ), one used a time-lag design ( Twenge et al., 2018 ), one was a scoping review ( Hamm et al., 2015 ), three used a focus-group interview design ( Burnette et al., 2017 ; O’Reilly et al., 2018 ; Throuvala et al., 2019 ), and one study used a combined survey and focus-group design ( Best et al., 2014 ).

The most common study settings were schools [ N = 42 (54%)] ( Best et al., 2014 ; Bourgeois et al., 2014 ; Meier and Gray, 2014 ; Neira and Barber, 2014 ; O’Connor et al., 2014 ; Banjanin et al., 2015 ; Hanprathet et al., 2015 ; Hase et al., 2015 ; Sampasa-Kanyinga and Lewis, 2015 ; Frison and Eggermont, 2016 ; Sampasa-Kanyinga and Chaput, 2016 ; Woods and Scott, 2016 ; Banyai et al., 2017 ; Brunborg et al., 2017 ; Colder Carras et al., 2017 ; Foerster and Roosli, 2017 ; Geusens and Beullens, 2017 , 2018 ; Kim, 2017 ; Larm et al., 2017 , 2019 ; Merelle et al., 2017 ; Nesi et al., 2017a , b ; Przybylski and Bowes, 2017 ; Rousseau et al., 2017 ; Salmela-Aro et al., 2017 ; Tiggemann and Slater, 2017 ; de Lenne et al., 2018 ; Fredrick and Demaray, 2018 ; Houghton et al., 2018 ; Lai et al., 2018 ; Marengo et al., 2018 ; Niu et al., 2018 ; Nursalam et al., 2018 ; Sampasa-Kanyinga et al., 2018 ; Scott and Woods, 2018 ; Settanni et al., 2018 ; van den Eijnden et al., 2018 ; Wang et al., 2018 ; Holfeld and Mishna, 2019 ; Kim et al., 2019 ). Fourteen of the included studies were based on data from a home setting ( Cross et al., 2015 ; Koo et al., 2015 ; Spears et al., 2015 ; Boyle et al., 2016 ; de Vries et al., 2016 ; Harbard et al., 2016 ; Barry et al., 2017 ; Frison and Eggermont, 2017 ; Oberst et al., 2017 ; Yan et al., 2017 ; Booker et al., 2018 ; Marques et al., 2018 ; Wartberg et al., 2018 ; Critchlow et al., 2019 ). Eleven publications were reviews or meta-analyses and included primary studies from different settings ( Aboujaoude et al., 2015 ; Best et al., 2015 ; Hamm et al., 2015 ; Richards et al., 2015 ; Fisher et al., 2016 ; Foody et al., 2017 ; Marchant et al., 2017 ; Curtis et al., 2018 ; Erfani and Abedin, 2018 ; John et al., 2018 ; Memon et al., 2018 ). One study used both a home and school setting ( Erreygers et al., 2018 ), and 11 (14%) of the included studies did not mention the study setting for data collection ( Ferguson et al., 2014 ; Tseng and Yang, 2015 ; Fahy et al., 2016 ; Burnette et al., 2017 ; Jafarpour et al., 2017 ; Przybylski and Weinstein, 2017 ; Wolke et al., 2017 ; O’Reilly et al., 2018 ; Twenge et al., 2018 ; Throuvala et al., 2019 ; Twenge and Campbell, 2019 ).

Mental Health Foci of Included Studies

For a visual overview of the mental health foci of the included studies see Figures 2 , 3 . Most studies had a focus on different negative aspects of mental health, as evident from the frequently used terms in Figures 2 , 3 . The most studied aspect was depression, with 23 (29%) studies examining the relationship between social media use and depressive symptoms ( Ferguson et al., 2014 ; Neira and Barber, 2014 ; O’Connor et al., 2014 ; Banjanin et al., 2015 ; Richards et al., 2015 ; Spears et al., 2015 ; Tseng and Yang, 2015 ; Fahy et al., 2016 ; Frison and Eggermont, 2016 , 2017 ; Woods and Scott, 2016 ; Banyai et al., 2017 ; Brunborg et al., 2017 ; Colder Carras et al., 2017 ; Larm et al., 2017 ; Nesi et al., 2017a ; Salmela-Aro et al., 2017 ; Fredrick and Demaray, 2018 ; Houghton et al., 2018 ; Niu et al., 2018 ; Twenge et al., 2018 ; Wang et al., 2018 ; Wartberg et al., 2018 ). Twenty of the included studies focused on different aspects of good mental health, such as well-being, happiness, or quality of life ( Best et al., 2014 , 2015 ; Bourgeois et al., 2014 ; Ferguson et al., 2014 ; Cross et al., 2015 ; Koo et al., 2015 ; Richards et al., 2015 ; Spears et al., 2015 ; Fahy et al., 2016 ; Foerster and Roosli, 2017 ; Przybylski and Bowes, 2017 ; Przybylski and Weinstein, 2017 ; Yan et al., 2017 ; Booker et al., 2018 ; de Lenne et al., 2018 ; Erfani and Abedin, 2018 ; Erreygers et al., 2018 ; Lai et al., 2018 ; van den Eijnden et al., 2018 ; Twenge and Campbell, 2019 ). Nineteen studies had a more broad-stroke approach, and covered general mental health or psychiatric problems ( Aboujaoude et al., 2015 ; Hanprathet et al., 2015 ; Hase et al., 2015 ; Sampasa-Kanyinga and Lewis, 2015 ; Spears et al., 2015 ; Fisher et al., 2016 ; Barry et al., 2017 ; Jafarpour et al., 2017 ; Kim, 2017 ; Merelle et al., 2017 ; Oberst et al., 2017 ; Wolke et al., 2017 ; Marengo et al., 2018 ; Marques et al., 2018 ; Sampasa-Kanyinga et al., 2018 ; Holfeld and Mishna, 2019 ; Kim et al., 2019 ; Larm et al., 2019 ). Eight studies examined the link between social media use and body dissatisfaction and eating disorder symptoms ( Ferguson et al., 2014 ; Meier and Gray, 2014 ; de Vries et al., 2016 ; Burnette et al., 2017 ; Rousseau et al., 2017 ; Tiggemann and Slater, 2017 ; Marengo et al., 2018 ; Wartberg et al., 2018 ). Anxiety was the focus of seven studies ( O’Connor et al., 2014 ; Koo et al., 2015 ; Spears et al., 2015 ; Fahy et al., 2016 ; Woods and Scott, 2016 ; Colder Carras et al., 2017 ; Yan et al., 2017 ), and 13 studies included a focus on the relationship between alcohol use and social media use ( O’Connor et al., 2014 ; Boyle et al., 2016 ; Sampasa-Kanyinga and Chaput, 2016 ; Brunborg et al., 2017 ; Geusens and Beullens, 2017 , 2018 ; Larm et al., 2017 ; Merelle et al., 2017 ; Nesi et al., 2017b ; Curtis et al., 2018 ; Sampasa-Kanyinga et al., 2018 ; Critchlow et al., 2019 ; Kim et al., 2019 ). Seven studies examined the effect of social media use on sleep ( Harbard et al., 2016 ; Woods and Scott, 2016 ; Yan et al., 2017 ; Nursalam et al., 2018 ; Sampasa-Kanyinga et al., 2018 ; Scott and Woods, 2018 ; Larm et al., 2019 ). Five studies saw how drug use and social media use affected each other ( O’Connor et al., 2014 ; Merelle et al., 2017 ; Sampasa-Kanyinga et al., 2018 ; Kim et al., 2019 ; Larm et al., 2019 ). Self-harm and suicidal behavior was the focus of eleven studies ( O’Connor et al., 2014 ; Sampasa-Kanyinga and Lewis, 2015 ; Tseng and Yang, 2015 ; Kim, 2017 ; Marchant et al., 2017 ; Merelle et al., 2017 ; Fredrick and Demaray, 2018 ; John et al., 2018 ; Memon et al., 2018 ; Twenge et al., 2018 ; Kim et al., 2019 ). Other areas of focus other than the aforementioned are loneliness, self-esteem, fear of missing out and other non-pathological measures ( Neira and Barber, 2014 ; Banyai et al., 2017 ; Barry et al., 2017 ; Colder Carras et al., 2017 ).

Social Media Metrics of Included Studies

The studies included in the current scoping review often focus on specific, widely used, social media and social networking services, such as 31 (39%) studies focusing on Facebook ( Bourgeois et al., 2014 ; Meier and Gray, 2014 ; Banjanin et al., 2015 ; Cross et al., 2015 ; Hanprathet et al., 2015 ; Richards et al., 2015 ; Sampasa-Kanyinga and Lewis, 2015 ; Spears et al., 2015 ; Boyle et al., 2016 ; de Vries et al., 2016 ; Frison and Eggermont, 2016 ; Harbard et al., 2016 ; Sampasa-Kanyinga and Chaput, 2016 ; Banyai et al., 2017 ; Barry et al., 2017 ; Brunborg et al., 2017 ; Larm et al., 2017 ; Merelle et al., 2017 ; Nesi et al., 2017a , b ; Rousseau et al., 2017 ; Tiggemann and Slater, 2017 ; Booker et al., 2018 ; de Lenne et al., 2018 ; Lai et al., 2018 ; Marengo et al., 2018 ; Marques et al., 2018 ; Memon et al., 2018 ; Sampasa-Kanyinga et al., 2018 ; Settanni et al., 2018 ; Twenge et al., 2018 ), 11 on Instagram ( Sampasa-Kanyinga and Lewis, 2015 ; Boyle et al., 2016 ; Sampasa-Kanyinga and Chaput, 2016 ; Barry et al., 2017 ; Brunborg et al., 2017 ; Frison and Eggermont, 2017 ; Nesi et al., 2017a ; Marengo et al., 2018 ; Memon et al., 2018 ; Sampasa-Kanyinga et al., 2018 ), 11 including Twitter ( Richards et al., 2015 ; Sampasa-Kanyinga and Lewis, 2015 ; Spears et al., 2015 ; Harbard et al., 2016 ; Sampasa-Kanyinga and Chaput, 2016 ; Barry et al., 2017 ; Brunborg et al., 2017 ; Merelle et al., 2017 ; Nesi et al., 2017a ; Memon et al., 2018 ; Sampasa-Kanyinga et al., 2018 ), and five studies asking about Snapchat ( Boyle et al., 2016 ; Barry et al., 2017 ; Brunborg et al., 2017 ; Nesi et al., 2017a ; Marengo et al., 2018 ). Eight studies mentioned Myspace ( Richards et al., 2015 ; Sampasa-Kanyinga and Lewis, 2015 ; de Vries et al., 2016 ; Harbard et al., 2016 ; Sampasa-Kanyinga and Chaput, 2016 ; Larm et al., 2017 ; Booker et al., 2018 ; Sampasa-Kanyinga et al., 2018 ) and two asked about Tumblr ( Barry et al., 2017 ; Nesi et al., 2017a ). Other media such as Skype ( Merelle et al., 2017 ), Youtube ( Richards et al., 2015 ), WhatsApp ( Brunborg et al., 2017 ), Ping ( Merelle et al., 2017 ), Bebo ( Booker et al., 2018 ), Hyves ( de Vries et al., 2016 ), Kik ( Brunborg et al., 2017 ), Ask ( Brunborg et al., 2017 ), and Qzone ( Niu et al., 2018 ) were only included in one study each.

Almost half ( n = 34, 43%) of the included studies focus on use of social network sites or online communication in general, without specifying particular social media sites, leaving this up to the study participants to decide ( Best et al., 2014 , 2015 ; Ferguson et al., 2014 ; Neira and Barber, 2014 ; O’Connor et al., 2014 ; Koo et al., 2015 ; Tseng and Yang, 2015 ; Fahy et al., 2016 ; Woods and Scott, 2016 ; Burnette et al., 2017 ; Colder Carras et al., 2017 ; Foerster and Roosli, 2017 ; Foody et al., 2017 ; Geusens and Beullens, 2017 , 2018 ; Jafarpour et al., 2017 ; Kim, 2017 ; Marchant et al., 2017 ; Oberst et al., 2017 ; Przybylski and Weinstein, 2017 ; Salmela-Aro et al., 2017 ; Yan et al., 2017 ; Curtis et al., 2018 ; Erfani and Abedin, 2018 ; Erreygers et al., 2018 ; Nursalam et al., 2018 ; Scott and Woods, 2018 ; van den Eijnden et al., 2018 ; Wartberg et al., 2018 ; Critchlow et al., 2019 ; Holfeld and Mishna, 2019 ; Larm et al., 2019 ; Throuvala et al., 2019 ; Twenge and Campbell, 2019 ). Seven of the included studies examined the relationship between virtual game worlds or socially oriented video games and mental health ( Ferguson et al., 2014 ; Best et al., 2015 ; Spears et al., 2015 ; Yan et al., 2017 ; van den Eijnden et al., 2018 ; Larm et al., 2019 ; Twenge and Campbell, 2019 ).

In the 79 studies included in this scoping review, several approaches to measuring social media use are utilized. The combination of frequency and duration of social media use is by far the most used measurement of social media use, and 44 (56%) of the included studies collected data on these parameters ( Ferguson et al., 2014 ; Meier and Gray, 2014 ; Neira and Barber, 2014 ; Banjanin et al., 2015 ; Best et al., 2015 ; Hanprathet et al., 2015 ; Sampasa-Kanyinga and Lewis, 2015 ; Tseng and Yang, 2015 ; Boyle et al., 2016 ; de Vries et al., 2016 ; Frison and Eggermont, 2016 , 2017 ; Harbard et al., 2016 ; Sampasa-Kanyinga and Chaput, 2016 ; Woods and Scott, 2016 ; Banyai et al., 2017 ; Brunborg et al., 2017 ; Colder Carras et al., 2017 ; Foerster and Roosli, 2017 ; Jafarpour et al., 2017 ; Kim, 2017 ; Larm et al., 2017 , 2019 ; Merelle et al., 2017 ; Nesi et al., 2017b ; Oberst et al., 2017 ; Rousseau et al., 2017 ; Tiggemann and Slater, 2017 ; Yan et al., 2017 ; Booker et al., 2018 ; de Lenne et al., 2018 ; Erreygers et al., 2018 ; Houghton et al., 2018 ; Lai et al., 2018 ; Marengo et al., 2018 ; Marques et al., 2018 ; Niu et al., 2018 ; Nursalam et al., 2018 ; Sampasa-Kanyinga et al., 2018 ; Scott and Woods, 2018 ; Settanni et al., 2018 ; Twenge et al., 2018 ; van den Eijnden et al., 2018 ; Twenge and Campbell, 2019 ). Eight studies focused on the relationship between social media addiction or excessive use and mental health ( Banjanin et al., 2015 ; Tseng and Yang, 2015 ; Banyai et al., 2017 ; Merelle et al., 2017 ; Nursalam et al., 2018 ; Settanni et al., 2018 ; Wang et al., 2018 ). Bergen Social Media Addiction Scale is a commonly used questionnaire amongst the included studies ( Hanprathet et al., 2015 ; Banyai et al., 2017 ; Settanni et al., 2018 ). Seven studies asked about various specific actions on social media, such as liking or commenting on photos, posting something or participating in a discussion ( Meier and Gray, 2014 ; Koo et al., 2015 ; Nesi et al., 2017b ; Geusens and Beullens, 2018 ; Marques et al., 2018 ; van den Eijnden et al., 2018 ; Critchlow et al., 2019 ).

Five studies had a specific and sole focus on the link between social media use and alcohol, and examined how various alcohol-related social media use affected alcohol intake ( Boyle et al., 2016 ; Geusens and Beullens, 2017 , 2018 ; Nesi et al., 2017b ; Critchlow et al., 2019 ). Some studies had a more theory-based focus and investigated themes such as peer comparison, social media intrusion or pro-social behavior on social media and its effect on mental health ( Bourgeois et al., 2014 ; Rousseau et al., 2017 ; de Lenne et al., 2018 ). One of the included studies looked into night-time specific social media use ( Scott and Woods, 2018 ) and one looked into pre-bedtime social media behavior ( Harbard et al., 2016 ) to study the link between this use and sleep.

Amongst the 79 included studies, only six (8%) studies had participants of one gender ( Ferguson et al., 2014 ; Meier and Gray, 2014 ; Best et al., 2015 ; Burnette et al., 2017 ; Jafarpour et al., 2017 ; Tiggemann and Slater, 2017 ). Sixteen studies (20%) did not mention the gender distribution of the participants ( Aboujaoude et al., 2015 ; Best et al., 2015 ; Hamm et al., 2015 ; Richards et al., 2015 ; Fisher et al., 2016 ; Woods and Scott, 2016 ; Foody et al., 2017 ; Marchant et al., 2017 ; Przybylski and Weinstein, 2017 ; Curtis et al., 2018 ; Erfani and Abedin, 2018 ; John et al., 2018 ; Memon et al., 2018 ; O’Reilly et al., 2018 ; Twenge et al., 2018 ; Twenge and Campbell, 2019 ). Several of these were meta-analyses or reviews ( Aboujaoude et al., 2015 ; Best et al., 2014 ; Curtis et al., 2018 ; Foody et al., 2017 ; John et al., 2018 ; Erfani and Abedin, 2018 ; Wallaroo, 2020 ). The studies that included both genders as participants generally had a well-balanced gender distribution with no gender below 40% of the participants. Eight of the studies did not report gender-specific results ( Harbard et al., 2016 ; Nesi et al., 2017b ; Curtis et al., 2018 ; de Lenne et al., 2018 ; Niu et al., 2018 ; Nursalam et al., 2018 ; Wang et al., 2018 ; Twenge and Campbell, 2019 ). Of the included studies, gender was seldom examined as an explanatory variable, and other sociodemographic variables (e.g., ethnicity, socioeconomic status) were not included at all.

Implicit Causation Based on Direction of Association

Sixty-one (77%) of the included studies has social media use as the independent variable and some of the mentioned measurements of mental health as the dependent variable ( Aboujaoude et al., 2015 ; Banjanin et al., 2015 ; Banyai et al., 2017 ; Barry et al., 2017 ; Best et al., 2014 ; Booker et al., 2018 ; Bourgeois et al., 2014 ; Boyle et al., 2016 ; Brunborg et al., 2017 ; Colder Carras et al., 2017 ; Critchlow et al., 2019 ; Cross et al., 2015 ; Curtis et al., 2018 ; de Lenne et al., 2018 ; de Vries et al., 2016 ; Erfani and Abedin, 2018 ; Fahy et al., 2016 ; Fisher et al., 2016 ; Foerster and Roosli, 2017 ; Fredrick and Demaray, 2018 ; Frison and Eggermont, 2016 ; Geusens and Beullens, 2018 ; Hamm et al., 2015 ; Hanprathet et al., 2015 ; Harbard et al., 2016 ; Hase et al., 2015 ; Holfeld and Mishna, 2019 ; Jafarpour et al., 2017 ; John et al., 2018 ; Kim et al., 2019 ; Kim, 2017 ; Lai et al., 2018 ; Larm et al., 2017 , 2019 ; Marengo et al., 2018 ; Marques et al., 2018 ; Meier and Gray, 2014 ; Memon et al., 2018 ; Neira and Barber, 2014 ; Nesi et al., 2017b ; Niu et al., 2018 ; Nursalam et al., 2018 ; O’Connor et al., 2014 ; O’Reilly et al., 2018 ; Przybylski and Bowes, 2017 ; Przybylski and Weinstein, 2017 ; Richards et al., 2015 ; Sampasa-Kanyinga and Chaput, 2016 ; Sampasa-Kanyinga and Lewis, 2015 ; Sampasa-Kanyinga et al., 2018 ; Scott and Woods, 2018 ; Spears et al., 2015 ; Tseng and Yang, 2015 ; Twenge and Campbell, 2019 ; Twenge et al., 2018 ; van den Eijnden et al., 2018 ; Wang et al., 2018 ; Wartberg et al., 2018 ; Wolke et al., 2017 ; Woods and Scott, 2016 ; Yan et al., 2017 ). Most of the included studies hypothesize social media use pattern will affect youth mental health in certain ways. The majority of the included studies tend to find a correlation between more frequent social media use and poor well-being and/or mental health (see Supplementary Table 2 ). The strength of this correlation is however heterogeneous as social media use is measured substantially different across studies. Four (5%) of the included studies focus explicitly on how mental health can affect social media use ( Merelle et al., 2017 ; Nesi et al., 2017a ; Erreygers et al., 2018 ; Settanni et al., 2018 ). Fourteen studies included a mediating factor or focus on reciprocal relationships between social media use and mental health ( Ferguson et al., 2014 ; Koo et al., 2015 ; Tseng and Yang, 2015 ; Frison and Eggermont, 2017 ; Geusens and Beullens, 2017 ; Marchant et al., 2017 ; Oberst et al., 2017 ; Rousseau et al., 2017 ; Salmela-Aro et al., 2017 ; Tiggemann and Slater, 2017 ; Houghton et al., 2018 ; Marques et al., 2018 ; Niu et al., 2018 ; Wang et al., 2018 ). An example is a cross-sectional study by Ferguson et al. (2014) suggesting that exposure to social media contribute to later peer competition which was found to be a predictor of negative mental health outcomes such as eating disorder symptoms.

Cyberbullying as a Nexus

Thirteen of the 79 (17%) included studies investigated cyberbullying as the measurement of social media use ( Aboujaoude et al., 2015 ; Cross et al., 2015 ; Hamm et al., 2015 ; Hase et al., 2015 ; Spears et al., 2015 ; Fahy et al., 2016 ; Fisher et al., 2016 ; Foody et al., 2017 ; Przybylski and Bowes, 2017 ; Wolke et al., 2017 ; Fredrick and Demaray, 2018 ; John et al., 2018 ; Holfeld and Mishna, 2019 ). Most of the systematic reviews and meta-analyses included focused on cyberbullying. A cross-sectional study from 2017 suggests that cyberbullying has similar negative effects as direct or relational bullying, and that cyberbullying is “mainly a new tool to harm victims already bullied by traditional means” ( Wolke et al., 2017 ). A meta-analysis from 2016 concludes that “peer cybervictimization is indeed associated with a variety of internalizing and externalizing problems among adolescents” ( Fisher et al., 2016 ). A systematic review from 2018 concludes that both victims and perpetrators of cyberbullying are at greater risk of suicidal behavior compared with non-victims and non-perpetrators ( John et al., 2018 ).

Strengths and Limitations of Present Study

The main strength of this scoping review lies in the effort to give a broad overview of published research related to use of social media, and mental health and well-being among adolescents. Although a range of reviews on screen-based activities in general and mental health and well-being exist ( Dickson et al., 2018 ; Orben, 2020 ), they do not necessarily discern between social media use and other types of technology-based media. Also, some previous reviews tend to be more particular regarding mental health outcome ( Best et al., 2014 ; Seabrook et al., 2016 ; Orben, 2020 ), or do not focus on adolescents per se ( Seabrook et al., 2016 ). The main limitation is that, despite efforts to make the search strategy as comprehensive and inclusive as possible, we probably have not been able to identify all relevant studies – this is perhaps especially true when studies do include relevant information about social media and mental health/well-being, but this information is part of sub-group analyses or otherwise not the main aim of the studies. In a similar manner, related to qualitative studies, we do not know if our search strategy were as efficient in identifying studies of relevance if this was not the main theme or focus of the study. Despite this, we believe that we were able to strike a balance between specificity and sensitivity in our search strategy.

Description of Central Themes and Core Concepts

The findings from the present scoping review on social media use and mental health and well-being among adolescents revealed that the majority (about 3/4) of the included studies focused on social media and pathology. The core concepts identified are social media use and its statistical association with symptoms of depression, general psychiatric symptoms and other symptoms of psychopathology. Similar findings were made by Keles et al. (2020) in a systematic review from 2019. Focus on the potential association between social media use and positive outcomes seems to be rarer in the current literature, even though some studies focused on well-being which also includes positive aspects of mental health. Studies focusing on screen-based media in general and well-being is more prevalent than studies linking social media specifically with well-being ( Orben, 2020 ). The notion that excessive social media use is associated with poor mental health is well established within mainstream media. Our observation that this preconception seems to be the starting point for much research is not conducive to increased knowledge, but also alluded to elsewhere ( Coyne et al., 2020 ).

Why the Focus on Poor Mental Health/Pathology?

The relationship between social media and mental health is likely to be complex, and social media use can be beneficial for maintaining friendships and enriching social life ( Seabrook et al., 2016 ; Birkjær and Kaats, 2019 ; Coyne et al., 2020 ; Orben, 2020 ). This scoping review reveals that the majority of studies focusing on effects of social media use has a clearly stated focus on pathology and detrimental results of social media use. Mainstream media and the public discourse has contributed in creating a culture of fear around social media, with a focus on its negative elements ( Ahn, 2012 ; O’Reilly et al., 2018 ). It is difficult to pin-point why the one-sided focus on the negative effects of social media has been established within the research literature. But likely reasons are elements of “moral panic,” and reports of increases in mental health problems among adolescents in the same period that social media were introduced and became wide-spread ( Birkjær and Kaats, 2019 ). The phenomenon of moral panic typically resurges with the introduction and increasing use of new technologies, as happened with video games, TV, and radio ( Mueller, 2019 ).

The Metrics of Social Media

Social media trends change rapidly, and it is challenging for the research field to keep up. The included studies covered some of the most frequently used social media, but the amount of studies focusing on each social media did not accurately reflect the contemporary distribution of users. Even though sites such as Instagram and Snapchat were covered in some studies, the coverage did not do justice to the amount of users these sites had. Newer social media sites such as TikTok were not mentioned in the included studies even though it has several hundred million daily users ( Mediakix, 2019 ; Wallaroo, 2020 ).

Across the included studies there was some variation in how social media were gauged, but the majority of studies focused on the mere frequency and duration of use. There were little focus on separating between different forms of (inter)actions on social media, as these can vary between being a victim of cyberbullying to participating in healthy community work. Also, few studies differentiated between types of actions (i.e., posting, scrolling, reading), active and passive modes of social media use (i.e., production versus consumption, and level of interactivity), a finding similar to other reports ( Seabrook et al., 2016 ; Verduyn et al., 2017 ; Orben, 2020 ). There is reason to believe that different modes of use on social media platforms are differentially associated with mental health, and a recent narrative review highlight the need to address this in future research ( Orben, 2020 ). One of the included studies found for instance that it is not the total time spent on Facebook or the internet, but the specific amount of time allocated to photo-related activities that is associated with greater symptoms of eating disorders such as thin ideal internalization, self-objectification, weight dissatisfaction, and drive for thinness ( Meier and Gray, 2014 ). This observation can possibly be explained by social comparison mechanisms ( Appel et al., 2016 ) and passive use of social media ( Verduyn et al., 2017 ). The lack of research differentiating social media use and its association with mental health is an important finding of this scoping review and will hopefully contribute to this being included in future studies.

Few studies examined the motivation behind choosing to use social media, or the mental health status of the users when beginning a social media session. It has been reported that young people sometimes choose to enter sites such as Facebook and Twitter as an escape from threats to their mental health such as experiencing overwhelming pressure in daily life ( Boyd, 2014 ). This kind of escapism can be explained through uses and gratifications theory [see for instance ( Coyne et al., 2020 )]. On the other hand, more recent research suggest that additional motivational factors may include the need to control relationships, content, presentation, and impressions ( Throuvala et al., 2019 ), and it is possible that social media use can act as an reinforcement of adolescents’ current moods and motivations ( Birkjær and Kaats, 2019 ). Regardless, it seems obvious that the interplay between online and offline use and underlying motivational mechanisms needs to be better understood.

There has also been some questions about the accuracy when it comes to deciding the amount and frequency of one’s personal social media use. Without measuring duration and frequency of use directly and objectively it is unlikely that subjective self-report of general use is reliable ( Kobayashi and Boase, 2012 ; Scharkow, 2016 , 2019 ; Naab et al., 2019 ). Especially since the potential for social media use is almost omnipresent and the use itself is diverse in nature. Also, due to processes such as social desirability, it is likely that some participants report lower amounts of social media use as excessive use is seen largely undesirable ( Krumpal, 2013 ). Inaccurate reporting of prior social media use could also be a threat to the validity of the reported numbers and thus bias the results reported. Real-time tracking of actual use and modes of use is therefore recommended in future studies to ensure higher accuracy of these aspects of social media use ( Coyne et al., 2020 ; Orben, 2020 ), despite obvious legal and ethical challenges. Another aspect of social media use which does not seem to be addressed is potential spill-over effects, where use of social media leads to potential interest in or thinking about use of – and events or contents on – social media when the individual is offline. When this aspect has been addressed, it seems to be in relation to preoccupations and with a focus on excessive use or addictive behaviors ( Griffiths et al., 2014 ). Conversely, given the ubiquitous and important role of social media, experiences on social media – for better or for worse – are likely to be interconnected with the rest of an individual’s lived experience ( Birkjær and Kaats, 2019 ).

The Studies Seem to Implicitly Think That the Use of Social Media “Causes”/“Affects” Mental Health (Problems)

Most of the included studies establish an implicit causation between social media and mental health. It is assumed that social media use has an impact on mental health. The majority of studies included establish some correlation between more frequent use of social media and poor well-being/mental health, as evident from Supplementary Table 2 . As formerly mentioned, most of the included studies are cross-sectional and cannot shed light into temporality or cause-and-effect. In total, only 16 studies had a longitudinal design, using different types of regression models, latent growth curve models and cross-lagged models. Yet there seems to be an unspoken expectation that the direction of the association is social media use affecting mental health. The reason for this supposition is unclear, but again it is likely that the mainstream media discourse dominated by mostly negative stories and reports of social media use has some impact together with the observed moral panic.

With the increased popularity of social media and internet arrived a reduction of face-to-face contact and supposed increased social isolation ( Kraut et al., 1998 ; Espinoza and Juvonen, 2011 ). This view is described as the displacement hypothesis [see for instance ( Coyne et al., 2020 )]. Having a thriving social life and community with meaningful relations are for many considered vital for well-being and good mental health, and the supposed reduction of sociality were undoubtedly met with skepticism by some. Social media use has increased rapidly among young people over the last two decades along with reports that mental health problems are increasing. Several studies report that there is a rising prevalence of symptom of anxiety and depression among our adolescents ( Bor et al., 2014 ; Olfson et al., 2015 ). The observation that increases in social media use and mental health issues happened in more or less the same time period can have contributed to focus on how use of social media affects mental health problems.

The existence of an implicit causation is supported by the study variables chosen and the lack of positively worded outcomes. Depression, anxiety, alcohol use, psychiatric problems, suicidal behavior and eating disorders are amongst the most studied outcome-variables. On the other side of the spectrum we have well-being, which can oscillate from positive to negative, whilst the measures of pathology only vary from “ill” to “not ill” with positive outcomes not possible.

What Is the Gap in the Literature?

The current literature on social media and mental health among youth is still developing and has several gaps and shortcomings, as evident from this scoping review and other publications ( Seabrook et al., 2016 ; Coyne et al., 2020 ; Keles et al., 2020 ; Orben, 2020 ). Some of the gaps and shortcomings in the field we propose solutions for has been identified in a systematic review from 2019 by Keles et al. (2020) . The majority of the included studies in the current scoping review were cross-sectional, were limited in their inclusion of potential confounders and 3rd variables such as sociodemographics and personality, preventing knowledge about possible cause-and-effect between social media and mental health. There is a lack of longitudinal studies examining the effects of social media over extended periods of time, as well as investigations longitudinally of how mental health impacts social media use. However, since the formal search was ended for this scoping review, some innovative studies have emerged using longitudinal data ( Brunborg and Andreas, 2019 ; Orben et al., 2019 ; Coyne et al., 2020 ). More high quality longitudinal studies of social media use and mental health could help us identify the patterns over time and help us learn about possible cause-and-effect relationships, as well as disentangling between- and within-person associations ( Coyne et al., 2020 ; Orben, 2020 ). Furthermore, both social media use and mental health are complex phenomena in themselves, and future studies need to consider which aspects they want to investigate when trying to understand their relationship. Mechanisms linking social media use and eating disorders are for instance likely to be different than mechanisms linking social media use and symptoms of ADHD.

Our literature search also revealed a paucity of qualitative studies exploring the why’s and how’s of social media use in relation to mental health among adolescents. Few studies examine how youth themselves experience and perceive the relationship between social media and mental health, and the reasons for their continued and frequent use. Qualitatively oriented studies would contribute to a deeper understanding of adolescent’s social media sphere, and their thoughts about the relationship between social media use and mental health [see for instance ( Burnette et al., 2017 )]. For instance, O’Reilly et al. (2018) found that adolescents viewed social media as a threat to mental well-being, and concluded that they buy into the idea that “inherently social media has negative effects on mental wellbeing” and seem to “reify the moral panic that has become endemic to contemporary discourses.” On the other hand, Weinstein found using both quantitative and qualitative data that adolescents’ perceptions of the relationship between social media use and well-being probably is more nuanced, and mostly positive. Another clear gap in the research literature is the lack of focus on potentially positive aspects of social media use. It is obvious that there are some positive sides of the use of social media, and these also need to be investigated further ( Weinstein, 2018 ; Birkjær and Kaats, 2019 ). Gender-specific analyses are also lacking in the research literature, and there is reason to believe that social media use have different characteristics between the genders with different relationships to mental health. In fact, recent findings indicate that not only gender should be considered an important factor when investigating the role of social media in adolescents’ lives, but individual characteristics in general ( Orben et al., 2019 ; Orben, 2020 ). Analyses of socioeconomic status and geographic location are also lacking and it is likely that these factors might play a role the potential association between social media use and mental health. And finally, several studies point to the fact that social media potentially could be a fruitful arena for promoting mental well-being among youth, and developing mental health literacy to better equip our adolescents for the challenges that will surely arise ( O’Reilly et al., 2018 ; Teesson et al., 2020 ).

Research into the association between social media use and mental health and well-being among adolescents is rapidly emerging. The field is characterized by a focus on the association between social media use and negative aspects of mental health and well-being, and where studies focusing on the potentially positive aspects of social media use are lacking. Presently, the majority of studies in the field are quantitatively oriented, with most utilizing a cross-sectional design. An increase in qualitatively oriented studies would add to the field of research by increasing the understanding of adolescents’ social-media life and their own experiences of its association with mental health and well-being. More studies using a longitudinal design would contribute to examining the effects of social media over extended periods of time and help us learn about possible cause-and-effect relationships. Few studies look into individual factors, which may be important for our understanding of the association. Social media use and mental health and well-being are complex phenomena, and future studies could benefit from specifying the type of social media use they focus on when trying to understand its link to mental health. In conclusion, studies including more specific aspects of social media, individual differences and potential intermediate variables, and more studies using a longitudinal design are needed as the research field matures.

Author Contributions

JS conceptualized the review approach and provided general guidance to the research team. VS and JS drafted the first version of this manuscript. JS, GH, and LA developed the draft further based on feedback from the author group. All authors reviewed and approved the final version of the manuscript and have made substantive intellectual contributions to the development of this manuscript.

This review was partly funded by Regional Research Funds in Norway, funding #RFF297031. No other specific funding was received for the present project. The present project is associated with a larger innovation-project lead by Bergen municipality in Western Norway related to the use of social media and mental health and well-being. The innovation-project is funded by a program initiated by the Norwegian Directorate of Health, and in Vestland county coordinated by the County Council (County Authority). The project aims to explore social media as platform for health-promotion among adolescents.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We would like to thank Bergen municipality, Hordaland County Council and Western Norway University of Applied Sciences for their collaboration and help with the review. We would also like to thank Senior Librarian Marita Heinz at the Norwegian Institute for Public Health for vital help conducting the literature search.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2020.01949/full#supplementary-material

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Keywords : scoping review, social media, mental health, adolescence, well-being

Citation: Schønning V, Hjetland GJ, Aarø LE and Skogen JC (2020) Social Media Use and Mental Health and Well-Being Among Adolescents – A Scoping Review. Front. Psychol. 11:1949. doi: 10.3389/fpsyg.2020.01949

Received: 11 March 2020; Accepted: 14 July 2020; Published: 14 August 2020.

Reviewed by:

Copyright © 2020 Schønning, Hjetland, Aarø and Skogen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Viktor Schønning, [email protected]

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• Use of social network analysis in health research: a scoping review protocol
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• Eshleen Grewal 1 ,
• Jenny Godley 2 , 3 , 4 ,
• Justine Wheeler 5 ,
• http://orcid.org/0000-0001-9008-2289 Karen L Tang 1 , 3 , 4
• 1 Department of Medicine , University of Calgary , Calgary , Alberta , Canada
• 2 Department of Sociology , University of Calgary , Calgary , Alberta , Canada
• 3 Department of Community Health Sciences , University of Calgary , Calgary , Alberta , Canada
• 4 O’Brien Institute for Public Health , University of Calgary , Calgary , Alberta , Canada
• 5 Libraries and Cultural Resources , University of Calgary , Calgary , Alberta , Canada
• Correspondence to Dr Karen L Tang; klktang{at}ucalgary.ca

Introduction Social networks can affect health beliefs, behaviours and outcomes through various mechanisms, including social support, social influence and information diffusion. Social network analysis (SNA), an approach which emerged from the relational perspective in social theory, has been increasingly used in health research. This paper outlines the protocol for a scoping review of literature that uses social network analytical tools to examine the effects of social connections on individual non-communicable disease and health outcomes.

Methods and analysis This scoping review will be guided by Arksey and O’Malley’s framework for conducting scoping reviews. A search of the electronic databases, Ovid Medline, PsycINFO, EMBASE and CINAHL, will be conducted in April 2024 using terms related to SNA. Two reviewers will independently assess the titles and abstracts, then the full text, of identified studies to determine whether they meet inclusion criteria. Studies that use SNA as a tool to examine the effects of social networks on individual physical health, mental health, well-being, health behaviours, healthcare utilisation, or health-related engagement, knowledge, or trust will be included. Studies examining communicable disease prevention, transmission or outcomes will be excluded. Two reviewers will extract data from the included studies. Data will be presented in tables and figures, along with a narrative synthesis.

Ethics and dissemination This scoping review will synthesise data from articles published in peer-reviewed journals. The results of this review will map the ways in which SNA has been used in non-communicable disease health research. It will identify areas of health research where SNA has been heavily used and where future systematic reviews may be needed, as well as areas of opportunity where SNA remains a lesser-used method in exploring the relationship between social connections and health outcomes.

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This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/ .

https://doi.org/10.1136/bmjopen-2023-078872

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STRENGTHS AND LIMITATIONS OF THIS STUDY

This is a novel scoping review that fills an important gap—how and where social network analysis (SNA) (as a data collection and analytical tool) has been used in health research has not been systematically documented despite its increasing use in the discipline.

The breadth of the scoping review allows for a comprehensive mapping of the use of SNA to examine social connections and non-communicable disease and health outcomes, without limiting to any one population group or setting.

The use of the Arksey and O’Malley framework as well as the Levac et al recommendations to guide our scoping review will ensure that a rigorous and transparent process is undertaken.

Due to the scope of the review and the large volume of anticipated studies, only published articles in the English language will be included.

Introduction

Social connections are known to influence health. 1 People with many supportive social connections tend to be healthier and live longer than people who have fewer supportive social connections, while social isolation, or the absence of supportive social connections, is associated with the deterioration of physical and psychological health, and even death. 2–5 These associations hold even when accounting for socioeconomic status and health practices. 6 Additionally, having a low quantity of supportive social connections is associated with the development or worsening of medical conditions, such as atherosclerosis, hypertension, cardiovascular disease and cancer, potentially through chronic inflammation and changes to autonomic regulation and immune responses. 7–13 Unsupportive social connections can also have adverse effects on health due to emotional stress, which can then lead to poor health habits, psychological distress and negative physiological responses (eg, increased heart rate and blood pressure), all of which are detrimental to health over time. 14 The health of individuals is therefore connected to the people around them. 15

Social networks can influence health via five pathways. 15 16 First, networks can provide social support, to meet the needs of the individual. Dyadic relationships can provide informational, instrumental (ie, aid and assistance with tangible needs), appraisal (ie, help with decision-making) and/or emotional support; this support can be enhanced or hindered by the overall network structure. 17 In addition to the tangible aid and resources that are provided, social support—either perceived or actual—also has direct effects on mental health, well-being and feelings of self-efficacy. 18–20 Social support may also act as a buffer to stress. 16 19 The second pathway by which social networks influence health, and in particular health behaviours such as alcohol and cigarette use, physical activity, food intake patterns and healthcare utilisation, is through social influence. 16 21 That is, the attitudes and behaviour of individuals are guided and altered in response to other network members. 22 23 Social influence is difficult to disentangle from social selection from an empirical standpoint. That is, similarities in behaviour may be due to influences within a network, or alternatively, they may reflect the known phenomenon where individuals tend to form close connections with others who are like them. 22 24 The third pathway is through the promotion of social engagement and participation. Individuals derive a sense of identity, value and meaning through the roles they play (eg, parental roles, community roles, professional roles, etc) in their networks, and the opportunities for participation in social contexts. 16 The fourth pathway by which networks affect health is through transmission of communicable diseases through person-to-person contact. Finally, social networks overlap, resulting in differential access to resources and opportunities (eg, finances, information and jobs). 15 16 An individual’s structural position can result in differential health outcomes, similar to the inequities that stem from differences in social status. 16

There has been an explosion of literature in the area of social networks and health. In their bibliometric analysis, Chapman et al found that the number of studies that examine social networks and health has sextupled since 2000. 25 Similarly, the value of grants and contracts in this topic area, as awarded by the National Science Foundation and the National Institutes of Health, has increased 10-fold. 25 A turning point in the field was the HIV epidemic, where there was an urgent need to better understand its spread. 25 The exponential rise in the number of studies since then that examine social networks and health appears to reflect a widespread understanding that an individual’s health cannot be isolated from his or her social networks and context. There is, however, significant heterogeneity in what aspect of, and how, social networks are being studied. For example, many health research studies use proxies for social connectedness such as marital status or living alone status (as these variables tend to be commonly included in health surveys), without considering the quality of those social connections, and without further exploring the broader social network and their characteristics. 16 26 These proxy measures do little to describe the structure, quantity, quality or characteristics of social connections within which individuals are embedded. Another common approach in health research is to focus on social support measures and their effects on health. Individuals are asked about perceived, or received, social support (for example, through questions that ask about the availability of people who provide emotional support, informational support and/or assistance with daily tasks, with either binary or a Likert scale of responses). 27 28 While important, social support measures do not assess the structure of social networks and represent only one of many different mechanisms by which social networks influence health. 17 23

Social network analysis

Social network analysis (SNA) is a methodological tool, developed in the 1930s by social psychologists, used to study the structure and characteristics of the social networks within which individuals are embedded. 16 29 It has evolved over the past 100 years and has been used by researchers in many social science disciplines to analyse how structures of relationships impact social life. 29 30 SNA has the following key properties 3 30 31 : (1) it relies on empirical relational data (ie, data on actors (nodes) and the connections (ties) between them); (2) it uses mathematical models and graph theory to examine the structure of relationships within which individual actors are embedded; and (3) it models social action at both the group and the individual level arising from the opportunities and constraints determined by the system of relationships. The premise of SNA is that social ties are both drivers and consequences of human behaviour, and are therefore the object of study. 15 16 23 32 Social networks are comprised of nodes, representing the members within a network, connected by ties, representing relations among those individuals. 33 There are two types of SNA: egocentric network analysis and whole network analysis. Egocentric network analysis describes the characteristics of an individual’s (ie, the ‘egos’) personal network, while whole network analysis examines the structure of relationships among all the individuals in a bounded group, such as a school or classroom. 3

In egocentric network analysis, a list of ‘alters’ (ie, nodes) to whom the ego is connected, is obtained through a name generator. Name generator questions ask for a list of alters based on role relations (eg, friends or family), affect (eg, people to whom the ego feels close), interaction (eg, people with whom the ego has been in contact) or exchange (eg, people who provide social and/or financial support). 34 These are followed by name interpreters, where the ego is asked questions about the characteristics of each named alter. 35 Analyses of these data involve constructing measures that describe these egocentric networks. Such measures include network size, network density (ie, how tightly knit the network is), the strength of relationships (ie, the intensity and duration of relationships between ego and alter), network function (ie, the resources and/or support provided through the network) and the diversity of relations within the network (‘heterogeneity’). 23 36 In whole network analysis, the network boundary is determined a priori and network members are known, for example, through membership lists or rosters. 37 Each network member is surveyed, to identify the other network members with whom they are connected and/or affiliated; attributes of each member are obtained through surveying the network members themselves. Variables are constructed at the individual and network levels. Individual-level measures include the number of ties to other network members (‘degree’), types of relationships, and the strength and diversity of relationships. Network-level measures include but are not limited to: density (representing how tightly knit or ‘glued’ together the network is), reciprocity (ie, the proportion of network ties that are reciprocated), isolates (ie, nodes with no ties to other network members), centralisation (or the extent to which the network ties are focused on one node or a set of nodes), cliques and equivalence (ie, sets of nodes that have the same pattern of ties and therefore occupy the same position in the network). 33 38 The constructed measures can then be included in statistical models to explore associations between individual and/or network-level measures, and outcomes. 33 39

Study rationale

In medicine and health research, there has traditionally been a dichotomy between the individual and the context in which the individual is situated—such as in their relationships with others. 40 As such, epidemiology of diseases has historically focused on individual-level traditional risk and protective factors—such as biological markers, genetics, lifestyle and health behaviours, and psychological conditions. 41 While criticisms of this individualistic focus abound, attempts to develop and use different approaches in medicine and research have lagged behind. 42 The use and adoption of methods, like SNA, that frame issues of health and wellness differently, has the potential to offer new insights and solutions to clinical and healthcare delivery problems, 42 by more holistically considering ‘different levels of change’ beyond the individual. 41 We seek to examine the extent to which SNA has transcended the boundaries of its disciplines of origin in the social sciences, into health research. For example, while Chapman et al have clearly shown an explosion of publications at this intersection, 25 it remains unclear whether these studies use SNA tools (which were developed specifically to interrogate the nature and characteristics of social networks), or whether they suffer from the known problem of conflation of constructs like social support, social capital and social integration. 15 43 Many studies that report the impact of ‘social networks’ on health outcomes do not use SNA methods but rather use self-reported network size (without probing the network and its structure), 44 45 social support, 46 marital status 47 48 and/or household members 47 as proxies.

We will therefore undertake a scoping review to map the use of SNA as a data collection and analytical method in health research. More specifically, the scoping review will examine how SNA has been used to study associations across social networks and individual health and well-being (including both physical and psychological health), health knowledge, health engagement, health service use and health behaviours. Scoping reviews are a knowledge synthesis approach that aims to uncover the volume, range, reach and coverage of a body of literature on a specific topic. 49 They differ from systematic reviews, another type of knowledge synthesis, in their objectives. Systematic reviews seek to answer clinical or epidemiological questions and are conducted to fill gaps in knowledge. 50 Systematic reviews are used to establish the effectiveness of an intervention or associations between specific exposures and outcomes. On the other hand, scoping reviews do not seek to provide an answer to a question, but rather, aim to create a map of the existing literature. 49 They are used to provide clarity to the concepts and definitions used in literature, examine the way in which research is conducted in a specific field or on a specific topic, and uncover knowledge gaps. 49 A scoping review, therefore, is well suited as a research method to address our research question, of mapping the ways in which SNA has been used in health research. This scoping review can identify areas (eg, specific populations and specific health outcomes) where there has been a plethora of SNA research warranting future systematic reviews. It can also identify areas within health research where the use of SNA is scarce, highlighting topics, populations or outcomes for future study.

This scoping review will be limited to studies that use SNA in exploring network components and their associations with non-communicable diseases and health and well-being outcomes, for three reasons. The first is feasibility, given the large volume of studies anticipated, based on Chapman et al ’s bibliometric study on this topic. 25 Second, the use of SNA in understanding disease transmission of communicable diseases (such as sexually transmitted infections) is well established; its application to HIV was in fact one of the catalysts, as previously mentioned, to its broader uptake in health research. 25 Third, SNA in health research has shifted from focusing on communicable diseases to focusing on non-communicable diseases and their risk factors; SNA is now being applied much more frequently to the latter conditions than the former ones. 51

Methods and analysis

The scoping review will be informed by the framework developed by Arksey and O’Malley 52 for conducting scoping reviews, as well as the additional recommendations made by Levac et al . 53 Arksey and O’Malley’s framework recommends that the review process be organised into the following five steps: identifying the research question; identifying relevant studies; study selection; charting the data; and collating, summarising and reporting the results. 52 The reporting of this review will adhere to the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews. 54

Patient and public involvement

No patients will be involved.

Step 1: identifying the research question

A preliminary search of the literature identified a gap related to SNA and how it has been used to study the relationship between social networks and individual well-being and health outcomes. This led to the development of the research question that will guide this scoping review: how have social network analytical tools been used to study the associations between social networks and individual patient health? In this case, SNA is defined as a data analysis technique that uses either an egocentric or whole network analysis approach. For egocentric network analysis, we will include studies that involve peer nomination (ie, use of a name generator) and the collection of one or more characteristics of alters (ie, use of name interpreter(s)).

Step 2: identifying relevant studies

A search strategy will be constructed through consultation with an academic librarian (JW). The main concepts from the research question will be used for a preliminary search in Google Scholar. Additionally, the lead authors will provide the librarian with key studies that will be text-mined for relevant terms. These key studies will include a variety of populations (across different countries and age groups) and health outcomes. 55–58 Key studies will be searched in Ovid MEDLINE for appropriate subject headings. In consultation with team members, the librarian (JW) will construct a pilot search strategy. A title/abstract/keyword search will be conducted in Ovid MEDLINE against the known seed/key studies. Table 1 lists example keywords and terms relating to social networks that will be used, with the full search strategy detailed in online supplemental appendix A .

Supplemental material

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Search terms relating to social network analysis

Due to a significant number of irrelevant articles surrounding communicable diseases using this search strategy, we will exclude records with these terms in either the title or keyword fields. Table 2 lists the terms related to communicable diseases.

Search terms relating to communicable diseases

Of note, the search strategy will not include terms that relate to health-related outcomes of interest (outside of excluding communicable diseases). Prior literature has shown that the inclusion of outcome concepts in a search strategy reduces the recall and sensitivity of a search strategy. 59 60 This problem is further exacerbated when only generic health terms (for example, ‘morbidity’ or ‘health status’) or specific health terms (eg, specific diseases or conditions such as ‘diabetes mellitus’) are used. 61 Because the objective of this scoping review is to examine and map the use of SNA in health research, the outcomes of interest are very broad, including: physical health and well-being, psychological health and well-being, healthcare engagement, health knowledge, health behaviours, healthcare access and use, disease prevalence and outcomes (spanning every organ system), and mortality. It will be impossible for a search strategy to be sufficiently comprehensive, to capture all possible generic and specific terms relating to this broad range of outcomes. In keeping with recommendations to minimise the number of elements in a search strategy 62 —and in particular outcome elements 63 —our search strategy will entail searching for SNA terms in health databases without specifying health outcomes.

The search strategy will first be created in Medline (Ovid), then translated and adapted for the databases: (1) EMBASE (Ovid), (2) APA PsycInfo (Ovid) and (3) CINAHL (EBSCO). A search will be completed in April 2024. No date filters will be applied to the search. However, animal-only studies will be excluded. The current version of the search strategy including limits and filters, for all databases, is included in online supplemental appendix A .

Step 3: study selection

The criteria that will be used to determine which studies to include are as follows:

Studies that employ SNA as a data collection and/or analysis technique, as defined above. Of note, studies that elicit only the number of friends or other social contacts, without collection of any information about these social contacts, are not considered to be SNA and are therefore not included in the scoping review.

Studies that explore the social networks of individuals in whom the health outcome is measured.

Studies must include the exploration of non-communicable health outcomes. Examples include self-rated health or other global measures of health (including measures of physical health, mental health and well-being), health practices (eg, physical activity, dietary patterns, smoking, alcohol use, substance use), sexual and reproductive health, healthcare-seeking behaviours (eg, medication adherence, acute care use, attachment to a primary care provider), health knowledge, health beliefs, healthcare engagement, non-communicable disease prevalence and mortality.

The criteria that will be used to exclude studies are as follows:

Studies that explore the social networks of organisations or healthcare providers, rather than the social networks of the individual about whom the health outcome is measured or reported.

Studies that describe or use data analysis techniques other than SNA (eg, using proxies for social networks/social support that do not include peer nomination (such as marital status or living alone status), or studies where study participants report the number of social contacts but where no other information about each social contact is collected).

Studies that focus exclusively on online social networks (eg, social media, online forums, online support groups).

Studies related to prevention, transmission or outcomes of communicable diseases.

Non-English studies, for feasibility purposes.

We will not limit studies based on the study population or country in which the SNA is conducted. Studies in paediatric and adult populations will be included. The reasons for excluding SNA studies that focus solely on social media and online networks are twofold. First, we anticipate a very large number of articles, given the broad populations and outcomes of interest, and for feasibility purposes, we have needed to narrow the research objective to in-person and/or offline social networks only. Second, there are likely inherent differences in online and offline social networks. Individuals use health-related social networking sites and online networks primarily for information seeking, connection with others who share a similar lived experience while being able to maintain some emotional distance and interacting with health professionals 64 ; this differs from in-person networks, which individuals go to more for emotional and tangible or instrumental support. Friends met on online networks vary from friends met in person in other important ways. They tend to have less similarity in terms of age, gender and place of residence, 65 and the network ties more commonly arise spontaneously—that is, without common acquaintances or affiliations. 66 The social patterns and interactions among individuals and their online network contacts are also different—with entire relationships built on text-based interactions. 66 Therefore, while online social networks are an important area of study, they appear to be inherently different from the study of offline social networks, and are therefore excluded from this scoping review.

For the first step of the screening process, after removing duplicate articles, two reviewers will independently assess the titles and abstracts of the studies to determine whether they meet the inclusion criteria. Any studies that do not meet the inclusion criteria will be excluded from the review. Studies that either one of the two reviewers feels are potentially relevant will be included in the full-text review, to ensure that no article is prematurely excluded at this stage. During the second step of the screening process, two reviewers will independently review the full texts of the studies to ensure they meet the inclusion criteria. Conflicts will be resolved by third and fourth reviewers with expertise in SNA (JG) and health outcomes (KLT). The number of studies included in each step of the screening process will be reported using the Preferred Reporting Items for Systematic reviews and Meta-Analyses diagram. 67

Step 4: charting the data

A data charting document ( online supplemental appendix B ) will be created to extract data from the studies in the review. This document will include information about the authors, year of publication, study location, study population characteristics, outcomes of interest to this scoping review, and the scales and measures used for each outcome. Data about the social network analytical method will also be extracted, including whether studies used egocentric versus whole networks, the name generator used (in egocentric network studies) or the relationship being explored, the maximum number of peer nominations allowed, the lookback period used, whether (and which) alter attributes were collected, and whether alter-to-alter tie data were collected. Data extraction will be performed by at least one reviewer, with a second reviewer separately checking and confirming the inputted data. Disagreements in data extraction will be resolved through a consensus, and through the input of reviewers with content and methods expertise (KLT, JG).

Step 5: collating, summarising and reporting results

The results of the review will be presented in the form of figures and tables and will include descriptive numerical summaries. The numerical summary will include information about the number of studies included in the review, where the studies were conducted, when they were published and characteristics of the populations, such as the sample sizes and mean age. It will also include characteristics of the SNA conducted in these studies, including the number that are whole network studies versus egocentric network studies, the data sources used and the attributes of the social connections that are collected and analysed. Results will be synthesised in text, as well as through tables and figures.

Ethics and dissemination

This review does not require ethics approval. Data will be extracted from published material. Once the scoping review is complete, an article will be written to convey the findings of this review, and it will be submitted for publication in a peer-reviewed journal. We anticipate the results of this review will map out the ways in which SNA has been used in health research. Specifically, this scoping review will identify areas of potential saturation where SNA has been heavily used, opportunities for future systematic reviews (where there is a large body of primary research studies requiring synthesis) and health research gaps (eg, the health outcomes where SNA has been minimally used). The scoping review will also shed light on characteristics of SNA that have been used (eg, whether egocentric networks vs whole networks are used and in what settings, and whether a broad range of social network characteristics are captured and analysed), which will serve to inform the conduct of future SNA studies in health research.

Ethics statements

Patient consent for publication.

Not applicable.

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Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

• Data supplement 1
• Data supplement 2

Contributors KLT and JG conceived of the study protocol. KLT, JG, EG and JW developed and revised the study protocol, the search strategy and the inclusion/exclusion criteria. EG and KLT drafted the protocol manuscript, and all authors provided critical revisions.

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None declared.

Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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Economic Disparities, Life Events, and the Gender Mental Health Gap

• Original Research
• Open access
• Published: 03 September 2024

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• Thi Thao Nguyen   ORCID: orcid.org/0000-0003-2089-7874 1 ,
• Kim Huong Nguyen 2 &
• Nicholas Rohde 3  

This paper studies factors explaining the gender mental health gap using Australian data. We show that men have significantly higher mean outcomes and the left tail of the combined distribution is disproportionately female. Using regression-based decompositions, we examine the degree that both socioeconomic inequalities and life experience account for this phenomenon. We find that disparities in income play a substantial role, and subject to an assumption of exogeneity, would be enough to account for the gender gap amongst individuals with very poor psychological wellbeing. We also examine the mental health effects of various negative life experience, such as the death of a family member or being a victim of violence. At the individual level, these variables have large effect sizes but are not strongly correlated with gender to explain our mental health disparities.

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1 Introduction

Understanding the drivers of inequality in mental health is important, because the social and economic cost for individuals at the low end of the poor mental health spectrum can be catastrophic. People who live with acute and chronic mental illness often have significantly higher risk of suicide (Gili et al., 2019 ; Hoertel et al., 2015 ) and reduced capacity to engage in productive activities, such as education, work and social interactions (Cornaglia et al., 2015 ; Chopra, 2009 ; Christensen et al., 2021 ; Lu et al., 2009 ). Additionally, people with mental illness require more healthcare services while healthcare interventions, including medications, social supports and psychological therapies, have been shown to have lower effectiveness for people in lower socioeconomic groups (Beauchamp et al., 2014 ; Lorant et al., 2003 ; Rojas-Garcia et al., 2015 ).

The existing literature on mental health disparities often focuses on the mean differences between genders, overlooking the nuances across the entire distribution of mental health outcomes. While some studies have explored specific factors contributing to the gender mental health gap, such as the role of social norms (Caroli & Weber-Baghdiguian, 2016 ), dimensions of the self (Rosenfield et al., 2000 ) or the exposure to work and family stressors (Marchand et al., 2016 ), relatively less is known about the factors that explain the gender mental health gap beyond the mean. To our knowledge, only Churchill et al. ( 2020 ) have examined gender mental health differences at different quantiles, and these authors focused on locus of control as the primary explanatory factor. Most research tends to examine either socioeconomic factors or life experiences in isolation, without considering their interplay and differential impacts on mental health outcomes for men and women (see, e.g., Hashmi et al., 2020 ; Watson and Osberg, 2017 ). There remains a notable gap in understanding how economic and life experiences collectively shape this disparity across the entire distribution.

This study bridges the gap by adopting a holistic approach that considers both economic and life experience factors simultaneously and evaluates their distinct contributions to the gender mental health gap across the full distribution of outcomes, with a special focus on the low end. By doing so, we move beyond the traditional focus on mean differences and provide valuable insights for policymakers aiming to develop targeted interventions to reduce mental health disparities and promote mental wellbeing for all individuals, regardless of gender.

We address the research gap by employing econometric models and performing statistical decompositions to contrast two potential explanations for the gender differences in mental health. The first is a socioeconomic account that attributes the metal health gap to differentials in socioeconomic outcomes, while the second is a social, or life-experience explanation that attributes inequalities to differences in exposure to various social experiences. Health inequalities and disparities in economic and social outcomes are mutually linked, via positive and self-reinforcing associations between these various facets of wellbeing (see, e.g., Grossman, 2017 ; Huguet et al., 2008 ; Kim and Koh, 2021 ; Kawachi and Kennedy, 1999 ; Kulhánová et al., 2014 ; Li and Powdthavee, 2015 ). The relationships between health and socioeconomic inequalities are of significant interest to policymakers, as interventions that equalise socioeconomic variables could generate positive spillovers affecting the distribution of public health outcomes (Clark et al., 2008 ; Clark & D’Ambrosio, 2015 ; Osmani & Sen, 2003 ). By examining the full distribution of mental health outcomes, we extend beyond traditional analyses of the mean, providing a more nuanced understanding of the gender gap in mental health.

On average, men and women have different economic and social experiences (Berry & Welsh, 2010 ; Suter & Miller, 1973 ). For example, men typically have higher labour market participation rates, longer and less interrupted careers, and greater incomes. Conversely, women often face disadvantages in the paid labor market due to childrearing and other familial responsibilities. Given that these economic factors influence individuals’ psychological wellbeing (Clark et al., 2021 ; Zimmerman & Katon, 2005 ), they likely contribute to the gender mental health gap.

Differences in psychological makeup, social networks, and support systems between men and women could also play a role in how they handle adverse life events. Theories, such as those proposed by Bem ( 1981 ), suggest that masculinity and femininity are not fixed traits but rather roles that individuals adopt within societal norms. For instance, women are often expected to fulfill caregiving roles, which can increase their exposure to stress and potentially limit their access to personal support networks. The idea that taking a job allows women to mitigate the uncertainties of home life underscores the unique pressures they face (Hochschild, 2018 ). Moreover, early-life attachment styles, where women tend to be more emotionally responsive and dependent, contrast with men’s tendency towards independence and self-reliance, influencing stress responses throughout life (Bowlby, 1969 ). Additionally, women are more inclined to seek and receive social support, and there are notable differences in how men and women find satisfaction-women through intimate conversations, while men through companionship and task completion (Cohen & Wills, 1985 ). These dynamics suggest that men and women may process and react to stress differently.

Furthermore, the nature of life events experienced by men and women varies. For example, women were found to be more likely to experience financial distress than men (Zhou et al., 2023 ). Boys from disadvantaged families were more likely to have disciplinary issues and lower academic performance than girls from similar backgrounds (Autor et al., 2019 ). If these experiences occur more frequently for women than men (or vice versa), or have differing effects upon mental wellbeing that are correlated with gender, they may potentially drive health outcome differences.

Our study utilizes recent, high-quality Australian microdata to analyze the gender mental health gap. We reproduce the established result that women have poorer mental health than men on average (Australian Bureau of Statistics, 2021 ; Berry & Welsh, 2010 ), and show that they are strongly over-represented amongst individuals with very poor outcomes (i.e., there is a larger percentage of women at the lower end of the mental health distribution). Using regression-based decompositions based upon the Recentered Influence Function (RIF), we then show that socioeconomic variables such as income and employment do indeed play important roles in understanding aggregate outcome gaps. Subject to an assumption of exogeneity, our economic variables are sufficient to explain the entire gender gap amongst individuals with very poor mental health, which was defined as individuals at the bottom 10th quantile of the mental health score distribution. However, not all socioeconomic variables reinforce gender mental health inequalities. For examples, differentials in educational outcomes that favour women partially close the gender mental health gap.

We then turn our attention to the roles played by differing life experiences, such as being divorced, experiencing a death of spouse or child, or having an injured family member, in shaping mental health outcomes. Interestingly, while our life experience variables are extremely potent in predicting psychological health at the individual level, they contribute little to explain male–female differences in mental health. For example, negative life events such as experiencing physical violence have dire consequences for mental health for both men and women, especially for those who already located in the lower quantiles of the mental health distribution. However, the data indicate that these experiences are relatively rare, and not correlated strongly with gender; thus such experience does not explain why men in our panel appear to have better psychological wellbeing.

In addition to negative life experiences being rare events that drive our unexpected results, several potential factors may contribute to these findings. Other underlying risk factors, unrelated to the life experiences we examined, may be influential. For example, biological differences might affect mental health outcomes (HILDA does not collect variables such as blood group, heart rate, or blood pressure for us to control for in our study). Furthermore, differences in reporting or responding to mental illness between genders could contribute to the observed results. For instance, women are more likely to blame themselves when they are victims of violence, potentially exacerbating their mental health issues. Men and women may also perceive and report their mental health symptoms differently, affecting the apparent prevalence and severity of mental health issues in our study. Therefore, while adverse life experiences are crucial for understanding individual mental health trajectories, they do not sufficiently account for the gender gap in mental health within our Australian context.

Our research makes two contributions to the body of literature on health inequality. First, our econometric method goes beyond an analysis of the conditional mean by providing a full-distribution decomposition of psychological wellbeing. This approach allows us to disentangle the relative importance of various factors in driving the observed disparities. Second, to our knowledge, this study is the first that contrasts the impacts of economic and life experiences on gender mental health gap. By considering both sets of factors, policymakers can develop more effective interventions to address mental health disparities, focusing on modifiable factors that contribute to gender differences in mental wellbeing.

The paper is structured as follows. Section  2 describes the dataset and variables of interest. Section  3 describes our unconditional quantile regression models, which are still relatively new in the analysis of inequality, presents our model estimates and uses these models to decompose health inequalities into contributions from covariates. Section  4 suggests policies that might be effective in alleviating very poor mental health, and for closing the gender mental health gap.

2.1 The HILDA Dataset

We use data from Release 20 of the Household, Income and Labour Dynamics in Australia (HILDA) survey, which is an ongoing longitudinal study that began in 2001 with an approximately nationally representative sample of Australian households (Watson & Wooden, 2021 ). HILDA is comparable to the USA’s Panel Study of Income Dynamics (PSID), the British Household Panel Survey (BHPS) or the German Socio-Economic Panel (SOEP), so our results are generalisable. The first wave (2001) comprised 13,969 participants from 7682 households. In 2011, 2153 households (5462 individuals) were added to the panel.

HILDA collects information about the composition of households, income, employment, family relationships, and personal (individual) wellbeing. Individual person questionnaires are administered to every member of the household aged 15 years or older (with parental consent sought before interviewing persons aged under 18 years and living with their parents). All respondents completing an individual questionnaire are also asked to complete a separate self-completion questionnaire (SCQ) about general health and wellbeing, lifestyle and living situation, personal and household finances, job and the workplace, and parenting. We use data from both the household and individual surveys in our analyses.

Our study begins with a comprehensive dataset comprising 410,658 observations. We screen this sample to exclude individuals who did not complete interviews, focusing on the integrity and reliability of our data. This process results in a reduction to 305,143 observations.

Further refining our sample, we exclude participants with negative Mental Health Inventory-5 (MHI-5) scores, which indicated either No SCQ, Multiple Response SCQ, or Refused/Not Stated. This exclusion criterion aim to maintain the quality and relevance of our data, leading to the removal of an additional 31,921 observations. Consequently, our sample size is reduced to 273,222 observations.

Subsequently, we apply restrictions to ensure the meaningfulness of our analyses by focusing on relevant variables such as marital status and life events. This step further narrow our sample to 269,694 observations. Among these refined observations, we identify 887 cases where the after-tax equivalized income was zero. Given the nature of our analysis, which required logarithmic transformation of income, these cases are excluded. This step left us with 268,807 observations, evenly distribute between males (125,870) and females (142,937).

The implementation of multiple imputations address the remaining missing values, enhancing the robustness of our analyses. We document the results of these imputations in the Appendix A, demonstrating that the outcomes derived from the imputed data closely align with those presented in our main text.

2.2 Key Variables

Our analyses employ four variable types and we preview each below. These are the dependent variable (mental health aggregates), a range of economic indicators, a series of life experience markers, and a standard set of demographic controls.

2.2.1 MHI-5 Mental Health Aggregates

We measure individual-level mental health using the MHI-5 subscale of the SF-36 health aggregate. The SF-36 is a commonly used multi-dimensional instrument to measure health outcomes at population level, supported by substantial international research evidence of its responsiveness, validity and reliability in a range of studies (see, e.g., Brazier et al., 1992 ; Butterworth and Crosier, 2004 ; Jenkinson et al., 1994 ; Wu et al., 2023 ). Specifically, the reliability coefficient for the composite mental health measures was shown to exceed the recommended level of 0.85, with Cronbach’s \(\alpha = 0.95\) (Brazier et al., 1992 ). This high coefficient indicates the consistency and dependability of the composite scores derived from the mental health assessments, ensuring that our findings on gender differences in mental health are based on robust and reliable measures.

The MHI-5 score was derived following the procedure described in Ware et al. ( 1994 ). This is a 0–100 scale designed to be approximately cardinal in interpretation, where greater values are indicative of better outcomes. The five questions used to construct MHI-5 score are (1) been a nervous person, (2) felt so down in the dumps nothing could cheer you up, (3) felt calm and peaceful, (4) felt down, and (4) been a happy person. Each participant was asked to rate how often they had the aforementioned feeling in the past four weeks: (1) All of the time, (2) Most of the time, (3) A good bit of the time, (4) Some of the time, (5) A little of the time, (6) None of the time. The (reversed) answers were then added, subtracting 5, dividing by 25, and multiplying the sum by 100 with 0 implies a serious mental health problem and 100 means very good mental health (Roy & Schurer, 2013 ). Although MHI-5 includes only a subset of SF-36, its validity and reliability in detecting mental health issues in general population have been validated (see, e.g., Hoeymans et al., 2004 ; Rumpf et al., 2001 ; Thorsen et al., 2013 ).

2.2.2 Economic Variables

We measure the statistical relationships between economic disparities and mental health states using a range of economic indicators, focusing on income, labour force participation and education. Footnote 1

Household income is an indicator of individuals’ access to economic resources. This variable is the sum of all inflows such as labour earnings and government transfers, minus taxes. We then adjust for economies of scale within the household using the “OECD-modified scale”, following the formula Footnote 2

We opt not to use individual income because household income serves as a better indicator of economic welfare at the individual level. This decision is grounded in the recognition that within households, income is often shared among members, particularly in the case of married couples with children. Consequently, household income emerges as a more relevant and comprehensive measure in capturing the economic wellbeing of individuals (Buhmann et al., 1988 ). Footnote 3 While we regard household income as the best single measure of individual economic welfare, this variable assumes perfect sharing of income within households, which may result in an overestimate of the welfare of women because in many households, women do not work or earn less than men.

Labour market participation and education levels are included as sequences of dummy variables. Labour force status has three categories: employed, unemployed and not in the labour force, with the last group treated as the reference group. The “not in the labour force” group includes those who perform unpaid work in the home as a primary carer, either for children or for older adults in their family. The highest education level achieved has four categories: certificates and diplomas, undergraduate, postgraduate, and grade 12 or below, with the last group treated as the reference group.

Lastly, we supplement measures of economic wellbeing with quintiles of the socioeconomic indexes for areas SEIFA (Summerfield et al., 2020 ). The index has been used widely to capture the additional unobserved variation in the distribution of income and wealth that might associate with residential location.

2.2.3 Life Experience

The role of social factors in an individual’s mental health is potentially crucial. Typically, significant life events affect men and women differently, with varying frequencies. For instance, domestic violence is more likely to affect women. Additionally, men and women have different emotional reactions to life experiences, as they prioritize different factors. When men and women encounter distinct rates of life events or experience diverse impacts on their mental well-being, it can contribute to the gender mental health disparity.

A series of life experience markers are used to capture an individuals’ social experiences. These are dummy variables, with a value of 1 indicating that an individual had experienced the given event in the past year. We consider a broad range of these experiences, including death of spouse or child, death of close relative or family member, serious injury or illness to family member, natural disaster that damaged or destroyed home, fire or made redundant, victim of physical violence, close family member detained in jail, and being detained in jail.

2.2.4 Control Variables

We include standard demographic variables, such as marital status, household size, and individual age to control for omitted variable biases. Marital status has three categories: legally married or de facto, divorced/separated, and single; “marital status = single” is then used as the reference group. Marital status is likely to be an important determinant of mental health as it is indicative of an individual’s family support network. Similarly, household size reflects both the presence of a support structure and potential care responsibilities. Age and its square are used to control for trajectories in wellbeing over the lifecycle.

2.3 Descriptive Statistics

Figures  1 and  2 describe the distribution of mental health score MHI-5 by gender. In the 20-year HILDA panel, men have a significantly higher mean MHI-5 score than women in every period of the data (2001–2020, shown in the left panel of Fig.  1 ). The vertical gaps between the two lines (i.e. differences in means of MHI-5 scores for men and women over time) are relatively constant from one year to the next, indicating that the higher MHI-5 mean score for males is a highly persistent feature in the sample, Additionally, there is a higher variance for women, resulting in a much greater proportion of very low scores. Footnote 4 The percentage of women who have MHI-5 scores of less than or equal to 50 is consistently higher than that of men in each of those years, as shown in the right panel of Fig.  1 . Together, these facts imply that the left tail of the MHI-5 distribution for women is always thicker than that for men, as shown in Fig.  2 .

MHI-5 scores, HILDA, 2001–20. Notes The left graph shows the means of MHI-5 scores for men and women across all years from 2001 to 2020. The right graph shows the percentages of men and women with MHI-5 scores less than or equal to 50 in all those years

Kernel Density of MHI-5 scores, HILDA, 2001–20. Notes The figure depicts kernel densities of MHI-5 scores for men (solid line) and women (dashed line). The densities are estimated with fixed-bandwidth and boundary corrections at 0 and 100. The left figure is the kernel density estimated for men and women in 2020, which is the latest year in our data. The right figure is the kernel density estimate for the pooled sample

Table  1 below provides summary statistics for our variables of interest. We give mean values for men and women in the left and middle columns, with the difference in average outcomes on the right. Variables are grouped by outcome, demographics, economic and life experiences. It is important to note that there are significant differences in mean values for men and women for most variables.

With regards to economic variables, on average, men had significantly higher income than women, with almost 5% more after-tax equivalised annual income. Women were more likely to report being “not in the labour force” compared to men, with around 38% of women reporting not working compared to 26% of men. On the other hand, men were more likely to report being employed with almost 70% working compared to about 59% of women working. Women were also more likely to have an undergraduate degree, and made up a larger proportion of the lower education group (i.e. Grade 12 or below) than men, while men were more likely to obtain certificates, diplomas and postgraduate degrees, as shown in Table  1 .

The proportion of male that experience any major negative life events were reportedly different from that of female, with the exception of experiencing a natural disaster that damaged or destroyed their home. While men were more likely to be sacked, made redundant or jailed, women reported more of other negative life events. For example, the percentage of men reported being fired or made redundant was 1.5% higher compared to that of women. However, this could be because women were also more likely to report being “not in the labour force”. Among other negative life events that women were more likely to experience than men, we note that being of physical violence, death of a spouse or child, and close family member being jailed are rarer events (less than 2% of the sample) compared to having a serious injury or illness to family member or experiencing death of close relative or family member (between 10 and 20% of the sample).

3 Models and Results

Our regression-based decompositions of mental health inequality use unconditional quantile regression (UQR). This is a relatively new technique developed by Firpo et al. ( 2009 ) for unconditional quantiles modelling. As shown in the non-parametric estimates of the densities for mental health scores stratified by gender in Fig.  2 , an analysis of the conditional mean will mask disproportional impacts of the economic and social factors on both the absolute levels of mental health (of men and women) and its relative differences (between genders). Thus, an analysis of unconditional quantiles is useful for understanding how the relationship differs according to the location in the distribution.

The UQR was done by first transforming the outcome variable (MHI-5) using a recentered influence function (RIF) and then regressing the transformed MHI-5 against explanatory factors. The UQR allows us to evaluate the impact of changes in the distribution of various factors on quantiles of the unconditional distribution of mental health scores. UQR, therefore, looks at the entire distribution of the outcome variable, and thus is relevant for studying inequality, which is intrinsically linked to distributions rather than means (Kneib et al., 2021 ).

The analysis contains two parts. In the first part, we first apply the UQR method to estimate the correlates of the mental health state (MHI-5 score), for men and women separately. The regressions for men and women are:

where \(y_{Mit}\) and \(y_{Wit}\) are the i ’s observation of mental health in year t for men and women, respectively; \(\alpha _M\) and \(\alpha _W\) are constant terms; \(\gamma _t\) is the year fixed effects; \(\textbf{x}_{Mit}\) and \(\textbf{x}_{Wit}\) are vectors of demographic, economic and life event variables for men and women i in year t ; \(\varvec{\beta }_{M,\tau } \) and \(\varvec{\beta }_{W,\tau }\) are vectors of coefficients of the corresponding covariates at the \(\tau \) quantile for men and women; \(\epsilon _{Mit,\tau }\) and \(\epsilon _{Wit,\tau }\) are error terms. Footnote 5

In the second part of the paper, we use the RIF regression results to decompose the predicted outcome gap to examine how each factor contributes to the differences in mental health between men and women. Amongst various ways to perform decomposition in economics (e.g., Fortin et al., 2011 ), we focus on the aggregate effect of each explanatory variable. We compute the gap at the \(\tau \) th quantile following the formula:

where \({\hat{\Delta }}_{j,\tau }\) is the gap at quantile \(\tau \) th associated with variable j ; \(\bar{x}_{jM}\) and \(\bar{x}_{jW}\) are the means of variable j for the men and women group; \({\hat{\beta }}_{jM, \tau }\) and \({\hat{\beta }}_{jW, \tau }\) are the estimates of variable j from regression ( 1 ) and ( 2 ) respectively. This decomposition allows us to econometrically study factors that account for the differentials in mental health scores between men and women.

3.2 Results

3.2.1 correlates of mental health inequality.

We estimate our models over five main quantiles (10th, 25th, 50th, 75th and 90th) for men and women. The results are shown in Tables  2 and  3 , respectively. Footnote 6 Most variables are statistically significant and have the expected signs, with the economic variables having positive association with mental health score while life event shocks have negative impacts on mental health score, and the effect magnitudes being disproportionately larger at the lower quantiles.

Economic variables like after-tax equivalised income, being employed or having a higher level of education were correlated positively with MHI-5 scores. Although the positive correlation between mental health and economics variables has been shown in the literature (see, e.g., Araya et al., 2003 ; Kiely et al., 2015 ; Ross and Mirowsky, 1995 ), the larger size effects at lower quantiles reflect increasing returns to scale in mental health: that is, individuals who scored lower in the MHI-5 might experience much higher returns from any improvement in other aspects of life than individuals who already were in the relatively good mental health states. Income is more strongly correlated with MHI-5 scores for men at the 10th and 25th quantiles than women, but the impact’s magnitude reduces in higher quantiles. At the 50th, 75th and 90th quantiles, increasing income appeared to benefit the mental health of women more than men. For example, a 1% increase in income would increase the average 10th quantile of MHI-5 for men and women by 0.03 and 0.02, but would increase the average 90th quantile of mental health for men and women by 0.0093 and 0.011, respectively.

Being employed predicts improvements in the mental health state for both men and women, and the impacts are amplified at lower quantiles. Interestingly, unemployment appeared to be uncorrelated with the MHI-5 score for men while it was statistically correlated with lower state of mental health in women for those in 25th and 50th quintiles (with the correlation stronger in the former). This may be due to a tendency that women were more worried about their ability to secure a job compared to their counterparts, while it has been shown that greater job insecurity is associated with reduced self-reported health (Lepinteur, 2021 ).

Another important observation is that higher level of education was not always associated with higher MHI-5 scores. Having a higher level of education is associated with higher scores for those at lower quantiles but with lower scores at higher quantiles. An explanation for this could be that higher education is not necessarily translated into higher paid jobs, which might partly contribute to reduced mental health. For example, in order to work in certain professional fields, higher education is required but relative pay, compared to other occupations, might not be better.

All life events had negative impacts on mental health score for both men and women, which is also consistent with existing literature (see, e.g., Brose et al., 2021 ; Di Giuseppe et al., 2020 ; Gonzalez et al., 2011 ; Watson and Osberg, 2017 ). Footnote 7 However, our full-distribution regressions showed that the impact magnitudes were always larger for the lower quantiles. Among the eight major life shocks, the top three that significantly reduced the mental health state of men in the 10th quantile include: being a victim of physical violence, being detained in jail, and experiencing the death of a spouse or child.

While it is common knowledge that women who experienced physical assaults often suffered from poor mental health, the findings highlight that being a victim of physical violence was also traumatic for men, for both physical and mental health. However, being a victim of physical violence reduced women’s mental health score more than men’s; this held true at all five quantiles across the distribution. For example, if the proportion of victims of physical violence in the population was to increase by 10 percentage points, the average 10th quantile of MHI-5 for women would drop by 5.29 points, while that of men would drop by 3.32 points.

Experiencing the death of a spouse or child and being sacked or made redundant have stronger negative consequences for the mental health of women than men, ranking as the second and the third major correlates of poor mental health in women. The negative impact of these negative events might have implications for intergenerational mobility, as mothers’ mental health is found as a channel that translates financial problems on the noncognitive outcomes of their children (Clark et al., 2020 ).

In summary, economic outcomes like household income or education were observed to be less important contributors to low MHI-5 score at the individual level than negative life events such as being a victim of violence, especially for those at the bottom of the MHI-5 distribution. We speculate that individuals at the lower end of the mental health distribution experience more severe consequences from adverse events, as indicated by larger coefficients, because they have fewer resources or coping mechanisms available to them. With larger effect sizes and higher prevalence of exposure to negative life events among women, this conclusion aligns with our hypothesis that negative life events are strongly correlated with worse mental health in women who already are in the low mental health score range.

3.2.2 Decomposition of Gender Mental Health Gap

We now employ the regression estimates to gain insight into the factors that contribute to the disparities in mental health between genders. Based on the RIF regression results presented in Section  3.2.1 , we decompose the predicted outcome gap using Eq. ( 3 ). The results presented in Table  4 show a strong link between economic disparities and gender mental health gap, especially at the lower end of the mental health score distribution.

Among economic factors, income and labour market participation contribute to increasing the gender mental health gap. Specifically, disparity in income is sufficient to explain the aggregate gender mental health differences at the 10th and 25th quantiles. Indeed, even closing 58% of the income gap would fully close the mental health differential between men and women at the 10th quantile. Our results extend the literature on social determinants of mental health, which shows that poverty or income deprivation is negative and significantly related with mental health (Cuesta & Budría, 2015 ; Isaacs et al., 2018 ). Thus, not only low income is strongly associated with poor mental health, income differences between men and women is also the single most important predictor of the gender mental health gap for those with poor psychological wellbeing.

On the contrary, education narrows the gender mental health differences with “certificates and diploma” and “undergraduate” having the strongest impact at the low end of the mental health score distribution. As one moves toward the high end of mental health score distribution and the high end of the education system (i.e. postgraduate study), education tends to further the mental health gap between men and women. The fact that economic factors play important roles in gender mental health gap has positive implication for policy because socioeconomic variables are partially modifiable. Efforts to reduce the variation in economic inequalities may therefore plausibly feed through to close outcome gaps in health.

All the negative life events were associated with increasing gender-based mental health gap with the exception of being detained in jail. This result held across quantiles, even though their contribution to the gap decreased as one moved to higher quantiles. Among negative life events, serious injury or illness to a family member contributed most to the mental health gap between men and women. One possible explanation is that women often took a carer role after such events, regardless of whether or not the family member was their own or their spouse’s family. If the women facing such situations also carry on normal professional work and housework, then the additional caring responsibility might be perceived as an unfair division of labour, leading to lower life satisfaction and mental health (Flèche et al., 2020 ). Note that serious injury or illness of a family member did not have the largest absolute impact on MHI-5 scores, but physical assault experience, imprisonment and family’s death. The contribution of all these negative life events to the gender mental health gap was relatively small compared to the contribution of economic factors such as income. Although the impacts of some life shocks were extremely severe at the individual level, these types of events are infrequent for both men and women, contributing marginally to the gender-based mental health gap.

We note that apart from income, differences in the age distribution played an important role in the gender mental health gap, possibly because men and women have different paths through life and their trajectories through life are different. The contemporary literature on mental health inequality includes similar findings (e.g., Hashmi et al., 2020 ; Veisani and Delpisheh, 2015 ) and attributes this fact to the ageing or retirement effect. Although male and female health needs at older ages are different (Gómez-Costilla et al., 2021 ), it is hypothesised that as people get older, they become more psychologically stable, thus men’s and women’s mental health states tend to converge. The constant in Table  4 accounts for common underlying risk factors that are unrelated to other variables examined in our studies, such as biological differences, or differences in reporting or responding to mental illness. Overall, the gender mental health gap decreases as one moves from the lower quantiles to the higher quantiles of the distribution, as shown in the last row of Table  4 . Footnote 8

4 Discussion and Conclusion

This paper has studied the structure of the mental health gap between men and women in Australia. Men are shown to have a highly robust advantage in MHI-5 Mental Health scores; one that is particularly large in the left tail of the health-outcome distribution. We considered two potential sources of this disparity—(1) the persistent economic disadvantage experienced by women in terms of income, and (2) the differences that men and women have in terms of their life experiences.

Using regression-based decompositions based upon the Recentered Influence Function, we showed that income is a highly significant determinant of mental health for individuals in the lower quantiles of the unconditional MHI-5 distribution. This feature, combined with lower mean incomes for women and higher rates of poverty and disadvantage, suggests that the over-representation of women amongst individuals with very poor mental health is strongly linked with economic disparities. Although regression models such as these may be influenced by reverse causal effects or omitted variable bias, our results are robust, consistent with expectations, and line up with existing findings in related literature. Policies that compress the income distribution and mitigate poverty are therefore likely to narrow the gender gap in individuals with very poor mental health. Other socioeconomic variables such as education have relatively small effect sizes, and in some instances generate female advantages in psychological health.

We also considered the possibility that social experiences (captured with life event variables) may contribute to this outcome gap. Men and women on average lead different types of lives, and care different amounts about relationships, risk, caring, family, and many other such factors. Since these variables are also likely to be important determinants of mental wellbeing, they offer an alternative to the economic explanation for the mental health gaps. Our models show that factors like divorce, job loss, incarceration, experiencing violence, and the death of friends or family members are indeed highly influential over individuals’ mental states. However, these social factors are relatively rare and do not differ substantially between men and women, and are therefore not a meaningful source of gender disparity. Policies that offer social support for individuals suffering from adverse life events are likely to reduce health inequalities, as these variables have unusually large effect sizes for individuals with already poor mental health. However, they will not meaningfully lower gender gaps.

Data Availability

The unit record data from the HILDA Survey were obtained from the Australian Data Archive, which is hosted by The Australian National University. The HILDA Survey was initiated and is funded by the Australian Government Department of Social Services (DSS) and is managed by the Melbourne Institute of Applied Economic and Social Research (Melbourne Institute). The findings and views based on the data, however, are those of the author and should not be attributed to the Australian Government, DSS, the Melbourne Institute, the Australian Data Archive or The Australian National University and none of those entities bear any responsibility for the analysis or interpretation of the unit record data from the HILDA Survey provided by the author.

All monetary values used in this study are adjusted for inflation. We calculate the Consumer Price Index (CPI) for each state each year as the average of four quarters—all commodity groups, following the data from the Australian Bureau of Statistics with index reference period: 2011–12 = 100.0.

https://www.oecd.org/els/soc/OECD-Note-EquivalenceScales.pdf .

We present results using individual income as a robustness check in Appendix C.

Some studies show that women report being happier than men and have higher average “life-satisfaction” scores on average (Graham and Chattopadhyay, 2013 ; Joshanloo and Jovanović 2020 ). However, it could be because women have different reference points when answering the same life satisfaction question (Montgomery, 2016 ) or men less likely to report their problems for fear of stigma or to do with dominant notions of masculinity (Affleck et al., 2018 ). Stevenson and Wolfers ( 2009 ) found that women experienced an absolute and relative decline in happiness across multiple datasets spanning a number of Western industrialised countries.

Although HILDA collects panel data, we do not use standard panel data techniques like random effects or fixed effects because we are interested in inequality; using fixed effects will wipe out the dispersion that we care about, and there is no random effects version of our approach. Instead, we run the pooled model with year fixed-effects. The estimates for selected years are presented in Appendixes D and E.

We acknowledge that it is important to account for past mental health conditions because there is likely correlation between \(\epsilon _{it}\) and \(\epsilon _{i, t-1}\) that is not necessarily controlled for in the model (i.e., an individual responses can be similar over the years). However, we do not include a lag dependent variable, because if we did, what explains inequality in \(y_t\) will be in \(y_{t-1}\) , and what explains inequality in \(y_{t-1}\) will be in \(y_{t-2}\) . This recursion will not help us to get to the root source of inequality and create endogeneity issue in short panel. We are unaware of any dynamic technique for Quantile Regression. As a result, instead of accounting for the correlations as coefficients in the model, we resolve this issue using clustered robust standard error at the individual level.

The full results are provided in Appendix B.

Hashmi et al. ( 2020 ) did not study the mental health of men and women separately, but they found that exposure to negative life events was most harmful for the mental health of people in the most disadvantaged socioeconomic groups.

We expand our analysis by examining the impact of gender on outcomes and introducing further metrics to assess inequality, such as the interquantile share ratio, the Gini coefficient, and the log variance. The findings derived from these expanded analyses corroborate the conclusions reached in the main text, as detailed in Appendix F.

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A Results Using Multiple Imputation

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B Full RIF Regression Results

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C Results Using Individual Income

See Tables  9 and 10 .

D Results Using Wave 9

See Tables  11 , 12 and 13 .

E Results Using Wave 20

See Tables  14 and 15 .

F Treatment Effects of Gender

See Tables  16 , 17 and 18 .

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Nguyen, T.T., Nguyen, K.H. & Rohde, N. Economic Disparities, Life Events, and the Gender Mental Health Gap. Soc Indic Res (2024). https://doi.org/10.1007/s11205-024-03424-6

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The Role of Social Determinants of Health in Promoting Health Equality: A Narrative Review

Khushbu chelak.

1 Public Health and Epidemiology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Medical Sciences, Wardha, IND

Swarupa Chakole

2 Community Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Medical Sciences, Wardha, IND

Significant health disparities exist locally and even throughout the nation. Dipping health inequalities necessitates a focus on the inadequate spread of power, money, and resources, as well as the situations of daily living, which may be addressed through social determinants of health. This study aimed to review the role of health-related social factors in overcoming health disparities. We conducted a search of English-language literature, including studies published on health and health equalities or inequalities. Most reports show that social determinants of health have a higher effect on health. The elimination process of these health inequities occurs through well-designed economic and social policies. Every aspect of social determinants influences the health aspects of people; hence, some areas to focus on include employment, education, socioeconomic status, social support networks, health policies, and healthcare access. Launching interventions to reduce health disparities can help improve the community’s health and health equality.

Introduction and background

According to the World Health Organization (WHO), social determinants of health (SDH) are defined as the circumstances in which humans are born, develop, live, earn, and age. At the international, regional, and state or local levels, the distribution of money, power, and resources shapes these circumstances [ 1 ]. The WHO Commission on Social determinants of Health (CSDH) has stated that progress on SDH is the most successful means of enhancing all people’s well-being and raising disparities [ 2 ]. The WHO established the CSDH based on SDH intervention, which is the most effective strategy to improve well-being and reduce inequality [ 2 ]. Important aspects include governmental, financial, and traditional organizations, based on factors such as manageable healthcare and learning organizations; safe ecological conditions; aesthetically pleasant neighborhoods; and the availability of nutritious food [ 3 ]. Nowadays, health challenges such as being overweight, cardiovascular diseases, diabetes, and depression are prominent, wreaking havoc upon people because of the increasing demands of a high lifestyle. This leads to people suffering from non-communicable diseases. These socioeconomic variables contribute to societal stratification and health disparities among persons of different social and economic classes, genders, and ethnicity.

History of social determinants of health inequality

In the 19th century, people started becoming aware of the factors that had an impact on the health of the population [ 4 ]. Rudolf Virchow, a pioneer in this field, testified on the role of poverty in generating a disease that led to a plague outbreak in Prussia [ 4 ]. Friedrich Engels also studied to find out about the increased mortality. After that, Salvador Allende tried in Chile to demonstrate the importance of political and social variables in people’s health inequities [ 4 ]. All of them tried to frame how factors influence health and what role they play. Marmot emphasized that the workplace may be an important location for addressing disparities. Similarly, changing housing might have an impact on physical and mental health [ 5 ]. Cutting across the structural inequalities, health inequality is a more contemporary challenge and possibly a consequence of the imbalances in development planning and economic design [ 6 ]. Interventions on health and its disparities help overcome further problems [ 7 ]. There is a long history of housing evidence from several reviews [ 8 ]. People suffering from the financial crisis and economic disparities were also among the many who were deeply affected by the growing socioeconomic demands in the early days.

Methodology

This article presents a narrative review of SDH in promoting health equality. PubMed and Google Scholar were used to find all original and review articles with original reports. A set of keywords and Medical Subject Headings (MeSH) terms related to health inequalities and SDH were used. Keywords used were social inequalities, social inequities, poverty, health determinants, behavior, economic status, and social movement. The following MeSH terms were used interchangeably and in combination to find all relevant articles: social determinants, health inequities, and social movement. All free full-text PubMed Central articles were searched using Pubmed and Google Scholar. Studies that discussed the relationship between health inequities, the importance of social determinants, health inequities, health policies, social factors, health equality, and social movement were included. Articles that reviewed SDH in a more general way and whose main focus was not health inequalities and equalities were excluded (Figure ​ (Figure1 1 ).

Social determinants of health

A subcategory of health factors is SDH, as shown in Figure ​ Figure2 2 .

Source: Open access journal under a CC-BY license contributed by social determinants of health. Available at: https://www.who.int/health-topics/social-determinants-of-health#tab=tab_1 [ 9 ].

The most significant health factors include government policy, medical availability, individual behavioral choices, and biological and genetic features [ 3 ]. Examples of SDH include occupation, job status, workplace safety, level of income, opportunities for education, job and place of work protection, inequity between men and women, and segregation based on race. The various health aspects of SDH include food poverty and limitations of access to nutritious food options, housing, and helpful facilities available; early childhood growth and experiences; inclusion in the community and social assistance; the prevalence of crime and exposure to violent behavior; neighborhood circumstances and physical environment; and possibilities for recreation and leisure, as shown in Table ​ Table1 1 .

Source: Open access journal under a CC-BY license contributed by social determinants of health (2018). Available at: https://www.christenseninstitute.org/wp-content/uploads/2018/10/Social-Determinants-of-Health-Table.png [ 10 ].

Social determinants of health indicators

Social determinants determine how health is affected, how they play a significant role in influencing health, and how we can improve health for all. Some of the effects of social determinants affect health in the long term. For example, a less educated person might have less knowledge about how to utilize resources which may affect their ability to use resources to the fullest. Thus, social determinants play a role which must be recognized and improved.

Socioeconomic Status

Financial stress and socioeconomic status are a combination of a family’s or ordinary citizen’s profession, academic performance, wealth, and economic standing. Wealth and power are characteristics that influence a person’s socioeconomic status. The total amount earned in earnings or compensation over a year is income. Income limits a family’s overall lifestyle and influences their consumption habits [ 11 ]. Discrimination based on caste, creed, and gender makes a person vulnerable, enabling them to stop asking for more. They are subjugated to extreme pressure, which only worsens their mental status and health. Health education and programs must be used to educate people about how beneficial it is to count everyone as a whole. The promotion of health equality and equity remains the most significant goal of balancing financial stress.

Equality Education

Education is a means of improving one’s socioeconomic standing. A wealthy family’s socioeconomic status suggests a higher chance of enrolling and graduating from college. Family background, rather than other factors, such as supplemental educational services, significantly influence how much and what kind of education people get and what kind of employment they obtain [ 11 ]. The goal of improving income equality and eradicating poverty through education has not been met. Higher wages and social policies that support low-income families are needed to enhance students’ social and economic conditions [ 11 ]. Educational perspectives allow us to take a comprehensive and clearer view of the causes of health and disease in a population and must be paid attention to [ 4 ].

Gender Inequality and Age Inequality

Women earn less and are more likely to be poor. Institutional discrimination in a patriarchal society, where women were supposed to be mothers and spouses rather than part of the formal workforce, is to blame for gender inequality [ 11 ]. Poverty is more prevalent among the youngest and oldest population groups. Children are more likely to be poor than other age groups. School attainment, high school graduation rates, and reading skills are all impacted by poverty [ 11 ]. Health inequality does not mean just some kind of health difference but the differences in health like that of a pregnant woman who has fewer resources and is deficient and the newborn child who might be underweight leading to problems such as stunting and growth retardation. This adversely affects the opportunities and performances of those afflicted by it and can be corrected by successfully evaluating the determinants affecting health [ 6 ].

Economic Inequality

The United States has the least poverty rates and the most restrictive social policies regarding escaping poverty. Except for Mexico and Turkey, all other developed countries have more significant income disparity than the United States. Young people, full-time workers in low-status jobs, people of color, illiterate people, and women are most likely to be poor [ 11 ]. In influenced market knowledge and customer needs individualized society, incomes or wealth are alternate socioeconomic indicators [ 3 ].

Economic Power

Economic power is the ability to improve the standard of living of a country or business. Economic power represents the status of people with higher socioeconomic status wielding more power than those with lower socioeconomic status. For example, employment provides income that shapes choices about housing, child care, education, medical care, and food, among others. Influence is a factor of being able to produce, buy, and sell. Power is the primary force in today’s era. Because the curriculum is governed by teachers, school board members, and national standards, teachers and students have power connections [ 11 ].

Implications of a System Approach

In a systems approach, the current state of affairs and its factors are both causes and outcomes. Rather than a linear path of socioeconomic variables leading to various health outcomes, they are interconnected in a causal web. The feedback loops result in outcomes that influence the causes. Low income and deprivation, for example, lead to inferior health outcomes, exacerbating the group’s poor and worsening health. A more advanced model, such as the system dynamic model, is necessary to operationalize [ 12 ]. Early events and life cycle, occupational considerations, social ties (social networks and support, discrimination, neighborhood characteristics), and healthcare are all identified as social risk factors [ 13 ]. Eliminating these health inequities indicates that well-designed economic and social policies can promote health and health equity. It outlines 10 guidelines to keep in mind while launching interventions to reduce health disparities [ 14 ]. The circumstances increase their impact on life [ 15 ]. The physical atmosphere, opportunities for learning, suitable housing, occupation, and wealth are examples of these circumstances, known as SDH [ 16 ]. Recommendations such as improving living conditions and inequalities among people are justified in their own right but the way these are linked to health is problematic [ 17 ]. The magnitude of inequalities should be viewed with caution because the study does not take caste into account, potentially exaggerating socioeconomic inequalities [ 18 ].

Policies for Improving Health Equality

While numerous public policies contribute to public health and equality, enhancing public health is not the society’s or the government’s only goal. Although these initiatives have been effective at commencing actions addressing SDH, continuing inequities, and diverse social, economic, and cultural differences across India, more cooperation is required across the current programs of different ministries [ 18 ], resulting in policy incoherence may develop. Due to a lack of policy coherence throughout the government, one branch of the government may ensure the introduction of a national development plan of action, for example, TB free response to the change of the WHO [ 19 ]. At the same time, other parts promote exports, industrialization, and proposals that are dangerous to human life. The single cause for these discrepancies is a lack of knowledge among areas of the connections between health and quality of life, on the one hand, and more significant health determinants, such as productivity expansion, on the other. Another cause is that unrelated initiatives may have unexpected consequences that are not monitored or addressed. In its preamble, the Indian Constitution provides core values for establishing a social order in the country. An orderly society is built on these core values. Equality, various freedoms, socioeconomic justice, and individual dignity are fundamental principles for governing a democratic country like India. The policy approach will protect the social rights of people [ 20 ].

The healthcare system must comprehend the obligations of other parts and establish mutual consideration of health, its consequences, and great social welfare or life characteristics to contribute to policy coherence across government. It needs novel solutions and institutions that create avenues for debate and decision-making that cut across typical policy silos in government. In practice, this entails taking a variety of acts, such as facilitating seminars of government policymakers, program leaders, and healthcare provider organizations, to promote policy, service, and program coherence in response to the needs of disadvantaged groups, such as via conferences conducted at numerous organizational stages and with private and government providers. For evaluation of policy progress and pitfalls, from a theoretical perspective, several policy-making frameworks can be used to describe how programs are developed and executed [ 21 ].

The policy windows model by Kingdom (1995) is crucial as it illustrates how and why issues become part of the policy agenda before implementation [ 22 ]. Three streams are coupled or decoupled problem, policy, and politics; according to Kingdom, open and close strategy opportunities. The gathering of proof regarding health inequality is essential but not enough for policy change. Problems must be viewed or identified as problems that can be addressed by legislation. Complicated by the fact that general populace initiatives in largely unrelated domains may have population health implications [ 23 ]. The collection of facts, particularly the Acheson Report, has aided in the designation of health disparities as a political issue. Similar inquiries have formed the issue in other places [ 24 ].

Socioeconomic Determinants and Health Inequities

SDH must all be incorporated into public health services to reduce health disparities. Health services must be adapted to the demands of distinct population groups. Due to the build-up of difficulty through several areas and over the life course, different social groups in the population differ in their empowerment to participate in health interventions. Many public health programs have not met or are not meeting their health equity targets due to a lack of healthcare-specific interventions and a failure to reach out to vulnerable people and address significant social variables that affect public health. Disparities exist between public and private health systems [ 25 ]. Policy efforts at the health system level are required to monitor and improve these disparities [ 25 ]. The coronavirus disease 2019 (COVID-19) pandemic has had the greatest impact on groups that have faced discrimination and historical injustices [ 26 ]. Poor living conditions and exploitative labor have become more prevalent, allowing for inequitable income distribution and health risks [ 26 ]. Governments have exploited the pandemic to further erode civil and human rights and promote extractives [ 26 ]. A post-COVID-19 world must ensure equity, social justice, solidarity, and a shift in the balance of power and resources for poor and marginalized people [ 26 ].

Lower-income societies with lower smoking rates have a lower incidence of lung cancer [ 27 ]. Individual smoking patterns or different rates of illness prevalence and incidence among social groups, i.e., inequalities, are caused by balances or imbalances in community norms and social structures. Sick people are diametrically opposed to the overall healthy population [ 27 ]. The term health inequalities in SDH (SDHI) has recently been taken to refer to settings, social structures, social norms, and some determinants. Three primary paths have been proposed to describe how the social environment causes fitness inequities [ 28 ]. Social choice, or mobility of community, suggests that health, relatively more than the other way around, determines socioeconomic status. As a result, healthier people will be happier. They move toward a higher socioeconomic status than others who were less beneficial, resulting in inequities. Social causation claims that discrepancies in health outcomes are caused by a variety of unequally distributed material, psychosocial, and behavioral factors [ 29 , 30 ].

A life path viewpoint indicates various features throughout life (e.g., malnutrition in the maternal prenatal period, low learning services in infancy, physically hazardous employment, influence, and manifest illness trends across time). The eco-social method tries to assimilate these organic, communal, and natural variables in illness through a vigorous process of incorporation which means we accurately integrate natural effects from the substantial and the social world [ 7 , 31 ]. Over the last 40 years, research on health inequalities and growth has shed light on the income well-being trend [ 32 ]. Measuring the disparity between subgroups requires using different health data based on the relevant dimension of inequality (i.e., demographic, socioeconomic, or geographic factors) [ 33 ]. Monitoring health inequality at the national level assists in assessing the impact of policies, programs, and practices on the disadvantaged subgroup [ 33 ]. This priority will be given to the proposed Sustainable Development Goals. which ask countries to increase the income, gender, geographical location, race, age, ethnicity, disability, migrant status, and other relevant characteristics at the national level [ 34 ].

Conceptual Limitations of Inequalities

SDHI covertly and overtly embraces substantial parts of a Newtonian view of reality (i.e., reductionism, linearity, and hierarchy), as do most notions connected to health outcomes [ 35 ]. This reductive approach is represented by a factor influencing health outcomes, for example, socioeconomic stratification of mortality due to asthma and the selection of interventions that focus on a single determinant, for example, improving thermal comfort in homes with insufficient heat [ 36 ]. Another common assumption in this debate is linearity, which argues that determinants of inequalities can be used in a variety of situations [ 30 ]. Differential access to healthcare or education is presumed to be health disparities in results [ 37 ], essentially in a linear pattern, whether overtly or implicitly [ 38 ]. In the case of what works in terms of tackling health inequalities, disappointingly very less relevant reviews have been conducted [ 39 ].

Conclusions

After reviewing the current literature on SDH and health inequalities, we conclude that economic and social factors such as poverty, social exclusion, and others are usually regarded as SDH. Interventions are the most effective strategies to improve everyone’s well-being and reduce inequalities. The severity of employment, geography, and education imply that better healthcare management and expanded education and work prospects are required. Additional efforts in this area will likely help overcome social health inequalities in communities and achieve health equality. Policies that reduce social disadvantage can reduce health inequalities. The current state of the health sector, for which the union and state governments are equally responsible, and the right to health is not equally distributed can only be corrected if the union and state governments start practicing and introducing more efforts to achieve health equality. Health rights should be given to all people, encouraging them to use more services. Hence, making them healthier, more productive, and fit.

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