Source: Table created by authors from the following information on surveys: OHS: National Research Foundation ( 63 ); GHS and CS: Stats SA web-based Nesstar information and Stats SA electronic reports ( 54 , 69 , 89 ); DHS: Department of Health et al ., 2002 ( 47 ), Department of Health et al ., 2007 ( 68 ); NIDS: Moultrie & Dorrington, 2009 ( 90 ), Leibbrant et al ., 2009 ( 71 ).
The establishment of the October Household Surveys (OHS) programme in 1993 marked the beginning of the national collection of demographic information on an annual basis. The OHS was a cross-sectional sample survey undertaken by Stats SA from 1993 to 1999, aiming to collect individual and household information that covered a range of development and poverty indicators. The OHS was replaced by the General Household Survey. The surveys were based on a probability sample of a large number of households, targeting residents in private households and workers–hostels countrywide. Fieldworkers visited sampled households and filled the survey questionnaire during face-to-face interviews ( 63 ). See Tables 1 and and2 2 and the Stats SA ( 64 ) and University of Cape Town's DataFirst ( 65 ) websites for more information.
The General Household Survey (GHS) has been conducted annually by Stats SA from 2002 to 2011 and was in the field until September 2012 for the next round. The GHS was instituted to monitor development indicators and development programmes on a regular basis. The survey aims to measure multiple facets of the living conditions of the country's households, and the quality of service delivery in selected service sectors. The GHS is a cross-sectional survey, based on a representative sample drawn from the total population. The target population is private households and residents in workers–hostels. Using probability-proportional-to-size principles, a multi-stage, stratified random sample is drawn. Households are visited by fieldwork teams and an extensive questionnaire is filled by enumerators during face-to-face interviews ( 66 , 67 ). Further information is available in Tables 1 and and2, 2 , and at the Stats SA ( 64 ) and DataFirst websites ( 65 ).
Post-democracy, two national Demographic and Health Surveys (DHSs) were conducted collaboratively by the Department of Health, SA MRC, and OrcMacro. The 1998 SADHS employed a two-stage sample based on 1996 census demarcations and stratified according to the nine provinces, each subsequently stratified by urban/non-urban residence ( 47 ). The 2003 survey sample, based on the enumeration areas created during the 2001 census, was designed as a nationally representative sample of households. Stratification took place according to the provinces and subsequently by urban/non-urban residence ( 68 ). Eligible women were prompted for full birth histories in both surveys. Tables 1 and and2 2 highlight more information about the types of mortality data collected. More information about the 1998 and 2003 surveys is available in the final full reports ( 47 , 68 ).
The Community Survey (CS), conducted by Stats SA, was a large-scale nationally representative inter-census household survey conducted in 2007, designed to provide information on the trends of selected demographic, social, and socio-economic profiles of the population. The sampling procedure included a two-stage stratified random sampling process, the first involving the selection of enumerator areas within each municipality, and the second the selection of dwelling units within enumerator areas. Enumerators visited the selected sampled dwelling units and completed questionnaires during face-to-face interviews with study participants ( 69 ). The realised sample was adjusted to replicate the national population in a way that the data are consistent internally and with other censuses and surveys ( 70 ). Tables 1 and and2 2 and the Stats SA ( 64 ) and DataFirst ( 65 ) websites hold more information.
The National Income Dynamics Study (NIDS) was South Africa's first national panel study to document the dynamic structure of households and changes in the incomes, expenditures, assets, access to services, education, health, and well-being of household members. The target population was private households in all provinces and residents in workers–hostels, convents, and monasteries. Households were sampled with a stratified, two-stage cluster sample design, randomly selecting 400 primary sampling units in the first stage from Stats SA's 2003 master sample. In each primary sampling unit, two dwelling-unit clusters were selected. The first fieldwork wave commenced in February 2008. Information was collected on all household members, both resident and non-resident. A household questionnaire and an individual questionnaire for each adult and child in the household were administered via face-to-face interviews in each household ( 71 ). See Tables 1 and and2 2 and the NIDS methodology report ( 71 ) for more information.
Mortality was assessed for different age or interest groups across the surveys, and mortality items sometimes varied within surveys across time ( Tables 1 – 2 ). Items to assess mortality via both direct and indirect measures were included in these surveys. Assessments of deaths in the household, parental survival, and summary birth histories were included most frequently across time and surveys. When a death was reported, selected further information about the death and deceased were collected. The years of enumeration, interest group, and mortality data items used are shown in Tables 1 and and2 2 .
Unit record data from the OHS can be requested for use in the DataFirst Research Data Centre at the University of Cape Town. The Research Data Centre makes data, statistical analysis software, and trained staff available, free of charge, for this purpose ( 65 ). OHS data and metadata are available on compact disc for a fee from Stats SA ( 72 ). Unit record data from the GHS and CS are available on disc from Stats SA via request. Stats SA's web-based data repositories ( 54 ) contain information about mortality as collected in the GHS and CS. From the GHS, parental survival data for 2002–2010 can be found via SuperWEB, and for 2002–2008 via Nesstar ( 54 ). From the CS, parental survival data are available via Nesstar and SuperWEB, and summary birth histories and death-in-the-household data via Nesstar ( 54 ). Users can tabulate CS death-in-the-household data by selected demographic variables (e.g. age and sex) and sub-national entities (e.g. province and district council). Stats SA also responds to special requests for tabulations from the Stats SA surveys without requiring a data user's agreement. Due to the poor quality of the data, the 2003 DHS failed to provide reliable estimates of adult and maternal mortality from the parental and sibling survival questions ( 68 ). The birth history data, however, were used to estimate levels and trends in infant and child mortality, and apart from a chapter presenting these results ( 68 ), unit record data from the 1998 and 2003 DHS surveys are available per request and by signing a data user's agreement, from the Department of Health and SA MRC. For NIDS, data and supportive documentation for Wave 1 are available via DataFirst servers upon completion of an online form and agreement to the terms of data use ( 71 ). Publicly available datasets from any of these surveys contain only non-confidential data.
Extensive resources and information related to these surveys, including questionnaires, reports, metadata, code lists, public data downloads, and microdata-request forms, are available from the University of Cape Town's DataFirst webpage ( 65 ).
Three INDEPTH HDSSs collect longitudinal health and demographic data in three rural surveillance areas. The Agincourt HDSS in the Bushbuckridge district of Mpumalanga province has collected data since 1992 ( 73 ); Dikgale HDSS in the Mankweng district of Limpopo, since 1996 ( 74 ); and the Africa Centre Demographic Information System (ACDIS) in the Umkhanyakude district in KwaZulu-Natal, since 2000 ( 75 ). Agincourt had a population of approximately 90,000 people in 2011 ( 73 ); Dikgale, approximately 8,000 in 2008 ( 74 ); and ACDIS, approximately 85,000 in 2008 ( 75 ).
Mortality information and a range of other socio-demographic and health information are collected through annual censuses and updates of vital events in each household in the site. Verbal autopsies, a well-established method in the absence of routine death registration, are used for classifying causes of death from population-based inquiries ( 76 – 78 ), and are conducted by specially trained fieldworkers who interview a close relative or caregiver of the deceased. Efforts to refine the approach, have led to international standards for verbal autopsy and strengthening standardised interpretation of verbal autopsy data through the InterVA tool, recently culminating in the launch of the InterVA-4 model ( 14 , 79 , 80 ). At Agincourt and ACDIS, the probable cause of death has been attributed via subsequent physician assessment of the verbal autopsy information ( 73 , 81 , 82 ). However, more recently all three sites have been utilising the automated InterVA tool for probabilistic verbal autopsy interpretation and probable cause attribution ( 83 – 85 ) (e-mail communication from Dr. Chifundo Kanjala and Prof. Marianne Alberts, 4–5 April 2012).
Mortality and population data from ACDIS and Agincourt are available through data products, data downloads, and accompanying supportive documentation at the HDSS’ websites ( 86 , 87 ). Public access to ACDIS and Agincourt data is available via links to downloadable datasets comprising an approximate 1%- and 10%-sample, respectively, of the full datasets ( 86 , 87 ). These sample datasets can be used for teaching, familiarising potential users with the structure and availability of data, or developing selected analyses before requesting the full dataset. Unit record data that are not publically available can be requested from senior site staff at ACDIS and Agincourt, accompanied by a motivation, analysis plan, and data user's agreement ( 86 , 87 ). The INDEPTH Network is committed to the principles and practice of data sharing, and has launched the iSHARE portal aiming to make data from the HDSSs publicly available ( 13 ).
Apart from South Africa's routine notification system currently reporting incidence and deaths from 33 notifiable medical conditions to local, provincial, and/or national health departments, the country's particularly sizeable burden of disease from cancer, injury, and tuberculosis is reflected in facility-based surveillance systems to help assess the extent and impact of these conditions. Table 3 provides key information about these surveillance systems. Recognising the current international pressure to provide reliable information about maternal and child health to monitor MDGs 4 and 5, and acknowledging the shortcomings in reporting such mortality via civil registration, Table 3 refers additionally to three facility-based structured mortality audits for peri-natal, child and maternal deaths. The value of other national reporting systems, including the South African Birth Defects Surveillance System (SABDSS), South African Dialysis and Transplantation Registry (SADTR), Surveillance of Work-Related and Respiratory Diseases in South Africa (SORDSA), as well as injury-reporting of the Mine Health and Safety Inspectorate, National Transport Information System, and the South African Police Services, are acknowledged but not described here.
Selected facility-based data sources that may complement vital registration mortality data
Condition- or age-specific data sources | ||
---|---|---|
Programme | Enumeration years | Selected key information about source |
Confidential Enquiry into Maternal Deaths (CEMD) | 1998–current | |
Peri-natal Problem Identification Programme (PPIP) | 2000–current | |
Child Healthcare Problem Identification Programme (Child PIP) | 2005–current | |
National Cancer Registry (NCR) | 1986–current | |
National Injury Mortality Surveillance System (NIMSS) | 1999–current | |
National Tuberculosis Registry (NTR) and Electronic Tuberculosis Register (ETR.Net) | 1995–current |
Source: Table created by authors from the following: CEMD: National Committee CEMD, 2008 ( 91 ); PPIP: Pattinson (ed), 2011 ( 92 ), Bradshaw et al ., 2008 ( 93 ); Child PIP: Stephen et al ., 2008 ( 94 ), Bradshaw et al ., 2008 ( 93 ); NCR: Albrecht, 2006 ( 95 ), Mqoqi et al ., 2003 ( 96 ); NIMSS: Matzopoulos, 2002 ( 97 ); TB: Dept of Health, 2004 ( 98 ), Dept of Health, 2007 ( 99 ).
Despite having improved vital registration data and nationally representative HIV prevalence data, these data sources do not provide decision makers with a direct measure of the mortality impact of the country's extensive HIV/AIDS epidemic ( 56 , 100 ). Mathematical models have hence become necessary, and local actuarial researchers have developed the ASSA AIDS and Demographic Model ( 101 ) to estimate such impact. The model has been calibrated to empirical data sources, including vital registration, census, and survey data adjusted for biases ( 100 , 102 ). As time passed and more relevant empirical data became available, updated revisions of the model were released. A number of mortality and population indicators are available from the models and are widely used as a basis for health policy and planning by both government and the public health research community in South Africa ( 103 – 110 ). While these models are of much practical use, they should be considered ‘interim’ measures until complete vital registration and improved medical certification of causes of death are achieved. Upon online registration, mortality indicators such as 5 q 0 (under-5 mortality) and 45 q 15 (adult mortality) are freely available at the website of the Actuarial Society of South Africa ( 101 ).
As this article aims to review mortality data sources and not results from these sources, Fig. 1 is merely provided to indicate the variety of data sources available for estimating adult mortality, specifically the probability of dying between ages 15 and 50 ( 35 q 15 ). Estimates of 35 q 15 were derived from using both direct and indirect methods, as indicated in Fig. 1 . A fairly consistent trend of increasing mortality for most of the 1990s and early 2000s is produced by the different data sources and methods, with a levelling and decline in mortality in more recent years.
The probability of dying between ages 15 and 50 ( 35 q 15 ) from different data sources.
Source: OHS and DHS estimates extracted from Figure 6.2 in Dorrington RE, Moultrie TA, Timæus IM, 2004 ( 44 ). ASSA2008 estimates calculated from ASSA life table values which are based on vital registration data ( 101 ).
This review demonstrates a rich and varied list of mortality data sources in South Africa. However, it is important to be aware of the strengths and limitations of the different sources and the quality of their data to ensure suitable and strategic utilisation thereof. Different mortality indicators are required for different purposes, and at varied levels of population aggregation. For instance, reliable measures of peri-natal and under-5 mortality at the health-district level are of critical importance in planning and providing for maternal and child health services. Therefore, it is necessary to have robust measures of these indicators at this level, or even at sub-district level. Mortality rates for specific conditions such as tuberculosis, HIV/AIDS, injuries, cardiovascular conditions, neoplasms, and respiratory disease – the high-burden conditions in South Africa – should ideally be measured at magisterial level and at least at health-district level to inform planning for prevention, detection and treatment optimally. Mortality from maternal conditions and other less-prevalent non-communicable diseases, in contrast, can at best be effectively monitored for differences and change at the provincial level, given their relatively infrequent occurrence.
The under-5 mortality rate (U5MR) is a key indicator of child health and overall development. While its measurement at national level is important for monitoring countries’ progress towards the targets of MDG 4, timely and accurate measurement at sub-national levels are critical for evaluating and prioritising child health care needs and services. Although vital registration is the optimal data source for this, the under-reporting of stillbirths, live births and childhood deaths in South Africa results in under-estimates of child mortality ( 43 , 46 , 57 ). Furthermore, in the context of rapid epidemiological change, the current 2-year reporting delay reduces the utility of the rates. Data from complete birth histories collected in DHSs, are generally a key source for measuring U5MR trends ( 111 , 112 ), but do not permit estimates lower than provincial/state level. In addition, data quality problems in both the 2001 Census and 2003 DHS have rendered it impossible to derive reliable estimates of under-5 mortality from these sources ( 113 ). Census-based summary birth histories may yield estimates at the health-district level, but apart from recall and omission biases, such estimates are limited by their availability only once in 5 or 10 years, and for a reference period of several years preceding the census. Finally, the existing audit programmes for child (Child PIP) and peri-natal (PPIP) events are rich in their content, but biased in that only facility-based events are recorded, and participation in most provinces continues to be voluntarily.
For as long as birth and death registration are incomplete, a strategy is required that would distinguish and integrate useful, quality data from different well-administered sources towards deriving robust data on the levels and determinants of U5MRs at district level. Research in Indonesia, for example, has demonstrated the low-cost, time-efficient potential to adapt the DHS model into a ‘mini-DHS’ to collect data and provide robust under-5 mortality measures at the district level, allowing researchers to point out significant differentials at this level, thus assisting health-district officials to plan for a locally-appropriate response towards achieving national targets for MDG4 ( 114 ). Alternatively, South Africa needs to further strengthen the efforts towards complete birth and death registration, a process that has significantly progressed in a short period ( 57 , 115 ). Particular efforts for children aged 1–4 years are needed ( 46 ). The APAI-CRVS ( 18 ) initiative and the recommendations of the Health Data Advisory and Co-ordination Committee ( 109 ) show great potential for further stimulating vital registration towards completeness.
Overshadowed by a focus on child health for many decades, it has taken a severe epidemic to modify the neglect of adult mortality in sub-Saharan Africa. This neglect was partly due to the lack of reliable empirical data to measure adult mortality in the region. For most of the past century, South Africa was no exception ( 41 , 116 ). During the 1980s and 1990s, however, meticulous research efforts started putting together pieces from the disjointed vital registration puzzle ( 42 , 117 – 119 ). As alternative data sources became available in tandem with improved civil registration and vital statistics practices, researchers were in a position to triangulate and interrogate different sources and started having a better handle on estimating adult mortality levels ( 18 , 34 , 43 , 44 , 56 , 120 – 124 ). Differences in adult mortality estimates are shown in these publications, indicating data limitations such as event omission and recall bias in data from censuses and surveys, age misreporting, violation of selected assumptions in indirect methods, and uncertainty about the level of completeness of death registration.
More challenging has been deriving cause-specific mortality estimates. The vital registration system is likely the optimal source to calculate cause-specific estimates from, but a number of problems limit its utilisation, including an incomplete national cause profile due to incomplete death registration, and an urban bias in registration. For reported deaths, limitations of cause data include incomplete medical certification of the cause(s) of death, relatively high proportions of deaths in the ill-defined natural and undetermined unnatural categories, and misclassification of causes of death ( 104 , 110 , 117 , 119 , 125 , 126 ). Cause limitations are exacerbated by the continued practice that traditional headmen, on the basis of relatives’ information about the deaths, are allowed to certify deaths from natural causes. This may affect up to 10% of primarily rural registered deaths ( 127 ). While it is a welcome practice in terms of improving completeness of death reporting, it is not ideal for cause-of-death data.
Alternative sources of causes of adult deaths could be useful for mortality estimates from specific causes. Tuberculosis reporting is compulsory in terms of the National Health Act, and the Register operated by the National Tuberculosis Control Program could play a complementary role, both as a tool to keep track of deaths at the health-district level, and, where data could be matched via linking variables, as a means of assessing agreement of cause attribution between the Tuberculosis Register and vital registration. In addition, this may be useful in assessing the completeness of tuberculosis reporting on the death notification form. The close association between HIV and tuberculosis calls for appropriate strategies to cross-reference data from these two sources for verifying event occurrence and capturing suitable additional data to guide programme improvement to prevent or curb mortality. Similarly, towards optimally informing MDG5, teasing out differing estimates of maternal mortality ( 109 , 128 ) may gain from linking death records from the Confidential Enquiry into Maternal Deaths and vital registration databases, and triangulation with census data.
Cancer has a considerable impact on the country's disease burden as the 4th leading category of cause of death in 2000 ( 104 ). One in 12 cancer causes, accounting for almost 2,400 cases, were ill-defined and could not be attributed to a site-specific cancer ( 129 ), thereby diminishing the utility of the information. It may hence be useful to link records from the Cancer Registry and vital registration databases in a capture–recapture design towards reducing ill-defined cancer diagnoses in the vital registration database. Compared to vital registration data, such registers or audits generally have a considerable advantage in terms of disease control in that they have the potential to measure cause-specific incidence, prevalence, treatment, and case fatality at the health-district or at least provincial level, thus being able to point out differentials at this level, and assisting health-district and provincial officials to identify potential patient load and priorities for locally-appropriate health services.
For injuries, there is a system problem because the South African death notification form does not include a field for the apparent manner of death (homicide, suicide or accident). All deaths from injuries are certified as ‘unnatural’ deaths, and must undergo a medico-legal investigation at a state mortuary. However, some forensic pathologists consider that, in terms of the Inquest Act, they cannot indicate the circumstance of the death on the death certificate. Thus, the external cause (e.g. burn, firearm discharge, or fall) of many injury deaths is undetermined. While the National Transport Information System records information for selected motor vehicle collision deaths, the Mine Health and Safety Inspectorate records fatal mining injuries, and the South African Police Services violence-related injury, mortality from other external causes is not monitored by any agency ( 130 ). The National Injury Mortality Surveillance System (NIMSS) data are filling this gap by providing more comprehensive external cause information which would be valuable in the design and evaluation of injury control programs, but are limited by the lack of full-country coverage and an urban bias ( 104 , 107 ). For as long as the civil registration data do not include the external cause of injury deaths and NIMSS data do not include all injury deaths, the response to this large cause of premature death and disability can neither be comprehensive nor adequate. It may therefore be worthwhile to adapt the death notification form to include the external cause of injuries, apparent manner of death, scene of injury (e.g. private house, or street/highway), and district of injury (which may differ to the district of death). The value of these data items for injury prevention and safety and peace promotion speak for themselves.
Finally, HIV/AIDS was estimated the single largest cause of both death and years of life lost (YLLs) in 2000, respectively, accounting for 30% of total deaths, and 39% of total YLLs ( 104 ). Despite its enormous impact on mortality and premature death, HIV is not notifiable in South Africa, and no register or audit are assigned to capture details of suspected or confirmed HIV cases. A number of studies have found HIV/AIDS under-certified in both adult and paediatric deaths ( 110 , 126 ) ( 131 – 134 ) and although these reports are valuable in alerting data users to problems with the accuracy of cause attribution, they should also be seen as valuable in alerting certifying officers, coders, and researchers to indicator conditions, alternate terminology, and euphemisms that are used to indicate HIV as a possible cause.
A national initiative to improve the quality of medical certification should emphasise the importance of appropriate recording of HIV on death notifications, particularly in the new political climate of acceptance of the role of HIV in causing AIDS ( 135 , 136 ), towards accurately informing local responses and reliably reporting progress on Target 6A of MDG6 (i.e. have halted by 2015 and begun to reverse the spread of HIV/AIDS). This initiative should be monitored by a medical record review in a representative sample of death notifications to ascertain the veracity of certification and coding practices. Additionally, the HDSSs have built considerable relationships of trust in their communities, and matching HDSS and vital registration records may generate valuable knowledge of the extent of HIV/AIDS misclassification in registered rural deaths. While substantial problems of accuracy have been identified with physician-assigned causes of death in national vital registration data ( 110 , 126 ), and even in deaths that occurred in tertiary health facilities ( 39 , 132 , 133 ), local HDSS studies using verbal autopsy data have shown successful detection and a substantial presence of HIV-related mortality with closely comparable findings between physician- and InterVA-assessments. ( 84 , 85 )
While our review suggests that there are a number of potentially useful data sources on mortality, some of which could be used complementary, it is also clear that data use and analysis based on these collections have been restricted by limitations. At times, mortality data collection has been poor and mortality levels could not be derived due to the extent of missing or illogical data in selected surveys and censuses. For some earlier data sources, the quality of the data was unassessed and the data unused. This may have resulted from a lack of knowledge on how to assess data quality issues, limited capacity to apply selected methods of mortality estimation, prolonged time periods before data become available for public use, financial costs to obtain data, or bureaucratic processes that hinder data access and use. Additionally, sample sizes varied across years for surveys – at times substantially, and not necessarily congruent with national population numbers; the age ranges of respondents for the same data items at times differ across surveys, or across years within a survey; and changing administrative borders and place names have sometimes affected mortality reporting and measurement.
Recognising these challenges presents an important step towards improving mortality measurement, from the planning of enquiry/reporting systems through data collection, processing, and compilation, to depositing data in the public domain for independent evaluation and analysis. Stats SA has greatly improved availability of national mortality data over the past 20 years, has reduced public use waiting time, and has collaborated with strategic partners to improve completeness of vital registration. Mortality measurement will further gain from creating opportunities for wider public knowledge about the importance and public health uses of reliable and valid mortality data; further improvements in completeness of registration and timeliness of data availability; adequate, on-going training of certifiers and coders in cause attribution; and strategic strengthening of analytical capacity at Stats SA and research and academic institutions.
In a continent often reported as lacking the basic data to infer levels and trends of all-cause and cause-specific mortality, this article has identified a number of data sources in South Africa that, after critical review and adjustment, could yield valuable policy insights into mortality change over the past two decades. Data sources with mortality items are many and varied, offering a promising scenario for improved population health planning from an evidence base informed by multiple sources. However, it is clear that more can and must be gained for mortality measurement by tackling three key issues: data quality, data triangulation, and analytic capacity.
Data quality in surveys and censuses can be improved by demanding nothing less than excellent fieldworker training and excellent quality control measures in the field. For improved quality in vital statistics, further focussed advances in completeness of death registration, and, in particular, a strong, co-ordinated national response towards improved coverage and accuracy of medical certification of causes of death is recommended. The latter is simply critical. Moreover, with studies pointing to problems in physician-certified causes, ( 137 , 138 ) such causes should not be taken as automatically having content validity, and the possibility of routinely comparing a sample of death certificates with hospital records/doctors’ notes/clinic or day hospital cards, should be pursued.
A focussed agenda is recommended towards data triangulation and contestability via linkage and validation studies that will allow drawing on complementary properties of different sources and, in particular, will assist in completeness estimation and improve our understanding of the accuracy in cause-of-death attribution. Such improved understanding holds clear gains for improved mortality estimates, enhanced resource and service distribution, and, eventually, better meeting the health needs of the population.
However, data quality assessment and triangulation, like other aspects of mortality measurement, require sufficient competent analytic capacity. As analytic capacity has not been expanded upon compared to mortality experiences in the population, nor the increase in national mortality data collection and availability, the expansion and strengthening of analytic capacity is a critical, overarching recommendation.
Achieving these will not be easy, and a co-ordinated research agenda for mortality data collection, evaluation, comparison, analysis, and use, along with an operational agenda for quality assurance and analytical capacity strengthening, are recommended. These should be generated and backed-up by adequate and independent human and fiscal resources. For the future, it will be important to adopt a strategic approach to data collection, streamlined by lessons from past experience, and enhanced by successes and innovative modes of data collection elsewhere.
The research was carried out while the first author was holding a University of Queensland Research Scholarship and Endeavour International Postgraduate Research Scholarship at the University of Queensland.
1 ‘Population group’ is used as a collective term that may elsewhere be called ‘race’ or ‘ethnic group’. The use of the terms ‘Black African’, ‘Coloured’, ‘Indian/Asian’, and ‘White’ are not intended to denote biological difference, neither to support a racial or ethnic classification system. Under the Population Registration Act of 1950, South Africans were classified into these groups. The classification was associated with disparities in many spheres of life, including health. To acknowledge this impact, and to help track progress in redressing past inequalities based on the classification, mortality and other data are still classified by these terms, although individuals self-classify. For this reason, and because a historical perspective is presented at times, it is necessary to reference these terms in this article.
JJ and CR conceptualised the article. JJ undertook the review and wrote the first draft with substantial inputs from CR. All co-authors gave expert inputs and contributed to the critical review of subsequent drafts. JJ produced the tables and graph with expert inputs from CR (tables) and RD (graph).
No funding was received to conduct the review. The authors have no conflict of interest.
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The National Party was elected in 1948 on the policy of Apartheid ('separateness'). This 'separateness' put South Africans of different racial groups on their own paths in a partitioned system of development. The policy goal of separate development allowed the National Party to maintain the status quo of white supremacy as well as control the African labour needed for rapid industrial development (Baldwin, 1975: 218). Separate development was supposed to allow Africans to develop themselves under their own self-government, but the economic structure of South Africa made that impossible (Marquard, 1969: 256). During the Second World War, there was rapid urbanization by Africans. The lack of infrastructure in South African cities led to the phenomenon of overcrowding and squatting on empty land by those seeking employment. In many cases, the scramble for housing created mixed neighbourhoods (Marquard, 1969: 43). The Group Areas Act was fashioned as the “cornerstone” of Apartheid policy and aimed to eliminate mixed neighbourhoods in favour of racially segregated ones which would allow South Africans to develop separately (South African Institute for Race Relations, 1950: 26).
When the Group Areas Act (GAA) was passed in 1950, it imposed control over interracial property transactions and property occupation throughout South Africa (Horrell, 1978: 71). It was amended almost annually and was re-enacted in the Consolidation Acts of 1957 and 1966. The GAA created the legal framework for varying levels of government to establish particular neighbourhoods as 'group areas', where only people of a particular race were able to reside (South African Institute for Race Relations, 1952: 32). The GAA displaced hundreds of thousands of people; breaking up families, friends, and communities. This was due in large part to the retroactive application of the law, meaning that once an area was declared a group area, the GAA had the power to demolish all the houses there and displace everyone who was not of the designated group (Mabin, 1992a: 422). The GAA added more restrictions to the lives of Africans and it was one of the first drastic rights infringements for the Indian and Coloured populations (Marquard, 1969: 163).
Origins of the Group Areas Act
The GAA was one of many pieces of legislation used to control the lives of Indians, Coloureds, and Africans, in this instance by limiting property rights. A distinction between the GAA and its predecessors was that segregationist measures passed before 1948 were less coherent and often provisional in nature. Legislation passed by the Apartheid government were done in a more clearly defined pattern to implement Apartheid (Horrell, 1978: xi). Many pieces of legislation passed under the pretence of segregation were in actuality attempts to control the labour of Africans. The GAA was another layer in controlling the movement and life of urban Black, Indian or Coloured persons (Baldwin, 1975: 218).
Rural immigration to cities began as early as the middle of the 19th century, though the amount of time these migrants stayed in urban centres was fairly different case by case. As the rapid expansion of the production of export products like wine and wool began, so too did African urbanization, as migrants found jobs working on railways, docks, and in manufacturing. Along with the varying amount of time spent working in cities, the extent to which families migrated to join the urban breadwinner varied greatly. This is a trend that continues in the present day (Mabin, 1992b: 14).
The Glen Gray Act of 1894 restricted the ability for African men to own land in the Cape (Root & Wachira, 2009: 668). The Act also imposed a labour tax of ten shillings a year on land owned by Africans unless they could prove that they had been employed outside of their designated reserve area for at least three months (Wilson, 1953: 243).
During the early 20th century, many of the impositions of segregation were associated with emergency epidemics. For example, shortly after the discovery of bubonic plague in Johannesburg in 1904, the city burned down its African slums which drove Africans to the Kilpspruit sewage farm, creating the first exclusively African settlement (Swanson, 1977: 388). The late 19th century approach of “containment” to control outbreaks in Europe with sanitary legislation was highly influential to South African policies towards slum and overcrowding which were identified as the source of the outbreaks of disease. European “containment” was seen by Whites in South Africa as the appropriate response to what they perceived as their country's social problems as from the 1870s onwards. Fear of cholera, small pox, and plague epidemics were used to justify the efforts to segregate Indians and Africans in urban areas (Swanson, 1977: 390). This method of behaviour is illustrated by the 1885 Asiatic Bazaar law (No. 3 of 1885) which authorized the government to create separate districts for Indians for reasons of sanitation (Dison and Mohamed, 1960: 24). The Asiatic Bazaar law was reinforced in 1932 by a committee in Transvaal known as the 'Transvaal Asiatic Land Tenure Act'. It included a clause that was later dropped which would have forced local authorities to set aside 'Asiatic' areas (Mabin, 1992a: 409). During the plague crisis at the beginning of the twentieth century in the Cape, the government used the Public Health Act to move between six-thousand and seventhousand Africans to Uitvlugt, a sewage farm several miles from the city in the Cape Flats (Swanson, 1977: 393).
The Locations Act of 1903 in Port Elizabeth aimed to control the settlement of Africans in the region. The Mayor estimated that about ten thousand Africans lived within the city limits, and another four-thousand had moved beyond the city limits. It tried to facilitate African migration to an area called 'New Brighton' but was largely unsuccessful in this goal (Swanson, 1977: 401).
The 1913 Natives Land Act was one of the first pieces of legislation that limited property rights of Africans in South Africa. Before the Natives Land Act, Africans were able to purchase land outside of their reserves, with differing degrees of restrictions, but the Natives Land Act limited the landmass where they were allowed to own land to approximately 9 million hectares, and created specific areas (which came to be known as 'homelands') within which whites were not allowed to buy land (Horrell, 1978: 3). The outcome of the act was, as Sol Plaajte said, to make the African “a pariah in the land of his birth.” (1914: 1). Africans who had purchased land outside of homelands were generally allowed to keep it in cases where it was adjacent to a homeland. In cases where a African owned farm was surrounded by White-owned farms, it became known as a “black spot”. Later in the 20th century, the government took steps to eliminate these black spots. The Natives Land Act initially allocated approximately 8 per cent of South African land to be purchasable by Africans (Beinhart, 2001, 10).
Ten years after the Natives Land Act was passed, the Smuts government passed the 1923 Native Urban Areas Act which gave municipalities greater powers to segregate housing, to police African communities and to control movement by imposing passes. It specified that alternative housing had to be provided for those who were moved by its implementation (Horrell, 1978: 2). An amendment to the Act in 1937 forbid Africans from acquiring property from non-Africans in cities and townships (Kirkwood, 2: 1951). However, the act led to constant housing shortages throughout the 20th century. Since Whites were legally stipulated to be paid more than Africans for the same work, the regulations that required Whites to do most of the work on public housing projects significantly exacerbated their cost (Horrell, 1978: 77).
In 1924, the Smuts and then the Pact government aimed to pass the “Class Areas Bill,” which would have restricted Indian residential and trading rights. However, the Bill was never passed, as it was faced with vehement opposition within South Africa as well as abroad (Horrell, 1978: 5).
New powers were given to the Governor-General by the 1927 Native Administration Act. It allowed Governors-General to govern Africans by proclamation, including the ability to order that any tribe, section of a tribe, or African to move from one place to another without notice. It also allowed the Governor-General to be able to decree that Africans were not to leave any stated area for a specific period (Horrell, 1978: 4).
The Native Land Act was updated in 1936 via the Native Trust and Land Act which added an additional 6.2 million hectares to the reserves where Africans could purchase land (Horrell, 1978: 4). This expanded the total percentage of land in South Africa which could be purchased by an African from 8% to 13%. (Hellmann, 1949: 174). While Africans did still occupy land owned either by the Crown or by absentee white landlords, as nearly a quarter of a million did by the late 1920s, their tenure was insecure as they were not legally entitled to hold the deed for that land, and thus could be evicted at any time (Bundy, 1972: 384). The Native Trust and Land Act gave new powers to the Department of Native Administration and Development which allowed it to begin to evict owners of 'black spots' (land owned by Africans surrounded by White-owned farms) (Horrell, 1978: 203). Furthermore, during the 1930s, White farmers tried to force Africans to labour on their farms for six months as a payment of rent towards land tenure. While they were unsuccessful in doing so, this demonstrates the degree of ambiguity that African tenure held during the early 20th century (Beinhart, 2001: 136). Africans were prohibited from acquiring land from non-Africans by the Native Laws Amendment Act of 1937. The only circumstance under which it was allowed was with the consent of the Governor-General. Additionally, it prohibited the construction of new churches, schools, or other institutions which would mainly cater to Africans in urban areas without approval from the Minister of Native Affairs, though this did not affect institutions established before 1938. It allowed towns to apply to have areas declared as closed to entry of Africans other than those who were either visiting, employed, or seeking to be employed there (Horrell, 1978: 3).
In 1950, the Population Registration Act provided for compulsory racial classification on a national register. Documents were issued to people based on the racial group they were designated. The groups named were Europeans, Coloured, and Natives. Coloured people and Natives were also subcategorized according to their ethnic group. A Race Classification Board was set up to adjudicate disputed cases. Identity cards were issued to all people over the age of sixteen and authorized officials (e.g. police officers) were empowered to demand anyone of that age or older to produce their identity cards. Those who failed to produce their identity cards had seven days to report to a police station (South African Institute for Race Relations, 1950: 24).
The Group Areas Act: 1950
In April 1950, the Minister of the Interior introduced the “Group Areas Bill,” which became law on July 12th of that year. The Group Areas Act (GAA) systematized segregation in the control of transfers of land and immovable property (property which cannot be moved without being severely altered or destroyed, like a house) as well as occupation rights throughout the Union of South Africa, with the exception of reserves. The consequence was that one could only buy property from people of the same race (South African Institute for Race Relations, 1950: 26). Many of the measures in the Act were interim measures until the establishment of 'full group areas', or complete residential segregation (Kirkwood, 1951: 24).
One of the first major changes in South African property law made by the GAA was the creation of 'controlled' areas. Controlled areas were any area which was not a group area; a Native area, location, or village; or a Coloured persons settlement, mission station, or communal reserve. Controlled areas had particular provisions for the ownership of immovable property (1.v). In specified and controlled areas, inter-racial property transactions and changes in occupation of property were subject to permit. In controlled areas, the criteria for occupation was the group of the owner, whereas in specified areas it was the group of the occupier. So for instance in a controlled area, if the owner of the property was White then the occupant needed to be White, while in a specified area the owner could be White while the occupant could be Indian. Disqualified persons or companies were allowed to enter, or continue, an agreement for a lease or sub-lease of a property in a specified area but could not do so in a controlled area without a permit (South African Institute for Race Relations, 1953, 45).
The GAA was administered by the Land Tenure Advisory Board (the Board) and the Minister of the Interior (the Minister). Group areas were created, after the bill passed, by making a proposal to the Board, which was appointed by the Minister. The Board had to make a report to the Minister as to whether or not an area should have been proclaimed a group area. Before it advised the Minister, the Board had to give the public an opportunity to have input on the decision (27.2, 27.3). The Board also had to take into consideration the availability of accommodations for the people displaced by the decision. However, it was the role of other departments, not the Board, to find alternative accommodation for people displaced by the declaration of group areas (27.5). The Minister was not compelled to listen to the advisement of the Board but was required to read it before issuing a proclamation (26). The Board modelled after the Asiatic Land Tenure Board (from the previously mentioned Transvaal Asiatic Land Tenure Act of 1932). It was composed of no more than seven members who could be dismissed by the Minister (24.1 and 24.4d). Unlike the Asiatic Land Tenure Board, members could serve for no longer than five years and there were no explicit provisions allowing Coloureds or Africans to serve on the Board (24.3).
Section 2 of the Act defined the 'groups' as White, Native, and Coloured. The group of women was determined by who they married to or cohabited with (2.1.abc). African or Coloured groups could be further subdivided by the Governor-General on ethnic, linguistic, or cultural grounds (2.2). The Governor-General would declare areas as 'group areas' and give the residents who are not of the specified group at least one year to leave (ie, that a particular neighbourhood is now a White neighbourhood so all Coloured or Native residents must leave the area by the date specified in the proclamation)(3.1a). The GAA did not itself create 'group areas' but established the machinery to create them (Kirkwood, 1951: 8).
The creation process of group areas was explained in Section 3. It specified that, for the first five years after the passage of the GAA, the Governor-General had the power to create group areas for Whites in provinces of the Cape of Good Hope and Natal, or areas already set aside in the Transvaal for people who were Native or Coloured. After the first five years had elapsed, these proclamations would be subject to approval by both Houses of Parliament (3.3a). Proclamations could only be given once the Minister had considered a report by the Board and had to consult with the Administrator of the Province affected, the Minister of Mines in the case of any land proclaimed under any mining related law and the Natural Resources Development Council if the area was on any land which they controlled (3.3b). However, proclamations would be withdrawn at any time by the Minister or the Governor-General (33).
Those who were not of the 'group' of an area became a 'disqualified person' under Section 4. Disqualified persons were not allowed to occupy any land or premises in any group area to which a proclamation relates, except under the authority of a permit (4.1). The exceptions to this section were any servant or employee of either the state, a statutory body, or of those who were lawfully occupying the land; a visitor staying in a home of a lawfully residing person or at hotel for no more than ninety days; or any person under the control of the state, either as a patient of a hospital or of a similar institution or as an inmate of a prison or similar institution (4.2). This section also made the proclamation of a group area override any legal agreements which would prohibit or restrict those who were designated as the lawful occupants (ie, if an African is leasing an apartment in a White area, the lease would expire after the date specified in the proclamation) (4.3). No one lawfully occupying any land was allowed to help a disqualified person occupy land without the authority of a permit (10.1). The GAA would not interfere with arrangements made by the Housing Act (1920) or Housing (Emergency Powers) Act of 1945 as long as they were of the appropriate group for the group area (10.2d). It also made exceptions for native labourers governed by the Native Labour Regulation Act (1911) as along as the company which recruited them was lawfully occupying the land or premises where they were housing them (10.2i). The ownership rights of disqualified people and companies were further clarified in Section 5, as to the acquisition of property in group areas, and Section 8, as to the acquisition of property by disqualified people in controlled areas. Disqualified persons and companies were not allowed to acquire any property in group areas without a permit (5.1a). The inheritance of any immovable property by a disqualified person was made illegal unless they had a permit to do so. The executor of the will had a year (or longer if the Minister of the Interior allowed it) to sell the now illegally owned property. The inheritor was entitled to the profit of the sale, as long as it was done in time (5.3). The ownership of property by disqualified companies in group areas were required to pursue a permit to continue to own it and their ownership would expire after ten years (5.1bc). However, mining companies were exempt from this portion of the law (5.2) Section 8 detailed the restrictions on disqualified persons and immovable property in controlled areas. It disallowed disqualified people from acquiring immovable property, unless they were a Native acquiring property already governed by preceding legislation, and made state officials as being authorized to act on the behalf of those who owned the property (8).
If any immovable property was acquired or held illegally without the correct permit, Section 20 of the GAA empowered the Minister to force the owner to sell the property within a three months, but if it was not sold within one month the Minister could sell it at public auction (20.1). The proceeds of the sale were first used to cover any associated costs and, in the case of the property being owned by a company, the rest would go to the company. In any other cases, unless otherwise directed by the Minister, the money would be directed into the Consolidated Revenue Fund (20.2). Furthermore, the Minister and any officer designated by him were empowered to do whatever was necessary to effect the transfer of any property sold to whomever purchased it (20.6). Any property registered in the name of someone who was not lawfully able to hold or acquire it was subject to penalties (22.2).
The GAA stringently restricted trading licenses. Any officer of licenses could withhold or not issue a license to an applicant who they “[had] reason to believe” is applying on the behalf of someone for whom it would be illegal to hold such a license (23.1). Controlled and specified areas also had consequences for businesses. Controlled areas allowed only persons of the same racial group as the owner to trade on the premises, while in specified areas only members of the same racial group as the people who occupied the area could trade on the premises (Kirkwood, 1951: 27).
Failure to comply with the GAA had significant consequences. Violators of the act could receive fines of up to two hundred pounds, imprisonment for a period not exceeding two years, or both punishments (34.1). Violators of the act could have also been faced with forced evictions. If they did not comply they received either fines of sixty pounds, imprisonment for six months or both, as well as an additional fine of five pounds for each day they failed to comply with the eviction order (34.2 and 34.3).
Effects of the Group Areas Act
The GAA had strange implications for governance and responsibility as it became more elaborate and amended. For example, the Coloured townships of Coronationville, Noordgesig, Newclare, Riverlea, and Western Township are administrated by Johannesburg City Council while Bosmont is the responsibility of the Department of Community Development (South African Institute of Race Relations, 1964: 216). The work of welfare organizations was made more difficult by the GAA, like Lunalegwaba House, a group home for African boys, in Johannesburg could not operate because the regulations of the GAA did not allow the White charity to own the property (South African Institute for Race Relations, 1967: 306). People attempted to use the courts to overturn the GAA, though each time they were unsuccessful (Dugard, 1978, 324). Others decided to use civil disobedience and other protests, like ‘sit-ins’ at restaurants, were experienced across South Africa in the early 60s. The 'sit-ins' were not ill-received by the average White citizen, which the South African Institute of Race Relations believed proved that they did not object to sharing restaurants with the other racial groups (1961: 183). There was also resistance from Cape Town City Council who voted before 1964 to keep District Six and the central business district not dedicated to any one racial group; they had the support of the Cape Town Chamber of Commerce on this decision (South African Institute of Race Relations, 1964: 213).
The GAA had immense effects on communities and people across South Africa. By 1983, over 600 000 people had been removed and relocated from their original homes (Pirie, 1983: 348). There were varying levels of consolidation of the Act in different regions. For example in Port Elizabeth by 1960, approximately 90% of the African population was housed within designated 'African' areas, by 1985 it was 96% (Christopher, 1987: 200). The fight for Indian occupation of Cato Manor in Durban was fierce. There were major clearances of the shacklands in Cato Manor by the government in 1951, when the government declared it to be a White zoned area (Maharaj,1994: 6). The GAA destroyed District Six, a crowded, racially mixed neighbourhood with a unique, traditional character of its own (South African Institute for Race Relations, 1966: 187). Many parts of the contemporary urban landscape in South Africa are still structured in the image of the GAA (Maharaj,1994: 21). Squatting, which became prevalent during the existence of the GAA, was a symptom of the massive housing shortage in the Cape. In 1977, the Government removed of 15 000 squatters from Modderdam without alternative accommodation, many of the squatters had been displaced by the GAA in the first place (South African Institute for Race Relations, 1977: 438-439)
As the terms of the Native Urban Areas Act required local authorities to set aside areas for African occupation, they were not the worst hit in the rezoning of neighbourhoods. Colouredpeople suffered because housing schemes for them were often delayed due to plans for racial zoning (South African Institute for Race Relations, 1954, 57). The GAA particularly hurt Indian South Africans because many of them had historically been present in other ethnic communities as traders and landlords. This is clearly documented by the South African Indian National Congress. In 1963, it was estimated that over a quarter of Indian men and women were employed as traders (South African Institute for Race Relations, 1963: 13). Indians were also known as landlords, especially in Natal. For example, in Cato Manor Indian landlords allowed Africans to built shacks on their land for rent. Before the GAA, this method of housing was accepted because the city of Durban was unable to provide alternative housing (Maharaj, 1994: 5). It was condemned for economic reasons by the Natal Indian Congress in 1955 . The National Government completely ignored the protests of the Indian community. In June 1977, the Minister of Community Development stated that he knew of no instances where an Indian trader was resettled and was not pleased with their new accommodation, and the subsequent day a number of Indian traders expressed their dissatisfaction in the Sunday Express in the Transvaal (South African Institute for Race Relations, 1977: 437)
This article was written by Patricia Johnson-Castle and forms part of the SAHO Public History Internship
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The FDA understands that there is increasing interest in the potential utility of cannabis for a variety of medical conditions, as well as research on the potential adverse health effects from use of cannabis.
To date, the FDA has not approved a marketing application for cannabis for the treatment of any disease or condition. The agency has, however, approved one cannabis-derived drug product: Epidiolex (cannabidiol), and three synthetic cannabis-related drug products: Marinol (dronabinol), Syndros (dronabinol), and Cesamet (nabilone). These approved drug products are only available with a prescription from a licensed healthcare provider. Importantly, the FDA has not approved any other cannabis, cannabis-derived, or cannabidiol (CBD) products currently available on the market.
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FDA has approved Epidiolex, which contains a purified form of the drug substance cannabidiol (CBD) for the treatment of seizures associated with Lennox-Gastaut syndrome or Dravet syndrome in patients 2 years of age and older. That means FDA has concluded that this particular drug product is safe and effective for its intended use.
The agency also has approved Marinol and Syndros for therapeutic uses in the United States, including for nausea associated with cancer chemotherapy and for the treatment of anorexia associated with weight loss in AIDS patients. Marinol and Syndros include the active ingredient dronabinol, a synthetic delta-9- tetrahydrocannabinol (THC) which is considered the psychoactive intoxicating component of cannabis (i.e., the component responsible for the “high” people may experience from using cannabis). Another FDA-approved drug, Cesamet, contains the active ingredient nabilone, which has a chemical structure similar to THC and is synthetically derived. Cesamet, like dronabinol-containing products, is indicated for nausea associated with cancer chemotherapy.
FDA is aware that unapproved cannabis and/or unapproved cannabis-derived products are being used to treat a number of medical conditions including, AIDS wasting, epilepsy, neuropathic pain, spasticity associated with multiple sclerosis, and cancer and chemotherapy-induced nausea. Caregivers and patients can be confident that FDA-approved drugs have been carefully evaluated for safety, efficacy, and quality, and are monitored by the FDA once they are on the market. However, the use of unapproved cannabis and cannabis-derived products can have unpredictable and unintended consequences, including serious safety risks. Also, there has been no FDA review of data from rigorous clinical trials to support that these unapproved products are safe and efficacious for the various therapeutic uses for which they are being used.
FDA understands the need to develop therapies for patients with unmet medical needs, and does everything it can to facilitate this process. FDA has programs such as Fast Track, Breakthrough Therapy, Accelerated Approval and Priority Review that are designed to facilitate the development of and expedite the approval of drug products. In addition, the FDA’s expanded access (sometimes called “compassionate use”) statutory and regulatory provisions are designed to facilitate the availability of investigational products to patients with serious diseases or conditions when there is no comparable or satisfactory alternative therapy available, either because the patients have exhausted treatment with or are intolerant of approved therapies, or when the patients are not eligible for an ongoing clinical trial. Through these programs and the drug approval process, FDA supports sound, scientifically-based research into the medicinal uses of drug products containing cannabis or cannabis-derived compounds and will continue to work with companies interested in bringing safe, effective, and quality products to market.
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The FDA has an important role to play in supporting scientific research into the medical uses of cannabis and its constituents in scientifically valid investigations as part of the agency’s drug review and approval process. As a part of this role, the FDA supports those in the medical research community who intend to study cannabis by:
To conduct clinical research that can lead to an approved new drug, including research using materials from plants such as cannabis, researchers need to work with the FDA and submit an IND application to CDER. The IND application process gives researchers a path to follow that includes regular interactions with the FDA to support efficient drug development while protecting the patients who are enrolled in the trials. An IND includes protocols describing proposed studies, the qualifications of the investigators who will conduct the clinical studies, and assurances of informed consent and protection of the rights, safety, and welfare of the human subjects. The FDA reviews the IND to ensure that the proposed studies, generally referred to as “clinical trials,” do not place human subjects at an unreasonable risk of harm. The FDA also requires obtaining the informed consent of trial subjects and human subject protection in the conduct of the clinical trials. For research intending to develop an animal drug product, researchers would establish an INAD file with the Center for Veterinary Medicine (CVM) to conduct their research, rather than an IND with CDER.
FDA is committed to encouraging the development of cannabis-related drug products, including CBD. Those interested in cannabis-derived and cannabis-related drug development are encouraged to contact the relevant CDER review division and CDER’s Botanical Review Team (BRT) to answer questions related to their specific drug development program. The BRT serves as an expert resource on botanical issues and has developed the Botanical Drug Development Guidance for Industry to assist those pursuing drug development in this area. FDA encourages researchers to request a Pre-Investigational New Drug application (PIND) meeting to discuss questions related to the development of a specific cannabis-derived and cannabis-related drug product.
Please note that certain cultivars and parts of the Cannabis sativa L. plant are controlled under the Controlled Substances Act (CSA) since 1970 under the drug class "Marihuana" (commonly referred to as "marijuana") [21 U.S.C. 802(16)]. "Marihuana" is listed in Schedule I of the CSA due to its high potential for abuse, which is attributable in large part to the psychoactive intoxicating effects of THC, and the absence of a currently accepted medical use in the United States. From 1970 until December of 2018, the definition of “marihuana” included all types of Cannabis Sativa L. , regardless of THC content. However, in December 2018, the Agriculture Improvement Act of 2018 (also known as the Farm Bill) removed hemp, a type of cannabis that is very low in THC (cannabis or cannabis derivatives containing no more than 0.3% THC on a dry weight basis), from controls under the CSA. This change in the law may result in a more streamlined process for researchers to study cannabis and its derivatives, including CBD, that fall under the definition of hemp, a result which could speed the development of new drugs containing hemp.
Conducting clinical research using cannabis-derived substances that are considered controlled substances under the CSA often involves interactions with several federal agencies. For example:
Sponsor obtains pre-IND number through CDER review division to request a pre-IND meeting. For new animal drug research, a sponsor may engage with CVM to establish an INAD file. A pre-IND meeting with CDER is optional, and an opportunity to obtain FDA guidance on sponsor research plans and required content for an IND submission .
The sponsor contacts NIDA or another DEA-registered source of cannabis and/or cannabis-derived substances to obtain information on the specific cultivars available, so that all necessary chemistry, manufacturing, and controls (CMC) and botanical raw material (BRM) information can be included in the IND. Importation of products controlled under the CSA are subject to DEA authorization.
The sponsor may contact DEA to discuss Schedule I drug research plans that may require DEA inspection for an investigator and study site Schedule I license.
Step 4: If the selected BRM or drug substance manufacturer holds a Drug Master File (DMF) , the sponsor must obtain a Letter of Authorization (LOA) to reference CMC and BRM information. Alternatively, an IND submission would need to contain all necessary CMC data characterizing their study drug and ensuring it is safe for use in humans.
The sponsor sends a copy of the IND and clinical protocol, including a LOA (if applicable), to FDA.
FDA reviews the submitted IND. The sponsor must wait 30 calendar days following IND submission before initiating any clinical trials, unless FDA notifies the sponsor that the trials may proceed sooner. During this time, FDA has an opportunity to review the submission for safety to assure that research subjects will not be subjected to unreasonable risk.
If the IND is authorized by FDA as “safe to proceed” the sponsor may then submit their clinical protocol registration application, including referenced IND number, to DEA to obtain the protocol registration. Once this is received, the sponsor contacts NIDA or another DEA-registered source to obtain the cannabis and/or cannabis-derived substances and they can then begin the study.
For nonclinical research, including research conducted under an INAD file submitted established with CVM, there is no requirement of prior authorization of the protocol by FDA before the investigators may proceed with a protocol registration application submitted to DEA. For these nonclinical protocols, investigators may immediately pursue investigator and study site licensure, and protocol registration with DEA, so they may then obtain their Schedule I cannabis-derived study drug from supplier.
Sponsor provides all applicable chemistry, manufacturing, and controls (CMC) and botanical raw material (BRM) information in the IND for review by FDA, including hemp cultivars.
If the selected hemp manufacturer holds a Drug Master File (DMF) , the sponsor must obtain a Letter of Authorization (LOA) to reference CMC and BRM information. Alternatively, an IND submission would need to contain all necessary CMC data characterizing their study drug and ensuring it is safe for use in humans.
The FDA’s role in the regulation of drugs, including cannabis and cannabis-derived products, also includes review of applications to market drugs to determine whether proposed drug products are safe and effective for their intended indications. The FDA’s drug approval process requires that clinical trials be designed and conducted in a way that provides the agency with the necessary scientific data upon which the FDA can make its approval decisions. Without this review, the FDA cannot determine whether a drug product is safe and effective. It also cannot ensure that a drug product meets appropriate quality standards. For certain drugs that have not been approved by the FDA, the lack of FDA approval and oversight means the safety, effectiveness, and quality of the drug – including how potent it is, how pure it is, and whether the labeling is accurate or false – may vary considerably.
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The Population Registration Act of 1950 required that each inhabitant of South Africa be classified and registered in accordance with their racial characteristics as part of the system of apartheid. [1] [2] [3] Race classification certificate issued in terms of the Population Registration Act. Explanation of South African identity numbers in an ...
Updated on August 03, 2019. South Africa's Population Registration Act No. 30 (commenced on July 7) was passed in 1950 and defined in clear terms who belonged to a particular race. Race was defined by physical appearance and the act required people to be identified and registered from birth as belonging to one of four distinct racial groups ...
The Population Registration Act, Act No 30 of 1950 . The Population Registration Act provided that all South Africans be racially classified in one of three categories: White, Black or Coloured. According to this Act Indians fell under the Coloured category. The criteria used to determine the qualification into each of these categories was ...
The Population Registration Act (1950) classified every South African by race. The Prohibition of Mixed Marriages Act (1949) and the Immorality Act (1950) prohibited interracial marriage or sex. The Suppression of Communism Act (1950) defined communism and its aims broadly to include any opposition to the…. Read More.
The Population Registration Act of 1950, which preceded both Group Areas and the Bewysburo (Bureau of Proof), required every South African to secure an Identity Card - a laminated certificate that contained a photograph submitted by the applicant, recorded an address, a simple identity number and a racial classification.
The cornerstone of this racially stratified legal system was the Population Registration Act of 1950, which required that all South Africans needed to be registered and assigned to an official ...
Population Registration Act: 1950: Created a national register in which every individual's race was officially recorded. Group Areas Act: 1950: Legally codified segregation by creating distinct residential areas for each race. Immorality Act: 1950: Prohibited sex between whites and non-whites. Suppression of Communism Act: 1950: Outlawed ...
We estimate the causal effect of racial classification by exploiting a change in the racial classification process that applied to children born after 1951. At the onset of apartheid, an individual's racial classification was determined by the criteria set out in the Population Registration Act of 1950, and the 1951 Census was the key source ...
The Population Registration Act (1950) divided South Africa's inhabitants into four racial groups—Africans, Coloureds, Asiatics, and Whites—as the basis to create racially differentiated citizens and subjects, residential areas, employment, education, and political, economic, and social rights.
The apartheid state was defined by a system of legislation - the Population Registration Act of 1950 that legally categorized people according to race, the Immorality Act that forbade sexual relations between different race groups, and the Group Areas Act which segregated racial groups in urban areas.
Although colonialist and race essentialist policies and practices predated the South African Nationalist government in 1948 (Parry, 1983), it was this government's Population Registration Act of 1950 which officially codified race and race hierarchies in South African society.Whereas racial classification attempts prior to 1948 'produced a dense conceptual fog' (p. 91), the Population ...
Racial segregation had long existed in white minority-governed South Africa, but the practice was extended under the government led by the National Party (1948-94), and the party named its racial segregation policies apartheid (Afrikaans: "apartness").The Population Registration Act of 1950 classified South Africans as Bantu (black Africans), Coloured (those of mixed race), or white; an ...
Population Registration Act, 1950 - This Act demanded that people be registered according to their racial group. This meant that the Department of Home Affairs would have a record of people according to whether they were White, Coloured, Black, Indian or Asian. People would then be treated differently according to their population group, and so ...
The Population Registration Act of 1950 provided the basic framework for apartheid by classifying all South Africans by race, including Bantu (Black Africans), Coloured (mixed race) and white.
It is the product of almost two decades of research and includes analyses, chronologies, historical documents, and interviews from the apartheid and post-apartheid eras. ... 1950. Population Registration Act No 30. This "provided for the compilation of a register of the entire South African population" (Dyzenhaus 1991: 40). The South African ...
At the onset of apartheid, an individual's racial classification was determined by the criteria set out in the Population Registration Act of 1950, and the 1951 Census was the key source of information used for implementation of the Act. Racial classification involved three criteria; appearance, social acceptability, and ancestry or descent.
Population Registration Act, Act No 30 of 1950. Date of Publication: 1990-07-00. The Act was to make provision for the compilation of a Register of the Population of the Union; for the issue of Identity cards to persons whose names are included in the Register; and for matters incidental thereto.
The act used the Population Registration Act (also passed in 1950) for definitions of the racial categories into which the country would be divided. ... The board would research and draw areas that its members considered to be apt for segregation and submit a map to the minister, who in turn would approve the creation of the new areas. ...
This chapter draws on interviews conducted in 2018-2019 in South Africa with research participants who were activists and organic intellectuals ... statistical purposes, to indicate their race as African, Indian/Asian, Colored, or White, although the repeal of the Population Registration Act has meant there are no legal definitions of 'race
The Population Registration Act, the legal foundation of apartheid, was formally repealed by the white-controlled Parliament on Monday, ending four decades of cradle-to-grave racial labeling in ...
The Population Registration Act of 1950 made provision for the compilation of a manual population register that, counter-intuitively, played a minor role in producing vital statistics (22, 23). In 1972, a computerised population register was initiated, but did not capture the civil details of Black Africans until 1986 ( 21 , 22 ).
Our research explored experiences related to culture, society, language, religion, education, traditions, family life, dress, food and values. ... Population Registration Act of 1950, Natives Act ...
The Group Areas Act was fashioned as the "cornerstone" of Apartheid policy and aimed to eliminate mixed neighbourhoods in favour of racially segregated ones which would allow South Africans to develop separately (South African Institute for Race Relations, 1950: 26). ... the Population Registration Act provided for compulsory racial ...
Though its overall population will grow by 0.9% or 800,000 people within that timeframe due to net migration, the number of people between 20 and 67 years old is set to fall by 2%, the study from ...
Protocols to conduct research with controlled substances listed in Schedule I are required to be conducted under a site-specific DEA investigator registration. For more information, see 21 CFR ...
2000 and 2019, the Asian population in the United States grew by 81%, and the population is projected to pass 35 million by 2060. Native Hawaiians and Pacific Islanders were the third-fastest growing group, growing by 61% from 2000 to 2019. Their population is projected to pass 2 million by 2030.