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Background: In the context of an epidemiologic transition in South Africa, in which cardiovascular disease is increasing, little is known about the stroke burden, particularly morbidity in rural populations. Risk factors for stroke are high, with hypertension prevalence of more than 50%. Accurate, up-to-date information on disease burden is essential in planning health services for stroke management. This study estimates the burden of stroke in rural South Africa using the epidemiological parameters of incidence, mortality and disability adjusted life year (DALY) metric, a time-based measure that incorporates both mortality and morbidity. Methods: Data from the Agincourt health and socio-demographic surveillance system was utilised to calculate stroke mortality for the period 2007-2011. Dismod, an incidence-prevalence-mortality model, was used to estimate incidence and duration of disability in Agincourt sub-district and ‘mostly rural’ municipalities of South Africa. Using these values, burden of disease in years of life lost (YLL), years lived with disability (YLD) and DALYs was calculated for Agincourt sub-district. Results: Over 5 years, there were an estimated 842 incident cases of stroke in Agincourt sub-district, a crude stroke incidence rate of 244 per 100,000 person years. We estimate that 1,070 DALYs are lost due to stroke yearly. Of this, YLDs contributed 8.7% (3.5 – 10.5%) in sensitivity analysis). Crude stroke mortality was 114 per 100,000 person-years in 2007-11 in Agincourt sub-district. Burden of stroke in entire rural South Africa, a population of some 13,000,000 people, was high, with an estimated 33, 500 strokes occurring in 2011. Conclusions: This study provides the first estimates of stroke burden in terms of incidence, and disability in rural South Africa. High YLL and DALYs lost amongst the rural populations demand urgent measures for preventing and mitigating impacts of stroke. Longitudinal surveillance sites provide a platform through which a changing stroke burden can be monitored in rural South Africa.
The term ‘rural’ suggests many contrasting images to people, such as agricultural landscapes, isolation, small towns, and low-population density [17]. We use the classification by Palmer Development Group (PDG), a public sector consulting firm which classifies rural areas as either ‘small towns’ or ‘mostly rural’ municipalities (Additional file 1: Table S2 and Table S3) [18]. The Agincourt sub-district falls under Bushbuckridge municipality ‘mostly rural’; in this study, ‘rural’ refers explicitly to such municipalities. This analysis is based on a population of approximately 70,000 people residing in the Agincourt sub-district of Mpumalanga province, north-eastern of South Africa between 2007 and 2011 (Figure 1) [19]. The area is completely covered by a health and demographic surveillance system (HDSS). Comprehensive data on mortality and causes of death, births, and inward and outward migration have been collected through a yearly census update since 1992. Additional data on labour participation and educational status have been collected at different time intervals to complement demographic data and provide contextual information. Agincourt sub-district has characteristics similar to many other rural South African populations. Though the sub-district’s socio-economic status has improved since 1994, the majority of the population relies on social assistance grants particularly pension and child care grants. Labour migration is high, with approximately 50-70% of men aged 20–59 years migrating to work outside the study area in 2011 [20]. The proportion is lower for women but increasing over the years with 25-35% of women considered a temporary migrant in 2011, an increase from 20-25% observed in 2000 [20]. The map of Agincourt HDSS, located in rural North-Eastern South Africa. As of 2013, the surveillance site covered 32 villages and a population of more than 100,000 people. The health status profile in the area is characterized by the persistent burden of TB and HIV/AIDS, maternal and child health problems, and emerging non-communicable diseases. Data from the HDSS suggest that between 1992 and 2005, cardiovascular disease (CVD) remained the top cause of death amongst women 50–64 years old [21]. In men, CVD showed a sustained increase. By 2002 it was the third-leading cause of death in men aged 50–64 and second-leading cause of death in men 65 years and above. The sub-district, measuring some 420 km2 is served by six clinics and one health centre. Hospital services are provided by three hospitals situated between 25 km and 45 km from the Agincourt study site. Imaging equipment for the diagnosis of stroke is lacking within the district but accessible at the provincial capital, Nelspruit, some 120 km south of Agincourt. At the time of this study, the stroke register set up in the early 2000s as part of the Southern African Stroke Prevention Initiative (SASPI) was no longer functional and there were no dedicated stroke units in the South African public health system. Ethical approval for the study was granted by the Committee for Research on Human Subjects (Medical) of the University of the Witwatersrand, Johannesburg, South Africa for both the MRC/Wits Rural Public Health and Health Transitions Research Unit’s (Agincourt) Health and Socio-Demographic Surveillance System and add-on modules (Clearance certificate no. M131050). DALYs were calculated by predominantly applying the methodological principles employed in the Global Burden of Disease Studies (detailed description in Additional file 1). DALYs are the sum of years of life lost due to premature mortality (YLL) plus years of life lost due to time lived in states of less than optimal health, loosely referred to as “disability” (YLD) [22]. YLL due to stroke among all persons that die of stroke is the sum of years that victims would have lived if they had completed the life expectancy attributed to their age (as assessed by a standard population) at the time of their death. In this study, the reference life table used in GBD 1990 study was chosen to ensure comparability with previous burden of disease studies. This was based on the highest life expectancy at the time, Japanese females with a life expectancy at birth of 82.5 years. YLLs measure the fatal burden of disease. The YLD figure expresses the consequences of living with less than perfect health conditions. It is an estimate based on the length of time that a condition persisted along with any accompanying disability and thus is an indicator of the non-fatal burden of disease. YLD can be calculated from an incidence perspective as the product of incidence, disability weights and average duration of disease. Alternatively YLD can be measured from a prevalence perspective as the product of prevalence of disease and disability weights. To ensure consistency with the YLL calculation, which takes an inherently incidence perspective, and for comparison with earlier GBD studies, we compute incidence YLDs. Prevalence-based YLDs were calculated mainly for comparison with the GBD 2010 study. To be consistent with GBD 2010, we did not discount or apply age weighting in computing prevalence- based DALYs but apply discounting when YLDs are calculated using incidence. The latter allows comparison with earlier studies that discounted DALYs. Comparative analysis of the incident and prevalence YLDs is warranted as the two are not directly comparable. The incidence approach does not reflect the current prevalent burden of disabling sequelae for a condition for which incidence might have been substantially reduced. Secondly, in an incidence perspective, all YLDs for a condition are assigned to the age-groups at which the condition is incident, whereas in many cases for health policy-making, the ages at which the loss of health is experienced are of most interest. We conducted a systematic literature search of studies conducted in rural South Africa on prevalence of stroke. The search yielded one study; the Southern African Stroke Prevention Initiative (SASPI) study conducted in 2001 within the Agincourt population [6]. In that study, fieldworkers questioned each household informant, systematically reviewed every individual in the household using a previously validated questionnaire, and asked the following question: “Has (person) ever had weakness down one side of the body?” and “Has (person) ever had a stroke?” If either question was answered positively, a clinician/neurologist visited individuals aged >15 years to clinically assess the possible diagnosis of stroke by performing a detailed assessment of the patient. Clinical assessment of possible stroke victims was lowest amongst migrant males 25–44 years. To account for this non-contact, the investigators adjusted the stroke rates in each 10-year age stratum and assumed the same proportion of stroke survivors in employed men as among predominantly unemployed men. Migrant labourers were included in the local population denominator as they consider the sub-district home and return to seek health care when too ill to work [23]. Given the rising trend in risk factors, notably hypertension, and the potential impact of such changes in prevalence on YLD within the population, a sensitivity analysis was conducted around the point estimate, using a range of ±15%. Disability following a stroke spans a wide spectrum. Most contemporary stroke research has assessed disability using the modified rankin scales (MRS), a commonly used ordinal scale that measures disability or dependence in conducting activities of daily living in stroke victims [24]. In the GBD 2010 study, five sequelae of stroke were assessed and disability was ranked according to the lay definitions shown in Table 1 [25]. The disability weights were calculated based on personal interviews in Bangladesh, Indonesia, Peru, and Tanzania; telephone interviews in the USA; and an open access web-based survey. To identify the distribution of the severity of stroke within the Agincourt population, we used Hoffmann’s study (2000) carried out in KwaZulu Natal, South Africa [26]. In the study, patients were recruited into a stroke database from 1992–1998 and a retrospective analysis undertaken of all patients aged 15–40 to establish whether their disability resulted from stroke. Though disability was assessed based on the MRS, similar distribution of severity on the basis of the GBD sequela definition was assumed in this study. The slight difference between the definition of disability on MRS and categories in GBD 2010 was regarded as acceptable by a consensus of members of an international collaborative stroke expert group. A weighted disability weight (DW) was calculated by multiplying each disability weight by the proportion of the population it represented. Disability weights for stroke at each disability level, South Africa Weighted average disability weight across all disability levels: 0.18. Dismod II was used to calculate incidence and duration of stroke-related disability. It models transitions from being healthy, to the incidence of a specific disease, to death from the disease under study or death from other causes. Given three input parameters such as remission, case fatality and prevalence, DisMod II can generate age-specific and sex-specific estimates of disease incidence. Because remission is defined as ‘cure’ in Dismod, no remission (i.e. improvement from the input condition) is possible when modelling stroke survivors. Consequently, prevalence (from SASPI study 2001), post-28 day relative risk of mortality, and a remission rate of zero were used to yield estimates of incidence and duration as outputs. Due to the high risk of mortality in the first 28 days following a stroke, prevalence reflects only those who survive this period; there is thus a need to calculate mortality post-28 days. We could not identify South African specific studies that assessed post-28 day mortality amongst stroke survivors. The best available data chosen as input parameters was based on a prospective study conducted in a rural demographic surveillance site in Hai district, Tanzania between June 2003 and June 2006 [27]. The results of post-stroke case fatality relate to follow up until June 2009, which is at least 3 years of follow-up amongst the cases (Additional file 1: Table S6 and Table S7) [28]. To the best of the authors’ knowledge this is the first published data of post-stroke mortality in Sub-Saharan Africa, based on an incident population and that reports on long-term case fatality. Incidence calculated through Dismod reflects those who survive the high mortality period (first 28 days after stroke) since the prevalence of stroke that was used as input data also reflects those who survive the high mortality period. To show incidence of all cases (those who die within 28 days plus those who survive past 28 days), equation 1 is used to make an adjustment. Equation 1: In South Africa, two hospital-based studies found case fatality rates of 33% and 34%, the weighted average of which is 33% [29,30]. Because the studies are hospital based and many people die before they reach facilities, we elected to use the mortality rates at 28 days post-stroke from the study described above conducted in Tanzania [28]. Incidence and YLD of stroke in the whole of rural South Africa were extrapolated based on mortality rates and prevalence observed in Agincourt sub-district. The total population for rural South Africa was based on 2011 estimates by Statistics South Africa for the ‘mostly rural’ municipalities (Additional file 1: Table S2).
