The authors have declared that no competing interests exist.
South Africa faces an epidemic of chronic non-communicable diseases (NCDs), yet national surveillance is limited due to the lack of recent data. We used data from the first comprehensive national survey on NCDs—the South African National Health and Nutrition Examination Survey (SANHANES-1 (2011–2012))—to evaluate the prevalence of and health system response to diabetes through a diabetes care cascade. We defined diabetes as a Hemoglobin A1c equal to or above 6.5% or currently on treatment for diabetes. We constructed a diabetes care cascade by categorizing the population with diabetes into those who were unscreened, screened but undiagnosed, diagnosed but untreated, treated but uncontrolled, and treated and controlled. We then used multivariable logistic regression models to explore factors associated with diagnosed and undiagnosed diabetes. The age-standardized prevalence of diabetes in South Africans aged 15+ was 10.1%. Prevalence rates were higher among the non-white population and among women. Among individuals with diabetes, a total of 45.4% were unscreened, 14.7% were screened but undiagnosed, 2.3% were diagnosed but untreated, 18.1% were treated but uncontrolled, and 19.4% were treated and controlled, suggesting that 80.6% of the diabetic population had unmet need for care. The diabetes care cascade revealed significant losses from lack of screening, between screening and diagnosis, and between treatment and control. These results point to significant unmet need for diabetes care in South Africa. Additionally, this analysis provides a benchmark for evaluating efforts to manage the rising burden of diabetes in South Africa.
Diabetes is an important cause of global morbidity and mortality.[
In South Africa, diabetes currently ranks second among the top ten leading natural causes of death, accounting for 5.4% of deaths.[
The actual prevalence of diabetes in South Africa, as well as the magnitude of unmet need for diabetes care, remains unclear, as prior estimates for South Africa are largely based on self-reported data, which do not capture undiagnosed cases and often come from sub-national samples and/or pooled estimates drawing on data from multiple countries.[
In 2013, South Africa outlined its strategy for the prevention and control of non-communicable diseases (NCDs).[
Monitoring the population level management of diabetes can be achieved using a care cascade—a method of representing the proportion of people that reach each stage of care from screening for the disease to control.[
In this study, we use data from a unique national survey of the adult population of South Africa conducted in 2011–2012 [
This study draws on data from South African adults who participated in the first South African National Health and Nutrition Examination Survey (SANHANES-1). The SANHANES-1 is a national survey of the non-institutionalized population of South Africa conducted by the Human Sciences Research Council in 2011–2012 to measure the nutrition and health status of the population.[
The survey included an interview, a medical examination, and blood sampling for biomarker analysis. At the household level, 8,166 of the 10,000 households were occupied and contactable. These households yielded 27,580 individuals of all ages who were eligible to be interviewed and agreed to participate, 25,532 (92.6%) of whom completed the interview. Of the latter number, 12,025 (43.6%) and 8,078 (29.3%) individuals volunteered to undergo a medical examination and provide a blood sample for biomarker analysis, respectively. Additional details of SANHANES-1 methodology, content, and laboratory procedures are reported elsewhere.[
The analysis reported here was restricted to South Africans aged 15 and above with non-missing information on race, sex, and province. Among those who consented to providing a blood sample for biomarker analysis, 17% had missing data on HbA1c and were excluded. Additional exclusion criteria were applied in order to conduct the diabetes care cascade and analysis. Participants were excluded because of missing data on self-reports of diabetes screening and diagnosis. Of the respondents who reported a prior diagnosis, only those who reported whether they were currently taking tablets or insulin to lower their blood sugar were retained.
The WHO and American Diabetes Association recommended threshold of 6.5% for HbA1c was used for the diagnosis of diabetes in the current analysis.[
We developed a care cascade to examine unmet need for diabetes care and identify opportunities for intervention across the domains of screening, diagnosis, treatment, and control. This analysis entailed grouping the diabetes subpopulation into five mutually exclusive and exhaustive categories: 1) unscreened (HbA1c ≥ 6.5%; never tested for high blood sugar or sugar diabetes; no reported prior diagnosis) 2) screened, undiagnosed (HbA1c ≥ 6.5%; reported being tested ever; no reported prior diagnosis of diabetes); 3) diagnosed, untreated (prior reported diagnosis of diabetes, but no reported current use of oral glycemic medication or insulin therapy); 4) treated, uncontrolled (reported current use of oral glycemic medication or insulin therapy with HbA1c greater than or equal to 7.0%); and 5) treated, controlled (reported current use of diabetes medication with HbA1c value of less than 7.0%). The criteria for each category are summarized in
The five categories defined above form a diabetes care cascade that helps pinpoint where people with diabetes are lost across the continuum of care. We examined the proportion of respondents who reached each stage using the number of respondents in the subsequent stage as the denominator. For example, among those with diabetes who reported prior testing for high blood sugar, we calculated the proportion who were then diagnosed. In addition to the stages in the cascade, we defined “unmet need” as the sum of the first four of the diabetes subcategories (unscreened; screened but undiagnosed; diagnosed but untreated; treated but uncontrolled). Respondents with controlled diabetes were not considered to have unmet need for care.
To examine differences in unmet need for diabetes care across different sub-groups of the South African population, we integrated data on a number of covariates. Age, gender, race, and geographic location were ascertained by interview. Race was classified as African, Coloured, White, Indian/Asian, or other per South African standards, and the Indian/Asian and “other” categories were collapsed for analysis. Geographic status was defined using the categories urban informal, urban formal, rural informal (tribal areas), and rural formal (farms).[
We then used multivariable logistic regression analysis to investigate social, demographic, and anthropometric predictors of 1) prevalent diabetes and 2) undiagnosed diabetes. Analyses for this study were performed using Stata Version 14 (StataCorp, Texas, USA). Descriptive statistics were generated using means for continuous and proportions for categorical variables. Estimates of prevalence and unmet need for diabetes care were age-standardized to the age-distribution of the South African adult population, using mid-year population estimates for 2012.[
The final analytic sample included 4,083 total respondents, of whom 521 had diabetes. Descriptive statistics for the sample and the South African adult population as a whole are shown in
Final Analytic SANHANES Sample, 2011–2012 | Mid-Year Population Estimates, 2012 Census | ||
---|---|---|---|
No. | % | % | |
Sex | |||
Men | 1459 | 47.4 | 48.1 |
Women | 2624 | 52.6 | 51.9 |
Age Categories | |||
15–34 | 1791 | 49.1 | 52.0 |
35–54 | 1270 | 33.4 | 32.3 |
55–74 | 864 | 15.2 | 13.6 |
≥ 75 | 158 | 2.3 | 2.2 |
Race | |||
African | 2659 | 72.1 | 77.7 |
White | 95 | 13.2 | 10.3 |
Coloured | 1132 | 11.5 | 9.3 |
Indian/Asian/Other | 197 | 3.3 | 2.8 |
Province | |||
Western Cape | 872 | 16.0 | 11.8 |
Eastern Cape | 677 | 13.4 | 12.0 |
Northern Cape | 306 | 2.7 | 2.2 |
Free State | 347 | 6.9 | 5.4 |
KwaZulu-Natal | 423 | 13.3 | 18.8 |
North West | 581 | 7.9 | 6.7 |
Gauteng | 444 | 28.7 | 25.7 |
Mpumalanga | 271 | 4.3 | 7.5 |
Limpopo | 162 | 6.7 | 10.0 |
Sample Size (n) | 4083 |
Sample weights were incorporated to adjust the percentage estimates in the SANHANES sample for unequal probabilities of selection and nonresponse in the laboratory component of the survey. Mid-year population estimates for 2012 were obtained from South African census data (Statistics South Africa, 2012).
The age-standardized prevalence of normal blood sugar and prediabetic blood sugar in the sample was 60.2% and 29.7% respectively. The age-standardized prevalence of diabetes (HbA1c ≥ 6.5% or currently taking medication for high blood sugar) in the sample was 10.1%. Estimates of clinical blood sugar categories by population group can be found in
Normal HbA1c < 5.7% | Prediabetes 5.7% ≤ HbA1c < 6.5% | Diabetes HbA1c ≥ 6.5% or taking medication | ||||
---|---|---|---|---|---|---|
Prev | SE | Prev | SE | Prev | SE | |
Ages ≥ 15 | ||||||
Crude | 59.0 | 1.5 | 30.3 | 1.4 | 10.7 | 1.1 |
Age-standardized | 60.2 | 1.6 | 29.7 | 1.3 | 10.1 | 1.1 |
Age Categories | ||||||
15–34 | 74.1 | 1.9 | 20.9 | 1.4 | 5.0 | 1.6 |
35–54 | 50.0 | 2.6 | 39.2 | 2.6 | 10.8 | 1.5 |
55–74 | 36.4 | 3.9 | 39.4 | 3.5 | 24.1 | 2.8 |
≥ 75 | 25.6 | 5.2 | 41.5 | 7.8 | 32.9 | 5.1 |
Sex | ||||||
Men | 61.2 | 2.4 | 29.1 | 2.1 | 9.7 | 1.7 |
Women | 59.1 | 1.8 | 30.1 | 1.7 | 10.7 | 1.0 |
Sex by Age | ||||||
Men | ||||||
15–34 | 71.3 | 3.3 | 22.0 | 2.6 | 6.7 | 3.0 |
35–54 | 55.2 | 3.7 | 36.7 | 4.2 | 8.0 | 2.1 |
55–74 | 40.9 | 6.1 | 37.3 | 5.3 | 21.9 | 3.6 |
≥ 75 | 35.0 | 7.8 | 33.9 | 12.6 | 31.1 | 7.7 |
Women | ||||||
15–34 | 76.9 | 1.9 | 19.4 | 1.6 | 3.7 | 1.1 |
35–54 | 45.6 | 3.7 | 41.3 | 3.6 | 13.1 | 1.8 |
55–74 | 30.3 | 3.9 | 41.6 | 4.5 | 28.1 | 3.1 |
≥ 75 | 17.1 | 4.4 | 48.4 | 7.0 | 34.5 | 7.0 |
Race | ||||||
African | 59.4 | 1.7 | 31.0 | 1.4 | 9.7 | 1.2 |
White | 74.7 | 3.3 | 18.3 | 3.8 | 7.1 | 2.2 |
Coloured | 52.4 | 2.4 | 36.3 | 2.4 | 11.3 | 1.4 |
Indian/Asian/Other | 56.5 | 5.0 | 17.9 | 2.7 | 25.6 | 4.8 |
Residential Location | ||||||
Urban Formal | 60.4 | 2.4 | 28.7 | 2.0 | 10.9 | 1.4 |
Urban Informal | 64.2 | 2.8 | 27.2 | 2.3 | 8.6 | 1.6 |
Rural Informal | 58.6 | 3.0 | 31.8 | 2.5 | 9.6 | 2.4 |
Rural Formal | 59.8 | 3.6 | 32.6 | 2.7 | 7.5 | 2.0 |
BMI Category | ||||||
Underweight | 61.7 | 4.0 | 37.2 | 4.0 | 1.2 | 0.5 |
Normal | 67.8 | 2.2 | 26.7 | 1.7 | 5.5 | 1.5 |
Overweight | 65.6 | 2.4 | 25.1 | 2.2 | 9.3 | 1.4 |
Obese | 48.8 | 3.1 | 33.5 | 2.4 | 17.7 | 2.3 |
Prev = prevalence, SE = Standard Error. Normal = HbA1c < 5.7%; Prediabetes = 5.7% ≤ HbA1c < 6.5%; Diabetes = HbA1c ≥ 6.5% or currently taking medication. The following BMI categories were used: underweight (BMI < 18.5 kg/m^2), normal (18.5 ≤ BMI < 25), overweight (25 ≤ BMI < 30), and obese (BMI ≥ 30). Estimates for the overall population and by sex, race, geography and BMI were age-standardized using five-year age-categories between 15 and 74 and an open-ended category of 75 and above. Standard values were obtained from mid-year population estimates for 2012 (Statistics South Africa, 2012).
Results of the diabetes care cascade are displayed in
Of those with diabetes, 55% have ever been screened for diabetes, a 45% loss. Of those who have ever had their blood sugar measured, 73% received a diagnosis of high blood sugar or sugar diabetes, a 27% loss. Of those who received a diagnosis, 94% were being treated with oral glycemic medication or insulin, a 6% loss. Of those who were currently taking medication, 51% had controlled blood sugar (HbA1c < 7.0%), a 49% loss.
Total Diabetes | Unscreened, Undiagnosed | Screened, Undiagnosed | Diagnosed, Untreated | Treated, Uncontrolled | Treated, Controlled | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Prev | SE | Prev | SE | % of diab | Prev | SE | % of diab | Prev | SE | % of diab | Prev | SE | % of diab | Prev | SE | % of diab | |
Ages ≥ 15 | |||||||||||||||||
Crude | 10.7 | 1.1 | 3.5 | 0.7 | 32.8 | 2.2 | 0.8 | 20.4 | 0.4 | 0.2 | 4.2 | 2.8 | 0.3 | 26.2 | 1.8 | 0.3 | 16.5 |
Age-standardized | 10.1 | 1.1 | 3.3 | 0.6 | 45.4 | 2.0 | 0.7 | 14.7 | 0.4 | 0.2 | 2.3 | 2.6 | 0.3 | 18.1 | 1.7 | 0.3 | 19.4 |
Age Categories | |||||||||||||||||
15–34 | 5.0 | 1.6 | 2.2 | 1.0 | 55.8 | 1.7 | 1.2 | 15.8 | 0.0 | 0.0 | 0.2 | 0.4 | 0.2 | 8.8 | 0.6 | 0.3 | 19.3 |
35–54 | 10.8 | 1.5 | 3.7 | 0.7 | 38.4 | 1.3 | 0.3 | 11.5 | 0.7 | 0.4 | 4.5 | 3.3 | 0.8 | 25.0 | 1.9 | 0.7 | 20.5 |
55–74 | 24.1 | 0.0 | 6.6 | 0.0 | 27.5 | 4.0 | 0.0 | 16.1 | 1.1 | 0.0 | 5.1 | 8.7 | 0.0 | 37.5 | 3.7 | 0.0 | 13.8 |
≥ 75 | 32.9 | 5.1 | 3.7 | 1.3 | 11.3 | 9.4 | 3.6 | 28.5 | 0.2 | 0.2 | 0.7 | 5.9 | 2.2 | 18.0 | 13.6 | 5.5 | 41.5 |
Sex | |||||||||||||||||
Men | 9.7 | 1.7 | 3.3 | 1.2 | 44.0 | 2.5 | 1.4 | 14.8 | 0.4 | 0.3 | 2.7 | 2.2 | 0.5 | 30.1 | 1.3 | 0.4 | 8.3 |
Women | 10.7 | 1.0 | 3.5 | 0.5 | 46.3 | 1.7 | 0.4 | 14.0 | 0.4 | 0.1 | 1.8 | 2.9 | 0.4 | 15.1 | 2.2 | 0.6 | 22.8 |
Sex by Age | |||||||||||||||||
Men | |||||||||||||||||
15–34 | 6.7 | 3.0 | 2.8 | 2.1 | 54.9 | 2.8 | 2.6 | 13.6 | 0.0 | 0.0 | 0.0 | 0.7 | 0.5 | 26.3 | 0.3 | 0.2 | 5.2 |
35–54 | 8.0 | 2.1 | 2.4 | 1.0 | 34.7 | 0.9 | 0.4 | 14.8 | 0.9 | 0.7 | 6.7 | 3.0 | 1.1 | 35.9 | 0.8 | 0.5 | 7.9 |
55–74 | 21.9 | 3.6 | 7.3 | 2.9 | 30.2 | 3.9 | 1.3 | 18.2 | 0.9 | 0.9 | 4.0 | 5.9 | 1.8 | 34.4 | 3.8 | 1.9 | 13.1 |
≥ 75 | 31.1 | 7.7 | 1.9 | 1.9 | 6.1 | 7.5 | 4.3 | 24.0 | 0.5 | 0.5 | 1.6 | 2.6 | 2.3 | 8.4 | 18.6 | 9.5 | 59.9 |
Women | |||||||||||||||||
15–34 | 3.7 | 1.1 | 1.7 | 0.6 | 56.5 | 0.8 | 0.6 | 15.0 | 0.1 | 0.1 | 0.9 | 0.2 | 0.1 | 3.8 | 0.9 | 0.6 | 23.7 |
35–54 | 13.1 | 1.8 | 4.8 | 0.9 | 40.8 | 1.6 | 0.4 | 11.0 | 0.4 | 0.3 | 2.3 | 3.3 | 0.8 | 20.6 | 3.1 | 1.4 | 25.3 |
55–74 | 28.1 | 3.1 | 7.2 | 1.7 | 25.0 | 3.9 | 1.1 | 14.1 | 1.5 | 0.7 | 4.5 | 11.6 | 2.1 | 43.6 | 3.9 | 1.1 | 12.8 |
≥ 75 | 34.5 | 7.0 | 5.3 | 2.0 | 15.5 | 11.1 | 5.3 | 32.1 | 0.0 | 0.0 | 0.0 | 8.9 | 3.3 | 25.9 | 9.1 | 3.8 | 26.5 |
Race | |||||||||||||||||
African | 9.7 | 1.2 | 3.7 | 0.7 | 54.1 | 2.0 | 0.8 | 12.6 | 0.3 | 0.1 | 1.9 | 2.4 | 0.4 | 15.3 | 1.3 | 0.3 | 16.1 |
White | 7.1 | 2.2 | 1.3 | 1.0 | 7.6 | 0.1 | 0.1 | 3.2 | 0.2 | 0.2 | 4.6 | 2.2 | 1.1 | 46.0 | 3.1 | 1.6 | 38.6 |
Coloured | 11.3 | 1.4 | 2.8 | 0.6 | 38.4 | 2.4 | 0.6 | 15.5 | 0.9 | 0.7 | 3.8 | 3.3 | 0.7 | 26.8 | 1.8 | 0.7 | 15.6 |
Indian/Asian/Oth | 25.6 | 4.8 | 4.9 | 1.6 | 14.4 | 10.5 | 3.8 | 43.5 | 0.3 | 0.2 | 0.9 | 7.0 | 1.6 | 20.2 | 3.0 | 1.2 | 20.9 |
Residential Location | |||||||||||||||||
Urban Formal | 10.9 | 1.4 | 3.5 | 1.0 | 40.4 | 1.7 | 0.4 | 11.1 | 0.5 | 0.2 | 2.5 | 3.1 | 0.5 | 23.4 | 2.1 | 0.5 | 22.6 |
Urban Informal | 8.6 | 1.6 | 4.0 | 1.1 | 68.1 | 0.9 | 0.4 | 5.3 | 0.1 | 0.1 | 5.1 | 1.4 | 0.6 | 8.5 | 2.2 | 1.0 | 13.0 |
Rural Informal | 9.6 | 2.4 | 3.1 | 0.8 | 65.1 | 3.5 | 2.2 | 17.2 | 0.2 | 0.2 | 1.6 | 1.9 | 0.4 | 10.2 | 0.8 | 0.3 | 5.8 |
Rural Formal | 7.5 | 2.0 | 2.6 | 1.2 | 43.2 | 1.1 | 0.5 | 11.1 | 0.1 | 0.1 | 0.5 | 1.5 | 0.7 | 17.9 | 2.2 | 1.0 | 27.3 |
BMI Category | |||||||||||||||||
Underweight | 1.2 | 0.5 | 0.1 | 0.1 | 27.4 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3 | 0.3 | 5.4 | 0.8 | 0.4 | 67.2 |
Normal | 5.5 | 1.5 | 1.2 | 0.3 | 40.2 | 2.0 | 1.4 | 16.0 | 0.2 | 0.1 | 0.8 | 1.4 | 0.4 | 34.1 | 0.7 | 0.2 | 8.8 |
Overweight | 9.3 | 1.4 | 3.2 | 0.8 | 41.8 | 0.8 | 0.2 | 19.6 | 1.0 | 0.7 | 4.0 | 2.7 | 0.6 | 17.7 | 1.7 | 0.6 | 16.9 |
Obese | 17.7 | 2.3 | 6.2 | 1.8 | 47.7 | 3.6 | 0.8 | 13.7 | 0.5 | 0.2 | 1.9 | 4.1 | 0.6 | 17.1 | 3.4 | 1.2 | 19.6 |
Prev = prevalence, SE = Standard Error. Diabetes was defined as a Hemoglobin A1c equal to or above 6.5% or currently on treatment for diabetes. For the category of treated and controlled, HbA1c < 7.0% was used per South African standards. The following BMI categories were used: underweight (BMI < 18.5 kg/m^2), normal (18.5 ≤ BMI < 25), overweight (25 ≤ BMI < 30), and obese (BMI ≥ 30). Estimates for the overall population and by sex, race, geography, and BMI were age standardized using five-year age-categories between 15 and 74 and an open-ended category of 75 and above. Standard values were obtained from mid-year population estimates for 2012 (Statistics South Africa, 2012).
Among individuals with diabetes, nearly half were unscreened (45.4%). An additional 14.7% were screened but undiagnosed, 2.3% were diagnosed but untreated, and 18.1% were treated but uncontrolled. Only 19.4% of diabetic respondents were treated and controlled. Results of the diabetes care analysis by age, sex, race, residential location, and BMI are also presented in
Although individuals in the age category 15–34 had lower prevalence of diabetes overall—estimated at 5.0% compared to 10.8% for those 35–54, 24.1% for those 55–74, and 32.9% for those over 75—younger people were also at higher risk of being unscreened: 55.8% of individuals with diabetes between the ages 15 and 34 were unscreened, compared to 11.3% in the age category 75+.
The age-standardized prevalence of diabetes was higher in women as compared with men (10.7% versus 9.7%). The likelihood of being unscreened was also slightly higher in women (46.3%) than in men (44.0%), while the likelihood of being treated but uncontrolled was higher in men than in women (30.1% of men with diabetes were treated but uncontrolled compared to 15.1% of women with diabetes). Women with diabetes were more likely to have controlled blood sugar than men (22.8% versus 8.3%).
Overall, the age-standardized prevalence of diabetes was higher in the non-white population, particularly among Indians/Asians/others. The proportion unscreened was also higher in non-whites compared to whites; 54.1% in Africans, 38.4% in the Coloured population and 14.4% among Indians/Asians/others, compared to 7.6% of white South Africans. A similar trend was seen for those with diabetes who were screened but undiagnosed (12.6% in Africans, 15.5% in the Coloured population, and 43.5% among Indians/Asians/others, compared with 3.2% among Whites). Whites were more likely to be both treated, uncontrolled and treated, controlled than non-white subgroups.
Although the prevalence of diabetes was highest in those living in urban formal areas, the proportion unscreened was lower in urban formal areas than in other settings (40.4% compared to 68.1% in urban informal areas and 65.1% in rural informal areas). With respect to BMI, prevalence increased monotonically between weight categories, progressing from 1.2% in those with a BMI of less than 18.5 kg/m2 to 17.7% in individuals with BMI 30 kg/m2 and above. The highest proportion of undiagnosed diabetes was found in the obese category, where 61.4% of individuals were either unscreened or screened but undiagnosed.
The transitions between stages of the diabetes care cascade are displayed in
In the multivariable analysis of predictors of diabetes prevalence (
Predictors of |
Predictors of |
|||||||
---|---|---|---|---|---|---|---|---|
OR | 95% CI | P value | OR | 95% CI | P value | |||
Age Categories | ||||||||
15–34 | 1.00 | 1.00 | ||||||
35–54 | 1.51 | 0.69 | 3.30 | 0.30 | 0.80 | 0.30 | 2.08 | 0.64 |
55–74 | 3.55 | 1.53 | 8.21 | 0.00 | 1.74 | 0.66 | 4.57 | 0.26 |
≥ 75 | 4.79 | 1.61 | 14.21 | 0.00 | 2.69 | 0.78 | 9.34 | 0.12 |
Sex | ||||||||
Men | 1.00 | 1.00 | ||||||
Women | 0.58 | 0.29 | 1.17 | 0.13 | 0.43 | 0.17 | 1.06 | 0.07 |
Race | ||||||||
African | 1.00 | 1.00 | ||||||
White | 0.60 | 0.24 | 1.54 | 0.29 | 0.23 | 0.04 | 1.26 | 0.09 |
Coloured | 1.38 | 0.79 | 2.41 | 0.26 | 1.12 | 0.56 | 2.23 | 0.75 |
Indian/Asian/Other | 4.06 | 1.99 | 8.29 | 0.00 | 4.23 | 1.70 | 10.53 | 0.00 |
Residential Location | ||||||||
Urban Formal | 1.00 | 1.00 | ||||||
Urban Informal | 0.71 | 0.37 | 1.37 | 0.31 | 0.67 | 0.28 | 1.59 | 0.36 |
Rural Informal | 1.15 | 0.43 | 3.02 | 0.78 | 1.47 | 0.43 | 5.08 | 0.54 |
Rural Formal | 1.06 | 0.50 | 2.27 | 0.87 | 1.04 | 0.33 | 3.28 | 0.95 |
BMI Category | ||||||||
Normal | 1.00 | 1.00 | ||||||
Overweight | 2.60 | 1.19 | 5.68 | 0.02 | 2.40 | 0.82 | 7.03 | 0.11 |
Obese | 5.73 | 2.37 | 13.86 | 0.00 | 7.28 | 2.09 | 25.33 | 0.00 |
Family History of Diabetes | 2.33 | 1.46 | 3.72 | 0.00 | 0.83 | 0.48 | 1.44 | 0.50 |
OR = odds ratio; BMI = body mass index; CI = confidence interval. Diabetes was defined as a Hemoglobin A1c equal to or above 6.5% or currently on treatment for diabetes. The analysis of predictors of having undiagnosed diabetes was restricted to those with diabetes. “Undiagnosed” here refers to all diabetic respondents who have never been screened for high blood sugar and those who have been screened but never received a diagnosis. The following BMI categories were used: normal (18.5 ≤ BMI < 25), overweight (25 ≤ BMI < 30), and obese (BMI ≥ 30).
In this study, using data from a unique national survey which combined questionnaires with medical examination and biomarker analysis, we report the first nationally representative estimate of the burden of diabetes among South African adults. Our analysis revealed both high diabetes prevalence and substantial unmet need for diabetes care in the South African population, as well as notable disparities across groups. Of the 10.1% of those aged 15+ with diabetes, only 19.4% were treated and controlled; 45.4% were unscreened, 14.7% were screened but undiagnosed, 2.3% were diagnosed but untreated, and 18.1% were treated but uncontrolled.
Our estimates indicate a higher prevalence of diabetes than previously reported by the National Income Dynamics Study (NIDS) in 2012 for South Africans aged 40+ (18.9% prevalence for SANHANES compared to 14.3% for NIDS).[
Estimates presented in this analysis indicate a slightly lower diabetes prevalence but substantially greater unmet need for diabetes care in South Africa than in the United States. Recent estimates for the U.S. place age-standardized diabetes prevalence at 12.3% for adults aged 20+,[
The diabetes care cascade reveals that one of the key gaps in the national management of diabetes is proper screening; nearly half of diabetic respondents reported never even having their blood sugar measured. Of those with diabetes who reported prior screening/testing, 72.5% received a diagnosis, indicating another significant loss between the stage of screening and diagnosis. Although some of the respondents who reported having their blood sugar measured may have been screened before becoming diabetic, 79.2% of the people who were screened but undiagnosed reported that they had their blood sugar measured within the last year. In total, only 39.9% of those with diabetic level HbA1c reported awareness of their condition.
In addition to the poor rates of screening and diagnosis, the care cascade suggests that a gap exists in terms of effective treatment with diabetes medication. Among those who reported use of oral glycemic medication or insulin, only 51.2% had controlled blood sugar. Contributing factors to this effective treatment gap likely include lack of health education and poor medicine adherence on the part of patients.[
Several factors may contribute to the high rate of unmet need for diabetes care in South Africa across the care continuum, among them problems with access, health-seeking behavior, and health system quality. Insufficient access to health care services is widespread, with the most important barriers to access relating to low socio-economic status, racial background, lack of health insurance,[
Many of these barriers are highlighted in a recent qualitative study among low-income black women in Soweto, in which a large proportion of participants had no health insurance and sought diabetes care in public health facilities with limited availability of diabetes counseling and treatment and low quality of care.[
Strengths of the current study included use of data from a large national sample and the measurement of Hemoglobin A1c, an objective criterion for defining diabetes, which allowed us to obtain estimates of the total prevalence of diabetes in South Africa as well as to investigate control status. Another strength is the use of a care cascade to identify gaps in the population-level management of diabetes.
A limitation of this study was the low response rate to the laboratory component of the SANHANES, since testing was conducted in referral sites, not at the point of survey administration (in contrast, for example, to the US NHANES, which employed mobile examination units). We conducted a bias analysis to compare demographic characteristics of the final analytic sample and those excluded between interview and analysis (
Until recently, health policy and programming in South Africa have largely focused on infectious and communicable diseases, and the majority of all health expenditure has been directed to the prevention and management of these diseases. With the recent launch of the national NCD strategy, however, momentum to tackle the burden of NCDs is growing. Given South Africa’s large past investments in chronic HIV care, the national NCD strategy outlines an approach that integrates diabetes care into these existing systems.
This study documents high levels of unmet need for diabetes care among South African adults with diabetes and points to stages in the diabetes care continuum with the biggest gaps in population-level management. The current estimates should serve as a benchmark for evaluating the effectiveness of the proposed reforms, particularly the re-engineering of primary care, and motivate policies aimed at redressing unmet need for diabetes care in South Africa.
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The columns "All Excluded Observations from SANHANES interview sample" show all people excluded between the adult interview sample and the final analytic sample including those who did not complete the lab portion of the exam. The columns "Excluded observations from the SANHANES lab sample" show only the observations that were excluded from the lab sample based on criteria unique to this analysis. Sample weights were incorporated to adjust the percentage estimates in the SANHANES samples for unequal probabilities of selection and nonresponse in the laboratory component of the survey.
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The authors express their gratitude to the Human Sciences Research Council (HSRC) in Cape Town for granting access to this data. Additionally, we would like to thank everyone who contributed to designing and administering the SANHANES survey; a complete list of contributors can be found elsewhere.[