Participation Dynamics in Population-Based Longitudinal HIV Surveillance in Rural South Africa

Population-based HIV surveillance is crucial to inform understanding of the HIV pandemic and evaluate HIV interventions, but little is known about longitudinal participation patterns in such settings. We investigated the dynamics of longitudinal participation patterns in a high HIV prevalence surveillance setting in rural South Africa between 2003 and 2012, taking into account demographic dynamics. At any given survey round, 22,708 to 30,495 persons were eligible. Although the yearly participation rates were relatively modest (26% to 46%), cumulative rates increased substantially with multiple recruitment opportunities: 68% of eligible persons participated at least once, 48% at least twice and 31% at least three times after five survey rounds. We identified two types of study fatigue: at the individual level, contact and consent rates decreased with multiple recruitment opportunities and, at the population level, these rates also decreased over calendar time, independently of multiple recruitment opportunities. Using sequence analysis and hierarchical clustering, we identified three broad individual participation profiles: consenters (20%), switchers (43%) and refusers (37%). Men were over represented among refusers, women among consenters, and temporary non-residents among switchers. The specific subgroup of persons who were systemically not contacted or refusers constitutes a challenge for population-based surveillance and interventions.     

Sex and Life Expectancy

BackgroundA sexual dimorphism in human life expectancy has existed in almost every country for as long as records have been kept. Although human life expectancy has increased each year, females still live longer, on average, than males. Undoubtedly, the reasons for the sex gap in life expectancy are multifaceted, and it has been discussed from both sociological and biological perspectives. However, even if biological factors make up only a small percentage of the determinants of the sex difference in this phenomenon, parity in average life expectancy should not be anticipated.ObjectiveThe aim of this review is to highlight biological mechanisms that may underlie the sexual dimorphism in life expectancy.MethodsUsing PubMed, ISI Web of Knowledge, and Google Scholar, as well as cited and citing reference histories of articles through August 2012, English-language articles were identified, read, and synthesized into categories that could account for biological sex differences in human life expectancy.ResultsThe examination of biological mechanisms accounting for the female-based advantage in human life expectancy has been an active area of inquiry; however, it is still difficult to prove the relative importance of any 1 factor. Nonetheless, biological differences between the sexes do exist and include differences in genetic and physiological factors such as progressive skewing of X chromosome inactivation, telomere attrition, mitochondrial inheritance, hormonal and cellular responses to stress, immune function, and metabolic substrate handling among others. These factors may account for at least a part of the female advantage in human life expectancy.ConclusionsDespite noted gaps in sex equality, higher body fat percentages and lower physical activity levels globally at all ages, a sex-based gap in life expectancy exists in nearly every country for which data exist. There are several biological mechanisms that may contribute to explaining why females live longer than men on average, but the complexity of the human life experience makes research examining the contribution of any single factor for the female advantage difficult. However, this information may still prove important to the development of strategies for healthy aging in both sexes.     

Need for Timely Paediatric HIV Treatment within Primary Health Care in Rural South Africa

Background

In areas where adult HIV prevalence has reached hyperendemic levels, many infants remain at risk of acquiring HIV infection. Timely access to care and treatment for HIV-infected infants and young children remains an important challenge. We explore the extent to which public sector roll-out has met the estimated need for paediatric treatment in a rural South African setting.

Methods

Local facility and population-based data were used to compare the number of HIV infected children accessing HAART before 2008, with estimates of those in need of treatment from a deterministic modeling approach. The impact of programmatic improvements on estimated numbers of children in need of treatment was assessed in sensitivity analyses.

Findings

In the primary health care programme of HIV treatment 346 children <16 years of age initiated HAART by 2008; 245(70.8%) were aged 10 years or younger, and only 2(<1%) under one year of age. Deterministic modeling predicted 2,561 HIV infected children aged 10 or younger to be alive within the area, of whom at least 521(20.3%) would have required immediate treatment. Were extended PMTCT uptake to reach 100% coverage, the annual number of infected infants could be reduced by 49.2%.

Conclusion

Despite progress in delivering decentralized HIV services to a rural sub-district in South Africa, substantial unmet need for treatment remains. In a local setting, very few children were initiated on treatment under 1 year of age and steps have now been taken to successfully improve early diagnosis and referral of infected infants.     

Gender,Migration and HIV in Rural KwaZulu-Natal,South Africa

Objectives

Research on migration and HIV has largely focused on male migration, often failing to measure HIV risks associated with migration for women. We aimed to establish whether associations between migration and HIV infection differ for women and men, and identify possible mechanisms by which women''s migration contributes to their high infection risk.

Design

Data on socio-demographic characteristics, patterns of migration, sexual behavior and HIV infection status were obtained for a population of 11,677 women aged 15–49 and men aged 15–54, resident members of households within a demographic surveillance area participating in HIV surveillance in 2003–04.

Methods

Logistic regression was conducted to examine whether sex and migration were independently associated with HIV infection in three additive effects models, using measures of recent migration, household presence and migration frequency. Multiplicative effects models were fitted to explore whether the risk of HIV associated with migration differed for males and females. Further modeling and simulations explored whether composition or behavioral differences accounted for observed associations.

Results

Relative to non-migrant males, non-migrant females had higher odds of being HIV-positive (adjusted odds ratio [aOR] = 1.72; 95% confidence interval [1.49–1.99]), but odds were higher for female migrants (aOR = 2.55 [2.07–3.13]). Female migrants also had higher odds of infection relative to female non-migrants (aOR = 1.48 [1.23–1.77]). The association between number of sexual partners over the lifetime and HIV infection was modified by both sex and migrant status: For male non-migrants, each additional partner was associated with 3% higher odds of HIV infection (aOR = 1.03 [1.02–1.05]); for male migrants the association between number of partners and HIV infection was non-significant. Each additional partner increased odds of HIV infection by 22% for female non-migrants (aOR = 1.22 [1.12–1.32]) and 46% for female migrants (aOR = 1.46 [1.25–1.69]).

Conclusions

Higher risk sexual behavior in the context of migration increased women''s likelihood of HIV infection.     

Obesity in South Africa: The South African Demographic and Health Survey

Objectives: To ascertain the anthropometric profile and determinants of obesity in South Africans who participated in the Demographic and Health Survey in 1998. Research Methods and Procedures: A sample of 13,089 men and women (age, ≥15 years) were randomly selected and then stratified by province and urban and nonurban areas. Height, weight, mid-upper arm circumference, and waist and hip circumference were measured. Body mass index (BMI) was used as an indicator of obesity, and the waist/hip ratio (WHR) was used as an indicator of abdominal obesity. Multivariate regression identified sociodemographic predictors of BMI and waist circumference in the data. Results: Mean BMI values for men and women were 22.9 kg/m² and 27.1 kg/m², respectively. For men, 29.2% were overweight or obese (≥25 kg/m²) and 9.2% had abdominal obesity (WHR ≥1.0), whereas 56.6% of women were overweight or obese and 42% had abdominal obesity (WHR >0.85). Underweight (BMI <18.5 kg/m²) was found in 12.2% of men and 5.6% of women. For men, 19% of the variation of BMI and 34% of the variation in waist circumference could be explained by age, level of education, population group, and area of residence. For women, these variables explained 16% of the variation of BMI and 24% of the variation in waist circumference. Obesity increased with age, and higher levels of obesity were found in urban African women. Discussion: Overnutrition is prevalent among adult South Africans, particularly women. Determinants of overnutrition include age, level of education, ethnicity, and area of residence.     

Adjusting HIV Prevalence for Survey Non-Response Using Mortality Rates: An Application of the Method Using Surveillance Data from Rural South Africa

Background

The main source of HIV prevalence estimates are household and population-based surveys; however, high refusal rates may hinder the interpretation of such estimates. The study objective was to evaluate whether population HIV prevalence estimates can be adjusted for survey non-response using mortality rates.

Methodology/Principal Findings

Data come from the longitudinal Africa Centre Demographic Information System (ACDIS), in rural South Africa. Mortality rates for persons tested and not tested in the 2005 HIV surveillance were available from routine household surveillance. Assuming HIV status among individuals contacted but who refused to test (non-response) is missing at random and mortality among non-testers can be related to mortality of those tested a mathematical model was developed. Non-parametric bootstrapping was used to estimate the 95% confidence intervals around the estimates. Mortality rates were higher among untested (16.9 per thousand person-years) than tested population (11.6 per thousand person-years), suggesting higher HIV prevalence in the former. Adjusted HIV prevalence for females (15–49 years) was 31.6% (95% CI 26.1–37.1) compared to observed 25.2% (95% CI 24.0–26.4). For males (15–49 years) adjusted HIV prevalence was 19.8% (95% CI 14.8–24.8), compared to observed 13.2% (95% CI 12.1–14.3). For both sexes (15–49 years) combined, adjusted prevalence was 27.5% (95% CI 23.6–31.3), and observed prevalence was 19.7% (95% CI 19.6–21.3). Overall, observed prevalence underestimates the adjusted prevalence by around 7 percentage points (37% relative difference).

Conclusions/Significance

We developed a simple approach to adjust HIV prevalence estimates for survey non-response. The approach has three features that make it easy to implement and effective in adjusting for selection bias than other approaches. Further research is needed to assess this approach in populations with widely available HIV treatment (ART).     

The Promise of Prevention: The Effects of Four Preventable Risk Factors on National Life Expectancy and Life Expectancy Disparities by Race and County in the United States

Background

There has been substantial research on psychosocial and health care determinants of health disparities in the United States (US) but less on the role of modifiable risk factors. We estimated the effects of smoking, high blood pressure, elevated blood glucose, and adiposity on national life expectancy and on disparities in life expectancy and disease-specific mortality among eight subgroups of the US population (the “Eight Americas”) defined on the basis of race and the location and socioeconomic characteristics of county of residence, in 2005.

Methods and Findings

We combined data from the National Health and Nutrition Examination Survey and the Behavioral Risk Factor Surveillance System to estimate unbiased risk factor levels for the Eight Americas. We used data from the National Center for Health Statistics to estimate age–sex–disease-specific number of deaths in 2005. We used systematic reviews and meta-analyses of epidemiologic studies to obtain risk factor effect sizes for disease-specific mortality. We used epidemiologic methods for multiple risk factors to estimate the effects of current exposure to these risk factors on death rates, and life table methods to estimate effects on life expectancy. Asians had the lowest mean body mass index, fasting plasma glucose, and smoking; whites had the lowest systolic blood pressure (SBP). SBP was highest in blacks, especially in the rural South—5–7 mmHg higher than whites. The other three risk factors were highest in Western Native Americans, Southern low-income rural blacks, and/or low-income whites in Appalachia and the Mississippi Valley. Nationally, these four risk factors reduced life expectancy at birth in 2005 by an estimated 4.9 y in men and 4.1 y in women. Life expectancy effects were smallest in Asians (M, 4.1 y; F, 3.6 y) and largest in Southern rural blacks (M, 6.7 y; F, 5.7 y). Standard deviation of life expectancies in the Eight Americas would decline by 0.50 y (18%) in men and 0.45 y (21%) in women if these risks had been reduced to optimal levels. Disparities in the probabilities of dying from cardiovascular diseases and diabetes at different ages would decline by 69%–80%; the corresponding reduction for probabilities of dying from cancers would be 29%–50%. Individually, smoking and high blood pressure had the largest effect on life expectancy disparities.

Conclusions

Disparities in smoking, blood pressure, blood glucose, and adiposity explain a significant proportion of disparities in mortality from cardiovascular diseases and cancers, and some of the life expectancy disparities in the US. Please see later in the article for the Editors'' Summary     

Population-Based CD4 Counts in a Rural Area in South Africa with High HIV Prevalence and High Antiretroviral Treatment Coverage

Background

Little is known about the variability of CD4 counts in the general population of sub-Saharan Africa countries affected by the HIV epidemic. We investigated factors associated with CD4 counts in a rural area in South Africa with high HIV prevalence and high antiretroviral treatment (ART) coverage.

Methods

CD4 counts, health status, body mass index (BMI), demographic characteristics and HIV status were assessed in 4990 adult resident participants of a demographic surveillance in rural KwaZulu-Natal in South Africa; antiretroviral treatment duration was obtained from a linked clinical database. Multivariable regression analysis, overall and stratified by HIV status, was performed with CD4 count levels as outcome.

Results

Median CD4 counts were significantly higher in women than in men overall (714 vs. 630 cells/µl, p<0.0001), both in HIV-uninfected (833 vs. 683 cells/µl, p<0.0001) and HIV-infected adults (384.5 vs. 333 cells/µl, p<0.0001). In multivariable regression analysis, women had 19.4% (95% confidence interval (CI) 16.1–22.9) higher CD4 counts than men, controlling for age, HIV status, urban/rural residence, household wealth, education, BMI, self-reported tuberculosis, high blood pressure, other chronic illnesses and sample processing delay. At ART initiation, HIV-infected adults had 21.7% (95% CI 14.6–28.2) lower CD4 counts than treatment-naive individuals; CD4 counts were estimated to increase by 9.2% (95% CI 6.2–12.4) per year of treatment.

Conclusions

CD4 counts are primarily determined by sex in HIV-uninfected adults, and by sex, age and duration of antiretroviral treatment in HIV-infected adults. Lower CD4 counts at ART initiation in men could be a consequence of lower CD4 cell counts before HIV acquisition.     

Trends in Longevity in the Americas: Disparities in Life Expectancy in Women and Men, 1965-2010

Objective

We describe trends in life expectancy at birth (LE) and between-country LE disparities since 1965, in Latin America and the Caribbean.

Methods & Findings

LE trends since 1965 are described for three geographical sub-regions: the Caribbean, Central America, and South America. LE disparities are explored using a suite of absolute and relative disparity metrics, with measurement consensus providing confidence to observed differences. LE has increased throughout Latin America and the Caribbean. Compared to the Caribbean, LE has increased by an additional 6.6 years in Central America and 4.1 years in South America. Since 1965, average reductions in between-country LE disparities were 14% (absolute disparity) and 23% (relative disparity) in the Caribbean, 55% and 51% in Central America, 55% and 52% in South America.

Conclusions

LE in Latin America and the Caribbean is exceeding ‘minimum standard’ international targets, and is improving relative to the world region with the highest human longevity. The Caribbean, which had the highest LE and the lowest between-country LE disparities in Latin America and the Caribbean in 1965-70, had the lowest LE and the highest LE disparities by 2005-10. Caribbean Governments have championed a collaborative solution to the growing burden of non-communicable disease, with 15 territories signing on to the Declaration of Port of Spain, signalling regional commitment to a coordinated public-health response. The persistent LE inequity between Caribbean countries suggests that public health interventions should be tailored to individual countries to be most effective. Between- and within-country disparity monitoring for a range of health metrics should be a priority, first to guide country-level policy initiatives, then to contribute to the assessment of policy success.     

Hypertension and Obesity in Adults Living in a High HIV Prevalence Rural Area in South Africa

Hypertension and excess body weight are major risk factors of cardiovascular morbidity and mortality in developing countries. In countries with a high HIV prevalence, it is unknown how increased antiretroviral treatment and care (ART) coverage has affected the prevalence of overweight, obesity, and hypertension. We conducted a health survey in 2010 based on the WHO STEPwise approach in 14,198 adult resident participants of a demographic surveillance area in rural South Africa to investigate factors associated with hypertension and excess weight including HIV infection and ART status. Women had a significantly higher median body mass index (BMI) than men (26.4 vs. 21.2 kg/m², p<0.001). The prevalence of obesity (BMI≥30 kg/m²) in women (31.3%, 95% confidence interval (CI) 30.2–32.4) was 6.5 times higher than in men (4.9%, 95% CI 4.1–5.7), whereas prevalence of hypertension (systolic or diastolic blood pressure≥140 or 90 mm Hg, respectively) was 1.4 times higher in women than in men (28.5% vs 20.8%, p<0.001). In multivariable regression analysis, both hypertension and obesity were significantly associated with sex, age, HIV and ART status. The BMI of women and men on ART was on average 3.8 (95% CI 3.2–3.8) and 1.7 (95% CI 0.9–2.5) kg/m² lower than of HIV-negative women and men, respectively. The BMI of HIV-infected women and men not on ART was on average 1.2 (95% CI 0.8–1.6) and 0.4 (95% CI -0.1–0.9) kg/m² lower than of HIV-negative women and men, respectively. Obesity was a bigger risk factor for hypertension in men (adjusted odds ratio (aOR) 2.99, 95% CI 2.00–4.48) than in women (aOR 1.64, 95% CI 1.39–1.92) and overweight (25≤BMI<30) was a significant risk factor for men only (aOR 1.53 95% CI 1.14–2.06). Our study suggests that, cardiovascular risk factors of hypertension and obesity differ substantially between women and men in rural South Africa.     

Evaluation of Two Influenza Surveillance Systems in South Africa

Background

The World Health Organisation recommends outpatient influenza-like illness (ILI) and inpatient severe acute respiratory illness (SARI) surveillance. We evaluated two influenza surveillance systems in South Africa: one for ILI and another for SARI.

Methodology

The Viral Watch (VW) programme has collected virological influenza surveillance data voluntarily from patients with ILI since 1984 in private and public clinics in all 9 South African provinces. The SARI surveillance programme has collected epidemiological and virological influenza surveillance data since 2009 in public hospitals in 4 provinces by dedicated personnel. We compared nine surveillance system attributes from 2009–2012.

Results

We analysed data from 18,293 SARI patients and 9,104 ILI patients. The annual proportion of samples testing positive for influenza was higher for VW (mean 41%) than SARI (mean 8%) and generally exceeded the seasonal threshold from May to September (VW: weeks 21–40; SARI: weeks 23–39). Data quality was a major strength of SARI (most data completion measures >90%; adherence to definitions: 88–89%) and a relative weakness of the VW programme (62% of forms complete, with limited epidemiologic data collected; adherence to definitions: 65–82%). Timeliness was a relative strength of both systems (e.g. both collected >93% of all respiratory specimens within 7 days of symptom onset). ILI surveillance was more nationally representative, financially sustainable and expandable than the SARI system. Though the SARI programme is not nationally representative, the high quality and detail of SARI data collection sheds light on the local burden and epidemiology of severe influenza-associated disease.

Conclusions

To best monitor influenza in South Africa, we propose that both ILI and SARI should be under surveillance. Improving ILI surveillance will require better quality and more systematic data collection, and SARI surveillance should be expanded to be more nationally representative, even if this requires scaling back on information gathered.     

Integrating Community-Based Interventions to Reverse the Convergent TB/HIV Epidemics in Rural South Africa

The WHO recommends integrating interventions to address the devastating TB/HIV co-epidemics in South Africa, yet integration has been poorly implemented and TB/HIV control efforts need strengthening. Identifying infected individuals is particularly difficult in rural settings. We used mathematical modeling to predict the impact of community-based, integrated TB/HIV case finding and additional control strategies on South Africa’s TB/HIV epidemics. We developed a model incorporating TB and HIV transmission to evaluate the effectiveness of integrating TB and HIV interventions in rural South Africa over 10 years. We modeled the impact of a novel screening program that integrates case finding for TB and HIV in the community, comparing it to status quo and recommended TB/HIV control strategies, including GeneXpert, MDR-TB treatment decentralization, improved first-line TB treatment cure rate, isoniazid preventive therapy, and expanded ART. Combining recommended interventions averted 27% of expected TB cases (95% CI 18–40%) 18% HIV (95% CI 13–24%), 60% MDR-TB (95% CI 34–83%), 69% XDR-TB (95% CI 34–90%), and 16% TB/HIV deaths (95% CI 12–29). Supplementing these interventions with annual community-based TB/HIV case finding averted a further 17% of TB cases (44% total; 95% CI 31–56%), 5% HIV (23% total; 95% CI 17–29%), 8% MDR-TB (68% total; 95% CI 40–88%), 4% XDR-TB (73% total; 95% CI 38–91%), and 8% TB/HIV deaths (24% total; 95% CI 16–39%). In addition to increasing screening frequency, we found that improving TB symptom questionnaire sensitivity, second-line TB treatment delays, default before initiating TB treatment or ART, and second-line TB drug efficacy were significantly associated with even greater reductions in TB and HIV cases. TB/HIV epidemics in South Africa were most effectively curtailed by simultaneously implementing interventions that integrated community-based TB/HIV control strategies and targeted drug-resistant TB. Strengthening existing TB and HIV treatment programs is needed to further reduce disease incidence.     

Identifying and Targeting Mortality Disparities: A Framework for Sub-Saharan Africa Using Adult Mortality Data from South Africa

Background

Health inequities in developing countries are difficult to eradicate because of limited resources. The neglect of adult mortality in Sub-Saharan Africa (SSA) is a particular concern. Advances in data availability, software and analytic methods have created opportunities to address this challenge and tailor interventions to small areas. This study demonstrates how a generic framework can be applied to guide policy interventions to reduce adult mortality in high risk areas. The framework, therefore, incorporates the spatial clustering of adult mortality, estimates the impact of a range of determinants and quantifies the impact of their removal to ensure optimal returns on scarce resources.

Methods

Data from a national cross-sectional survey in 2007 were used to illustrate the use of the generic framework for SSA and elsewhere. Adult mortality proportions were analyzed at four administrative levels and spatial analyses were used to identify areas with significant excess mortality. An ecological approach was then used to assess the relationship between mortality “hotspots” and various determinants. Population attributable fractions were calculated to quantify the reduction in mortality as a result of targeted removal of high-impact determinants.

Results

Overall adult mortality rate was 145 per 10,000. Spatial disaggregation identified a highly non-random pattern and 67 significant high risk local municipalities were identified. The most prominent determinants of adult mortality included HIV antenatal sero-prevalence, low SES and lack of formal marital union status. The removal of the most attributable factors, based on local area prevalence, suggest that overall adult mortality could be potentially reduced by ∼90 deaths per 10,000.

Conclusions

The innovative use of secondary data and advanced epidemiological techniques can be combined in a generic framework to identify and map mortality to the lowest administration level. The identification of high risk mortality determinants allows health authorities to tailor interventions at local level. This approach can be replicated elsewhere.     

Invasive Group B Streptococcal Disease in South Africa: Importance of Surveillance Methodology

Data on neonatal group B streptococcal (GBS) invasive disease burden are needed to refine prevention policies. Differences in surveillance methods and investigating for cases can lead to varying disease burden estimates. We compared the findings of laboratory-based passive surveillance for GBS disease across South Africa, and for one of the provinces compared this to a real-time, systematic, clinical surveillance in a population-defined region in Johannesburg, Soweto. Passive surveillance identified a total of 799 early-onset disease (EOD, <7 days age) and 818 LOD (late onset disease, 7–89 days age) cases nationwide. The passive surveillance provincial incidence varied for EOD (range 0.00 to 1.23/1000 live births), and was 0.03 to 1.04/1000 live births for LOD. The passive surveillance rates for Soweto, were not significantly different compared to those from the systematic surveillance (EOD 1.23 [95%CI 1.06–1.43] vs. 1.50 [95%CI 1.30–1.71], respectively, rate ratio 0.82 [95%CI 0.67–1.01]; LOD 1.04 [95% CI 0.90–1.23] vs. 1.22 [95%CI 1.05–1.42], rate ratio 0.85 [95% CI 0.68–1.07]). A review of the few cases missed in the passive system in Soweto, suggested that missing key identifiers, such as date of birth, resulted in their omission during the electronic data extraction process. Our analysis suggests that passive surveillance provides a modestly lower estimate of invasive GBS rates compared to real time sentinel-site systematic surveillance, however, this is unlikely to be the reason for the provincial variability in incidence of invasive GBS disease in South Africa. This, possibly reflects that invasive GBS disease goes undiagnosed due to issues related to access to healthcare, poor laboratory capacity and varying diagnostic procedures or empiric antibiotic treatment of neonates with suspected sepsis in the absence of attempting to making a microbiological diagnosis. An efficacious GBS vaccine for pregnant women, when available, could be used as a probe to better quantify the burden of invasive GBS disease in low-middle resourced settings such as ours. From our study passive systems are important to monitor trends over time as long as they are interpreted with caution; active systems give better detailed information and will have greater representivity when expanded to other surveillance sites.     

Sex differences in obesity rates in poor countries: Evidence from South Africa

Globally, men and women face markedly different risks of obesity. In all but of handful of (primarily Western European) countries, obesity is much more prevalent among women than men. We examine several potential explanations for this phenomenon. We analyze differences between men and women in reports and effects of potential underlying causes of obesity—childhood and adult poverty, depression, and attitudes about obesity. We evaluate the evidence for each explanation using data collected in an urban African township in the Cape Town metropolitan area. Three factors explain the greater obesity rates we find among women. Women who were nutritionally deprived as children are significantly more likely to be obese as adults, while men who were deprived as children face no greater risk. In addition, women of higher adult socioeconomic status are significantly more likely to be obese, which is not true for men. These two factors - childhood circumstances and adult SES - can fully explain the difference in obesity rates between men and women that we find in our sample. More speculatively, in South Africa, women's perceptions of an ‘ideal’ female body are larger than men's perceptions of the ‘ideal’ male body, and individuals with larger ‘ideal’ body images are significantly more likely to be obese.