Transitions between body mass index categories, South Africa.

OBJECTIVE: To profile the prevalence of the three body mass index (BMI) categories by sociodemographic characteristics, and to calculate the percentage transitioning (or not) from one BMI category to another, to inform South African health policy for the control of obesity and noncommunicable diseases. METHODS: We used data from the National Income Dynamics Study, including sociodemographic characteristics and BMI measurements collected in 2008, 2010, 2012, 2014 and 2017. For each data collection wave and each population group, we calculated mean BMI and prevalence by category. We also calculated the percentage making an upwards transition (e.g. from overweight to obese), a downwards transition or remaining within a particular category. We used a multinomial logistic regression model to estimate transition likelihood. FINDINGS: Between 2008 and 2017, mean BMI increased by 2.3 kg/m2. We calculated an increased prevalence of obesity from 19.7% (3686/18 679) to 23.6% (3412/14 463), with the largest increases in prevalence for those aged 19-24 years and those with at least high school education. The percentages of upwards transitions to overweight or obese categories increased sharply between the ages of 19 and 50 years. Once overweight or obese, the likelihood of transitioning to a normal BMI is low, particularly for women, those of higher age groups, and those with a higher income and a higher level of education. CONCLUSION: In the development of national strategies to control obesity and noncommunicable diseases, our results will allow limited public health resources to be focused on the relevant population groups.

An assessment of adverse drug reactions among HIV positive patients receiving antiretroviral treatment in South Africa.

BACKGROUND: Antiretroviral treatment (ART) has been effective in reducing HIV/AIDS related morbidity and mortality. However, the use and uptake of ART has resulted in adverse reactions, due mainly to the medicine's toxicity and interactions with other medicines. The timing of adverse drug reactions (ADRs) among these patients is a critical public health issue for antiretroviral (ARV) treatment adherence and retention. Reliable monitoring of HIV patients on ART is through a structured pharmacovigilance surveillance system. However, recurrent nature of these data pose challenges in their analyses. This study aimed at modelling the timing of ADR events in HIV patients on ART using correlated time-to-event models. METHODS: The data concern 590 HIV patients registered onto the Medunsa National ARV Pharmacovigilance Surveillance System within 6 months of ART initiation between February 2007 and July 2011. Recurrent times of ADRs and baseline characteristics: patient gender, and age, ART regimen, clinic and initiation period were extracted from the data. The recurrent ADR events data were modelled using both shared frailty and marginal models on the five patients' characteristics as covariates. RESULTS: Out of 590 patients, 67% were female, 68% started on regimen: Stavudine, Lamivudine and Efavirenz; 37% had experienced at least one ADR and 67% started ART in 2009-2011. Age (p-value = 0.0210), clinic (p-value < 0.0001) and period of ART initiation (p-value = 0.0002) were significantly associated with timing of first ADR. There was a significantly higher rates of ADR recurrences in patients aged 38-44 years [HR = 2.45; 95% CI = (1.47; 4.10)] vs. 30 years and less, patients taking regimen: Zidovudine, Lamivudine and Nevarapine) vs. regimen: Stavudine, Lamivudine and Efavirenz [HR = 2.09; 95% CI = (1.35; 3.22)], while the rate was lower among those who started ART in 2009-2011 vs. those who initiated in 2007-2008 [HR = 0.55; 95% CI = (0.40; 0.76)]. CONCLUSION: More realistic time-to-event models for recurrent events data have been used to analyse timing of ADR events in HIV patients taking ARV treatment. Age, antiretroviral regimen type and period of initiation of ART were associated with the timing of HIV/AIDS drug related adverse reactions regardless of the analysis model used. This study has public health policy implications in addressing the added morbidity among HIV patients taking ARV treatment in the context of universal scaling up of ARV treatment.

A prediction risk score for HIV among adolescent girls and young women in South Africa: identifying those in need of HIV pre-exposure prophylaxis.

BACKGROUND: In sub-Saharan Africa (SSA), adolescent girls and young women (AGYW) have the highest risk of acquiring HIV. This has led to several studies aimed at identifying risk factors for HIV in AGYM. However, a combination of the purported risk variables in a multivariate risk model could be more useful in determining HIV risk in AGYW than one at a time. The purpose of this study was to develop and validate an HIV risk prediction model for AGYW. METHODS: We analyzed HIV-related HERStory survey data on 4,399 AGYW from South Africa. We identified 16 purported risk variables from the data set. The HIV acquisition risk scores were computed by combining coefficients of a multivariate logistic regression model of HIV positivity. The performance of the final model at discriminating between HIV positive and HIV negative was assessed using the area under the receiver-operating characteristic curve (AUROC). The optimal cut-point of the prediction model was determined using the Youden index. We also used other measures of discriminative abilities such as predictive values, sensitivity, and specificity. RESULTS: The estimated HIV prevalence was 12.4% (11.7% - 14.0) %. The score of the derived risk prediction model had a mean and standard deviation of 2.36 and 0.64 respectively and ranged from 0.37 to 4.59. The prediction model's sensitivity was 16. 7% and a specificity of 98.5%. The model's positive predictive value was 68.2% and a negative predictive value of 85.8%. The prediction model's optimal cut-point was 2.43 with sensitivity of 71% and specificity of 60%. Our model performed well at predicting HIV positivity with training AUC of 0.78 and a testing AUC of 0.76. CONCLUSION: A combination of the identified risk factors provided good discrimination and calibration at predicting HIV positivity in AGYW. This model could provide a simple and low-cost strategy for screening AGYW in primary healthcare clinics and community-based settings. In this way, health service providers could easily identify and link AGYW to HIV PrEP services.

Joint disease mapping using six cancers in the Yorkshire region of England.

OBJECTIVES: The aims of this study were to model jointly the incidence rates of six smoking related cancers in the Yorkshire region of England, to explore the patterns of spatial correlation amongst them, and to estimate the relative weight of smoking and other shared risk factors for the relevant disease sites, both before and after adjustment for socioeconomic background (SEB). METHODS: Data on the incidence of oesophagus, stomach, pancreas, lung, kidney, and bladder cancers between 1983 and 2003 were extracted from the Northern & Yorkshire Cancer Registry database for the 532 electoral wards in the Yorkshire region. Using postcode of residence, each case was assigned an area-based measure of SEB using the Townsend index. Standardised incidence ratios (SIRs) were calculated for each cancer site and their correlations investigated. The joint analysis of the spatial variation in incidence used a Bayesian shared-component model. Three components were included to represent differences in smoking (for all six sites), bodyweight/obesity (for oesophagus, pancreas and kidney cancers) and diet/alcohol consumption (for oesophagus and stomach cancers). RESULTS: The incidence of cancers of the oesophagus, pancreas, kidney, and bladder was relatively evenly distributed across the region. The incidence of stomach and lung cancers was more clustered around the urban areas in the south of the region, and these two cancers were significantly associated with higher levels of area deprivation. The incidence of lung cancer was most impacted by adjustment for SEB, with the rural/urban split becoming less apparent. The component representing smoking had a larger effect on cancer incidence in the eastern part of the region. The effects of the other two components were small and disappeared after adjustment for SEB. CONCLUSION: This study demonstrates the feasibility of joint disease modelling using data from six cancer sites. Incidence estimates are more precise than those obtained without smoothing. This methodology may be an important tool to help authorities evaluate healthcare system performance and the impact of policies.

Geographic variation of hypertension in sub-saharan Africa: a case study of South Africa.

BACKGROUND: Hypertension is of public health importance in sub-Saharan Africa, with considerable underdiagnosis, poor management, and lack of community-wide preventive strategies. We examined the geographic variation of hypertension at the province level in South Africa, while accounting for individual-level risk factors such as sociodemographics, lifestyle, and cardiovascular comorbidities. METHODS: Our analysis was based on the South African Demographic and Health Survey, including 13,596 men and women aged 15 years and over. Individual data were collected on lifestyle habits and cardiovascular comorbidities, but were aggregated to the nine country's provinces. We used a Bayesian geo-additive mixed model to map the geographic distribution of hypertension at the province level, accounting for individual risk factors. RESULTS: The overall prevalence of hypertension (blood pressure ≥140/90 mmHg or self-reported diagnosis or on medication) was 30.4%. In multivariate Bayesian geo-additive models, current smokers (odds ratio [OR] and 95% credible region [CR]: 1.14 [1.03, 1.26]), current drinkers (1.17 [1.05, 1.29]), people reporting sleep problems (1.16 [1.02, 1.31]), and participants with prevalent cardiovascular comorbidities, such as type 2 diabetes (2.49 [1.92, 3.13]), were significantly associated with a higher prevalence of hypertension. There was a striking variation in hypertension prevalence across provinces, the highest being in North West (1.33 [1.14, 1.61]), Free State (1.32 [1.08, 1.68]) and Northern Cape (1.30 [1.02, 1.55]), the lowest in Limpopo (0.68 [0.56, 0.84]). CONCLUSIONS: This study showed distinct geographic patterns in hypertension prevalence in South Africa, suggesting the potential role of socioeconomic, nutritional, and environmental factors at the province level, beyond individual-level risk factors in this setting.

Risk of Death among HIV Co-Infected Multidrug Resistant Tuberculosis Patients, Compared To Mortality in the General Population of South Africa.

BACKGROUND: Even though highly effective drugs are available in South Africa, multidrug resistant tuberculosis (MDR-TB) patients with HIV infection have higher mortality compared to HIV-uninfected MDR-TB patients. This trend has been observed in similar countries with high HIV prevalence. This study sought to determine excess mortality attributable to HIV among MDR-TB patients in South Africa using relative survival methods. METHODS: Data available were from a cohort of 2079 MDR-TB patients enrolled in a Standardized Programmatic Management of MDR-TB from 2000 to 2004 in South Africa. A Poisson-based model adjusted for age, gender, year of diagnosis, TB history, and resistance to ethambutol, anti-TB injectable drugs and fluoroquinolones antibiotics was constructed to assess the excess mortality among HIV co-infected MDR-TB patients. Excess hazard ratios (EHRs) were used to describe the effect of the predictors on net mortality, controlling for the general mortality in the South African population. RESULTS: Death was recorded on 1619 patients, of whom 367 (22.7%) had died within 2 years. Out of the 1413 patients that tested for HIV infection, 554 (39.2%) tested positive. Excess mortality was higher in HIV infected, compared to HIV uninfected, MDR-TB patients (adjusted excess hazard ratio, 5.6 [95% CI, 3.2-9.7]); in patients whose TB isolates' resistance to ethambutol and kanamycin was unknown (3.7 [2.1-6.2] and 4.87 [1.9-13.3], respectively) vs. known. There were no differences in excess mortality between age and gender of the patient, year of diagnosis and TB history. CONCLUSION: Adjusting for some important predictors, MDR-TB patients with HIV infection experienced higher excess mortality compared to HIV-uninfected MDR-TB patients, after accounting for the general mortality in South Africa. An appropriate, though complex method has produced predictor effect estimates similar to those obtained from classical methods. Thus, the use of relative survival methods should be encouraged in the analysis of causespecific mortality, when ascertainment of cause of death is inaccurate or unknown.