Spatial clustering of drug-resistant tuberculosis in Hlabisa subdistrict, KwaZulu-Natal, 2011-2015.

SETTING: Incidencerates of tuberculosis (TB) in South Africa are among the highest in the world, and drug resistance is a major concern. Understanding geographic variations in disease may guide targeted interventions. OBJECTIVE: To characterise the spatial distribution of drug-resistant TB (DR-TB) in a rural area of KwaZulu-Natal, South Africa, and to test for clustering. DESIGN: This was a cross-sectional analysis of DR-TB patients managed at a rural district hospital from 2011 to 2015. We mapped all patients in hospital data to local areas, and then linked to a population-based demographic surveillance system to map the patients to individual homesteads. We used kernel density estimation to visualise the distribution of disease and tested for clustering using spatial scan statistics. RESULTS: There were 489 patients with DR-TB in the subdistrict; 111 lived in the smaller demographic surveillance area. Spatial clustering analysis identified a high-risk cluster (relative risk of DR-TB inside vs. outside cluster 3.0, P < 0.001) in the south-east, a region characterised by high population density and a high prevalence of human immunodeficiency virus infection. CONCLUSION: We have demonstrated evidence of a geographic high-risk cluster of DR-TB. This suggests that targeting interventions to spatial areas of highest risk, where transmission may be ongoing, could be effective

Need for timely paediatric HIV treatment within primary health care in rural South Africa

&lt;p&gt;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.&lt;/p&gt; &lt;p&gt;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.&lt;/p&gt; &lt;p&gt;Findings: In the primary health care programme of HIV treatment 346 children &#60;16 years of age initiated HAART by 2008; 245(70.8%) were aged 10 years or younger, and only 2(&#60;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%.&lt;/p&gt; &lt;p&gt;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.&lt;/p&gt

Validating child vaccination status in a demographic surveillance system using data from a clinical cohort study: evidence from rural South Africa

&lt;p&gt;&lt;b&gt;Background:&lt;/b&gt; Childhood vaccination coverage can be estimated from a range of sources. This study aims to validate vaccination data from a longitudinal population-based demographic surveillance system (DSS) against data from a clinical cohort study.&lt;/p&gt; &lt;p&gt;&lt;b&gt;Methods:&lt;/b&gt; The sample includes 821 children in the Vertical Transmission cohort Study (VTS), who were born between December 2001 and April 2005, and were matched to the Africa Centre DSS, in northern KwaZulu-Natal. Vaccination information in the surveillance was collected retrospectively, using standardized questionnaires during bi-annual household visits, when the child was 12 to 23 months of age. DSS vaccination information was based on extraction from a vaccination card or, if the card was not available, on maternal recall. In the VTS, vaccination data was collected at scheduled maternal and child clinic visits when a study nurse administered child vaccinations. We estimated the sensitivity of the surveillance in detecting vaccinations conducted as part of the VTS during these clinic visits.&lt;/p&gt; &lt;p&gt;&lt;b&gt;Results:&lt;/b&gt; Vaccination data in matched children in the DSS was based on the vaccination card in about two-thirds of the cases and on maternal recall in about one-third. The sensitivity of the vaccination variables in the surveillance was high for all vaccines based on either information from a South African Road-to-Health (RTH) card (0.94-0.97) or maternal recall (0.94-0.98). Addition of maternal recall to the RTH card information had little effect on the sensitivity of the surveillance variable (0.95-0.97). The estimates of sensitivity did not vary significantly, when we stratified the analyses by maternal antenatal HIV status. Addition of maternal recall of vaccination status of the child to the RTH card information significantly increased the proportion of children known to be vaccinated across all vaccines in the DSS.&lt;/p&gt; &lt;p&gt;&lt;b&gt;Conclusion:&lt;/b&gt; Maternal recall performs well in identifying vaccinated children aged 12-23 months (both in HIV-infected and HIV-uninfected mothers), with sensitivity similar to information extracted from vaccination cards. Information based on both maternal recall and vaccination cards should be used if the aim is to use surveillance data to identify children who received a vaccination.&lt;/p&gt

Spatiotemporal analysis of insecticide-treated net use for children under 5 in relation to socioeconomic gradients in Central and East Africa

Background: Insecticide-treated net (ITN) use is the core intervention among the strategies against malaria in subSaharan Africa (SSA) and the percentage of ITN ownership has increased from 47% in 2010 to 72% in 2017 across countries in SSA. Regardless of this massive expansion of ITN distribution, considerable gap between ownership and use of ITNs has been reported. Using data from more than 100,000 households in Central and East Africa (CEA) countries, the main aim of this study was to identify barriers associated with low ITN use and conduct geospatial analyses to estimate numbers and locations of vulnerable children living in areas with high malaria and low ITN use. Methods: Main sources of data for this study were the Demographic and Health Surveys and Malaria Indicator Surveys conducted in 11 countries in CEA. Logistic regression models for each country were built to assess the association between ITN ownership or ITN use and several socioeconomic and demographic variables. A density map of children under 5 living in areas at high-risk of malaria and low ITN use was generated to estimate the number of children who are living in these high malaria burden areas. Results: Results obtained suggest that factors such as the number of members in the household, total number of children in the household, education and place of residence can be key factors linked to the use of ITN for protecting children against malaria in CEA. Results from the spatiotemporal analyses found that although total rates of ownership and use of ITNs across CEA have increased up to 70% and 48%, respectively, a large proportion of children under 5 (19,780,678; 23% of total number of children) still lives in high-risk malaria areas with low use of ITNs. Conclusion: The results indicate that despite substantial progress in the distribution of ITNs in CEA, with about 70% of the households having an ITN, several socioeconomic factors have compromised the effectiveness of this control intervention against malaria, and only about 48% of the households protect their children under 5 with ITNs. Increasing the effective ITN use by targeting these factors and the areas where vulnerable children reside can be a core strategy meant to reducing malaria transmission

Capturing the spatial variability of HIV epidemics in South Africa and Tanzania using routine healthcare facility data

Background: Large geographical variations in the intensity of the HIV epidemic in sub-Saharan Africa call for geographically targeted resource allocation where burdens are greatest. However, data available for mapping the geographic variability of HIV prevalence and detecting HIV ‘hotspots’ is scarce, and population-based surveillance data are not always available. Here, we evaluated the viability of using clinic-based HIV prevalence data to measure the spatial variability of HIV in South Africa and Tanzania. Methods: Population-based and clinic-based HIV data from a small HIV hyper-endemic rural community in South Africa as well as for the country of Tanzania were used to map smoothed HIV prevalence using kernel interpolation techniques. Spatial variables were included in clinic-based models using co-kriging methods to assess whether cofactors improve clinic-based spatial HIV prevalence predictions. Clinic- and population-based smoothed prevalence maps were compared using partial rank correlation coefficients and residual local indicators of spatial autocorrelation. Results: Routinely-collected clinic-based data captured most of the geographical heterogeneity described by population-based data but failed to detect some pockets of high prevalence. Analyses indicated that clinic-based data could accurately predict the spatial location of so-called HIV ‘hotspots’ in &gt; 50% of the high HIV burden areas. Conclusion: Clinic-based data can be used to accurately map the broad spatial structure of HIV prevalence and to identify most of the areas where the burden of the infection is concentrated (HIV ‘hotspots’). Where population-based data are not available, HIV data collected from health facilities may provide a second-best option to generate valid spatial prevalence estimates for geographical targeting and resource allocation

Population uptake of antiretroviral treatment through primary care in rural South Africa

<p>Abstract</p> <p>Background</p> <p>KwaZulu-Natal is the South African province worst affected by HIV and the focus of early modeling studies investigating strategies of antiretroviral treatment (ART) delivery. The reality of antiretroviral roll-out through primary care has differed from that anticipated and real world data are needed to inform the planning of further scaling up of services. We investigated the factors associated with uptake of antiretroviral treatment through a primary healthcare system in rural South Africa.</p> <p>Methods</p> <p>Detailed demographic, HIV surveillance and geographic information system (GIS) data were used to estimate the proportion of HIV positive adults accessing antiretroviral treatment within northern KwaZulu-Natal, South Africa in the period from initiation of antiretroviral roll-out until the end of 2008. Demographic, spatial and socioeconomic factors influencing the likelihood of individuals accessing antiretroviral treatment were explored using multivariable analysis.</p> <p>Results</p> <p>Mean uptake of ART among HIV positive resident adults was 21.0% (95%CI 20.1-21.9). Uptake among HIV positive men (19.2%) was slightly lower than women (21.8%, P = 0.011). An individual's likelihood of accessing ART was not associated with level of education, household assets or urban/rural locale. ART uptake was strongly negatively associated with distance from the nearest primary healthcare facility (aOR = 0.728 per square-root transformed km, 95%CI 0.658-0.963, <it>P </it>= 0.002).</p> <p>Conclusions</p> <p>Despite concerns about the equitable nature of antiretroviral treatment rollout, we find very few differences in ART uptake across a range of socio-demographic variables in a rural South African population. However, even when socio-demographic factors were taken into account, individuals living further away from primary healthcare clinics were still significantly less likely to be accessing ART</p

HIV seroconcordance among heterosexual couples in rural KwaZulu-Natal, South Africa: a population-based analysis

Introduction High levels of HIV seroconcordance at the population level reduce the potential for effective HIV transmission. However, the level of HIV seroconcordance is largely unknown among heterosexual couples in sub‐Saharan Africa. We aimed to quantify the population level HIV seroconcordance in stable heterosexual couples in rural South Africa. Methods We followed adults (≥15 years old) using a population‐based, longitudinal and open surveillance system in KwaZulu‐Natal, South Africa, from 2003 to 2016. Sexual partnerships and HIV status were confirmed via household surveys and annual HIV surveillance. We calculated the proportions of HIV seroconcordance and serodiscordance in stable sexual partnerships and compared them to the expected proportions under the assumption of random mixing using individual‐based microsimulation models. Among unpartnered individuals, we estimated the incidence rates and hazard of sexual partnership formation with HIV‐positive or HIV‐negative partners by participants' own time‐varying HIV status. Competing risks survival regressions were fitted adjusting for sociodemographic and clinical factors. We also calculated Newman's assortativity coefficients. Results A total of 18,341 HIV‐negative and 11,361 HIV‐positive individuals contributed 154,469 person‐years (PY) of follow‐up. Overall, 28% of the participants were in stable sexual partnerships. Of the 677 newly formed stable sexual partnerships, 7.7% (95% CI: 5.8 to 10.0) were HIV‐positive seroconcordant (i.e. both individuals in the partnership were HIV‐positive), which was three times higher than the expected proportion (2.3%) in microsimulation models based on random mixing. The incidence rates of sexual partnership formation were 0.54/1000PY with HIV‐positive, 1.12/1000PY with HIV‐negative and 2.65/1000PY with unknown serostatus partners. HIV‐positive individuals had 2.39 (95% CI: 1.43 to 3.99) times higher hazard of forming a sexual partnership with an HIV‐positive partner than did HIV‐negative individuals after adjusting for age, opposite‐sex HIV prevalence (by 5‐years age groups), HIV prevalence in the surrounding community, ART coverage and other sociodemographic factors. Similarly, forming a sexual partnership with an HIV‐negative partner was 1.47 (95% CI: 1.01 to 2.14) times higher in HIV‐negative individuals in the adjusted model. Newman's coefficient also showed that assortativity by participant and partner HIV status was moderate (r = 0.35). Conclusions A high degree of population level HIV seroconcordance (both positive and negative) was observed at the time of forming new sexual partnerships. Understanding factors driving these patterns may help the development of strategies to bring the HIV epidemic under control

Multi-drug-resistant tuberculosis clusters in Mpumalanga province, South Africa, 2013–2016 : a spatial analysis

OBJECTIVE : To identify spatial clusters with unusually high levels of MDR-TB, which are highly unlikely to have arisen by chance in Mpumalanga Province, South Africa. METHODS : Home addresses of all MDR-TB patients were collected from four MDR-TB facilities from 2013 to 2016. We mapped all addresses, linking them to the nearest ward with population estimates. A spatial analysis was conducted using kernel density in ArcGIS to estimate and map the distribution of the disease and used Gertis-Ord Gi to test for significant clustering. RESULTS : A total of 4065 MDR-TB patients were mapped. Ten significant clusters (p-value <0.05) were found across the province in six sub-districts: Mbombela, Nkomazi, Emalahleni, Govan Mbeki, Lekwa and Mkhondo. Mbombela has the highest number of significant clusters. The central region did not have any MDR-TB clusters. CONCLUSION : There is clear evidence of MDR-TB clustering in Mpumalanga. This calls for concentrated TB prevention efforts and proper allocation of resources. Further investigations are needed to identify MDR-TB predictors.http://www.wileyonlinelibrary.com/journal/tmihj2022School of Health Systems and Public Health (SHSPH

BMI and All-Cause Mortality in a Population-Based Cohort in Rural South Africa

OBJECTIVE: This study evaluates the association between BMI and all-cause and cause-specific mortality in South Africa. METHODS: Prospective, population-based observational cohort data from rural South Africa were analyzed. BMI was measured in 2010. Demographic characteristics were recorded and deaths were verified with verbal autopsy interview. The InterVA-5 tool was used to assign causes of death. HIV testing was conducted annually. Cox proportional hazards models were fit to estimate the effect of BMI on all-cause and cause-specific mortality, accounting for the competing risk of death from other causes. Models were adjusted for sociodemographic characteristics and HIV status, and inverse probability weighting for survey nonparticipation was used. RESULTS: The cohort consisted of 9,728 individuals. In adjusted models, those with BMI of 25.0 to 29.9 kg/m2 or 30.0 to 34.9 kg/m2 had a lower hazard of death (adjusted hazard ratio: 0.80; 95% CI: 0.69-0.92 and adjusted hazard ratio: 0.75; 95% CI: 0.60-0.93, respectively) compared with those with BMI of 18.5 to 24.9 kg/m^{2}. CONCLUSIONS: Individuals in South Africa who meet clinically defined criteria for overweight or obesity had a lower risk of all-cause mortality than those with a normal BMI. These findings were stronger for women and communicable conditions

The tuberculosis challenge in a rural South African HIV programme.

BACKGROUND: South Africa remains the country with the greatest burden of HIV-infected individuals and the second highest estimated TB incidence per capita worldwide. Within South Africa, KwaZulu-Natal has one of the highest rates of TB incidence and an emerging epidemic of drug-resistant tuberculosis. METHODS: Review of records of consecutive HIV-infected people initiated onto ART between 1st January 2005 and 31st March 2006. Patients were screened for TB at initiation and incident episodes recorded. CD4 counts, viral loads and follow-up status were recorded; data was censored on 5th August 2008. Geographic cluster analysis was performed using spatial scanning. RESULTS: 801 patients were initiated. TB prevalence was 25.3%, associated with lower CD4 (AHR 2.61 p = 0.01 for CD4 25 copies/ml (OR 1.75 p = 0.11). A low-risk cluster for incident TB was identified for patients living near the local hospital in the geospatial analysis. CONCLUSION: There is a large burden of TB in this population. Rate of incident TB stabilises at a rate higher than that of the overall population. These data highlight the need for greater research on strategies for active case finding in rural settings and the need to focus on strengthening primary health care