Temporal Variations in Malaria Risk in Africa

In sub-Saharan Africa, malaria is a major cause of morbidity and mortality especially among children less than five years of age and pregnant women. Malaria situations are very diverse because of many factors involved in malaria transmission and the great variety of their local combinations. These include climatic, ecologic, social, economic and cultural factors. A number of epidemiological approaches have been used to try and reduce malaria situations to a manageable number of types and classes for efficient planning and targeting of appropriate malaria control strategies. Modelling and mapping of malaria have long been recognized as important means to developing empirical knowledge of this kind. Recently, the availability of new data sets, innovative analytical tools and statistical methods has resulted in the development of more comprehensive malaria maps for east, west and central Africa. However, most risk maps that have been produced so far do not take into account seasonal variation in malaria transmission. Seasonality affects the dynamic relationship between vector mosquito densities, inoculation rate, parasite prevalence and disease outcome. Quantitative description and mapping of malaria seasonality is therefore important for modelling malaria transmission dynamics and for timely spatial targeting of interventions. This thesis is part of an on going effort within the MARA/ARMA (Mapping Malaria Risk in Africa/Atlas du risqué de la Malaria en Afrique) collaboration towards the development of improved malaria risk maps for Africa. The main objective is the development of an empirical model of malaria seasonality by fitting classical and modern statistical models to clinical and / or entomological indices where available. This work also intended to identify important determinants of between-year and between-area variation that may be useful for developing climate based seasonal forecasting models for malaria epidemics. Chapter 1 gives an overview of the transmission and epidemiology of malaria in Africa and set the rational for this work. The initial focus of the analysis was on southern Africa, until recently this was the only region with reasonably comprehensive clinical malaria case data in the continent and therefore offered an ideal starting point. This region has a long history of successful malaria vector control by indoor residual spraying (IRS) with insecticides and this may have an impact on the level of malaria endemicity and consequently what we are modelling. Chapter 2 therefore reviews the historical impact of IRS in southern Africa. Chapters 3 evaluate the impact of the El Nino Southern Oscillation (ENSO) phenomenon on annual malaria incidence in Southern Africa. This is the main driver of inter-annual and seasonal variability in climate in most regions in Africa, and is important because ENSO events alter seasonality in climate in a way that influences malaria seasonality. Chapter 4 uses Zimbabwe to examine the spatio-temporal role of climate on year to year variation of malaria incidence. This country has a heterogeneity of climatic suitability for malaria transmission and reflects varying epidemiological profiles that occur in Southern Africa. Chapter 5 uses Zimbabwe as an example towards the development of an empirical model of malaria seasonality based on clinical malaria case data. Chapter 6 assesses the potential for use of the entomological inoculation rate (EIR) to describe malaria seasonality in Africa. Chapter 7 improves on work done in chapter 6 by modelling and mapping seasonal transmission of malaria transmission using an approximation based on discrete Fourier transformations which remove noise in the original time series and allows for the description important / main seasonal components in EIR in relation to those of meteorological covariates. The work described in these chapters culminated in five scientific publications and one working paper Chapter 2 showed that Southern African countries that sustained the application of IRS reduced the level of transmission from hyper- to meso-endemicity and from meso- to hypo-endemicity. This means that in instances where pre-control malariometric indices are not available one can not assume to be modelling baseline endemicity. Preferably, where the data are available the ideal situation will be to develop pre- and post-control models to evaluate changes in the malaria risk pattern over time. Chapter 3 found that contrary to east Africa where ENSO events and in particular El Nino has been linked to changes in climatic condition and increase in epidemic risk, in Southern Africa, ENSO has the opposite effect during El Nino years, with heightened incidence during La Nina years. However, the impact of ENSO also varies over time within countries, depending on existing malaria control efforts and response capacity. From this analysis it is clear that in order to lay an empirical basis for epidemic forecasting models there is a need for spatial-temporal models that at the same time consider both ENSO driven climate anomalies and non ENSO factors influencing epidemic risk potential. Chapter 4 confirmed that there is considerable inter-annual variation in the timing and intensity of malaria incidence in Zimbabwe. The modelling approach adjusted for unmeasured space-time varying risk factors and showed that while year to year variation in malaria incidence is driven mainly by climate the resultant spatial risk pattern may to large extent be influenced by other risk factors except during high and low risk years following the occurrence of extremely wet and dry conditions, respectively. It is likely therefore that only years characterized by extreme climatic conditions may be important for delineating areas prone to climate driven epidemics, and for developing climate based seasonal forecasting models for malaria epidemics. Chapter 5 employed the Bayesian spatial statistical method to quantify the relative amount of transmission in each month. This method smoothed for unobserved or unmeasured residual variation in malaria case rates while adjusting for environmental covariates enabling us to interpret the spatial pattern of malaria in seasonality. This work also demonstrated the feasibility of using Markham’s seasonality index (previously used for rainfall) to describe malaria seasonality. In this analysis the index was used to summarize the spatial pattern of the modelled seasonal trend by displaying the concentration of malaria case load during the peak season across, which is important for malaria control. Chapter 6 adopted Markham’s seasonality index to characterize seasonality in EIR in relation to environment covariates. This work successfully identified rainfall seasonality and minimum temperature as predictors of malaria seasonality across a number of sites in Africa. However, model predictions were poor in areas characterized by two rainfall peaks and irrigation activities. The seasonality concentration index performed better in areas with a unimodal seasonal pattern, and this might have had an adverse effect in the analysis in areas with a bimodal seasonal pattern. This highlighted the need for an improved quantification of malaria seasonality to model the complex and varied seasonal dynamics across the continent. Chapter 7 used an approximation of the discrete Fourier transform to the model relationship between seasonality in EIR and meteorological covariates. This was used to predict the seasonal average as well as the magnitude and timing of the main seasonal cycles. This allowed for the estimation of the overall degree and timing malaria seasonality and the duration of transmission across sub-Saharan Africa. Model predictions can be used to estimate the average seasonal pattern of malaria transmission across the continent. This analysis presents the first step towards the development of improved models of malaria seasonality, and as more data become available the models can be further refined. In conclusion the Bayesian analytical framework used in this study enhanced our ability to evaluate the relationship between malaria and climatic / environmental factors, and improved considerably the identification of important associations and covariates. Climatic and associated environmental determinants of seasonal and between yearvariation in malaria, including the impact of ENSO identified in this work, provide valuable information for the development of climate based seasonal forecasting models for malaria. Furthermore, an approximation of the discrete Fourier transformation of the data enabled us for the first time to develop empirical models and maps of the seasonality of transmission of malaria at a continental level. These are positive developments for the malaria modelling, mapping and control community in general

Environmental factors influencing the distribution hookworm infection in KwaZulu-Natal, South Africa [sic].

Thesis (M.Sc.)-University of Natal, Durban, 1998.The aim of this study was to investigate the occurrence of the soil transmitted parasitic nematode Necator americanus ("Old World" hookworm) in soils of different texture in KwaZulu-Natal. The key questions being asked were: (i) Is hookworm infection in KwaZulu-Natal confined to the coastal plain? (ii) Is there any association between hookworm prevalence and the different soil types in the province? (iii) Since several examples exist in the province of soil types on which hookworm is transmitted on the coastal plain, occurring inland, what is the status of infection in communities situated in these areas? (iv) What properties of soil are important in the transmission ecology of hookworm larvae? All available hookworm prevalence data of KwaZulu-Natal were mapped on Land Type maps of the province (Land Type Survey Staff, 1986). Several additional surveys were carried out to supplement this database. Faecal egg counts were obtained by the Formal-Ether Concentration Method and positive infections were confirmed as N. americanus by larval morphology after coproculture using the Harada-Mori Technique. Univariate analysis was carried out for significant associations between hookworm prevalence, altitude, climatic variables (rainfall and temperature) and soil type. The results showed that areas ≤ 150m above sea level (i.e. the coastal plain) support high prevalences (x ‾ = 45 %), and are characterised by low-clay textured soils, warm temperatures and relatively high rainfall. Areas > 150 m (i.e. inland) have low hookworm prevalences (x ‾ = 6 %), and are characterised by high-clay textured soils, cool temperatures and moderate rainfall. Hookworm prevalence also decreased southwards as climatic conditions (rainfall and temperature) become unfavourable, and the coastal plain also narrows in this direction. Multivariate analysis was done to determine which environmental factors combine best to provide favourable conditions for hookworm transmission. From the variables used, prevalence of infection was most significantly correlated with the mean daily minimum temperature for January followed by the mean number of rainy days for January. This points to the importance of summer conditions in the transmission of hookworm infection in KwaZulu-Natal. Moderate hookworm prevalences (x ‾ = 17.3 %) were found in the inland sandy areas, dropping to low prevalences (x ‾ = 5.3 %) in the surrounding non-sandy areas. The intensity-related data could not be significantly correlated with the environmental variables used in this study. The Spearman Correlation Coefficient was used to test for relationships between hookworm prevalence and soil variables. In the results, only the fine and medium sand fractions showed positive correlations with hookworm prevalence. Clay showed a significant negative correlation with hookworm prevalence. No significant correlations were found between soil pH or its organic matter content and hookworm prevalence. Age and sex related infection data could not be drawn into the analysis due to the small sample size of study localities

El Niño Southern Oscillation (ENSO) and annual malaria incidence in Southern Africa

We evaluated the association between annual malaria incidence and El Niño Southern Oscillation (ENSO) as measured by the Southern Oscillation Index (SOI) in five countries in Southern Africa from 1988 to 1999. Below normal incidence of malaria synchronised with a negative SOI (El Niño) and above normal incidence with a positive SOI (La Niña), which lead to dry and wet weather conditions, respectively. In most countries there was a positive relationship between SOI and annual malaria incidence, especially where Anopheles arabiensis is a major vector. This mosquito breeds in temporary rain pools and is highly sensitive to fluctuations in weather conditions. South Africa and Swaziland have the most reliable data and showed the strongest associations, but the picture there may also be compounded by the moderating effect of other oscillatory systems in the Indian Ocean. The impact of ENSO also varies over time within countries, depending on existing malaria control efforts and response capacity. There remains a need for quantitative studies that at the same time consider both ENSO-driven climate anomalies and non-ENSO factors influencing epidemic risk potential to assess their relative importance in order to provide an empirical basis for malaria epidemic forecasting model

Is there risk compensation among HIV infected youth and adults 15 years and older on antiretroviral treatment in South Africa? Findings from the 2017 national HIV prevalence, incidence, behaviour and communication survey

In this paper, risk compensation among individuals on antiretroviral therapy (ART), using the 2017 South African national survey on HIV, is explored. A multi-stage stratified cluster random sampling approach was used to realize 11,130 participants 15 years and older. Logistic regression analysis assessed the association between multiple sexual partners, condom use at last sexual encounter, consistency of condom usage and potential explanatory variables using HIV status and ART exposure as a mediator variable. HIV positive participants who were aware and on ART were less likely to have multiple sexual partners, and less likely not to use a condom at last sex compared to HIV positive participants who were aware but not on ART. The odds of reporting multiple sexual partners were significantly lower among older age groups, females, non-Black Africans, and rural settings, and higher among those with tertiary level education, and risky alcohol users

Helminthiasis: A Systematic Review of the Immune Interactions Present in Individuals Coinfected with HIV and/or Tuberculosis

Helminth infections are highly endemic in parts of the world where the two killer epidemics caused by Mycobacterium tuberculosis (M.tb) and the human immunodeficiency virus (HIV) intersect. Sub-Saharan Africa is hardest hit by this epidemiological overlap. Consequently, several studies have investigated the immunological outcomes of helminth coinfection with either HIV or M.tb, to elucidate the central hypothesis that chronic infection with helminths exacerbates the course of HIV and tuberculosis disease. However, there is no conclusive evidence to confirm whether helminth-induced immunity modulates HIV- and TB-specific immune responses and their pathogenesis or vice versa. The present chapter summarizes the epidemiology, clinical course, and immune interactions during helminths and HIV/TB coinfections and undertakes a systematic review of the existing literature published from Africa on this subject. The aim was to determine if chronic helminthiasis has a negative impact on HIV and TB infections. A PubMed search was undertaken with no language and time restrictions. Search terms used included a varied combination of “Helminth coinfection and immunity and TB coinfection or TB immunity and HIV coinfection or HIV immunity and Africa.” Names of individual species were also permutated in the search terms. Reviews and bibliographies of selected articles were screened to identify additional relevant articles or studies. Of the total 1021 articles retrieved, 47 were relevant with 31 helminth and HIV coinfection and 16 helminths and TB coinfection articles. While many studies failed to find a negative impact of helminth infection on immune responses to HIV and/or TB, a significant number found evidence of deleterious effects of coinfection with helminths such as immune activation, impaired Th1 responses to TB antigens, higher viral loads, lower CD4+ counts, and increased risks of antiretroviral immunologic failure, mother to child HIV transmission or TB disease. Some of the helminth-induced immune dysregulation was reversed by deworming, while some studies found no benefit of antihelminthic treatment. More studies particularly in Southern Africa are needed to increase the much sought evidence of the impact of deworming among HIV-infected individuals as this seems the most feasible, cost-effective intervention with little or no serious adverse effects. Lastly, with the expansion of ART and increased access to HIV treatment, the effects of helminths on vaccines, TB, and antiretroviral treatments efficacy also need serious consideration, in light of the suggestive evidence of possible immunologic failure due to helminth coinfection

Developing a spatial-statistical model and map of historical malaria prevalence in Botswana using a staged variable selection procedure

<p>Abstract</p> <p>Background</p> <p>Several malaria risk maps have been developed in recent years, many from the prevalence of infection data collated by the MARA (Mapping Malaria Risk in Africa) project, and using various environmental data sets as predictors. Variable selection is a major obstacle due to analytical problems caused by over-fitting, confounding and non-independence in the data. Testing and comparing every combination of explanatory variables in a Bayesian spatial framework remains unfeasible for most researchers. The aim of this study was to develop a malaria risk map using a systematic and practicable variable selection process for spatial analysis and mapping of historical malaria risk in Botswana.</p> <p>Results</p> <p>Of 50 potential explanatory variables from eight environmental data themes, 42 were significantly associated with malaria prevalence in univariate logistic regression and were ranked by the Akaike Information Criterion. Those correlated with higher-ranking relatives of the same environmental theme, were temporarily excluded. The remaining 14 candidates were ranked by selection frequency after running automated step-wise selection procedures on 1000 bootstrap samples drawn from the data. A non-spatial multiple-variable model was developed through step-wise inclusion in order of selection frequency. Previously excluded variables were then re-evaluated for inclusion, using further step-wise bootstrap procedures, resulting in the exclusion of another variable. Finally a Bayesian geo-statistical model using Markov Chain Monte Carlo simulation was fitted to the data, resulting in a final model of three predictor variables, namely summer rainfall, mean annual temperature and altitude. Each was independently and significantly associated with malaria prevalence after allowing for spatial correlation. This model was used to predict malaria prevalence at unobserved locations, producing a smooth risk map for the whole country.</p> <p>Conclusion</p> <p>We have produced a highly plausible and parsimonious model of historical malaria risk for Botswana from point-referenced data from a 1961/2 prevalence survey of malaria infection in 1–14 year old children. After starting with a list of 50 potential variables we ended with three highly plausible predictors, by applying a systematic and repeatable staged variable selection procedure that included a spatial analysis, which has application for other environmentally determined infectious diseases. All this was accomplished using general-purpose statistical software.</p

Early sexual debut: Voluntary or coerced? Evidence from longitudinal data in South Africa – the Birth to Twenty Plus study

Background. Early sexual debut, voluntary or coerced, increases risks to sexual and reproductive health. Sexual coercion is increasingly receiving attention as an important public health issue owing to its association with adverse health and social outcomes.Objective. To describe voluntary and coerced experience at sexual debut.Methods. A longitudinal perspective among 2 216 adolescents (1 149 females, 1 067 males) in a birth cohort study in South Africa, analysing data collected on six occasions between 11 and 18 years.Results. The median age of sexual debut was 16 years for females and 15 for males. Reported coerced sexual debut included children &lt;11 years of age. Males reported earlier sexual debut, with both voluntary and coerced sexual experience, than females (p&lt;0.0001). Sexual coercion at early sexual debut among both male and female adolescents occurred mostly through sexual intercourse with older adolescents and partners of the same age.Conclusion. The identified time periods and age groups need to be targeted for interventions to delay sexual debut and prevent sexual coercion among young people. More research is needed to understand underlying predisposing risk factors for sexual coercion at sexual debut, both early and not early

National South African HIV prevalence estimates robust despite substantial test non-participation

Background. South African (SA) national HIV seroprevalence estimates are of crucial policy relevance in the country, and for the worldwide HIV response. However, the most recent nationally representative HIV test survey in 2012 had 22% test non-participation, leaving the potential for substantial bias in current seroprevalence estimates, even after controlling for selection on observed factors. Objective. To re-estimate national HIV prevalence in SA, controlling for bias due to selection on both observed and unobserved factors in the 2012 SA National HIV Prevalence, Incidence and Behaviour Survey. Methods. We jointly estimated regression models for consent to test and HIV status in a Heckman-type bivariate probit framework. As selection variable, we used assigned interviewer identity, a variable known to predict consent but highly unlikely to be associated with interviewees’ HIV status. From these models, we estimated the HIV status of interviewed participants who did not test. Results. Of 26 710 interviewed participants who were invited to test for HIV, 21.3% of females and 24.3% of males declined. Interviewer identity was strongly correlated with consent to test for HIV; declining a test was weakly associated with HIV serostatus. Our HIV prevalence estimates were not significantly different from those using standard methods to control for bias due to selection on observed factors: 15.1% (95% confidence interval (CI) 12.1 - 18.6) v. 14.5% (95% CI 12.8 - 16.3) for 15 - 49-year-old males; 23.3% (95% CI 21.7 - 25.8) v. 23.2% (95% CI 21.3 - 25.1) for 15 - 49-year-old females. Conclusion. The most recent SA HIV prevalence estimates are robust under the strongest available test for selection bias due to missing data. Our findings support the reliability of inferences drawn from such data

Trends and determinants of ever having tested for HIV among youth and adults in South Africa from 2005–2017: Results from four repeated cross-sectional nationally representative household-based HIV prevalence, incidence, and behaviour surveys

Background HIV testing contributes to the prevention and control of the HIV epidemic in the general population. South Africa has made strides to improve HIV testing towards reaching the first of the UNAIDS 90–90–90 targets by 2020. However, to date no nationally representative analysis has examined temporal trends and factors associated with HIV testing among youth and adults in the country. Aim This study aimed to examine the trends and associations with ever having tested for HIV among youth and adults aged 15 years and older in South Africa using the 2005, 2008, 2012 and 2017 nationally representative population-based household surveys. Methods The analysis of the data collected used multi-stage stratified cluster randomised cross-sectional design. P-trend chi-squared test was used to identify any significant changes over the four study periods. Bivariate and multivariate logistic regression analysis was conducted to determine factors associated with HIV testing in each of the survey periods. Results Ever having tested for HIV increased substantially from 2005 (30.6%, n = 16 112), 2008 (50.4%, n = 13 084), 2012 (65.5%, n = 26 381), to 2017 (75.2%, n = 23 190). Those aged 50 years and older were significantly less likely to ever have tested for HIV than those aged 25–49 years. Those residing in rural areas were significantly less likely to have tested for HIV as compared to people from urban areas. There was a change in HIV testing among race groups with Whites, Coloureds and Indian/Asians testing more in 2005 and 2008 and Black Africans in 2017. Marriage, education and employment were significantly associated with increased likelihood of ever testing for HIV. Those who provided a blood specimen for laboratory HIV testing in the survey rounds and were found to have tested positive were more likely to have ever tested for HIV previously. Conclusion The results show that overall there has been an increase in ever having an HIV test in the South African population over time. The findings also suggest that for South Africa to close the testing gap and reach the first of the UNAIDS 90–90–90 targets by 2020, targeted programmes aimed at increasing access and utilization of HIV testing in young people, males, those not married, the less educated, unemployed and those residing in rural areas of South Africa should be prioritised

Socio-economic differences in the uptake of HIV testing and associated factors in South Africa

Background Improved understanding of barriers to HIV testing is important for reaching the first of the UNAIDS 90–90-90 targets, which states that 90% of HIV positive individuals ought to know their HIV status. This study examined socio-economic status (SES) differences in HIV testing uptake and associated factors among youth and adults 15 years and older in South Africa. Methods This study used data from a national cross-sectional, population-based household survey conducted in 2017 using a multi-stage sampling design. A composite SES score was created using multiple correspondence analyses of household assets; households were classified into wealth quintiles and dichotomised into low SES/poorest (lowest 3 quintiles) and high SES/less-poor (highest 2 quintiles). Bivariate and multivariate logistic regression models were used to examine factors associated with the uptake of HIV testing in low and high SES households. Results HIV testing uptake was 73.8 and 76.7% among low and high SES households, respectively, both of which were below the first 90 targets. Among both low and high SES households, increased HIV testing uptake was significantly associated with females than males. The decreased likelihood was significantly associated with residing in rural formal areas than urban areas, those with no education or low levels of educational attainment and alcohol drinkers among low SES households. Whites and Indians/Asians had a decreased likelihood than Black Africans in high SES households. Conclusions HIV testing interventions should target males, residents in rural formal areas, those with no or low education and those that consume alcohol in low SES households, including Whites and Indians/Asians from high SES households in order to bridge socio-economic disparities in the uptake of HIV testing. This should entail expanding HIV testing beyond traditional centres for voluntary counselling and testing through outreach efforts, including mobile testing and home-based testing