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Maps of the geographical variation in prevalence play an important role in large-scale programs for the control of neglected tropical diseases. Precontrol mapping is needed to establish the appropriate control intervention in each area of the country in question. Mapping is also needed postintervention to measure the success of control efforts. In the absence of comprehensive disease registries, mapping efforts can be informed by 2 kinds of data: empirical estimates of local prevalence obtained by testing individuals from a sample of communities within the geographical region of interest, and digital images of environmental factors that are predictive of local prevalence. In this article, we focus on the design and analysis of impact surveys, that is, prevalence surveys that are conducted postintervention with the aim of informing decisions on what further intervention, if any, is needed to achieve elimination of the disease as a public health problem. We show that geospatial statistical methods enable prevalence surveys to be designed and analyzed as efficiently as possible so as to make best use of hard-won field data. We use 3 case studies based on data from soil-transmitted helminth impact surveys in Kenya, Sierra Leone, and Zimbabwe to compare the predictive performance of model-based geostatistics with methods described in current World Health Organization (WHO) guidelines. In all 3 cases, we find that model-based geostatistics substantially outperforms the current WHO guidelines, delivering improved precision for reduced field-sampling effort. We argue from experience that similar improvements will hold for prevalence mapping of other neglected tropical diseases.

Background: Soil-transmitted helminths (STH) are among the most common parasitic infections globally, disproportionately affecting children. Treatment of STH in Kenya is often targeted at preschool (PSAC) and school aged (SAC) children delivered through annual mass drug administration (MDA) in primary schools. Understanding group-specific prevalence and dynamics between treatment and coverage is critical for continued treatment success. This study aims to provide detailed information on group-specific infection prevalence and relative reductions (RR), and their relationships with treatment coverage over time. Additionally, it aims to quantify the correlation between the observed school level infection prevalence and treatment coverage.Methods: Secondary analysis of existing data collected between 2012 and 2018 by the monitoring and evaluation (M&E) program of the National School-Based Deworming (NSBD) program was used. The M&E program conducted surveys utilizing cross-sectional study design, at four survey time points, in a nationally-representative sample of schoolchildren across counties in Kenya. In each participating school, the program randomly sampled 108 children per school, of both groups. Infection prevalence was estimated using binomial regression, RR in prevalence using multivariable mixed effects model, statistical correlations using structural equation modeling, and change-point-analysis using the binary segmentation algorithm.Results: Overall, STH prevalence for PSAC was 33.7, 20.2, 19.0, and 17.9% during Year 1 (Y1), Year 3 (Y3), Year 5 (Y5), and Year 6 (Y6) surveys, respectively with an overall RR of 46.9% (p = 0.001) from Y1 to Y6. Similarly, overall STH prevalence for SAC was 33.6, 18.4, 14.7, and 12.5% during Y1, Y3, Y5, and Y6 surveys, respectively with an overall RR of 62.6% (p < 0.001). An overall (all time points) significant but very weak negative correlation was found between treatment coverage and undifferentiated STH prevalence (r = −0.144, p = 0.002) among PSAC but not in SAC. Further, we observed inter-county heterogeneity variation in infection prevalence, RR, as well as correlations.Conclusion: The analysis showed that after six rounds of MDA, prevalence of STH has significantly declined among both groups of children, however not to a point where it is not a public health problem (below 1%). The analysis, additionally established an overall significant but weak negative correlation between treatment coverage and prevalence, indicating that the current treatment coverage might not be sufficient to drive the overall STH prevalence to below 1%. These findings will allow STH control programs in Kenya to make decisions that will accelerate the attainment of STH elimination as a public health problem.

IntroductionEswatini achieved a 44% decrease in new HIV infections from 2014 to 2019 through substantial scale-up of testing and treatment. However, it still has one of the highest rates of HIV incidence in the world, with 14 infections per 1,000 adults 15-49 years estimated for 2017. The Government of Eswatini has called for an 85% reduction in new infections by 2023 over 2017 levels. To make further progress towards this target and to achieve maximum health gains, this study aims to model optimized investments of available HIV resources.MethodsThe Optima HIV model was applied to estimate the impact of efficiency strategies to accelerate prevention of HIV infections and HIV-related deaths. We estimated the number of infections and deaths that could be prevented by optimizing HIV investments. We optimize across HIV programs, then across service delivery modalities for voluntary medical male circumcision (VMMC), HIV testing, and antiretroviral refill, as well as switching to a lower cost antiretroviral regimen.FindingsUnder an optimized budget, prioritising HIV testing for the general population followed by key preventative interventions may result in approximately 1,000 more new infections (2% more) being averted by 2023. More infections could be averted with further optimization between service delivery modalities across the HIV cascade. Scaling-up index and self-testing could lead to 100,000 more people getting tested for HIV (25% more tests) with the same budget. By prioritizing Fast-Track, community-based, and facility-based antiretroviral refill options, an estimated 30,000 more people could receive treatment, 17% more than baseline or US$5.5 million could be saved, 4% of the total budget. Finally, switching non-pregnant HIV-positive adults to a Dolutegravir-based antiretroviral therapy regimen and concentrating delivery of VMMC to existing fixed facilities over mobile clinics, US$4.5 million (7% of total budget) and US$6.6 million (10% of total budget) could be saved, respectively.SignificanceWith a relatively short five-year timeframe, even under a substantially increased and optimized budget, Eswatini is unlikely to reach their ambitious national prevention target by 2023. However, by optimizing investment of the same budget towards highly cost-effective VMMC, testing, and treatment modalities, further reductions in HIV incidence and cost savings could be realized.

Tight health budgets are increasingly strained in Low and Middle Income Countries (LMICs) through shifting burdens of disease, placing new constraints on how HIV is controlled. As HIV transitions from an acute to a chronic disease, control policies which potentially improve effectiveness, minimise cost or, both warrant investigation. Integration of HIV programs or services with other health system components is one such policy.Integration is a general term for processes of aligning two or more system components towards common goals. Alignment is in response to system inefficiency driven through differentiation of those components. Contention exists, however, as to how effective different integration modalities are within health systems. This is in part due to inherent methodological issues facing empirical evaluation of integration.Four issues limit the scope of evaluating the comparative impact of HIV integration modalities. Firstly, integration is unlikely to be randomised given it is often implemented fixed to health establishments with limited variation in how individuals access it. Secondly, integration is most frequently applied to places as opposed to individuals, which results in clustering. Thirdly, evaluation of integration is frequently performed ex-post meaning policy analysts have little control over the evaluation process. Finally, evaluation is complicated by contention around conceptualisations of integration itself and appropriate indicators reflecting differences in modalities being compared.This thesis aims to understand and address issues in evaluating integration. It primarily explores how methodological issues may be overcome through the combined use of quasi-experiments and MultiLevel regression Models (MLMs). Quasi-experiments exploit aspects of study design to recreate some benefits of randomisation. A class of quasi-experiment, the Propensity Score (PS); created through regression is used to reduce measured systematic biases. MLMs are regression models which allow inclusion of cluster level information. PSs have traditionally been constructed using naive, non-MLM regression. By instead constructing PSs using MLMs, known as a MLM-PS, non-randomisation and clustering may be simultaneously addressed. MLM-PS is sufficiently flexible to be applied ex-post with relatively little information on policy assignment compared to other quasi-experiments. The MLM-PS approach is potentially able to overcome methodological issues inhibiting HIV integration evaluation.To address issues of selecting plausible indicators for evaluation a theoretical framework is constructed for the thesis. It builds upon existing work deconstructing comparative integration modalities mapping them across a standard framework for evaluating quality in health programs. Indicators are described as reflecting the relative impact of comparable integration modalities through plausible mechanisms of action. Feasibility of MLM-PS and the theoretical framework are explored through application to two case studies of HIV integration. Both case studies originate from an implementation of integrated HIV services in Peru. Integration in both case studies has been applied non-randomly and clustered at district level with evaluation conducted ex-post reflecting the common issues described above.The first case study examines the impact of increasing HIV and Antenatal Care (ANC) integration, on the use of essential testing services. MLM-PS is shown to demonstrably reduce bias while controlling for clustering and including important cluster level information in the PS. Using the method, increased integration is shown to be highly effective. The second case study examines the impact of HIV/TB integration on essential testing services. MLM-PS method is also shown to reduce biases incorporating control for clustering, while using conceptually different data. Using the method, higher levels of integration are shown to have mixed results. As a demonstration of robustness, different iterations of MLM-PS for both case studies are explored. Here the importance of using MLMs in the PS when clustering is present is also established.MLM-PS is concluded to be a useful pragmatic alternative when more robust methods are not available. It is flexible enough to be applied when data are limited, and little control is available over evaluation. It is suggested as a potential tool for policy analysts and policy makers to improve estimates of integration impact, or indeed other similar polices. This is with a view to strengthening evidence-based decision making on policies critical to the control of HIV.

Background India has the world’s highest total number of under-five deaths of any nation. While progress towards Millennium Development Goal 4 has been documented at the state level, little information is available for greater disaggregation of child health markers within states. In 2000, new states were created within the country as a partial response to political pressures. State-level information on child health trends in the new states of Chhattisgarh and Jharkhand is scarce. To fill this gap, this article examines under-five and neonatal mortality across various equity markers within these two new states, pre-and post-split. Methods Both direct and indirect estimation using pooled data from five available sources were undertaken. Inter-population disparities were evaluated by mortality data stratification of rural–urban location, ethnicity, wealth and districts. Results Both states experienced an overall reduction in under-five and neonatal mortality, however, this has stagnated post-2001 and various disparities persist. In cases where disparities have declined, such as between urban–rural populations and low- and high-income groups, this has been driven by modest declines within the disadvantaged groups (i.e. low-income rural households) and stagnation or worsening of outcomes within the advantaged groups. Indeed, rising trends in mortality are most prevalent in urban middle-income households. Conclusions The results suggest that rural health improvements may have come at the expense of urban areas, where poor performance may be attributed to factors such as lack of access to quality private health facilities. In addition, the disparities may in part be associated with geographical access, traditional practices and district-level health resource allocation.

Introduction Tuberculosis, reported as the second most common infectious cause of death worldwide, is a key mortality contributor in developing countries and globally. The disease is endemic in Peru and while relative success was achieved during the 1990s in its control, this slowed as new complications, such as multi drug resistant TB arose. Health centre workers participating in the national DOTS program, create the front-line TB work-force in Peru meaning their knowledge and attitudes about the disease are key in its control. Methods A Spanish language, multiple choice knowledge and attitudes survey was designed based on previous successful studies and the national Peruvian TB control guidelines. It was applied to two health networks in Lima, Peru amongst 301health workers participating in the national TB control program from 66 different health centres. The study results were analysed to test mean knowledge scores amongst different groups, overall gaps in key areas of TB treatment and control knowledge, and attitudes towards the disease and the national TB control program. Results A mean knowledge score of 10.1 (+/- 1.7) out of 15 or 67.3% correct was shown. Demographics shown to have an effect on knowledge score were age and level of education. Major knowledge gaps were noted primarily in themes relating to treatment and diagnostics. Greater community involvement including better patient education about TB was seen as important in implementing the national TB control program. Participants were in disagreement about the current distribution of health resources throughout the study area. Discussion Serious knowledge gaps were identified from the survey; these reflect findings from a previous study in Lima and other studies from TB endemic areas throughout the world. Understanding these gaps and observations made by front-line TB workers in Lima may help to improve the national TB control program and other control efforts globally.