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Soil-transmitted helminth (STH) infections are among the most common neglected tropical diseases worldwide, causing high morbidity and mortality rates in endemic areas. STH impact assessment surveys provide information on the changing epidemiological situation so that control programme managers can adapt the frequency of any continuing preventive chemotherapy (PC). The cost associated with conducting these surveys is an important factor, especially in developing countries with severely limited financial resources. Using three case studies based on historical data on baseline prevalence, environmental risk-factors and treatment history from Kenya, Sierra Leone and Zimbabwe, we show that a model-based geostatistics approach drastically reduces the cost of STH impact surveys by comparison with a survey conducted according to the current WHO guidelines, whilst maintaining the same overall accuracy. The framework that we develop is disease-agnostic and could easily be repurposed for use with other NTDs.

BackgroundLimited understanding exists about the interactions between malaria and soil-transmitted helminths (STH), their potential geographical overlap and the factors driving it. This study characterised the geographical and co-clustered distribution patterns of malaria and STH infections among vulnerable populations in sub-Saharan Africa (SSA).Methodology/Principal findingsWe obtained continuous estimates of malaria prevalence from the Malaria Atlas Project and STH prevalence surveys from the WHO-driven Expanded Special Project for the Elimination of NTDs (ESPEN) covering 2000-2018 and used spatial autocorrelation methods to identify statistically significant clusters for both diseases across SSA. We used the inverse distance weighted kriging (interpolation) methods to estimate STH prevalence. We calculated the population-weighted prevalence of malaria and STH co-infection, and used the bivariate local indicator of spatial association (LISA analysis) to explore potential co-clustering of both diseases at the implementation unit levels.Our analysis shows spatial variations in the estimates of the prevalence of Plasmodium falciparum-STH co-infections and identified hotspots across many countries in SSA with inter-and intra-country variations. High P. falciparum and high hookworm co-infections were more prevalent in West and Central Africa, whereas high P. falciparum, high Ascaris lumbricoides, high P. falciparum, and high Trichuris trichiura co-infections were more predominant in Central Africa, compared to other sub-regions in SSA.Conclusions/SignificanceWide spatial heterogeneity exists in the prevalence of malaria and STH co-infection within the regions and within countries in SSA. The geographical overlap and spatial co-existence of malaria and STH could be exploited to achieve effective control and elimination agendas through the integration of the vertical programmes designed for malaria and STH into a more comprehensive and sustainable community-based paradigm.Author SummaryMalaria and worms frequently co-exist together among children living in the poorest countries of the world, but little is known about the specific locations of the combined infections involving the two major parasitic diseases and how they interact and change over the years.We used open access data collected by two public registries, that is, the Malaria Atlas Project and Expanded Special Project for the Elimination of NTDs, to understand the overlap of the two diseases in different parts of Africa, where their burden are more predominant.We found significant differences in the distributions of the combined diseases across different parts of Africa, with large concentrations identified in Central and West Africa. For example, double infections with malaria and hookworm were more common in West and Central Africa, whereas malaria and roundworm, and malaria and whipworm were predominantly found in Central Africa. A large collection of the dual infections was also found in some localities within the countries which appeared to have low burden of the two diseases.These findings provide a useful insight into the areas which could be serving as a reservoir to propagating the transmission of the two diseases. The results of this study could also be used to develop and implement integrated control programmes for malaria and worms, and this could help to achieve the WHO NTD roadmap to ending the neglect to attain Sustainable Development Goals by 2030.

BackgroundThe structural environment of urban slums, including physical, demographic and socioeconomic attributes, renders inhabitants more vulnerable to SARS-CoV-2 infection. Yet, little is known about the specific determinants that contribute to high transmission within these communities.Methods and findingsWe performed a serosurvey of an established cohort of 2,035 urban slum residents from the city of Salvador, Brazil between November 2020 and February 2021, following the first COVID-19 pandemic wave in the country. We identified high SARS-CoV-2 seroprevalence (46.4%, 95% confidence interval [CI] 44.3-48.6%), particularly among female residents (48.7% [95% CI 45.9-51.6%] vs. 43.2% [95% CI 39.8-46.6%] among male residents), and among children (56.5% [95% CI 52.3-60.5%] vs. 42.4% [95% CI 39.9-45.0%] among adults). In multivariable models that accounted for household-level clustering, the odds ratio for SARS-CoV-2 seropositivity among children was 1.96 (95% CI 1.42-2.72) compared to adults aged 30-44 years. Adults residing in households with children were more likely to be seropositive; this effect was particularly prominent among individuals with age 30-44 and 60 years or more. Women living below the poverty threshold (daily per capita household income ConclusionsDuring a single wave of the COVID-19 pandemic, cumulative incidence as assessed by serology approached 50% in a Brazilian urban slum population. In contrast to observations from industrialized countries, SARS-CoV-2 incidence was highest among children, as well as women living in extreme poverty. These findings emphasize the need for targeted interventions that provide safe environments for children and mitigate the structural risks posed by crowding and poverty for the most vulnerable residents of urban slum communities.

The lack of a WHO-recommended strategy for onchocerciasis treatment with ivermectin in hypo-endemic areas co-endemic with loiasis is an impediment to global onchocerciasis elimination. New loiasis diagnostics (LoaScope; Loa antibody rapid test) and risk prediction tools may enable safe mass treatment decisions in co-endemic areas. In 2017–2018, an integrated mapping strategy for onchocerciasis, lymphatic filariasis (LF), and loiasis, aimed at enabling safe ivermectin treatment decisions, was piloted in Gabon. Three ivermectin-naïve departments suspected to be hypo-endemic were selected and up to 100 adults per village across 30 villages in each of the three departments underwent testing for indicators of onchocerciasis, LF, and loiasis. An additional 67 communities in five adjoining departments were tested for loiasis to extend the prevalence and intensity predictions and possibly expand the boundaries of areas deemed safe for ivermectin treatment. Integrated testing in the three departments revealed within-department heterogeneity for all the three diseases, highlighting the value of a mapping approach that relies on cluster-based sampling rather than sentinel sites. These results suggest that safe mass treatment of onchocerciasis may be possible at the subdepartment level, even in departments where loiasis is present. Beyond valuable epidemiologic data, the study generated insight into the performance of various diagnostics and the feasibility of an integrated mapping approach utilizing new diagnostic and modeling tools. Further research should explore how programs can combine these diagnostic and risk prediction tools into a feasible programmatic strategy to enable safe treatment decisions where loiasis and onchocerciasis are co-endemic.

Current methods for the design and analysis of neglected tropical disease prevalence surveys largely rely on classical survey sampling ideas that treat prevalence data from different locations as an independent random sample from the probability distribution induced by a random sampling design. We set out an alternative, explicitly geospatial paradigm that can deliver much more precise estimates of the geospatial variation in prevalence over a country or region of interest. We describe the advantages of this approach under three headings: Streamlining, whereby more precise results can be obtained with smaller sample sizes; integrating, whereby a joint analysis of data from two or more diseases can bring further gains in precision; and adapting, whereby the choice of future sampling location is informed by past data. © 2021 The Author(s). Published by Oxford University Press on behalf of Royal Society of Tropical Medicine and Hygiene.

Background As the prevalences of neglected tropical diseases reduce to low levels in some countries, policymakers require precise disease estimates to decide whether the set public health targets have been met. At low prevalence levels, traditional statistical methods produce imprecise estimates. More modern geospatial statistical methods can deliver the required level of precision for accurate decision-making. Methods Using spatially referenced data from 3567 cluster locations in Ethiopia in the years 2017, 2018 and 2019, we developed a geostatistical model to estimate the prevalence of trachomatous trichiasis and to calculate the probability that the trachomatous trichiasis component of the elimination of trachoma as a public health problem has already been achieved for each of 482 evaluation units. We also compared the precision of traditional and geostatistical approaches by the ratios of the lengths of their 95% predictive intervals. Results The elimination threshold of trachomatous trichiasis (prevalence ≤ 0.2% in individuals aged ≥15 years) is met with a probability of 0.9 or more in 8 out of the 482 evaluation units assessed, and with a probability of ≤0.1 in 469 evaluation units. For the remaining five evaluation units, the probability of elimination is between 0.45 and 0.65. Prevalence estimates were, on average, 10 times more precise than estimates obtained using the traditional approach. Conclusions By accounting for and exploiting spatial correlation in the prevalence data, we achieved remarkably improved precision of prevalence estimates compared with the traditional approach. The geostatistical approach also delivers predictions for unsampled evaluation units that are geographically close to sampled evaluation units.

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.

Chronic Obstructive Pulmonary Disease (COPD) is one of the leading causes of mortality worldwide and is a major contributor to the number of emergency admissions in the UK. We introduce a modelling framework for the development of early warning systems for COPD emergency admissions. We analyse the number of COPD emergency admissions using a Poisson generalised linear mixed model. We group risk factors into three main groups, namely pollution, weather and deprivation. We then carry out variable selection within each of the three domains of COPD risk. Based on a threshold of incidence rate, we then identify the model giving the highest sensitivity and specificity through the use of exceedance probabilities. The developed modelling framework provides a principled likelihood-based approach for detecting the exceedance of thresholds in COPD emergency admissions. Our results indicate that socio-economic risk factors are key to enhance the predictive power of the model.

Background Life expectancy at birth (LEB), one of the main indicators of human longevity, has often been used to characterise the health status of a population. Understanding its relationships with the deprivation is key to develop policies and evaluate interventions that are aimed at reducing health inequalities. However, methodological challenges in the analysis of LEB data arise from the fact that different Government agencies often provide spatially aggregated information on LEB and the index of multiple deprivation (IMD) at different spatial scales. Our objective is to develop a geostatistical framework that, unlike existing methods of inference, allows to carry out spatially continuous prediction while dealing with spatial misalignment of the areal-level data. Methods We developed a model-based geostatistical approach for the joint analysis of LEB and IMD, when these are available over different partitions of the study region. We model the spatial correlation in LEB and IMD across the areal units using inter-point distances based on a regular grid covering the whole of the study area. The advantages and strengths of the new methodology are illustrated through an analysis of LEB and IMD data from the Liverpool district council. Results We found that the effect of IMD on LEB is stronger in males than in females, explaining about 63.35% of the spatial variation in LEB in the former group and 38.92% in the latter. We also estimate that LEB is about 8.5 years lower between the most and least deprived area of Liverpool for men, and 7.1 years for women. Finally, we find that LEB, both in males and females, is at least 80% likely to be above the England wide average only in some areas falling in the electoral wards of Childwall, Woolton and Church. Conclusion The novel model-based geostatistical framework provides a feasible solution to the spatial misalignment problem. More importantly, the proposed methodology has the following advantages over existing methods used model LEB: (1) it can deliver spatially continuous inferences using spatially aggregated data; (2) it can be applied to any form of misalignment with information provided at a range of spatial scales, from areal-level to pixel-level.

Additional file 1. The proof of the Eqs. (2)–(10).

In this paper, we develop a computationally efficient discrete approximation to log-Gaussian Cox process (LGCP) models for the analysis of spatially aggregated disease count data. Our approach overcomes an inherent limitation of spatial models based on Markov structures, namely that each such model is tied to a specific partition of the study area, and allows for spatially continuous prediction. We compare the predictive performance of our modelling approach with LGCP through a simulation study and an application to primary biliary cirrhosis incidence data in Newcastle-Upon-Tyne, UK. Our results suggest that when disease risk is assumed to be a spatially continuous process, the proposed approximation to LGCP provides reliable estimates of disease risk both on spatially continuous and aggregated scales. The proposed methodology is implemented in the open-source R package SDALGCP., Comment: 25 pages, 6 figures

This paper reports the prevalence and intensity of soil-transmitted helminth (STH) infections measured in Zimbabwe before and after a control intervention based on annual deworming of school-age children (SAC) conducted from 2012 to 2018. In 2010, epidemiological data were collected from 13 195 SAC in 255 randomly selected schools in all districts nationwide using, as diagnostic methods, the Kato–Katz and the formal ether stool concentration technique. At follow up, conducted in 2017, only Kato–Katz was performed; specimens were collected from 13 352 children in 336 schools. The data were evaluated using a geospatial approach. The national prevalence of STH infection in SAC was estimated at 5.8% at baseline, with 0.8% of infections of moderate and heavy intensity. Preventive chemotherapy (PC) targeted all 2.5 million children of school age enrolled in Zimbabwe, with coverage ranging from 49% to 85%. At follow up, national prevalence of STH in SAC was estimated at 0.8%; infections of moderate and heavy intensity almost disappeared (0.1% prevalence). As a result, Zimbabwe can suspend deworming activities in 54 districts and reduce the frequency of PC in the remaining six districts. The total amount of albendazole tablets needed will be approximately 100 000 a year., Author summary We analysed the impact of 6 years of preventive chemotherapy intervention by the Ministry of Health and Child Care of Zimbabwe (MoHCC) to control STH infections in Zimbabwe. We applied a geostatistical method to analyse the epidemiological data before and after the intervention. The results are interesting because they demonstrate a complete elimination of infection of moderate heavy intensity (that are considered the ones causing most of STH morbidity and a drastic reduction in prevalence that entails an estimated reduction of 90% in the need for anthelminthic medicines to maintain the situation and a consequent reduction in programme costs.