Disease Risk Mapping Research Articles

Combining Morphological and Molecular Tools Can Enhance Tick Species Identification for Improved Tick-Borne Disease Surveillance Among Pastoral Communities in Kenya.

Background: Ticks are ecto-parasites of domestic animals, rodents, and wildlife living for periods at a time on one or more vertebrate hosts. They are important vectors of viral, bacterial, or parasitic diseases in livestock and humans. Crimean-Congo haemorrhagic fever virus and the spotted fever rickettsiae are some of the tick-borne diseases of public health importance reported in Kenya. Their distribution and public health risks among communities, especially pastoralists, remain poorly characterized due to limited surveillance, affected partly by inadequate capacity for tick identification arising from a limited number of skilled taxonomists. Materials and Methods: The aim of this survey was to identify tick species currently circulating in different livestock hosts in northern Kenya. Ticks were sampled from cattle, sheep, goats, and camels in Turkana, Isiolo, Baringo, and West Pokot counties, and differential identification was carried out using morphological identification keys followed by molecular characterization based on the cytochrome c oxidase I gene (cox1). Haplotypes were determined using the DnaSP v6 software and phylogenetic relationships inferred using the maximum likelihood algorithm. Results: A total of 12,206 ticks were collected, from Turkana (45.4%), Isiolo (23.1%), Baringo (22.7%), and West Pokot (8.8%) counties in Kenya. Ten species were confirmed by molecular analysis; H. rufipes, H. impeltatum, H. dromedarii, R. pravus, R. camicasi, R. pulchellus, R. evertsi evertsi, A. variegatum, A. gemma, and A. lepidum. There was no disparity in the morphological and molecular identification of Amblyomma species. However, molecular analysis provided insight into the complexity of morphological identification especially among Hyalomma and Rhipicephalus species. High haplotype diversities (0.857-1.000) and low nucleotide diversities (0.00719-0.06319) were observed in all the tick samples tested. Conclusion: The findings highlight the diversity of tick species in dry pastoral ecologies in Kenya and the importance of confirming morphological identification by molecular analysis thus contributing to accurate mapping of tick-borne disease distribution and risk.

Waterborne Disease Risk Assessment and Mapping for a Floating Village by Combining 3D Hydraulic Simulation and Quantitative Microbial Risk Assessment

Spatiotemporal changes in waterborne disease risk were evaluated for the Chhnok Tru floating village in the Tonle Sap Lake by combining a hydraulic simulation and quantitative microbial risk assessment (QMRA). First, a three-dimensional (3D) hydraulic simulation was performed, and the transport of Escherichia coli (E. coli) was simulated. Prior to the simulation, by coupling satellite imagery analysis using the normalized difference water index (NDWI) and a sounding survey using an acoustic Doppler current profiler (ADCP), a new digital elevation model was generated for the complex channel network with high resolution. The results of the 3D hydraulic simulation revealed the flow regime and nonuniform pathogen distribution in the floating village. QMRA was performed for the village using the E. coli distribution calculated by the 3D hydraulic model. Subsequently, the disease risk in the village was visualized through an effective and easy-to-understand disease risk map. To demonstrate the usefulness of the hydraulic-simulation-based disease risk map, the map was used to quantitatively compare simple policies by evaluating their reduction in disease risk.

Space-time modelling of monthly malaria incidence for seasonal associated drivers and early epidemic detection in Southern Ethiopia

BackgroundAlthough Ethiopia has made great strides in recent years to reduce the threat of malaria, the disease remains a significant issue in most districts of the country. It constantly disappears in parts of the areas before reappearing in others with erratic transmission rates. Thus, developing a malaria epidemic early warning system is important to support the prevention and control of the incidence.MethodsSpace-time malaria risk mapping is essential to monitor and evaluate priority zones, refocus intervention, and enable planning for future health targets. From August 2013 to May 2019, the researcher considered an aggregated count of genus Plasmodium falciparum from 149 districts in Southern Ethiopia. Afterwards, a malaria epidemic early warning system was developed using model-based geostatistics, which helped to chart the disease’s spread and future management.ResultsRisk factors like precipitation, temperature, humidity, and nighttime light are significantly associated with malaria with different rates across the districts. Districts in the southwest, including Selamago, Bero, and Hamer, had higher rates of malaria risk, whereas in the south and centre like Arbaminch and Hawassa had moderate rates. The distribution is inconsistent and varies across time and space with the seasons.ConclusionDespite the importance of spatial correlation in disease risk mapping, it may occasionally be a good idea to generate epidemic early warning independently in each district to get a quick picture of disease risk. A system like this is essential for spotting numerous inconsistencies in lower administrative levels early enough to take corrective action before outbreaks arise.

Understanding the importance of spatial correlation in identifying spatio-temporal variation of disease risk, in the case of malaria risk mapping in southern Ethiopia

Malaria remains a major health problem in developing countries despite a significant reduction in incidence in the last few years. Disease mapping thus helps to understand the spatial pattern and identify areas characterized by unusual risks. Several spatial models have been used to analyze the incidence of malaria. We aim to compare the predictive performance of these models and investigate the effect of ignoring spatial correlation. The reported malaria case counts of genus P.falciparum in 149 districts of southern Ethiopia from January 2016 to May 2019 were analyzed using the spatial time series model (STS) that ignores spatial correlation, Spatio-temporal conditional autoregressive model (STCAR), Spatio-temporal geostatistical model (STG) and Spatio-temporal spatial discrete approximation to log Gaussian cox process (STSDALGCP). We assess the predictive performance of the models using root mean square error, mean absolute error, and coverage probability. We found that monthly average rainfall, temperature, humidity, and EVI are significantly associated with malaria risk. The spatial variation of malaria incidence changes with time, in particular, the high incidence was observed from November to December, months after heavy rainfall, and more pronounced in the southwest of the country. STSDALGCP gives a small prediction error in test set and captures the uncertainties better than other models, while the STS model gives a high prediction error. Accounting for spatial correlation is crucial for disease risk mapping and leads to better prediction of disease risk. Since malaria transmission operates in a spatially continuous manner, a spatially continuous model should be considered when it is computationally feasible.

EpiGeostats: An R Package to Facilitate Visualization of Geostatistical Disease Risk Maps

AbstractWith the emergence of the coronavirus disease 2019 (COVID-19) pandemic in Portugal, a geostatistical tool was developed to model the spatial distribution of COVID-19 risk to support decision-making and policymakers. Based on a block direct sequential simulation algorithm, the model provides detailed disease risk estimates and associated spatial uncertainty. However, uncertainty is difficult to visualize with the estimated risk, and is usually overlooked as a tool to support decision-making. Ignoring uncertainty can be misleading in evaluating risk, since the amount of uncertainty varies throughout the spatial domain. The EpiGeostats R package was developed to solve this problem, since it integrates the geostatistical model and visualization tools to deliver a single map summarizing disease risk and spatial uncertainty. This paper briefly describes the methodology and package functions implemented for interfacing with the tools in question. The use of EpiGeostats is illustrated by applying it to real data from COVID-19 incidence rates on mainland Portugal. EpiGeostats is a powerful tool for supporting decision-making in the context of epidemics, since it combines a well-established geostatistical model for disease risk mapping with simple and intuitive ways of visualizing results, which prevent fine-scale inference in regions with high-risk uncertainty. The package may be used for similar problems such as mortality risk, or applied to other fields such as ecology or environmental epidemiology.

A scoping review of zoonotic parasites and pathogens associated with abattoirs in Eastern Africa and recommendations for abattoirs as disease surveillance sites.

Abattoirs are facilities where livestock are slaughtered and are an important aspect in the food production chain. There are several types of abattoirs, which differ in infrastructure and facilities, sanitation and PPE practices, and adherence to regulations. In each abattoir facility, worker exposure to animals and animal products increases their risk of infection from zoonotic pathogens. Backyard abattoirs and slaughter slabs have the highest risk of pathogen transmission because of substandard hygiene practices and minimal infrastructure. These abattoir conditions can often contribute to environmental contamination and may play a significant role in disease outbreaks within communities. To assess further the risk of disease, we conducted a scoping review of parasites and pathogens among livestock and human workers in abattoirs across 13 Eastern African countries, which are hotspots for zoonoses. Our search results (n = 104 articles) showed the presence of bacteria, viruses, fungi, and macroparasites (nematodes, cestodes, etc.) in cattle, goats, sheep, pigs, camels, and poultry. Most articles reported results from cattle, and the most frequent pathogen detected was Mycobacterium bovis, which causes bovine tuberculosis. Some articles included worker survey and questionnaires that suggested how the use of PPE along with proper worker training and safe animal handling practices could reduce disease risk. Based on these findings, we discuss ways to improve abattoir biosafety and increase biosurveillance for disease control and mitigation. Abattoirs are a 'catch all' for pathogens, and by surveying animals at abattoirs, health officials can determine which diseases are prevalent in different regions and which pathogens are most likely transmitted from wildlife to livestock. We suggest a regional approach to biosurveillance, which will improve testing and data gathering for enhanced disease risk mapping and forecasting. Next generation sequencing will be key in identifying a wide range of pathogens, rather than a targeted approach.

Epidemic intelligence data of Crimean-Congo haemorrhagic fever, European Region, 2012 to 2022: a new opportunity for risk mapping of neglected diseases.

BackgroundThe Epidemic Intelligence from Open Sources (EIOS) system, jointly developed by the World Health Organisation (WHO), the Joint Research Centre (JRC) of the European Commission and various partners, is a web-based platform that facilitate the monitoring of information on public health threats in near real-time from thousands of online sources.AimsTo assess the capacity of the EIOS system to strengthen data collection for neglected diseases of public health importance, and to evaluate the use of EIOS data for improving the understanding of the geographic extents of diseases and their level of risk.MethodsA Bayesian additive regression trees (BART) model was implemented to map the risk of Crimean-Congo haemorrhagic fever (CCHF) occurrence in 52 countries and territories within the European Region between January 2012 and March 2022 using data on CCHF occurrence retrieved from the EIOS system.ResultsThe model found a positive association between all temperature-related variables and the probability of CCHF occurrence, with an increased risk in warmer and drier areas. The highest risk of CCHF was found in the Mediterranean basin and in areas bordering the Black Sea. There was a general decreasing risk trend from south to north across the entire European Region.ConclusionThe study highlights that the information gathered by public health intelligence can be used to build a disease risk map. Internet-based sources could aid in the assessment of new or changing risks and planning effective actions in target areas.

Exposure to and infection by Leishmania infantum among domestic dogs in an area of the Cerrado biome, Maranhão, Brazil

Visceral Leishmaniasis is a serious public health problem and dogs are considered to be the main source of infection in urban areas. In Brazil, this disease is present in all regions, but with high concentration of cases in the Northeast, and the state of Maranhão is considered to be an endemic region. The aim of this study was to conduct an epidemiological, spatial, molecular and serological survey on Leishmania infantum among domestic dogs in the municipality of Belágua, Maranhão. Blood samples were collected from dogs and questionnaires were applied to their owners to obtain epidemiological data and risk factors relating to this zoonosis in the region. The coordinates of the dogs' homes were obtained to produce a disease risk map. Serological diagnoses were made using the indirect immunofluorescence reaction (IFAT) and the dual-path platform chromatographic immunoassay test (DPP®) (Bio-Manguinhos/FIOCRUZ, Brazil). A molecular investigation was undertaken using the polymerase chain reaction (PCR). Georeferencing was performed using the global positioning system (GPS) and cases of canine visceral leishmaniasis in the municipality were spatially represented and analyzed using QGIS version 3.16.6 (QGIS Development Team, 2021). A total of 205 blood samples were collected, of which 122 (59.51%) were seroreactive for L. infantum through IFAT, while the DPP test showed 84 reactive samples (40.97%). IFAT and DPP detected 16 positive animals simultaneously. One sample that was seroreactive through IFAT was also positive through PCR. In the clinical evaluation, it was observed that among the seropositive dogs, 112 (91.80%) were symptomatic and 10 (8.20%) were asymptomatic. In the spatial analysis, the Kernel density estimator enabled determination of the place at greatest risk of occurrence of the disease. The areas with the highest concentrations of cases were in districts with large quantities of precarious housing and lack of basic sanitation. This was the first report on the occurrence of L. infantum among dogs in the municipality of Belágua. The results show that canine visceral leishmaniasis is well dispersed in this municipality, thus putting at risk the human population.

Abstract C005: Social determinants and environmental exposures and their localized contribution to risk of cancer mortality

Abstract When considering environmental exposures and/or social determinants of health (SDOH), data are typically available by counties, zip codes or census tracts. We consider two quantitative challenges in this setting when modeling cancer outcomes. The first is that multiple, potentially highly correlated exposures may contribute to risk. The second is that the spatial correlation of such features may not follow the original models designed for disease risk mapping. While several conditional autoregressive (CAR) models are typically utilized (Besag et al. (1991), Leroux et al. (1999), Stern and Cressie (1999)), a critical drawback is that they impose a global level of autocorrelation across the study area, presuming areas nearest each other are most similar. Spatial data may actually include step changes in the risk surface, indicative of more complex patterns either due to physical barriers and/or social isolation. Thus, identifying these step changes is of interest, as they may reflect the underlying complexity of the relationship between environmental exposures, SDOH and cancer mortality. We consider a two-step model to explore how clusters of environmental exposures and SDOH may be able to identify boundaries in the risk surface of cancer outcomes. In the first step, we estimate clusters of exposures using Bayesian profile regression, a nonparametric approach based on Dirichlet process mixture methods. In the second step, we model cancer mortality using locally adaptive spatial smoothing with the clusters as dissimilarity metrics (Lee and Mitchell, 2012). Several diagnostic tools are used to assess convergence in each step. With this work, we bridge the gap between incorporating mixtures of correlated exposures with the identification of localized areas with elevated risk of cancer mortality. We apply this approach to a nationwide database of county-level breast cancer mortality, utilize environmental exposure data from the National Air Toxics Assessment and assess SDOH from the American Community Survey. Citation Format: Kara McCormack. Social determinants and environmental exposures and their localized contribution to risk of cancer mortality [abstract]. In: Proceedings of the 15th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2022 Sep 16-19; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr C005.

Tackling AMR: A Call for a(n Even) More Integrated and Transdisciplinary Approach between Planetary Health and Earth Scientists

Antibiotic resistance is a pressing global, one health and planetary health challenge. Links between climate change, antibiotic use, and the emergence of antibiotic resistance have been well documented, but less attention has been given to the impact(s) of earth systems on specific bacterial livestock diseases at a more granular level. Understanding the precise impacts of climate change on livestock health—and in turn the use of antibiotics to address that ill-health—is important in providing an evidence base from which to tackle such impacts and to develop practical, implementable, and locally acceptable solutions within and beyond current antibiotic stewardship programs. In this paper, we set out the case for better integration of earth scientists and their specific disciplinary skill set (specifically, problem-solving with incomplete/fragmentary data; the ability to work across four dimensions and at the interface between the present and deep/geological time) into planetary health research. Then, using a case study from our own research, we discuss a methodology that makes use of risk mapping, a common methodology in earth science but less frequently used in health science, to map disease risk against changing climatic conditions at a granular level. The aim of this exercise is to argue that, by enabling livestock farmers, veterinarians, and animal health observatories to better predict future disease risk and risk impacts based on predicted future climate conditions, earth science can help to provide an evidence base from which to influence policy and develop mitigations. Our example—of climate conditions’ impact on livestock health in Karnataka, India—clearly evidences the benefit of integrating earth scientists into planetary health research.

The COMBAT project: controlling and progressively minimizing the burden of vector-borne animal trypanosomosis in Africa.

Vector-borne diseases affecting livestock have serious impacts in Africa. Trypanosomosis is caused by parasites transmitted by tsetse flies and other blood-sucking Diptera. The animal form of the disease is a scourge for African livestock keepers, is already present in Latin America and Asia, and has the potential to spread further. A human form of the disease also exists, known as human African trypanosomosis or sleeping sickness. Controlling and progressively minimizing the burden of animal trypanosomosis (COMBAT) is a four-year research and innovation project funded by the European Commission, whose ultimate goal is to reduce the burden of animal trypanosomosis (AT) in Africa. The project builds on the progressive control pathway (PCP), a risk-based, step-wise approach to disease reduction or elimination. COMBAT will strengthen AT control and prevention by improving basic knowledge of AT, developing innovative control tools, reinforcing surveillance, rationalizing control strategies, building capacity, and raising awareness. Knowledge gaps on disease epidemiology, vector ecology and competence, and biological aspects of trypanotolerant livestock will be addressed. Environmentally friendly vector control technologies and more effective and adapted diagnostic tools will be developed. Surveillance will be enhanced by developing information systems, strengthening reporting, and mapping and modelling disease risk in Africa and beyond. The socio-economic burden of AT will be assessed at a range of geographical scales. Guidelines for the PCP and harmonized national control strategies and roadmaps will be developed. Gender equality and ethics will be pivotal in all project activities. The COMBAT project benefits from the expertise of African and European research institutions, national veterinary authorities, and international organizations. The project consortium comprises 21 participants, including a geographically balanced representation from 13 African countries, and it will engage a larger number of AT-affected countries through regional initiatives.

A framework for integrating inferred movement behavior into disease risk models

Movement behavior is an important contributor to habitat selection and its incorporation in disease risk models has been somewhat neglected. The habitat preferences of host individuals affect their probability of exposure to pathogens. If preference behavior can be incorporated in ecological niche models (ENMs) when data on pathogen distributions are available, then variation in such behavior may dramatically impact exposure risk. Here we use data from the anthrax endemic system of Etosha National Park, Namibia, to demonstrate how integrating inferred movement behavior alters the construction of disease risk maps. We used a Maximum Entropy (MaxEnt) model that associated soil, bioclimatic, and vegetation variables with the best available pathogen presence data collected at anthrax carcass sites to map areas of most likely Bacillus anthracis (the causative bacterium of anthrax) persistence. We then used a hidden Markov model (HMM) to distinguish foraging and non-foraging behavioral states along the movement tracks of nine zebra (Equus quagga) during the 2009 and 2010 anthrax seasons. The resulting tracks, decomposed on the basis of the inferred behavioral state, formed the basis of step-selection functions (SSFs) that used the MaxEnt output as a potential predictor variable. Our analyses revealed different risks of exposure during different zebra behavioral states, which were obscured when the full movement tracks were analyzed without consideration of the underlying behavioral states of individuals. Pathogen (or vector) distribution models may be misleading with regard to the actual risk faced by host animal populations when specific behavioral states are not explicitly accounted for in selection analyses. To more accurately evaluate exposure risk, especially in the case of environmentally transmitted pathogens, selection functions could be built for each identified behavioral state and then used to assess the comparative exposure risk across relevant states. The scale of data collection and analysis, however, introduces complexities and limitations for consideration when interpreting results.

The COMBAT project: controlling and progressively minimizing the burden of vector-borne animal trypanosomosis in Africa

Vector-borne diseases affecting livestock have serious impacts in Africa. Trypanosomosis is caused by parasites transmitted by tsetse flies and other blood-suckingDiptera. The animal form of the disease is a scourge for African livestock keepers, is already present in Latin America and Asia, and has the potential to spread further. A human form of the disease also exists, known as human African trypanosomosis or sleeping sickness. Controlling and progressively minimizing the burden of animal trypanosomosis (COMBAT) is a four-year research and innovation project funded by the European Commission, whose ultimate goal is to reduce the burden of animal trypanosomosis (AT) in Africa. The project builds on the progressive control pathway (PCP), a risk-based, step-wise approach to disease reduction or elimination. COMBAT will strengthen AT control and prevention by improving basic knowledge of AT, developing innovative control tools, reinforcing surveillance, rationalizing control strategies, building capacity, and raising awareness. Knowledge gaps on disease epidemiology, vector ecology and competence, and biological aspects of trypanotolerant livestock will be addressed. Environmentally friendly vector control technologies and more effective and adapted diagnostic tools will be developed. Surveillance will be enhanced by developing information systems, strengthening reporting, and mapping and modelling disease risk in Africa and beyond. The socio-economic burden of AT will be assessed at a range of geographical scales. Guidelines for the PCP and harmonized national control strategies and roadmaps will be developed. Gender equality and ethics will be pivotal in all project activities. The COMBAT project benefits from the expertise of African and European research institutions, national veterinary authorities, and international organizations. The project consortium comprises 21 participants, including a geographically balanced representation from 13 African countries, and it will engage a larger number of AT-affected countries through regional initiatives.

Indexing Estimates of GFR to Body Surface Area in Low-Resource Settings With a High Burden of Malnutrition: Evidence From Guatemala

Indexing Estimates of GFR to Body Surface Area in Low-Resource Settings With a High Burden of Malnutrition: Evidence From Guatemala

Health-Based Geographic Information Systems for Mapping and Risk Modeling of Infectious Diseases and COVID-19 to Support Spatial Decision-Making.

Infectious diseases remain an essential global challenge in public health. For instance, novel coronavirus (COVID-19) has resulted in significant negative impacts on public health, infecting more than 214 million people and causing 4.47 million deaths worldwide as of August 2021. Geographic Information Systems have played an essential role in managing, storing, analyzing, and mapping disease and related risk information. This article provides an overview of a broad topic on applications of GIS into infectious disease research. Our review follows the framework of human-environment interactions, focusing on the environmental and social factors that cause the disease outbreak and the role of humans in disease control, including public health policies and interventions such as social distancing/face covering practice and mobility modeling. The work identifies key spatial decision-making issues where GIS becomes valued in the agenda for infectious disease research and highlights the importance of adopting science-based policies to protect the public during the current and future pandemics.

A Bayesian conditional autoregressive with INLA: A case study of tuberculosis in Java, Indonesia

Most disease risk mapping models are modeled by using the Bayesian approach. In this approach, the posterior distribution is typically estimated by using Markov Chain Monte Carlo (MCMC) method. However, it is known that the MCMC method suffers from convergence issues and requires a high demand for computational resources. This study aims to identify potential covariates in tuberculosis reduction and to map the relative risk of tuberculosis disease in the Java region by using Bayesian Conditional Autoregressive (CAR) with Integrated Nested Laplace Approximation (INLA). The posterior distribution of Bayes was estimated analytically using INLA that resulting in a more efficient, fast, and accurate inference. The results showed that Besag York Mollié (BYM) has the smallest Deviance Information Criterion (DIC) and Mean Absolute Deviance (MAD), so this model gives the best prediction. The significant covariates in reducing the number of TB cases in Java are percentages of healthy homes, household clean and healthy behavior, non-smokers, and complete tuberculosis treatment. In addition, 52.1% of districts/cities in Java were found to have a relative risk greater than one, and most of them were located in West Java.

Maplaria: a user friendly web-application for spatio-temporal malaria prevalence mapping

BackgroundModel-based geostatistical (MBG) methods have been extensively used to map malaria risk using community survey data in low-resource settings where disease registries are incomplete or non-existent. However, the wider adoption of MBG methods by national control programmes to inform health policy decisions is hindered by the lack of advanced statistical expertise and suitable computational equipment. Here, Maplaria, an interactive, user-friendly web-application that allows users to upload their own malaria prevalence data and carry out geostatistical prediction of annual malaria prevalence at any desired spatial scale, is introduced.MethodsIn the design of the Maplaria web application, two main criteria were considered: the application should be able to classify subnational divisions into the most likely endemicity levels; the web application should allow only minimal input from the user in the set-up of the geostatistical inference process. To achieve this, the process of fitting and validating the geostatistical models is carried out by statistical experts using publicly available malaria survey data from the Harvard database. The stage of geostatistical prediction is entirely user-driven and allows the user to upload malaria data, as well as vector data that define the administrative boundaries for the generation of spatially aggregated inferences.ResultsThe process of data uploading and processing is split into a series of steps spread across screens through the progressive disclosure technique that prevents the user being immediately overwhelmed by the length of the form. Each of these is illustrated using a data set from the Malaria Indicator carried out in Tanzania in 2017 as an example.ConclusionsMaplaria application provides a user-friendly solution to the problem making geostatistical methods more accessible to users that have not undertaken formal training in statistics. The application is a useful tool that can be used to foster ownership, among policy makers, of disease risk maps and promote better use of data for decision-making in low resource settings.

The Relative Risk Estimation of Pneumonia in Malaysia using Standardized Morbidity Ratio (SMR)

Pneumonia is the most serious inflammatory disease of the respiratory system which cause millions of deaths worldwide especially for children under age five and older adults. The numbers of pneumonia cases reported keep on increasing from year to year in Malaysia. Moreover, this type of disease can become outbreak if it is not controlled. Besides using as prevention strategies for a disease risk, statistical modelling of disease mapping can also be used in monitoring as it clearly shows risk areas. The objective of this paper is to estimate the value of relative risk for pneumonia transmission by using Standardized Morbidity Ratio (SMR) method which is the conventional statistic method used in disease mapping and subsequently propose a disease risk map to show the pneumonia risk areas. Pneumonia data in Malaysia is used in estimating the relative risk. The results show that, Terengganu has the highest risk of contracting pneumonia while Pulau Pinang shows the lowest risk.

Geostatistical analysis, web-based mapping, and environmental determinants of under-5 stunting: evidence from the 2014 Ghana Demographic and Health Survey

Stunting rates in children younger than 5 years are among the most important health indicators globally. At the national level, malnutrition accounts for about 40% of under-5 deaths in Ghana. Disease risk mapping provides opportunities for disease surveillance and targeted interventions. We aimed to estimate and map under-5 stunting prevalence in Ghana, with the goal of identifying communities at higher risk where interventions and further research can be targeted. For this modelling study, we used data from the 2014 Ghana Demographic and Health Survey. Analyses were done on 2734 children residing in 415 geographical clusters. The outcome variable was the number of stunted children younger than 5 years in each sampled cluster. We employed a Bayesian geostatistical model to investigate both measured and unmeasured spatial risk factors for child stunting, comparing the performance of non-spatial (adjusting for selected covariates without spatial correlation), spatial (including spatial correlation), and null spatial (without the selected covariates) models. We then visualised the stunting prevalence across Ghana by mapping the predicted prevalence and exceedance probabilities to resolutions as refined as 5 km × 5 km. In 2014, 535 (19·6%) of 2734 children surveyed in Ghana were stunted. Elevation (log odds mean -0·0017, 95% credible interval -0·0034 to -0·0001), precipitation (0·0403, 0·0192 to 0·0615), and aridity (-3·7013, -6·5478 to -0·8723) were environmental and climatic factors associated with stunting in the non-spatial model, but were not significant in the spatial model. Substantial geographical variations in prevalence of childhood stunting were found. The predicted mean stunting prevalence was 27·7% (SD 3·7%) with predicted prevalence ranging from 4·2% to 45·1% across Ghana. Children residing in parts of the Northern region were at highest risk of stunting, whereas parts of the Greater Accra, Brong-Ahafo, Ashanti, and Eastern regions showed some of the lowest prevalence. There are substantial geographical differences in childhood stunting across Ghana. Our prevalence maps can be used as an effective tool to identify communities that require targeted interventions by programme managers and implementers, as part of an overall strategy to reduce the burden of malnutrition in a country with limited public health resources. None.

Bayesian Modelling for Spatially Misaligned Health Areal Data: A Multiple Membership Approach

AbstractDiabetes prevalence is on the rise in the United Kingdom, and for public health strategy, estimation of relative disease risk and subsequent mapping is important. We consider an application to London data on diabetes prevalence and mortality. In order to improve the estimation of relative risks, we analyse jointly prevalence and mortality data to ensure borrowing strength over the two outcomes. The available data involve two spatial frameworks, areas (Middle Layer Super Output Areas, MSOAs) and general practices (GPs) recruiting patients from several areas. This raises a spatial misalignment issue that we deal with by employing the multiple membership principle. Specifically, we translate areal spatial effects to explain GP practice prevalence according to proportions of GP populations resident in different areas. A sparse implementation in RStan of both the multivariate conditional autoregressive (MCAR) and generalised MCAR (GMCAR) with multiple membership allows the comparison of these bivariate priors as well as exploring the different implications for the mapping patterns for both outcomes. The necessary causal precedence of diabetes prevalence over mortality allows a specific conditionality assumption in the GMCAR, not always present in the context of disease mapping. Additionally, an area-locality comparison is considered to locate high versus low relative risk clusters.