Differences between gridded population data impact measures of geographic access to healthcare in sub-Saharan Africa.

Background: Access to healthcare is imperative to health equity and well-being. Geographic access to healthcare can be modeled using spatial datasets on local context, together with the distribution of existing health facilities and populations. Several population datasets are currently available, but their impact on accessibility analyses is unknown. In this study, we model the geographic accessibility of public health facilities at 100-meter resolution in sub-Saharan Africa and evaluate six of the most popular gridded population datasets for their impact on coverage statistics at different administrative levels. Methods: Travel time to nearest health facilities was calculated by overlaying health facility coordinates on top of a friction raster accounting for roads, landcover, and physical barriers. We then intersected six different gridded population datasets with our travel time estimates to determine accessibility coverages within various travel time thresholds (i.e., 30, 60, 90, 120, 150, and 180-min). Results: Here we show that differences in accessibility coverage can exceed 70% at the sub-national level, based on a one-hour travel time threshold. The differences are most notable in large and sparsely populated administrative units and dramatically shape patterns of healthcare accessibility at national and sub-national levels. Conclusions: The results of this study show how valuable and critical a comparative analysis between population datasets is for the derivation of coverage statistics that inform local policies and monitor global targets. Large differences exist between the datasets and the results underscore an essential source of uncertainty in accessibility analyses that should be systematically assessed.

High-resolution population estimation using household survey data and building footprints.

The national census is an essential data source to support decision-making in many areas of public interest. However, this data may become outdated during the intercensal period, which can stretch up to several decades. In this study, we develop a Bayesian hierarchical model leveraging recent household surveys and building footprints to produce up-to-date population estimates. We estimate population totals and age and sex breakdowns with associated uncertainty measures within grid cells of approximately 100 m in five provinces of the Democratic Republic of the Congo, a country where the last census was completed in 1984. The model exhibits a very good fit, with an R2 value of 0.79 for out-of-sample predictions of population totals at the microcensus-cluster level and 1.00 for age and sex proportions at the province level. This work confirms the benefits of combining household surveys and building footprints for high-resolution population estimation in countries with outdated censuses.

Association between environmental and climatic risk factors and the spatial distribution of cystic and alveolar echinococcosis in Kyrgyzstan.

BACKGROUND: Cystic and alveolar echinococcosis (CE and AE) are neglected tropical diseases caused by Echinococcus granulosus sensu lato and E. multilocularis, and are emerging zoonoses in Kyrgyzstan. In this country, the spatial distribution of CE and AE surgical incidence in 2014-2016 showed marked heterogeneity across communities, suggesting the presence of ecological determinants underlying CE and AE distributions. METHODOLOGY/PRINCIPAL FINDINGS: For this reason, in this study we assessed potential associations between community-level confirmed primary CE (no.=2359) or AE (no.=546) cases in 2014-2016 in Kyrgyzstan and environmental and climatic variables derived from satellite-remote sensing datasets using conditional autoregressive models. We also mapped CE and AE relative risk. The number of AE cases was negatively associated with 10-year lag mean annual temperature. Although this time lag should not be considered as an exact measurement but with associated uncertainty, it is consistent with the estimated 10-15-year latency following AE infection. No associations were detected for CE. We also identified several communities at risk for CE or AE where no disease cases were reported in the study period. CONCLUSIONS/SIGNIFICANCE: Our findings support the hypothesis that CE is linked to an anthropogenic cycle and is less affected by environmental risk factors compared to AE, which is believed to result from spillover from a wild life cycle. As CE was not affected by factors we investigated, hence control should not have a geographical focus. In contrast, AE risk areas identified in this study without reported AE cases should be targeted for active disease surveillance in humans. This active surveillance would confirm or exclude AE transmission which might not be reported with the present passive surveillance system. These areas should also be targeted for ecological investigations in the animal hosts.

Epidemic cystic and alveolar echinococcosis in Kyrgyzstan: an analysis of national surveillance data.

BACKGROUND: Human cystic and alveolar echinococcosis are among the priority neglected zoonotic diseases for which WHO advocates control. The incidence of both cystic echinococcosis and alveolar echinococcosis has increased substantially in the past 30 years in Kyrgyzstan. Given the scarcity of adequate data on the local geographical variation of these focal diseases, we aimed to investigate within-country incidence and geographical variation of cystic echinococcosis and alveolar echinococcosis at a high spatial resolution in Kyrgyzstan. METHODS: We mapped all confirmed surgical cases of cystic echinococcosis and alveolar echinococcosis reported through the national echinococcosis surveillance system in Kyrgyzstan between Jan 1, 2014, and Dec 31, 2016, from nine regional databases. We then estimated crude surgical incidence, standardised incidence, and standardised incidence ratios (SIRs) of primary cases (ie, excluding relapses) based on age and sex at country, region, district, and local community levels. Finally, we tested the SIRs for global and local spatial autocorrelation to identify disease hotspots at the local community level. All incidence estimates were calculated per 100 000 population and averaged across the 3-year study period to obtain annual estimates. FINDINGS: The surveillance system reported 2359 primary surgical cases of cystic echinococcosis and 546 primary surgical cases of alveolar echinococcosis. Country-level crude surgical incidence was 13·1 per 100 000 population per year for cystic echinococcosis and 3·02 per 100 000 population per year for alveolar echinococcosis. At the local community level, we found annual crude surgical incidences up to 176 per 100 000 population in Sary-Kamysh (Jalal-Abad region) for cystic echinococcosis and 246 per 100 000 population in Uch-Dobo (Alay district, Osh region) for alveolar echinococcosis. Significant hotspots of cystic echinococcosis were found in four regions: Osh (five local communities in Uzgen district and four in Alay district), Naryn (three local communities in Jumgal district and one in Naryn district), Talas (three local communities in Talas district), and Chuy (one local community in Jayyl district). Significant alveolar echinococcosis hotspots were detected in the Osh region (11 communities in Alay district, including the local community of Sary Mogol, and one in Chong-Alay district) and in the Naryn region (five communities in Jumgal district and three in At-Bashy district), in the southwest and centre of the country. INTERPRETATION: Our analyses reveal remarkable within-country variation in the surgical incidence of cystic echinococcosis and alveolar echinococcosis in Kyrgyzstan. These high-resolution maps identify precise locations where interventions and epidemiological research should be targeted to reduce the burden of human cystic echinococcosis and alveolar echinococcosis. FUNDING: Swiss National Science Foundation.

A grid-based sample design framework for household surveys.

Traditional sample designs for household surveys are contingent upon the availability of a representative primary sampling frame. This is defined using enumeration units and population counts retrieved from decennial national censuses that can become rapidly inaccurate in highly dynamic demographic settings. To tackle the need for representative sampling frames, we propose an original grid-based sample design framework introducing essential concepts of spatial sampling in household surveys. In this framework, the sampling frame is defined based on gridded population estimates and formalized as a bi-dimensional random field, characterized by spatial trends, spatial autocorrelation, and stratification. The sampling design reflects the characteristics of the random field by combining contextual stratification and proportional to population size sampling. A nonparametric estimator is applied to evaluate the sampling design and inform sample size estimation. We demonstrate an application of the proposed framework through a case study developed in two provinces located in the western part of the Democratic Republic of the Congo. We define a sampling frame consisting of settled cells with associated population estimates. We then perform a contextual stratification by applying a principal component analysis (PCA) and k-means clustering to a set of gridded geospatial covariates, and sample settled cells proportionally to population size. Lastly, we evaluate the sampling design by contrasting the empirical cumulative distribution function for the entire population of interest and its weighted counterpart across different sample sizes and identify an adequate sample size using the Kolmogorov-Smirnov distance between the two functions. The results of the case study underscore the strengths and limitations of the proposed grid-based sample design framework and foster further research into the application of spatial sampling concepts in household surveys.

Pan-European Study on the Prevalence of the Feline Leukaemia Virus Infection - Reported by the European Advisory Board on Cat Diseases (ABCD Europe).

Feline leukaemia virus (FeLV) is a retrovirus associated with fatal disease in progressively infected cats. While testing/removal and vaccination led to a decreased prevalence of FeLV, recently, this decrease has reportedly stagnated in some countries. This study aimed to prospectively determine the prevalence of FeLV viraemia in cats taken to veterinary facilities in 32 European countries. FeLV viral RNA was semiquantitatively detected in saliva, using RT-qPCR as a measure of viraemia. Risk and protective factors were assessed using an online questionnaire to report geographic, demographic, husbandry, FeLV vaccination, and clinical data. The overall prevalence of FeLV viraemia in cats visiting a veterinary facility, of which 10.4% were shelter and rescue cats, was 2.3% (141/6005; 95% CI: 2.0%-2.8%) with the highest prevalences in Portugal, Hungary, and Italy/Malta (5.7%-8.8%). Using multivariate analysis, seven risk factors (Southern Europe, male intact, 1-6 years of age, indoor and outdoor or outdoor-only living, living in a group of ≥5 cats, illness), and three protective factors (Northern Europe, Western Europe, pedigree cats) were identified. Using classification and regression tree (CART) analysis, the origin of cats in Europe, pedigree, and access to outdoors were important predictors of FeLV status. FeLV-infected sick cats shed more viral RNA than FeLV-infected healthy cats, and they suffered more frequently from anaemia, anorexia, and gingivitis/stomatitis than uninfected sick cats. Most cats had never been FeLV-vaccinated; vaccination rates were indirectly associated with the gross domestic product (GDP) per capita. In conclusion, we identified countries where FeLV was undetectable, demonstrating that the infection can be eradicated and highlighting those regions where awareness and prevention should be increased.

Exploring Uncertainty in Canine Cancer Data Sources Through Dasymetric Refinement.

In spite of the potentially groundbreaking environmental sentinel applications, studies of canine cancer data sources are often limited due to undercounting of cancer cases. This source of uncertainty might be further amplified through the process of spatial data aggregation, manifested as part of the modifiable areal unit problem (MAUP). In this study, we explore potential explanatory factors for canine cancer incidence retrieved from the Swiss Canine Cancer Registry (SCCR) in a regression modeling framework. In doing so, we also evaluate differences in statistical performance and associations resulting from a dasymetric refinement of municipal units to their portion of residential land. Our findings document severe underascertainment of cancer cases in the SCCR, which we linked to specific demographic characteristics and reduced use of veterinary care. These explanatory factors result in improved statistical performance when computed using dasymetrically refined units. This suggests that dasymetric mapping should be further tested in geographic correlation studies of canine cancer incidence and in future comparative studies involving human cancers.

The importance of regional models in assessing canine cancer incidences in Switzerland.

Fitting canine cancer incidences through a conventional regression model assumes constant statistical relationships across the study area in estimating the model coefficients. However, it is often more realistic to consider that these relationships may vary over space. Such a condition, known as spatial non-stationarity, implies that the model coefficients need to be estimated locally. In these kinds of local models, the geographic scale, or spatial extent, employed for coefficient estimation may also have a pervasive influence. This is because important variations in the local model coefficients across geographic scales may impact the understanding of local relationships. In this study, we fitted canine cancer incidences across Swiss municipal units through multiple regional models. We computed diagnostic summaries across the different regional models, and contrasted them with the diagnostics of the conventional regression model, using value-by-alpha maps and scalograms. The results of this comparative assessment enabled us to identify variations in the goodness-of-fit and coefficient estimates. We detected spatially non-stationary relationships, in particular, for the variables related to biological risk factors. These variations in the model coefficients were more important at small geographic scales, making a case for the need to model canine cancer incidences locally in contrast to more conventional global approaches. However, we contend that prior to undertaking local modeling efforts, a deeper understanding of the effects of geographic scale is needed to better characterize and identify local model relationships.

Assessing effects of structural zeros on models of canine cancer incidence: a case study of the Swiss Canine Cancer Registry.

Epidemiological research of canine cancers could inform comparative studies of environmental determinants for a number of human cancers. However, such an approach is currently limited because canine cancer data sources are still few in number and often incomplete. Incompleteness is typically due to under-ascertainment of canine cancers. A main reason for this is because dog owners commonly do not seek veterinary care for this diagnosis. Deeper knowledge on under-ascertainment is critical for modelling canine cancer incidence, as an indication of zero incidence might originate from the sole absence of diagnostic examinations within a given sample unit. In the present case study, we investigated effects of such structural zeros on models of canine cancer incidence. In doing so, we contrasted two scenarios for modelling incidence data retrieved from the Swiss Canine Cancer Registry. The first scenario was based on the complete enumeration of incidence data for all Swiss municipal units. The second scenario was based on a filtered sample that systematically discarded structural zeros in those municipal units where no diagnostic examination had been performed. By means of cross-validation, we assessed and contrasted statistical performance and predictive power of the two modelling scenarios. This analytical step allowed us to demonstrate that structural zeros impact on the generalisability of the model of canine cancer incidence, thus challenging future comparative studies of canine and human cancers. The results of this case study show that increased awareness about the effects of structural zeros is critical to epidemiological research.