Geospatial Health最新文献

Clubfoot is a congenital anomaly affecting 1/1,000 live births. Ponseti casting is an effective and affordable treatment. About 75% of affected children have access to Ponseti treatment in Bangladesh, but 20% are at risk of drop-out. We aimed to identify the areas in Bangladesh where patients are at high or low risk for drop-out. This study used a cross-sectional design based on publicly available data. The nationwide clubfoot program: 'Walk for Life' identified five risk factors for drop-out from the Ponseti treatment, specific to the Bangladeshi setting: household poverty, household size, population working in agriculture, educational attainment and travel time to the clinic. We explored the spatial distribution and clustering of these five risk factors. The spatial distribution of children <5 years with clubfoot and the population density differ widely across the different sub-districts of Bangladesh. Analysis of risk factor distribution and cluster analysis showed areas at high risk for dropout in the Northeast and the Southwest, with poverty, educational attainment and working in agriculture as the most prevalent driving risk factor. Across the entire country, twenty-one multivariate high-risk clusters were identified. As the risk factors for drop-out from clubfoot care are not equally distributed across Bangladesh, there is a need in regional prioritization and diversification of treatment and enrolment policies. Local stakeholders and policy makers can identify high-risk areas and allocate resources effectively.

This paper aimed to analyse the spatio-temporal patterns of the diffusion of SARS-CoV-2, the virus causing coronavirus 2019 (COVID-19, in the city of Bologna, the capital and largest city of the Emilia-Romagna Region in northern Italy. The study took place from February 1st, 2020 to November 20th, 2021 and accounted for space, sociodemographic characteristics and health conditions of the resident population. A second goal was to derive a model for the level of risk of being infected by SARS-CoV-2 and to identify and measure the place-specific factors associated with the disease and its determinants. Spatial heterogeneity was tested by comparing global Poisson regression (GPR) and local geographically weighted Poisson regression (GWPR) models. The key findings were that different city areas were impacted differently during the first three epidemic waves. The area-to-area influence was estimated to exert its effect over an area with 4.7 km radius. Spatio-temporal heterogeneity patterns were found to be independent of the sociodemographic and the clinical characteristics of the resident population. Significant single-individual risk factors for detected SARS-CoV-2 infection cases were old age, hypertension, diabetes and co-morbidities. More specifically, in the global model, the average SARS-CoV-2 infection rate decreased 0.93-fold in the 21-65 years age group compared to the >65 years age group, whereas hypertension, diabetes, and any other co-morbidities (present vs absent), increased 1.28-, 1.39- and 1.15-fold, respectively. The local GWPR model had a better fit better than GPR. Due to the global geographical distribution of the pandemic, local estimates are essential for mitigating or strengthening security measures.

A complete sampling frame (CSF) is needed for the development of probability sampling structures; utilisation of a spatial sampling frame (SSF) was the objective of the present study. We used two sampling methods, simple random sampling (SRS) and stratified random sampling (STRS), to compare the prevalence estimates delivered by a CSF to that by a SSF when applied to self-reported hypertension and diabetes mellitus in a semi-urban setting and in a rural one. A CSF based on Geodatabase of all households and all individuals was available for our study that focused on adults aged 18-69 years in the two settings. A single digitized shapefile of solely household regions/structures as SSF was developed using Google Earth and employed for the study. The results from the two sampling frames were similar and not significantly different. All 95%CI calculations contained the prevalence rates of the two medical conditions except for one occasion based on STRS and CSF. The SRS based on CSF showed a minimum 95% CI width for diabetes mellitus, whereas SSF showed a minimum 95% CI width for hypertension. The coefficient of variation exceeded 10.0% on six occasions for CSF but only once for SSF, which was found to be as efficient as CSF.

Anaemia is still a public health problem in Indonesia. The iron supplement program, known as Tablet Tambah Darah (Blood Add Tablet) has not yet produced optimal results. This study aimed to identify the cause of anaemia and the factors that influence it. Biochemical indicator data are haemoglobin (Hb), C-reactive protein (CRP), ferritin and serum transferrin receptor (sTfR) from 9,463 women of reproduction age. Data from the Basic Health Research (Riskesdas) project of 2013 were used for the study. ANOVA as well as global and local regression approaches (classical regression and geo-weighted regression) were used to compare the mean Hb and CRP values between provinces and to determine the factors that influence Hb concentrations. The results showed that the distribution of anaemia in Indonesia is uneven and not always caused by iron deficiency. The lowest Hb mean coupled with the highest iron deficiency was found in Papua, where there are high rates of parasitic infections. In contrast, the highest mean Hb coupled with low iron deficiency, and also low infection rates, was found in North Sulawesi. The Hb concentrations were significantly associated by ferritin, CRP and sTfR and there were varying magnitudes between provinces. Although anaemia is mainly influenced by the iron concentration, CRP, ferritin and sTfR can also affect it through their association with inflammatory reactions. Identification of all causes of anaemia in each province needs to be done in the future, while blanket iron supplementation should be reviewed.

Rabies continues to be one of the deadliest, high risk diseases worldwide, posing a severe threat to public health. The lack of human-to-human transmission means that the spread of rabies is not significantly affected by the distribution of humans or migra- tion. Thus, the spatiotemporal dynamic of cases in both wild and domestic animals is an important issue that can result in human cases. This paper gives an overview of the methodologies for the spatial and temporal dynamic analysis of this disease. It introduces the most representative research progress of spatial aggregation, dynamic transmission, spatiotemporal distribution, epidemiologi- cal analysis and application of modelling in the study of rabies transmission in recent years. This overview should be useful for investigating the spatial and temporal dynamics of rabies, as it could help understanding the spread of cases as well as contribute to the development of better prevention and control strategies in ecology and epidemiology.

The burden of diabetes mellitus (DM), one of the major noncommunicable diseases (NCDs), has been significantly rising globally. In the Asia-Pacific region, Thailand ranks within the top ten of diabetic patient populations and the disease has increased from 2.3% in 1991 to 8.0% in 2015. This study applied local indicators of spatial association (LISA) and spatial regression to examine the local associations in Thailand with night-time light, spatial density of alcohol/convenience stores, concentration of elderly population and prevalence of DM among middle-aged and elderly people. Univariate LISA identified the statistically significant cluster of DM prevalence in the upper north-eastern region. For multivariate spatial analysis, the obtained R2 values of the spatial lag model (SLM) and spatial error model (SEM) were 0.310 and 0.316, respectively. These two models indicated a statistical significant association of several sociodemographic and environmental characteristics with the DM prevalence: food shops (SLM coefficient = 9.625, p<0.001; SEM coefficient = 9.695, p<0.001), alcohol stores (SLM coefficient = 1.936, p<0.05; SEM coefficient = 1.894, p<0.05), population density of elderly people (SLM coefficient = 0.156, p<0.05; SEM coefficient = 0.188, p<0.05) and night-time light density (SLM coefficient = -0.437, p<0.001; SEM coefficient = -0.437, p<0.001). These findings are useful for policymakers and public health professionals in formulating measures aimed at reducing DM burden in the country.

We provide a novel approach to understanding the multiple causations of maternal anaemia in a geospatial context, highlighting how genetics, environment and socioeconomic disparities at the micro-geographical level lead to the inequitable distribution of anaemia. All first-trimester pregnant women registered for the antenatal care programme in Anuradhapura District, Sri Lanka from July to September 2019 were invited to the Rajarata Pregnancy Cohort (RaPCo), which assessed the prevalence of anaemia in early pregnancy. The combination of the prevalence of anaemia and minor haemoglobinopathy-related anaemia (MHA) with the poverty headcount index of the 22 health divisions in the district was investigated using GeoDa spatial K-means clustering. Sociodemographic and economic data at the divisional level were compared between identified clusters. Combining the analysis with the geographical and environmental characteristics of the region, further hypotheses regarding anaemia in this community were formulated. The study included data from 3,137 pregnant women in early pregnancy. The anaemia and MHA prevalence varied from 13.6 to 21.7% and from 2.6% to 5%, respectively. We identified four distinct spatial clusters. The cluster with the highest anaemia prevalence also included high poverty and the highest prevalence of MHA. The clusters had significant differences with regard to ethnic distribution, access to water, sanitation and dietary patterns. Areas supplied by major irrigation projects had significantly low levels of anaemia, probably attributable to internal migration and improved livelihood. It was evident that genetic, socioeconomic and environmental risk factors were grouped at the divisional level, and that their complex interactions make controlling anaemia with blanket interventions unsuccessful. Analysis of the distribution of heterogeneous risk factors at the micro-geospatial level helped identify context-specific approaches to tackle anaemia in pregnancy.

Leptospirosis is a serious public health problem in Brazil, which can be observed after flooding events. Using an exploratory mixed clustering method, this ecological study analyzes whether spatial-temporal clustering patterns of leptospirosis occur in Brazil. Data from the Brazilian Unified Health System (SUS) were used to calculate the prevalence of leptospirosis between 2007 and 2017 in all counties of the country. Clustering techniques, including spatial association indicators, were used for analysis and evaluation of disease yearly spatial distribution. Based on Local Indicators of Spatial Association (LISA) with Empirical Bayesian rates detected spatial patterns of leptospirosis ranging from 0.137 (p = 0.001 in 2009) to 0.293 (p = 0.001 in 2008). Over the whole period, the rate was 0.388 (p = 0.001). The main pattern showed permanence of leptospirosis clusters in the South and emergence and permanence of such clusters in northern Brazil. The municipalities with leptospirosis cases and at least one flood occurrence registered in the Brazilian Integrated Disaster Information System were incorporated into the LISA cluster map with Empirical Bayesian rates. These counties were expected to exhibit clustering, not all did. The results of the cluster analysis suggest allocation of health resources in areas with leptospirosis clustering.

We intend to tackle two under-addressed issues in access to healthcare services during the COVID-19 pandemic: first, the spatiotemporal dynamic of access during the pandemic of acute communicable disease; second, the demographic and socioeconomic access disparities. We used the two-step floating catchment area (2SFCA) method to measure the spatial access to public hospitals during the second COVID-19 wave (September 28th-February 28th, 2021) in Nottinghamshire, UK. To investigate the temporal variation in access along with the development of the pandemic, we divided our study period into 11 sections and applied the 2SFCA to each of them. The results indicate that western Nottinghamshire is better than the eastern part from a spatial perspective and the north-western urban area represents the highest spatial access; temporally, the accessibility of the public hospitals generally decreased when the number of cases increased. Particular low accessibility was observed at the beginning of the pandemic when the outbreak hit the university region and its vicinities during the back-to-school season. Our disparity analysis found that i) the access of the senior population to public hospitals deviated from that of the general population, ii) the access was positively associated with socioeconomic status, and iii) all disparities were related to the urban-rural discrepancy. These findings can help to plan temporary clinics or hospitals during epidemic emergencies. More generally, they provide scientific support to pandemic-related healthcare resource allocation and policy- making, particularly for people in vulnerable areas.

The mother-to-child transmission (MTCT) of the human immunodeficiency virus (HIV) remains a serious public health problem in the Brazilian Rainforest. This study aimed to spatially analyze this type of infection between 2007 and 2018 in Pará, which is the second-largest Brazilian state in the Brazilian Rainforest and also has the highest MTCT of HIV in Brazil. We analyzed the incidence rates of HIV (including the acquired immunodeficiency syndrome (AIDS) by MTCT as the main route of infection in children younger than 13 years old and whose mothers live in Pará. We employed spatial autocorrelation, spatial scanning, and geographic-weighted spatial regression techniques. In the period of this study, 389 new HIV/AIDS were noted, with territorial expansion of the incidence rates in the municipalities in northern and southern Pará having the highest rates. São Francisco do Pará had high spatial risk and high-spatiotemporal risk clusters comprising municipalities in western and south-western Pará between 2013 and 2016. The spatial variability of HIV/AIDS incidence rates was found to be common in the number of men and women with formal jobs; unemployed ≥18 years old people; elementary school pupils; and families enrolled in the "Single Registry for Social Programs". The social equity approach in Pará guarantee pregnant women access to preventive, diagnostic and treatment health services and their children should be supported to eliminate the MTCT of HIV in Pará.