Spatial and Spatio-Temporal Epidemiology最新文献

This paper investigated the spatiotemporal pattern of COVID-19 mortality and its socioeconomic and environmental determinants in the first and second wave of the pandemic in England. The COVID-19 mortality rates for middle super output areas from March 2020 to April 2021 were used in the analysis. SaTScan was used in the analysis of spatiotemporal pattern of COVID-19 mortality and geographically weighted Poisson regression (GWPR) was used to investigate the association with socioeconomic and environmental factors. The results show that there was significant spatiotemporal variation in hotspots of COVID-19 deaths with the hotspots moving from regions where the COVID-19 outbreak initiated and then spread to other parts of the country. The GWPR analysis revealed that age composition, ethnic composition, deprivation, care home and pollution were all related to COVID-19 mortality. Althoughthe relationship varied over space the association with these factors was fairly consistent over the first and second wave.

Our study investigated the spatial distribution of tuberculosis and the influence of social determinants in Russia between 2006 and 2018 using the regional incidence of multi-drug resistance tuberculosis, HIV-TB coinfection, and mortality data. The “space-time cube” method identified the uneven geographical distribution of the tuberculosis burden. There is a clear distinction between a healthier European Russia, where a statistically significant stable trend towards a decrease in incidence and mortality was found, and the eastern part of the country, where there is no such trend. Generalized linear logistic regression analysis found that the challenging situation was associated with HIV-TB coinfection incidence, with a high incidence rate being detected even in more prosperous regions of European Russia. HIV-TB coinfection incidence was determined by a set of socioeconomic variables, out of which the influence of income and urbanization were the most pronounced. The influence of crime could indicate the spread of tuberculosis in socially disadvantaged regions.

Lung cancer is the most common type of cancer worldwide. This study assessed the spatio-temporal variations of the incidence rate of lung cancer between 2014 and 2020 in Chlef, a province in the North West of Algeria.

Case data recoded by municipality, sex and age were collected from the oncology department in a local hospital. A hierarchical Bayesian spatial model, adjusted by urbanization level, with zero inflated Poisson distribution was used to study the variation of lung cancer incidence.

A total of 250 lung cancer cases were registered during the study period, with a crude incidence rate of 4.12 per 100,000 inhabitants. The results of the model showed that residents in urban municipalities had a significantly higher risk of lung cancer than those in rural municipalities: incidence ratio rate (IRR) = 2.83 (95% CI: 1.91 – 4.31) and 1.80 (95% CI: 1.02 - 3.16) for men and women, respectively. In addition, the estimated incidence rate by the model for both sexes in the Chlef province indicated that only three urban municipalities had a higher incidence rate of lung cancer than the average of the province.

The results of our study suggest that the risk factors for lung cancer in the North West of Algeria were mainly related to the level of urbanization. Our findings provide important information to guide the health authorities in designing measures for the surveillance and control of lung cancer.

Monitoring cancer incidence data by geography is useful for planning public health activities. However, due to anticipated confidentiality and statistical reliability issues, data on cancer incidence and mortality are more often displayed at a national, state, or county level, rather than at more local levels. To address this gap in displaying cancer data at the local level, the CDC's National Environmental Public Health Tracking Program and 21 National Program of Cancer Registries worked together on a pilot project to examine the feasibility of displaying sub-county-level incidence of selected cancer types diagnosed during 2007–2016. The results from this project are important steps for building sub-county cancer displays into data visualizations and using the data in a way that provides meaningful insights. The availability of sub-county cancer data may allow researchers to better examine cancer data at a local level which may help guide public health decisions regarding community-based interventions and screening services.

The rapid spread of COVID-19 worldwide led to the implementation of various non-pharmaceutical interventions to limit transmission and hence reduce the number of infections. Using telecom-operator-based mobility data and a spatio-temporal dynamic model, the impact of mobility on the evolution of the pandemic at the level of the 581 Belgian municipalities is investigated. By decomposing incidence, particularly into within- and between-municipality components, we noted that the global epidemic component is relatively more important in larger municipalities (e.g., cities), while the local component is more relevant in smaller (rural) municipalities. Investigation of the effect of mobility on the pandemic spread showed that reduction of mobility has a significant impact in reducing the number of new infections.

Bogota, the capital and largest city of Colombia, constantly fights against easily transmitted and endemic–epidemic diseases that lead to enormous public health problems. Pneumonia is currently the leading cause of mortality attributable respiratory infections in the city. Its recurrence and impact have been partially explained by biological, medical, and behavioural factors. Against this background, this study investigates Pneumonia mortality rates in Bogota from 2004 and 2014. We identified a set of environmental, socioeconomic, behavioural, and medical care factors whose interaction in space could explain the occurrence and impact of the disease in the Iberoamerican city. We adopted a spatial autoregressive models framework to study the spatial dependence and heterogeneity of Pneumonia mortality rates associated with well-known risk factors. The results highlight the different types of spatial processes governing Pneumonia mortality. Furthermore, they demonstrate and quantify the driving factors that stimulate the spatial spread and clustering of mortality rates. Our study stresses the importance of spatial modelling of context-dependent diseases such as Pneumonia. Likewise, we emphasize the need to develop comprehensive public health policies that consider the space and contextual factors.

Anaemia which is a condition that describes low haemoglobin (Hb) levels has been recognized as a major public health problem amongst pregnant women in many sub-Saharan African countries including Nigeria. The causes of maternal anaemia which are interconnected and complex vary between countries and can vary within a country. This study aimed to investigate the spatial pattern and identify demographic and socio-economic determinants associated with anaemia amongst Nigerian pregnant women aged 15–49 years using data from the 2018 Nigeria Demographic and Health Survey (NDHS). This study utilized chi-square tests of independence and semiparametric structured additive models to describe the relationship between the presumed factors and anaemia status or Hb level while also taking spatial effects at state level into account. The Gaussian and Binomial distributions were used for Hb level and anaemia status respectively. The overall observed prevalence of anaemia in pregnant women and average Hb level in Nigeria were 64% and 10.4 (SD = 1.6) g/dL respectively while the prevalence of mild, moderate and severe anaemia were 27.2%, 34.6% and 2.2% respectively. Higher education, older age, and currently breastfeeding were associated with higher Hb level. Low education, being unemployed and recently having a sexually transmitted infection were identified as risk factors for maternal anaemia. Body mass index (BMI) and household size had a nonlinear effect on Hb level while BMI and age were nonlinearly related to odds of anaemia. Bivariate analysis indicated that living in rural area, low wealth class, using unsafe water and non-usage of internet were significantly associated with increased risk of anaemia. Maternal anaemia prevalence was highest in the South Eastern part of Nigeria with Imo state producing the highest prevalence of maternal anaemia while Cross River state yielded the lowest prevalence of maternal anaemia. The spatial effects associated with states were significant but unstructured indicating that states in close proximity do not necessarily share similar spatial effects. Hence, unobserved characteristics shared by states in close proximity do not influence maternal anaemia and Hb level. The findings from this study can undoubtedly help in the planning and designing of anaemia interventions that match local conditions taking into consideration the aetiology of anaemia in Nigeria.

Background

In the past, deprivation has been mostly captured through simple and univariate measures such as low income or poor educational attainment in research on health and social inequalities in Belgium. This paper presents a shift towards a more complex, multidimensional measure of deprivation at the aggregate level and describes the development of the first Belgian Indices of Multiple Deprivation (BIMDs) for the years 2001 and 2011.

Methods

The BIMDs are constructed at the level of the smallest administrative unit in Belgium, the statistical sector. They are a combination of six domains of deprivation: income, employment, education, housing, crime and health. Each domain is built on a suite of relevant indicators representing individuals that suffer from a certain deprivation in an area. The indicators are combined to create the domain deprivation scores, and these scores are then weighted to create the overall BIMDs scores. The domain and BIMDs scores can be ranked and assigned to deciles from 1 (the most deprived) to 10 (the least deprived).

Results

We show geographical variations in the distribution of the most and least deprived statistical sectors in terms of individual domains and overall BIMDs, and we identify hotspots of deprivation. The majority of the most deprived statistical sectors are located in Wallonia, whereas most of the least deprived statistical sectors are in Flanders.

Conclusion

The BIMDs offer a new tool for researches and policy makers for analyzing patterns of deprivation and identifying areas that would benefit from special initiatives and programs.

Nepal aims to eliminate malaria by 2026. This study analyzed district-level spatio-temporal patterns of malaria in Nepal from 2005 to 2018, following the introduction of Long-Lasting Insecticidal Nets (LLINs) for vector control intervention. The spatial variation in a temporal trend (SVTT) method in SaTScan was used to detect significantly high or low temporal trends of five malaria indicators: Indigenous, Imported, PV (Plasmodium vivax), PF (Plasmodium falciparum), and Total Malaria; results were mapped as clusters with associated trends. Spatial clusters of increasing malaria were found for all five indicators. Indigenous Malaria increased 113.71% in a cluster of three previously non-endemic mountainous districts. The most prominent cluster of Imported Malaria increased by 156.22%, and included the capital, Kathmandu. While some clusters had decreasing malaria, the rate of decrease in clusters was lower than outside the clusters. Overall, malaria burden is decreasing in Nepal as the country progresses closer to the elimination deadline. However, spatial clusters of increasing malaria, and clusters of lower rates of decreasing malaria, point to a need to focus vector control interventions on these clusters.