Spatial and Spatio-Temporal Epidemiology最新文献

While the extant literature has established that transgender people face significant barriers to accessing healthcare, no studies to date have offered an explicitly spatial analysis of their access to trans-specific care. This study aims to fill that gap by providing a spatial analysis of access to gender-affirming hormone therapy (GAHT) using Texas as a case study. We used the three-step floating catchment area method, which relies on census tract-level population data and location data for healthcare facilities to quantify spatial access to healthcare within a specific drive-time window, in our case 120 min. For our tract-level population estimates we adapt estimates of the rates of transgender identification from a recent data source, the Household Pulse Survey, and use these in tandem with a spatial database of GAHT providers of the lead author's creation. We then compare results of the 3SFCA with data on urbanicity and rurality, as well as which areas are deemed medically underserved. Finally, we conduct a hot-spot analysis that identifies specific areas where health services could be planned in ways that could improve both access to GAHT for trans people and access to primary care for the general population. Ultimately, we conclude that our results illustrate that patterns of access to trans-specific medical care, like GAHT, do not neatly follow patterns of access to primary care for the general population and that therefore trans communities’ access to healthcare warrants specific, further investigation.

We constructed county-level models to examine properties of the SARS-CoV-2 B.1.617.2 (Delta) variant wave of infections in North Carolina and assessed immunity levels (via prior infection, via vaccination, and overall) prior to the Delta wave. To understand how prior immunity shaped Delta wave outcomes, we assessed relationships among these characteristics. Peak weekly infection rate and total percent of the population infected during the Delta wave were negatively correlated with the proportion of people with vaccine-derived immunity prior to the Delta Wave, signaling that places with higher vaccine uptake had better outcomes. We observed a positive correlation between immunity via infection prior to Delta and percent of the population infected during the Delta wave, meaning that counties with poor pre-Delta outcomes also had poor Delta wave outcomes. Our findings illustrate geographic variation in outcomes during the Delta wave in North Carolina, highlighting regional differences in population characteristics and infection dynamics.

Unmatched spatially stratified random sampling (SSRS) of non-cases selects geographically balanced controls by dividing the study area into spatial strata and randomly selecting controls from all non-cases within each stratum. The performance of SSRS control selection was evaluated in a case study spatial analysis of preterm birth in Massachusetts. In a simulation study, we fit generalized additive models using controls selected by SSRS or simple random sample (SRS) designs. We compared mean squared error (MSE), bias, relative efficiency (RE), and statistically significant map results to the model results with all non-cases. SSRS designs had lower average MSE (0.0042–0.0044) and higher RE (77–80%) compared to SRS designs (MSE: 0.0072–0.0073; RE across designs: 71%). SSRS map results were more consistent across simulations, reliably identifying statistically significant areas. SSRS designs improved efficiency by selecting controls that are geographically distributed, particularly from low population density areas, and may be more appropriate for spatial analyses.

To monitor the COVID-19 epidemic in Cuba, data on several epidemiological indicators have been collected on a daily basis for each municipality. Studying the spatio-temporal dynamics in these indicators, and how they behave similarly, can help us better understand how COVID-19 spread across Cuba. Therefore, spatio-temporal models can be used to analyze these indicators. Univariate spatio-temporal models have been thoroughly studied, but when interest lies in studying the association between multiple outcomes, a joint model that allows for association between the spatial and temporal patterns is necessary. The purpose of our study was to develop a multivariate spatio-temporal model to study the association between the weekly number of COVID-19 deaths and the weekly number of imported COVID-19 cases in Cuba during 2021. To allow for correlation between the spatial patterns, a multivariate conditional autoregressive prior (MCAR) was used. Correlation between the temporal patterns was taken into account by using two approaches; either a multivariate random walk prior was used or a multivariate conditional autoregressive prior (MCAR) was used. All models were fitted within a Bayesian framework.

Cardiovascular disease (CVD) is the leading cause of death globally, coronary heart disease (CHD) is the main category of it. It has been shown that the urban built environment affects the occurrence of CHD, but most focus on single environmental factors. This study developed two multicomponent Urban Heart Health Environment (UHHE) Indexes (unweighted index and weighted index), which were based on the four main behavioral risk factors for CHD (unhealthy diet, lack of physical activity, smoking, and drinking). And we examined the relationship between the indexes and the prevalence of CHD. The prevalence calculation is based on the database of F Hospital patients, who have had coronary stent implantation (CSI). Furthermore, these single-center data were corrected to reduce underestimation of prevalence. We performed global (Ordinal Least Square) and local (Geographically Weighed Regression) regression analyses to assess the relationship between the two UHHE indexes and CHD prevalence. Both indexes showed a significant negative relationship with CHD prevalence. In its spatial relationship, a non-stationary was discovered. The UHHE indexes may help identify and prioritize geographical areas for CHD prevention and may be beneficial to urban design in China.

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.