Residential greenness and risks of depression: Longitudinal associations with different greenness indicators and spatial scales in a Finnish population cohort

We carried out a longitudinal study on the associations between residential greenness and depression risk in urban areas in Finland. Residential greenness indicators were estimated within various buffer sizes around individuals' home locations (selected n = 14424) using time-series of normalized differential vegetation index (NDVI) and CORINE land cover data (CLC). We estimated individuals’ cumulative exposure to residential greenness over a 5-years and 14-years follow-up. We used doctor-diagnosed depression and Beck Depression Inventory for depression assessment. Our multi-logistic model showed an inverse association between residential greenness and depression, implying lowered depression risk for individuals with higher residential greenness. The association was particularly evident when using NDVI-based residential greenness (within a buffer of 100 m radius) and doctor-diagnosis depression data, adjusted with individual-level covariates. The odds ratio was 0.56 (95% CI 0.33 to 0.96) for the 5-years follow-up, and 0.54 (95% CI 0.30 to 0.98) for the 14-years follow-up. The associations between CLC-based total residential green space and depression varied across the different buffer sizes. In general, all the associations depended on the type of depression assessment, quality of greenness indicators, and the spatial scale of analysis. The associations also varied across the socio-demographic groups and neighborhood socioeconomic disadvantage level.     

Bayesian consensus clustering for multivariate longitudinal data

In clinical and epidemiological studies, there is a growing interest in studying the heterogeneity among patients based on longitudinal characteristics to identify subtypes of the study population. Compared to clustering a single longitudinal marker, simultaneously clustering multiple longitudinal markers allow additional information to be incorporated into the clustering process, which reveals co-existing longitudinal patterns and generates deeper biological insight. In the current study, we propose a Bayesian consensus clustering (BCC) model for multivariate longitudinal data. Instead of arriving at a single overall clustering, the proposed model allows each marker to follow marker-specific local clustering and these local clusterings are aggregated to find a global (consensus) clustering. To estimate the posterior distribution of model parameters, a Gibbs sampling algorithm is proposed. We apply our proposed model to the primary biliary cirrhosis study to identify patient subtypes that may be associated with their prognosis. We also perform simulation studies to compare the clustering performance between the proposed model and existing models under several scenarios. The results demonstrate that the proposed BCC model serves as a useful tool for clustering multivariate longitudinal data.     

Geographic Characteristics of Various Cancers in Yogyakarta Province,Indonesia: A Spatial Analysis at the Community Level

Background: Cancer remains a significant public health problem in Indonesia and worldwide. Yogyakarta Province has the largest number of cancer cases in Indonesia. Maps of the distribution of cancer cases are useful tools for stratification of cancer risk and for selective prevention strategies. The aim of this study was to determine the spatial distribution of cancer cases in Yogyakarta Province. Methods: Cancer patient data registered by the Yogyakarta Provincial Health Office during 2019-2020 were analysed in this study (n=9,933). To evaluate cancer pattern distributions, ArcGIS 10.2 and Excel 2016 software were used. Results: The mean participant age (± standard deviation) was 55.08 ± 15.46 years, and 79.40% were female. Breast and cervical cancer were the most frequently diagnosed, and the majority of patients were located in Sleman district. The incidence of all cancer types varied by county-level. The majority of cancer patients lived below the poverty line. Cancer screening rates were low, and screening was limited to breast and cervical cancer. Conclusion: Various types of cancers were identified in Yogyakarta, Indonesia; of them, breast and cervical cancer predominated. Most of the cancer patients were from Sleman district and economically poor areas. Geospatial techniques are useful for identifying environmental factors related to cancer and improving cancer control strategies and resource allocation.     

Applications of geospatial analyses in health research among homeless people: A systematic scoping review of available evidence

BackgroundUnequal housing access resulted in more than 150 million homeless people worldwide, with millions more expected to be added every year due to the ongoing climate-related crises. Homeless population has a counterproductive effect on the social, psychological integration efforts by the community and exposure to other severe health-related issues. Geographic Information Systems (GIS) have long been applied in urban planning and policy, housing and homelessness, and health-related research.MethodsWe used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method to systematically review 24 articles collected from multiple databases (n = 10) that focused on health-related issues among homeless people and used geospatial analysis techniques in their research.ResultsOur findings indicated a geographic clustering of case study locations– 26 out of the 31 case study sites are from the USA and Canada. Studies used spatial analysis techniques to identify hotspots, clusters and patterns of patient location and population distribution. Studies also reported relationships among the location of homeless shelters and substance use, discarded needles, different infectious and non-infectious disease clusters.ConclusionMost studies were restricted in analyzing and visualizing the patterns and disease clusters; however, geospatial analyses techniques are useful and offer diverse techniques for a more sophisticated understanding of the spatial characteristics of the health issues among homeless people. Better integration of GIS in health research among the homeless would help formulate sensible policies to counter health inequities among this vulnerable population group.     

Association of regional socioeconomic deprivation and rurality with global developmental delay in early childhood: Data from mandatory school entry examinations in Germany

BackgroundFrom birth to young adulthood, health and development of young people are strongly linked to their living situation, including their family’s socioeconomic position (SEP) and living environment. The impact of regional characteristics on development in early childhood beyond family SEP has been rarely investigated. This study aimed to identify regional predictors of global developmental delay at school entry taking family SEP into consideration.MethodWe used representative, population-based data from mandatory school entry examinations of the German federal state of Brandenburg in 2018/2019 with n=22,801 preschool children. By applying binary multilevel models, we hierarchically analyzed the effect of regional deprivation defined by the German Index of Socioeconomic Deprivation (GISD) and rurality operationalized as inverted population density of the children’s school district on global developmental delay (GDD) while adjusting for family SEP (low, medium and high).ResultsFamily SEP was significantly and strongly linked to GDD. Children with the highest family SEP showed a lower odds for GDD compared to a medium SEP (female: OR=4.26, male: OR=3.46) and low SEP (female: OR=16.58, male: OR=12.79). Furthermore, we discovered a smaller, but additional and independent effect of regional socioeconomic deprivation on GDD, with a higher odds for children from a more deprived school district (female: OR=1.35, male: OR=1.20). However, rurality did not show a significant link to GDD in preschool children beyond family SEP and regional deprivation.ConclusionFamily SEP and regional deprivation are risk factors for child development and of particular interest to promote health of children in early childhood and over the life course.     

学校结核病影响因素及防控策略概述

结核病是一种严重的呼吸系统传染性疾病,在世界范围内广泛传播。某些特殊群体结核病发病风险较高,如学校学生及相关工作人员。学校学生聚集,人口密度大,有利于疾病的传播与扩散,导致聚集性疫情的发生。学校结核病疫情的出现主要与学生免疫力低、就诊延误现象、人口密度大、性别、年龄、知晓率等因素有关。为了尽早发现传染源,减少结核病在校园内的传播,使用合理的方法对密切接触者进行筛查极为重要。应明确各机构在学校结核病防控工作中的职责与重点,共同做好防控工作,减少学校结核病疫情的发生。     

National and subnational estimates of coverage and travel time to emergency obstetric care in Afghanistan: Modeling of spatial accessibility

In Afghanistan, the risk of maternal death is among the highest in the world, with wide variation across the country. One explanation may be wide geographic disparities in access and use of maternal health care services. This study describes the spatial distribution of public facilities providing maternal health care in Afghanistan, specifically emergency obstetric care (EmOC), and the differences in travel time estimates using different transportation modes from 2010 to 2015 at the national and subnational levels. We conducted mapping and spatial analyses to measure the proportion of pregnant women able to access any EmOC health facility within 2 h by foot, animal, motor vehicle and a combination of transport modes. In 2015, adequate coverage of active public health facilities within 2 h of travel time was 36.6% by foot and 71.2% by a combination of transport modes. We found an 8.3% and 63.2% increase in access to EmOC facilities within 2 h of travel time by a combination of transport modes and by foot only, respectively, by 2015. Access to a combination of transportation options such as motor vehicles and animals may benefit pregnant women in reaching health facilities efficiently. Afghanistan made impressive gains in maternal healthcare access; despite these improvements, large disparities remain in geographic access by province and overall access to facilities is still poor.     

K-Means Clustering to Elucidate Vulnerable Subpopulations Among Medicare Patients Undergoing Total Joint Arthroplasty

BackgroundThe role of preoperative laboratory values for risk stratification following joint arthroplasty is currently ambiguous. In order to improve upon existing risk stratification within joint arthroplasty, this study sought to define novel phenotypes of total hip or total knee arthroplasty patients based entirely on preoperative laboratory measures. These phenotypes (“clusters”) were compared to elucidate statistically and clinically significant differences in outcomes.MethodsA total of 134,252 patients were gathered from the National Surgical Quality Improvement Program database between 2005 and 2015. “K-means” with 3 clusters was applied using 9 preoperative laboratory values: sodium, blood urea nitrogen (BUN), creatinine, albumin, bilirubin, white blood cell count, hematocrit, platelet count, and international normalized ratio of prothrombin values (INR). Outcome measures included 30-day readmissions, severe adverse events, and discharge to nonhome.ResultsCluster 2 was characterized by elevated preoperative BUN, creatinine, and INR and demonstrated almost twice the rate of adverse events (3.52% vs 2.20% and 2.22%), 30-day readmissions (6.39% vs 3.31% and 3.71%), and discharge to nonhome (47.97% vs 30.50% and 35.85%). Cluster 3 was characterized by a slightly higher risk of discharge to nonhome than cluster 1 and was overwhelmingly female (79.5% female, 35.8% discharge to nonhome). Cluster 1 represents the lowest-risk subgroup, experiencing the lowest rates of readmissions, adverse events, and discharge to nonhome.ConclusionPreoperative laboratory values, namely BUN, creatinine, and INR, are useful in identifying patients at risk of adverse outcomes. This analysis supports the existing surgical literature pushing for preoperative hydration as a targeted intervention to expedite recovery.