Identifying Infectious Diarrhea Hot spots and Associated Socioeconomic Factors in Anhui Province, China.
ABSTRACT: Infectious diarrhea cases have increased during the past years in the Anhui Province of China, but little is known about its spatial cluster pattern and associated socioeconomic factors. We obtained county-level total cases of infectious diarrhea in 105 counties of Anhui in 2016 and computed age-adjusted rates. Socioeconomic factors were collected from the Statistical Yearbook. Hot spot analysis was used to identify hot and cold spot counties for infectious diarrhea incidence. We then applied binary logistic regression models to determine the association between socioeconomic factors and hot spot or cold spot clustering risk. Hot spot analysis indicated there were both significant hot spot (29 counties) and cold spot (18 counties) clustering areas for infectious diarrhea in Anhui (P < 0.10). Multivariate binary logistic regression results showed that infectious diarrhea hot spots were positively associated with per capita gross domestic product (GDP), with an adjusted odds ratio (AOR): 3.51, 95% CI: 2.09-5.91, whereas cold spots clustering were positively associated with the number of medical staffs (AOR: 1.18, 95% CI: 1.08-1.29) and negatively associated with the number of public health physicians (AOR: 0.27, 95% CI: 0.09-0.86). We identified locations for hot and cold spot clusters of infectious diarrhea incidence in Anhui, and the clustering risks were significantly associated with health workforce resources and the regional economic development. Targeted interventions should be carried out with considerations of regional socioeconomic conditions.
Project description:The aims of the study were: (1) compare sociodemographic characteristics among active tuberculosis (TB) cases and their household contacts in cold and hot spot transmission areas, and (2) quantify the influence of locality, genotype and potential determinants on the rates of latent tuberculosis infection (LTBI) among household contacts of index TB cases. Parallel case-contact studies were conducted in two geographic areas classified as "cold" and "hot" spots based on TB notification and spatial clustering between January and June 2018 in Guangxi, China, using data from field contact investigations, whole genome sequencing, tuberculin skin tests (TSTs), and chest radiographs. Beijing family strains accounted for 64.6% of Mycobacterium tuberculosis (Mtb) strains transmitted in hot spots, and 50.7% in cold spots (p-value = 0.02). The positive TST rate in hot spot areas was significantly higher than that observed in cold spot areas (p-value < 0.01). Living in hot spots (adjusted odds ratio (aOR) = 1.75, 95%, confidence interval (CI): 1.22, 2.50), Beijing family genotype (aOR = 1.83, 95% CI: 1.19, 2.81), living in the same room with an index case (aOR = 2.29, 95% CI: 1.5, 3.49), travelling time from home to a medical facility (aOR = 4.78, 95% CI: 2.96, 7.72), history of Bacillus Calmette-Guérin vaccination (aOR = 2.02, 95% CI: 1.13 3.62), and delay in diagnosis (aOR = 2.56, 95% CI: 1.13, 5.80) were significantly associated with positive TST results among household contacts of TB cases. The findings of this study confirmed the strong transmissibility of the Beijing genotype family strains and this genotype's important role in household transmission. We found that an extended traveling time from home to the medical facility was an important socioeconomic factor for Mtb transmission in the family. It is still necessary to improve the medical facility infrastructure and management, especially in areas with a high TB prevalence.
Project description:BACKGROUND:At present, there are few studies on polymorphism of Mycobacterium tuberculosis (Mtb) gene and how it affects the TB epidemic. This study aimed to document the differences of polymorphisms between tuberculosis hot and cold spot areas of Guangxi Zhuang Autonomous Region, China. METHODS:The cold and hot spot areas, each with 3 counties, had been pre-identified by TB incidence for 5?years from the surveillance database. Whole genome sequencing analysis was performed on all sputum Mtb isolates from the detected cases during January and June 2018. Single nucleotide polymorphism (SNP) of each isolate compared to the H37Rv strain were called and used for lineage and sub-lineage identification. Pairwise SNP differences between every pair of isolates were computed. Analyses of Molecular Variance (AMOVA) across counties of the same hot or cold spot area and between the two areas were performed. RESULTS:As a whole, 59.8% (57.7% sub-lineage 2.2 and 2.1% sub-lineage 2.1) and 39.8% (17.8% sub-lineage 4.4, 6.5% sub-lineage 4.2 and 15.5% sub-lineage 4.5) of the Mtb strains were Lineage 2 and Lineage 4 respectively. The percentages of sub-lineage 2.2 (Beijing family strains) are significantly higher in hot spots. Through the MDS dimension reduction, the genomic population structure in the three hot spot counties is significantly different from those three cold spot counties (T-test p?=?0.05). The median of SNPs distances among Mtb isolates in cold spots was greater than that in hot spots (897 vs 746, Rank-sum test p?<?0.001). Three genomic clusters, each with genomic distance ?12 SNPs, were identified with 2, 3 and 4 consanguineous strains. Two clusters were from hot spots and one was from cold spots. CONCLUSION:Narrower genotype diversity in the hot area may indicate higher transmissibility of the Mtb strains in the area compared to those in the cold spot area.
Project description:Diet is an important modifiable risk factor for chronic diseases. In the search for effective strategies to improve dietary patterns in order to promote healthy ageing, new approaches considering contextual factors in public health medicine are warranted. The aim of this study is to examine the spatial clustering of dietary patterns in a large representative sample of adults.Dietary patterns were defined on the basis of a 111 item Food Frequency Questionnaire among n?=?117,570 adults using principal components analysis. We quantified the spatial clustering of dietary pattern scores at the neighborhood level using the Global Moran's I spatial statistic, taking into consideration individual demographic and (neighborhood) socioeconomic indicators.Four dietary patterns explaining 27% of the variance in dietary data were extracted in this population and named the "bread and cookies" pattern, the "snack" pattern, the "meat and alcohol" pattern and the "vegetable, fruit and fish" pattern. Significant spatial clustering of high (hot spot) and low (cold spot) dietary pattern scores was found for all four dietary patterns irrespective of age and gender differences. Educational attainment and neighborhood income explained the global clustering to some extent, although clustering at smaller regional scales persisted.The significant region-specific hot and cold spots of the four dietary patterns illustrate the existence of regional "food cultures" and underscore the need for interventions targeted at the sub-national level in order to tackle unhealthy dietary behavior and to stimulate people to make healthy dietary choices.
Project description:PURPOSE:The goals of this study were to identify geographic and racial/ethnic variation in breast cancer mortality, and evaluate whether observed geographic differences are explained by county-level characteristics. METHODS:We analyzed data on breast cancer deaths among women in 3,108 contiguous United States (US) counties from years 2000 through 2015. We applied novel geospatial methods and identified hot spot counties based on breast cancer mortality rates. We assessed differences in county-level characteristics between hot spot and other counties using Wilcoxon rank-sum test and Spearman correlation, and stratified all analysis by race/ethnicity. RESULTS:Among all women, 80 of 3,108 (2.57%) contiguous US counties were deemed hot spots for breast cancer mortality with the majority located in the southern region of the US (72.50%, p value?<?0.001). In race/ethnicity-specific analyses, 119 (3.83%) hot spot counties were identified for NH-Black women, with the majority being located in southern states (98.32%, p value?<?0.001). Among Hispanic women, there were 83 (2.67%) hot spot counties and the majority was located in the southwest region of the US (southern?=?61.45%, western?=?33.73%, p value?<?0.001). We did not observe definitive geographic patterns in breast cancer mortality for NH-White women. Hot spot counties were more likely to have residents with lower education, lower household income, higher unemployment rates, higher uninsured population, and higher proportion indicating cost as a barrier to medical care. CONCLUSIONS:We observed geographic and racial/ethnic disparities in breast cancer mortality: NH-Black and Hispanic breast cancer deaths were more concentrated in southern, lower SES counties.
Project description:The stroke belt is described as an 8-state region with high stroke mortality across the southeastern United States. Using spatial statistics, we identified clusters of high stroke mortality (hot spots) and adjacent areas of low stroke mortality (cool spots) for US counties and evaluated for regional differences in county-level risk factors.A cross-sectional study of stroke mortality was conducted using Multiple Cause of Death data (Centers for Disease Control and Prevention) to compute age-adjusted adult stroke mortality rates for US counties. Local indicators of spatial association statistics were used for hot-spot mapping. County-level variables were compared between hot and cool spots.Between 2008 and 2010, there were 393?121 stroke-related deaths. Median age-adjusted adult stroke mortality was 61.7 per 100?000 persons (interquartile range=51.4-74.7). We identified 705 hot-spot counties (22.4%) and 234 cool-spot counties (7.5%); 44.5% of hot-spot counties were located outside of the stroke belt. Hot spots had greater proportions of black residents, higher rates of unemployment, chronic disease, and healthcare utilization, and lower median income and educational attainment.Clusters of high stroke mortality exist beyond the 8-state stroke belt, and variation exists within the stroke belt. Reconsideration of the stroke belt definition and increased attention to local determinants of health underlying small area regional variability could inform targeted healthcare interventions.
Project description:BACKGROUND:Tuberculosis (TB) is the leading cause of death from an infectious disease in Ethiopia, killing more than 30 thousand people every year. This study aimed to determine whether the rates of poor TB treatment outcome varied geographically across Ethiopia at district and zone levels and whether such variability was associated with socioeconomic, behavioural, health care access, or climatic conditions. METHODS:A geospatial analysis was conducted using national TB data reported to the health management information system (HMIS), for the period 2015-2017. The prevalence of poor TB treatment outcomes was calculated by dividing the sum of treatment failure, death and loss to follow-up by the total number of TB patients. Binomial logistic regression models were computed and a spatial analysis was performed using a Bayesian framework. Estimates of parameters were generated using Markov chain Monte Carlo (MCMC) simulation. Geographic clustering was assessed using the Getis-Ord Gi* statistic, and global and local Moran's I statistics. RESULTS:A total of 223,244 TB patients were reported from 722 districts in Ethiopia during the study period. Of these, 63,556 (28.5%) were cured, 139,633 (62.4%) completed treatment, 6716 (3.0%) died, 1459 (0.7%) had treatment failure, and 12,200 (5.5%) were lost to follow-up. The overall prevalence of a poor TB treatment outcome was 9.0% (range, 1-58%). Hot-spots and clustering of poor TB treatment outcomes were detected in districts near the international borders in Afar, Gambelia, and Somali regions and cold spots were detected in Oromia and Amhara regions. Spatial clustering of poor TB treatment outcomes was positively associated with the proportion of the population with low wealth index (OR: 1.01; 95%CI: 1.0, 1.01), the proportion of the population with poor knowledge about TB (OR: 1.02; 95%CI: 1.01, 1.03), and higher annual mean temperature per degree Celsius (OR: 1.15; 95% CI: 1.08, 1.21). CONCLUSIONS:This study showed significant spatial variation in poor TB treatment outcomes in Ethiopia that was related to underlying socioeconomic status, knowledge about TB, and climatic conditions. Clinical and public health interventions should be targeted in hot spot areas to reduce poor TB treatment outcomes and to achieve the national End-TB Strategy targets.
Project description:The United Nations' Sustainable Development Goal 3 is to ensure health and well-being for all at all ages with a specific target to end malaria by 2030. Aligned with this goal, the primary objective of this study is to determine the effectiveness of utilizing local spatial variations to uncover the statistical relationships between malaria incidence rate and environmental and behavioral factors across the counties of Kenya. Two data sources are used-Kenya Demographic and Health Surveys of 2000, 2005, 2010, and 2015, and the national Malaria Indicator Survey of 2015. The spatial analysis shows clustering of counties with high malaria incidence rate, or hot spots, in the Lake Victoria region and the east coastal area around Mombasa; there are significant clusters of counties with low incidence rate, or cold spot areas in Nairobi. We apply an analysis technique, geographically weighted regression, that helps to better model how environmental and social determinants are related to malaria incidence rate while accounting for the confounding effects of spatial non-stationarity. Some general patterns persist over the four years of observation. We establish that variables including rainfall, proximity to water, vegetation, and population density, show differential impacts on the incidence of malaria in Kenya. The El-Nino-southern oscillation (ENSO) event in 2015 was significant in driving up malaria in the southern region of Lake Victoria compared with prior time-periods. The applied spatial multivariate clustering analysis indicates the significance of social and behavioral survey responses. This study can help build a better spatially explicit predictive model for malaria in Kenya capturing the role and spatial distribution of environmental, social, behavioral, and other characteristics of the households.
Project description:Pneumonia is a leading cause of death in New York City (NYC). We identified spatial clusters of pneumonia-associated hospitalisation for persons residing in NYC, aged ⩾18 years during 2010-2014. We detected pneumonia-associated hospitalisations using an all-payer inpatient dataset. Using geostatistical semivariogram modelling, local Moran's I cluster analyses and χ2 tests, we characterised differences between 'hot spots' and 'cold spots' for pneumonia-associated hospitalisations. During 2010-2014, there were 141 730 pneumonia-associated hospitalisations across 188 NYC neighbourhoods, of which 43.5% (N = 61 712) were sub-classified as severe. Hot spots of pneumonia-associated hospitalisation spanned 26 neighbourhoods in the Bronx, Manhattan and Staten Island, whereas cold spots were found in lower Manhattan and northeastern Queens. We identified hot spots of severe pneumonia-associated hospitalisation in the northern Bronx and the northern tip of Staten Island. For severe pneumonia-associated hospitalisations, hot-spot patients were of lower mean age and a greater proportion identified as non-Hispanic Black compared with cold spot patients; additionally, hot-spot patients had a longer hospital stay and a greater proportion experienced in-hospital death compared with cold-spot patients. Pneumonia prevention efforts within NYC should consider examining the reasons for higher rates in hot-spot neighbourhoods, and focus interventions towards the Bronx, northern Manhattan and Staten Island.
Project description:Visceral leishmaniasis (VL) remains a serious public health problem in China. To explore the temporal, spatial, and spatiotemporal characteristics of visceral leishmaniasis (VL), the spatial and spatiotemporal clustering distribution and their relationships with the surrounding geographic environmental factors were analyzed. In this study, the average nearest-neighbor distance (ANN), Ripley's K-function and Moran's I statistics were used to evaluate spatial autocorrelation in the VL distribution of the existing case patterns. Getis?Ord Gi* was used to identify the hot-spot and cold-spot areas based on Geographic Information System (GIS), and spatiotemporal retrospective permutation scan statistics was used to detect the spatiotemporal clusters. The results indicated that VL continues to be a serious public health problem in Kashi Prefecture, China, particularly in the north-central region of Jiashi County, which is a relatively high-risk area in which hot spots are distributed. Autumn and winter months were the outbreak season for VL cases. The detection of spatial and spatiotemporal patterns can provide epidemiologists and local governments with significant information for prevention measures and control strategies.
Project description:This study evaluated clustering of stroke hospitalization rates, patterns of the clustering over time, and associations with community-level characteristics.We used Medicare hospital claims data from 1995-1996 to 2005-2006 with a principal discharge diagnosis of stroke to calculate county-level stroke hospitalization rates. We identified statistically significant clusters of high- and low-rate counties by using local indicators of spatial association, tracked cluster status over time, and assessed associations between cluster status and county-level socioeconomic and healthcare profiles.Clearly defined clusters of counties with high- and low-stroke hospitalization rates were identified in each time. Approximately 75% of counties maintained their cluster status from 1995-1996 to 2005-2006. In addition, 243 counties transitioned into high-rate clusters, and 148 transitioned out of high-rate clusters. Persistently high-rate clusters were located primarily in the Southeast, whereas persistently low-rate clusters occurred mostly in New England and in the West. In general, persistently low-rate counties had the most favorable socioeconomic and healthcare profiles, followed by counties that transitioned out of or into high-rate clusters. Persistently high-rate counties experienced the least favorable socioeconomic and healthcare profiles.The persistence of clusters of high- and low-stroke hospitalization rates during a 10-year period suggests that the underlying causes of stroke in these areas have also persisted. The associations found between cluster status (persistently high, transitional, persistently low) and socioeconomic and healthcare profiles shed new light on the contributions of community-level characteristics to geographic disparities in stroke hospitalizations.