Spatial patterns and sociodemographic predictors of chronic obstructive pulmonary disease in Florida.

Background: Chronic obstructive pulmonary disease (COPD) is a chronic, inflammatory respiratory disease that obstructs airflow and decreases lung function and is a leading cause death globally. In the United States (US), the prevalence among adults is 6.2%, but increases with age to 12.8% among those 65 years or older. Florida has one of the largest populations of older adults in the US, accounting for 4.5 million adults 65 years or older. This makes Florida an ideal geographic location for investigating COPD as disease prevalence increases with age. Understanding the geographic disparities in COPD and potential associations between its disparities and environmental factors as well as population characteristics is useful in guiding intervention strategies. Thus, the objectives of this study are to investigate county-level geographic disparities of COPD prevalence in Florida and identify county-level socio-demographic predictors of COPD prevalence. Methods: This ecological study was performed in Florida using data obtained from the US Census Bureau, Florida Health CHARTS, and County Health Rankings and Roadmaps. County-level COPD prevalence for 2019 was age-standardized using the direct method and 2020 US population as the standard population. High-prevalence spatial clusters of COPD were identified using Tango's flexible spatial scan statistics. Predictors of county-level COPD prevalence were investigated using multivariable ordinary least squares model built using backwards elimination approach. Multicollinearity of regression coefficients was assessed using variance inflation factor. Shapiro-Wilks, Breusch Pagan, and robust Lagrange Multiplier tests were used to assess for normality, homoskedasticity, and spatial autocorrelation of model residuals, respectively. Results: County-level age-adjusted COPD prevalence ranged from 4.7% (Miami-Dade) to 16.9% (Baker and Bradford) with a median prevalence of 9.6%. A total of 6 high-prevalence clusters with prevalence ratios >1.2 were identified. The primary cluster, which was also the largest geographic cluster that included 13 counties, stretched from Nassau County in north-central Florida to Charlotte County in south-central Florida. However, cluster 2 had the highest prevalence ratio (1.68) and included 10 counties in north-central Florida. Together, the primary cluster and cluster 2 covered most of the counties in north-central Florida. Significant predictors of county-level COPD prevalence were county-level percentage of residents with asthma and the percentage of current smokers. Conclusions: There is evidence of spatial clusters of COPD prevalence in Florida. These patterns are explained, in part, by differences in distribution of some health behaviors (smoking) and co-morbidities (asthma). This information is important for guiding intervention efforts to address the condition, reduce health disparities, and improve population health.

Using Spatial Scan Statistics and Geographic Information Systems to Detect Monthly Human Mobility Clusters and Analyze Cluster Area Characteristics.

Introduction: This study evaluated the detection of monthly human mobility clusters and characteristics of cluster areas before the coronavirus disease 2019 (COVID-19) outbreak using spatial epidemiological methods, namely, spatial scan statistics and geographic information systems (GIS). Methods: The research area covers approximately 10.3 km2, with a population of about 350,000 people. Analysis was conducted using open data, with the exception of one dataset. Human mobility and population data were used on a 1-km mesh scale, and business location data were used to examine the area characteristics. Data from January to December 2019 were utilized to detect human mobility clusters before the COVID-19 pandemic. Spatial scan statistics were performed using SaTScan to calculate relative risk (RR). The detected clusters and other data were visualized in QGIS to explore the features of the cluster areas. Results: Spatial scan statistics identified 33 clusters. The detailed analysis focused on clusters with an RR exceeding 1.5. Meshes with an RR over 1.5 included one with clusters for 1 year which is identified in all months of the year, one with clusters for 9 months, three with clusters for 6 months, three with clusters for 3 months, and four with clusters for 1 month. September had the highest number of clusters (eight), followed by April and November (seven each). The remaining months had five or six clusters. Characteristically, the cluster areas included the vicinity of railway stations, densely populated business areas, ball game fields, and large-scale construction sites. Conclusions: Statistical analysis of human mobility clusters using open data and open-source tools is crucial for the advancement of evidence-based policymaking based on scientific facts, not only for novel infectious diseases but also for existing ones, such as influenza.

A Shared-Frailty Spatial Scan Statistic Model for Time-to-Event Data.

Spatial scan statistics are well-known methods widely used to detect spatial clusters of events. Furthermore, several spatial scan statistics models have been applied to the spatial analysis of time-to-event data. However, these models do not take account of potential correlations between the observations of individuals within the same spatial unit or potential spatial dependence between spatial units. To overcome this problem, we have developed a scan statistic based on a Cox model with shared frailty and that takes account of the spatial dependence between spatial units. In simulation studies, we found that (i) conventional models of spatial scan statistics for time-to-event data fail to maintain the type I error in the presence of a correlation between the observations of individuals within the same spatial unit and (ii) our model performed well in the presence of such correlation and spatial dependence. We have applied our method to epidemiological data and the detection of spatial clusters of mortality in patients with end-stage renal disease in northern France.

Geographic disparities and temporal changes of diabetes-related mortality risks in Florida: a retrospective study.

Background: Over the last few decades, diabetes-related mortality risks (DRMR) have increased in Florida. Although there is evidence of geographic disparities in pre-diabetes and diabetes prevalence, little is known about disparities of DRMR in Florida. Understanding these disparities is important for guiding control programs and allocating health resources to communities most at need. Therefore, the objective of this study was to investigate geographic disparities and temporal changes of DRMR in Florida. Methods: Retrospective mortality data for deaths that occurred from 2010 to 2019 were obtained from the Florida Department of Health. Tenth International Classification of Disease codes E10-E14 were used to identify diabetes-related deaths. County-level mortality risks were computed and presented as number of deaths per 100,000 persons. Spatial Empirical Bayesian (SEB) smoothing was performed to adjust for spatial autocorrelation and the small number problem. High-risk spatial clusters of DRMR were identified using Tango's flexible spatial scan statistics. Geographic distribution and high-risk mortality clusters were displayed using ArcGIS, whereas seasonal patterns were visually represented in Excel. Results: A total of 54,684 deaths were reported during the study period. There was an increasing temporal trend as well as seasonal patterns in diabetes mortality risks with high risks occurring during the winter. The highest mortality risk (8.1 per 100,000 persons) was recorded during the winter of 2018, while the lowest (6.1 per 100,000 persons) was in the fall of 2010. County-level SEB smoothed mortality risks varied by geographic location, ranging from 12.6 to 81.1 deaths per 100,000 persons. Counties in the northern and central parts of the state tended to have high mortality risks, whereas southern counties consistently showed low mortality risks. Similar to the geographic distribution of DRMR, significant high-risk spatial clusters were also identified in the central and northern parts of Florida. Conclusion: Geographic disparities of DRMR exist in Florida, with high-risk spatial clusters being observed in rural central and northern areas of the state. There is also evidence of both increasing temporal trends and Winter peaks of DRMR. These findings are helpful for guiding allocation of resources to control the disease, reduce disparities, and improve population health.

Cancer patterns in Iran: a gender-specific spatial modelling of cancer incidence during 2014-2017.

BACKGROUND: Cancer is a significant public health concern and the second leading cause of death. This study aims to visualize spatial patterns of top common cancer types and identify high-risk and low-risk counties for these cancers in Iran from 2014 to 2017. METHODS: In this study, we analyzed 482,229 newly diagnosed cancer cases recorded by the Iranian National Population-Based Cancer Registry from 2014 to 2017. We employed a purely spatial scanning model and local Moran I analysis to explore spatial patterns across Iran. RESULTS: Approximately 53% of all cases were male. The average age of cancer diagnosis was 62.58 ± 17.42 years for males and 56.11 ± 17.33years for females. Stomach cancer was the most common cancer in men. The northern and northwestern regions of Iran were identified as high-risk areas for stomach cancer in both genders, with a relative risk (RR) ranging from 1.26 to 2.64 in males and 1.19 to 3.32 in females. These areas recognized as high-risk areas for trachea, bronchus, and lung (TBL) cancer specifically in males (RR:1.15-2.02). Central regions of Iran were identified as high-risk areas for non-melanoma skin cancers in both genders, ranking as the second most common cancer (RR:1.18-5.93 in males and 1.24-5.38 in females). Furthermore, bladder cancer in males (RR:1.32-2.77) and thyroid cancer in females (RR:1.88-3.10) showed concentration in the central part of Iran. Breast cancer, being the most common cancer among women (RR:1.23-5.54), exhibited concentration in the northern regions of the country. Also, northern regions of Iran were identified as high-risk clusters for colon cancer (RR:1.31-3.31 in males and 1.33-4.13 in females), and prostate cancer in males (RR:1.22-2.31). Brain, nervous system cancer, ranked sixth among women (RR:1.26-5.25) in central areas. CONCLUSIONS: The study's revelations on the spatial patterns of common cancer incidence in Iran provide crucial insights into the distribution and trends of these diseases. The identification of high-risk areas equips policymakers with valuable information to tailor targeted screening programs, facilitating early diagnosis and effective disease control strategies.

High-risk spatiotemporal patterns of cutaneous leishmaniasis: a nationwide study in Iran from 2011 to 2020.

BACKGROUND: Cutaneous leishmaniasis (CL) is a wide-reaching infection of major public health concern. Iran is one of the six most endemic countries in the world. This study aims to provide a spatiotemporal visualization of CL cases in Iran at the county level from 2011 to 2020, detecting high-risk zones, while also noting the movement of high-risk clusters. METHODS: On the basis of clinical observations and parasitological tests, data of 154,378 diagnosed patients were obtained from the Iran Ministry of Health and Medical Education. Utilizing spatial scan statistics, we investigated the disease's purely temporal, purely spatial, spatial variation in temporal trends and spatiotemporal patterns. At P = 0.05 level, the null hypothesis was rejected in every instance. RESULTS: In general, the number of new CL cases decreased over the course of the 9-year research period. From 2011 to 2020, a regular seasonal pattern, with peaks in the fall and troughs in the spring, was found. The period of September-February of 2014-2015 was found to hold the highest risk in terms of CL incidence rate in the whole country [relative risk (RR) = 2.24, P < 0.001)]. In terms of location, six significant high-risk CL clusters covering 40.6% of the total area of the country were observed, with the RR ranging from 1.87 to 9.69. In addition, spatial variation in the temporal trend analysis found 11 clusters as potential high-risk areas that highlighted certain regions with an increasing tendency. Finally, five space-time clusters were found. The geographical displacement and spread of the disease followed a moving pattern over the 9-year study period affecting many regions of the country. CONCLUSIONS: Our study has revealed significant regional, temporal, and spatiotemporal patterns of CL distribution in Iran. Over the years, there have been multiple shifts in spatiotemporal clusters, encompassing many different parts of the country from 2011 to 2020. The results reveal the formation of clusters across counties that cover certain parts of provinces, indicating the importance of conducting spatiotemporal analyses at the county level for studies that encompass entire countries. Such analyses, at a finer geographical scale, such as county level, might provide more precise results than analyses at the scale of the province.

Exploration of the COVID-19 pandemic at the neighborhood level in an intra-urban setting.

The COVID-19 pandemic represents a worldwide threat to health. Since its onset in 2019, the pandemic has proceeded in different phases, which have been shaped by a complex set of influencing factors, including public health and social measures, the emergence of new virus variants, and seasonality. Understanding the development of COVID-19 incidence and its spatiotemporal patterns at a neighborhood level is crucial for local health authorities to identify high-risk areas and develop tailored mitigation strategies. However, analyses at the neighborhood level are scarce and mostly limited to specific phases of the pandemic. The aim of this study was to explore the development of COVID-19 incidence and spatiotemporal patterns of incidence at a neighborhood scale in an intra-urban setting over several pandemic phases (March 2020-December 2021). We used reported COVID-19 case data from the health department of the district Berlin-Neukölln, Germany, additional socio-demographic data, and text documents and materials on implemented public health and social measures. We examined incidence over time in the context of the measures and other influencing factors, with a particular focus on age groups. We used incidence maps and spatial scan statistics to reveal changing spatiotemporal patterns. Our results show that several factors may have influenced the development of COVID-19 incidence. In particular, the far-reaching measures for contact reduction showed a substantial impact on incidence in Neukölln. We observed several age group-specific effects: school closures had an effect on incidence in the younger population (< 18 years), whereas the start of the vaccination campaign had an impact primarily on incidence among the elderly (> 65 years). The spatial analysis revealed that high-risk areas were heterogeneously distributed across the district. The location of high-risk areas also changed across the pandemic phases. In this study, existing intra-urban studies were supplemented by our investigation of the course of the pandemic and the underlying processes at a small scale over a long period of time. Our findings provide new insights for public health authorities, community planners, and policymakers about the spatiotemporal development of the COVID-19 pandemic at the neighborhood level. These insights are crucial for guiding decision-makers in implementing mitigation strategies.

A retrospective investigation of spatial clusters and determinants of diabetes prevalence: scan statistics and geographically weighted regression modeling approaches.

Background: Diabetes and its complications represent a significant public health burden in the United States. Some communities have disproportionately high risks of the disease. Identification of these disparities is critical for guiding policy and control efforts to reduce/eliminate the inequities and improve population health. Thus, the objectives of this study were to investigate geographic high-prevalence clusters, temporal changes, and predictors of diabetes prevalence in Florida. Methods: Behavioral Risk Factor Surveillance System data for 2013 and 2016 were provided by the Florida Department of Health. Tests for equality of proportions were used to identify counties with significant changes in the prevalence of diabetes between 2013 and 2016. The Simes method was used to adjust for multiple comparisons. Significant spatial clusters of counties with high diabetes prevalence were identified using Tango's flexible spatial scan statistic. A global multivariable regression model was fit to identify predictors of diabetes prevalence. A geographically weighted regression model was fit to assess for spatial non-stationarity of the regression coefficients and fit a local model. Results: There was a small but significant increase in the prevalence of diabetes in Florida (10.1% in 2013 to 10.4% in 2016), and statistically significant increases in prevalence occurred in 61% (41/67) of counties in the state. Significant, high-prevalence clusters of diabetes were identified. Counties with a high burden of the condition tended to have high proportions of the population that were non-Hispanic Black, had limited access to healthy foods, were unemployed, physically inactive, and had arthritis. Significant non-stationarity of regression coefficients was observed for the following variables: proportion of the population physically inactive, proportion with limited access to healthy foods, proportion unemployed, and proportion with arthritis. However, density of fitness and recreational facilities had a confounding effect on the association between diabetes prevalence and levels of unemployment, physical inactivity, and arthritis. Inclusion of this variable decreased the strength of these relationships in the global model, and reduced the number of counties with statistically significant associations in the local model. Conclusions: The persistent geographic disparities of diabetes prevalence and temporal increases identified in this study are concerning. There is evidence that the impacts of the determinants on diabetes risk vary by geographical location. This implies that a one-size-fits-all approach to disease control/prevention would be inadequate to curb the problem. Therefore, health programs will need to use evidence-based approaches to guide health programs and resource allocation to reduce disparities and improve population health.

Revisiting the Geographical Distribution of Thyroid Cancer Incidence in Fukushima Prefecture: Analysis of Data From the Second- and Third-round Thyroid Ultrasound Examination.

BACKGROUND: After the first-round (Preliminary Baseline Survey) ultrasound-based examination for thyroid cancer in response to the accident at the Fukushima Daiichi Nuclear Power Plant in 2011, two rounds of surveys (Full-scale Survey) have been carried out in Fukushima Prefecture. Using the data from these surveys, the geographical distribution of thyroid cancer incidence over 6 or 7 years after the disaster was examined. METHODS: Children and adolescents who underwent the ultrasound-based examinations in the second- and/or third-round (Full-scale) survey in addition to the first-round survey were included. With a discrete survival model, we computed age, sex, and body mass index standardized incidence ratios (SIRs) for municipalities. Then, we employed spatial statistics to assess geographic clustering tendency in SIRs and Poisson regression to assess the association of SIRs with the municipal average absorbed dose to the thyroid gland at the 59-municipality level. RESULTS: Throughout the second- and third-round surveys, 99 thyroid cancer cases were diagnosed in the study population of 252,502 individuals. Both flexibly shaped spatial scan statistics and maximized excess events test did not detect statistically significant spatial clustering (P = 0.17 and 0.54, respectively). Poisson regression showed no significant dose-response relationship: the estimated relative risks of lowest, middle-low, middle-high, and highest areas were 1.16 (95% confidence interval [CI], 0.52-2.59), 0.55 (95% CI, 0.31-0.97), 1.05 (95% CI, 0.79-1.40), and 1.24 (95% CI, 0.89-1.74). CONCLUSION: There was no statistical support for geographic clustering or regional association with radiation dose measures of the thyroid cancer incidence in the cohort followed up to the third-round survey (fiscal years 2016-2017) in Fukushima Prefecture.

Contrasting Methods of Measurement in Spatial Analyses Examining the Alcohol Environment and Child Maltreatment.

Child physical abuse is a major public health issue in the United States. Environmental child welfare research has focused on neighborhood characteristics and the influence of alcohol and marijuana establishments. To our knowledge, child welfare studies have singularly examined the outcome in terms of victims, that is, at the level of child population, and have not considered the parent population. Thus, in this exploratory study, we use spatial scan statistics to analyze patterns of child physical abuse at the child and household level, and we use Bayesian hierarchical spatial conditional autoregressive models to determine the relative influence of alcohol availability and other environmental factors. We find that household clusters are nested in child clusters and that controlling for alcohol establishments reduces cluster size. In the Bayesian regression models, alcohol availability increased risk slightly, while neighborhood diversity (measured using Blau's Index) elevated risk considerably. Immediate implications for child welfare agencies are discussed.

An Evaluation of Geo-located Twitter Data for Measuring Human Migration.

This study evaluates the spatial patterns of flows generated from geo-located Twitter data to measure human migration. Using geo-located tweets continuously collected in the U.S. from 2013 to 2015, we identified Twitter users who migrated per changes in county-of-residence every two years and compared the Twitter-estimated county-to-county migration flows with the ones from the U.S. Internal Revenue Service (IRS). To evaluate the spatial patterns of Twitter migration flows when representing the IRS counterparts, we developed a normalized difference representation index to visualize and identify those counties of over-/under-representations in the Twitter estimates. Further, we applied a multidimensional spatial scan statistic approach based on a Poisson process model to detect pairs of origin and destination regions where the over-/under-representativeness occurred. The results suggest that Twitter migration flows tend to under-represent the IRS estimates in regions with a large population and over-represent them in metropolitan regions adjacent to tourist attractions. This study demonstrated that geo-located Twitter data could be a sound statistical proxy for measuring human migration. Given that the spatial patterns of Twitter-estimated migration flows vary significantly across the geographic space, related studies will benefit from our approach by identifying those regions where data calibration is necessary.

Anemia Among Children Under 5 Years in India: A District-Level Spatial Analysis Using NFHS-5 Data.

The 2015/2016 National Family Health Survey (NFHS-4) revealed that the prevalence of anemia among children under five years is 58% in India. Lack of nutritional supplementation and lack of health care facilities are found to be important influential factors of anemia among children. We aimed to examine district-level spatial heterogeneity and clustering of associated factors with childhood anemia in India. Geographically weighted regression was applied on the NFHS-5 data for 335 districts. Factors such as prevalence of nutritional supplementation in children and mothers, birth order, antenatal care, diarrhea in children, and stunting were found to be significantly associated. Spatial scan statistics technique identified three significant local spatial clusters of anemia. This study provides findings based on the latest available data which can further assist in the design and execution of tailor-made policies.

Geographic disparities and socio-demographic predictors of pertussis risk in Florida.

BACKGROUND: Pertussis is a toxin-mediated respiratory illness caused by Bordetella pertussis that can result in severe complications and death, particularly in infants. Between 2008 and 2011, children less than 3 months old accounted for 83% of the pertussis deaths in the United States. Understanding the geographic disparities in the distribution of pertussis risk and identifying high risk geographic areas is necessary for guiding resource allocation and public health control strategies. Therefore, this study investigated geographic disparities and temporal changes in pertussis risk in Florida from 2010 to 2018. It also investigated socioeconomic and demographic predictors of the identified disparities. METHODS: Pertussis data covering the time period 2010-2018 were obtained from Florida HealthCHARTS web interface. Spatial patterns and temporal changes in geographic distribution of pertussis risk were assessed using county-level choropleth maps for the time periods 2010-2012, 2013-2015, 2016-2018 and 2010-2018. Tango's flexible spatial scan statistics were used to identify high-risk spatial clusters which were displayed in maps. Ordinary least squares (OLS) regression was used to identify significant predictors of county-level risk. Residuals of the OLS model were assessed for model assumptions including spatial autocorrelation. RESULTS: County-level pertussis risk varied from 0 to 116.31 cases per 100,000 people during the study period. A total of 11 significant (p < 0.05) spatial clusters were identified with risk ratios ranging from 1.5 to 5.8. Geographic distribution remained relatively consistent over time with areas of high risk persisting in the western panhandle, northeastern coast, and along the western coast. Although county level pertussis risks generally increased from 2010-2012 to 2013-2015, risk tended to be lower during the 2016-2018 time period. Significant predictors of county-level pertussis risk were rurality, percentage of females, and median income. Counties with high pertussis risk tended to be rural (p = 0.021), those with high median incomes (p = 0.039), and those with high percentages of females (p < 0.001). CONCLUSION: There is evidence that geographic disparities exist and have persisted over time in Florida. This study highlights the application and importance of Geographic Information Systems (GIS) technology and spatial statistical/epidemiological tools in identifying areas of highest disease risk so as to guide resource allocation to reduce health disparities and improve health for all.

Geographic disparities, determinants, and temporal changes in the prevalence of pre-diabetes in Florida.

BACKGROUND: Left unchecked, pre-diabetes progresses to diabetes and its complications that are important health burdens in the United States. There is evidence of geographic disparities in the condition with some areas having a significantly high risks of the condition and its risk factors. Identifying these disparities, their determinants, and changes in burden are useful for guiding control programs and stopping the progression of pre-diabetes to diabetes. Therefore, the objectives of this study were to investigate geographic disparities of pre-diabetes prevalence in Florida, identify predictors of the observed spatial patterns, as well as changes in disease burden between 2013 and 2016. METHODS: The 2013 and 2016 Behavioral Risk Factor Surveillance System data were obtained from the Florida Department of Health. Counties with significant changes in the prevalence of the condition between 2013 and 2016 were identified using tests for equality of proportions adjusted for multiple comparisons using the Simes method. Flexible scan statistics were used to identify significant high prevalence geographic clusters. Multivariable regression models were used to identify determinants of county-level pre-diabetes prevalence. RESULTS: The state-wide age-adjusted prevalence of pre-diabetes increased significantly (p ≤ 0.05) from 8.0% in 2013 to 10.5% in 2016 with 72% (48/67) of the counties reporting statistically significant increases. Significant local geographic hotspots were identified. High prevalence of pre-diabetes tended to occur in counties with high proportions of non-Hispanic black population, low median household income, and low proportion of the population without health insurance coverage. CONCLUSIONS: Geographic disparities of pre-diabetes continues to exist in Florida with most counties reporting significant increases in prevalence between 2013 and 2016. These findings are critical for guiding health planning, resource allocation and intervention programs.

Investigation of geographic disparities of pre-diabetes and diabetes in Florida.

BACKGROUND: Diabetes is a leading cause of death and disability in the United States, and its precursor, pre-diabetes, is estimated to occur in one-third of American adults. Understanding the geographic disparities in the distribution of these conditions and identifying high-prevalence areas is critical to guiding control and prevention programs. Therefore, the objective of this study was to investigate clusters of pre-diabetes and diabetes risk in Florida and identify significant predictors of the conditions. METHODS: Data from the 2013 Behavioral Risk Factor Surveillance System were obtained from the Florida Department of Health. Spatial scan statistics were used to identify and locate significant high-prevalence local clusters. The county prevalence proportions of pre-diabetes and diabetes and the identified significant clusters were displayed in maps. Logistic regression was used to identify significant predictors of the two conditions for individuals living within and outside high-prevalence clusters. RESULTS: The study included a total of 34,186 respondents. The overall prevalence of pre-diabetes and diabetes were 8.2 and 11.5%, respectively. Three significant (p < 0.05) local, high-prevalence spatial clusters were detected for pre-diabetes, while five were detected for diabetes. The counties within the high-prevalence clusters had prevalence ratios ranging from 1.29 to 1.85. There were differences in the predictors of the conditions based on whether respondents lived within or outside high-prevalence clusters. Predictors of both pre-diabetes and diabetes regardless of region or place of residence were obesity/overweight, hypertension, and hypercholesterolemia. Income and physical activity level were significant predictors of diabetes but not pre-diabetes. Arthritis, sex, and marital status were significant predictors of diabetes only among residents of high-prevalence clusters, while educational attainment and smoking were significant predictors of diabetes only among residents of non-cluster counties. CONCLUSIONS: Geographic disparities of pre-diabetes and diabetes exist in Florida. Information from this study is useful for guiding resource allocation and targeting of intervention programs focusing on identified modifiable predictors of pre-diabetes and diabetes so as to reduce health disparities and improve the health of all Floridians.

Disparities in Temporal and Geographic Patterns of Myocardial Infarction Hospitalization Risks in Florida.

Knowledge of geographical disparities in myocardial infarction (MI) is critical for guiding health planning and resource allocation. The objectives of this study were to identify geographic disparities in MI hospitalization risks in Florida and assess temporal changes in these disparities between 2005 and 2014. This study used retrospective data on MI hospitalizations that occurred among Florida residents between 2005 and 2014. We identified spatial clusters of hospitalization risks using Kulldorff's circular and Tango's flexible spatial scan statistics. Counties with persistently high or low MI hospitalization risks were identified. There was a 20% decline in hospitalization risks during the study period. However, we found persistent clustering of high risks in the Big Bend region, South Central and southeast Florida, and persistent clustering of low risks primarily in the South. Risks decreased by 7%-21% in high-risk clusters and by 9%-28% in low-risk clusters. The risk decreased in the high-risk cluster in the southeast but increased in the Big Bend area during the last four years of the study. Overall, risks in low-risk clusters were ahead those for high-risk clusters by at least 10 years. Despite MI risk declining over the study period, disparities in MI risks persist. Eliminating/reducing those disparities will require prioritizing high-risk clusters for interventions.

Spatial heterogeneity of KRAS mutations in colorectal cancers in northern France.

BACKGROUND: Somatic mutations in the KRAS gene are the most common oncogenic mutations found in human cancers. However, no clinical features have been linked to KRAS mutations in colorectal cancer [CRC]. PURPOSE: In this study, we attempted to identify the potential geographical population clusters of KRAS mutations in CRC patients in northern France. PATIENTS AND METHODS: All patients with CRC who were identified to have KRAS mutations between 2008 and 2014 at the Regional Molecular Biology Platform at Lille University Hospital were included. 2,486 patients underwent a KRAS status available, with 40.9% of CRC with KRAS mutations in northern France. We retrospectively collected demographic and geographic data from these patients. The proportions of KRAS mutation were smoothed to take into account the variability related to low frequencies and spatial autocorrelation. Geographical clusters were searched using spatial scan statistical models. RESULTS: A mutation at KRAS codon 12 or 13 was found in 1,018 patients [40.9%]. We report 5 clusters of over-incidence but only one elongated cluster that was statistically significant [Cluster 1; proportion of KRAS mutation among CRC: 0.4570; RR=1.29; P=0.0314]. We made an ecological study which did not highlight a significant association between KRAS mutations and the distance to the Closest Waste Incineration Plant, and between KRAS mutations and The French Ecological Deprivation Index but few socio-economic and environmental data were available. CONCLUSION: There was a spatial heterogeneity and a greater frequency of KRAS mutations in some areas close to major highways and big cities in northern France. These data demand deeper epidemiological investigations to identify environmental factors such as air pollution as key factors in the occurrence of KRAS mutations.

Spatial analysis of HIV-TB co-clustering in Uganda.

BACKGROUND: Tuberculosis (TB) is the leading cause of death for individuals infected with Human immunodeficiency virus (HIV). Conversely, HIV is the most important risk factor in the progression of TB from the latent to the active status. In order to manage this double epidemic situation, an integrated approach that includes HIV management in TB patients was proposed by the World Health Organization and was implemented in Uganda (one of the countries endemic with both diseases). To enable targeted intervention using the integrated approach, areas with high disease prevalence rates for TB and HIV need to be identified first. However, there is no such study in Uganda, addressing the joint spatial patterns of these two diseases. METHODS: This study uses global Moran's index, spatial scan statistics and bivariate global and local Moran's indices to investigate the geographical clustering patterns of both diseases, as individuals and as combined. The data used are TB and HIV case data for 2015, 2016 and 2017 obtained from the District Health Information Software 2 system, housed and maintained by the Ministry of Health, Uganda. RESULTS: Results from this analysis show that while TB and HIV diseases are highly correlated (55-76%), they exhibit relatively different spatial clustering patterns across Uganda. The joint TB/HIV prevalence shows consistent hotspot clusters around districts surrounding Lake Victoria as well as northern Uganda. These two clusters could be linked to the presence of high HIV prevalence among the fishing communities of Lake Victoria and the presence of refugees and internally displaced people camps, respectively. The consistent cold spot observed in eastern Uganda and around Kasese could be explained by low HIV prevalence in communities with circumcision tradition. CONCLUSIONS: This study makes a significant contribution to TB/HIV public health bodies around Uganda by identifying areas with high joint disease burden, in the light of TB/HIV co-infection. It, thus, provides a valuable starting point for an informed and targeted intervention, as a positive step towards a TB and HIV-AIDS free community.

Geographic distribution of staphylococcus spp. infections and antimicrobial resistance among dogs from Gauteng Province presented at a veterinary teaching hospital in South Africa.

The objective of this study was to investigate spatial patterns of staphylococcal infections and resistance patterns of clinical isolates among dogs from Gauteng province in South Africa. Data from records of 1497 dog clinical samples submitted to a veterinary teaching hospital between 2007 and 2012 were used in the study. Spatial empirical Bayesian smoothed risk maps were used to investigate spatial patterns of staphylococcal infections, antimicrobial resistance (AMR), and multidrug resistance (MDR). Moran's I and spatial scan statistics were used to investigate spatial clusters at municipal and town spatial scales. Significant clusters of staphylococcal infections were identified at both the municipal (Relative Risk [RR] = 1.71, p = 0.003) and town (RR = 1.65, p = 0.039) scales. However, significant clusters of AMR (p = 0.003) and MDR (p = 0.007) were observed only at the town scale. Future larger studies will need to investigate local determinants of geographical distribution of the clusters so as to guide targeted control efforts.

Do sedentary behavior and physical activity spatially cluster? Analysis of a population-based sample of Boston adolescents.

Sedentary behavior and lack of physical activity are key modifiable behavioral risk factors for chronic health problems, such as obesity and diabetes. Little is known about how sedentary behavior and physical activity among adolescents spatially cluster. The objective was to detect spatial clustering of sedentary behavior and physical activity among Boston adolescents. Data were used from the 2008 Boston Youth Survey Geospatial Dataset, a sample of public high school students who responded to a sedentary behavior and physical activity questionnaire. Four binary variables were created: 1) TV watching (>2 hours/day), 2) video games (>2 hours/day), 3) total screen time (>2 hours/day); and 4) 20 minutes/day of physical activity (≥5 days/week). A spatial scan statistic was utilized to detect clustering of sedentary behavior and physical activity. One statistically significant cluster of TV watching emerged among Boston adolescents in the unadjusted model. Students inside the cluster were more than twice as likely to report > 2 hours/day of TV watching compared to respondents outside the cluster. No significant clusters of sedentary behavior and physical activity emerged. Findings suggest that TV watching is spatially clustered among Boston adolescents. Such findings may serve to inform public health policymakers by identifying specific locations in Boston that could provide opportunities for policy intervention. Future research should examine what is linked to the clusters, such as neighborhood environments and network effects.