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ObjectivesSmall area analysis is a health services research technique that facilitates geographical comparison of services supply and utilization rates between health service areas (HSAs). HSAs are functionally relevant regions around medical facilities within which most residents undergo treatment. We aimed to identify HSAs for psychiatric outpatient care (HSA-PSY) in Switzerland.MethodsWe used HSAr, a new and automated methodological approach, and comprehensive psychiatric service use data from insurances to identify HSA-PSY based on travel patterns between patients' residences and service sites. Resulting HSA-PSY were compared geographically, demographically and regarding the use of inpatient and outpatient psychiatric services.ResultsWe identified 68 HSA-PSY, which were reviewed and validated by local mental health services experts. The population-based rate of inpatient and outpatient service utilization varied considerably between HSA-PSY. Utilization of inpatient and outpatient services tended to be positively associated across HSA-PSY.ConclusionsWide variation of service use between HSA-PSY can hardly be fully explained by underlying differences in the prevalence or incidence of disorders. Whether other factors such as the amount of services supply did add to the high variation should be addressed in further studies, for which our functional mapping on a small-scale regional level provides a good analytical framework.

Abstract Background Mortality rate estimation in small areas can be difficult due the low number of events/exposure (i.e. stochastic error). If the death records are not completed, it adds a systematic uncertainty on the mortality estimates. Previous studies in Brazil have combined demographic and statistical methods to partially overcome these issues. We estimated age- and sex-specific mortality rates for all 5,565 Brazilian municipalities in 2010 and forecasted probabilistic mortality rates and life expectancy between 2010 and 2030. Methods We used a combination of the Tool for Projecting Age-Specific Rates Using Linear Splines (TOPALS), Bayesian Model, Spatial Smoothing Model and an ad-hoc procedure to estimate age- and sex-specific mortality rates for all Brazilian municipalities for 2010. Then we adapted the Lee-Carter model to forecast mortality rates by age and sex in all municipalities between 2010 and 2030. Results The adjusted sex- and age-specific mortality rates for all Brazilian municipalities in 2010 reveal a distinct regional pattern, showcasing a decrease in life expectancy in less socioeconomically developed municipalities when compared to estimates without adjustments. The forecasted mortality rates indicate varying regional improvements, leading to a convergence in life expectancy at birth among small areas in Brazil. Consequently, a reduction in the variability of age at death across Brazil’s municipalities was observed, with a persistent sex differential. Conclusion Mortality rates at a small-area level were successfully estimated and forecasted, with associated uncertainty estimates also generated for future life tables. Our approach could be applied across countries with data quality issues to improve public policy planning.

Abstract Since their inception, small area studies intended to measure health system performance have been challenged by concerns that regional variation in health care may be primarily explained by differences in patient health risk. Controlling for regional population differences depends on appropriate risk adjustment, but the adequacy of the methods used in early analyses was contested. A novel response to these concerns was the development of end-of-life cohorts by Dartmouth Atlas investigators. These were used initially to control for differences in population health status in studies investigating relative efficiency across regions. Later, they became useful for studying hospital-level variation in chronic illness care, and for measuring utilization and patient experiences at the very end of life. Altogether, end-of-life cohorts have been invaluable for clarifying the contribution of health system and provider factors to health care variation and outcomes.

Background: Mortality rate estimation in small areas can be difficult due the low number of events/exposure (i.e. stochastic error). If the death records are not completed, it adds a systematic uncertainty on the mortality estimates. Previous studies in Brazil have combined demographic and statistical methods to partially overcome these issues. We estimated age- and sex-specific mortality rates for all 5,565 Brazilian municipalities in 2010 and forecasted probabilistic mortality rates and life expectancy between 2010 and 2030. Methods: We used a combination of the Tool for Projecting Age-Specific Rates Using Linear Splines (TOPALS), Bayesian Model, Spatial Smoothing Model and an ad-hoc procedure to estimate age- and sex-specific mortality rates for all Brazilian municipalities for 2010. Then we adapted the Lee-Carter model to forecast mortality rates by age and sex in all municipalities between 2010 and 2030. Results: The adjusted sex- and age-specific mortality rates for all Brazilian municipalities in 2010 reveal a distinct regional pattern, showcasing a decrease in life expectancy in less socioeconomically developed municipalities when compared to estimates without adjustments. The forecasted mortality rates indicate varying regional improvements, leading to a convergence in life expectancy at birth among small areas in Brazil. Consequently, a reduction in the variability of age at death across Brazil's municipalities was observed, with a persistent sex differential. Conclusion: Mortality rates at a small-area level were successfully estimated and forecasted, with associated uncertainty estimates also generated for future life tables. Our approach could be applied across countries with data quality issues to improve public policy planning. [ABSTRACT FROM AUTHOR]

Since their inception, small area studies intended to measure health system performance have been challenged by concerns that regional variation in health care may be primarily explained by differences in patient health risk. Controlling for regional population differences depends on appropriate risk adjustment, but the adequacy of the methods used in early analyses was contested. A novel response to these concerns was the development of end-of-life cohorts by Dartmouth Atlas investigators. These were used initially to control for differences in population health status in studies investigating relative efficiency across regions. Later, they became useful for studying hospital-level variation in chronic illness care, and for measuring utilization and patient experiences at the very end of life. Altogether, end-of-life cohorts have been invaluable for clarifying the contribution of health system and provider factors to health care variation and outcomes. [ABSTRACT FROM AUTHOR]

Abstract Background Cancer is a significant health issue globally and it is well known that cancer risk varies geographically. However in many countries there are no small area-level data on cancer risk factors with high resolution and complete reach, which hinders the development of targeted prevention strategies. Methods Using Australia as a case study, the 2017–2018 National Health Survey was used to generate prevalence estimates for 2221 small areas across Australia for eight cancer risk factor measures covering smoking, alcohol, physical activity, diet and weight. Utilising a recently developed Bayesian two-stage small area estimation methodology, the model incorporated survey-only covariates, spatial smoothing and hierarchical modelling techniques, along with a vast array of small area-level auxiliary data, including census, remoteness, and socioeconomic data. The models borrowed strength from previously published cancer risk estimates provided by the Social Health Atlases of Australia. Estimates were internally and externally validated. Results We illustrated that in 2017–2018 health behaviours across Australia exhibited more spatial disparities than previously realised by improving the reach and resolution of formerly published cancer risk factors. The derived estimates revealed higher prevalence of unhealthy behaviours in more remote areas, and areas of lower socioeconomic status; a trend that aligned well with previous work. Conclusions Our study addresses the gaps in small area level cancer risk factor estimates in Australia. The new estimates provide improved spatial resolution and reach and will enable more targeted cancer prevention strategies at the small area level. Furthermore, by including the results in the next release of the Australian Cancer Atlas, which currently provides small area level estimates of cancer incidence and relative survival, this work will help to provide a more comprehensive picture of cancer in Australia by supporting policy makers, researchers, and the general public in understanding the spatial distribution of cancer risk factors. The methodology applied in this work is generalisable to other small area estimation applications and has been shown to perform well when the survey data are sparse.

Background: Cancer is a significant health issue globally and it is well known that cancer risk varies geographically. However in many countries there are no small area-level data on cancer risk factors with high resolution and complete reach, which hinders the development of targeted prevention strategies. Methods: Using Australia as a case study, the 2017–2018 National Health Survey was used to generate prevalence estimates for 2221 small areas across Australia for eight cancer risk factor measures covering smoking, alcohol, physical activity, diet and weight. Utilising a recently developed Bayesian two-stage small area estimation methodology, the model incorporated survey-only covariates, spatial smoothing and hierarchical modelling techniques, along with a vast array of small area-level auxiliary data, including census, remoteness, and socioeconomic data. The models borrowed strength from previously published cancer risk estimates provided by the Social Health Atlases of Australia. Estimates were internally and externally validated. Results: We illustrated that in 2017–2018 health behaviours across Australia exhibited more spatial disparities than previously realised by improving the reach and resolution of formerly published cancer risk factors. The derived estimates revealed higher prevalence of unhealthy behaviours in more remote areas, and areas of lower socioeconomic status; a trend that aligned well with previous work. Conclusions: Our study addresses the gaps in small area level cancer risk factor estimates in Australia. The new estimates provide improved spatial resolution and reach and will enable more targeted cancer prevention strategies at the small area level. Furthermore, by including the results in the next release of the Australian Cancer Atlas, which currently provides small area level estimates of cancer incidence and relative survival, this work will help to provide a more comprehensive picture of cancer in Australia by supporting policy makers, researchers, and the general public in understanding the spatial distribution of cancer risk factors. The methodology applied in this work is generalisable to other small area estimation applications and has been shown to perform well when the survey data are sparse. [ABSTRACT FROM AUTHOR]

Abstract Background Compulsory hospitalisation in mental health care restricts patients’ liberty and is experienced as harmful by many. Such hospitalisations continue to be used due to their assumed benefit, despite limited scientific evidence. Observed geographical variation in compulsory hospitalisation raises concern that rates are higher and lower than necessary in some areas. Methods/discussion We present a specific normative ethical analysis of how geographical variation in compulsory hospitalisation challenges four core principles of health care ethics. We then consider the theoretical possibility of a “right”, or appropriate, level of compulsory hospitalisation, as a general norm for assessing the moral divergence, i.e., too little, or too much. Finally, we discuss implications of our analysis and how they can inform the future direction of mental health services.

ObjectivesTo explore effects of disease prevalence adjustment on ambulatory care-sensitive hospitalization (ACSH) rates used for quality comparisons.Data sources/study settingCounty-level hospital administrative data on adults discharged from German hospitals in 2011 and prevalence estimates based on administrative ambulatory diagnosis data were used.Study designA retrospective cross-sectional study using in- and outpatient secondary data was performed.Data collectionHospitalization data for hypertension, diabetes, heart failure, chronic obstructive pulmonary disease, and asthma were obtained from the German Diagnosis Related Groups (DRG) database. Prevalence estimates were obtained from the German Central Research Institute of Ambulatory Health Care.Principal findingsCrude hospitalization rates varied substantially across counties (coefficients of variation [CV] 28-37 percent across conditions); this variation was reduced by prevalence adjustment (CV 21-28 percent). Prevalence explained 40-50 percent of the observed variation (r = 0.65-0.70) in ACSH rates for all conditions except asthma (r = 0.07). Between 30 percent and 38 percent of areas moved into or outside condition-specific control limits with prevalence adjustment.ConclusionsUnadjusted ACSH rates should be used with caution for high-stakes public reporting as differences in prevalence may have a marked impact. Prevalence adjustment should be considered in models analyzing ACSH.

Background: Compulsory hospitalisation in mental health care restricts patients' liberty and is experienced as harmful by many. Such hospitalisations continue to be used due to their assumed benefit, despite limited scientific evidence. Observed geographical variation in compulsory hospitalisation raises concern that rates are higher and lower than necessary in some areas. Methods/discussion: We present a specific normative ethical analysis of how geographical variation in compulsory hospitalisation challenges four core principles of health care ethics. We then consider the theoretical possibility of a "right", or appropriate, level of compulsory hospitalisation, as a general norm for assessing the moral divergence, i.e., too little, or too much. Finally, we discuss implications of our analysis and how they can inform the future direction of mental health services. [ABSTRACT FROM AUTHOR]

Purpose: Vaccinations are reported at the state level, but services are delivered at the county level through health departments (HD). This research contributes statistical models to predict county level HPV vaccination.Methods: Using a cross sectional study design, secondary data were analyzed for the years 2016-2018 for all counties of GA. Study population was male and female adolescents aged 13-17 who received the tetanus, diphtheria and pertussis (Tdap) vaccine. The number of administered HPV vaccine doses and HPV vaccination coverage rate were modeled using indicators of HD clinic access, age, sex, race/ethnicity, socioeconomic status, education, median household income, health insurance, and urban/rural residence.Results: By county the number of administered HPV vaccine doses showed a statistically significant positive association with indicators of HD clinic access: public transit and the number of HD private clinics. HPV vaccination coverage showed a statistically significant negative association with White race and rural residency.Conclusion: Examining Tdap vaccinated adolescents conservatively predicted HPV vaccination and controlled for multiple confounders such as vaccination ineligibility, vaccine exemption, and vaccine opposition. Within this population, public health professionals and clinicians could use these statistical models to target HPV vaccination efforts among non-Hispanic whites and rural communities at the county level. [ABSTRACT FROM AUTHOR]

Introduction: This study analyzes changes in hospital emergency department (ED) visit ratesbefore and after the 2014 Affordable Care Act (ACA) insurance expansions in Illinois. We comparethe association between population insurance status change and ED visit rate change between a24-month (2012-2013) pre-ACA period and a 24-month post-ACA (2014-2015) period across 88socioeconomically diverse areas of Illinois.Methods: We used annual American Community Survey estimates for 2012-2015 to obtain insurancestatus changes for uninsured, private, Medicaid, and Medicare (disability) populations of 88 IllinoisPublic Use Micro Areas (PUMAs), areas with a mean of about 90,000 age 18-64 residents. Over 12million ED visits to 201 non-federal Illinois hospitals were used to calculate visit rates by residents ofeach PUMA, using population-based mapping weights to allocate visits from zip codes to PUMAs. Wethen estimated n=88 correlations between population insurance-status changes and changes in EDvisit rates per 1,000 residents comparing the two years before and after ACA implementation.Results: The baseline PUMA uninsurance rate ranged from 6.7% to 41.1% and there was 4.6-fold variation in baseline PUMA ED visit rates. The top quartile of PUMAs had >21,000 reductionsin uninsured residents; 16 PUMAs had at least a 15,000 person increase in Medicaid enrollment.Compared to 2012-2013, 2014-2015 average monthly ED visits by the uninsured dropped 42%,but increased 42% for Medicaid and 10% for the privately insured. Areas with the largest increasesin Medicaid enrollment experienced the largest growth in ED use; change in Medicaid enrollmentwas the only significant correlate of area change in total ED visits and explained a third of variationacross the 88 PUMAs.Conclusion: ACA implementation in Illinois accelerated existing trends towards greater use of hospitalED care. It remains to be seen whether providing better access to primary and preventive care tothe formerly uninsured will reduce ED use over time, or whether ACA insurance expansion is a partof continued, long-term growth. Monitoring ED use at the local level is critical to the success of newhome- and community-based care coordination initiatives. [West J Emerg Med. 2017;18(5)811-820.]

Abstract Objective A variety of measures are used for reporting levels of compulsory psychiatric hospitalisation. This complicates comparisons between studies and makes it hard to establish the extent of geographic variation. We aimed to investigate how measures based on events, individuals and duration portray geographical variation differently and perform over time, how they correlate and how well they predict future ranked levels of compulsory hospitalisation. Methods Small‐area analysis, correlation analysis and linear regressions of data from a Norwegian health registry containing whole population data from 2014 to 2018. Results The average compulsory hospitalisation rate per 100,000 inhabitant was 5.6 times higher in the highest area, compared to the lowest, while the difference for the compulsory inpatient rate was 3.2. Population rates based on inpatients correlate strongly with rates of compulsory hospitalisations (r = 0.88) and duration (r = 0.78). 68%–81% of ranked compulsory hospitalisation rates could be explained by each area's rank the previous year. Conclusion There are stable differences in service delivery between catchment areas in Norway. In future research, multiple measures of the level of compulsory hospitalisation should ideally be included when investigating geographical variation. It is important that researchers describe accurately the measure upon which their results are based.

Objective: A variety of measures are used for reporting levels of compulsory psychiatric hospitalisation. This complicates comparisons between studies and makes it hard to establish the extent of geographic variation. We aimed to investigate how measures based on events, individuals and duration portray geographical variation differently and perform over time, how they correlate and how well they predict future ranked levels of compulsory hospitalisation. Methods: Small‐area analysis, correlation analysis and linear regressions of data from a Norwegian health registry containing whole population data from 2014 to 2018. Results: The average compulsory hospitalisation rate per 100,000 inhabitant was 5.6 times higher in the highest area, compared to the lowest, while the difference for the compulsory inpatient rate was 3.2. Population rates based on inpatients correlate strongly with rates of compulsory hospitalisations (r = 0.88) and duration (r = 0.78). 68%–81% of ranked compulsory hospitalisation rates could be explained by each area's rank the previous year. Conclusion: There are stable differences in service delivery between catchment areas in Norway. In future research, multiple measures of the level of compulsory hospitalisation should ideally be included when investigating geographical variation. It is important that researchers describe accurately the measure upon which their results are based. [ABSTRACT FROM AUTHOR]

Abstract Introduction Emergency Departments experience a significant census burst after disasters. The aim of this study is to describe patient presentations at Emergency Departments in Contra Costa County, California following chemical release incidents at an oil refinery in 2007 and 2012. Specific areas of focus include hospital and community burden with an emphasis on disease classes. Methods Searching 4 weeks before through 4 weeks after each event, Emergency Department abstracts identified patients living in Contra Costa County and seeking care there or in neighboring Alameda County. City and ZIP-code of residence established proximity to the refinery. This provided the following contrast groups: Event (2007, 2012), time (before, after), location (bayside, rest of county), and within bayside, warned or not warned to shelter in place. Using the Multi-Level Clinical Classification Software, we classified primary health groups recorded 4 weeks before and after the events, then summarized the data, calculated rates, and made tables, graphs, and maps to highlight findings. Results Number of visits meeting selection criteria totalled 105020 records. Visits increased modestly but statistically significantly after the 2007 incident. After the 2012 incident, two Emergency Departments took the brunt of the surge. Censuses increased from less than 600 a week each to respectively 5719 and 3072 the first week, with the greatest number 2 days post-event. It took 4 weeks for censuses to return to normal. The most common diagnosis groups that spiked were nervous/sensory, respiratory, circulatory, and injury. Bayside communities had statistically significant increases in residents seeking care. Specifically, visits of residents in warned communities nearest the refinery increased by a factor of 3.7 while visits of residents in other nearby un-warned communities increased by a factor of 1.5. Conclusions The 2012 Emergency Department census peaked in the first week and did not return to normal for 4 weeks. Diagnoses changed to reflect conditions associated with reactions to chemical exposures. Surrounding communities and nearby hospitals experienced significant emergent burdens. In addition to changes from such events in patient diagnoses and community burden, the discussion highlights the long-term implications of failures to require adequate monitoring and warning systems and failures of health planning.

Objective: To identify geographic variation in mental health service use in the Department of Veterans Affairs (VA), the authors constructed utilization-based VA mental health service areas (MHSAs) for outpatient treatment and mental health referral regions (MHRRs) for residential and acute inpatient treatment.Methods: MHSAs are empirically derived geographic groupings of one or more counties containing one or more VA outpatient mental health clinics. For each county within an MHSA, patients received most of their VA-provided outpatient mental health care within that MHSA. MHSAs were aggregated into MHRRs according to where VA users in each MHSA received most of their residential and acute inpatient mental health care. Attribution loyalty was evaluated with the localization index-the fraction of VA users living in each geographic area who used their designated MHSA and MHRR facility. Variation in outpatient mental health visits and in acute inpatient and residential mental health stays was determined for the 2008-2018 period.Results: A total of 441 MHSAs were aggregated to 115 MHRRs (representing 3,909,080 patients with 52,372,303 outpatient mental health visits). The mean±SD localization index was 59.3%±16.4% for MHSAs and 67.8%±12.7% for MHRRs. Adjusted outpatient mental health visits varied from a mean of 0.88 per year in the lowest quintile of MHSAs to 3.14 in the highest. Combined residential and acute inpatient days varied from 0.29 to 1.79 between the lowest and highest quintiles.Conclusions: MHSAs and MHRRs validly represented mental health utilization patterns in the VA and displayed considerable variation in mental health service provision across different locations. [ABSTRACT FROM AUTHOR]

Abstract Background Life expectancy at birth (LE) has been calculated for states and counties. LE estimates at these levels mask health disparities in local communities. There are no nationwide estimates at the sub-county level. We present a stepwise approach for calculating LE using census tracts in New York state to identify health disparities. Methods Our study included 2751 census tracts in New York state, but excluded New York City. We used population data from the 2010 United States Census and 2008–2010 mortality data from the state health department. Tracts were assigned to 99.97% of the deaths. We removed tracts which had a majority of people living in group quarters. Deaths in these tracts are often recorded elsewhere. Of the remaining 2679 tracts, 6.6% of the tracts had standard errors ≥ 2 years. A geographic aggregation tool was used to aggregate tracts with fewer than 60 deaths, and then aggregate areas that had standard errors of ≥ 2 years. Results Aggregation resulted in a 9.9% reduction in the number of areas. Tracts with

Abstract Background This study seeks to quantify variation in healthcare utilization and per capita costs using system-defined geographic regions based on enrollee residence within the Military Health System (MHS). Methods Data for fiscal years 2007 – 2010 were obtained from the Military Health System under a data sharing agreement with the Defense Health Agency (DHA). DHA manages all aspects of the Department of Defense Military Health System, including TRICARE. Adjusted rates were calculated for per capita costs and for two procedures with high interest to the MHS- back surgery and Cesarean sections for TRICARE Prime and Plus enrollees. Coefficients of variation (CoV) and interquartile ranges (IQR) were calculated and analyzed using residence catchment area as the geographic unit. Catchment areas anchored by a Military Treatment Facility (MTF) were compared to catchment areas not anchored by a MTF. Results Variation, as measured by CoV, was 0.37 for back surgery and 0.13 for C-sections in FY 2010- comparable to rates documented in other healthcare systems. The 2010 CoV (and average cost) for per capita costs was 0.26 ($3,479.51). Procedure rates were generally lower and CoVs higher in regions anchored by a MTF compared with regions not anchored by a MTF, based on both system-wide comparisons and comparisons of neighboring areas. Conclusions In spite of its centrally managed system and relatively healthy beneficiaries with very robust health benefits, the MHS is not immune to unexplained variation in utilization and cost of healthcare.

The purpose of the meeting was to identify ways in which the Center for Spatially Integrated Social Science could better achieve its goal of supporting the development and dissemination of spatial theories, tools, concepts and techniquesin the social sciences, with reference to studying and addressing problems of social inequality and equity. The meeting of scholars took place in Santa Barbara, CA, November 12-14, 2000. The conceptual issues addressed included incorporating temporal considerations into spatial analysis, data problems in spatial approaches to social science, and problems realted to data access, statistical analysis, and visualization.

Introduction: Emergency Departments experience a significant census burst after disasters. The aim of this study is to describe patient presentations at Emergency Departments in Contra Costa County, California following chemical release incidents at an oil refinery in 2007 and 2012. Specific areas of focus include hospital and community burden with an emphasis on disease classes.Methods: Searching 4 weeks before through 4 weeks after each event, Emergency Department abstracts identified patients living in Contra Costa County and seeking care there or in neighboring Alameda County. City and ZIP-code of residence established proximity to the refinery. This provided the following contrast groups: Event (2007, 2012), time (before, after), location (bayside, rest of county), and within bayside, warned or not warned to shelter in place. Using the Multi-Level Clinical Classification Software, we classified primary health groups recorded 4 weeks before and after the events, then summarized the data, calculated rates, and made tables, graphs, and maps to highlight findings.Results: Number of visits meeting selection criteria totalled 105020 records. Visits increased modestly but statistically significantly after the 2007 incident. After the 2012 incident, two Emergency Departments took the brunt of the surge. Censuses increased from less than 600 a week each to respectively 5719 and 3072 the first week, with the greatest number 2 days post-event. It took 4 weeks for censuses to return to normal. The most common diagnosis groups that spiked were nervous/sensory, respiratory, circulatory, and injury. Bayside communities had statistically significant increases in residents seeking care. Specifically, visits of residents in warned communities nearest the refinery increased by a factor of 3.7 while visits of residents in other nearby un-warned communities increased by a factor of 1.5.Conclusions: The 2012 Emergency Department census peaked in the first week and did not return to normal for 4 weeks. Diagnoses changed to reflect conditions associated with reactions to chemical exposures. Surrounding communities and nearby hospitals experienced significant emergent burdens. In addition to changes from such events in patient diagnoses and community burden, the discussion highlights the long-term implications of failures to require adequate monitoring and warning systems and failures of health planning. [ABSTRACT FROM AUTHOR]

Abstract In an article in this Journal, Mendlovic and colleagues report on regional variation in medical care across Israeli regions. This study joins a growing literature demonstrating generally high variation in the provision of health care services within developed countries. This commentary summarizes the status of medical care epidemiology and its studies of unwarranted variation in health care, and provides a conceptual framework to guide future studies. Recommendations are offered for advancing studies in Israel that could guide policy development and clinical improvement.

Background & Aim: Awareness of adolescents’ general health status is important. The aim of this study was an assessment of general health status by estimating the mean of general health score of high school students in eight counties of Bushehr province in 2014 with small area method. Methods & Materials: In this cross-sectional study, students’ general health score at the counties levels was estimated by small area method. The data were collected based on the general health questionnaire GHQ28) and the accuracy of estimations was measured by using of standard error. Results: The overall estimated mean±standard error of general health score for 138 students, using the synthetic estimation of small area method was 26.65±0.21. The means of general health at eight counties were 26.96, 26.85, 26.41, 26.48, 26.74, 26.76, 26.62 and 27.41, respectively Conclusion: A little difference between the averages and the small standard errors of estimations in eight counties, showed that the synthetic estimator has a high accuracy for estimating the mean of adolescents’ general health scores.

Introduction: Marin is a medium-sized county in California's San Francisco Bay Area. Despite its historically higher-than-average life expectancy and socioeconomic level, known economic and health disparities by race, ethnicity, and geography became more visible during the COVID-19 pandemic.  Methods: We calculated life expectancy, measured years of potential life lost (YPLLs), and described premature mortality for the five years of 2017-2021 by race, ethnicity, census tract, and resource level (as measured by Healthy Places Index [HPI]) to provide data on inequities to guide community-centered action to reduce premature mortality.  Results: Life expectancy for the county was 85.2 years. The non-Hispanic African American/Black population experienced the lowest life expectancy of 77.1 years, 11.6 years lower than the non-Hispanic Asian population which had the highest life expectancy (88.7 years). There was a 14.9-year difference in life expectancy between the census tracts with the lowest (77.1 years) and highest (92.0 years) estimates. We found a moderate, positive association between census tract resource level (HPI) and life expectancy (r=0.58, p<0.01). The leading causes of premature death were cancer, diseases of the circulatory system, and accidental overdoses, with variation by subgroup.  Conclusion: These data highlight health disparities that persist in Marin County and can inform data-driven public health strategies to narrow gaps in longevity between communities., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2023, Soriano et al.)

Background: Life expectancy at birth (LE) has been calculated for states and counties. LE estimates at these levels mask health disparities in local communities. There are no nationwide estimates at the sub-county level. We present a stepwise approach for calculating LE using census tracts in New York state to identify health disparities. Methods: Our study included 2751 census tracts in New York state, but excluded New York City. We used population data from the 2010 United States Census and 2008-2010 mortality data from the state health department. Tracts were assigned to 99.97% of the deaths. We removed tracts which had a majority of people living in group quarters. Deaths in these tracts are often recorded elsewhere. Of the remaining 2679 tracts, 6.6% of the tracts had standard errors ≥ 2 years. A geographic aggregation tool was used to aggregate tracts with fewer than 60 deaths, and then aggregate areas that had standard errors of ≥ 2 years. Results: Aggregation resulted in a 9.9% reduction in the number of areas. Tracts with < 2% of population living below the poverty level had a LE of 82.8 years, while tracts with a poverty level ≥ 25% had a LE of 75.5. We observed differences in LE in border areas, of up to 10.4 years, when excluding or including deaths of study area residents that occurred outside the study area. The range and standard deviation at the county level (77.5-82.8, SD = 1.2 years) were smaller than our final sub-county areas (64.7-92.0, SD = 3.3 years). The correlation between LE and poverty were similar and statistically significant (p < 0.0001) at the county (r = - 0.58) and sub-county level (r = - 0.58). The correlations between LE and percent African-American at the county level were (r = 0.11, p = 0.43) and at the sub-county level (r = - 0.25, p < 0.0001). Conclusion: The proposed approach for geocoding and aggregation of mortality and population data provides a solution for health departments to produce stable empirically-derived LE estimates using data coded to the tract. Reliable estimates within sub-county areas are needed to aid public health officials in focusing preventive health programs in areas where health disparities would be masked by county level estimates. [ABSTRACT FROM AUTHOR]

This article reports on the use of sequential and integrated mixed-methods approach in a focused population and small-area analysis. The study framework integrates focus groups, survey research, and community engagement strategies in a search for evidence related to prostate cancer screening services utilization as a component of cancer prevention planning in a marginalized African American community in the United States. Research and data analysis methods are synthesized by aggregation, configuration, and interpretive analysis. The results of synthesis show that qualitative and quantitative data validate and complement each other in advancing our knowledge of population characteristics, variable associations, the complex context in which variables exist, and the best options for prevention and service planning. Synthesis of findings and interpretive analysis provided two important explanations which seemed inexplicable in regression outputs: (a) Focus group data on the limitations of the church as an educational source explain the negative association between preferred educational channels and screening behavior found in quantitative analysis. (b) Focus group data on unwelcoming provider environments explain the inconsistent relationship between knowledge of local sites and screening services utilization found in quantitative analysis. The findings suggest that planners, evaluators, and scientists should grow their planning and evaluation evidence from the community they serve. [ABSTRACT FROM PUBLISHER]

Background: This study seeks to quantify variation in healthcare utilization and per capita costs using system-defined geographic regions based on enrollee residence within the Military Health System (MHS).Methods: Data for fiscal years 2007 - 2010 were obtained from the Military Health System under a data sharing agreement with the Defense Health Agency (DHA). DHA manages all aspects of the Department of Defense Military Health System, including TRICARE. Adjusted rates were calculated for per capita costs and for two procedures with high interest to the MHS- back surgery and Cesarean sections for TRICARE Prime and Plus enrollees. Coefficients of variation (CoV) and interquartile ranges (IQR) were calculated and analyzed using residence catchment area as the geographic unit. Catchment areas anchored by a Military Treatment Facility (MTF) were compared to catchment areas not anchored by a MTF.Results: Variation, as measured by CoV, was 0.37 for back surgery and 0.13 for C-sections in FY 2010- comparable to rates documented in other healthcare systems. The 2010 CoV (and average cost) for per capita costs was 0.26 ($3,479.51). Procedure rates were generally lower and CoVs higher in regions anchored by a MTF compared with regions not anchored by a MTF, based on both system-wide comparisons and comparisons of neighboring areas.Conclusions: In spite of its centrally managed system and relatively healthy beneficiaries with very robust health benefits, the MHS is not immune to unexplained variation in utilization and cost of healthcare. [ABSTRACT FROM AUTHOR]

Objetivo: Describir la frecuencia de utilización de los servicios de urgencias en las áreas sanitarias de Asturias desde 1994 a 2001 y analizar su variabilidad. Métodos: Se estimó la demanda de urgencias hospitalarias y de atención primaria en las 8 áreas sanitarias de Asturias, y se analizó su evolución y las diferencias entre áreas, usando la estandarización indirecta y estadísticos de variabilidad. Resultados: Entre 1994 y 2001 se realizaron casi 6,5 millones de urgencias (un 43,8% en hospitales), con un crecimiento medio anual del 6,2% (un 7,8% en atención primaria y un 5,1% en hospitales) con gran heterogeneidad entre áreas. La variabilidad fue mayor en atención primaria y disminuyó en el período estudiado (coeficientes de variación: 0,38 y 0,27 para 1994 y 2001 en atención primaria, y 0,14 y 0,11 en hospitales, respectivamente). Conclusiones: La utilización de las urgencias creció en el período estudiado y se observa una gran variabilidad entre áreas sanitarias.Objective: To describe primary care and hospital emergency utilization rates in Asturias health districts from 1994 to 2001 and to analyse their variability. Methods: Hospital and primary care rates from 1994 to 2001 in the 8 Asturias health districts were estimated. Their variability was analysed using indirect standardisation and small area variation statistics. Results: Almost 6.5 million of emergencies (hospitals: 43.8%) were attended in Asturias from 1994 to 2001. The average annual growth was 6.2% (primary care: 7.8%; hospitals: 5.1%) with differences among districts. Primary care variability was higher (variation coefficient: 0.38 and 0.27 in 1994 and 2001) than hospital variability (variation coefficient: 0.14 and 0.11) and it decreased in the period. Conclusions: Emergency Health Services utilization growth between 1994 and 2001, with strong variability among health districts.

Background: Institutional deaths (hospitals and nursing homes) are an important issue because they are often at odds with patient preference and associated with high healthcare costs. The aim of this study was to examine deaths in institutions and the role of individual, regional, and healthcare supply characteristics in explaining variation across Swiss Hospital Service Areas (HSAs). Methods: Retrospective study of individuals ≥66 years old who died in a Swiss institution (hospital or nursing homes) in 2010. Using a two-level logistic regression analysis we examined the amount of variation across HSAs adjusting for individual, regional and healthcare supply measures. The outcome was place of death, defined as death in hospital or nursing homes. Results: In 2010, 41,275 individuals ≥66 years old died in a Swiss institution; 54 % in nursing homes and 46 % in hospitals. The probability of dying in hospital decreased with increasing age. The OR was 0.07 (95 % CI: 0.05-0.07) for age 91+ years compared to those 66-70 years. Living in peri-urban areas (OR = 1.06 95 % CI: 1.00-1.11) and French speaking region (OR = 1.43 95 % CI: 1.22-1.65) was associated with higher probability of hospital death. Females had lower probability of death in hospital (OR = 0.54 95 % CI: 0.51-0.56). The density of ambulatory care physicians (OR = 0. 81 95 % CI: 0.67-0.97) and nursing homes beds (OR = 0.67 95 % CI: 0.56-0.79) was negatively associated with hospital death. The proportion of dying in hospital varied from 38 % in HSAs with lowest proportion of hospital deaths to 60 % in HSAs with highest proportion of hospital deaths (1.6-fold variation). Conclusions: We found evidence for variation across regions in Switzerland in dying in hospital versus nursing homes, indicating possible overuse and underuse of end of life (EOL) services. [ABSTRACT FROM AUTHOR]

Research Objective: This study examines small area variations in readmission rates to assess whether higher readmission rate in an area is associated with higher clusters of patients with multiple chronic conditions.Study Design: The study uses hospital discharge data of adult (18+) patients in 6 U.S. states for 2009 from the Healthcare Cost and Utilization Project of the Agency for Healthcare Research and Quality, linked to contextual and provider data from Health Resources and Services Administration. A multivariate cross sectional design at primary care service area (PCSA) level is used.Principal Findings: Adjusting for area characteristics, the readmission rates were significantly higher in PCSAs having higher proportions of patients with 2-3 chronic conditions and those with 4+ chronic conditions, compared with areas with a higher concentration of patients with 0-1 chronic conditions.Conclusions: Using small area analysis, the study shows that areas with higher concentration of patients with increased comorbid conditions are more likely to have higher readmission rates. [ABSTRACT FROM AUTHOR]

Objetivos: Describir las desigualdades socioeconómicas en la mortalidad en la Comunidad Autónoma del País Vasco, utilizando datos de mortalidad y socioeconómicos por secciones censales. Métodos: Los datos sobre las defunciones y sobre la población fueron obtenidos del Instituto Vasco de Estadística. Se asignó a cada defunción las características socioeconómicas de su sección censal y se construyó un índice de privación que combinaba cuatro indicadores socioeconómicos. Se estimaron tasas de mortalidad ajustadas por edad para cada quintil del índice de privación por sexo, grupo de edad (0-64, ≥ 65 años) y causa de defunción. Mediante modelos de regresión de Poisson se estimaron las razones de tasas ajustadas por edad y el exceso de mortalidad atribuible a las desigualdades. Resultados: La mortalidad mostró un gradiente según el índice de privación en ambos sexos, siendo la mortalidad mayor en las secciones más desfavorecidas. Las mayores desigualdades se observaron en los varones menores de 65 años. Un 9,3 y un 4,9% de las defunciones en los varones y en las mujeres, respectivamente, fueron atribuibles a las desigualdades socioeconómicas. La importancia relativa de las causas de muerte fue distinta según la medida de desigualdad utilizada, y destacaron las causas relacionadas con los estilos de vida. Conclusiones: Este estudio ilustra la utilidad de los datos por sección censal para describir las desigualdades en la mortalidad y para identificar prioridades de intervención.Objectives: To describe socioeconomic inequalities in mortality in the Basque Country, using mortality and socioeconomic data by census sections. Methods: Mortality and population data were obtained from the Basque Institute of Statistics. Socioeconomic characteristics of the census sections were assigned to each death and a deprivation index combining information from four socioeconomic indicators was computed. Age-adjusted mortality rates by sex, age group (0-64 ≥ 65) and cause of death were calculated for each quintile of the deprivation index. Poisson regression models were fitted to estimate age-adjusted rate ratios and excess mortality attributable to inequalities. Results: Mortality showed a gradient according to the deprivation index in men and women. Mortality was greater in the most deprived sections. Mortality inequalities were observed in men younger than 65 years. A total of 9.3% of deaths in men and 4.9% of those in women were attributable to socioeconomic inequalities. The relative importance of the cause of death differed according to the inequality measure used. Lifestyle-related causes of death were notable. Conclusion: This study illustrates the potential utility of census section socioeconomic indicators both to describe socioeconomic inequalities in mortality and to identify priorities for interventions.

Background: Preventable mortality is a good indicator of possible problems to be investigated in the primary prevention chain, making it also a useful tool with which to evaluate health policies particularly public health policies. This study describes inequalities in preventable avoidable mortality in relation to socioeconomic status in small urban areas of thirty three Spanish cities, and analyses their evolution over the course of the periods 1996-2001 and 2002-2007. Methods: We analysed census tracts and all deaths occurring in the population residing in these cities from 1996 to 2007 were taken into account. The causes included in the study were lung cancer, cirrhosis, AIDS/HIV, motor vehicle traffic accidents injuries, suicide and homicide. The census tracts were classified into three groups, according their socioeconomic level. To analyse inequalities in mortality risks between the highest and lowest socioeconomic levels and over different periods, for each city and separating by sex, Poisson regression were used. Results: Preventable avoidable mortality made a significant contribution to general mortality (around 7.5%, higher among men), having decreased over time in men (12.7 in 1996-2001 and 10.9 in 2002-2007), though not so clearly among women (3.3% in 1996-2001 and 2.9% in 2002-2007). It has been observed in men that the risks of death are higher in areas of greater deprivation, and that these excesses have not modified over time. The result in women is different and differences in mortality risks by socioeconomic level could not be established in many cities. Conclusions: Preventable mortality decreased between the 1996-2001 and 2002-2007 periods, more markedly in men than in women. There were socioeconomic inequalities in mortality in most cities analysed, associating a higher risk of death with higher levels of deprivation. Inequalities have remained over the two periods analysed. This study makes it possible to identify those areas where excess preventable mortality was associated with more deprived zones. It is in these deprived zones where actions to reduce and monitor health inequalities should be put into place. Primary healthcare may play an important role in this process. [ABSTRACT FROM AUTHOR]

Background: While research continues into indicators such as preventable and amenable mortality in order to evaluate quality, access, and equity in the healthcare, it is also necessary to continue identifying the areas of greatest risk owing to these causes of death in urban areas of large cities, where a large part of the population is concentrated, in order to carry out specific actions and reduce inequalities in mortality. This study describes inequalities in amenable mortality in relation to socioeconomic status in small urban areas, and analyses their evolution over the course of the periods 1996–99, 2000–2003 and 2004–2007 in three major cities in the Spanish Mediterranean coast (Alicante, Castellón, and Valencia). Methods: All deaths attributed to amenable causes were analysed among non-institutionalised residents in the three cities studied over the course of the study periods. Census tracts for the cities were grouped into 3 socioeconomic status levels, from higher to lower levels of deprivation, using 5 indicators obtained from the 2001 Spanish Population Census. For each city, the relative risks of death were estimated between socioeconomic status levels using Poisson’s Regression models, adjusted for age and study period, and distinguishing between genders. Results: Amenable mortality contributes significantly to general mortality (around 10%, higher among men), having decreased over time in the three cities studied for men and women. In the three cities studied, with a high degree of consistency, it has been seen that the risks of mortality are greater in areas of higher deprivation, and that these excesses have not significantly modified over time. Conclusions: Although amenable mortality decreases over the time period studied, the socioeconomic inequalities observed are maintained in the three cities. Areas have been identified that display excesses in amenable mortality, potentially attributable to differences in the healthcare system, associated with areas of greater deprivation. Action must be taken in these areas of greater inequality in order to reduce the health inequalities detected. The causes behind socioeconomic inequalities in amenable mortality must be studied in depth. [ABSTRACT FROM AUTHOR]

The hospital admission for ambulatory care sensitive conditions (ACSCs) is a validated indicator of impeded access to good primary and preventive care services. The authors examine the predictors of ACSC admissions in small geographic areas in two cross-sections spanning an 11-year time interval (1995–2005). Using hospital discharge data from the Healthcare Cost and Utilization Project of the Agency for Healthcare Research and Quality for Arizona, California, Massachusetts, Maryland, New Jersey, and New York for the years 1995 and 2005, the study includes a multivariate cross-sectional design, using compositional factors describing the hospitalized populations and the contextual factors, all aggregated at the primary care service area level. The study uses ordinary least squares regressions with and without state fixed effects, adjusting for heteroscedasticity. Data is pooled over 2 years to assess the statistically significant changes in associations over time. ACSC admission rates were inversely related to the availability of local primary care physicians, and managed care was associated with declines in ACSC admissions for the elderly. Minorities, aged elderly, and percent under federal poverty level were found to be associated with higher ACSC rates. The comparative analysis for 2 years highlights significant declines in the association with ACSC rates of several factors including percent minorities and rurality. The two policy-driven factors, primary care physician capacity and Medicare-managed care penetration, were not found significantly more effective over time. Using small area analysis, the study indicates that improvements in socioeconomic conditions and geographic access may have helped improve the quality of primary care received by the elderly over the last decade, particularly among some minority groups. [ABSTRACT FROM AUTHOR]

Background Although efforts have been made to articulate rural-urban health inequalities in recent years, results have been inconsistent due to different geographical scales used in these studies. Small-area level investigations of health inequalities will likely show more detailed pictures of health inequalities among diverse rural communities, but they are difficult to conduct, particularly in a small population region. The objectives of this study were: 1) to compare life expectancy at birth for females and males across small-areas classified by locally defined settlement types for a small province in Canada; 2) to assess whether any of the settlement types explains variations in life expectancy over and above the extent of socioeconomic disadvantage and social isolation; and 3) to examine variations in life expectancies within a (larger) area unit used as the basis of health inequality investigations in previous studies. Methods Seven settlement types were determined for the 'community' units based on population perkilometre- road density and settlement forms. Mean life expectancies at birth for both genders were compared by settlement type, both for the entire province and within the Halifax Regional Municipality–the province's only census designated metropolitan area, but also contains rural settlements. Linear regression analyses were conducted to assess the statistical associations between life expectancy and the settlement types, adjusting for indicators of community-level deprivation. Results While types of communities considered as 'rural' generally had lower life expectancy for both genders, the effects of living in any settlement type were attenuated once adjusted for socioeconomic deprivation and social isolation. An exception was the village and settlement cluster type, which had additionally negative effects on health for females. There were some variations observed within the Halifax Regional Municipality, suggesting the importance of further investigating a variety of health and disease outcomes at smaller area-levels than those employed in previous studies. Conclusions This paper highlighted the importance of further articulating the differences in the characteristics of rural at finer area-levels and the differential influence they may have on health. Further efforts are desirable to overcome various data challenges in order to extend the investigation of health inequalities to hard-to-study provinces. [ABSTRACT FROM AUTHOR]

Background: Artificial fluoridation of drinking water to improve dental health has long been a topic of controversy. Opponents of this public health measure have cited the possibility of bone cancer induction. The study objective was to examine whether increased risk of primary bone cancer was associated with living in areas with higher concentrations of fluoride in drinking water.Methods: Case data on osteosarcoma and Ewing sarcoma, diagnosed at ages 0-49 years in Great Britain (GB) (defined here as England, Scotland and Wales) during the period 1980-2005, were obtained from population-based cancer registries. Data on fluoride levels in drinking water in England and Wales were accessed through regional water companies and the Drinking Water Inspectorate. Scottish Water provided data for Scotland. Negative binomial regression was used to examine the relationship between incidence rates and level of fluoride in drinking water at small area level.Results: The study analysed 2566 osteosarcoma and 1650 Ewing sarcoma cases. There was no evidence of an association between osteosarcoma risk and fluoride in drinking water [relative risk (RR) per one part per million increase in the level of fluoride = 1·001; 90% confidence interval (CI) 0·871, 1·151] and similarly there was no association for Ewing sarcoma (RR = 0·929; 90% CI 0·773, 1·115).Conclusions: The findings from this study provide no evidence that higher levels of fluoride (whether natural or artificial) in drinking water in GB lead to greater risk of either osteosarcoma or Ewing sarcoma. [ABSTRACT FROM AUTHOR]

Abstract: Improving the health and wellbeing of citizens ranks highly on the agenda of most governments. Policy action to enhance health and wellbeing can be targeted at a range of geographical levels and in England the focus has tended to shift away from the national level to smaller areas, such as communities and neighbourhoods. Our focus is to identify the potential for targeting policy interventions at the most appropriate geographical levels in order to enhance health and wellbeing. The rationale is that where variations in health and wellbeing indicators are larger, there may be greater potential for policy intervention targeted at that geographical level to have an impact on the outcomes of interest, compared with a strategy of targeting policy at those levels where relative variations are smaller. We use a multi-level regression approach to identify the degree of variation that exists in a set of health indicators at each level, taking account of the geographical hierarchical organisation of public sector organisations. We find that for each indicator, the proportion of total residual variance is greatest at smaller geographical areas. We also explore the variations in health indicators within a hierarchical level, but across the geographical areas for which public sector organisations are responsible. We show that it is feasible to identify a sub-set of organisations for which unexplained variation in health indicators is significantly greater relative to their counterparts. We demonstrate that adopting a geographical perspective to analyse the variation in indicators of health at different levels offers a potentially powerful analytical tool to signal where public sector organisations, faced increasingly with many competing demands, should target their policy efforts. This is relevant not only to the English context but also to other countries where responsibilities for health and wellbeing are being devolved to localities and communities. [Copyright &y& Elsevier]

Objectives To describe rates of Medicaid-funded services provided by orthodontists in Iowa to children and adolescents, identify factors associated with utilization, and describe geographic barriers to care. Methods We analyzed enrollment and claims data from the Iowa Medicaid program for a 3-year period, January 2008 through December 2010. Descriptive, bivariate, and multivariable logistic regression analyses were performed with utilization of orthodontic services as the main outcome variable. Service areas were identified by small area analysis in order to examine regional variability in utilization. Results The overall rate of orthodontic utilization was 3.1 percent. Medicaid enrollees living in small towns and rural areas were more likely to utilize orthodontic services than those living in urban areas. Children who had an oral evaluation by a primary care provider in the year prior to the study period were more likely to receive orthodontic services. Service areas with lower population density and greater mean travel distance to participating orthodontists had higher utilization rates than smaller, more densely populated areas. Conclusions Rural residency and increased travel distances do not appear to act as barriers to orthodontic care for this population. The wide variability of utilization rates seen across service areas may be related to workforce supply in the form of orthodontists who accept Medicaid-insured patients. Referrals to orthodontists from primary care dentists may improve access to specialty care for Medicaid enrollees. [ABSTRACT FROM AUTHOR]

Abstract: Regions affected by deindustrialisation are often characterised by unfavourable local health profiles. This was the situation in coalfield areas in England, where the scale and suddenness of the job losses in the 1980s and 1990s left these communities experiencing difficult socioeconomic conditions, and associated poor health status. Using data from the Health Survey for England, this paper examines whether poorer health outcomes still characterise coalfield areas today. Findings confirm a ‘coalfield health effect’ related to limiting long-term illness. With regards to self reported general health and mental health outcomes, results are less clear. The population health profile of coalfield communities is not homogeneous, with some coalfield communities faring worse than others, indicating more localised health issues. [Copyright &y& Elsevier]

Abstract: Indices of socio-economic deprivation are often used as a proxy for differences in the health behaviours of populations within small areas, but these indices are a measure of the economic environment rather than the health environment. Sets of synthetic estimates of the ward-level prevalence of low fruit and vegetable consumption, obesity, raised blood pressure, raised cholesterol and smoking were combined to develop an index of unhealthy lifestyle. Multi-level regression models showed that this index described about 50% of the large-scale geographic variation in CHD mortality rates in England, and substantially adds to the ability of an index of deprivation to explain geographic variations in CHD mortality rates. [Copyright &y& Elsevier]

Abstract: Space–time analysis of disease data has historically involved the search for patterns in aggregated data to identify how regions of high and low risk change through time. Space–time analysis of aggregated data has great value, but represents only a subset of space–time epidemiologic applications. Technological advances for tracking and mapping individuals (e.g., global positioning systems) have introduced mobile populations as an important element in space–time epidemiology. We review five domains critical to the developing field of spatio-temporal epidemiology: (1) spatio-temporal epidemiologic theory, (2) selection of appropriate spatial scale of analysis, (3) choice of spatial/spatio-temporal method for pattern identification, (4) individual-level exposure assessment in epidemiologic studies, and (5) assessment and consideration of locational and attribute uncertainty. This review provides an introduction to principles of space–time epidemiology and highlights future research opportunities. [Copyright &y& Elsevier]

Many statistical tests have been developed to assess the significance of clusters of disease located around known sources of environmental contaminants, also known as focused disease clusters. The majority of focused-cluster tests were designed to detect a particular spatial pattern of clustering, one in which the disease cluster centers around the pollution source and declines in a radial fashion with distance. However, other spatial patterns of environmentally related disease clusters are likely given that the spatial dispersion patterns of environmental contaminants, and thus human exposure, depend on a number of factors (i.e., meteorology and topography). For this study, data were simulated with five different spatial patterns of disease clusters, reflecting potential pollutant dispersion scenarios: (1) a radial effect decreasing with increasing distance, (2) a radial effect with a defined peak and decreasing with distance, (3) a simple angular effect, (4) an angular effect decreasing with increasing distance and (5) an angular effect with a defined peak and decreasing with distance. The power to detect each type of spatially distributed disease cluster was evaluated using Stone’s Maximum Likelihood Ratio Test, Tango’s Focused Test, Bithell’s Linear Risk Score Test, and variations of the Lawson–Waller Score Test. Study findings underscore the importance of considering environmental contaminant dispersion patterns, particularly directional effects, with respect to focused-cluster test selection in cluster investigations. The effect of extra variation in risk also is considered, although its effect is not substantial in terms of the power of tests. [ABSTRACT FROM AUTHOR]

Objective: Knowledge is lacking on the extent to which area-level characteristics contribute to variations observed in the use of mental health services. This study examined the influence of area- and individual-level characteristics on the use of mental health services. Methods: Data from a nationally representative, population-based, cross-sectional survey, the Canadian Community Health Survey-Mental Health and Well-Being, consisting of adults aged 15 years or older (n = 36 984), were linked to Canadian 2001 Census profiles according to health region boundaries (n = 97). Multilevel multivariable logistic regression modelling was used to: estimate variation in 12-month self-reported use of health services for mental health reasons between health regions; and, estimate the effects of individual- and area-level need, health resources, and sociodemographic factors on self-reported 12-month use of medical services for mental health reasons. Results: There was a 2.1% and 3.5% regional variation for general practitioner-family physician (GP-FP) and psychiatric health service use during 12 months, respectively. Most of the regional variation observed was explained by number of physicians per health region and regional and individual need factors. Adults who were middle-aged, had a post-secondary education, low-income, were separated, widowed, or divorced, and Canadian-born were significantly more likely to use GP-FP and psychiatry services for mental health reasons at the individual level, even after adjusting for area- and individual-level need factors. Conclusions: Most area-level variation was explained by the availability of health region resources and individual-level need factors. After accounting for need, numerous sociodemographic factors retained their association with use of mental health services. Additional efforts are needed at the area and individual level to reduce inequities through appropriate targeted care. [ABSTRACT FROM AUTHOR]

Background. Awareness of chronic kidney disease (CKD) has been prompted by the publication of several large epidemiological studies since 2002. This has led to various initiatives for the early identification and management of CKD, including the introduction of automated glomerular filtration rate (GFR) reporting and renal indicators in the primary care quality and outcomes framework (QOF) since April 2006. These initiatives were intended to promote identification of CKD and have had an impact on referral patterns to renal services. The aim of this study was to understand the nature of this impact in a catchment population of 1.2 million people. [ABSTRACT FROM PUBLISHER]

Abstract: The geography of small areas has important implications for studying the contextual determinants of health because of potential errors when measuring ecologic exposures and estimating their effects on health. In this paper, we present an approach for designing homogeneous zones optimising the spatial distribution of an area-level exposure, active living potential (ALP), based on data collected in Montreal, Canada. The objectives are to (1) assess and compare variation in walking behaviours between these purposefully designed zones and between standard administrative units, census tracts; and (2) disentangle the relative influence of ALP and area-level socioeconomic conditions on walking using the alternative geographies. Zones were designed by statistically classifying smallest census areas (disseminations areas) into seven categories of exposure similar along three indicators of ALP: population density, land use mix, and geographic accessibility to services. Mapping of categories resulted in the delineation of zones characterised by one of seven levels of ALP. A sample of 2716 adults aged ≥45 years was geocoded and cross-classified in 270 zones and 112 census tracts. Individuals reported on minutes and motives of walking and provided socioeconomic information. Data were analysed using cross-classified multilevel models. Variation in utilitarian walking was larger across the purposefully defined zones than across census tracts. Total walking varied significantly between census tracts only. Greater ALP was associated with more utilitarian walking but with less recreational walking. Higher socioeconomic position in census tracts was positively associated with total, utilitarian, and recreational walking. The soundness of standard administrative units for measuring ecologic exposure and their associations with health should be considered prior to conducting analyses. The added value of different approaches for understanding how place relates to health remains to be established and should be the focus of further investigations. [Copyright &y& Elsevier]