The County Health Rankings: rationale and methods

Association of the Robert Wood Johnson Foundations' Social Determinants of Health and Medicare Ocular Hospitalizations: A Cross Sectional Data Analysis

INTRODUCTION

Social determinants of health (SDH) may influence inpatient utilization rates and outcomes but have yet to be associated with ocular diagnoses. The purpose of this paper was to determine whether the SDH are associated with ocular hospitalizations.

METHODS

Patients from the national Medicare 100% Inpatient Limited Dataset were examined and linked to SDH measures from the Robert Wood Johnson Foundation (RWJF) County Health Rankings. Patients were included in the study group with either an admitting or primary diagnosis of an ophthalmic condition. All other hospitalized Medicare patients served in the comparison group. Nested logistic regression of these Medicare patients was conducted in their respective communities at the county level. SDH measures were benchmarked above or below the national median.

RESULTS

Positively associated SDH factors included communities with air pollution exceeding 11.62 micro grams per cubic meter (OR 1.05; 95% CI 1.01-1.08), communities where severe housing problems exceeding 14.38% (OR 1.13; 95% CI 1.09-1.18), children in single parent households exceeding 32.13% (OR 1.06; 95% CI 1.02-1.11), violent crime rate exceeding 250.54 per 100,000 (OR 1.07; 95% CI 1.03-1.12), diabetes exceeding 10.95% (OR 1.09: 95% CI 1.04-1.14), and drug poisoning deaths including opioids exceeding 14.17 per 100,000 (OR 1.04; 95% CI 1.01-1.08).

CONCLUSION

When compared to an all-condition, hospitalized population, ocular hospitalizations tended to have small, yet statistically significant associations with health behaviors, socioeconomic, and physical environment factors. Further research will be needed on how the physical environment, social, and community variables affect ocular health relative to all-cause hospitalizations.

Mobilising cross-sector collaborations to improve population health in US rural communities: a qualitative study

OBJECTIVES

This study examines types and forms of cross-sector collaborations employed by rural communities to address community health issues and identifies factors facilitating or inhibiting such collaborations.

SETTING

We conducted case studies of four rural communities in the US state of Iowa that have demonstrated progress in creating healthier communities.

PARTICIPANTS

Key informants from local public health departments, hospitals and other health-promoting organisations and groups participated in this study. Twenty-two key-informant interviews were conducted. Participants were selected based on their organisation's involvement in community health initiatives.

RESULTS

Rural communities used different forms of collaborations, including cross-sector partnership, cross-sector interaction and cross-sector exploration, to address community health issues. Stakeholders from public health, healthcare, social services, education and business sectors were involved. Factors facilitating cross-sector collaborations include health-promoting local contexts, seed initiatives that mobilise communities, hospital visions that embrace broad views of health and shared collaboration leadership and governance. Challenges to developing and sustaining cross-sector collaborations include different institutional logics, financial and human resources constraints and geographic dispersion.

CONCLUSIONS

Rural communities use cross-sector collaborations to address community health issues in the forms of interaction and exploration, but real and lasting partnerships are rare. The development, operation and sustainment of cross-sector collaborations are influenced by a set of contextual and practical factors. Practical strategies and policy interventions may be used to enhance cross-sector collaborations in rural communities.

Burden of Cause-Specific Mortality Associated With PM2.5 Air Pollution in the United States

IMPORTANCE

Ambient fine particulate matter (PM2.5) air pollution is associated with increased risk of several causes of death. However, epidemiologic evidence suggests that current knowledge does not comprehensively capture all causes of death associated with PM2.5 exposure.

OBJECTIVE

To systematically identify causes of death associated with PM2.5 pollution and estimate the burden of death for each cause in the United States.

DESIGN, SETTING, AND PARTICIPANTS

In a cohort study of US veterans followed up between 2006 and 2016, ensemble modeling was used to identify and characterize morphology of the association between PM2.5 and causes of death. Burden of death associated with PM2.5 exposure in the contiguous United States and for each state was then estimated by application of estimated risk functions to county-level PM2.5 estimates from the US Environmental Protection Agency and cause-specific death rate data from the Centers for Disease Control and Prevention.

MAIN OUTCOMES AND MEASURES

Nonlinear exposure-response functions of the association between PM2.5 and causes of death and burden of death associated with PM2.5.

EXPOSURES

Annual mean PM2.5 levels.

RESULTS

A cohort of 4 522 160 US veterans (4 243 462 [93.8%] male; median [interquartile range] age, 64.1 [55.7-75.5] years; 3 702 942 [82.0%] white, 667 550 [14.8%] black, and 145 593 [3.2%] other race) was followed up for a median (interquartile range) of 10.0 (6.8-10.2) years. In the contiguous United States, PM2.5 exposure was associated with excess burden of death due to cardiovascular disease (56 070.1 deaths [95% uncertainty interval {UI}, 51 940.2-60 318.3 deaths]), cerebrovascular disease (40 466.1 deaths [95% UI, 21 770.1-46 487.9 deaths]), chronic kidney disease (7175.2 deaths [95% UI, 5910.2-8371.9 deaths]), chronic obstructive pulmonary disease (645.7 deaths [95% UI, 300.2-2490.9 deaths]), dementia (19 851.5 deaths [95% UI, 14 420.6-31 621.4 deaths]), type 2 diabetes (501.3 deaths [95% UI, 447.5-561.1 deaths]), hypertension (30 696.9 deaths [95% UI, 27 518.1-33 881.9 deaths]), lung cancer (17 545.3 deaths [95% UI, 15 055.3-20 464.5 deaths]), and pneumonia (8854.9 deaths [95% UI, 7696.2-10 710.6 deaths]). Burden exhibited substantial geographic variation. Estimated burden of death due to nonaccidental causes was 197 905.1 deaths (95% UI, 183 463.3-213 644.9 deaths); mean age-standardized death rates (per 100 000) due to nonaccidental causes were higher among black individuals (55.2 [95% UI, 50.5-60.6]) than nonblack individuals (51.0 [95% UI, 46.4-56.1]) and higher among those living in counties with high (65.3 [95% UI, 56.2-75.4]) vs low (46.1 [95% UI, 42.3-50.4]) socioeconomic deprivation; 99.0% of the burden of death due to nonaccidental causes was associated with PM2.5 levels below standards set by the US Environmental Protection Agency.

CONCLUSIONS AND RELEVANCE

In this study, 9 causes of death were associated with PM2.5 exposure. The burden of death associated with PM2.5 was disproportionally borne by black individuals and socioeconomically disadvantaged communities. Effort toward cleaner air might reduce the burden of PM2.5-associated deaths.

How to Be an Informed Consumer of Evidence Ratings: It's in the Details

What are evidence-based strategies and how can public health practitioners find evidence without conducting extensive literature reviews? We developed an inventory of clearinghouses and other resources that disseminate research on evidence of effectiveness. We examined differences in evidence classification among 6 evidence clearinghouses that rate the effectiveness of community-level strategies to address determinants of health. Most evidence clearinghouses clearly defined their scope, but only a few clearinghouses explicitly defined the types of strategies they assess (eg, programs, policies, practices). The term “evidence-based” was widely used, but definitions and standards were inconsistent across organizations and disciplines. Evidence clearinghouses varied in the way they used evidence rating classifications and criteria for assigning ratings. Attention to detail is important. The criteria for the top rating of some evidence clearinghouses, for example, require a more thorough literature review with more robust results than the criteria for the top rating of others. In addition, some clearinghouses report only on strategies considered to be evidence-based, whereas others also report on strategies that have no effect, mixed evidence, or no qualifying studies, demonstrating that a listing of a strategy by an evidence clearinghouse does not necessarily mean that it is effective. We conclude by providing guidance for users of evidence clearinghouses about how to interpret and effectively apply rating criteria across platforms: look closely at the details of how clearinghouses assign their ratings and be aware of similarities and differences when you are aligning potential strategies with your local priorities. We encourage communities to balance evidence with local needs, resources, and culture in strategy selection and funding decisions.

[Facilitating community oriented primary health care. Basic health indicators by small areas in Catalonia]

Health depends mostly on factors that lay outside the health system, such as socioeconomic determinants. Intersectorality and participation are key elements for an integrated care person and community centred. In Catalonia, the National Primary Health and Community Strategy aims to reinforce primary health care, so that it becomes the backbone of the health system and with a strong community orientation. To deploy a community oriented primary health care it is important to count with reliable and robust data by small areas. Thirty-eight basic indicators were selected for each of the 370 primary health care in Catalonia. Indicators were calculated and presented following the social determinants model: demographic (4), socioeconomic (3), morbidity (9), mortality (6), lifestyles (4), preventive practices (1), resources and use of health services (9) and physical environment (2). These indicators will allow health professionals to carry out local health assessments in a fast and systematic manner.

Evaluation of data availability on population health indicators at the regional level across the European Union

BACKGROUND

The ability to measure regional health inequalities across Europe and to build adequate population health indices depends significantly on the availability of reliable and comparable data at the regional level. Within the scope of the EU-funded project EURO-HEALTHY, a Population Health Index (PHI) was built. This model aggregates 39 indicators considered relevant by experts and stakeholders to evaluate and monitor population health on the regional level within the European Union (269 regions). The aim of this research was to assess the data availability for those indicators. As a subsequent aim, an adequate protocol to overcome issues arising from missing data will be presented, as well as key messages for both national and European statistical authorities meant to improve data collection on population health.

METHODS

The methodology for the study includes three consecutive phases: (i) assessing the data availability for the respective indicators at the regional level for the last year available (ii) applying a protocol for missing data and completing the database and (iii) developing a scoring system ranging from 0 (no data available; worst) to 1 (all data available; best) to evaluate the availability of data by indicator and EU region.

RESULTS

Although the missing data on the set of the PHI indicators was significant, the mean availability score for the EURO-HEALTHY PHI indicators is 0.8 and the regional availability score is 0.7, which reveal the strength of the indicators as well as the data completeness protocol for missing data.

CONCLUSIONS

This study provides a comprehensive data availability assessment for population health indicators from multiple areas of concern, at the EU regional level. The results highlight that the data completeness protocol and availability scores are suitable tools to apply on any indicator's data source mapping. It also raises awareness to the urgent need for sub-national data in several domains and for closing the data gaps between and within countries. This will require policies clearly focused on improving equity between regions and a coordinated effort from the producers of data (the EU28 national statistics offices and EUROSTAT) and the stakeholders who design policies at EU, regional and local level.

Integrating social and behavioral determinants of health into patient care and population health at Veterans Health Administration: a conceptual framework and an assessment of available individual and population level data sources and evidence-based measurements

The premise of this project was that social and behavioral determinants of health (SBDH) affect the use of healthcare services and outcomes for patients in an integrated healthcare system such as the Veterans Health Administration (VHA), and thus individual patient level socio-behavioral factors in addition to the neighborhood characteristics and geographically linked factors could add information beyond medical factors mostly considered in clinical decision making, patient care, and population health. To help VHA better address SBDH risk factors for the veterans it cares for within its primary care clinics, we proposed a conceptual and analytic framework, a set of evidence-based measures, and their data source. The framework and recommended SBDH metrics can provide a road map for other primary care-centric healthcare organizations wishing to use health analytic tools to better understand how SBDH affect health outcomes.

Factors Related to International Travel for Transplantation Among U.S.-Listed Kidney Transplant Candidates

Factors related to travel for transplantation were examined using data from the Scientific Registry for Transplant Recipients. Candidates who traveled abroad for a kidney transplant had higher odds of being male, Asian or Hispanic, college-educated, employed, privately insured, and a non-U.S. citizen/non-U.S. resident. Candidates with a body mass index greater than 30, a calculated panel reactive antibody (cPRA) greater than 80%, and a history of more than two years of hemodialysis or peritoneal dialysis had lower odds of traveling abroad for a kidney transplant. Geographically, candidates listed in the northeastern region of the United States (New York and Western Vermont) had the highest odds of traveling abroad for a kidney transplant. Findings of this study can be used to guide practice and education with transplant candidates, and to direct further investigation in this understudied but growing area of transplantation.

Community determinants of COPD exacerbations in elderly patients in Poland: protocol for a retrospective Big Data observational cohort study

INTRODUCTION

Analyses of large sets of electronic health-related data (Big Data), including local community indicators, may improve knowledge of the outcomes of chronic diseases among patients and healthcare systems. Our study will estimate the prevalence of chronic obstructive pulmonary disease (COPD) and its exacerbations in elderly patients in the Lodz region, Poland; it will also evaluate local community factors potentially associated with disease exacerbations and rank local communities according to health and local community indicators.

METHODS

and analysis : Local community factors, including medical/health, socioeconomic and environmental values potentially associated with COPD exacerbations will be identified. A retrospective analysis of a cohort of about half a million people 65 years old and older, living in local communities of the Lodz region in 2016 will be performed. Relevant data will be extracted from databases, including those of the National Health Fund, Tax Office and National Statistics Centre. This cross-sectional study will include data for a 1 year period, from 1 January until 31 December 2016. The data will first be checked for quality, cleaned and analysed using data mining techniques, and then multilevel logistic regression will be used to discover the community determinants of COPD exacerbations.

ETHICS AND DISSEMINATION

The study protocol has been approved by the Bioethical Committee of Medical University of Lodz (RNN/248/18/KE, 10 July 2018). Our findings will be published in peer-reviewed journals and reports.

The Intersection of Urban and Global Health

Providers from high-income countries are often drawn to practicing medicine in settings identified as having greater need, whether that be in a low- or middle-income country or within an underserved area within their own high-income country. Despite sharing the goal of fostering health equity, global health and urban health are often considered in dichotomous ways. This article points a lens at the intersection of these 2 fields, highlighting what they can learn from each other.

Disparities in opioid related mortality between United States counties from 2000 to 2014

BACKGROUND

The general increase in opioid-related deaths is well documented, and disparities by geographic regions and demographic characteristics have been observed as well. However, the distribution of opioid-related deaths among U.S. counties and the trends in that distribution have not been fully explored. This study examines the inequality in opioid death rates to assess convergence or divergence in opioid-related mortality between counties.

METHODS

Using mortality data from the NVSS for 2000-2014, this study examines the Gini coefficient of the county opioid mortality distribution.

RESULTS

The distribution of opioid mortality became more equal, with the Gini coefficient falling from 0.81 in 2000 to 0.61 in 2014. Counties with lower initial opioid mortality rates experienced faster growth in mortality than counties with high initial mortality.

CONCLUSIONS

Counties have experienced a convergence in opioid mortality rates. This poses potential challenges for addressing the crisis, as measures must become much broader in scope and be implemented in areas in which the dangers of the opioid crisis are not as apparent.

Adolescent Behavioral Cancer Prevention in the United States: Creating a Composite Variable and Ranking States' Performance

Preventive behaviors established during adolescence can reduce cancer throughout the life span. Understanding the combinations of multiple behaviors, and how these behaviors vary across states, is important for identifying where additional interventions are needed. Using data on 2011-2015 vaccination, energy balance, and substance use from national surveys, we created state-level composite scores for adolescent cancer prevention. Hierarchical Bayesian linear mixed models were used to predict estimates for states with no data on select behaviors. We used a Monte Carlo procedure with 100,000 simulations to generate states' ranks and 95% confidence intervals. Across states, hepatitis B vaccination was 84.3% to 97.1%, and human papillomavirus vaccination was 41.8% to 78.0% for girls and 19.0% to 59.3% for boys. For energy balance, 20.2% to 34.6% of adolescents met guidelines for physical activity, 4.1% to 15.8% for fruit and vegetable consumption, and 66.4% to 82.0% for healthy weight. For substance use, 82.5% to 93.5% reported abstaining from binge alcohol use, 84.3% to 95.4% from cigarette smoking, and 62.9% to 92.8% from marijuana use. (1) Rhode Island, (2) Colorado, (4) Hawaii and New Hampshire (tied), and (5) Vermont performed the best for adolescent cancer prevention, and (47) Missouri, (48) Arkansas, Mississippi, and South Carolina (tied), and (51) Kentucky performed the worst. However, 95% CIs around ranks often overlapped, indicating lack of statistical differences. Adolescent cancer prevention behaviors clustered into a composite index. States varied on their performance on this index, especially for states at the high and low extremes, but most states did not differ statistically. These findings can inform decision makers about where and how to intervene to improve cancer prevention among adolescents.

A Bayesian spatio-temporal analysis on racial disparities in hypertensive disorders of pregnancy in Florida, 2005-2014

Disparities in hypertensive disorders of pregnancy (HDP) exist among racial and ethnic groups in the US. However, little is known about spatio-temporal variations in HDP disparities. We used a Bayesian hierarchical regression approach to investigate spatio-temporal variations in HDP disparities from 2005 to 2014. County-level variation was firstly examined, followed by census tract-level variation assessment in counties where high HDP disparities were observed. A significant disadvantage in HDP was revealed for African Americans in Florida overall (Odds Ratio: 1.27, 95% Confidence Interval: 1.25, 1.29), with significant spatial variations. The greatest HDP disparities between African Americans and non-African Americans occurred in North Central Florida counties (the Big Bend region of Florida), with consistent patterns from 2005 to 2014. Analyses at census tract-level further revealed significant neighborhood disparities within these counties. Findings from this study provide important information for public health agencies and policymakers to reduce HDP disparities at the population level.

Identification of Factors Associated With Variation in US County-Level Obesity Prevalence Rates Using Epidemiologic vs Machine Learning Models

IMPORTANCE

Obesity is a leading cause of high health care expenditures, disability, and premature mortality. Previous studies have documented geographic disparities in obesity prevalence.

OBJECTIVE

To identify county-level factors associated with obesity using traditional epidemiologic and machine learning methods.

DESIGN, SETTING, AND PARTICIPANTS

Cross-sectional study using linear regression models and machine learning models to evaluate the associations between county-level obesity and county-level demographic, socioeconomic, health care, and environmental factors from summarized statistical data extracted from the 2018 Robert Wood Johnson Foundation County Health Rankings and merged with US Census data from each of 3138 US counties. The explanatory power of the linear multivariate regression and the top performing machine learning model were compared using mean R2 measured in 30-fold cross validation.

EXPOSURES

County-level demographic factors (population; rural status; census region; and race/ethnicity, sex, and age composition), socioeconomic factors (median income, unemployment rate, and percentage of population with some college education), health care factors (rate of uninsured adults and primary care physicians), and environmental factors (access to healthy foods and access to exercise opportunities).

MAIN OUTCOMES AND MEASURES

County-level obesity prevalence in 2018, its association with each county-level factor, and the percentage of variation in county-level obesity prevalence explained by linear multivariate and gradient boosting machine regression measured with R2.

RESULTS

Among the 3138 counties studied, the mean (range) obesity prevalence was 31.5% (12.8%-47.8%). In multivariate regressions, demographic factors explained 44.9% of variation in obesity prevalence; socioeconomic factors, 33.0%; environmental factors, 15.5%; and health care factors, 9.1%. The county-level factors with the strongest association with obesity were census region, median household income, and percentage of population with some college education. R2 values of univariate regressions of obesity prevalence were 0.238 for census region, 0.218 for median household income, and 0.160 for percentage of population with some college education. Multivariate linear regression and gradient boosting machine regression (the best-performing machine learning model) of obesity prevalence using all county-level demographic, socioeconomic, health care, and environmental factors had R2 values of 0.58 and 0.66, respectively (P < .001).

CONCLUSIONS AND RELEVANCE

Obesity prevalence varies significantly between counties. County-level demographic, socioeconomic, health care, and environmental factors explain the majority of variation in county-level obesity prevalence. Using machine learning models may explain significantly more of the variation in obesity prevalence..

City-Level Measures of Health, Health Determinants, and Equity to Foster Population Health Improvement: The City Health Dashboard

OBJECTIVES

To support efforts to improve urban population health, we created a City Health Dashboard with area-specific data on health status, determinants of health, and equity at city and subcity (census tract) levels.

METHODS

We developed a Web-based resource that includes 37 metrics across 5 domains: social and economic factors, physical environment, health behaviors, health outcomes, and clinical care. For the largest 500 US cities, the Dashboard presents metrics calculated to the city level and, where possible, subcity level from multiple data sources, including national health surveys, vital statistics, federal administrative data, and state education data sets.

RESULTS

Iterative input from city partners shaped Dashboard development, ensuring that measures can be compared across user-selected cities and linked to evidence-based policies to spur action. Reports from early deployment indicate that the Dashboard fills an important need for city- and subcity-level data, fostering more granular understanding of health and its drivers and supporting associated priority-setting.

CONCLUSIONS

By providing accessible city-level data on health and its determinants, the City Health Dashboard complements local surveillance efforts and supports urban population health improvement on a national scale.

A community health case for psychiatric care: A cross-sectional study of county health rankings

OBJECTIVES

To determine if there is an association between the provision of mental health services and county health rankings in the United States.

METHODS

We used retrospective population-based, 2016 U.S. county level cross-sectional analysis to determine the association of mental health services provision on U.S. counties health rankings. The key dependent variables in this study were the county health factor rankings (CHR). The presence of inpatient, outpatient and other facilities which may provide mental health services are identified for each county. Multilevel mixed effects ordinal logistic regression models were used to account for nesting effects utilizing two levels of data which include hospital- and county-level data.

RESULTS

Better county health rankings were associated with the presence of Outpatient services (OR = 0.69, 95% CI: 0.55-0.85) and Psychiatric hospital (OR = 0.55, 95% CI: 0.40-0.74).

CONCLUSION

These findings suggest a significant association between psychiatric care and community health. Access to psychiatric services is associated with improved population health.

Associations between implementation of Project Lazarus and opioid analgesic dispensing and buprenorphine utilization in North Carolina, 2009-2014

Background

Project Lazarus (PL) is a seven-strategy, community-coalition-based intervention designed to reduce opioid overdose and dependence. The seven strategies include: community education, provider education, hospital emergency department policy change, diversion control, support programs for patients with pain, naloxone policies, and addiction treatment expansion. PL was originally developed in Wilkes County, NC. It was made available to all counties in North Carolina starting in March 2013 with funding of up to $34,400 per county per year. We examined the association between PL implementation and 1) overall dispensing rate of opioid analgesics, and 2) utilization of buprenorphine. Buprenorphine is often used in connection with medication assisted treatment (MAT) for opioid dependence.

Methods

Observational interrupted time series analysis of 100 counties over 2009–2014 (n = 7200 county-months) in North Carolina. The intervention period was March 2013–December 2014. 74 of 100 counties implemented the intervention. Exposure data sources comprised process surveys, training records, Prescription Drug Monitoring Program (PDMP) data, and methadone treatment program quality data. Outcomes were PDMP-derived counts of opioid prescriptions and buprenorphine patients. Incidence Rate Ratios were estimated with adjusted GEE Poisson regression models of all seven PL strategies.

Results

In adjusted models, diversion control efforts were positively associated with increased dispensing of opioid analgesics (IRR: 1.06; 95% CI: 1.03, 1.09). None of the other PL strategies were associated with reduced prescribing of opioid analgesics. Support programs for patients with pain were associated with a non-significant decrease in buprenorphine utilization (IRR: 0.93; 95% CI: 0.85, 1.02), but addiction treatment expansion efforts were associated with no change in buprenorphine utilization (IRR: 0.98; 95% CI: 0.91, 1.06).

Conclusions

Implementation of PL strategies did not appreciably reduce opioid dispensing and did not increase buprenorphine utilization. These results are consistent with previous findings of limited impact of PL strategies on overdose morbidity and mortality. Future studies should analyze the uptake of MAT using a more expansive view of institutional barriers, treating community coalition activity around MAT as an effect modifier.

Lifespan Socioeconomic Context: Associations With Cognitive Functioning in Later Life

Objectives

Early socioeconomic status (SES) correlates with later-life cognition. However, the effect of socioeconomic context (SEC), which reflects influences from broader ecological contexts, has not been examined. The present study developed a measure of SEC using lifetime residential addresses and examined SEC and residential mobility effects on later-life cognition.

Method

Older adults (N = 117, Mage = 75) reported addresses since birth. Latent SEC was constructed from census income, employment, and education (1920-2010) for each county and census year, extrapolated between census years. Controlling for current SES, SEC in childhood (ages 0-18) and adulthood (ages 19-60), with finer granulations in young adulthood (ages 19-39) and midlife (ages 40-60), predicted later-life cognition. Effects of residential mobility on later-life cognition were also examined.

Results

Higher childhood and adulthood SEC were associated with better Auditory Verbal Learning Test recognition (β = .24, p = .012) and immediate recall (β = .26, p = .008). Higher midlife SEC was associated with faster task switching (β = .26, p = .025) and better task switching efficiency (β = .27, p = .022). Higher residential mobility in childhood was associated with higher crystallized intelligence (β = .194, p = .040).

Discussion

Independent of current SES, childhood and adulthood SEC predicted later-life cognition, which may be sensitive to effects of social institutions and environmental health. SEC assessed across the lifespan, and related residential mobility information may be important complements to SES in predicting later-life cognitive health.

Home and community-based services coordination for homebound older adults in home-based primary care

BACKGROUND

Medically complex vulnerable older adults often face social challenges that affect compliance with their medical care plans, and thus require home and community-based services (HCBS). This study describes how non-medical social needs of homebound older adults are assessed and addressed within home-based primary care (HBPC) practices, and to identify barriers to coordinating HCBS for patients.

METHODS

An online survey of members of the American Academy of Home Care Medicine (AAHCM) was conducted between March through November 2016 in the United States. A 56-item survey was developed to assess HBPC practice characteristics and how practices identify social needs and coordinate and evaluate HCBS. Data from 101 of the 150 surveys received were included in the analyses. Forty-four percent of respondents were physicians, 24% were nurse practitioners, and 32% were administrators or other HBPC team members.

RESULTS

Nearly all practices (98%) assessed patient social needs, with 78% conducting an assessment during the intake visit, and 88% providing ongoing periodic assessments. Seventy-four percent indicated 'most' or 'all' of their patients needed HCBS in the past 12 months. The most common needs were personal care (84%) and medication adherence (40%), and caregiver support (38%). Of the 86% of practices reporting they coordinate HCBS, 91% followed-up with patients, 84% assisted with applications, and 83% made service referrals. Fifty-seven percent reported that coordination was 'difficult.' The most common barriers to coordinating HCBS included cost to patient (65%), and eligibility requirements (63%). Four of the five most frequently reported barriers were associated with practices reporting it was 'difficult' or 'very difficult' to coordinate HCBS (OR from 2.49 to 3.94, p-values < .05).

CONCLUSIONS

Despite the barriers to addressing non-medical social needs, most HBPC practices provided some level of coordination of HCBS for their high-need, high-cost homebound patients. More efforts are needed to implement and scale care model partnerships between medical and non-medical service providers within HBPC practices.

Patchwork of contrasting medication cultures across the USA

Health in the United States is markedly heterogeneous, with large disparities in disease incidence, treatment choices and health spending. Drug prescription is one major component of health care-reflecting the accuracy of diagnosis, the adherence to evidence-based guidelines, susceptibility to drug marketing and regulatory factors. Using medical claims data covering nearly half of the USA population, we have developed and validated a framework to compare prescription rates of 600 popular drugs in 2334 counties. Our approach uncovers geographically separated sub-Americas, where patients receive treatment for different diseases, and where physicians choose different drugs for the same disease. The geographical variation suggests influences of racial composition, state-level health care laws and wealth. Some regions consistently prefer more expensive drugs, even when they have not been proven more efficacious than cheaper alternatives. Our study underlines the benefit of aggregating massive information on medical practice into a summarized and actionable form.

Factors Associated With Cancer Disparities Among Low-, Medium-, and High-Income US Counties

BACKGROUND

There are concerns about the degree to which county income level might underlie the stark disparities in cancer death rates among US counties; at the same time, there is uncertainty about the factors that may mediate the disparities.

OBJECTIVES

To assess county-level cancer death rates and to identify possible mediators of the association between county-level median incomes and cancer death rates.

DESIGN, SETTING, AND PARTICIPANTS

Cross-sectional study using death records from the National Center for Health Statistics in 2014, with data collected and analyzed between October 1, 2016, and July 31, 2017. All US counties and county equivalents were included.

EXPOSURES

County-level median household income.

MAIN OUTCOMES AND MEASURES

County-level age-standardized cancer death rate.

RESULTS

In 3135 counties, median incomes ranged from $22 126 to $121 250 per year. Low-income counties (median income, $33 445) vs high-income counties (median income, $55 780) had higher proportions of residents who were non-Hispanic black, lived in rural areas, or reported poor or fair health. The mean (SD) cancer death rate was 185.9 (24.4) per 100 000 person-years in high-income counties, compared with 204.9 (26.3) and 229.7 (32.9) per 100 000 person-years in medium- and low-income counties, respectively. In mediation models, health risk behaviors (smoking, obesity, and physical inactivity); clinical care factors (unaffordable care and low-quality care); health environments (food insecurity); and health policies (state smoke-free laws and Medicaid payment rates) in aggregate accounted for more than 80% of the income-related disparity. The strongest possible mediators were food insecurity (explaining 19.1% [95% CI, 12.5%-26.5%] of the association between county incomes and cancer deaths), low-quality care (17.9%; 95% CI, 14.0%-21.8%), smoking (12.7%; 95% CI, 9.4%-15.6%), and physical inactivity (12.2%; 95% CI, 9.4%-15.6%).

CONCLUSIONS AND RELEVANCE

There are wide gaps in cancer death rates between low-, medium-, and high-income counties. Future studies are needed to assess whether targeting the possible mediators might ameliorate the substantial socioeconomic cancer disparities.

The Intersection of Residence, Community Vulnerability, and Premature Mortality

PURPOSE

Rural communities often experience higher rates of mortality than their urban counterparts, with gaps widening in the foreseeable future. However, the underlying level of socioeconomic vulnerability (area deprivation) among rural communities can vary widely. This study examines rural-urban differences in mortality-related outcomes within comparable levels of deprivation.

METHODS

Rural-urban differences in Years of Potential Life Lost (YPLL), derived from the County Health Rankings, were examined across comparable levels of area deprivation using a quantile regression approach. Rural-urban differences in YPLL were estimated at the 10th, 25th, 50th, 75th, and 90th percentiles across levels of deprivation.

FINDINGS

Compared to the reference population (urban counties/least deprived) a clear increase in YPLL among both rural and urban counties was noted across levels of deprivation, with the highest level of YPLL occurring in counties with the most deprivation. While YPLL increased across levels of deprivation, the magnitude of these differences was markedly higher in rural counties compared to urban, particularly among the most deprived counties. Rural counties experienced an advantage at the lowest percentiles and levels of deprivation. However, this advantage quickly deteriorated, revealing significant rural disparities at the highest level of deprivation.

CONCLUSIONS

This study noted a differential effect in mortality-related outcomes among rural counties within comparable levels of community deprivation. Findings contribute to evidence that many, but not all rural communities face a double disadvantage. This underscores the need for a continued focus on the development and implementation of multiple policies aimed at reducing differences in poverty, education, and access to care.

Fitting a square peg into a round hole: Perceptions of Appalachian physicians on the incorporation of chronic disease prevention into their practice

This study used a focus group in August 2017 (n = 9) to explore the perceptions of rural physicians to a state request to incorporate diabetes prevention screening into their West Virginia medical practice. Analysis of the data revealed that the participants did not think private physicians were equipped to incorporate diabetes prevention programming into their practice. Three categories emerged from the data analysis to explain the reasoning of the health practitioners on the incorporation of pre-diabetes screening and management into their practice.⁎

The practice of medicine

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Prevention is a mismatch

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Social determinants of health

In the end, the study revealed that a request for physicians to identify and refer at risk patients to a diabetes prevention program is problematic due to conceptual and structural issues. Based on the findings it does not appear at this time that private physicians in rural settings can incorporate diabetes prevention into their existing practice. To address conceptual and structural barriers the invitation to rural physicians must: 1) present evidence on how physicians may be effective in a diabetes management team; 2) include a model that demonstrates a limited, specific role and duties for the physician within a team setting; and last, 3) integrate physicians into an existing community-based network of social and human service providers set up to provide diabetes prevention services.

The 2016 global and national burden of diabetes mellitus attributable to PM2·5 air pollution

BACKGROUND

PM_2·5 air pollution is associated with increased risk of diabetes; however, a knowledge gap exists to further define and quantify the burden of diabetes attributable to PM_2·5 air pollution. Therefore, we aimed to define the relationship between PM_2·5 and diabetes. We also aimed to characterise an integrated exposure response function and to provide a quantitative estimate of the global and national burden of diabetes attributable to PM_2·5.

METHODS

We did a longitudinal cohort study of the association of PM_2·5 with diabetes. We built a cohort of US veterans with no previous history of diabetes from various databases. Participants were followed up for a median of 8·5 years, we and used survival models to examine the association between PM_2·5 and the risk of diabetes. All models were adjusted for sociodemographic and health characteristics. We tested a positive outcome control (ie, risk of all-cause mortality), negative exposure control (ie, ambient air sodium concentrations), and a negative outcome control (ie, risk of lower limb fracture). Data for the models were reported as hazard ratios (HRs) and 95% CIs. Additionally, we reviewed studies of PM_2·5 and the risk of diabetes, and used the estimates to build a non-linear integrated exposure response function to characterise the relationship across all concentrations of PM_2·5 exposure. We included studies into the building of the integrated exposure response function if they scored at least a four on the Newcastle-Ottawa Quality Assessment Scale and were only included if the outcome was type 2 diabetes or all types of diabetes. Finally, we used the Global Burden of Disease study data and methodologies to estimate the attributable burden of disease (ABD) and disability-adjusted life-years (DALYs) of diabetes attributable to PM_2·5 air pollution globally and in 194 countries and territories.

FINDINGS

We examined the relationship of PM_2·5 and the risk of incident diabetes in a longitudinal cohort of 1 729 108 participants followed up for a median of 8·5 years (IQR 8·1-8·8). In adjusted models, a 10 μg/m³ increase in PM_2·5 was associated with increased risk of diabetes (HR 1·15, 95% CI 1·08-1·22). PM_2·5 was associated with increased risk of death as the positive outcome control (HR 1·08, 95% CI 1·03-1·13), but not with lower limb fracture as the negative outcome control (1·00, 0·91-1·09). An IQR increase (0·045 μg/m³) in ambient air sodium concentration as the negative exposure control exhibited no significant association with the risk of diabetes (HR 1·00, 95% CI 0·99-1·00). An integrated exposure response function showed that the risk of diabetes increased substantially above 2·4 μg/m³, and then exhibited a more moderate increase at concentrations above 10 μg/m³. Globally, ambient PM_2·5 contributed to about 3·2 million (95% uncertainty interval [UI] 2·2-3·8) incident cases of diabetes, about 8·2 million (95% UI 5·8-11·0) DALYs caused by diabetes, and 206 105 (95% UI 153 408-259 119) deaths from diabetes attributable to PM_2·5 exposure. The burden varied substantially among geographies and was more heavily skewed towards low-income and lower-to-middle-income countries.

INTERPRETATION

The global toll of diabetes attributable to PM_2·5 air pollution is significant. Reduction in exposure will yield substantial health benefits.

FUNDING

US Department of Veterans Affairs.

Are US degree-granting institutions associated with better community health determinants and outcomes?

OBJECTIVES

The impact of individual education level on health outcomes is well-established, but the effect of degree-granting institutions on county health rankings (CHRs) is unknown. The objective of this study is to determine if there is an association between the presence of degree-granting institutions and CHRs.

STUDY DESIGN

This is an ecologic study.

METHODS

Data for 3062 counties were derived from the Robert Wood Johnson County Health Rankings and the Integrated Postsecondary Education Data System for year 2016. Ordinal logistic regression was utilized to determine the association between presence of a degree-granting institution and county rankings for health behaviors, health outcomes, clinical care, social and economic factors, and physical environment. All models were adjusted for potential confounding factors including age, sex, race/ethnicity, English language proficiency, and urban/rural location.

RESULTS

The presence of a degree-granting institution was positively associated with CHRs for length of life (odds ratio [OR] = 1.28; 95% confidence interval [CI]: 1.05, 1.58), health behaviors (OR = 1.46; 95% CI: 1.19, 1.79), and clinical care (OR = 1.36; 95% CI: 1.11, 1.67). Counties with the highest rankings in one health category were more likely to score in the highest rankings for other health categories.

CONCLUSIONS

These findings suggest that degree-granting institutions are associated with CHRs. Partnerships between public health agencies and degree-granting institutions are recommended to improve population health.

Active Ottumwa: Adapting Evidence-Based Recommendations to Promote Physical Activity in a Micropolitan New Destination Community

Background: Evidence-based interventions have been developed and tested to promote physical activity, but fewer studies have focused on identifying effective intervention strategies for mid-size rural communities, especially new immigrant destinations. We report here on the design and implementation of Active Ottumwa, a community-wide intervention using a lay health advisor approach to increase physical activity in a micropolitan new destination community in the rural state of Iowa. Methods: The Active Ottumwa study is part of a community-academic partnership in Ottumwa, IA. Evidence-based strategies recommended by the Community Guide for Preventive Services guided study implementation and included behavioral and social, campaign and informational, and environmental and policy approaches. Evaluation methods for this study are multi-faceted and include a cross-sectional community survey, longitudinal cohort assessment, observational data, key informant interviews, and project records. Results: We are currently in our second year of intervention implementation, with 45 lay health advisors (termed physical activity leaders here) trained to carry out behavioral and social intervention approaches, including walking groups, tai chi, and yoga. We have completed a communication and informational campaign utilizing five channels. Our longitudinal cohort has been recruited, with baseline and 12-month data collection completed. Conclusions: This study will assess the effectiveness and impact of a community-wide intervention to support physical activity.

Rochester Epidemiology Project Data Exploration Portal

Introduction

The goal of this project was to develop an interactive, web-based tool to explore patterns of prevalence and co-occurrence of diseases using data from the expanded Rochester Epidemiology Project (E-REP) medical records-linkage system.

Methods

We designed the REP Data Exploration Portal (REP DEP) to include summary information for people who lived in a 27-county region of southern Minnesota and western Wisconsin on January 1, 2014 (n = 694,506; 61% of the entire population). We obtained diagnostic codes of the International Classification of Diseases, 9th edition, from the medical records-linkage system in 2009 through 2013 (5 years) and grouped them into 717 disease categories. For each condition or combination of 2 conditions (dyad), we calculated prevalence by dividing the number of persons with a specified condition (numerator) by the total number of persons in the population (denominator). We calculated observed-to-expected ratios (OERs) to test whether 2 conditions co-occur more frequently than would co-occur as a result of chance alone.

Results

We launched the first version of the REP DEP in May 2017. The REP DEP can be accessed at http://rochesterproject.org/portal/. Users can select 2 conditions of interest, and the REP DEP displays the overall prevalence, age-specific prevalence, and sex-specific prevalence for each condition and dyad. Also displayed are OERs overall and by age and sex and maps of county-specific prevalence of each condition and OER.

Conclusion

The REP DEP draws upon a medical records-linkage system to provide an innovative, rapid, interactive, free-of-charge method to examine the prevalence and co-occurrence of 717 diseases and conditions in a geographically defined population.

Assessing the validity of a data driven segmentation approach: A 4 year longitudinal study of healthcare utilization and mortality

BACKGROUND

Segmentation of heterogeneous patient populations into parsimonious and relatively homogenous groups with similar healthcare needs can facilitate healthcare resource planning and development of effective integrated healthcare interventions for each segment. We aimed to apply a data-driven, healthcare utilization-based clustering analysis to segment a regional health system patient population and validate its discriminative ability on 4-year longitudinal healthcare utilization and mortality data.

METHODS

We extracted data from the Singapore Health Services Electronic Health Intelligence System, an electronic medical record database that included healthcare utilization (inpatient admissions, specialist outpatient clinic visits, emergency department visits, and primary care clinic visits), mortality, diseases, and demographics for all adult Singapore residents who resided in and had a healthcare encounter with our regional health system in 2012. Hierarchical clustering analysis (Ward's linkage) and K-means cluster analysis using age and healthcare utilization data in 2012 were applied to segment the selected population. These segments were compared using their demographics (other than age) and morbidities in 2012, and longitudinal healthcare utilization and mortality from 2013-2016.

RESULTS

Among 146,999 subjects, five distinct patient segments "Young, healthy"; "Middle age, healthy"; "Stable, chronic disease"; "Complicated chronic disease" and "Frequent admitters" were identified. Healthcare utilization patterns in 2012, morbidity patterns and demographics differed significantly across all segments. The "Frequent admitters" segment had the smallest number of patients (1.79% of the population) but consumed 69% of inpatient admissions, 77% of specialist outpatient visits, 54% of emergency department visits, and 23% of primary care clinic visits in 2012. 11.5% and 31.2% of this segment has end stage renal failure and malignancy respectively. The validity of cluster-analysis derived segments is supported by discriminative ability for longitudinal healthcare utilization and mortality from 2013-2016. Incident rate ratios for healthcare utilization and Cox hazards ratio for mortality increased as patient segments increased in complexity. Patients in the "Frequent admitters" segment accounted for a disproportionate healthcare utilization and 8.16 times higher mortality rate.

CONCLUSION

Our data-driven clustering analysis on a general patient population in Singapore identified five patient segments with distinct longitudinal healthcare utilization patterns and mortality risk to provide an evidence-based segmentation of a regional health system's healthcare needs.

Health systems around the world - a comparison of existing health system rankings

Background

Existing health systems all over the world are different due to the different combinations of components that can be considered for their establishment. The ranking of health systems has been a focal points for many years especially the issue of performance. In 2000 the World Health Organization (WHO) performed a ranking to compare the Performance of the health system of the member countries. Since then other health system rankings have been performed and it became an issue of public discussion. A point of contention regarding these rankings is the methodology employed by each of them, since no gold standard exists. Therefore, this review focuses on evaluating the methodologies of each existing health system performance ranking to assess their reproducibility and transparency.

Methods

A search was conducted to identify existing health system rankings, and a questionnaire was developed for the comparison of the methodologies based on the following indicators: (1) General information, (2) Statistical methods, (3) Data (4) Indicators. Overall nine rankings were identified whereas six of them focused rather on the measurement of population health without any financial component and were therefore excluded. Finally, three health system rankings were selected for this review: “Health Systems: Improving Performance” by the WHO, “Mirror, Mirror on the wall: How the Performance of the US Health Care System Compares Internationally” by the Commonwealth Fund and “the Most efficient Health Care” by Bloomberg.

Results

After the completion of the comparison of the rankings by giving them scores according to the indicators, the ranking performed the WHO was considered the most complete regarding the ability of reproducibility and transparency of the methodology.

Conclusions

This review and comparison could help in establishing consensus in the field of health system research. This may also help giving recommendations for future health rankings and evaluating the current gap in the literature.

Assessing Social Contagion in Body Mass Index, Overweight, and Obesity Using a Natural Experiment

IMPORTANCE

Little is known about whether the substantial clustering of obesity and overweight within social and geographic networks results from causal pathways, such as social contagion and shared environments, or from self-selection.

OBJECTIVES

This study aimed to determine whether exposure to communities with higher rates of obesity increases the body mass index (BMI) of individuals, calculated as weight in kilograms divided by height in meters squared, and their risk of being overweight or obese, and whether social contagion, shared environments, or self-selection can account for identified differences.

DESIGN, SETTING, AND PARTICIPANTS

This natural experiment study used the routine assignment of military service members to installations as a source of exogenous variation in exposure to communities with higher vs lower rates of obesity. The study, which used data collected by the Military Teenagers' Environments, Exercise, and Nutrition Study, examined families from 38 military installations around the United States to determine if individuals had higher BMI and greater odds of overweight and obesity when assigned to installations in counties with higher rates of obesity. The study also examined if the relationship persisted after controlling for shared built environments. The participants included 1 parent and 1 child aged 12 or 13 years from 1519 families of Army-enlisted personnel. Data analysis was completed from November 2016 to October 2017.

EXPOSURES

Adult obesity rate in the county where the assigned installation of the service member was located. Time at installation and location of residence (on-installation vs off-installation) were used to measure the degree of exposure.

MAIN OUTCOMES AND MEASURES

For parents, outcomes were BMI, overweight/obesity (BMI, ≥25) and obesity (BMI, ≥30). For children, outcomes were BMI z score, overweight/obesity (BMI percentile for age and sex, ≥85), and obesity (BMI percentile for age and sex, ≥95). These outcomes were based on self-reports for parents, self-reports and parent reports for all children, and anthropometric measurements for a subsample of children.

RESULTS

Members of 1519 families participated, including 1314 adults (of whom 740, or 56%, were fathers) and 1111 children (of whom 576, or 52%, were boys); anthropometric measurements were performed on 458 children. The sample was 40% white, 22% black, 24% Hispanic, and 14% other races/ethnicities. A 1-percentage point higher county obesity rate was associated with a higher BMI (a difference of 0.08; 95% CI, 0.02-0.13) and greater odds of obesity (adjusted odds ratio [aOR], 1.05; 95% CI, 1.02-1.08) in parents, and a higher BMI z score (0.01; 95% CI, 0.003-0.02) and greater odds of overweight/obesity (aOR, 1.04; 95% CI, 1.01-1.06) in children. The evidence supported stronger associations among families with more time at installation and off-installation residence. Associations persisted even after controlling for shared built environments.

CONCLUSIONS AND RELEVANCE

Exposure to counties with higher rates of obesity was associated with higher BMI and higher odds of overweight and/or obesity in parents and children. There was no evidence to support self-selection or shared built environments as possible explanations, which suggests the presence of social contagion in obesity.

Patient-Centered Communication

As the population ages, morbidity and mortality associated with chronic disease will increase. Some patient-centered improvements have been made in health care services, but optimal health has not been fully realized. Only when pharmacists have a holistic understanding of an individual patient, including their experience of illness and medication, can they effectively assess appropriateness, safety, efficacy, and adherence to medications and develop realistic treatment plans. When patients are involved in their care, they are better able to manage complex chronic conditions by understanding and adhering to their plan of care. Pharmacists can enable patients to participate fully using patient-centered communication. There are relatively few published articles on patient-centered communication specific to pharmacists, but the Calgary-Cambridge guide and Four Habits model have applicability to pharmacy practice. The Patient-Centered Communication Tools (PaCT), created for use in pharmacy education and loosely based on the Four Habits model, can assist pharmacists in developing their patient-centered communication skills. Lastly, best practices for patient-centered communication in pharmacy practice are described.

A statewide evaluation of seven strategies to reduce opioid overdose in North Carolina

BACKGROUND

In response to increasing opioid overdoses, US prevention efforts have focused on prescriber education and supply, demand and harm reduction strategies. Limited evidence informs which interventions are effective. We evaluated Project Lazarus, a centralised statewide intervention designed to prevent opioid overdose.

METHODS

Observational intervention study of seven strategies. 74 of 100 North Carolina counties implemented the intervention. Dichotomous variables were constructed for each strategy by county-month. Exposure data were: process logs, surveys, addiction treatment interviews, prescription drug monitoring data. Outcomes were: unintentional and undetermined opioid overdose deaths, overdose-related emergency department (ED) visits. Interrupted time-series Poisson regression was used to estimate rates during preintervention (2009-2012) and intervention periods (2013-2014). Adjusted IRR controlled for prescriptions, county health status and time trends. Time-lagged regression models considered delayed impact (0-6 months).

RESULTS

In adjusted immediate-impact models, provider education was associated with lower overdose mortality (IRR 0.91; 95% CI 0.81 to 1.02) but little change in overdose-related ED visits. Policies to limit ED opioid dispensing were associated with lower mortality (IRR 0.97; 95% CI 0.87 to 1.07), but higher ED visits (IRR 1.06; 95% CI 1.01 to 1.12). Expansions of medication-assisted treatment (MAT) were associated with increased mortality (IRR 1.22; 95% CI 1.08 to 1.37) but lower ED visits in time-lagged models.

CONCLUSIONS

Provider education related to pain management and addiction treatment, and ED policies limiting opioid dispensing showed modest immediate reductions in mortality. MAT expansions showed beneficial effects in reducing ED-related overdose visits in time-lagged models, despite an unexpected adverse association with mortality.

Reducing Cardiovascular Disparities Through Community-Engaged Implementation Research: A National Heart, Lung, and Blood Institute Workshop Report

Cardiovascular disparities remain pervasive in the United States. Unequal disease burden is evident among population groups based on sex, race, ethnicity, socioeconomic status, educational attainment, nativity, or geography. Despite the significant declines in cardiovascular disease mortality rates in all demographic groups during the last 50 years, large disparities remain by sex, race, ethnicity, and geography. Recent data from modeling studies, linked micromap plots, and small-area analyses also demonstrate prominent variation in cardiovascular disease mortality rates across states and counties, with an especially high disease burden in the southeastern United States and Appalachia. Despite these continued disparities, few large-scale intervention studies have been conducted in these high-burden populations to examine the feasibility of reducing or eliminating cardiovascular disparities. To address this challenge, on June 22 and 23, 2017, the National Heart, Lung, and Blood Institute convened experts from a broad range of biomedical, behavioral, environmental, implementation, and social science backgrounds to summarize the current state of knowledge of cardiovascular disease disparities and propose intervention strategies aligned with the National Heart, Lung, and Blood Institute mission. This report presents the themes, challenges, opportunities, available resources, and recommended actions discussed at the workshop.

"Top Performing" US Hospitals and the Health Status of Counties they Serve

This study (a) examined the relationships between "top performing" US hospitals and the health status of counties they serve and (b) compared the health status of "top performing" US hospital counties versus that of remaining US counties. Statistical analyses considered US News and World Report Honor Roll ranking data, as a measure of hospital performance, and County Health Rankings (CHR) data, as a measure of county health status. "Top performing" hospital Honor Roll scores were correlated with measures of Clinical Care (p < 0.001). Counties with "top performing" US hospitals presented greater health status with regard to All Health Outcomes (p < 0.001), Length of Life (p < 0.001), Quality of Life (p < 0.001), All Health Factors (p < 0.001), Health Behaviors (p < 0.001), and Clinical Care (p < 0.001), than compared to remaining US counties. Hospital impact on county health status remains primarily recognized in clinical care and not in overall health. Also, counties that contain a "top performing" US hospital tend to present lower health risks to their citizens than compared to other US counties.

A county-level cross-sectional analysis of positive deviance to assess multiple population health outcomes in Indiana

OBJECTIVE

To test a positive deviance method to identify counties that are performing better than statistical expectations on a set of population health indicators.

DESIGN

Quantitative, cross-sectional county-level secondary analysis of risk variables and outcomes in Indiana. Data are analysed using multiple linear regression to identify counties performing better or worse than expected given traditional risk indicators, with a focus on 'positive deviants' or counties performing better than expected.

PARTICIPANTS

Counties in Indiana (n=92) constitute the unit of analysis.

MAIN OUTCOME MEASURES

Per cent adult obesity, per cent fair/poor health, low birth weight per cent, per cent with diabetes, years of potential life lost, colorectal cancer incidence rate and circulatory disease mortality rate.

RESULTS

County performance that outperforms expectations is for the most part outcome specific. But there are a few counties that performed particularly well across most measures.

CONCLUSIONS

The positive deviance approach provides a means for state and local public health departments to identify places that show better health outcomes despite demographic, social, economic or behavioural disadvantage. These places may serve as case studies or models for subsequent investigations to uncover best practices in the face of adversity and generalise effective approaches to other areas.

Associations of ambient coarse particulate matter, nitrogen dioxide, and carbon monoxide with the risk of kidney disease: a cohort study

BACKGROUND

Experimental evidence and preliminary clinical evidence suggest that environmental air pollution adversely effects kidney health. Previous work has examined the association between fine particulate matter and risk of kidney disease; however, the association between ambient coarse particulate matter (PM₁₀; ≤10 μm in aerodynamic diameter), nitrogen dioxide (NO₂), and carbon monoxide (CO) and risk of incident chronic kidney disease, chronic kidney disease progression, and end-stage renal disease is not clear.

METHODS

We merged multiple large databases, including those of the Environmental Protection Agency and the Department of Veterans Affairs, to build a cohort of US veterans, and used survival models to evaluate the association between PM₁₀, NO₂, and CO concentrations and risk of incident estimated glomerular filtration rate (eGFR) of less than 60 mL/min per 1·73 m², incident chronic kidney disease, eGFR decline of 30% or more, and end-stage renal disease. We treated exposure as time-varying when it was updated annually and as cohort participants moved.

FINDINGS

Between Oct 1, 2003, and Sept 30, 2012, 2 010 398 cohort participants were followed up over a median of 8·52 years (IQR 8·05-8·80). An increased risk of eGFR of less than 60 mL/min per 1·73 m² was associated with an IQR increase in concentrations of PM₁₀ (hazard ratio 1·07, 95% CI 1·06-1·08), NO₂ (1·09, 1·08-1·10), and CO (1·09, 1·08-1·10). An increased risk of incident chronic kidney disease was associated with an IQR increase in concentrations of PM₁₀ (1·07, 1·05-1·08), NO₂ (1·09, 1·08-1·11), and CO (1·10, 1·08-1·11). An increased risk of an eGFR decline of 30% or more was associated with an IQR increase in concentrations of PM₁₀ (1·08, 1·07-1·09), NO₂ (1·12, 1·10-1·13), and CO (1·09, 1·08-1·10). An increased risk of end-stage renal disease was associated with an IQR increase in concentrations of PM₁₀ (1·09, 1·06-1·12), NO₂ (1·09, 1·06-1·12), and CO (1·05, 1·02-1·08). Spline analyses suggested a monotonic increasing association between PM₁₀, NO₂, and CO concentrations and risk of kidney outcomes.

INTERPRETATION

Environmental exposure to higher concentrations of PM₁₀, NO₂, and CO is associated with increased risk of incident chronic kidney disease, eGFR decline, and end-stage renal disease.

FUNDING

US Department of Veterans Affairs.

Particulate Matter Air Pollution and the Risk of Incident CKD and Progression to ESRD

Elevated levels of fine particulate matter <2.5 µm in aerodynamic diameter (PM_2.5) are associated with increased risk of cardiovascular outcomes and death, but their association with risk of CKD and ESRD is unknown. We linked the Environmental Protection Agency and the Department of Veterans Affairs databases to build an observational cohort of 2,482,737 United States veterans, and used survival models to evaluate the association of PM_2.5 concentrations and risk of incident eGFR <60 ml/min per 1.73 m², incident CKD, eGFR decline ≥30%, and ESRD over a median follow-up of 8.52 years. County-level exposure was defined at baseline as the annual average PM_2.5 concentrations in 2004, and separately as time-varying where it was updated annually and as cohort participants moved. In analyses of baseline exposure (median, 11.8 [interquartile range, 10.1-13.7] µg/m³), a 10-µg/m³ increase in PM_2.5 concentration was associated with increased risk of eGFR<60 ml/min per 1.73 m² (hazard ratio [HR], 1.21; 95% confidence interval [95% CI], 1.14 to 1.29), CKD (HR, 1.27; 95% CI, 1.17 to 1.38), eGFR decline ≥30% (HR, 1.28; 95% CI, 1.18 to 1.39), and ESRD (HR, 1.26; 95% CI, 1.17 to 1.35). In time-varying analyses, a 10-µg/m³ increase in PM_2.5 concentration was associated with similarly increased risk of eGFR<60 ml/min per 1.73 m², CKD, eGFR decline ≥30%, and ESRD. Spline analyses showed a linear relationship between PM_2.5 concentrations and risk of kidney outcomes. Exposure estimates derived from National Aeronautics and Space Administration satellite data yielded consistent results. Our findings demonstrate a significant association between exposure to PM_2.5 and risk of incident CKD, eGFR decline, and ESRD.

Ranking States on Coverage of Cancer-Preventing Vaccines Among Adolescents: The Influence of Imprecision

OBJECTIVES

Identifying the best and worst states for coverage of cancer-preventing vaccines (hepatitis B [HepB] and human papillomavirus [HPV]) may guide public health officials in developing programs, such as promotion campaigns. However, acknowledging the imprecision of coverage and ranks is important for avoiding overinterpretation. The objective of this study was to examine states' vaccination coverage and ranks, as well as the imprecision of these estimates, to inform public health decision making.

METHODS

We used data on coverage of HepB and HPV vaccines among adolescents aged 13-17 from the 2011-2015 National Immunization Survey-Teen (n = 103 729 from 50 US states and Washington, DC). We calculated coverage, 95% confidence intervals (CIs), and ranks for vaccination coverage in each state, and we generated simultaneous 95% CIs for ranks using a Monte Carlo method with 100 000 simulations.

RESULTS

Across years, HepB vaccination coverage was 92.2% (95% CI, 91.8%-92.5%; states' range, 84.3% in West Virginia to 97.0% in Connecticut). HPV vaccination coverage was 57.4% (95% CI, 56.6%-58.2%; range, 41.8% in Kansas to 78.0% in Rhode Island) for girls and 31.0% (95% CI, 30.3%-31.8%; range, 19.0% in Utah to 59.3% in Rhode Island) for boys. States with the highest and lowest ranks generally had narrow 95% CIs; for example, Rhode Island was ranked first (95% CI, 1-1) and Kansas was ranked 51st (95% CI, 49-51) for girls' HPV vaccination. However, states with intermediate ranks had wider and more imprecise 95% CIs; for example, New York was 26th for girls' HPV vaccination coverage, but its 95% CI included ranks 18-35.

CONCLUSIONS

States' ranks of coverage of cancer-preventing vaccines were imprecise, especially for states in the middle of the range; thus, performance rankings presented without measures of imprecision could be overinterpreted. However, ranks can highlight high-performing and low-performing states to target for further research and vaccination promotion programming.

The Epidemic of Despair Among White Americans: Trends in the Leading Causes of Premature Death, 1999-2015

OBJECTIVES

To evaluate trends in premature death rates by cause of death, age, race, and urbanization level in the United States.

METHODS

We calculated cause-specific death rates using the Compressed Mortality File, National Center for Health Statistics data for adults aged 25 to 64 years in 2 time periods: 1999 to 2001 and 2013 to 2015. We defined 48 subpopulations by 10-year age groups, race/ethnicity, and county urbanization level (large urban, suburban, small or medium metropolitan, and rural).

RESULTS

The age-adjusted premature death rates for all adults declined by 8% between 1999 to 2001 and 2013 to 2015, with decreases in 39 of the 48 subpopulations. Most decreases in death rates were attributable to HIV, cardiovascular disease, and cancer. All 9 subpopulations with increased death rates were non-Hispanic Whites, largely outside large urban areas. Most increases in death rates were attributable to suicide, poisoning, and liver disease.

CONCLUSIONS

The unfavorable recent trends in premature death rate among non-Hispanic Whites outside large urban areas were primarily caused by self-destructive health behaviors likely related to underlying social and economic factors in these communities.

Uses of Population Health Rankings in Local Policy Contexts: A Multisite Case Study

Population health rankings are a common strategy to spur evidence-informed health policy making, but little is known about their uses or impacts. The study aims were to (1) understand how and why the County Health Rankings (CH-Rankings) are used in local policy contexts, (2) identify factors that influence CH-Rankings utilization, and (3) explore potentially negative impacts of the CH-Rankings. Forty-four interviews were conducted with health organization officials and public policy makers in 15 purposively selected counties. The CH-Rankings were used instrumentally to inform internal planning decisions, conceptually to educate the public and policy makers about determinants of population health, and politically to advance organizational agendas. Factors related to organizational capacity, county political ideology, and county rank influenced if, how, and why the CH-Rankings were used. The CH-Rankings sometimes had the negative impacts of promoting potentially ineffective interventions in politically conservative counties and prompting negative media coverage in some counties with poor rank.

Does hospital ownership influence hospital referral region health rankings in the United States

Extensive evidence demonstrates that a hospital's organizational ownership structure impacts its overall performance, but little is known concerning the influence of hospital structure on the health of its community. This paper explores the association between US hospital referral region (HRR) health rankings and hospital ownership and performance. Data from the 2016 Commonwealth Fund Scorecard on Local Health System Performance, the American Hospital Association dataset, and the Hospital Value-Based Purchasing dataset are utilized to conduct a cross-sectional analysis of 36 quality measures across 306 HRRs. Multivariate regression analysis was used to estimate the association among hospital ownership, system performance measures-access and affordability, prevention and treatment, avoidable hospital use and cost, and healthy lives-and performance as measured by value-based purchasing total performance scores. We found that indicators of access and affordability, as well as prevention and treatment, were significantly associated across all 3 hospitals' organizational structures. Hospital referral regions with a greater number of not-for-profit hospitals demonstrated greater indications of access and affordability, as well as better prevention and treatment rankings than for-profit and government hospitals. Hospital referral regions with a greater number of government, nonfederal hospitals had worse scores for healthy lives. Furthermore, the greater the total performance scores score, the better the HRR score on prevention and treatment rankings. The greater the per capita income, the better the score across all 4 dimensions. As such, this inquiry supports the assertion that performance of a local health system is dependent on its community's resources of health care delivery entities and their structure.