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Introduction: Produce prescriptions are one of several approaches that have been receiving national attention to increase food security while also improving diet for patients with diet-related health challenges. Implementation of produce prescriptions vary widely, from food vouchers to tailored, pre-arranged weekly home deliveries. Our study focuses on sending weekly curated produce boxes, scaled by family size up to 5 members. We curated 5 different culturally themed boxes for participants to choose from with the ability to change theme at any time throughout the study period. It was our hypothesis that pre-curated, culturally varied boxes would allow to address patient food preferences to assure continued retention. Methods: This produce prescriptions pre-randomized clinical trial is being conducted at Kaiser Permanente Southern California, an integrated health care system with over 4.5M members. Adult members with uncontrolled diabetes with recent Hemoglobin A1c >7.5% or 8% (if >65 years or certain comorbid conditions) were pre-randomized into a high or low-dose (intervention) versus usual care group. Surveys are conducted at 4 different time points (2 and 8-week, 3 and 6 months) for participants pre-randomized to the intervention group. The eight week and 3 months survey that are now completed provide participant feedback on utility and satisfaction of both the food and recipes provided. Results: Recruitment has closed to enrollment; the intervention is slated to end January 2023. There were 150 participants enrolled in each arm for a total 450 participants. The average age at enrollment was 59 years. Over 62% reported currently being unemployed, retired or disabled. Just over half (51%), reported Spanish as their preferred language. Although diabetes was the main eligibility for this study, participants on average had 2-3 additional comorbidities, with 65% also being diagnosed with hypertension and 8.5% having congestive heart failure. Generally, respondents (N=187) considered the foods culturally appropriate with 69% indicating being familiar with the foods delivered and 70% knew how to prepare the foods. Recipes were moderately useful familiarizing participants with new foods (26%). Only 41% reported that of the foods new to them, they would use again in the future. Deliveries could be improved for future with participants overwhelmingly asking for more fruit, vegetables, legumes (98%, 94%, 71%), while also stating diary would be important to add to the deliveries (94%). Overall, 72% of participants reporting being very satisfied with the weekly produce deliveries and 81% stated the food helped them eat healthier throughout the week. Conclusions: Weekly produce deliveries are a promising approach to promote healthier eating providing more variety and autonomy in choices will likely increase use of produce and overall satisfaction in such programs.

Objective Develop and validate a risk score using variables available during an Emergency Department (ED) encounter to predict adverse events among patients with suspected COVID-19. Methods A retrospective cohort study of adult visits for suspected COVID-19 between March 1 – April 30, 2020 at 15 EDs in Southern California. The primary outcomes were death or respiratory decompensation within 7-days. We used least absolute shrinkage and selection operator (LASSO) models and logistic regression to derive a risk score. We report metrics for derivation and validation cohorts, and subgroups with pneumonia or COVID-19 diagnoses. Results 26,600 ED encounters were included and 1079 experienced an adverse event. Five categories (comorbidities, obesity/BMI ≥ 40, vital signs, age and sex) were included in the final score. The area under the curve (AUC) in the derivation cohort was 0.891 (95% CI, 0.880–0.901); similar performance was observed in the validation cohort (AUC = 0.895, 95% CI, 0.874–0.916). Sensitivity ranging from 100% (Score 0) to 41.7% (Score of ≥15) and specificity from 13.9% (score 0) to 96.8% (score ≥ 15). In the subgroups with pneumonia (n = 3252) the AUCs were 0.780 (derivation, 95% CI 0.759–0.801) and 0.832 (validation, 95% CI 0.794–0.870), while for COVID-19 diagnoses (n = 2059) the AUCs were 0.867 (95% CI 0.843–0.892) and 0.837 (95% CI 0.774–0.899) respectively. Conclusion Physicians evaluating ED patients with pneumonia, COVID-19, or symptoms suspicious for COVID-19 can apply the COVAS score to assist with decisions to hospitalize or discharge patients during the SARS CoV-2 pandemic.

ImportanceUnaffordable housing is associated with adverse health-related outcomes, but little is known about the associations between moving due to unaffordable housing and health-related outcomes.ObjectiveTo characterize the association of recent cost-driven residential moves with health-related outcomes.Design, Setting, and ParticipantsThis cross-sectional study involved a weighted multivariable regression analysis of California Health Interview Survey data from January 1, 2011, to December 31, 2017. A population-based sample of 52 646 adult renters and other nonhomeowners in California were included. Data were analyzed from March 2, 2021, to January 6, 2023.ExposureCost-driven moves in the past 3 years relative to no move and to non–cost-driven moves.Main Outcomes and MeasuresFive outcomes were assessed: psychological distress (low, moderate, or severe, as categorized by the 6-item Kessler Psychological Distress Scale), emergency department [ED] visits in the past year (any vs none), preventive care visits in the past year (any vs none), general health (poor or fair vs good, very good, or excellent), and walking for leisure in the past 7 days (in minutes).ResultsAmong 52 646 adult renters and other nonhomeowners, 50.3% were female, 85.2% were younger than 60 years, 45.3% were Hispanic, and 55.1% had income lower than 200% of the federal poverty level. Overall, 8.9% of renters reported making a recent cost-driven move, with higher prevalence among Hispanic (9.9%) and non-Hispanic Black (11.3%) renters compared with non-Hispanic White renters (7.2%). In multivariable models, compared with not moving, cost-driven moving was associated with a 4.2 (95% CI, 2.6-5.7) percentage point higher probability of experiencing moderate psychological distress; a 3.2 (95% CI, 1.9-4.5) percentage point higher probability of experiencing severe psychological distress; a 2.5 (95% CI, 0-4.9) percentage point higher probability of ED visits; a 5.1 (95% CI, 1.6-8.6) percentage point lower probability of having preventive care visits; a 3.7 (95% CI, 1.2-6.2) percentage point lower probability of having good, very good, or excellent general health; and 16.8 (95% CI, 6.9-26.6) fewer minutes of walking for leisure. General health, psychological distress, and walking for leisure were also worse with cost-driven moves relative to non–cost-driven moves, with a 3.2 (95% CI, 1.7-4.7) percentage point higher probability of experiencing moderate psychological distress; a 2.5 (95% CI, 1.2-3.9) percentage point higher probability of experiencing severe psychological distress; a 4.6 (95% CI, 2.1-7.2) percentage point lower probability of having good, very good, or excellent general health; and 13.0 (95% CI, 4.0-21.9) fewer minutes of walking for leisure. However, the incidence of preventive care and ED visits did not differ between those who made cost-driven vs non–cost-driven moves.Conclusions and RelevanceIn this study, cost-driven moves were associated with adverse health-related outcomes relative to not moving and to non–cost-driven moves. These findings suggest that policies to improve housing affordability, prevent displacement, and increase access to health care for groups vulnerable to cost-driven moves may have the potential to improve population health equity, especially during the current national housing affordability crisis.

Few studies have tested multiple socio-ecological risk factors assocated with firearm injury among pediatric populations and distinguished self-inflicted from non-self-inflicted injury. To address this gap, the current study examined demographic, individual psychosocial, and neighborhood variables as risk factors for firearm injury among a large cohort of children and adolescents.Retrospective cohort study. Data were obtained from the electronic health records of a large integrated healthcare system. The cohort included children18 years with at least one clinical encounter between January 1, 2010 and December 31, 2018. Poisson regression was used to examine demographic (age, gender, race and ethnicity, Medicaid status), psychosocial (depression, substance use disorder, medical comorbidities), and neighborhood education variables as potential risk factors for non-self-inflicted and self-inflicted firearm injuries.For non-self-inflicted injury, the highest relative risk was found for children age 12-17 years old compared to 0-5 year olds (RR = 37.57); other risk factors included male gender, Black and Hispanic race and ethnicity (compared to White race), being a Medicaid recipient, lower neighborhood education, and substance use disorder diagnosis. For self-inflicted injury, only age 12-17 years old and male gender were associated with increased risk.These results reinforce the established higher risk for firearm injury among adolescent males, highlight differences between self-inflicted and non-self-inflicted injuries, and the need to consider demographic, psychosocial, and neighborhood variables as risk factors to inform interventions aimed to reduce firearm injuries among children and adolescents.

Background We estimated longitudinal trajectories of body mass index (BMI) z-score and percentile, weight for height (WFH) z-score and percentile, and percentage of the 95th BMI percentile (BMIp95) among low-income Hispanic children ages 2–5 years to provide normative data for this population and compare the behavior of different measures. Methods Longitudinal height and weight measurements obtained from 18,072 Hispanic children aged 2–5 years enrolled in the Special Supplemental Nutrition Program for Women, Infants and Children in Los Angeles County were analyzed. Trajectories of adiposity-related measures were estimated using mixed models, stratified by sex and BMI percentile at age 2 years. Results For children in the 5th–85th BMI percentile at age 2 years, all adiposity-related measures rose during ages 2–3.5 years; during ages 3.5–5 years, BMI-based measures increased, BMIp95 decreased, and WFH-based measures were stable. For children exceeding the 85th BMI percentile at age 2 years, measures generally trended downward during ages 2–5 years, except for BMIp95, which had variable trends. Conclusions Adiposity measures changed at different rates as children grew during ages 2–3.5 years compared to ages 3.5–5 years, and different measures displayed different trends. Studies should consider examining multiple measures and focusing on change relative to a comparison group. Impact To address the childhood obesity epidemic, information on normative trajectories of adiposity-related measures in at-risk populations of young children is needed. Longitudinal analysis of data collected from low-income Hispanic children during ages 2–5 years revealed different patterns for different adiposity measures and for ages 2–3.5 years versus 3.5–5 years. Child obesity studies should consider examining multiple adiposity measures and focus on change relative to a comparison group to avoid misinterpreting longitudinal patterns.

Objective To evaluate whether a natural language processing (NLP) algorithm could be adapted to extract, with acceptable validity, markers of residential instability (ie, homelessness and housing insecurity) from electronic health records (EHRs) of 3 healthcare systems. Materials and methods We included patients 18 years and older who received care at 1 of 3 healthcare systems from 2016 through 2020 and had at least 1 free-text note in the EHR during this period. We conducted the study independently; the NLP algorithm logic and method of validity assessment were identical across sites. The approach to the development of the gold standard for assessment of validity differed across sites. Using the EntityRuler module of spaCy 2.3 Python toolkit, we created a rule-based NLP system made up of expert-developed patterns indicating residential instability at the lead site and enriched the NLP system using insight gained from its application at the other 2 sites. We adapted the algorithm at each site then validated the algorithm using a split-sample approach. We assessed the performance of the algorithm by measures of positive predictive value (precision), sensitivity (recall), and specificity. Results The NLP algorithm performed with moderate precision (0.45, 0.73, and 1.0) at 3 sites. The sensitivity and specificity of the NLP algorithm varied across 3 sites (sensitivity: 0.68, 0.85, and 0.96; specificity: 0.69, 0.89, and 1.0). Discussion The performance of this NLP algorithm to identify residential instability in 3 different healthcare systems suggests the algorithm is generally valid and applicable in other healthcare systems with similar EHRs. Conclusion The NLP approach developed in this project is adaptable and can be modified to extract types of social needs other than residential instability from EHRs across different healthcare systems.

BackgroundAsthma exerts a substantial burden on patients and health care systems. To facilitate preventive care for asthma management and improve patient outcomes, we recently developed two machine learning models, one on Intermountain Healthcare data and the other on Kaiser Permanente Southern California (KPSC) data, to forecast asthma-related hospital visits, including emergency department visits and hospitalizations, in the succeeding 12 months among patients with asthma. As is typical for machine learning approaches, these two models do not explain their forecasting results. To address the interpretability issue of black-box models, we designed an automatic method to offer rule format explanations for the forecasting results of any machine learning model on imbalanced tabular data and to suggest customized interventions with no accuracy loss. Our method worked well for explaining the forecasting results of our Intermountain Healthcare model, but its generalizability to other health care systems remains unknown.ObjectiveThe objective of this study is to evaluate the generalizability of our automatic explanation method to KPSC for forecasting asthma-related hospital visits.MethodsThrough a secondary analysis of 987,506 data instances from 2012 to 2017 at KPSC, we used our method to explain the forecasting results of our KPSC model and to suggest customized interventions. The patient cohort covered a random sample of 70% of patients with asthma who had a KPSC health plan for any period between 2015 and 2018.ResultsOur method explained the forecasting results for 97.57% (2204/2259) of the patients with asthma who were correctly forecasted to undergo asthma-related hospital visits in the succeeding 12 months.ConclusionsFor forecasting asthma-related hospital visits, our automatic explanation method exhibited an acceptable generalizability to KPSC.International Registered Report Identifier (IRRID)RR2-10.2196/resprot.5039

Background The American Community Survey (ACS) is the largest household survey conducted by the US Census Bureau. We sought to describe the community-level characteristics derived from the ACS among enrollees of Kaiser Permanente Southern California (KPSC), evaluate the associations between ACS estimates and selective individual-level health outcomes, and explore how using different scales of the census geography and the linearity assumption affect the associations. Methods We examined the associations between track-level and block group-level ACS 5-year estimates and 4 individual-level Healthcare Effectiveness Data and Information Set (HEDIS) outcome measures (comprehensive diabetes care, postpartum care, antidepressant medication management, and childhood immunization status) using multilevel generalized linear models. Odds ratios and their 95% confidence intervals were estimated for every 10% increase in ACS measures. Results 6,357,841 addresses were successfully geocoded to at least the tract level. The community-level demographic, socioeconomic, residential, and other ACS measures varied among KPSC health plan enrollees. A majority of these ACS measures were associated with the selected HEDIS health outcomes. The directions of the effects were consistent across health outcomes; however, the magnitudes of the effect sizes varied. Within each HEDIS health outcome, the relative size of the effects appeared to remain similar. Differences between the census tract- and block group-level estimates were minor, especially for measures related to race/ethnicity, education, income, and occupation. Conclusion These findings support the use of many ACS measures at neighborhood levels to predict health outcomes. The geographic units might have little effect on the results. The linearity assumption should be made with caution.

Emerging reports suggest that obese patients who are hospitalized with COVID-19 may have worse outcomes; whether this association extends to those who are not hospitalized is unclear. This study examines the association between obesity and death 21 days after diagnosis of COVID-19 among patients who receive care in an integrated health care system, accounting for obesity-related comorbidities and sociodemographic factors., Visual Abstract. Obesity and Mortality Among Patients Diagnosed With COVID-19 Emerging reports suggest that obese patients who are hospitalized with COVID-19 may have worse outcomes; whether this association extends to those who are not hospitalized is unclear. This study examines the association between obesity and death 21 days after diagnosis of COVID-19 among patients who receive care in an integrated health care system, accounting for obesity-related comorbidities and sociodemographic factors. Visual Abstract. Obesity and Mortality Among Patients Diagnosed With COVID-19 Emerging reports suggest that obese patients who are hospitalized with COVID-19 may have worse outcomes; whether this association extends to those who are not hospitalized is unclear. This study examines the association between obesity and death 21 days after diagnosis of COVID-19 among patients who receive care in an integrated health care system, accounting for obesity-related comorbidities and sociodemographic factors., Background: Obesity, race/ethnicity, and other correlated characteristics have emerged as high-profile risk factors for adverse coronavirus disease 2019 (COVID-19)–associated outcomes, yet studies have not adequately disentangled their effects. Objective: To determine the adjusted effect of body mass index (BMI), associated comorbidities, time, neighborhood-level sociodemographic factors, and other factors on risk for death due to COVID-19. Design: Retrospective cohort study. Setting: Kaiser Permanente Southern California, a large integrated health care organization. Patients: Kaiser Permanente Southern California members diagnosed with COVID-19 from 13 February to 2 May 2020. Measurements: Multivariable Poisson regression estimated the adjusted effect of BMI and other factors on risk for death at 21 days; models were also stratified by age and sex. Results: Among 6916 patients with COVID-19, there was a J-shaped association between BMI and risk for death, even after adjustment for obesity-related comorbidities. Compared with patients with a BMI of 18.5 to 24 kg/m2, those with BMIs of 40 to 44 kg/m2 and greater than 45 kg/m2 had relative risks of 2.68 (95% CI, 1.43 to 5.04) and 4.18 (CI, 2.12 to 8.26), respectively. This risk was most striking among those aged 60 years or younger and men. Increased risk for death associated with Black or Latino race/ethnicity or other sociodemographic characteristics was not detected. Limitation: Deaths occurring outside a health care setting and not captured in membership files may have been missed. Conclusion: Obesity plays a profound role in risk for death from COVID-19, particularly in male patients and younger populations. Our capitated system with more equalized health care access may explain the absence of effect of racial/ethnic and socioeconomic disparities on death. Our data highlight the leading role of severe obesity over correlated risk factors, providing a target for early intervention. Primary Funding Source: Roche–Genentech.

There are currently no direct observation environmental audit tools that measure diverse aspects of the obesity-related environment efficiently and reliably in a variety of geographical settings. The goal was to develop a new instrument to reliably characterize the overall properties and features of rural, suburban, and urban settings along multiple dimensions. The Community Audit of Social, Civil, and Activity Domains in Diverse Environments (CASCADDE) is an iPad-based instrument that incorporates global positioning system coordinates and photography and is comprised of 214 items yielding 7 summary indices. A comprehensive spatial sampling strategy, training manual, and supporting data analysis code were also developed. Random geospatial sampling using GIS was used to captured features of the community as a whole. A single auditor collected 510 observation points in 30 communities (2013–2015). This analysis was done in 2015–2016. Correlation coefficients were used to compare items and indices to each other and to standard measures. Multilevel unconditional means models were used to calculate intraclass correlation coefficients (ICC) to determine if there was significant variation between communities. Results suggest that CASCADDE measures aspects of communities not previously captured by secondary data sources. Additionally, 7 summary indices capture meaningful differences between communities based on 15 observations per community. Community audit tools such as CASCADDE complement secondary data sources and have the potential to offer new insights about the mechanisms through which communities affect obesity and other health outcomes.

Objective To determine weight-related characteristics of adults with overweight or obesity who had not attempted weight loss in the prior 12 months. Methods Adults from the obesity cohort of the PORTAL clinical data research network were randomly selected to complete a survey in 2015. It included topics of experiences with health care providers, weight history, and health-promoting behaviors. Results A total of 2811 adults completed the survey, of whom 24% reported not attempting weight loss in the previous 12 months. Controlling for sociodemographic characteristics and body mass index, respondents who reported that their health care provider never talked about weight were more likely to not attempt weight loss in the previous 12 months compared with respondents who reported providers frequently brought up weight (OR: 0.45; 95% CI: 0.24, 0.66). Respondents who perceived themselves as normal weight or underweight were 4.22 times more likely (95% CI: 3.53, 5.05) to not attempt weight loss compared with respondents who perceived themselves as overweight. Respondents who reported that they never tried to lose at least 10 lbs, ate 0−2 servings of fruits and vegetables daily, or engaged in 0−29 min of physical activity weekly were less likely to attempt weight loss. Conclusions Receiving advice on weight loss from health care providers to improve awareness of overweight and obesity status may be important factors for a person’s weight management. Provider discussions about weight may be a strategy to motivate weight loss attempts.

The obesity epidemic progresses everywhere across the globe, and implementing frequent nationwide surveys to measure the percentage of obese population is costly. Conversely, country-level food sales information can be accessed inexpensively through different suppliers on a regular basis. This study applies a methodology to predict obesity prevalence at the country-level based on national sales of a small subset of food and beverage categories. Three machine learning algorithms for nonlinear regression were implemented using purchase and obesity prevalence data from 79 countries: support vector machines, random forests and extreme gradient boosting. The proposed method was validated in terms of both the absolute prediction error and the proportion of countries for which the obesity prevalence was predicted satisfactorily. We found that the most-relevant food category to predict obesity is baked goods and flours, followed by cheese and carbonated drinks.

Purpose The purpose of this study was to characterize the differences in myopic progression in children by race/ethnicity and age. Methods Patients enrolled in Kaiser Permanente Southern California between 2011 and 2016 and between the ages of 4 and 11 years old with a documented refraction between −6 and −1 diopters (Ds) were included in this retrospective cohort study. Patients with a history of amblyopia, strabismus, retinopathy of prematurity, or prior ocular surgery were excluded from analyses. Patients’ race/ethnicity and language information were used to create the following groups for analysis: white, Black, Hispanic, South Asian, East/Southeast Asian, Other Asian, and other/unknown. A growth curve analysis using linear mixed-effects modeling was used to trace longitudinal progression of spherical equivalents over time, modeled by race/ethnicity. Analyses adjusted for potential confounders, including body mass index (BMI), screen time, and physical activity. Results There were 11,595 patients who met the inclusion criteria. Patients were 53% girls, 55% Latino, 15% white, 9% black, 9% East/Southeast Asian, and 2% South Asian. Mean age (standard deviation [SD]) at the time of initial refraction was 8.9 years (1.6 years). Patients had an average (SD) of 3.4 (1.5) refractions, including the baseline measurement, during the study period. A three-way interaction model that assessed the effects of age at baseline, time since baseline, and race/ethnicity found that children of East/Southeast Asian descent showed significantly faster myopia progression across time (P < 0.001). East/Southeast Asian patients who presented with myopia between 6 to < 8 years progressed similarly to white patients in the same age group and significantly faster compared with white patients in other age groups. Conclusions Myopia progression differed significantly between East/Southeast Asian and white patients depending on the patients’ age.

Background Asthma causes numerous hospital encounters annually, including emergency department visits and hospitalizations. To improve patient outcomes and reduce the number of these encounters, predictive models are widely used to prospectively pinpoint high-risk patients with asthma for preventive care via care management. However, previous models do not have adequate accuracy to achieve this goal well. Adopting the modeling guideline for checking extensive candidate features, we recently constructed a machine learning model on Intermountain Healthcare data to predict asthma-related hospital encounters in patients with asthma. Although this model is more accurate than the previous models, whether our modeling guideline is generalizable to other health care systems remains unknown. Objective This study aims to assess the generalizability of our modeling guideline to Kaiser Permanente Southern California (KPSC). Methods The patient cohort included a random sample of 70.00% (397,858/568,369) of patients with asthma who were enrolled in a KPSC health plan for any duration between 2015 and 2018. We produced a machine learning model via a secondary analysis of 987,506 KPSC data instances from 2012 to 2017 and by checking 337 candidate features to project asthma-related hospital encounters in the following 12-month period in patients with asthma. Results Our model reached an area under the receiver operating characteristic curve of 0.820. When the cutoff point for binary classification was placed at the top 10.00% (20,474/204,744) of patients with asthma having the largest predicted risk, our model achieved an accuracy of 90.08% (184,435/204,744), a sensitivity of 51.90% (2259/4353), and a specificity of 90.91% (182,176/200,391). Conclusions Our modeling guideline exhibited acceptable generalizability to KPSC and resulted in a model that is more accurate than those formerly built by others. After further enhancement, our model could be used to guide asthma care management. International Registered Report Identifier (IRRID) RR2-10.2196/resprot.5039

Health research on the effects of crime has been hampered by a lack of small-scale, reliable crime data. Our objective is to assess the accuracy of a set of commercially available crime indices for use in health research. The 2016 release of Applied Geographic Solutions’ (AGS) crime indices are based on data from 2010 to 2014 and provide tract-level information on crime.We use crime rates for 1069 tracts of the Los Angeles Police Department (LAPD) jurisdiction for the same years to assess (1) Spearman Correlations of major crime categories, and (2) accuracy of AGS indices in predicting falling above/below the median and into the highest/lowest quartile of LAPD crime. We also test if adding variables from the American Community Survey (ACS) to regression analyses can help to reduce measurement bias.We found that five of ten AGS indices correlated moderately well with LAPD crime. In unadjusted regressions, robbery, homicide, aggravated assault, motor-vehicle theft and personal crime achieved c-statistics from 0.81 to 0.90. C-statistics improved up to 0.13 points after adding ACS variables. Some AGS crime indices may be reliable proxies for crime in an urban area. The AGS index for total crime, most commonly used in prior research, performed poorly. Keywords: Crime, Public health, Residence characteristics

Racial/ethnic disparities in the prevalence of diagnosed hypertension are persistent but may be partially explained by racial/ethnic differences in weight category and neighborhood socioeconomic status. The authors compared hypertension prevalence rates among 4 060 585 adults with overweight or obesity across 10 healthcare systems by weight category and neighborhood education level in geographically and racially diverse individuals. Data were obtained from electronic health records. Hypertension was defined as at least two outpatient visits or one inpatient hospitalization with a coded diagnosis. Logistic regression, adjusted for age, sex, and site, with two-way interactions between race/ethnicity and weight category or neighborhood education, was used to examine the association between hypertension and race/ethnicity, with whites as the reference. Results documented that odds ratios for hypertension prevalence were greater for blacks, American Indians/Alaskan Natives, Asians, and Native Hawaiians/other Pacific Islanders compared with whites and lower for Hispanics in similar weight categories and neighborhood education levels. Although two-way interactions were statistically significant, the magnitude of the odds of hypertension compared with whites did not substantially vary across weight or neighborhood education. Hypertension odds were almost double relative to whites for blacks and Native Hawaiians/other Pacific Islanders across most weight categories and all neighborhood education levels. Odds of hypertension were about 50% greater for Asians relative to whites across weight categories. Results suggest that other factors might be associated with racial/ethnic disparities in hypertension. More research is needed to understand the many factors that may contribute to variation in diagnosed hypertension across racial/ethnic groups with overweight or obesity.

Background: Products in corner stores may be affected by the network of suppliers from which storeowners procure food and beverages. To date, this supplier network has not been well characterized. Methods: Using network analysis, we examined the connections between corner stores (n = 24) in food deserts of Baltimore City (MD, USA) and their food/beverage suppliers (n = 42), to determine how different store and supplier characteristics correlated. Results: Food and beverage suppliers fell into two categories: Those providing primarily healthy foods/beverages (n = 15) in the healthy supplier network (HSN) and those providing primarily unhealthy food/beverages (n = 41) in the unhealthy supplier network (UHSN). Corner store connections to suppliers in the UHSN were nearly two times greater (t = 5.23, p &lt, 0.001), and key suppliers in the UHSN core were more diverse, compared to the HSN. The UHSN was significantly more cohesive and densely connected, with corner stores sharing a greater number of the same unhealthy suppliers, compared to HSN, which was less cohesive and sparsely connected (t = 5.82, p &lt, 0.001). Compared to African Americans, Asian and Hispanic corner storeowners had on average −1.53 (p &lt, 0.001) fewer connections to suppliers in the HSN (p &lt, 0.001). Conclusions: Our findings indicate clear differences between corner stores’ HSN and UHSN. Addressing ethnic/cultural differences of storeowners may also be important to consider.

We investigate whether communities with improving population health will subsequently experience rising real estate prices. Home price indices (HPIs) for 371 MSAs from 1990 to 2010 are regressed against life-expectancy five years prior. HPIs come from the Federal Housing Finance Agency. Life expectancy estimates come from the Institute of Health Metrics. Our analysis uses random and fixed effect models with a comprehensive set of controls. Life expectancy predicted increases in the HPI controlling for potential confounders. We found that, this effect varied spatially. Communities that invest their revenue from property taxes in public health infrastructure could benefit from a virtuous cycle of better health leading to higher property values. Communities that do not invest in health could enter vicious cycles and this could widen geospatial health and wealth disparities.

Background In the 20th century, periods of macroeconomic growth have been associated with increases in population mortality. Factors that cause or mitigate this association are not well understood. Evidence suggests that social policy may buffer the deleterious impact of economic growth. We sought to explore associations between changing unemployment (as a proxy for economic change) and trends in mortality over 30 years in the context of varying social protection expenditures. Methods We model change in all-cause mortality in 21 OECD (Organization for Economic Cooperation and Development) countries from 1980 to 2010. Data from the Comparative Welfare States Data Set and the WHO Mortality Database were used. A decrease in the unemployment rate was used as a proxy for economic growth and age-adjusted mortality rates as the outcome. Social protection expenditure was measured as percentage of gross domestic product expended. Results A 1% decrease in unemployment (i.e. the proxy for economic growth) was associated with a 0.24% increase in the overall mortality rate (95% confidence interval: 0.07;0.42) in countries with no changes in social protection. Reductions in social protection expenditure strengthened this association between unemployment and mortality. The magnitude of the association was diminished over time. Conclusions Our results are consistent with the hypothesis that social protection policies that accompany economic growth can mitigate its potential deleterious effects on health. Further research should identify specific policies that are most effective.

Background: Blacks have lower life expectancy than whites in the United States. That disparity could be due to racial differences in the causes of death, with blacks being more likely to die of causes that affect the young, or it could be due to differences in the average ages of blacks and whites who die of the same cause. Prior studies fail to distinguish these two possibilities. Objective: In this study we determine how much of the 2000-10 reduction in the racial gap in life expectancy resulted from narrowing differences in the cause-specific mean age at death for blacks and whites, as opposed to changing cause-specific probabilities for blacks and whites. Methods: We introduce a method for separating the difference-in-probabilities and difference-in-age components of group disparities in life expectancy. Results: Based on the new method, we find that 60Š of the decline in the racial gap in life expectancy from 2000 to 2010 was attributable to reduction in the age component, largely because of declining differences in the age at which blacks and whites die of chronic diseases. Conclusions: Our findings shed light on the sources of the declining racial gap in life expectancy in the United States, and help to identify where advances need to be made to achieve the goal of eliminating racial disparities in life expectancy.

Longitudinal studies of the role of community context in childhood obesity are lacking. The objective of this study was to examine associations of community socio economic deprivation (CSD) with trajectories of change in body mass index (BMI) in childhood

BACKGROUND: Childhood attention-deficit/hyperactivity disorder (ADHD) has been associated with childhood and adult obesity, and stimulant use with delayed childhood growth, but the independent influences are unclear. No longitudinal studies have examined associations of ADHD diagnosis and stimulant use on BMI trajectories throughout childhood and adolescence. METHODS: We used longitudinal electronic health record data from the Geisinger Health System on 163 820 children ages 3 to 18 years in Pennsylvania. Random effects linear regression models were used to model BMI trajectories with increasing age in relation to ADHD diagnosis, age at first stimulant use, and stimulant use duration, while controlling for confounding variables. RESULTS: Mean (SD) age at first BMI was 8.9 (5.0) years, and children provided a mean (SD) of 3.2 (2.4) annual BMI measurements. On average, BMI trajectories showed a curvilinear relation with age. There were consistent associations of unmedicated ADHD with higher BMIs during childhood compared with those without ADHD or stimulants. Younger age at first stimulant use and longer duration of stimulant use were each associated with slower BMI growth earlier in childhood but a more rapid rebound to higher BMIs in late adolescence. CONCLUSIONS: The study provides the first longitudinal evidence that ADHD during childhood not treated with stimulants was associated with higher childhood BMIs. In contrast, ADHD treated with stimulants was associated with slower early BMI growth but a rebound later in adolescence to levels above children without a history of ADHD or stimulant use. The findings have important clinical and neurobiological implications.

This study is the first to investigate whether and, if so, why Hispanics and non-Hispanic whites in the United States differ in the variability of their lifespans. Although Hispanics enjoy higher life expectancy than whites, very little is known about how lifespan variability—and thus uncertainty about length of life—differs by race/ethnicity. We use 2010 U.S. National Vital Statistics System data to calculate lifespan variance at ages 10+ for Hispanics and whites, and then decompose the Hispanic-white variance difference into cause-specific spread, allocation, and timing effects. In addition to their higher life expectancy relative to whites, Hispanics also exhibit 7 % lower lifespan variability, with a larger gap among women than men. Differences in cause-specific incidence (allocation effects) explain nearly two-thirds of Hispanics’ lower lifespan variability, mainly because of the higher mortality from suicide, accidental poisoning, and lung cancer among whites. Most of the remaining Hispanic-white variance difference is due to greater age dispersion (spread effects) in mortality from heart disease and residual causes among whites than Hispanics. Thus, the Hispanic paradox—that a socioeconomically disadvantaged population (Hispanics) enjoys a mortality advantage over a socioeconomically advantaged population (whites)—pertains to lifespan variability as well as to life expectancy. Efforts to reduce U.S. lifespan variability and simultaneously increase life expectancy, especially for whites, should target premature, young adult causes of death—in particular, suicide, accidental poisoning, and homicide. We conclude by discussing how the analysis of Hispanic-white differences in lifespan variability contributes to our understanding of the Hispanic paradox.

Background Product placement influences consumer choices in retail stores. While sugar sweetened beverage (SSB) manufacturers expend considerable effort and resources to determine how product placement may increase SSB purchases, the information is proprietary and not available to the public health and research community. This study aims to quantify the effect of non-SSB product placement in corner stores on adolescent beverage purchasing behavior. Corner stores are small privately owned retail stores that are important beverage providers in low-income neighborhoods – where adolescents have higher rates of obesity. Methods Using data from a community-based survey in Baltimore and parameters from the marketing literature, we developed a decision-analytic model to simulate and quantify how placement of healthy beverage (placement in beverage cooler closest to entrance, distance from back of the store, and vertical placement within each cooler) affects the probability of adolescents purchasing non-SSBs. Results In our simulation, non-SSB purchases were 2.8 times higher when placed in the “optimal location” – on the second or third shelves of the front cooler – compared to the worst location on the bottom shelf of the cooler farthest from the entrance. Based on our model results and survey data, we project that moving non-SSBs from the worst to the optional location would result in approximately 5.2 million more non-SSBs purchased by Baltimore adolescents annually. Conclusions Our study is the first to quantify the potential impact of changing placement of beverages in corner stores. Our findings suggest that this could be a low-cost, yet impactful strategy to nudge this population—highly susceptible to obesity—towards healthier beverage decisions.

Multistate life table models, which follow persons through more than one living state, have found increasing use in demographic analyses. Multistate stable populations, however, are infrequently used because the constant rate assumption is quite strong and such populations can take centuries to approach stability. Dynamic models, that is models where the rates can change over time, are examined to derive a new solution for the size and composition of a multistate population in terms of the sequence of underlying population projection matrices (PPMs). Constraints on the subordinate eigenvalues and the subordinate eigenvectors of the time-varying PPMs produce a model population that grows according to the dominant eigenvalues of each time-specific PPM and has a state composition that depends only on the most recent PPM. The two living state model is examined in detail, relationships between the PPM elements and the size and composition of the model are explored, and two illustrative applications of the mode...

Past research has assessed the association of single community characteristics with obesity, ignoring the spatial co-occurrence of multiple community-level risk factors. We used conditional random forests (CRF), a non-parametric machine learning approach to identify the combination of community features that are most important for the prediction of obesogenic and obesoprotective environments for children. After examining 44 community characteristics, we identified 13 features of the social, food, and physical activity environment that in combination correctly classified 67% of communities as obesoprotective or obesogenic using mean BMI-z as a surrogate. Social environment characteristics emerged as most important classifiers and might provide leverage for intervention. CRF allows consideration of the neighborhood as a system of risk factors.

Lifespans are both shorter and more variable for blacks than for whites in the United States. Because their lifespans are more variable, there is greater inequality in length of life—and thus greater uncertainty about the future—among blacks. This study is the first to decompose the black-white difference in lifespan variability in America. Are lifespans more variable for blacks because they are more likely to die of causes that disproportionately strike the young and middle-aged, or because age at death varies more for blacks than for whites among those who succumb to the same cause? We find that it is primarily the latter. For almost all causes of death, age at death is more variable for blacks than it is for whites, especially among women. Although some youthful causes of death, such as homicide and HIV/AIDS, contribute to the black-white disparity in variance, those contributions are largely offset by the higher rates of suicide and drug poisoning deaths for whites. As a result, differences in the causes of death for blacks and whites account, on net, for only about one-eighth of the difference in lifespan variance.

Introduction: The effects of economic crisis, like the current recession affecting Europe since 2008, on health have been widely debated. Our objective was to evaluate the effects of economic changes on mortality trends from ischemic heart disease (IHD) in Europe for the period 1980-2010. Hypothesis: We hypothesize that economic recession is associated with either increases in IHD mortality rates or halting of previous declining trends. Methods: We gathered economic (World Bank) and vital statistics data (WHO Mortality Database) for all Western European countries with more than 5 million inhabitants that had complete mortality data for the period 1980-2010. We defined a period of economic recession as an annual GDP (Gross Domestic Product) growth below 0%. We also computed the cumulative years lived through a given recession period (set to 0 if GDP growth rises above 0% in a given year). We computed age-adjusted (2013 European Standard Population) mortality rates for IHD (ICD9 Codes: 410-414, ICD10 Codes: I20-I25) for the study period and computed yearly changes. We modelled the association between recession and IHD mortality using Generalized Estimating Equations accounting for the correlation among data within country and time using first order autoregressive within-group correlation structures. Changes in ICD coding were introduced as a covariate to account for the shift from ICD-9 to ICD-10. We tested for associations using 0 to 10 year lags. Sensitivity analyses were conducted using unemployment as an alternative indicator of economic recession. Results: Twelve Western European countries were included (Austria, Finland, France, Germany, Greece, Ireland, Netherlands, Norway, Spain, Sweden, Switzerland and the United Kingdom). Economic recession was associated with a 2-year lagged 1.49% increase in IHD mortality in men (p=0.016) and 1.74% increase in women (p=0.019). Each additional year of recession was associated with a 2-year lagged 1.21% increase in IHD mortality in men (p=0.003) and 1.17% increase in women (p=0.016). Sensitivity analyses using unemployment as a second measure of economic recession showed analogous results with strongest associations seen in 0-year lags. GDP and Unemployment were highly correlated with two-year lags between GDP and Unemployment changes, confirming the robustness of these associations. Conclusions: This study showed a deleterious effect of economic recession on ischemic heart disease mortality rates in Europe for the period 1980-2010. Further studies should look into the specific mechanisms of this association and into the possible interaction with policy responses to economic crisis. 1

Objective Longitudinal studies of the role of community context in childhood obesity are lacking. The objective of this study was to examine associations of community socioeconomic deprivation (CSD) on trajectories of change in body mass index (BMI) in childhood and adolescence. Methods Data come from electronic health records on 163,473 children aged 3-18 residing in 1288 communities in Pennsylvania whose weight and height were measured longitudinally. CSD at the year of birth was measured using 6 census variables and modeled in quartiles. Trajectories of BMI within CSD quartiles were estimated using random effects growth-curve models accounting for differences by age, sex, race/ethnicity as well ascorrecting for non-constant residual variance across age groups. Results CSD was associated with higher BMI at average age (10.7 years) and with more rapid growth of BMI over time. Children born in communities with greater CSD had steeper increases of BMI at younger ages. Those born into the poorest communities displayed sustained accelerated BMI growth. CSD remained associated with BMI trajectories after adjustment for a measure of household socioeconomic deprivation. Conclusions Higher CSD may be associated with more obesogenic growth trajectories in early life. Findings suggest that individual-level interventions that ignore the effect of community context on obesity related behaviors may be less efficient.

Penn State’s 19th Annual Symposium on Family Issues incorporated diverse disciplinary perspectives on families and child health to advance understanding of how family processes contribute to health and health inequalities during childhood. This chapter critically examines the symposium’s contribution to understanding family influences on child health via social structural, psychosocial, and physiological pathways and their interactions. Advantages and disadvantages of potential health improvement strategies ranging from broadscale policies to interventions targeting at-risk groups are discussed. Incorporating multiple strategies via a portfolio approach is one means of addressing a wide range of long- and short-term goals relevant to multiple health domains. Directions for future research are considered. Priorities should include more precise conceptualization and measurement of child health and risk factors in large-scale studies along with development and testing of middle-range theories to unravel the complex processes connecting families and child health outcomes. Focusing on these priorities and fostering interdisciplinary collaborations are critical steps for generating the evidence that can be used by policy creators and other key decision makers.

Why is there greater variability in individual longevity in some populations than in others? We propose a decomposition method designed to address that question by quantifying the effects of population differences in the spread, allocation, and timing of the principal causes of death. Applying the method to the United States and Sweden, we find that spread effects account for about two-thirds of the greater variance in age at death among American adults, meaning that two-thirds of the U.S.-Sweden difference would persist if the two countries differed only with respect to within-cause variance among adults. The remainder of the difference is due largely to allocation effects, with the greater incidence of homicides and fatal traffic accidents alone accounting for more than one-fourth of the greater variance in age at death among adults in the United States.

The current study examines how poverty and education in both the family and school contexts influence adolescent weight. Prior research has produced an incomplete and often counterintuitive picture. We develop a framework to better understand how income and education operate alone and in conjunction with each other across families and schools. We test it by analyzing data from Wave 1 of the U.S.-based National Longitudinal Study of Adolescent Health (N = 16,133 in 132 schools) collected in 1994-1995. Using hierarchical logistic regression models and parallel indicators of family- and school-level poverty and educational resources, we find that at the family-level, parent's education, but not poverty status, is associated with adolescent overweight. At the school-level, the concentration of poverty within a school, but not the average level of parent's education, is associated with adolescent overweight. Further, increases in school poverty diminish the effectiveness of adolescents' own parents' education for protecting against the risks of overweight. The findings make a significant contribution by moving beyond the investigation of a single socioeconomic resource or social context. The findings push us to more fully consider when, where, and why money and education matter independently and jointly across health-related contexts.