Influence of the food environment on obesity risk in a large cohort of US veterans by community type
OBJECTIVE:The aim of this study was to examine relationships between the food environment and obesity by community type. METHODS:Using electronic health record data from the US Veterans Administration Diabetes Risk (VADR) cohort, we examined associations between the percentage of supermarkets and fast-food restaurants with obesity prevalence from 2008 to 2018. We constructed multivariable logistic regression models with random effects and interaction terms for year and food environment variables. We stratified models by community type. RESULTS:Mean age at baseline was 59.8 (SD = 16.1) years; 93.3% identified as men; and 2,102,542 (41.8%) were classified as having obesity. The association between the percentage of fast-food restaurants and obesity was positive in high-density urban areas (odds ratio [OR] = 1.033; 95% CI: 1.028-1.037), with no interaction by time (p = 0.83). The interaction with year was significant in other community types (p < 0.001), with increasing odds of obesity in each follow-up year. The associations between the percentage of supermarkets and obesity were null in high-density and low-density urban areas and positive in suburban (OR = 1.033; 95% CI: 1.027-1.039) and rural (OR = 1.007; 95% CI: 1.002-1.012) areas, with no interactions by time. CONCLUSIONS:Many healthy eating policies have been passed in urban areas; our results suggest such policies might also mitigate obesity risk in nonurban areas.
Beyond traffic jam alleviation: evaluating the health and health equity impacts of New York City's congestion pricing plan
New York City (NYC) is slated to be the first jurisdiction in the USA to implement a cordon-based congestion tax, which will be levied on vehicles entering its Central Business District. Several cities around the world, for example, London and Stockholm, have had similar cordon-based pricing programmes, defined as road pricing that charges drivers a fee for entering a specified area (typically a congested urban centre). In addition to reducing congestion and creating revenue, projections suggest the NYC congestion pricing plan may yield meaningful traffic-related air quality improvements that could result in health benefits. NYC is a large city with high air pollution and substantial racial/ethnic and socioeconomic health inequities. The distinct geography and meteorological conditions of the city also suggest that the policy's impact on air quality may extend beyond the NYC metropolitan area. As such, the potential breadth, directionality and magnitude of health impacts on communities who might be heavily affected by the nation's first congestion pricing plan should be empirically investigated. We briefly review evaluation studies of other cordon-based congestion pricing policies and argue that implementation of this policy provides an excellent opportunity to employ a quasi-experimental study design to evaluate the policy's impacts on air quality and health outcomes across population subgroups using a health equity lens. We discuss why real-time evaluations of the NYC congestion pricing plan can potentially help optimise benefits for communities historically negatively affected by traffic-related air pollution. Assessing intended and unintended impacts on health equity is key to achieving these goals.
Cigarette Prices and Disparities in Cessation in the United States
INTRODUCTION/BACKGROUND:Achieving cessation in people with established smoking patterns remains a challenge. Increasing cigarette prices has been one of the most effective strategies for lowering smoking rates. It remains unclear how effective it has been in encouraging cessation among adults in recent years and how the effectiveness varies by sociodemographic characteristics. METHODS:Using repeated cross-sectional data collected by the Tobacco Use Supplement of the Current Population Survey, we investigate the relationship between cigarette prices and cessation from 2003 to 2019 in adults 25+. We examine the associations between price and cessation in the population overall and by sex, race/ethnicity, and socioeconomic status. RESULTS:We found mixed support for associations between greater local prices and cessation. Unadjusted models showed that greater local prices were associated with greater odds of cessation, but the associations did not persist after controlling for sociodemographic characteristics. The associations did not significantly differ by respondent characteristics. Sensitivity analysis using alternative specifications and retail state price as the main predictor showed similar results. Sensitivity analysis with controls for e-cigarette use in the 2014-2019 period showed that greater local price was associated with cessation among adults with less than a high school degree. When stratified by year of data collection, results show that greater local prices were associated with cessation after 2009. CONCLUSIONS:Overall, the study adds to the conflicting evidence on the effectiveness of increasing prices on smoking cessation among adults with established smoking patterns. IMPLICATIONS/CONCLUSIONS:Higher cigarette prices have been one of the most effective tools for lowering smoking prevalence. It remains unclear how effective they are in encouraging adults with established smoking patterns to quit. Results show that greater local prices were associated with higher odds of cessation, but the association did not persist after sociodemographic adjustment. In a sensitivity analysis, greater local price was associated with cessation among people with less than a high school degree in models controlling for e-cigarette use. We also found evidence that greater local price was associated with cessation after 2009. More comprehensive smoke-free coverage was also associated with greater odds of cessation. The study's results highlight that encouraging cessation among adults with an established smoking pattern remains a challenging policy problem even when cigarette prices rise.
Associations between a Novel Measure of Census Tract-Level Credit Insecurity and Frequent Mental Distress in US Urban Areas, 2020
Access to and utilization of consumer credit remains an understudied social determinant of health. We examined associations between a novel, small-area, multidimensional credit insecurity index (CII), and the prevalence of self-reported frequent mental distress across US cities in 2020. The census tract-level CII was developed by the Federal Reserve Bank of New York using Census population information and a nationally representative sample of anonymized Equifax credit report data. The CII was calculated for tracts in 766 cities displayed on the City Health Dashboard at the time of analysis, predominantly representing cities with over 50,000 residents. The CII combined data on tract-level participation in the formal credit economy with information on the percent of individuals without revolving credit, percent with high credit utilization, and percent with deep subprime credit scores. Tracts were classified as credit-assured, credit-likely, mid-tier, at-risk, or credit-insecure. We used linear regression to examine associations between the CII and a modeled tract-level measure of frequent mental distress, obtained from the CDC PLACES project. Regression models were adjusted for neighborhood economic and demographic characteristics. We examined effect modification by US region by including two-way interaction terms in regression models. In adjusted models, credit-insecure tracts had a modestly higher prevalence of frequent mental distress (prevalence difference = 0.38 percentage points; 95% CI = 0.32, 0.44), compared to credit-assured tracts. Associations were most pronounced in the Midwest. Local factors impacting credit access and utilization are often modifiable. The CII, a novel indicator of community financial well-being, may be an independent predictor of neighborhood health in US cities and could illuminate policy targets to improve access to desirable credit products and downstream health outcomes.
Differential care-seeking behaviors during the beginning of the COVID-19 pandemic in Michigan: a population-based cross-sectional study
BACKGROUND:At the beginning of the COVID-19 pandemic in the United States in the spring of 2020, many Americans avoided the healthcare system, while those with COVID-19 symptoms were faced with decisions about seeking healthcare services for this novel virus. METHODS:Using a probability sample (n = 1088) from the Michigan adult population of PCR-confirmed COVID-19 cases who were diagnosed prior to July 31, 2020, we used logistic regression to examine sociodemographic and symptom severity predictors of care-seeking behaviors. The analyses examined three different outcomes: (1) whether respondents sought care and, among those who sought care, whether they sought care from (2) a primary care provider or (3) an emergency room. Final models were adjusted for sex, age, race and ethnicity, income, education, marital status, living arrangement, health insurance, and self-reported symptom severity. RESULTS:We found that participants ages 65 and older had 4.00 times higher odds of seeking care than 18-34-year-olds (95% CI: 2.21, 7.24), while adults reporting very severe symptoms had roughly 15 times higher odds of seeking care than those with mild symptoms (95% CI: 7.73, 27.01). Adults who were non-Hispanic Black or were uninsured had lower odds of seeking care from a primary care physician versus seeking care from other locations in comparison to adults who were non-Hispanic White or were privately insured, respectively (non-Hispanic Black: aOR = 0.27, 95% CI: 0.16, 0.44; Uninsured: aOR = 0.19, 95% CI: 0.09, 0.42). Conversely, adults who were older or reported more severe symptoms had higher odds of seeking care from an emergency room versus other locations in comparison to adults who were younger or reported less severe symptoms (Age 65+: aOR = 2.96, 95% CI: 1.40, 6.28; Very Severe Symptoms: aOR = 6.63, 95% CI: 3.33, 13.20). CONCLUSIONS:Our results suggest differential utilization of healthcare services early in the COVID-19 pandemic. Further analyses are needed to examine the reasons for these differences.
Demographic, social and geographic factors associated with glycaemic control among US Veterans with new onset type 2 diabetes: a retrospective cohort study
OBJECTIVES:This study evaluated whether a range of demographic, social and geographic factors had an influence on glycaemic control longitudinally after an initial diagnosis of diabetes. DESIGN, SETTING AND PARTICIPANTS:We used the US Veterans Administration Diabetes Risk national cohort to track glycaemic control among patients 20-79-year old with a new diagnosis of type 2 diabetes. PRIMARY OUTCOME AND METHODS:We modelled associations between glycaemic control at follow-up clinical assessments and geographic factors including neighbourhood race/ethnicity, socioeconomic, land use and food environment measures. We also adjusted for individual demographics, comorbidities, haemoglobin A1c (HbA1c) at diagnosis and duration of follow-up. These factors were analysed within strata of community type: high-density urban, low-density urban, suburban/small town and rural areas. RESULTS:We analysed 246 079 Veterans who developed a new type 2 diabetes diagnosis in 2008-2018 and had at least 2 years of follow-up data available. Across all community types, we found that lower baseline HbA1c and female sex were strongly associated with a higher likelihood of within-range HbA1c at follow-up. Surprisingly, patients who were older or had more documented comorbidities were more likely to have within-range follow-up HbA1c results. While there was variation by community type, none of the geographic measures analysed consistently demonstrated significant associations with glycaemic control across all community types.
Patterns and predictors of depressive and anxiety symptoms within a population-based sample of adults diagnosed with COVID-19 in Michigan
PURPOSE/OBJECTIVE:The COVID-19 pandemic has had wide-ranging impacts on mental health, however, less is known about predictors of mental health outcomes among adults who have experienced a COVID-19 diagnosis. We examined the intersection of demographic, economic, and illness-related predictors of depressive and anxiety symptoms within a population-based sample of adults diagnosed with COVID-19 in the U.S. state of Michigan early in the pandemic. METHODS:Data were from a population-based survey of Michigan adults who experienced a COVID-19 diagnosis prior to August 1, 2020 (N = 1087). We used weighted prevalence estimates and multinomial logistic regression to examine associations between mental health outcomes (depressive symptoms, anxiety symptoms, and comorbid depressive/anxiety symptoms) and demographic characteristics, pandemic-associated changes in accessing basic needs (accessing food/clean water and paying important bills), self-reported COVID-19 symptom severity, and symptom duration. RESULTS:Relative risks for experiencing poor mental health outcomes varied by sex, age, race/ethnicity, and income. In adjusted models, experiencing a change in accessing basic needs associated with the pandemic was associated with higher relative risks for anxiety and comorbid anxiety/depressive symptoms. Worse COVID-19 symptom severity was associated with a higher burden of comorbid depressive/anxiety symptoms. "Long COVID" (symptom duration greater than 60 days) was associated with all outcomes. CONCLUSION/CONCLUSIONS:Adults diagnosed with COVID-19 may face overlapping risk factors for poor mental health outcomes, including pandemic-associated disruptions to household and economic wellbeing, as well as factors related to COVID-19 symptom severity and duration. An integrated approach to treating depressive/anxiety symptoms among COVID-19 survivors is warranted.
Life Expectancy and Built Environments in the U.S.: A Multilevel Analysis
INTRODUCTION:The purpose of this study is to examine the associations between built environments and life expectancy across a gradient of urbanicity in the U.S. METHODS:Census tract‒level estimates of life expectancy between 2010 and 2015, except for Maine and Wisconsin, from the U.S. Small-Area Life Expectancy Estimates Project were analyzed in 2022. Tract-level measures of the built environment included: food, alcohol, and tobacco outlets; walkability; park and green space; housing characteristics; and air pollution. Multilevel linear models for each of the 4 urbanicity types were fitted to evaluate the associations, adjusting for population and social characteristics. RESULTS:Old housing (built before 1979) and air pollution were important built environment predictors of life expectancy disparities across all gradients of urbanicity. Convenience stores were negatively associated with life expectancy in all urbanicity types. Healthy food options were a positive predictor of life expectancy only in high-density urban areas. Park accessibility was associated with increased life expectancy in all areas, except rural areas. Green space in neighborhoods was positively associated with life expectancy in urban areas but showed an opposite association in rural areas. CONCLUSIONS:After adjusting for key social characteristics, several built environment characteristics were salient risk factors for decreased life expectancy in the U.S., with some measures showing differential effects by urbanicity. Planning and policy efforts should be tailored to local contexts.
The association between local tobacco retail licensing and adult cigarette smoking by race/ethnicity, income, and education in California (2012-2019)
This study investigates the association between the strength of TRL ordinances and adult cigarette use, and differences in the relationship by sociodemographic characteristics, using California as a case study. We merged geocoded data from the California Health Interview Survey with the State of Tobacco Control Reports from the American Lung Association from 2012 to 2019. Each jurisdiction was graded (A-strongest to F-weakest) based on the strength of their TRL ordinance while current cigarette use was defined as respondents who had smoked 100 or more cigarettes in their lifetime and currently smoke cigarettes every day or some days. We estimated multilevel logistic regression models to test the relationship between the strength of the TRL ordinance and current cigarette use and tested for effect modification by including interaction terms for race/ethnicity, income, and education in separate models. 11.6 % of sample participants from all years (n = 132,209) were current cigarette smokers. Adults in jurisdictions with stronger grades (A-D) had lower odds of current cigarette use (OR = 0.89, 95 % CI: 0.79-1.01) compared to adults in jurisdictions with the weakest grade (F), but the association was not statistically significant (p < 0.07). We found no evidence of effect modification by race/ethnicity, income, or education. We found limited evidence that stronger TRL ordinances were associated with lower adult cigarette smoking in California. However, future studies testing the relationship between TRL ordinances and adult smoking outcomes should examine the role of TRL fees across jurisdictions and adult cigarette use.
Disparities in routine healthcare utilization disruptions during COVID-19 pandemic among veterans with type 2 diabetes
BACKGROUND:While emerging studies suggest that the COVID-19 pandemic caused disruptions in routine healthcare utilization, the full impact of the pandemic on healthcare utilization among diverse group of patients with type 2 diabetes is unclear. The purpose of this study is to examine trends in healthcare utilization, including in-person and telehealth visits, among U.S. veterans with type 2 diabetes before, during and after the onset of the COVID-19 pandemic, by demographics, pre-pandemic glycemic control, and geographic region. METHODS:We longitudinally examined healthcare utilization in a large national cohort of veterans with new diabetes diagnoses between January 1, 2008 and December 31, 2018. The analytic sample was 733,006 veterans with recently-diagnosed diabetes, at least 1 encounter with veterans administration between March 2018-2020, and followed through March 2021. Monthly rates of glycohemoglobin (HbA1c) measurements, in-person and telehealth outpatient visits, and prescription fills for diabetes and hypertension medications were compared before and after March 2020 using interrupted time-series design. Log-linear regression model was used for statistical analysis. Secular trends were modeled with penalized cubic splines. RESULTS:In the initial 3 months after the pandemic onset, we observed large reductions in monthly rates of HbA1c measurements, from 130 (95%CI,110-140) to 50 (95%CI,30-80) per 1000 veterans, and in-person outpatient visits, from 1830 (95%CI,1640-2040) to 810 (95%CI,710-930) per 1000 veterans. However, monthly rates of telehealth visits doubled between March 2020-2021 from 330 (95%CI,310-350) to 770 (95%CI,720-820) per 1000 veterans. This pattern of increases in telehealth utilization varied by community type, with lowest increase in rural areas, and by race/ethnicity, with highest increase among non-hispanic Black veterans. Combined in-person and telehealth outpatient visits rebounded to pre-pandemic levels after 3 months. Despite notable changes in HbA1c measurements and visits during that initial window, we observed no changes in prescription fills rates. CONCLUSIONS:Healthcare utilization among veterans with diabetes was substantially disrupted at the onset of the pandemic, but rebounded after 3 months. There was disparity in uptake of telehealth visits by geography and race/ethnicity.