Associations of Greenness, Parks, and Blue Space With Neurodegenerative Disease Hospitalizations Among Older US Adults.

Importance: Exposure to natural environments has been associated with health outcomes related to neurological diseases. However, the few studies that have examined associations of natural environments with neurological diseases report mixed findings. Objective: To evaluate associations of natural environments with hospital admissions for Alzheimer disease and related dementias (ADRD) and Parkinson disease (PD) among older adults in the US. Design, Setting, and Participants: This open cohort study included fee-for-service Medicare beneficiaries aged 65 years or older who lived in the contiguous US from January 1, 2000, to December 31, 2016. Beneficiaries entered the cohort on January 1, 2000, or January 1 of the year after enrollment. Data from US Medicare enrollment and Medicare Provider Analysis and Review files, which contain information about individual-level covariates and all hospital admissions for Medicare fee-for-service beneficiaries, were analyzed between January 2021 and September 2022. Exposures: Differences in IQRs for zip code-level greenness (normalized difference vegetation index [NDVI]), percentage park cover, and percentage blue space cover (surface water; ≥1.0% vs <1.0%). Main Outcomes and Measures: The main outcome was first hospitalizations with a primary or secondary discharge diagnosis of ADRD or PD. To examine associations of exposures to natural environments with ADRD and PD hospitalization, we used Cox-equivalent Poisson models. Results: We included 61 662 472 and 61 673 367 Medicare beneficiaries in the ADRD and PD cohorts, respectively. For both cohorts, 55.2% of beneficiaries were women. Most beneficiaries in both cohorts were White (84.4%), were not eligible for Medicaid (87.6%), and were aged 65 to 74 years (76.6%) at study entry. We observed 7 737 609 and 1 168 940 first ADRD and PD hospitalizations, respectively. After adjustment for potential individual- and area-level confounders (eg, Medicaid eligibility and zip code-level median household income), NDVI was negatively associated with ADRD hospitalization (hazard ratio [HR], 0.95 [95% CI, 0.94-0.96], per IQR increase). We found no evidence of an association of percentage park and blue space cover with ADRD hospitalization. In contrast, NDVI (HR, 0.94 [95% CI, 0.93-0.95], per IQR increase), percentage park cover (HR, 0.97 [95% CI, 0.97-0.98], per IQR increase), and blue space cover (HR, 0.97 [95% CI, 0.96-0.98], ≥1.0% vs <1.0%) were associated with a decrease in PD hospitalizations. Patterns of effect modification by demographics differed between exposures. Conclusions and Relevance: The findings of this cohort study suggest that some natural environments are associated with a decreased risk of ADRD and PD hospitalization.

Increasing Representation of Epidemiologists From Around the World in the Society for Epidemiologic Research: The Case of Mexico.

Mexico has a population of 129 million and is considered one of the most unequal countries in the world, suffering from widespread health disparities. There is a pressing need to strengthen epidemiologic capacity in Mexico, to help solve the complex health problems the country faces and to reduce health inequities. However, the representation of Mexican epidemiologists in the largest epidemiologic society in North America is low, despite the short distance to the United States. In this commentary, we discuss the barriers to higher representation of Mexican epidemiologists within the Society for Epidemiologic Research (SER), including language barriers, costs, and regional necessities. We also discuss opportunities to expand Mexican SER representation and collaboration. Overall, we hope that this is a call towards expanding SER global participation and starting a conversation on a common agenda for epidemiologic research.

Sustaining planetary health through systems thinking: Public health's critical role.

Understanding and responding to adverse human health impacts of global environmental change will be a major priority of 21st century public health professionals. The emerging field of planetary health aims to face this challenge by studying and promoting policies that protect the health of humans and of the Earth's natural systems that support them. Public health, drawing on its experience of guiding policies to improve population health, has contributed to planetary health's development. Yet, few public health practitioners are familiar with planetary health's systems-oriented approaches for understanding relationships between economic development, environmental degradation, and human health. In this narrative review, we present key planetary health concepts and show how systems thinking has guided its development. We discuss historical approaches to studying impacts of economic development on human health and the environment. We then review novel conceptual frameworks adopted by planetary health scientists to study and forecast impacts of policies that influence human health and Earth's natural systems at varying spatiotemporal scales. We conclude by presenting examples of how applying the "Doughnut" model (an economic framework where the needs of people are met without overshooting the world's ecological limits) could guide policies for promoting health co-benefits to humans and natural systems.

Neighborhood Child Opportunity Index and Adolescent Cardiometabolic Risk.

BACKGROUND AND OBJECTIVES: The Child Opportunity Index (ChOI) is a publicly available surveillance tool that incorporates traditional and novel attributes of neighborhood conditions that may promote or inhibit healthy child development. The extent to which ChOI relates to individual-level cardiometabolic risk remains unclear. METHODS: We geocoded residential addresses obtained from 743 participants in midchildhood (mean age 7.9 years) in Project Viva, a prebirth cohort from eastern Massachusetts, and linked each location with census tract-level ChOI data. We measured adiposity and cardiometabolic outcomes in midchildhood and early adolescence (mean age 13.1 years) and analyzed their associations with neighborhood-level ChOI in midchildhood using mixed-effects models, adjusting for individual and family sociodemographics. RESULTS: On the basis of nationwide distributions of ChOI, 11.2% (n = 83) of children resided in areas of very low overall opportunity (ChOI score <20 U) and 55.3% (n = 411) resided in areas of very high (ChOI score ≥80 U) overall opportunity. Children who resided in areas with higher overall opportunity in midchildhood had persistently lower levels of C-reactive protein from midchildhood to early adolescence (per 25-U increase in ChOI score: ß = .14 mg/L; 95% confidence interval, .28 to .00). Additionally, certain ChOI indicators, such as greater number of high-quality childhood education centers, greater access to healthy food, and greater proximity to employment in midchildhood, were associated with persistently lower adiposity, C-reactive protein levels, insulin resistance, and metabolic risk z scores from midchildhood to early adolescence. CONCLUSIONS: Our findings suggest more favorable neighborhood opportunities in midchildhood predict better cardiometabolic health from midchildhood to early adolescence.

Early life exposure to green space and insulin resistance: An assessment from infancy to early adolescence.

BACKGROUND: Recent studies suggest that greater exposure to natural vegetation, or "green space" is associated with lower diabetes risk, possibly through increasing physical activity. However, there is limited research on green space and insulin resistance in youth. We hypothesized greater green space at early-life sensitive time periods would be associated with lower insulin resistance in youth. METHODS: We used data from Project Viva (N = 460), a pre-birth cohort study that recruited pregnant women in eastern Massachusetts, 1999-2002, and followed offspring into adolescence. We defined residential green space exposure at infancy (median age - 1.1 years), early childhood (3.2 years), mid-childhood (7.7 years), and early adolescence (12.8 years), using 30 m resolution Landsat satellite imagery to estimate the Normalized Difference Vegetation Index [NDVI]. Our main outcome was early adolescence estimated insulin resistance (HOMA-IR). We used multiple imputation to account for missing data and multiple linear regression models adjusted for age, sex, race/ethnicity, parental education, household income, and neighborhood median household income. RESULTS: The highest green space tertile had the highest percentage of white participants (85%), college-educated mothers (87%) and fathers (85%), and households with income higher than US$70,000 (86%). Unadjusted models showed that participants living in the highest green space tertile at infancy had a 0.15 unit lower HOMA-IR (95% CI: -0.23, -0.06) in early adolescence, than those living in the lowest tertile. However, in adjusted models, we did not observe evidence of associations between green space from infancy to early adolescence and HOMA-IR in early adolescence, although some point estimates were in the hypothesized direction. For example, participants in the highest green space tertile in infancy had 0.03 units lower HOMA-IR (95%CI: -0.14, 0.08) than those living in the lowest tertile. CONCLUSIONS: Exposure to green space at early life sensitive time periods was not associated with HOMA-IR in youth. Early-life longitudinal studies across diverse populations are needed to confirm or refute our results.

The Relative Contributions of Socioeconomic and Genetic Factors to Variations in Body Mass Index Among Young Adults.

In light of recent findings on the small proportion of variance in body mass index (BMI) explained by shared environment, and growing interests in the role of genetic susceptibility, we assessed the relative contribution of socioeconomic status (SES) and genome-wide polygenic score for BMI to explaining variation in BMI. Our final analytic sample included 4,918 White and 1,546 Black individuals from the US National Longitudinal Study of Adolescent to Adult Health Wave IV (2007-2008) who had complete measures on BMI, demographics, SES, genetic data, and health behaviors. We used ordinary least-squares regression to assess variation in log(BMI) as a function of the aforementioned predictors, independently and mutually adjusted. All analyses were stratified by race/ethnicity in the main analysis, and further by sex. The age-adjusted variation in log(BMI) was 0.055 among Whites and 0.066 among Blacks. The contribution of SES and polygenic score ranged from less than1% to 6% and from 2% to 8%, respectively, and majority of the variation (87%-96%) in log(BMI) remained unexplained. Differential distribution of socioeconomic resources, stressors, and buffers may interact to produce systematically larger variation in vulnerable populations. More understanding of the contribution of biological, genetic, and environmental factors, as well as stochastic elements, in diverse phenotypic variance is needed in population health sciences.

Longitudinal associations of neighborhood socioeconomic status with cardiovascular risk factors: A 46-year follow-up study.

OBJECTIVES: Neighborhood characteristics are increasingly recognized as important determinants of cardiovascular disease (CVD) risk. However, longitudinal studies on the health impacts of neighborhood characteristics are rare. We sought to investigate whether neighborhood socioeconomic status (NSES) during birth, childhood and adulthood is associated with CVD risk factors in adulthood. METHODS: Using longitudinal data from the New England Family Study (n = 671) with 46-years of follow-up, participants' home addresses were geocoded at birth (mean age = 1.6 months), childhood (mean age = 7.1 years), and adulthood (mean age = 44.2 years) across Massachusetts and Rhode Island in the US from 1961 to 2007. We used multilevel models to evaluate associations of NSES across the life-course with systolic blood pressure, diastolic blood pressure and body mass index (BMI) in adulthood, adjusting for age, sex, race/ethnicity, mother's race, individual SES, and parental SES. RESULTS: In fully adjusted models, one standard deviation higher NSES at birth was associated with a 1.9 mmHg lower SBP (95% CI: 3.8, -0.1) and 1.3 mmHg lower DBP (95%CI: 2.6,-0.03) in adulthood; while one standard deviation of higher NSES at adulthood was associated with 0.87 kg/m2 lower BMI (95%CI: 1.7, -0.1). CONCLUSIONS: We found that living in a socioeconomically disadvantaged neighborhood early in life and in adulthood was associated with blood pressure and BMI, respectively, two established risk factors for CVD. Our findings support a longitudinal association between exposure to socioeconomically disadvantaged neighborhoods in early life and CVD risk factors in adulthood.

Early life disadvantage and adult adiposity: tests of sensitive periods during childhood and behavioural mediation in adulthood.

BACKGROUND: Early exposure to socioeconomic disadvantage is associated with obesity. Here we investigated how early, and conducted mediation analyses to identify behavioural factors in adulthood that could explain why. METHODS: Among 931 participants in the New England Family Study, we investigated the associations of family socioeconomic disadvantage measured before birth and at age 7 years with the following measures of adiposity in mid-adulthood (mean age = 44.4 years): body mass index (BMI), waist circumference and, among 400 participants, body composition from dual-energy X-ray absorption scans. RESULTS: In linear regressions adjusting for age, sex, race and childhood BMI Z-score, participants in the highest tertile of socioeconomic disadvantage at birth had 2.6 additional BMI units in adulthood [95% confidence interval (CI) = 1.26, 3.96], 5.62 cm waist circumference (95% CI = 2.69, 8.55), 0.73 kg of android fat mass (95% CI = 0.25, 1.21), and 7.65 higher Fat Mass Index (95% CI = 2.22, 13.09). Conditional on disadvantage at birth, socioeconomic disadvantage at age 7 years was not associated with adult adiposity. In mediation analyses, 10-20% of these associations were explained by educational attainment and 5-10% were explained by depressive symptoms. CONCLUSIONS: Infancy may be a sensitive period for exposure to socioeconomic disadvantage, as exposure in the earliest years of life confers a larger risk for overall and central adiposity in mid-adulthood than exposure during childhood. Intervention on these two adult risk factors for adiposity would, if all model assumptions were satisfied, only remediate up to one-fifth of the excess adult adiposity among individuals born into socioeconomically disadvantaged households.

Increasing ambient temperature reduces emotional well-being.

This study examines the impact of ambient temperature on emotional well-being in the U.S. population aged 18+. The U.S. is an interesting test case because of its resources, technology and variation in climate across different areas, which also allows us to examine whether adaptation to different climates could weaken or even eliminate the impact of heat on well-being. Using survey responses from 1.9 million Americans over the period from 2008 to 2013, we estimate the effect of temperature on well-being from exogenous day-to-day temperature variation within respondents' area of residence and test whether this effect varies across areas with different climates. We find that increasing temperatures significantly reduce well-being. Compared to average daily temperatures in the 50-60°F (10-16°C) range, temperatures above 70°F (21°C) reduce positive emotions (e.g. joy, happiness), increase negative emotions (e.g. stress, anger), and increase fatigue (feeling tired, low energy). These effects are particularly strong among less educated and older Americans. However, there is no consistent evidence that heat effects on well-being differ across areas with mild and hot summers, suggesting limited variation in heat adaptation.

Are There Spillover Effects from the GI Bill? The Mental Health of Wives of Korean War Veterans.

BACKGROUND: The Korean War GI Bill provided economic benefits for veterans, thereby potentially improving their health outcomes. However potential spillover effects on veteran wives have not been evaluated. METHODS: Data from wives of veterans eligible for the Korean War GI Bill (N = 128) and wives of non-veterans (N = 224) from the Health and Retirement Study were matched on race and coarsened birth year and childhood health using coarsened exact matching. Number of depressive symptoms in 2010 (average age = 78) were assessed using a modified, validated Center for Epidemiologic Studies-Depression Scale. Regression analyses were stratified into low (mother < 8 years schooling / missing data, N = 95) or high (mother ≥ 8 years schooling, N = 257) childhood socio-economic status (cSES) groups, and were adjusted for birth year and childhood health, as well as respondent's educational attainment in a subset of analyses. RESULTS: Husband's Korean War GI Bill eligibility did not predict depressive symptoms among veteran wives in pooled analysis or cSES stratified analyses; analyses in the low cSES subgroup were underpowered (N = 95, ß = -0.50, 95% Confidence Interval: (-1.35, 0.35), p = 0.248, power = 0.28). CONCLUSIONS: We found no evidence of a relationship between husband's Korean War GI Bill eligibility and wives' mental health in these data, however there may be a true effect that our analysis was underpowered to detect.

Can social policy influence socioeconomic disparities? Korean War GI Bill eligibility and markers of depression.

PURPOSE: The Korean War GI Bill provided socioeconomic benefits to veterans; however, its association with health is unclear; we hypothesize GI Bill eligibility is associated with fewer depressive symptoms and smaller disparities. METHODS: Data from 246 Korean War GI Bill eligible veterans and 240 nonveterans from the Health and Retirement Study were matched on birth year, southern birth, race, height, and childhood health using coarsened exact matching. Number of depressive symptoms in 2010 (average age = 78 years) was assessed using a modified, validated Center for Epidemiologic Studies-Depression Scale, dichotomized to reflect elevated depressive symptoms. Regression analyses were stratified into low (at least one parent < 8 years schooling/missing data, n = 167) or high (both parents ≥ 8 years schooling, n = 319) childhood socioeconomic status (cSES) groups. RESULTS: Korean War GI Bill eligibility predicted fewer depressive symptoms among individuals from low cSES backgrounds [ß = -0.64, 95% confidence interval (CI) = (-1.18, -0.09), P = .022]. Socioeconomic disparities were smaller among veterans than nonveterans for number of depressive symptoms [ß = -0.76, 95% CI = (-1.33, -0.18), P = .010] and elevated depressive symptoms [ß = -11.7, 95% CI = (-8.2, -22.6), P = .035]. CONCLUSIONS: Korean War GI Bill eligibility predicted smaller socioeconomic disparities in depression markers.

Neighborhood socioeconomic context and change in allostatic load among older Puerto Ricans: The Boston Puerto Rican health study.

Neighborhood context may influence health and health disparities. However, most studies have been constrained by cross-sectional designs that limit causal inference due to failing to establish temporal order of exposure and disease. We tested the impact of baseline neighborhood context (neighborhood socioeconomic status factor at the block-group level, and relative income of individuals compared to their neighbors) on allostatic load two years later. We leveraged data from the Boston Puerto Rican Health Study, a prospective cohort of aging Puerto Rican adults (aged 45-75 at baseline), with change in AL modeled between baseline and the 2nd wave of follow-up using two-level hierarchical linear regression models. Puerto Rican adults with higher income, relative to their neighbors, exhibited lower AL after two years, after adjusting for NSES, age, gender, individual-level SES, length of residence, and city. After additional control for baseline AL, this association was attenuated to marginal significance. We found no significant association of NSES with AL. Longitudinal designs are an important tool to understand how neighborhood contexts influence health and health disparities.