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Abstract ObjectiveThis study evaluated the association between maternal care deserts (MCDs)—defined by accessibility measures such as travel time and distance to obstetric and gynecological care—and maternal and infant health outcomes in North Carolina from 2016 to 2021. MethodsThis was a retrospective secondary data analysis examining residents of North Carolina from 2016 to 2021, using travel metrics from residential zip codes to the nearest clinical providers. Maternal and infant health outcomes were assessed using data from the National Plan and Provider Enumeration System (NPPES) from the Centers for Medicare & Medicaid Services (CMS) and inpatient hospitalization records for North Carolina. Outcomes of interest included cesarean delivery rates, severe maternal morbidity (SMM20 and SMM21), and hypertension, which were examined across rural‐urban disparities based on RUCA codes. Statistical analyses were conducted to link travel metrics with health outcomes, adjusting for age, race, and insurance status to control for potential confounding factors. ResultsThe study found that rural and low‐income areas in North Carolina had fewer health care providers. Increased travel times and distances to clinical care were associated with higher cesarean delivery rates, increased severe maternal morbidity, preterm birth, and higher rates of gestational diabetes. These associations remained significant even after adjusting for age, race, and insurance status. ConclusionWomen living in maternal care deserts in North Carolina, often in rural locations, are more likely to experience adverse health outcomes, including severe maternal morbidity and hypertension, likely due to limited access to essential obstetric and gynecological care. These findings highlight the negative impact of health care inaccessibility on maternal and infant health in underserved regions. 

Abstract Increasing evidence suggests that temperatures adversely impact mental and behavioral disorders (MBD). This study explores the effects of temperatures on mental health outcomes using over 5.9 million MBD-related emergency department (ED) visits across three geographical regions of North Carolina (i.e., Mountains, Piedmont, and Coast) from 2016 to 2019. A distributed lag non-linear model (DLNM) with a generalized linear model and quasi-Poisson distribution adjusted for humidity, long-term seasonal time trends, and day of the week examined the acute impact (i.e., 7-day) of temperature on daily MBD-related ED visits at zip code tabulation area (ZCTA) locations. Results were pooled at the region and state levels and reported in reference to the median temperature using a case-time series design for the analysis of small-area data. Stratified analyses were conducted for age, sex, and specific mental-health related ED visits (substance use, mood disorders, anxiety disorders). At the state level, we found significant positive associations between high temperatures (97.5th percentile) and an increase in relative risk (RR) for total MBDs (RR:1.04, 95% CI,1.03–1.05) and psychoactive substance use (RR:1.04, 95% CI, 1.02–1.06). Low air temperatures (2.5th percentile) only increased risk for the elderly (i.e., 65 and older) and predominantly white communities (RR: 1.03, CI: 1.03–1.05). During high temperatures (97.5th percentile), majority-white communities (RR:1.06, CI: 1.01–1.10) and low-income communities had the highest risk for MBDs (RR: 1.05, CI: 1.03–1.07). Our findings suggest there is a positive association between exposure to high temperatures and increased MBD-related ED visits, modified by patient age and place-based sociodemographic (ie., race and income) context.  

Abstract In 2021, the US Surgeon General issued a national advisory citing an epidemic of isolation and loneliness. Even before the onset of the COVID-19 pandemic, approximately half of people in the US reported experiencing measurable levels of loneliness. Despite localized and select cross-sectional studies highlighting even higher increases in isolation/loneliness during the COVID-19 pandemic, additional research is needed, particularly for youth and young adults. This work examines patterns of isolation/loneliness across the US from 2016 to 2022 among individuals aged 24 and younger. Our study leverages a unique dataset, Crisis Text Line, which provides complete spatiotemporal coverage of crisis conversations in the US. We conducted a geospatial analysis using Kuldroff’s Space–Time SatScan to identify statistically significant clustering of elevated isolation/loneliness-related conversations. The statistical significance of spatiotemporal clusters was determined using Monte Carlo simulations (n = 9999). Results demonstrated local relative risk as high as 1.47 in high-risk populations in Southern, Midwest, and Atlantic states, indicating areas where the actual case count is 147% of the expected cases (pvalue < 0.01) from May to July 2020. Results also identified co-occurrence of isolation/loneliness and other crises concerns, including depression/sadness, anxiety, and multiple suicidality indicators, with higher rates among racial/ethnic minority, transgender and gender diverse, and younger individuals. This work makes a unique contribution to the literature by elucidating spatiotemporal disparities in isolation/loneliness among young people, providing much-needed knowledge as to where future public health interventions are immediately needed. 

Abstract Mental distress among young people has increased in recent years. Research suggests that greenspace may benefit mental health. The objective of this exploratory study is to further understanding of place‐based differences (i.e., urbanity) in the greenspace‐mental health association. We leverage publicly available greenspace data sets to operationalize greenspace quantity, quality, and accessibility metrics at the community‐level. Emergency department visits for young people (ages 24 and under) were coded for: anxiety, depression, mood disorders, mental and behavioral disorders, and substance use disorders. Generalized linear models investigated the association between greenspace metrics and community‐level mental health burden; results are reported as prevalence rate ratios (PRR). Urban and suburban communities with the lowest quantities of greenspace had the highest prevalence of poor mental health outcomes, particularly for mood disorders in urban areas (PRR: 1.19, 95% CI: 1.16–1.21), and substance use disorders in suburban areas (PRR: 1.35, 95% CI: 1.28–1.43). In urban, micropolitan, and rural/isolated areas further distance to greenspace was associated with a higher prevalence of poor mental health outcomes; this association was most pronounced for substance use disorders (PRRUrban: 1.31, 95% CI: 1.29–1.32; PRRMicropolitan: 1.47, 95% CI: 1.43–1.51; PRRRural 2.38: 95% CI: 2.19–2.58). In small towns and rural/isolated communities, poor mental health outcomes were more prevalent in communities with the worst greenspace quality; this association was most pronounced for mental and behavioral disorders in small towns (PRR: 1.29, 95% CI: 1.24–1.35), and for anxiety disorders in rural/isolated communities (PRR: 1.61, 95% CI: 1.43–1.82). The association between greenspace metrics and mental health outcomes among young people is place‐based with variations across the rural‐urban continuum. 

Abstract Growing evidence indicates that extreme environmental conditions in summer months have an adverse impact on mental and behavioral disorders (MBD), but there is limited research looking at youth populations. The objective of this study was to apply machine learning approaches to identify key variables that predict MBD‐related emergency room (ER) visits in youths in select North Carolina cities among adolescent populations. Daily MBD‐related ER visits, which totaled over 42,000 records, were paired with daily environmental conditions, as well as sociodemographic variables to determine if certain conditions lead to higher vulnerability to exacerbated mental health disorders. Four machine learning models (i.e., generalized linear model, generalized additive model, extreme gradient boosting, random forest) were used to assess the predictive performance of multiple environmental and sociodemographic variables on MBD‐related ER visits for all cities. The best‐performing machine learning model was then applied to each of the six individual cities. As a subanalysis, a distributed lag nonlinear model was used to confirm results. In the all cities scenario, sociodemographic variables contributed the greatest to the overall MBD prediction. In the individual cities scenario, four cities had a 24‐hr difference in the maximum temperature, and two of the cities had a 24‐hr difference in the minimum temperature, maximum temperature, or Normalized Difference Vegetation Index as a leading predictor of MBD ER visits. Results can inform the use of machine learning models for predicting MBD during high‐temperature events and identify variables that affect youth MBD responses during these events.