Browsing by Subject "Social vulnerability"

Now showing 1 - 6 of 6
  • Thumbnail Image
    Item
    Area-Level Indices and Health Care Use in a Pediatric Brain and Central Nervous System Tumor Cohort: Observational Study
    (JMIR, 2025-05-02) Tran, Yvette H.; Park, Seho; Coven, Scott L.; Mendonca, Eneida A.; Health Policy and Management, Richard M. Fairbanks School of Public Health
    Background: While survival among pediatric patients with cancer has advanced, disparities persist. Public health tools such as the Area Deprivation Index, the Child Opportunity Index (COI), and the Social Vulnerability Index (SVI) are potential proxies for social determinants of health and could help researchers, public health practitioners, and clinicians identify neighborhoods or populations most likely to experience adverse outcomes. However, evidence regarding their relationship with health care use, especially in the pediatric population with cancer, remains mixed. Objective: We sought to evaluate the relationship between emergency department (ED) visits and hospitalizations with these area-level indices in our study population. Methods: We conducted a cross-sectional study of pediatric patients with brain and central nervous system tumors in a single Midwestern state who were diagnosed between 2010 and 2020. We fitted zero-inflated Poisson models for counts of ED and inpatient visits to determine if any of these use measures were associated with our 3 area-level indices. Finally, we mapped index quintiles onto neighborhoods to visualize and compare how each index differentially ranks neighborhoods. Results: Our study cohort consisted of 524 patients; 78.6% (n=412) of them had no recorded ED visit, and 39.7% (n=208) had no record of hospitalization. Moderate (coefficient=0.306; P=.01) and high (coefficient=0.315; P=.01) deprivation were associated with more ED visits. Both low child opportunity (coefficient=0.497; P<.001) and very high child opportunity (coefficient=0.328; P=.01) were associated with more ED visits. All quintiles of SVI were associated with ED visits, but the relationship was not dose-dependent. Low and very high deprivation were associated with hospitalizations, but COI and SVI were not. Additionally, by overlaying index quintiles onto census tracts and census block groups, we showed that most patients who had an ED visit lived in disadvantaged neighborhoods based on Area Deprivation Index rankings, but not necessarily COI or SVI rankings. Conclusions: Although indices provide useful context about the environment in which our patient population resides in, we found little evidence that neighborhood conditions as measured by these indices consistently or reliably relate to health care use.
  • Thumbnail Image
    Item
    Association Between Social Vulnerability Index and Cardiovascular Disease: A Behavioral Risk Factor Surveillance System Study
    (American Heart Association, 2022) Jain, Vardhmaan; Al Rifai, Mahmoud; Khan, Safi U.; Kalra, Ankur; Rodriguez, Fatima; Samad, Zainab; Pokharel, Yashashwi; Misra, Arunima; Sperling, Laurence S.; Rana, Jamal S.; Ullah, Waqas; Medhekar, Ankit; Virani, Salim S.; Medicine, School of Medicine
    Background: Social and environmental factors play an important role in the rising health care burden of cardiovascular disease. The Centers for Disease Control and Prevention developed the Social Vulnerability Index (SVI) from US census data as a tool for public health officials to identify communities in need of support in the setting of a hazardous event. SVI (ranging from a least vulnerable score of 0 to a most vulnerable score of 1) ranks communities on 15 social factors including unemployment, minoritized groups status, and disability, and groups them under 4 broad themes: socioeconomic status, housing and transportation, minoritized groups, and household composition. We sought to assess the association of SVI with self‐reported prevalent cardiovascular comorbidities and atherosclerotic cardiovascular disease (ASCVD). Methods and Results: We performed a retrospective cohort analysis of adults (≥18 years) in the Behavioral Risk Factor Surveillance System 2016 to 2019. Data regarding self‐reported prevalent cardiovascular comorbidities (including diabetes, hypertension, hyperlipidemia, smoking, substance use), and ASCVD was captured using participants' response to a structured telephonic interview. We divided states on the basis of the tertile of SVI (first—participant lives in the least vulnerable group of states, 0–0.32; to third—participant lives in the most vulnerable group of states, 0.54–1.0). Multivariable logistic regression models adjusting for age, race and ethnicity, sex, employment, income, health care coverage, and association with federal poverty line were constructed to assess the association of SVI with cardiovascular comorbidities. Our study sample consisted of 1 745 999 participants ≥18 years of age. States in the highest (third) tertile of social vulnerability had predominantly Black and Hispanic adults, lower levels of education, lower income, higher rates of unemployment, and higher rates of prevalent comorbidities including hypertension, diabetes, chronic kidney disease, hyperlipidemia, substance use, and ASCVD. In multivariable logistic regression models, individuals living in states in the third tertile of SVI had higher odds of having hypertension (odds ratio (OR), 1.14 [95% CI, 1.11–1.17]), diabetes (OR, 1.12 [95% CI, 1.09–1.15]), hyperlipidemia (OR, 1.09 [95% CI, 1.06–1.12]), chronic kidney disease (OR, 1.17 [95% CI, 1.12–1.23]), smoking (OR, 1.05 [95% CI, 1.03–1.07]), and ASCVD (OR, 1.15 [95% CI, 1.12–1.19]), compared with those living in the first tertile of SVI. Conclusions: SVI varies across the US states and is associated with prevalent cardiovascular comorbidities and ASCVD, independent of age, race and ethnicity, sex, employment, income, and health care coverage. SVI may be a useful assessment tool for health policy makers and health systems researchers examining multilevel influences on cardiovascular‐related health behaviors and identifying communities for targeted interventions pertaining to social determinants of health.
  • Thumbnail Image
    Item
    Loneliness Interacts With Cognition in Relation to Healthcare and Financial Decision Making Among Community-Dwelling Older Adults
    (Oxford University Press, 2020-11-23) Stewart, Christopher C.; Yu, Lei; Glover, Crystal M.; Mottola, Gary; Bennett, David A.; Wilson, Robert S.; Boyle, Patricia A.; Neurology, School of Medicine
    Background and objectives: Cognition is a known determinant of healthcare and financial decision making in old age. Social vulnerabilities also might play a role in such decisions; however, the evidence for this is less clear. Here, we examined the association of loneliness with decision making and tested the hypothesis that loneliness is associated with decision making via its interaction with global cognition. Research design and methods: Participants were 1,121 nondemented older adults from the Rush Memory and Aging Project. Healthcare and financial decision making was assessed via a performance-based measure; loneliness was assessed via the De Jong Gierveld Loneliness Scale; and cognition was assessed via a 19-test neuropsychological battery. Results: In a regression model adjusted for age, sex, and education, global cognition was associated with decision making (B = 2.43, SE = 0.14, p < .001) but loneliness was not (B = -0.04, SE = 0.11, p = .72). However, in a model including the interaction of loneliness with global cognition, the interaction was significant (B = 0.44, SE = 0.20, p = .03), such that the detrimental effect of loneliness on decision making was stronger when cognition was low. In secondary analyses examining the interaction of loneliness with 5 specific cognitive domains, the interaction between loneliness and working memory with decision making was significant (B = 0.35, SE = 0.15, p = .02). Discussion and implications: Our results suggest that loneliness compromises healthcare and financial decision making among older adults with lower global cognition and, more specifically, lower working memory.
  • Thumbnail Image
    Item
    Pediatric injury trends and relationships with social vulnerability during the COVID-19 pandemic: A multi-institutional analysis
    (Wolters Kluwer, 2023) Flynn-O'Brien, Katherine T.; Collings, Amelia T.; Farazi, Manzur; Fallat, Mary E.; Minneci, Peter C.; Speck, K. Elizabeth; Van Arendonk, Kyle; Deans, Katherine J.; Falcone, Richard A., Jr.; Foley, David S.; Fraser, Jason D.; Gadepalli, Samir; Keller, Martin S.; Kotagal, Meera; Landman, Matthew P.; Leys, Charles M.; Markel, Troy A.; Rubalcava, Nathan; St. Peter, Shawn D.; Sato, Thomas T.; Midwest Pediatric Surgery Consortium; Surgery, School of Medicine
    Background: The impact of the COVID-19 pandemic on pediatric injury, particularly relative to a community's vulnerability, is unknown. The objective of this study was to describe the change in pediatric injury during the first 6 months of the COVID-19 pandemic compared with prior years, focusing on intentional injury relative to the social vulnerability index (SVI). Methods: All patients younger than 18 years meeting inclusion criteria for the National Trauma Data Bank between January 1, 2016, and September 30, 2020, at nine Level I pediatric trauma centers were included. The COVID cohort (children injured in the first 6 months of the pandemic) was compared with an averaged historical cohort (corresponding dates, 2016-2019). Demographic and injury characteristics and hospital-based outcomes were compared. Multivariable logistic regression was used to estimate the adjusted odds of intentional injury associated with SVI, moderated by exposure to the pandemic. Interrupted time series analysis with autoregressive integrated moving average modeling was used to predict expected injury patterns. Volume trends and observed versus expected rates of injury were analyzed. Results: There were 47,385 patients that met inclusion criteria, with 8,991 treated in 2020 and 38,394 treated in 2016 to 2019. The COVID cohort included 7,068 patients and the averaged historical cohort included 5,891 patients (SD, 472), indicating a 20% increase in pediatric injury ( p = 0.031). Penetrating injuries increased (722 [10.2%] COVID vs. 421 [8.0%] historical; p < 0.001), specifically firearm injuries (163 [2.3%] COVID vs. 105 [1.8%] historical; p = 0.043). Bicycle collisions (505 [26.3%] COVID vs. 261 [18.2%] historical; p < 0.001) and collisions on other land transportation (e.g., all-terrain vehicles) (525 [27.3%] COVID vs. 280 [19.5%] historical; p < 0.001) also increased. Overall, SVI was associated with intentional injury (odds ratio, 7.9; 95% confidence interval, 6.5-9.8), a relationship which increased during the pandemic. Conclusion: Pediatric injury increased during the pandemic across multiple sites and states. The relationship between increased vulnerability and intentional injury increased during the pandemic.
  • Thumbnail Image
    Item
    The influence of social and environmental determinants of health on hospitalizations for pediatric asthma
    (Taylor & Francis, 2024) Rogerson, Colin; Owora, Arthur; Tu, Wanzhu; Mendonca, Eneida; Pediatrics, School of Medicine
    Background: Asthma is the most common chronic disease of childhood, and has several social, environmental, and demographic factors potentially influential to its disease burden. This study sought to determine the influence of these factors on hospital admissions and readmissions for pediatric asthma. Methods: This was a retrospective cohort study using data from the Indiana Network for Patient Care, a state-wide health information exchange in the United States. Study participants were children 2-18 years old admitted to the hospital with a respiratory diagnostic code between 2010 and 2021. Clinical variables were obtained from electronic health record data, and social and environmental determinants of health data were obtained from the Indiana Social Assets and Vulnerabilities Indicators using geocoding systems. Negative binomial models were used to examine community level social and environmental risk factors modifying the relationship between patient characteristics and the risk of asthma-related hospitalizations and 30-day readmissions. Results: The study sample included 25,063 patients with an average follow-up of 9 (SD = 5) years. Of these, there were 17,816 asthma-related admissions. There were a total of 1,037 asthma-related 30-day readmissions, with an incidence rate of readmissions relative to total visits of 0.028 per person-year. A high social vulnerability index (SVI) was associated with an increased rate of hospital admissions (Proportion attributable ratio: 1.09, 95%CI (1.03,1.15), p < 0.05). No environmental determinants of health were significantly associated with hospitalization rate. Conclusion: High SVI was significantly associated with increased risk of total hospital admissions for pediatric asthma.
  • Thumbnail Image
    Item
    Worlds Further Apart: The Widening Gap in Life Expectancy among Communities of the Indianapolis Metropolitan Area
    (IU Richard M. Fairbanks School of Public Health; The Polis Center at IUPUI, 2021-08) Weathers, T; Kiehl, NT; Colbert, JT; Nowlin, M; Comer, KF; Staten, LK
    Indianapolis metro area residents are a diverse group of people. What we have in common is that many of our best and worst days have been lived within this larger community. We may recall warm summer hours in our favorite park, a day spent at “the track,” or taking the kids to the Children’s Museum. We may also remember days spent at the bedside of a sick family member in an area hospital or places of tragic loss. Year after year, we build our lives within the Indianapolis metro area. In this way, our lives are linked by a shared community. However, in the neighborhoods we each call home, our daily lives are often vastly different. For some, getting groceries means lugging plastic sacks onto the IndyGo bus after waiting on a patch of worn grass. For others, grocery shopping is a quick drive to one of three favorite options, and the farmer’s market is a weekend routine for fresh produce. Some kids go to school with laptops and fresh smelling pages of new textbooks, while others have worn books and no internet access. Playing outside with friends in one neighborhood builds fitness and friendships, while in another playing outside triggers an asthma attack because of all the car exhaust along the busy roadway. Place differences add up over the days of our lives to affect our health and length of life. The children of one neighborhood have the same biological capacity for a long and healthy life as do the children of any other neighborhood, but where they live and grow and learn often unfairly cuts short their opportunities and their life. In our updated analysis of 104 ZIP Codes in the metro area (2014-2018), we identified the northern suburb of Fishers as our longest living community and just 17 miles away, within the Indianapolis city limits, is the shortest living community within the metro area. Though only 17 miles of distance separate them, their life expectancy is worlds apart. As the White River winds its way through the metro area, flowing northeast to southwest, it connects us as a larger community across time and space. The history of central Indiana is rooted in access to this shared life-supporting resource, where tribes, then towns and cities grew along its banks. Following the winding path of the White River, we see a pattern in life expectancy that also plays out throughout the metro area (See Life Expectancy Mapped Along the White River, 2014-2018, on next page). Life expectancy is lowest in places within the urban core of Indianapolis and also on the outer periphery of the metro area (red), while highest life expectancy is found in the suburban transitions from the city (green). Similar to our earlier findings residents of the longest-living community are living years longer than the U.S. average with a life expectancy comparable to the top high-income countries of the world.1 Residents of the shortest living community are living only as long as U.S. residents lived on average more than six decades ago, and the gap has widened. There is no genetic reason for this inequity. These data compel us to put equity at the forefront in addressing the economic and social policies and structures driving this unfairness. Inequity, in life and health, “saps the strength of the whole society.”2