Racial and ethnic differences in predictors of participation in an intergenerational social connectedness intervention for older adults.

BACKGROUND: Social connectedness is a key determinant of health and interventions have been developed to prevent social isolation in older adults. However, these interventions have historically had a low participation rate amongst minority populations. Given the sustained isolation caused by the COVID-19 pandemic, it is even more important to understand what factors are associated with an individual's decision to participate in a social intervention. To achieve this, we used machine learning techniques to model the racial and ethnic differences in participation in social connectedness interventions. METHODS: Data were obtained from a social connectedness intervention that paired college students with Houston-area community-dwelling older adults (> 65 yo) enrolled in Medicare Advantage plans. Eligible participants were contacted telephonically and asked to complete the 3-item UCLA Loneliness Scale. We used the following machine-learning methods to identify significant predictors of participation in the program: k-nearest neighbors, logistic regression, decision tree, gradient-boosted decision tree, and random forest. RESULTS: The gradient-boosted decision tree models yielded the best parameters for all race/ethnicity groups (96.1% test accuracy, 0.739 AUROC). Among non-Hispanic White older adults, key features of the predictive model included Functional Comorbidity Index (FCI) score, Medicare prescription risk score, Medicare risk score, and depression and anxiety indicators within the FCI. Among non-Hispanic Black older adults, key features included disability, Medicare prescription risk score, FCI and Medicare risk scores. Among Hispanic older adults, key features included depression, FCI and Medicare risk scores. CONCLUSIONS: These findings offer a substantial opportunity for the design of interventions that maximize engagement among minority groups at greater risk for adverse health outcomes.

ChatGPT's Performance in Cardiac Arrest and Bradycardia Simulations Using the American Heart Association's Advanced Cardiovascular Life Support Guidelines: Exploratory Study.

BACKGROUND: ChatGPT is the most advanced large language model to date, with prior iterations having passed medical licensing examinations, providing clinical decision support, and improved diagnostics. Although limited, past studies of ChatGPT's performance found that artificial intelligence could pass the American Heart Association's advanced cardiovascular life support (ACLS) examinations with modifications. ChatGPT's accuracy has not been studied in more complex clinical scenarios. As heart disease and cardiac arrest remain leading causes of morbidity and mortality in the United States, finding technologies that help increase adherence to ACLS algorithms, which improves survival outcomes, is critical. OBJECTIVE: This study aims to examine the accuracy of ChatGPT in following ACLS guidelines for bradycardia and cardiac arrest. METHODS: We evaluated the accuracy of ChatGPT's responses to 2 simulations based on the 2020 American Heart Association ACLS guidelines with 3 primary outcomes of interest: the mean individual step accuracy, the accuracy score per simulation attempt, and the accuracy score for each algorithm. For each simulation step, ChatGPT was scored for correctness (1 point) or incorrectness (0 points). Each simulation was conducted 20 times. RESULTS: ChatGPT's median accuracy for each step was 85% (IQR 40%-100%) for cardiac arrest and 30% (IQR 13%-81%) for bradycardia. ChatGPT's median accuracy over 20 simulation attempts for cardiac arrest was 69% (IQR 67%-74%) and for bradycardia was 42% (IQR 33%-50%). We found that ChatGPT's outputs varied despite consistent input, the same actions were persistently missed, repetitive overemphasis hindered guidance, and erroneous medication information was presented. CONCLUSIONS: This study highlights the need for consistent and reliable guidance to prevent potential medical errors and optimize the application of ChatGPT to enhance its reliability and effectiveness in clinical practice.

Patient- and Provider-Level Factors Associated with Patient Portal Usage Among Medicaid Recipients.

Background: Patient portals can improve access to electronic health information and enhance patient engagement. However, disparities in patient portal utilization remain, affecting disadvantaged communities disproportionately. This study examined patient- and provider-level factors associated with portal usage among Medicaid recipients in a large federally qualified health center (FQHC) network in Texas. Methods: Deidentified electronic medical records of patients 18 years or older from a large Texas FQHC network were analyzed. The dependent variable was a binary flag indicating portal usage during the study period. Independent variables included patient- and provider-level factors. Patient-level factors included sociodemographic, geographic, and clinical characteristics. Provider characteristics included primary service line, provider type, provider language, and years in practice. Because the analysis was at the individual level, a multivariable logistic regression model focused on adjusted associations between independent variables and portal usage. Results: The analytic sample consisted of 9,271 individuals. Compared with individuals 18-39 years, patients 50 years and older had lower odds (50-64 OR: 0.60, p < 0.001; 65+ OR: 0.51, p < 0.001) of portal usage. Males were less likely to use portals (OR: 0.44, p = 0.03), and compared to Non-Hispanic Whites, Non-Hispanic Black (OR: 0.86, p = 0.02) and Hispanics (OR: 0.83, p < 0.001) were significantly less likely to use portals. Individuals with 1 or more telemedicine consults had a two-times greater odds of portal usage (OR: 1.97, p < 0.001). Compared to individuals who had clinic visits in December 2018, portal usage was significantly higher in the pandemic months (March 2020-November 2020, all p's < 0.01). Importantly, the behavioral health service line had the greatest odds (OR: 1.52, p < 0.001), whereas the dental service line had the lowest odds (OR: 0.69, p = 0.01) compared to family practice. No other provider characteristics were significant. Conclusion: Our finding of significant patient-level factors is important and can contribute to developing appropriate patient-focused health information technology approaches to ensure equitable access and maximize the potential benefits of patient portals in health care delivery.

Telemedicine-Related Opioid Use Disorder Services in Underserved Populations: A Qualitative Evaluation of the Waiver Era.

Introduction: This pilot study examined access to telemedicine-related opioid use disorder (OUD) treatment in underserved communities in Houston, Texas before July 30, 2023. Methods: Participants, both patients and providers, were recruited in partnership with local substance use treatment clinics. Both groups shared experiences before and after the waiver era. Rapid qualitative analysis was conducted by the research team. Results: Fourteen qualitative interviews were conducted via Zoom with 5 treatment providers and 9 self-identified Black or Hispanic patient participants. Participants generally approved telemedicine for OUD treatment due to its technological accessibility and flexibility. However, concerns about technology, care quality, relationship building, and privacy were common among both patients and providers. Discussion: Our study highlights the underutilization of telemedicine for OUD treatment in underserved Houston communities. Efforts to address current limitations and leverage recent policy changes can help bridge the utilization gap in underserved areas.

Utilization Gaps During the COVID-19 Pandemic: Racial and Ethnic Disparities in Telemedicine Uptake in Federally Qualified Health Center Clinics.

BACKGROUND: The Coronavirus Aid, Relief, and Economic Security (CARES) Act led to the rapid implementation of telemedicine across healthcare office settings. This innovation has the potential to improve healthcare use and ensure continuity of care. However, this delivery model could have an unintended consequence of worsening racial/ethnic disparities in healthcare utilization if adoption varies across sub-populations. OBJECTIVE: To examine associations between telemedicine use and race/ethnicity between 3/1/2020 and 11/30/2020, and the influence of other individual- and geographical-level factors on this relationship. DESIGN: Cohort study PARTICIPANTS: EMR data from 55 clinics in a FQHC network MAIN MEASURES: The dependent variable was visit type (in-person vs. telemedicine). Predictors of interest were patient race and ethnicity. To account for repeated visits within each patient nested within clinic, a three-level, mixed-effects, multivariable, logistic regression model was used. Subgroup analyses examined correlates of telemedicine use in African American and Hispanic cohorts, separately. KEY RESULTS: The analytic sample included 233,302 visits for 67,733 unique patients. African Americans (OR = 0.65, 95% CI: 0.61, 0.69), Asians (OR = 0.58, 95% CI: 0.52, 0.65), and American Indians / Alaska Natives and other Pacific Islanders (OR = 0.82, 95% CI: 0.70, 0.98) were significantly less likely to use telemedicine compared to Whites. Hispanics were also less likely to have a telemedicine visit (OR = 0.49, 95% CI: 0.47, 0.51) compared to non-Hispanics. Nonacute visits were more likely to be conducted via telemedicine. Distance to clinic exhibited a dose-response relationship such that patients who lived farthest from the clinics were most likely to have telemedicine visits. In the subgroup analyses to examine predictors of telemedicine use, the dose-response relationship between distance from clinic and telemedicine use persisted, with increasing distance associated with increasing likelihood of telemedicine use, in both African American and Hispanic cohorts. Nonacute visits were associated with telemedicine use in the Hispanic cohort, but not in the Black / African American cohort. CONCLUSION: Racial/ethnic disparities in telemedicine use persisted among this cohort. However, telemedicine improved utilization for African Americans and Hispanics living farther away from the clinic.

Early Impacts of the COVID-19 Pandemic on Telehealth Patterns in Primary Care, Mental Health, and Specialty Care Facilities in Texas.

OBJECTIVES: Since the onset of the coronavirus disease 2019 (COVID-19) pandemic, many US clinics have shifted some or all of their practice from in-person to virtual visits. In this study, we assessed the use of telehealth among primary care and specialty clinics, by targeting healthcare administrators via multiple channels. METHODS: Using an online survey, we assessed the use of, barriers to, and reimbursement for telehealth. Respondents included clinic administrators (chief executive officers, vice presidents, directors, and senior-level managers). RESULTS: A total of 85 complete responses were recorded, 79% of which represented solo or group practices and 63% reported a daily patient census >50. The proportion of clinics that delivered ≥50% of their consults using telehealth increased from 16% in March to 42% in April, 35% in May, and 30% in June. Clinics identified problems with telehealth reimbursement; although 63% of clinics reported that ≥75% of their telehealth consults were reimbursed, only 51% indicated that ≥75% of their telehealth visits were reimbursed at par with in-person office visits. Sixty-five percent of clinics reported having basic or foundational telehealth services, whereas only 9% of clinics reported advanced telehealth maturity. Value-based care participating clinics were more likely to report advanced telehealth services (27%), compared with non-value-based care clinics (3%). CONCLUSIONS: These findings highlight the adaptability of clinics to quickly transition and adopt telehealth. Uncertainty about reimbursement and policy changes may make the shift temporal, however.

Impact of Patient-Centered Medical Home Implementation on Diabetes Control in the Veterans Health Administration.

BACKGROUND: Given its widespread dissemination across primary care, the Veterans Health Administration (VA) is an ideal setting to examine the impact of the patient-centered medical home (PCMH) on diabetes outcomes. OBJECTIVE: To assess the impact of PCMH implementation on diabetes outcomes among patients receiving care in the Veterans Health Administration. DESIGN: Retrospective cohort analysis and multilevel logistic regression. PATIENTS: Twenty thousand eight hundred fifty-eight patients in one Midwest VA network who had a diabetes diagnosis in both 2009 and 2012 and who received primary care between October 1, 2008 and September 30, 2009. MAIN MEASURES: Glycemic and lipid control using VA quality indicators [hemoglobin (Hb) A1c < 9%, low-density lipoprotein cholesterol (LDL-C) < 100 mg/dL]. KEY RESULTS: Odds of glycemic control were lower in 2012 than 2009 (OR = 0.72, 95% CI = 0.67-0.77, p < 0.001), and this change in control over time varied by race (OR of the interaction between time and race = 1.18, 95% CI = 1.02-1.36, p = 0.028). While the disparity in glycemic control between white and black patients persisted post-PCMH, the magnitude of the disparity was smaller in 2012 compared to 2009 (2012: OR = 1.32, 95% CI = 1.18-1.47, p < 0.0001 and 2009: OR = 1.59, 95% CI = 1.39-1.82, p < 0.0001). Odds of lipid control did not significantly change between 2009 and 2012 and change did not vary by race and/or gender. CONCLUSIONS: Although there were no significant improvements in odds of lipid control, and odds of glycemic control decreased following PCMH implementation, there was evidence of reduced racial disparities in glycemic control post-PCMH implementation.

Health Care Disparities in the Post-Affordable Care Act Era.

Disparities in health care have been targeted for elimination by federal agencies and professional organizations, including the American Public Health Association. Although the Affordable Care Act (ACA) provides a valuable first step in reducing the disparities gap, progress is contingent upon whether opportunities in the ACA help or hinder populations at risk for impaired health and limited access to medical care.

Behavioral and technological interventions targeting glycemic control in a racially/ethnically diverse population: a randomized controlled trial.

BACKGROUND: Diabetes self-care by patients has been shown to assist in the reduction of disease severity and associated medical costs. We compared the effectiveness of two different diabetes self-care interventions on glycemic control in a racially/ethnically diverse population. We also explored whether reductions in glycated hemoglobin (HbA1c) will be more marked in minority persons. METHODS: We conducted an open-label randomized controlled trial of 376 patients with type 2 diabetes aged ≥18 years and whose last measured HbA1c was ≥7.5% (≥58 mmol/mol). Participants were randomized to: 1) a Chronic Disease Self-Management Program (CDSMP; n = 101); 2) a diabetes self-care software on a personal digital assistant (PDA; n = 81); 3) a combination of interventions (CDSMP + PDA; n = 99); or 4) usual care (control; n = 95). Enrollment occurred January 2009-June 2011 at seven regional clinics of a university-affiliated multi-specialty group practice. The primary outcome was change in HbA1c from randomization to 12 months. Data were analyzed using a multilevel statistical model. RESULTS: Average baseline HbA1c in the CDSMP, PDA, CDSMP + PDA, and control arms were 9.4%, 9.3%, 9.2%, and 9.2%, respectively. HbA1c reductions at 12 months for the groups averaged 1.1%, 0.7%, 1.1%, and 0.7%, respectively and did not differ significantly from baseline based on the model (P = .771). Besides the participants in the PDA group reporting eating more high-fat foods compared to their counterparts (P < .004), no other significant differences were observed in participants' diabetes self-care activities. Exploratory sub-analysis did not reveal any marked reductions in HbA1c for minority persons but rather modest reductions for all racial/ethnic groups. CONCLUSIONS: Although behavioral and technological interventions can result in some modest improvements in glycemic control, these interventions did not fare significantly better than usual care in achieving glycemic control. More research is needed to understand how these interventions can be most effective in clinical practice. The reduction in HbA1c levels found in our control group that received usual care also suggests that good routine care in an integrated healthcare system can lead to better glycemic control. TRIAL REGISTRATION: Clinicaltrials.gov Identifier: NCT01221090.

Innovation in Healthcare Education: Using "Shark Tank" Approaches to Educate Teams of Interprofessional Students in Health Equity.

Inspired by the television show Shark Tank, we developed a summer research program that brought together students from the Colleges of Medicine, Pharmacy, Business, and Communications, to collaborate, find creative solutions, and spark innovations in population health. Under the guidance of faculty, student teams conducted literature reviews and data-thon exercises to examine trends to identify health equity gaps. Students then worked collaboratively to develop and pitch innovative solutions in front of a panel of expert "sharks" for feedback and to gain financial support to advance their ideas.

Exploring racial and secondary substance use differences in route of administration of opioid drugs: Analysis of the 2015-2019 treatment admission data.

INTRODUCTION: The opioid crisis continues to evolve with increasing opioid-related overdose deaths among under-represented minorities. A better understanding of substance use differences in the route of administration for people using heroin and other opioids can lead to targeted strategies and interventions. METHODS: Using the 2015-2019 Treatment Episode Data Set - Admissions (TEDS-A), a multinomial logistic regression model examined the relationship between race/ethnicity and secondary substance use with route of administration in a subset of 591,078 admissions. RESULTS: For individuals reporting heroin as their primary substance, minoritized clients were both more likely to smoke (NH Blacks RR: 2.28, 95 % CI 2.16-2.41; Hispanic RR: 1.80, 95 % CI: 1.74, 1.87; Other RR: 2.09, 95 % CI: 2.00, 2.20) or inhale heroin (Hispanic RR: 1.82, 95 % CI 1.78-1.85; Other RR: 1.30, 95 % CI 1.25, 1.34) compared to non-Hispanic (NH) Whites. NH Black clients were nearly seven and a half times more likely to report inhaling (RR: 7.45, 95 % CI 7.28, 7.62) heroin over injecting it. Clients were more likely to smoke heroin compared to injection if they reported secondary drug use of methamphetamines (RR: 2.28, 95 % CI 2.21, 2.35) and other opioids (RR: 1.21, 95 % CI 1.15, 1.28). For clients reporting other opioids as their primary substance, Hispanic (RR: 1.33, 95 % CI 1.19, 1.47) and other racial/ethnic minority clients (RR: 2.50, 95 % CI 2.23, 2.79) were more likely to smoke opioids vs take it orally compared to their NH White counterparts. Individuals who reported methamphetamine use as a secondary substance were significantly more than three times as likely to smoke (RR: 3.07, 95 % CI 2.74, 3.45) or inject (RR: 3.36, 95 % CI 3.17, 3.57) compared to orally ingesting opioids, while those who reported cocaine or crack cocaine use were more than twice as likely to inject (RR: 2.22, 95 % CI 2.09-2.36) opioids than taking them orally. CONCLUSION: Findings demonstrate significant racial and ethnic differences in the route of administration. This work expands on the understanding of the complex nature of polysubstance use in the evolving opioid crisis and the secondary substance use of clients on routes of administration of opioids and heroin, highlighting the need for tailored interventions to address the treatment needs of under-represented minorities.

Pre-pandemic preventable hospitalization is associated with increased telemedicine use in safety-net settings.

Introduction: The COVID-19 pandemic necessitated a major expansion in telemedicine use. The continued use of telemedicine post-pandemic has the potential to enhance healthcare use for people at risk for sub-optimal healthcare access and utilization, such as patients with previous preventable hospitalization. This study analyzed the association between pre-pandemic preventable hospitalizations (PPHs) and telemedicine use during the pandemic. Methods: This retrospective cohort study uses Medicaid administrative claims data (01/2018-06/2022) for patients of a large Federally Qualified Health Center in Arizona that implemented telemedicine in March 2020. Bivariate and multivariable generalized estimating equations were used to analyze the relationship between the outcome and predictor variables. We also analyze racial/ethnic and primary language disparities in telemedicine use among those with PPH and report the average predicted probability. Results: There was a statistically significant relationship between telemedicine use and PPH even after adjusting for comorbidity severity (OR:1.85; CI: 1.74, 1.96). Analyses restricted to those who had PPHs showed an seven-percentage point difference in the predicted probability of telemedicine use between non-Hispanic White individuals and Asian/Pacific Islanders, the group with the lowest probability of telemedicine use among our study sample. Conclusion: Telemedicine is a unique tool that can be leveraged by interventions that aim to optimize healthcare use among those with a history of preventable hospitalizations. However, the lack of targeted interventions to identify and address barriers to telemedicine use among minoritized groups could limit the impact of such interventions and widen disparities.

Access to technology, internet usage, and online health information-seeking behaviors in a racially diverse, lower-income population.

Background: This study examined access to technology, internet usage, and online health information-seeking behaviors, in a racially diverse, lower-income population. Methods: Data were obtained via a cross-sectional survey of low-income communities in Houston, Los Angeles, and New York between April and August 2023. Binary responses to the following online health information-seeking behaviors, internet and technology access, were examined: using the internet to (i) understand a medical diagnosis, (ii) fill a prescription, (iii) schedule a healthcare appointment, (iv) email communication with a healthcare provider, and (v) access electronic health records and medical notes. Results: 41% of survey respondents identified as non-Hispanic Black individuals, 33% as non-Hispanic White individuals, and 22% as Hispanic individuals. 69% reported a pre-tax annual household income of less than $35,000. 97% reported ownership/access to a smart device; 97% reported access to reliable internet. In the past year, only 59% reported using the internet to better understand their medical diagnosis, 36% reported filling a prescription online, 47% scheduled a medical appointment online, 47% viewed electronic health records online, and 56% emailed healthcare providers. Female sex, higher incomes, and having at least a bachelor's degree were significantly associated with all five online health information-seeking attributes. Conclusion: Despite high technology adoption rates, we observed suboptimal online health information-seeking behaviors. This underutilization has potential adverse implications for healthcare access and use given the documented advantage of HIT. Efforts to increase health information-seeking behaviors should explore the identification of HIT barriers, and patient education to increase familiarity and usage in this population.

Prevalence of youth experiencing homelessness and its association with suicidal thoughts and behaviors: Findings from a population-based study.

Although various studies have examined factors associated with suicidal behaviors among youth, few studies have investigated the association between youth experiencing homelessness (YEH) and suicidal thoughts and behaviors (STBs) using a large nationally representative sample. The objectives of this study were to investigate prevalence of YEH and its association with STBs. Data for this study came from the 2021 Youth Risk Behavior Survey. An analytic sample of 17,033 youth aged 14-18 (51.7 % male) was analyzed using binary logistic regression. Of the 17,033 youth examined, 3 % experienced homelessness during the past 30 days, 21.3 % experienced suicidal ideation, 17.3 % made a suicide plan, and 10.9 % attempted suicide during the past 12 months. Controlling for demographic characteristics and feeling sad or hopeless, YEH was associated with 2.48 times higher odds of experiencing suicidal ideation (AOR=2.48, p<.001), 2.46 times higher odds of making a suicide plan (AOR=2.46, p<.001), and 4.38 times higher odds of making a suicide attempt (AOR=4.38, p<.001). The findings of this study highlight the importance of identifying youth who are at risk of experiencing homelessness to ensure early interventions are put in place to prevent suicidal behaviors.

Proxy use of patient portals on behalf of children: Federally Qualified Health Centers as a case study.

Objective: This study examined the proxy use of patient portals for children in a large Federally Qualified Health Centers (FQHC) network in Texas. Methods: We used de-identified individual-level data of patients, 0-18 years, who had 1+ visits between December 2018 and November 2020. Logistic regression was used to examine patient-, clinic-, and geographic-level factors associated with portal usage by an assumed proxy (i.e. parent or guardian). Results: The proxy portal usage rate increased from 28% in the pre-pandemic months (November 2018-February 2020) to 34% in the pandemic months (March-Nov 2020). Compared to patients 0-5 years, patients aged 6 to 18 years had lower odds of portal usage (6-10 OR: 0.77, p < 0.001; 11-14 OR: 0.62, p < 0.001; 15-18 OR: 0.51, p < 0.001). Minoritized groups had significantly lower odds of portal usage when compared to their non-Hispanic White counterparts (non-Hispanic Black OR: 0.78, p < 0.001; Hispanic OR 0.63, p < 0.001; Asian OR: 0.69, p < 0.001). Having one chronic condition was associated with portal usage (OR: 1.57, p < 0.001); however, there were no significant differences in portal usage between those with none or multiple chronic conditions. Portal usage also varied by service lines, with obstetrics and gynecology (OR: 1.84, p < 0.001) and behavioral health (OR 1.82, p < 0.001) having the highest odds of usage when compared to pediatrics. Having a telemedicine visit was the strongest predictor of portal usage (OR: 2.30, p < 0.001), while residence in zip codes with poor broadband internet access was associated with lower odds of portal usage (OR: 0.97, p < 0.001). Conclusion: While others have reported portal usage rates as high as 64% in pediatric settings, our analysis suggests proxy portal usage rates of 30% in pediatric FQHC settings, with race/ethnicity, age group, and chronic disease status being significant drivers of portal non-usage. These findings highlight the need for appropriate and responsive health information technology approaches for vulnerable populations receiving care in low-resource settings.

Trends in Mental Health Utilization Before and During the COVID-19 Pandemic: Federally Qualified Health Centers As a Case Study.

This study examined differential mental health utilization patterns before and during the COVID-19 pandemic. Data were obtained from 12 Federally Qualified Health Center (FQHC)-behavioral health clinics and included 65,561 visits between February 2019 and November 2020 for adults aged 18-64 years. To allow for a year-over-year comparison, the pre-COVID-19 period was flagged as visits between February 2019 and November 2019 and the peri-COVID-19 period as visits between February 2020 and November 2020. A difference-in-difference model with Poisson regression was used to predict racial/ethnic differences in the number of visits between the pre- and peri-COVID-19 periods. Overall, the average number of visits per patient increased from 5.45 visits in the pre-COVID period to 5.78 visits in the peri-COVID-19 period. Hispanics had +0.6 visits, Blacks had +0.3 visits, and other racial groups had +0.9 visits during the peri-COVID-19 period than the pre-COVID-19 period. In contrast, Whites (-0.6 visits) and Asians (-1.7 visits) were less likely to use mental health services in these FQHC clinics in the peri-COVID-19 period as compared with the pre-COVID-19 period. Peri-COVID-19 utilization rates were significantly higher for virtual visits (incident-rate ratio [IRR] = 1.15, P < 0.01) and residence in a metropolitan area (IRR = 1.14, P < 0.01), but lower for persons aged 55+ years (IRR = 0.92, P < 0.01) and males (IRR = 0.87, P < 0.01). Racial/ethnic minority patients, except Asians, were more likely to use mental health services during the COVID-19 pandemic than in the preceding year. This may be attributable to policy changes such as the CARES Act, which expanded health care access options to include telemedicine.

Measuring Historic and Longitudinal Social Vulnerability in Disaster-Prone Communities: A Modification to the Centers for Disease Control and Prevention Social Vulnerability Index (CDC-SVI).

OBJECTIVE: Researchers have developed numerous indices to identify vulnerable sub-populations. The Centers for Disease Control and Prevention (CDC) Social Vulnerability Index (SVI) is the most common and highly serviceable, but it has some temporal limitations considering that some variables used in calculating the CDC-SVI were not available before 1980. Changes in societal composition over time can impact social vulnerability. This study defines an alternate, but similar, index that could serve as a surrogate for the CDC-SVI without the temporal limitations. METHODS: An inventory analysis of the historical census data (1960-2018) was used to develop a Modified SVI that allows for historic analyses. To consider the chronic effect of social vulnerabilities, a longitudinal SVI was introduced to elucidate how a community's multidimensional experiences exacerbate vulnerability to disaster events, such as the COVID-19 pandemic. We use Harris County, Texas, in this case study to examine how the Modified SVI performs against the original CDC-SVI. RESULTS: This Modified SVI was used to generate historical maps, find temporal patterns, and inform a longitudinal SVI measure. The results showed a good agreement among the developed indices and the CDC-SVI. We also observed satisfactory performance in identifying the areas that are most vulnerable to the COVID-19 pandemic. CONCLUSIONS: The Modified SVI overcomes temporal limitations associated with the CDC-SVI, and the longitudinal SVI captures a community's multidimensional experiences that exacerbate a community's vulnerability to disaster events over time.

Comparing In-Person Only, Telemedicine Only, and Hybrid Health Care Visits Among Older Adults in Safety-Net Clinics.

Introduction: Older adults face challenges in seeking health care. This study examined factors associated with in-person only versus telemedicine only versus hybrid health care visits among adults 65+ in safety-net clinics. Methods: Data were obtained from a large Texas-based Federally Qualified Health Center (FQHC) network. The dataset included 12,279 appointments for 3914 unique older adults between March and November 2020. The outcome of interest was a 3-level indicator of telemedicine visits: in-person visits only, telemedicine visits only, and hybrid (in person + telemedicine) visits during the study period. We used a multinomial logit model adjusting for patient level characteristics to assess the strength of the relationships. Results: Compared to their white counterparts, black and Hispanic older adults were significantly likely to have telemedicine only visits versus in-person only visits (black RRR: 0.59, 95% confidence interval [CI]: 0.41-0.86; Hispanic RRR: 0.46, 95% CI: 0.36-0.60). However, there were no significant racial and ethnic differences in hybrid utilization (black RRR: 0.91, 95% CI: 0.67-1.23; Hispanic RRR: 0.86, 95% CI: 0.70-1.07). Discussion: Our findings suggest that hybrid opportunities may bridge racial and ethnic disparities in access to care. Clinics should consider building capacity for both in-person and telemedicine opportunities as complementary strategies.

Evaluating Predictors of Participation in Telephone-Based Social-Connectedness Interventions for Older Adults: A Dual Machine-Learning and Regression Approach.

Social isolation is a well-documented contributor to poor mental and physical health, and interventions promoting social connectedness have been associated with various health benefits. This study examined predictors of participation in a telephone-based social connectedness intervention for socially isolated older adults. Data were obtained from a social-connectedness intervention that paired college students with Houston-area, community-dwelling adults aged 65 years and older and enrolled in Medicare Advantage plans. We combined machine learning and regression techniques to identify significant predictors of program participation. The following machine-learning methods were implemented: (1) k-nearest neighbors, (2) decision tree and ensembles of decision trees, (3) gradient-boosted decision tree, and (4) random forest. The primary outcome was a binary flag indicating participation in the telephone-based social-connectedness intervention. The most predictive variables in the ML models, with scores corresponding to the 90th percentile or greater, were included in the regression analysis. The predictive ability of each model showed high discriminative power, with test accuracies greater than 95%. Our findings suggest that telephone-based social-connectedness interventions appeal to individuals with disabilities, depression, arthritis, and higher risk scores. scores. Recognizing features that predict participation in social-connectedness programs is the first step to increasing reach and fostering patient engagement.

Rethinking access to care: A spatial-economic analysis of the potential impact of pharmacy closures in the United States.

Data chronicling the geo-locations of all 61,589 pharmacies in the U.S. (from the Homeland Infrastructure Foundation-Level Data (HIFLD) Open Data interface, updated on April 2018) across 215,836 census block groups were combined with Medically Underserved Areas (MUAs) information, and the Centers for Disease Control and Prevention's Social Vulnerability Index (CDC-SVI). Geospatial techniques were applied to calculate the distance between the center of each census block and the nearest pharmacy. We then modeled the expected additional travel distance if the nearest pharmacy to the center of a census block closed and estimated additional travel costs, CO2 emissions, and lost labor productivity costs associated with the additional travel. Our findings revealed that MUA residents have almost two times greater travel distances to pharmacies than non-MUAs (4,269 m (2.65 mi) vs. 2,388 m (1.48 mi)), and this disparity is exaggerated with pharmacy closures (107% increase in travel distance in MUAs vs. 75% increase in travel distance in non-MUAs). Similarly, individuals living in MUAs experience significantly greater average annual economic costs than non-MUAs ($34,834 ± $668 vs. $22,720 ± $326). Our findings suggest the need for additional regulations to ensure populations are not disproportionately affected by these closures and that there is a significant throughput with community stakeholders before any pharmacy decides to close.