Nir Menachemi

Introduction: US lockdowns and school closures implemented during the COVID-19 pandemic were intended to mitigate viral transmission and protect public health. However, the broader health effects of these interventions remain unclear. Methods: We conducted a systematic review of peer-reviewed studies that assessed the impact of US lockdowns and school closures on health-related outcomes excluding COVID-19 transmission and mortality. Results: A total of 132 studies met inclusion criteria, yielding 454 unique outcomes. Lockdowns and school closures were associated with detrimental health effects in the majority of outcomes analyzed, including over 90% of mental health, obesity-related, and health-related social need outcomes (child development/education, employment, access to food, and economic/financial stability). Analyses focused on vulnerable populations, such as racial and ethnic minorities, low-income groups, and individuals with disabilities, were significantly more likely to report detrimental outcomes than the general population. Conclusion: Given how lockdowns and school closures may affect population well-being, policymakers should carefully weigh both the benefits and harms of these interventions, including how they may affect vulnerable populations. We conclude with policy recommendations to mitigate ongoing harms and inform more evidence-based decision-making.

A substantial portion of the 20 million calls that emergency medical services (EMS) personnel respond to each year are considered preventable, including more than 1.5 million behavioral health calls. Despite goals of preventing behavioral health crises and reducing the burden on patients and EMS personnel, little is known about how demographic and community characteristics influence behavioral health calls. Using nationwide 2021 EMS call data, we identified counties with high behavioral health calls and examined their demographic and community characteristics. Low-income and racially diverse counties had a higher incidence of behavioral health EMS calls, while politically conservative counties had a lower incidence of behavioral health EMS calls. To better meet the emergency behavioral health needs of communities, policy and decision-makers should consider strategies that increase access to and awareness of alternative behavioral health crisis services (eg, 988 Suicide and Crisis Lifeline).

Objective: Patient data is subject to missingness and errors. Patient portals enable patients managing type 2 diabetes mellitus (T2DM) to review and correct data to avoid retesting, medication errors, and diagnostic mistakes. We examined whether patient portal use was associated with electronic health record (EHR) data timeliness in T2DM care. Research Design and methods: We analyzed EHR data from a panel of adult patients to determine whether portal use improved data timeliness. EHR data timeliness is measured as the number of days between patient encounters, accounting for mean attribute update periods, where available EHR attribute updates for T2DM measurements were present, including body mass, weight, glycated hemoglobin A1c, cholesterol, blood pressure, serum creatinine, and smoking status. We performed negative binomial regressions with fixed effects to estimate the association between patient portal use and EHR data timeliness. Sensitivity analyses were conducted using Poisson regressions. Results: Nearly a third (31.3%) of patients in our sample actively used the health portal. There were fewer days (111.9 days vs. 136.7 days; p < 0.001) between EHR attribute updates for patients who used health portals compared to patients who did not. Data timeliness was lower among female, non-Hispanic White Medicare beneficiaries. Based on regression analyses, portal use was associated with an expected 3.6 (p < 0.001) percentage point decrease in days between attribute updates, indicating improved EHR timeliness. Conclusion: Improving the quality of health information may streamline decision-making in partnership with patients who produce data points across clinical settings. Active use of patient portals and digital health tools in chronic disease care are critical for care management and clinical decision-making, especially for patients managing type 2 diabetes across clinical settings.

Indiana has a business -friendly environment, but historical underinvestment in public health has yielded poor health outcomes. In 2023, when trust in governmental public health was strained nationwide, Indiana increased public health spending by 1,500 percent. In this article, we explain how Indiana achieved this unprecedented legislative victory for public health, describing the context, approach, and lessons learned. Specifically, an Indiana University report linking economic vitality and overall health sparked the creation of a governor's commission charged with exploring ways to address Indiana's shortcomings. Working with the Indiana Department of Health, the commission developed multisectoral coalitions and business and government partnerships, and it maintained consistent and coordinated communication with policy makers. Lessons learned included the value of uncoupling public health from partisan narratives, appointing diverse commission membership with strategically selected cochairs, involving local leaders, and ensuring local decisionmaking control. We believe that Indiana's approach holds insights for other states interested in strengthening public health funding in the current era.

Purpose To examine the longitudinal relationship between the age or shelf-life of common type 2 diabetes laboratory tests for serum creatinine, cholesterol, and glycated hemoglobin A1c conducted in outpatient settings and subsequent inpatient hospitalizations and emergency department visits. Methods This study analyzes panel data from two healthcare delivery systems’ electronic health records (EHR) for patients aged 18 years and older managing type 2 diabetes. We used EHR data to quantify the age of three laboratory tests: serum creatinine, cholesterol, and glycated hemoglobin A1c. Encounter data were used to determine the frequency of inpatient hospitalizations and emergency department visits. Negative binomial regressions with fixed effects were performed to compute marginal effects, levels of statistical significance, and 95% confidence intervals. Results The average age for serum creatinine laboratory tests was 1.51 months (95%CI: 1.49–1.53). We computed older average ages for hemoglobin A1c (mean:6.17 months; 95%CI: 6.11–6.23) and serum creatinine tests (mean: 8.73; 95%CI: 8.65–8.81). Older laboratory tests were associated with an increase in the total expected counts of subsequent inpatient hospitalizations (ME = 0.047; p < 0.001) and ED visits (ME = 0.034; p < 0.001). Conclusion Findings from this study indicate that older type 2 diabetes laboratory tests are associated with increases in the total expected count of subsequent inpatient hospitalizations and emergency department visits. Future research should examine the actionability of laboratory test values to determine associations with healthcare outcomes.

Objective: To examine how in-person classroom instruction was related to risk of SARS-CoV-2 infection in undergraduate students. Participants: Indiana University undergraduate students (n = 69,606) enrolled in Fall 2020, when courses with in-person and remote instruction options were available. Methods: Students participated weekly in mandatory SARS-CoV-2 RT-PCR asymptomatic testing by random selection, supplemented with symptomatic testing as needed. We used log-binomial regression models to estimate the association between number of in-person credit hours and the risk of SARS-CoV-2 infection over the course of the semester. Results: Overall 5,786 SARS-CoV-2 cases were observed. Increased in-person credit hour exposures were not associated with increased risk of SARS-CoV-2 overall [aRR (95% CI): 0.98 (0.97,0.99)], nor within specific subgroups (Greek affiliation and class). Conclusions: In-person instruction did not appear to increase SARS-CoV-2 transmission in a university setting with rigorous protective measures in place, prior to mass vaccine rollout and prior to delta variant emergence.

Measuring the prevalence of active SARS-CoV-2 infections in the general population is difficult because tests are conducted on a small and non-random segment of the population. However, hospitalized patients are tested at very high rates, even those admitted for non-COVID reasons. We show how to use information on testing of non-COVID hospitalized patients to obtain tight bounds on population prevalence, under conditions weaker than those usually used. We apply our approach to the population of test and hospitalization data for Indiana, and validate our approach. Our bounds could be constructed at relatively low cost, and for other heavily tested populations.

Objective: To determine whether preventive dental visits are associated with fewer subsequent nonpreventive dental visits and lower dental expenditures.Data Sources: Indiana Medicaid enrollment and claims data (2015–2018) and the Area Health Resource File.Study Design: A repeated measures design with individual and year fixed effects examining the relationship between preventive dental visits (PDVs) and nonpreventive dental visits (NPVs) and dental expenditures.Data Collection/Extraction Methods: Not applicable.Principal Findings: Of 28,152 adults (108,349 observation‐years) meeting inclusion criteria, 36.0% had a dental visit, 27.8% a PDV, and 22.1% a NPV. Compared to no PDV in the prior year, at least one was associated with fewer NPVs (β = −0.13; 95% CI –0.12, −0.11), lower NPV expenditures (β = −$29.12.53; 95% CI –28.07, −21.05), and lower total dental expenditures (−$70.12; 95% –74.92, −65.31), as well as fewer PDVs (β = −0.24; 95% CI –0.26, −0.23).Conclusions: Our findings suggest that prior year PDVs are associated with fewer subsequent NPVs and lower dental expenditures among Medicaid‐enrolled adults. Thus, from a public insurance program standpoint, supporting preventive dental care use may translate into improved population oral health outcomes and lower dental costs among certain low‐income adult populations, but barriers to consistent utilization of PDV prohibit definitive findings.

Introduction: Nonresponse bias occurs when participants in a study differ from eligible nonparticipants in ways that can distort study conclusions. The current study uses successive wave analysis, an established but underutilized approach, to assess nonresponse bias in a large-scale SARS-CoV-2 prevalence study. Such an approach makes use of reminders to induce participation among individuals. Based on the response continuum theory, those requiring several reminders to participate are more like nonrespondents than those who participate in a study upon first invitation, thus allowing for an examination of factors affecting participation. Methods: Study participants from the Indiana Population Prevalence SARS-CoV-2 Study were divided into 3 groups (eg, waves) based upon the number of reminders that were needed to induce participation. Independent variables were then used to determine whether key demographic characteristics as well as other variables hypothesized to influence study participation differed by wave using chi-square analyses. Specifically, we examined whether race, age, gender, education level, health status, tobacco behaviors, COVID-19–related symptoms, reasons for participating in the study, and SARS-CoV-2 positivity rates differed by wave. Results: Respondents included 3658 individuals, including 1495 in wave 1 (40.9%), 1246 in wave 2 (34.1%), and 917 in wave 3 (25%), for an overall participation rate of 23.6%. No significant differences in any examined variables were observed across waves, suggesting similar characteristics among those needing additional reminders compared with early participants. Conclusions: Using established techniques, we found no evidence of nonresponse bias in a random sample with a relatively low response rate. A hypothetical additional wave of participants would be unlikely to change original study conclusions. Successive wave analysis is an effective and easy tool that can allow public health researchers to assess, and possibly adjust for, nonresponse in any epidemiological survey that uses reminders to encourage participation.