Characterizing participants who respond to text, email, phone calls, or postcards in a SARS-CoV-2 prevalence study.

INTRODUCTION: Multiple modalities and frequencies of contact are needed to maximize recruitment in many public health surveys. The purpose of this analysis is to characterize respondents to a statewide SARS-CoV-2 testing study whose participation followed either postcard, phone outreach or electronic means of invitation. In addition, we examine how participant characteristics differ based upon the number of contacts needed to elicit participation. METHODS: This is a cross-sectional analysis of survey data collected from participants who were randomly selected to represent Indiana residents and were invited to be tested for Covid-19 in April 2020. Participants received invitations via postcard, text/emails, and/or robocalls/texts based upon available contact information. The modality, and frequency of contacts, that prompted participation was determined by when the notification was sent and when the participant responded and subsequently registered to participate in the study. Chi square analyses were used to determine differences between groups and significant findings were analyzed using multinomial logistic regression. RESULTS: Respondents included 3,658 individuals and were stratified by postcards (7.9%), text/emails (26.5%), and robocalls/text (65.7%) with 19.7% registering after 1 contact, 47.9% after 2 contacts, and 32.4% after 3 contacts encouraging participation. Females made up 54.6% of the sample and responded at a higher rate for postcards (8.2% vs. 7.5%) and text/emails (28.1 vs. 24.6%) as compared to males (χ2 = 7.43, p = 0.025). Compared to males, females responded at a higher percentage after 1 contact (21.4 vs. 17.9%, χ2 = 7.6, p = 0.023). Those over 60 years responded most often after 2 contacts (χ2 = 27.5, p < 0.001) when compared to others at younger age groups. In regression analysis, participant sex (p = 0.036) age (p = 0.005), educational attainment (p = < 0.0001), and being motivated by "free testing" (p = 0.036) were correlated with participation in the prevalence study. DISCUSSION: Researchers should be aware that the modality of contact as well as the number of prompts used could influence differential participation in public health studies. Our findings can inform researchers developing studies that rely on selective participation by study subjects. We explore how to increase participation within targeted demographic groups using specific modalities and examining frequency of contact.

Bayesian estimation of SARS-CoV-2 prevalence in Indiana by random testing.

From 25 to 29 April 2020, the state of Indiana undertook testing of 3,658 randomly chosen state residents for the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, the agent causing COVID-19 disease. This was the first statewide randomized study of COVID-19 testing in the United States. Both PCR and serological tests were administered to all study participants. This paper describes statistical methods used to address nonresponse among various demographic groups and to adjust for testing errors to reduce bias in the estimates of the overall disease prevalence in Indiana. These adjustments were implemented through Bayesian methods, which incorporated all available information on disease prevalence and test performance, along with external data obtained from census of the Indiana statewide population. Both adjustments appeared to have significant impact on the unadjusted estimates, mainly due to upweighting data in study participants of non-White races and Hispanic ethnicity and anticipated false-positive and false-negative test results among both the PCR and antibody tests utilized in the study.

State Policies Associated with Availability of Mobile Crisis Teams.

Mobile crisis teams are comprised of multidisciplinary mental health professionals that respond to mental health crisis calls in community settings. This study identified counties with mobile crisis teams and examined state policies associated with mobile crisis teams. Descriptive statistics and geographic information system software were used to quantify and map counties with mobile crisis teams in the United States. Relationships between state policies and mobile crisis teams were examined using an adjusted logistic regression model, controlling for county characteristics and accounting for clustering by state. Approximately 40% (n = 1,245) of all counties in the US have at least one mobile crisis team. Counties in states with legislation in place to fund the 988 Suicide and Crisis Lifeline were more likely to have a mobile crisis team (Adjusted Odds Ratio (AOR): 2.0; Confidence Interval (CI): 1.23-3.26), whereas counties in states with 1115 waivers restricting Medicaid benefits were less likely to have a mobile crisis team (AOR: 0.43; CI: 0.21-0.86). Additionally, counties with the largest population were more likely to have a mobile crisis team (AOR: 2.20; CI:1.43-3.38) than counties with the smallest population. Having a mobile crisis teams was positively associated with legislation to fund 988. Legislation that encourages expansion of existing crisis care services, specifically funding aimed at mobile crisis teams, may help increase availability of services for people who are experiencing a mental health crisis in the community.

Toward more rigorous and informative nutritional epidemiology: The rational space between dismissal and defense of the status quo.

To date, nutritional epidemiology has relied heavily on relatively weak methods including simple observational designs and substandard measurements. Despite low internal validity and other sources of bias, claims of causality are made commonly in this literature. Nutritional epidemiology investigations can be improved through greater scientific rigor and adherence to scientific reporting commensurate with research methods used. Some commentators advocate jettisoning nutritional epidemiology entirely, perhaps believing improvements are impossible. Still others support only normative refinements. But neither abolition nor minor tweaks are appropriate. Nutritional epidemiology, in its present state, offers utility, yet also needs marked, reformational renovation. Changing the status quo will require ongoing, unflinching scrutiny of research questions, practices, and reporting-and a willingness to admit that "good enough" is no longer good enough. As such, a workshop entitled "Toward more rigorous and informative nutritional epidemiology: the rational space between dismissal and defense of the status quo" was held from July 15 to August 14, 2020. This virtual symposium focused on: (1) Stronger Designs, (2) Stronger Measurement, (3) Stronger Analyses, and (4) Stronger Execution and Reporting. Participants from several leading academic institutions explored existing, evolving, and new better practices, tools, and techniques to collaboratively advance specific recommendations for strengthening nutritional epidemiology.

Unmet mental health need and subsequent substance use in individuals with a history of depression: Are there differences between metro and nonmetro areas?

BACKGROUND AND OBJECTIVES: More than nine million U.S. adults have a co-occurring mental health and substance use disorder. The self-medication hypothesis suggests that individuals with unmet need may alleviate the symptoms of their mental illness by using alcohol or drugs. We examine the relationship between unmet mental health need and subsequent substance use among individuals with a history of depression as well as differences in metro and nonmetro areas. METHODS: We used repeated cross-sectional data from the National Survey on Drug Use and Health (NSDUH), 2015-2018 after identifying individuals with depression in the past year (n = 12,211). We used logistic regressions with interaction terms to examine the association between unmet need for mental health care and substance use by geographic location. RESULTS: Unmet mental health need was associated with increased use of marijuana (odds ratio [OR] = 1.32, 95% confidence interval [CI]: 1.08-1.64), illicit drugs (OR = 1.75, 95% CI: 1.19-2.58), and prescription drugs (OR = 1.89, 95% CI: 1.19-3.00) among individuals with depression, which did not vary by geographic location. Unmet need was not associated with increased heavy alcohol drinking (OR = 0.87, 95% CI: 0.60-1.26). DISCUSSION AND CONCLUSIONS: No differences in substance usage between metro and nonmetro populations were observed for those with an unmet need for mental health care. We found support for the self-medication hypothesis among individuals with depression with respect to alcohol. SCIENTIFIC SIGNIFICANCE: We examine whether individuals with depression and unmet care needs are more likely to self-medicate with substances including prescription drugs. Due to higher unmeet needs in nonmetro areas, we examine whether the likelihood of self-medication differs in metro and nonmetro areas.

Factors that differentiate COVID-19 vaccine intentions among Indiana parents: Implications for targeted vaccine promotion.

Given low rates of uptake of the COVID-19 vaccine for children 12-17 and 5-11 years old, research is needed to understand parental behaviors and behavioral intentions related to COVID-19 vaccination for their children. In the state of Indiana, we conducted a non-random, online survey of parents or caregivers (N = 10,266) about their COVID-19 vaccine intentions or behaviors, demographic characteristics, and potential motivating reasons for getting the vaccine. In terms of behaviors/intentions, 44.8% of participants indicated they were vaccine acceptors (i.e., had already had their children vaccinated or would as soon as it was possible), 13.0% indicated they were vaccine hesitators (i.e., wanted to wait and see), and 42.2% indicated they were vaccine rejecters (i.e., would not vaccinate or only would if mandated). Compared to vaccine rejecters, vaccine hesitators were more likely to be motivated by perceptions of vaccine safety and efficacy, normative influences such as close friends/family who had been vaccinated and a recommendation from a provider, as well as if they were vaccinated themselves. These findings have implications for the development of targeted vaccine promotion strategies, such as social norms messaging and a focus on vaccine safety, in order to increase COVID-19 vaccination for eligible children.

SARS-CoV-2 reinfections in a US university setting, Fall 2020 to Spring 2021.

BACKGROUND: SARS-CoV-2 reinfections are a public health concern because of the potential for transmission and clinical disease, and because of our limited understanding of whether and how well an infection confers protection against subsequent infections. Despite the public health importance, few studies have reported rigorous estimates of reinfection risk. METHODS: Leveraging Indiana University's comprehensive testing program to identify both asymptomatic and symptomatic SARS-CoV-2 cases, we estimated the incidence of SARS-CoV-2 reinfection among students, faculty, and staff across the 2020-2021 academic year. We contextualized the reinfection data with information on key covariates: age, sex, Greek organization membership, student vs faculty/staff affiliation, and testing type. RESULTS: Among 12,272 people with primary infections, we found a low level of SARS-CoV-2 reinfections (0.6%; 0.4 per 10,000 person-days). We observed higher risk for SARS-CoV-2 reinfections in Greek-affiliated students. CONCLUSIONS: We found evidence for low levels of SARS-CoV-2 reinfection in a large multi-campus university population during a time-period prior to widespread COVID-19 vaccination.

Characteristics and outcomes associated with two asthma quality of care measures.

OBJECTIVE: We investigated asthma quality measures to understand patient characteristics associated with non-attainment of quality care and measure the association with asthma-related emergency department (ED) visits or inpatient hospitalizations (IPs). METHODS: Using administrative data from ALL Kids, Alabama's Children's Health Insurance Program, from 2013 to 2019 we calculated non-attainment of the Medication Management for Asthma (MMA) and Asthma Medication Ratio (AMR) quality measures. Patient characteristics and asthma-related ED visits and IPs associated with non-attainment of the MMA and AMR measures were assessed using logit regression models and Marginal effects at the mean. RESULTS: Among 2528 children with asthma, 53.2% failed to attain the MMA measure and 8.5% the AMR measure. Prior asthma-related ED visits or IP stays increased likelihood of non-attainment by 14.8 percentage points (95% CI 8.6-20.9) for MMA and 7.3 percentage points (95% CI 2.8-11.8) for AMR. Among 868 children (34.3%) with three years of continuous enrollment, AMR non-attainment was associated with a 6.1 percentage point increase in ED or IP utilization (95% CI 1.3-10.9), however MMA non-attainment was not associated with either outcome. Prior ED visit/IP stay was associated with a 17.2 percentage point (95% CI 8.3-26.1) increase in the likelihood of a subsequent ED visit/IP stay among those with non-attainment MMA and a 15.5 percentage point increase (95% CI 6.9-24.2) for non-attainment AMR. CONCLUSIONS: Patient characteristics associated with non-attainment of asthma quality measures presents actionable evidence to guide improvement efforts as non-attainment AMR increases the risk of subsequent ED visits and IP stays.

Measuring rurality in health services research: a scoping review.

PURPOSE: This study is a scoping review of the different methods used to measure rurality in the health services research (HSR) literature. METHODS: We identified peer-reviewed empirical studies from 2010-2020 from seven leading HSR journals, including the Journal of Rural Health, that used any definition to measure rurality as a part of their analysis. From each study, we identified the geographic unit (e.g., county, zip code) and definition (e.g., Rural Urban Continuum Codes, Rural Urban Commuting Areas) used to classify categories of rurality. We analyzed whether geographic units and definitions used to classify rurality differed by focus area of studies, including costs, quality, and access to care. Lastly, we examined the number of rural categories used by authors to assess rural areas. FINDINGS: In 103 included studies, five different geographic units and 11 definitions were used to measure rurality. The most common geographic units used to measure rurality were county (n = 59, 57%), which was used most frequently in studies examining cost (n = 12, 75%) and access (n = 33, 57.9%). Rural Urban Commuting Area codes were the most common definition used to measure rurality for studies examining access (n = 13, 22.8%) and quality (n = 10, 44%). The majority of included studies made rural versus urban comparisons (n = 82, 80%) as opposed to focusing on rural populations only (n = 21, 20%). Among studies that compared rural and urban populations, most studies used only one category to identify rural locations (n = 49 of 82 studies, 60%). CONCLUSION: Geographic units and definitions to determine rurality were used inconsistently within and across studies with an HSR focus. This finding may affect how health disparities by rural location are determined and thus how resources and federal funds are allocated. Future research should focus on developing a standardized system to determine under what circumstances researchers should use different geographic units and methods to determine rurality by HSR focus area.

Institutional factors associated with hospital partnerships for population health: A pooled cross-sectional analysis.

BACKGROUND: Hospitals are increasingly engaging in partnerships to address population health in response to national policies, such as value-based payment models. However, little is known about how institutional factors influence hospital partnerships for population health. PURPOSE: Guided by institutional theory, we examine the association between institutional pressures (coercive, normative, and mimetic isomorphism) and hospital partnerships for population health. METHODOLOGY: A pooled cross-sectional analysis used an unbalanced panel of 10,777 hospital-year observations representing respondents to a supplemental question of the American Hospital Association's annual survey (2015-2017). The analysis included descriptive and bivariate statistics, and regression models that adjusted for repeated observations to examine the relationship between key independent variables and partnerships over time. FINDINGS: In regression analyses, we found the most support for measures of coercive (e.g., regulatory factors) isomorphism, with nonprofit status, participation in accountable care organizations, and acceptance of bundled payments, all being consistently and significantly associated with partnerships across all organization types. Modest increases were observed from 2015 to 2017 for hospital partnerships with public health organizations (+2.8% points, p < .001), governmental organizations (+2.0% points, p = .009), schools (+4.1% points, p < .001), and businesses (+2.2% points, p = .007). PRACTICE IMPLICATIONS: Our results suggest that institutional factors, particularly those related to regulatory policies and programs, may influence hospital partnerships to support population health. Findings from this study can assist hospital leaders in assessing the factors that can support or impede the creation of partnerships to support their population health efforts.

Successive Wave Analysis to Assess Nonresponse Bias in a Statewide Random Sample Testing Study for SARS-CoV-2.

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.

Work-life balance among health administration faculty before and during the COVID-19 pandemic.

This current study examines measures of work-life balance among health administration faculty prior to and during the COVID-19 pandemic. A repeated cross-sectional design is used to analyze data collected from a national survey in 2018 and 2021. Changes in six different outcome measures of work-life balance were examined using multivariable logistic regression, controlling for health administration faculty characteristics. Compared to 2018 respondents, faculty respondents in 2021 were more likely to report that family and personal matters were interfering with their ability to do their job (OR = 1.93, p=0.001). Females more frequently reported that their career had suffered because of personal issues/obligations (OR = 1.82, p=0.003) but were less likely to report having enough time to get their teaching (OR = 0.68, p=0.026). Respondents with children 18 years or younger reported higher rates of regularly having to miss a meeting or event at home (OR = 1.88, p<0.001) and an event at work (OR = 3.74, p<0.001). These faculty also more frequently reported that family or personal matters were interfering with their ability to do their job (OR = 3.04, p<0.001) and that their career suffered because of personal issues/obligations (OR = 2.09, p=0.001). Given the implications of work-life conflicts to organizational outcomes, academic leaders and university decision-makers should consider adopting strategies to mitigate the effects of these disruptions to the work-life equilibrium of academics.

Faculty salaries in health administration: trends and correlates 2015-2021.

In this study, we provide updated information on salaries of academic health administration (HA) faculty members based on data collected in 2015, 2018, and 2021 and examine characteristics associated with earnings. We present mean inflation-adjusted salaries by demographic characteristics, education, experience, productivity, and job activities. We find that salaries of assistant, associate, and full professors have kept up with inflation and there have not been significant changes in salary by any characteristics over time. As in previous iterations of similar survey data, there remain differences in salary by both gender and race. Higher salaries were associated with having a 12-month contract, being tenured or tenure-track, having an administrative position, and being in a department whose focus is not primarily teaching. Findings from our study will be of interest to individuals on the HA job market, hiring committees, and doctoral students preparing for a position after graduation.

Assessing Variation in State Opioid Tapering Laws: Comparing State Laws with the CDC Guideline.

OBJECTIVE: In 2016, the Center for Disease Control and Prevention released an opioid prescribing guideline for primary care in response to opioid overdose deaths. Despite efforts to encourage safer prescribing practices, experts and federal agencies suspect prescribing guidelines may be misapplied in clinical practice, resulting in abrupt tapering from opioid therapy. Although state laws likely influence prescriber behavior, little is known about state tapering laws. Thus, we examined the scope and variation of state tapering laws compared with federal opioid guidelines. METHODS: We conducted a comprehensive review of state laws through December 31, 2019, using keyword searches in LexisNexus. Identified laws were coded based on the inclusion of attributes derived from federal opioid guidelines and an expert consensus panel report. We examined whether law attributes were associated with state characteristics, including region, population, governor's political affiliation, opioid prescribing rates, and opioid overdose rates. RESULTS: We found 27 states and one federal district had law(s) mentioning tapering. Most laws were authored by medical boards or workers' compensation groups (65.6%) while some laws included a penalty (32.8%). Approximately half of guideline attributes (54.2%) were included in state laws; however, only two state's laws cautioned against abrupt tapering. States with higher overdose death rates were more likely to enact a tapering law (P < 0.001) and have a penalty (P = 0.007). CONCLUSIONS: State tapering laws incorporate some federal guideline attributes but most lack attributes deemed critical by experts. Without clear instruction, patients risk inappropriate tapering and discontinuation of opioid therapy. Given these findings, policymakers should consider addressing this gap.

Association of Health Status and Nicotine Consumption with SARS-CoV-2 positivity rates.

BACKGROUND: Much of what is known about COVID-19 risk factors comes from patients with serious symptoms who test positive. While risk factors for hospitalization or death include chronic conditions and smoking; less is known about how health status or nicotine consumption is associated with risk of SARS-CoV-2 infection among individuals who do not present clinically. METHODS: Two community-based population samples (including individuals randomly and nonrandomly selected for statewide testing, n = 8214) underwent SARS-CoV-2 testing in nonclinical settings. Each participant was tested for current (viral PCR) and past (antibody) infection in either April or June of 2020. Before testing, participants provided demographic information and self-reported health status and nicotine and tobacco behaviors (smoking, chewing, vaping/e-cigarettes). Using descriptive statistics and a bivariate logistic regression model, we examined the association between health status and use of tobacco or nicotine with SARS-CoV-2 positivity on either PCR or antibody tests. RESULTS: Compared to people with self-identified "excellent" or very good health status, those reporting "good" or "fair" health status had a higher risk of past or current infections. Positive smoking status was inversely associated with SARS-CoV-2 infection. Chewing tobacco was associated with infection and the use of vaping/e-cigarettes was not associated with infection. CONCLUSIONS: In a statewide, community-based population drawn for SARS-CoV-2 testing, we find that overall health status was associated with infection rates. Unlike in studies of COVID-19 patients, smoking status was inversely associated with SARS-CoV-2 positivity. More research is needed to further understand the nature of this relationship.

Defining safety net hospitals in the health services research literature: a systematic review and critical appraisal.

BACKGROUND: The aim of this study was to identify the range of ways that safety net hospitals (SNHs) have been empirically operationalized in the literature and determine the extent to which patterns could be identified in the use of empirical definitions of SNHs. METHODS: We conducted a PRISMA guided systematic review of studies published between 2009 and 2018 and analyzed 22 articles that met the inclusion criteria of hospital-level analyses with a clear SNH definition. RESULTS: Eleven unique SNH definitions were identified, and there were no obvious patterns in the use of a definition category (Medicaid caseload, DSH payment status, uncompensated care, facility characteristics, patient care mix) by the journal type where the article appeared, dataset used, or the year of publication. CONCLUSIONS: Overall, there is broad variability in the conceptualization of, and variables used to define, SNHs. Our work advances the field toward the development of standards in measuring, operationalizing, and conceptualizing SNHs across research and policy questions.

Hospital Partnerships for Population Health: A Systematic Review of the Literature.

EXECUTIVE SUMMARY: The U.S. healthcare system continues to experience high costs and suboptimal health outcomes that are largely influenced by social determinants of health. National policies such as the Affordable Care Act and value-based payment reforms incentivize healthcare systems to engage in strategies to improve population health. Healthcare systems are increasingly expanding or developing new partnerships with community-based organizations to support these efforts. We conducted a systematic review of peer-reviewed literature in the United States to identify examples of hospital-community partnerships; the main purposes or goals of partnerships; study designs used to assess partnerships; and potential outcomes (e.g., process- or health-related) associated with partnerships. Using robust keyword searches and a thorough reference review, we identified 37 articles published between January 2008 and December 2019 for inclusion. Most studies employed descriptive study designs (n = 21); health needs assessments were the most common partnership focus (n = 15); and community/social service (n = 21) and public health organizations (n = 15) were the most common partner types. Qualitative findings suggest hospital-community partnerships hold promise for breaking down silos, improving communication across sectors, and ensuring appropriate interventions for specific populations. Few studies in this review reported quantitative findings. In those that did, results were mixed, with the strongest support for improvements in measures of hospitalizations. This review provides an initial synthesis of hospital partnerships to address population health and presents valuable insights to hospital administrators, particularly those leading population health efforts.

Factors associated with the provision of inpatient care in hospices.

BACKGROUND: Hospices provide end-of-life care to patients who have complex health care needs and whose symptoms are difficult to control. Understanding why some hospices offer inpatient hospice care to patients could bring more evidence for policy makers and researchers to focus on the role of inpatient care in hospice. PURPOSE: The purpose of this study was to examine market and organizational factors that are associated with the provision of hospice inpatient care. METHODOLOGY: This study used a retrospective, longitudinal design (2009-2013). The study sample was drawn from three data resources: the Area Health Resources Files, the Provider of Services files, and Hospice Cost Reports from Centers for Medicare & Medicaid Services. The sample size was 2,391 hospices or 10,999 hospice observations over 5 years. A generalized linear mixed-effects model was used to examine the association between market and organizational factors and hospice inpatient services offering. RESULTS: On average, 94.59% of hospices offer inpatient services to patients. Proportion of adults who were over 65 years old (OR = 1.12) and Medicare-managed care penetration (OR = 1.02) were positively associated with the provision of hospice inpatient services. The number of hospitals with hospice program was negatively related to hospice inpatient services offering (OR = .95). Other factors such as nursing skill mix, volunteer dependence, and census region were also associated with inpatient services offering. PRACTICE IMPLICATIONS: The age demand of hospice care and Medicare-managed care penetration are related to hospice inpatient services offering. Hospices located in the market with more competition from hospitals that offer hospice program are less likely to offer inpatient care.

Hospice inpatient services provision, utilization, and financial performance.

BACKGROUND: Hospice performance is an overlooked area in the health care field due to the difficulty of measuring quality of care and the infrequent quality inspection. Based on the daily reimbursement mechanism for different levels of hospice care, inpatient services provision could influence both hospice-level length of stay (LOS) and financial performance. PURPOSE: The objective of this study was to explore the relationship between hospice inpatient services provision and hospice utilization and financial performance. METHODOLOGY/APPROACH: A longitudinal secondary data set (2009-2013) was merged from three sources: (a) Hospice Cost Reports from the Centers for Medicare & Medicaid Services, (b) the Provider of Services files, and (c) the Area Health Resources Files. The dependent variable in this study was hospice average LOS and financial performance measured by total operating margin (TOM) and return on assets. The independent variable was hospice inpatient services' offering. Mixed-effects regression models were used in the multivariate regression analyses. RESULTS: When comparing to hospices not providing inpatient services, offering inpatient services by staff was negatively related to average LOS (b = -0.063, p < .05) and TOM (b = -0.022, p < .05). The combination method with providing inpatient services by staff and under arrangement was negatively associated with return on assets (b = -0.073, p < .05). CONCLUSION: Hospice inpatient services provision was associated with average LOS and financial performance. PRACTICE IMPLICATIONS: Offering the inpatient services to patients by staff decreased average LOS and TOM. Hospice agencies may seek strategies to maintain their financial sustainability through outsourcing.

How Many SARS-CoV-2-Infected People Require Hospitalization? Using Random Sample Testing to Better Inform Preparedness Efforts.

CONTEXT: Existing hospitalization ratios for COVID-19 typically use case counts in the denominator, which problematically underestimates total infections because asymptomatic and mildly infected persons rarely get tested. As a result, surge models that rely on case counts to forecast hospital demand may be inaccurately influencing policy and decision-maker action. OBJECTIVE: Based on SARS-CoV-2 prevalence data derived from a statewide random sample (as opposed to relying on reported case counts), we determine the infection-hospitalization ratio (IHR), defined as the percentage of infected individuals who are hospitalized, for various demographic groups in Indiana. Furthermore, for comparison, we show the extent to which case-based hospitalization ratios, compared with the IHR, overestimate the probability of hospitalization by demographic group. DESIGN: Secondary analysis of statewide prevalence data from Indiana, COVID-19 hospitalization data extracted from a statewide health information exchange, and all reported COVID-19 cases to the state health department. SETTING: State of Indiana as of April 30, 2020. MAIN OUTCOME MEASURES: Demographic-stratified IHRs and case-hospitalization ratios. RESULTS: The overall IHR was 2.1% and varied more by age than by race or sex. Infection-hospitalization ratio estimates ranged from 0.4% for those younger than 40 years to 9.2% for those older than 60 years. Hospitalization rates based on case counts overestimated the IHR by a factor of 10, but this overestimation differed by demographic groups, especially age. CONCLUSIONS: In this first study of the IHR based on population prevalence, our results can improve forecasting models of hospital demand-especially in preparation for the upcoming winter period when an increase in SARS CoV-2 infections is expected.