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Enrollment in high-deductible health plans (HDHPs) has greatly increased in recent years. Policy makers and other stakeholders need the best available evidence about how these plans may affect health care cost and utilization, but the literature has not been comprehensively synthesized. We performed a systematic review of methodologically rigorous studies that examined the impact of HDHPs on health care utilization and costs. The plans were associated with a significant reduction in preventive care in seven of twelve studies and a significant reduction in office visits in six of eleven studies-which in turn led to a reduction in both appropriate and inappropriate care. Furthermore, bivariate analyses of data extracted from the included studies suggested that the plans may be associated with a reduction in appropriate preventive care and medication adherence. Current evidence suggests that HDHPs are associated with lower health care costs as a result of a reduction in the use of health services, including appropriate services.

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.

Objectives. We conducted a systematic review of the literature examining the relationship between built environments (e.g., parks, trails, sidewalks) and physical activity (PA) or obesity rates. Methods. We performed a 2-step inclusion protocol to identify empirical articles examining any form of built environment and any form of PA (or obesity rate) as the outcome. We extracted data from included abstracts for analysis by using a standard code sheet developed for this study. Results. Of 169 included articles, 89.2% reported beneficial relationships—but virtually all articles utilized simple observational study designs not suited for determining causality. Studies utilizing objective PA measures (e.g., pedometer) were 18% less likely to identify a beneficial relationship. Articles focusing on children in community settings (–14.2%), those examining direct measures of obesity (–6.2%), or those with an academic first author (–3.4%) were less likely to find a beneficial relationship. Conclusions. Policymakers at federal and local levels should encourage more rigorous scientific research to determine whether altered built environments will result in increased PA and decreased obesity rates.

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.

The Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009 that was signed into law as part of the \"stimulus package\" represents the largest US initiative to date that is designed to encourage widespread use of electronic health records (EHRs). In light of the changes anticipated from this policy initiative, the purpose of this paper is to review and summarize the literature on the benefits and drawbacks of EHR systems. Much of the literature has focused on key EHR functionalities, including clinical decision support systems, computerized order entry systems, and health information exchange. Our paper describes the potential benefits of EHRs that include clinical outcomes (eg, improved quality, reduced medical errors), organizational outcomes (eg, financial and operational benefits), and societal outcomes (eg, improved ability to conduct research, improved population health, reduced costs). Despite these benefits, studies in the literature highlight drawbacks associated with EHRs, which include the high upfront acquisition costs, ongoing maintenance costs, and disruptions to workflows that contribute to temporary losses in productivity that are the result of learning a new system. Moreover, EHRs are associated with potential perceived privacy concerns among patients, which are further addressed legislatively in the HITECH Act. Overall, experts and policymakers believe that significant benefits to patients and society can be realized when EHRs are widely adopted and used in a \"meaningful\" way.

This commentary reviews common myths and presumptions about obesity and also provides some useful evidence-based concepts about overweight and obesity. Passionate interests, the human tendency to seek explanations for observed phenomena, and everyday experience appear to contribute to strong convictions about obesity, despite the absence of supporting data. When the public, mass media, government agencies, and even academic scientists espouse unsupported beliefs, the result may be ineffective policy, unhelpful or unsafe clinical and public health recommendations, and an unproductive allocation of resources. In this article, we review some common beliefs about obesity that are not supported by scientific evidence and also provide some useful evidence-based concepts. We define myths as beliefs held to be true despite substantial refuting evidence, presumptions . . .

Prior studies examining symptoms of COVID-19 are primarily descriptive and measured among hospitalized individuals. Understanding symptoms of SARS-CoV-2 infection in pre-clinical, community-based populations may improve clinical screening, particularly during flu season. We sought to identify key symptoms and symptom combinations in a community-based population using robust methods. We pooled community-based cohorts of individuals aged 12 and older screened for SARS-CoV-2 infection in April and June 2020 for a statewide prevalence study. Main outcome was SARS-CoV-2 positivity. We calculated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for individual symptoms as well as symptom combinations. We further employed multivariable logistic regression and exploratory factor analysis (EFA) to examine symptoms and combinations associated with SARS-CoV-2 infection. Among 8214 individuals screened, 368 individuals (4.5%) were RT-PCR positive for SARS-CoV-2. Although two-thirds of symptoms were highly specific (>90.0%), most symptoms individually possessed a PPV <50.0%. The individual symptoms most greatly associated with SARS-CoV-2 positivity were fever (OR = 5.34, p<0.001), anosmia (OR = 4.08, p<0.001), ageusia (OR = 2.38, p = 0.006), and cough (OR = 2.86, p<0.001). Results from EFA identified two primary symptom clusters most associated with SARS-CoV-2 infection: (1) ageusia, anosmia, and fever; and (2) shortness of breath, cough, and chest pain. Moreover, being non-white (13.6% vs. 2.3%, p<0.001), Hispanic (27.9% vs. 2.5%, p<0.001), or living in an Urban area (5.4% vs. 3.8%, p<0.001) was associated with infection. Symptoms can help distinguish SARS-CoV-2 infection from other respiratory viruses, especially in community or urgent care settings where rapid testing may be limited. Symptoms should further be structured in clinical documentation to support identification of new cases and mitigation of disease spread by public health. These symptoms, derived from asymptomatic as well as mildly infected individuals, can also inform vaccine and therapeutic clinical trials.

Background/purpose To examine how the choice of rural measurements affects estimates of hospitalization rates for depression and substance use disorders (SUD). Methods We conducted cross-sectional analyses using the 2018 State Inpatient Database (SID) for 5 states, including Arizona, Kentucky, Maryland, Washington, and Florida, to determine how (1) estimates of hospitalization rates for depression and SUDs; and (2) patient characteristics among those hospitalized differ. Five measurements of rurality including rural-urban commuting areas (RUCA) codes, core-based statistical areas (CBSA), urban-rural category four (URCategory4) and two definitions of rural urban continuum codes (RUCC) were used. For each measurement, we calculated frequencies and percentages for age, race, sex, and insurance type. We conducted Spearman’s rank correlations to compare associations and internal agreement. We created an UpSet chart to visualize the overlap in different measurements. Results There were 152,771 hospitalizations for depression and 43,760 hospitalizations for SUDs. The percentage of hospitalizations for depression or SUD differed significantly (3.2–8.1% for depression and 5.0–11.6% for SUDs ) based on rurality measure. Race and insurance characteristics of those identified as rural varied by rural measurement for depression and SUD hospitalizations. Spearman’s correlations were higher for hospitalizations for SUD than for depression, ranging from r  = 0.61 (RUCC and RUCA) to r  = 0.99 (CBSA and URCategory4). Conclusions Different rurality measurements result in differing estimates of hospitalizations for SUD or depression. Stakeholders should be aware that the choice of rural measurements can impact policy decisions and resource allocation for programs intended to improve care in rural areas.

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 (χ 2  = 7.43, p  = 0.025). Compared to males, females responded at a higher percentage after 1 contact (21.4 vs. 17.9%, χ 2  = 7.6, p  = 0.023). Those over 60 years responded most often after 2 contacts (χ 2  = 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.

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.