Designing Difference-in-Difference Studies with Staggered Treatment Adoption: Key Concepts and Practical Guidelines.

Difference-in-difference (DID) estimators are a valuable method for identifying causal effects in the public health researcher's toolkit. A growing methods literature points out potential problems with DID estimators when treatment is staggered in adoption and varies with time. Despite this, no practical guide exists for addressing these new critiques in public health research. We illustrate these new DID concepts with step-by-step examples, code, and a checklist. We draw insights by comparing the simple 2 × 2 DID design (single treatment group, single control group, two time periods) with more complex cases: additional treated groups, additional time periods of treatment, and treatment effects possibly varying over time. We outline newly uncovered threats to causal interpretation of DID estimates and the solutions the literature has proposed, relying on a decomposition that shows how the more complex DIDs are an average of simpler 2 × 2 DID subexperiments.

High-Dose Opioid Prescribing in Individuals with Acute Pain: Assessing the Effects of US State Opioid Policies.

BACKGROUND: How state opioid policy environments with multiple concurrent policies affect opioid prescribing to individuals with acute pain is unknown. OBJECTIVE: To examine how prescription drug monitoring programs (PDMPs), pain management clinic regulations, initial prescription duration limits, and mandatory continued medical education affected total and high-dose prescribing. DESIGN: A county-level multiple-policy difference-in-difference event study framework. SUBJECTS: A total of 2,425,643 individuals in a large national commercial insurance deidentified claims database (aged 12-64 years) with acute pain diagnoses and opioid prescriptions from 2007 to 2019. MAIN MEASURES: The total number of acute pain opioid treatment episodes and number of episodes containing high-dose (> 90 morphine equivalent daily dosage (MEDD)) prescriptions. KEY RESULTS: Approximately 7.5% of acute pain episodes were categorized as high-dose episodes. Prescription duration limits were associated with increases in the number of total episodes; no other policy was found to have a significant impact. Beginning five quarters after implementation, counties in states with pain management clinic regulations experienced a sustained 50% relative decline in the number of episodes containing > 90 MEDD prescriptions (95 CIs: (Q5: - 0.506, - 0.144; Q12: - 1.000, - 0.290)). Mandated continuing medical education regarding the treatment of pain was associated with a 50-75% relative increase in number of high-dose episodes following the first year-and-a-half of enactment (95 CIs: (Q7: 0.351, 0.869; Q12: 0.413, 1.107)). Initial prescription duration limits were associated with an initial relative reduction of 25% in high-dose prescribing, with the effect increasing over time (95 CI: (Q12: - 0.967, - 0.335). There was no evidence that PDMPs affected high-dose opioids dispensed to individuals with acute pain. Other high-risk prescribing indicators were explored as well; no consistent policy impacts were found. CONCLUSIONS: State opioid policies may have differential effects on high-dose opioid dispensing in individuals with acute pain. Policymakers should consider effectiveness of individual policies in the presence of other opioid policies to address the ongoing opioid crisis.

Does paid sick leave encourage staying at home? Evidence from the United States during a pandemic.

We study the impact of a temporary U.S. paid sick leave mandate that became effective April 1st, 2020 on self-quarantining, proxied by physical mobility behaviors gleaned from cellular devices. We study this policy using generalized difference-in-differences methods, leveraging pre-policy county-level heterogeneity in the share of workers likely eligible for paid sick leave benefits. We find that the policy leads to increased self-quarantining as proxied by staying home. We also find that COVID-19 confirmed cases decline post-policy.

Recent tapering from long-term opioid therapy and odds of opioid-related hospital use.

PURPOSE: The number of patients tapered from long-term opioid therapy (LTOT) has increased in recent years in the United States. Some patients tapered from LTOT report improved quality of life, while others face increased risks of opioid-related hospital use. Research has not yet established how the risk of opioid-related hospital use changes across LTOT dose and subsequent tapering. Our objective was to examine associations between recent tapering from LTOT with odds of opioid-related hospital use. METHODS: Case-crossover design using 2014-2018 health information exchange data from Indiana. We defined opioid-related hospital use as hospitalizations, and emergency department (ED) visits for a drug overdose, opioid abuse, and dependence. We defined tapering as a 15% or greater dose reduction following at least 3 months of continuous opioid therapy of 50 morphine milligram equivalents (MME)/day or more. We used conditional logistic regression to estimate odds ratios (OR) with 95% confidence intervals (CI). RESULTS: Recent tapering from LTOT was associated with increased odds of opioid-related hospital use (OR: 1.50, 95%CI: 1.34-1.63), ED visit (OR: 1.52; 95%CI: 1.35-1.72), and inpatient hospitalization (OR: 1.40; 95%CI: 1.20-1.65). We found no evidence of heterogeneity of the effect of tapering on opioid-related hospital use by gender, age, and race. Recent tapering among patients on a high baseline dose (>300 MME) was associated with increased odds of opioid-related hospital use (OR: 2.95, 95% CI: 2.12-4.11, p < 0.001) compared to patients on a lower baseline doses. CONCLUSIONS: Recent tapering from LTOT is associated with increased odds of opioid-related hospital use.

Evidence from internet search data shows information-seeking responses to news of local COVID-19 cases.

The COVID-19 outbreak is a global pandemic with community circulation in many countries, including the United States, with confirmed cases in all states. The course of this pandemic will be shaped by how governments enact timely policies and disseminate information and by how the public reacts to policies and information. Here, we examine information-seeking responses to the first COVID-19 case public announcement in a state. Using an event study framework for all US states, we show that such news increases collective attention to the crisis right away. However, the elevated level of attention is short-lived, even though the initial announcements are followed by increasingly strong policy measures. Specifically, searches for "coronavirus" increased by about 36% (95% CI: 27 to 44%) on the day immediately after the first case announcement but decreased back to the baseline level in less than a week or two. We find that people respond to the first report of COVID-19 in their state by immediately seeking information about COVID-19, as measured by searches for coronavirus, coronavirus symptoms, and hand sanitizer. On the other hand, searches for information regarding community-level policies (e.g., quarantine, school closures, testing) or personal health strategies (e.g., masks, grocery delivery, over-the-counter medications) do not appear to be immediately triggered by first reports. These results are representative of the study period being relatively early in the epidemic, and more-elaborate policy responses were not yet part of the public discourse. Further analysis should track evolving patterns of responses to subsequent flows of public information.

Characteristics of statewide prescription drug monitoring programs and potentially inappropriate opioid prescribing to patients with non-cancer chronic pain: A machine learning application.

Unnecessary/unsafe opioid prescribing has become a major public health concern in the U.S. Statewide prescription drug monitoring programs (PDMPs) with varying characteristics have been implemented to improve safe prescribing practice. Yet, no studies have comprehensively evaluated the effectiveness of PDMP characteristics in reducing opioid-related potentially inappropriate prescribing (PIP) practices. The objective of the study is to apply machine learning methods to evaluate PDMP effectiveness by examining how different PDMP characteristics are associated with opioid-related PIPs for non-cancer chronic pain (NCCP) treatment. This was a retrospective observational study that included 802,926 adult patients who were diagnosed NCCP, obtained opioid prescriptions, and were continuously enrolled in plans of a major U.S. insurer for over a year. Four outcomes of opioid-related PIP practices, including dosage ≥50 MME/day and ≥90 MME/day, days supply ≥7 days, and benzodiazepine-opioid co-prescription were examined. Machine learning models were applied, including logistic regression, least absolute shrinkage and selection operation regression, classification and regression trees, random forests, and gradient boost modeling (GBM). The SHapley Additive exPlanations (SHAP) method was applied to interpret model results. The results show that among 1,886,146 NCCP opioid-related claims, 22.8% had an opioid dosage ≥50 MME/day and 8.9% ≥90 MME/day, 70.3% had days supply ≥7 days, and 10.3% were when benzodiazepine was filled ≤7 days ago. GBM had superior model performance. We identified the most salient PDMP characteristics that predict opioid-related PIPs (e.g., broader access to patient prescription history, monitoring Schedule IV controlled substances), which could be informative to the states considering the redesign of PDMPs.

Determinants of Disparities in Early COVID-19 Job Losses.

This study examines the sociodemographic divide in early labor market responses to the U.S. COVID-19 epidemic and associated policies, benchmarked against two previous recessions. Monthly Current Population Survey (CPS) data show greater declines in employment in April and May 2020 (relative to February) for Hispanic individuals, younger workers, and those with a high school diploma or some college. Between April and May, the demographic subgroups considered regained some employment. Reemployment in May was broadly proportional to the employment drop that occurred through April, except for Black individuals, who experienced a smaller rebound. Compared to the 2001 recession and the Great Recession, employment losses in the early COVID-19 recession were smaller for groups with low or high (vs. medium) education. We show that job loss was greater in occupations that require more interpersonal contact and that cannot be performed remotely, and that pre-COVID-19 sorting of workers into occupations and industries along demographic lines can explain a sizable portion of the demographic gaps in new unemployment. For example, while women suffered more job losses than men, their disproportionate pre-epidemic sorting into occupations compatible with remote work shielded them from even larger employment losses. However, substantial gaps in employment losses across groups cannot be explained by socioeconomic differences. We consider policy lessons and future research needs regarding the early labor market implications of the COVID-19 crisis.

Perceived COVID-19 vaccine attributes associated with early adoption among adults in rural Indiana.

2019 Novel coronavirus (COVID-19) vaccination rates in the United States have plateaued in specific populations, including rural areas. To improve COVID-19 vaccination rates and to encourage early vaccine uptake in future pandemics, this study aimed to examine vaccine attributes associated with early adoption. Data are from an anonymous online survey of adults using targeted Facebook pages of rural southern Indiana towns in January and February 2021 (n = 286). The diffusion of innovation theory states that the rate of adoption of a product in a specific population is explained by five perceived attributes: relative advantage, compatibility, observability, complexity and trialability. Binary logistic regression analyses were used to examine the association of Diffusion of Innovation theory attributes of the COVID-19 vaccine on early adoption. Results indicated that trialability [odds ratio (OR) = 3.307; 95% confidence interval (CI) = 1.964-5.571; P < 0.001], relative advantage (OR = 2.890; 95% CI = 1.789-4.667; P < 0.001) and compatibility (OR = 2.606; 95% CI = 1.476-4.601; P < 0.001) showed significant independent associations with early adoption. Furthermore, age and political ideology were significant moderators of complexity and relative advantage, respectfully. Health education strategies for early vaccine uptake should focus on building trust in vaccine safety, increasing short-term benefits of vaccination and promoting relatability to personal values.

Coverage Effects of the ACA's Medicaid Expansion on Adult Reproductive-Aged Women, Postpartum Mothers, and Mothers with Older Children.

OBJECTIVES: We estimate the effect of the Affordable Care Act's (ACA) Medicaid expansions on Medicaid coverage of reproductive-aged women at varying childbearing stages. METHODS: Using data from the American Community Survey (ACS) (n = 1,977,098) and a difference-in-differences approach, we compare Medicaid coverage among low-income adult women without children, postpartum mothers, and mothers of children older than one year in expansion states to non-expansion states, before and after the expansions. RESULTS: The ACA's Medicaid expansion increased Medicaid coverage among adult women with incomes between 101 and 200% of the federal poverty line (FPL) without children by 10.7 percentage points (54 percent, p < 0.01). Coverage of mothers with children older than one year increased by 9.5 percentage points (34 percent, p < 0.01). Coverage of mothers with infants rose by 7.9 percentage points (21 percent, p < 0.01). CONCLUSIONS FOR PRACTICE: Within the population of adult reproductive-aged women, we find a "fanning out" of effects from the ACA's Medicaid expansions. Childless women experience the largest gains in coverage while mothers of infants experience the smallest gains; mothers of children greater than one year old fall in the middle. These results are consistent with ACA gains being the smallest among the groups least targeted by the ACA, but also show substantial gains (one fifth) even among postpartum mothers.

Strategic use of health information exchange and market share, payer mix, and operating margins.

BACKGROUND: Health information exchange (HIE) capabilities are tied to health care organizations' strategic and business goals. As a technology that connects information from different organizations, HIE may be a source of competitive advantage and a path to improvements in performance. PURPOSE: The aim of the study was to identify the impact of hospitals' use of HIE capabilities on outcomes that may be sensitive to changes in various contracting arrangements and referral patterns arising from improved connectivity. METHODOLOGY: Using a panel of community hospitals in nine states, we examined the association between the number of different data types the hospital could exchange via HIE and changes in market share, payer mix, and operating margin (2010-2014). Regression models that controlled for the number of different data types shared intraorganizationally and other time-varying factors and included both hospital and time fixed effects were used for adjusted estimates of the relationships between changes in HIE capabilities and outcomes. RESULTS: Increasing HIE capability was associated with a 13 percentage point increase in a hospital's discharges that were covered by commercial insurers or Medicare (i.e., payer mix). Conversely, increasing intraorganizational information sharing was associated with a 9.6 percentage point decrease in the percentage of discharges covered by commercial insurers or Medicare. Increasing HIE capability or intraorganizational information sharing was not associated with increased market share nor with operating margin. CONCLUSIONS: Improving information sharing with external organizations may be an approach to support strategic business goals. PRACTICE IMPLICATIONS: Organizations may be served by identifying ways to leverage HIE instead of focusing on intraorganizational exchange capabilities.

Impacts of state COVID-19 reopening policy on human mobility and mixing behavior.

This study quantifies the effect of the 2020 state COVID economic activity reopening policies on daily mobility and mixing behavior, adding to the economic literature on individual responses to public health policy that addresses public contagion risks. We harness cellular device signal data and the timing of reopening plans to provide an assessment of the extent to which human mobility and physical proximity in the United States respond to the reversal of state closure policies. We observe substantial increases in mixing activities, 13.56% at 4 days and 48.65% at 4 weeks, following reopening events. Echoing a theme from the literature on the 2020 closures, mobility outside the home increased on average prior to these state actions. Furthermore, the largest increases in mobility occurred in states that were early adopters of closure measures and hard-hit by the pandemic, suggesting that psychological fatigue is an important barrier to implementation of closure policies extending for prolonged periods of time.

Levels of Employment and Community Engagement among Low-Income Adults: Implications for Medicaid Work Requirements.

CONTEXT: Twenty states are pursuing community engagement requirements ("work requirements") in Medicaid, though legal challenges are ongoing. While most nondisabled low-income individuals work, it is less clear how many engage in the required number of hours of qualifying community engagement activities and what heterogeneity may exist by race/ethnicity, age, and gender. The authors' objective was to estimate current levels of employment and other community engagement activities among potential Medicaid beneficiaries. METHODS: The authors analyzed the US Census Bureau's national time-use survey data for the years 2015 through 2018. Their main sample consisted of nondisabled adults between 19 and 64 years with family incomes less than 138% of the federal poverty level (N = 2,551). FINDINGS: Nationally, low-income adults who might become subject to Medicaid work requirements already spent an average of 30 hours per week on community engagement activities. However, 22% of the low-income population-particularly women, older adults, and those with less education-would not currently satisfy a 20-hour-per-week requirement. CONCLUSIONS: Although the majority of potential Medicaid beneficiaries already meet community engagement requirements or are exempt, 22% would not currently satisfy a 20-hour-per-week requirement and therefore could be at risk for losing coverage.

Prescription Contraceptive Sales Following the Affordable Care Act.

Objectives We examine trends in prescription contraceptive sales following the Affordable Care Act's (ACA) zero-copayment contraceptive coverage mandate in areas more likely to be affected by the provision relative to areas less likely to be affected. Methods Before the ACA, several states had their own contraceptive insurance coverage mandates. Using a national prescription claims database combined with wholesaler institutional sales activity from January 2008 through June 2014, we compare sales of the intrauterine device (IUD), implant, injectable, pill, ring, and patch in states that had a state-level insurance coverage mandate before the ACA to states that did not. Results Overall, our results imply the ACA increased sales of prescription contraceptives, with stronger effects for some methods than others. Specifically, we find the ACA increased sales of injectable contraceptives, but had no significant impact on sales of the IUD, implant, pill, or patch in states without a state-level mandate before the ACA relative to states that had a state-level mandate. We also find suggestive evidence of a reduction in sales of the ring. Conclusions for Practice Demand responses to changes in out-of-pocket expenses for contraception vary across methods. Eliminating copays could promote the use of contraceptives, but is not the only approach to increasing contraceptive utilization.

Designing Difference in Difference Studies: Best Practices for Public Health Policy Research.

The difference in difference (DID) design is a quasi-experimental research design that researchers often use to study causal relationships in public health settings where randomized controlled trials (RCTs) are infeasible or unethical. However, causal inference poses many challenges in DID designs. In this article, we review key features of DID designs with an emphasis on public health policy research. Contemporary researchers should take an active approach to the design of DID studies, seeking to construct comparison groups, sensitivity analyses, and robustness checks that help validate the method's assumptions. We explain the key assumptions of the design and discuss analytic tactics, supplementary analysis, and approaches to statistical inference that are often important in applied research. The DID design is not a perfect substitute for randomized experiments, but it often represents a feasible way to learn about casual relationships. We conclude by noting that combining elements from multiple quasi-experimental techniques may be important in the next wave of innovations to the DID approach.

Third Year of Survey Data Shows Continuing Benefits of Medicaid Expansions for Low-Income Childless Adults in the U.S.

BACKGROUND: The Affordable Care Act (ACA) of 2010 incentivized states to expand eligibility for their Medicaid programs. Many did so in 2014, and there has been great interest in understanding the effects of these expansions on access to health care, health care utilization, and population health. OBJECTIVE: To estimate the longer-term (three-year) impact of Medicaid expansions on insurance coverage, access to care, preventive care, self-assessed health, and risky health behaviors. DESIGN: A difference-in-differences model, exploiting variation across states and over time in Medicaid expansion, was estimated using data from the Behavioral Risk Factor Surveillance System (BRFSS) for 2010-2016. PARTICIPANTS: Low-income childless adults aged 19-64 years in the BRFSS. MAIN MEASURES: Outcomes included insurance coverage, access to care, several forms of preventive care (e.g., routine checkups, flu shots, HIV tests, dental visits, and cancer screening), risky health behaviors (e.g., smoking, alcohol abuse, obesity), and self-assessed health. KEY RESULTS: The previously documented benefits of Medicaid expansions on insurance coverage, access to care, preventive care, and self-assessed health have persisted 3 years after expansion. There was no detectable effect on risky health behaviors. CONCLUSIONS: The Affordable Care Act was motivated in part by a desire to increase health insurance coverage, improve access to care, and increase use of preventive care. The Medicaid expansions facilitated by the ACA are helping to achieve those objectives, and the benefits have persisted 3 years after expansion.

Effect of Medicaid Expansions of 2014 on Overall and Early-Stage Cancer Diagnoses.

OBJECTIVES: To determine whether the 2014 Medicaid expansions facilitated by the Affordable Care Act affected overall and early-stage cancer diagnosis for nonelderly adults. METHODS: We used Surveillance, Epidemiology, and End Results Cancer Registry data from 2010 through 2014 to estimate a difference-in-differences model of cancer diagnosis rates, both overall and by stage, comparing changes in county-level diagnosis rates in US states that expanded Medicaid in 2014 with those that did not expand Medicaid. RESULTS: Among the 611 counties in this study, Medicaid expansion was associated with an increase in overall cancer diagnoses of 13.8 per 100 000 population (95% confidence interval [CI] = 0.7, 26.9), or 3.4%. Medicaid expansion was also associated with an increase in early-stage diagnoses of 15.4 per 100 000 population (95% CI = 5.4, 25.3), or 6.4%. There was no detectable impact on late-stage diagnoses. CONCLUSIONS: In their first year, the 2014 Medicaid expansions were associated with an increase in cancer diagnosis, particularly at the early stage, in the working-age population. Public Health Implications. Expanding public health insurance may be an avenue for improving cancer detection, which is associated with improved patient outcomes, including reduced mortality.

The Impact of the Affordable Care Act Young Adult Provision on Childbearing: Evidence From Tax Data.

We use panel U.S. tax data spanning 2008-2013 to study the impact of the Affordable Care Act (ACA) young adult provision on an important demographic outcome: childbearing. The impact is theoretically ambiguous: gaining insurance may increase access to contraceptive services while also reducing the out-of-pocket costs of childbirth. Because employer-reported U.S. Wage and Tax Statements (W-2 forms) record access to employer-provided benefits, we can examine the impact of the coverage expansion by focusing on young adults whose parents have access to benefits. We compare those who are slightly younger than the age threshold with those who are slightly older. Our results suggest that the ACA young adult provision led to a modest decrease in childbearing.

Changes in Employment in the US Health Care Workforce, 2016-2022.

This study examines the rate of employment in US health care in the postpandemic period, through the end of 2022.

The Impact of Health Insurance on Preventive Care and Health Behaviors: Evidence from the First Two Years of the ACA Medicaid Expansions.

The U.S. population receives suboptimal levels of preventive care and has a high prevalence of risky health behaviors. One goal of the Affordable Care Act (ACA) was to increase preventive care and improve health behaviors by expanding access to health insurance. This paper estimates how the ACA-facilitated state-level expansions of Medicaid in 2014 affected these outcomes. Using data from the Behavioral Risk Factor Surveillance System, and a difference-in-differences model that compares states that did and did not expand Medicaid, we examine the impact of the expansions on preventive care (e.g., dental visits, immunizations, mammograms, cancer screenings), risky health behaviors (e.g., smoking, heavy drinking, lack of exercise, obesity), and self-assessed health. We find that the expansions increased insurance coverage and access to care among the targeted population of low-income childless adults. The expansions also increased use of certain forms of preventive care, but there is no evidence that they increased ex ante moral hazard (i.e., there is no evidence that risky health behaviors increased in response to health insurance coverage). The Medicaid expansions also modestly improved self-assessed health.