IU Indianapolis ScholarWorks :: Browsing by Author "Gupta, Sumedha"

Browsing by Author "Gupta, Sumedha"

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Assessment of Filled Buprenorphine Prescriptions for Opioid Use Disorder During the Coronavirus Disease 2019 Pandemic
(AMA, 2020-12) Nguyen, Thuy D.; Gupta, Sumedha; Ziedan, Engy; Simon, Kosali I.; Alexander, Caleb; Saloner, Brendan; Stein, Bradley D.; Economics, School of Liberal Arts
The coronavirus disease 2019 (COVID-19) pandemic has profoundly disrupted health care delivery in the US.1 The Centers for Disease Control and Prevention noted a 9.1% increase in reported 12-month counts of drug overdose deaths from March 2019 to March 2020, from 67 726 to 73 860.2 On March 13, 2020, a COVID-19 national emergency was declared. To diminish potential barriers to treatment access, 3 days later, federal guidelines on telemedicine use were released, providing authorized practitioners increased flexibility to prescribe buprenorphine to patients with opioid use disorder (OUD) during this public health emergency.3 Other local, state, and federal policy initiatives have also attempted to preserve access to medication treatment for OUD, yet the cumulative outcome of these undertakings is not clear.
Back to Business and (Re)employing Workers? Labor Market Activity During State COVID-19 Reopenings
(National Bureau of Economic Research, 2020-06) Cheng, Wei; Carlin, Patrick; Carroll, Joanna; Gupta, Sumedha; Rojas, Felipe Lozano; Montenovo, Laura; Nguyen, Thuy D.; Schmutte, Ian M.; Scrivner, Olga; Simon, Kosali I.; Wing, Coady; Weinberg, Bruce; Department of Economics, IU School of Liberal Arts
We study the effect of state reopening policies on a large set of labor market indicators through May 2020 to: (1) understand the recent increase in employment using longitudinal as well as cross-sectional data, (2) assess the likely trajectory of reemployment going forward, and (3) investigate the strength of job matches that were disrupted by COVID-19. Estimates from event studies and difference-in-difference regressions suggest that some of the recent increases in employment activity, as measured by cellphone data on work-related mobility, internet searches related to employment, and new and continuing unemployment insurance claims, were likely related to state reopenings, often predating actual reopening dates somewhat. We provide suggestive evidence that increases in employment stem from people returning to their prior jobs: reopenings are only weakly related to job postings, and longitudinal CPS data show that large shares of the unemployed-on-layoff and employed-but-absent in April who transitioned to employment in May remain in the same industry or occupation. Longitudinal CPS estimates further show declines in reemployment probabilities with time away from work. Taken together, these estimates suggest that employment relationships are durable in the short run, but raise concerns that employment gains requiring new employment matches may not be as rapid.
Effect Specification, Identification, Estimation, and Inference in a Fractional Outcome Regression Model with an Endogenous Causal Variable
(2024-08) Cheong, Taul; Terza, Joseph V.; Gupta, Sumedha; Steinberg, Richard; Liu, Ziyue
Empirical economic research is primarily driven by the desire to offer scientific evidence that helps assess policy relevant cause-and-effect. The approach most often applied in pursuit of this objective involves regression modeling and estimation. In this dissertation, we focus on the specification, identification, estimation, and causal inference of a causal effect (CE) in the context of the fractional regression model (FRM) for which the support of the outcome variable of interest is restricted to the unit interval. Empirical applications of such models abound in health economics, health services research and health policy literatures. Examples from other disciplines include labor economics, development economics, political economics, commerce or finance. Various full information maximum likelihood and quasi-maximum likelihood regression estimators and nonlinear least squares approach have been proposed to account for the inherent nonlinearity in the FRM due to the unit interval support restriction (UISR) on the outcome variable. Additional nonlinearity is induced in the FRM when the presumed causal variable is subject to unobservable confounding (UC) [i.e., when the presumed causal variable is endogenous]. In such cases, the additional analytic and implementation effort required to account for both sources of nonlinearity (fractional outcome and UC) while avoiding UC bias (which precludes causal interpretability) can be daunting. We seek to develop and implement regression model specifications that account for the inherent nonlinearity implied by this restriction, as well as the nonlinearity that could be additionally imposed by the endogeneity of the presumed causal variable. We focus on the case where the presumed causal variable is continuous. We develop new models for FRM-based CE estimation that implement two-stage residual inclusion (2SRI) methods, as suggested by Terza et al. (2008). We assess the accuracy of our proposed new methods and compare them with extant 2SRI approaches using simulation study. An empirical application demonstrates the working of our proposed method.
Effects of social distancing policy on labor market outcomes
(Wiley, 2023) Gupta, Sumedha; Montenovo, Laura; Nguyen, Thuy; Lozano-Rojas, Felipe; Schmutte, Ian; Simon, Kosali; Weinberg, Bruce A.; Wing, Coady; Economics, School of Liberal Arts
US workers receive unemployment benefits if they lose their job, but not for reduced working hours. In alignment with the benefits incentives, we find that the labor market responded to COVID-19 and related closure-policies mostly on the extensive (12 pp outright job loss) margin. Exploiting timing variation in state closure-policies, difference-in-differences (DiD) estimates show, between March 12 and April 12, 2020, employment rate fell by 1.7 pp for every 10 extra days of state stay-at-home orders (SAH), with little effect on hours worked/earnings among those employed. Forty percentage of the unemployment was due to a nationwide shock, rest due to social-distancing policies, particularly among "non-essential" workers.
Evaluation of an emergency department-based opioid overdose survivor intervention: Difference-in-difference analysis of electronic health record data to assess key outcomes
(Elsevier, 2021) Watson, Dennis P.; Weathers, Tess; McGuire, Alan; Cohen, Alex; Huynh, Philip; Bowes, Clay; O’Donnell, Daniel; Brucker, Krista; Gupta, Sumedha; Social and Behavioral Sciences, School of Public Health
Background: In recent years, a number of emergency department (ED)-based interventions have been developed to provide supports and/or treatment linkage for people who use opioids. However, there is limited research supporting the effectiveness of the majority of these interventions. Project POINT is an ED-based intervention aimed at providing opioid overdose survivors with naloxone and recovery supports and connecting them to evidence-based medications for opioid use disorder (MOUD). An evaluation of POINT was conducted. Methods: A difference-in-difference analysis of electronic health record data was completed to understand the difference in outcomes for patients admitted to the ED when a POINT staff member was working versus times when they were not. The observation window was January 1, 2012 to July 6, 2019, which included N = 1462 unique individuals, of which 802 were in the POINT arm. Outcomes of focus include MOUD opioid prescriptions dispensed, active non-MOUD opioid prescriptions dispensed, naloxone access, and drug poisonings. Results: The POINT arm had a significant increase in MOUD prescriptions dispensed, non-MOUD prescriptions dispensed, and naloxone access (all p-values < 0.001). There was no significant effect related to subsequent drug poisoning-related hospital admissions. Conclusions: The results support the assertion that POINT is meeting its two primary goals related to increasing naloxone access and connecting patients to MOUD. Generalization of these results is limited; however, the evaluation contributes to a nascent area of research and can serve a foundation for future work.
Exploring perceptions and experiences of patients who have chronic pain as state prescription opioid policies change: a qualitative study in Indiana
(BMJ, 2017-11-12) Al Achkar, Morhaf; Revere, Debra; Dennis, Barbara; MacKie, Palmer; Gupta, Sumedha; Grannis, Shaun; Medicine, School of Medicine
Objectives The misuse and abuse of prescription opioids (POs) is an epidemic in the USA today. Many states have implemented legislation to curb the use of POs resulting from inappropriate prescribing. Indiana legislated opioid prescribing rules that went into effect in December 2013. The rules changed how chronic pain is managed by healthcare providers. This qualitative study aims to evaluate the impact of Indiana’s opioid prescription legislation on the patient experiences around pain management. Setting This is a qualitative study using interviews of patient and primary care providers to obtain triangulated data sources. The patients were recruited from an integrated pain clinic to which chronic pain patients were referred from federally qualified health clinics (FQHCs). The primacy care providers were recruited from the same FQHCs. The study used inductive, emergent thematic analysis. Participants Nine patient participants and five primary care providers were included in the study. Results Living with chronic pain is disruptive to patients’ lives on multiple dimensions. The established pain management practices were disrupted by the change in prescription rules. Patient–provider relationships, which involve power dynamics and decision making, shifted significantly in parallel to the rule change. Conclusions As a result of the changes in pain management practice, some patients experienced significant challenges. Further studies into the magnitude of this change are necessary. In addition, exploring methods for regulating prescribing while assuring adequate access to pain management is crucial.
Exploring the Importance of Accounting for Nonlinearity in Correlated Count Regression Systems from the Perspective of Causal Estimation and Inference
(2021-07) Zhang, Yilei; Terza, Joseph V.; Vest, Joshua R.; Morrison, Wendy; Gupta, Sumedha
The main motivation for nearly all empirical economic research is to provide scientific evidence that can be used to assess causal relationships of interest. Essential to such assessments is the rigorous specification and accurate estimation of parameters that characterize the causal relationship between a presumed causal variable of interest, whose value is to be set and altered in the context of a relevant counterfactual and a designated outcome of interest. Relationships of this type are typically characterized by an effect parameter (EP) and estimation of the EP is the objective of the empirical analysis. The present research focuses on cases in which the regression outcome of interest is a vector that has count-valued elements (i.e., the model under consideration comprises a multi-equation system of equations). This research examines the importance of account for nonlinearity and cross-equation correlations in correlated count regression systems from the perspective of causal estimation and inference. We evaluate the efficiency and accuracy gains of estimating bivariate count valued systems-of-equations models by comparing three pairs of models: (1) Zellner’s Seemingly Unrelated Regression (SUR) versus Count-Outcome SUR - Conway Maxwell Poisson (CMP); (2) CMP SUR versus Single-Equation CMP Approach; (3) CMP SUR versus Poisson SUR. We show via simulation studies that it is more efficient to estimate jointly than equation-by-equation, it is more efficient to account for nonlinearity. We also apply our model and estimation method to real-world health care utilization data, where the dependent variables are correlated counts: count of physician office-visits, and count of non-physician health professional office-visits. The presumed causal variable is private health insurance status. Our model results in a reduction of at least 30% in standard errors for key policy EP (e.g., Average Incremental Effect). Our results are enabled by our development of a Stata program for approximating two-dimensional integrals via Gauss-Legendre Quadrature.
Impact of Volunteering on Cognitive Decline of the Elderly
(Elsevier, 2018-11) Gupta, Sumedha; Economics, School of Liberal Arts
Cognitive decline among the elderly imposes a large welfare and health care cost on the individual as well as society. Little however is known about factors that can mitigate cognitive decline. Using seven waves of the Health and Retirement Study and a fixed effects – instrumental variable methodology, this study estimates the effects of volunteering on old age cognitive decline. Although cognitive decline is an inevitable aspect of aging, our results suggest that volunteering participation significantly forestalls its progress among individuals aged 60 years and older.
Impacts of state COVID-19 reopening policy on human mobility and mixing behavior
(Wiley, 2021) Nguyen, Thuy D.; Gupta, Sumedha; Andersen, Martin S.; Bento, Ana I.; Simon, Kosali I.; Wing, Coady; Economics, School of Liberal Arts
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.
Impacts of State Reopening Policy on Human Mobility
(National Bureau of Economic Research, 2020-05) Nguyen, Thuy D.; Gupta, Sumedha; Andersen, Martin; Bento, Ana; Simon, Kosali I.; Wing, Coady; O’Neill School of Public and Environmental Affairs, IU & IUPUI
This study quantifies the effect of state reopening policies on daily mobility, travel, and mixing behavior during the COVID-19 pandemic. We harness cell device signal data to examine the effects of the timing and pace of reopening plans in different states. We quantify the increase in mobility patterns during the reopening phase by a broad range of cell-device-based metrics. Soon (four days) after reopening, we observe a 6% to 8% mobility increase. In addition, we find that temperature and precipitation are strongly associated with increased mobility across counties. The mobility measures that reflect visits to a greater variety of locations responds the most to reopening policies, while total time in vs. outside the house remains unchanged. The largest increases in mobility occur in states that were late adopters of closure measures, suggesting that closure policies may have represented more of a binding constraint in those states. Together, these four observations provide an assessment of the extent to which people in the U.S. are resuming movement and physical proximity as the COVID-19 pandemic continues.

Browsing by Author "Gupta, Sumedha"

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