Sources of Biased Inference in Alcohol and Drug Services Research: An Instrumental Variable Approach

Abstract

ABSTRACT. Objective: This study examined the potential for biased inference due to endogeneity when using standard approaches for modeling the utilization of alcohol and drug treatment. Method: Results from standard regression analysis were compared with those that controlled for endogeneity using instrumental variables estimation. Comparable models predicted the likelihood of receiving alcohol treatment based on the widely used Aday and Andersen medical care-seeking model. Data were from the National Epidemiologic Survey on Alcohol and Related Conditions and included a representative sample of adults in households and group quarters throughout the contiguous United States. Results: Findings suggested that standard approaches for modeling treatment utilization are prone to bias because of uncontrolled reverse causation and omitted variables. Compared with instrumental variables estimation, standard regression analyses produced downwardly biased estimates of the impact of alcohol problem severity on the likelihood of receiving care. Conclusions: Standard approaches for modeling service utilization are prone to underestimating the true effects of problem severity on service use. Biased inference could lead to inaccurate policy recommendations, for example, by suggesting that people with milder forms of substance use disorder are more likely to receive care than is actually the case. (J. Stud. Alcohol Drugs, 73, 144-153, 2012)

The Relationship between Alcohol Availability and Alcohol Consumption

“…In 2SLS, we used the population densities of the regions as instruments for the Alko outlets. The instrumental variable method has been used to solve endogenous problems [33][34][35]. The instrumental variable estimation results do not differ from the models where the supply is exogenous (OLS and FE).…”

Determinants of Obesity and Associated Population Attributability, South Africa: Empirical Evidence from a National Panel Survey, 2008-2012

“…cause precedes the effect. “The potential for reverse causation bias is likely to be greatest in cross-sectional studies that capture symptoms and treatment episodes over longer time frames, such as the lifetime or past 12 months.” [ 61 ] Given that the primary study design is a repeated cross sectional study, we cannot discount the possibility of reverse causation bias. The relatively high number of missing or invalid weight/height measurements may have introduced selection bias and may affected both the internal validity (“distortion of a statistical analysis”) as well as the representativeness (external validity) of the findings in the broader South African context.…”

Racial Disparities in Intimate Partner Violence and in Seeking Help With Mental Health

“…This behavior model has been widely applied to mental health services utilization (Cheng & Robinson, 2013;Dhingra, Zack, Strine, Pearson, & Balluz, 2010;Gamache, Rosenheck, & Tessler, 2000;Lipsky, Caetano, & Roy-Byrne, 2011;Maulik, Mendelson, & Tandon, 2010;Nejtek et al, 2011;Schmidt, Tam, & Larson, 2012;Stockdale, Tang, Zhang, Belin, & Wells, 2007;Theriot, Segal, & Cowsert, 2003). The present study was derived from Aday and Andersen's model and examined relationships between IPV-involved women's help seeking from mental health professionals, and treatment need, enabling factors (e.g., financial resources, insurance coverage), and predisposing factors (e.g., social structural characteristics, social-demographic characteristics).…”

“…Aday and Andersen’s behavior model of health services utilization (Aday, 1993; Aday & Andersen, 1974; Andersen, 1995) suggests that an individual’s access to or utilization of health services arises from the following individual indicators: need for treatment (e.g., illnesses, symptoms), social structural factors (e.g., ethnicity, education, employment), means to access (e.g., income, insurance coverage, geographic proximity to services), and demographic characteristics (e.g., age, gender, family size). This behavior model has been widely applied to mental health services utilization (Cheng & Robinson, 2013; Dhingra, Zack, Strine, Pearson, & Balluz, 2010; Gamache, Rosenheck, & Tessler, 2000; Lipsky, Caetano, & Roy-Byrne, 2011; Maulik, Mendelson, & Tandon, 2010; Nejtek et al, 2011; Schmidt, Tam, & Larson, 2012; Stockdale, Tang, Zhang, Belin, & Wells, 2007; Theriot, Segal, & Cowsert, 2003). The present study was derived from Aday and Andersen’s model and examined relationships between IPV-involved women’s help seeking from mental health professionals, and treatment need, enabling factors (e.g., financial resources, insurance coverage), and predisposing factors (e.g., social structural characteristics, social–demographic characteristics).…”