Mental Health Treatment Histories, Recovery, and Well-being

Epidemiological and sociological research on recovery from mental disorder is based on three rarely tested medical model assumptions: (1) recovery without treatment is the result of less severe illness, (2) treatment predicts recovery, and (3) recovery and well–being do not depend on individuals’ treatment histories. I challenge these assumptions using National Comorbidity Survey-Replication data for individuals with any disorder occurring prior to the current year ( N = 2,305). Results indicated that (1) untreated remissions were fully explained by less serious prior illness, (2) treated individuals were less likely to recover due to more serious illness, and (3) people who had past–only treatment were more likely to recover than the never–treated, while those in recurring and recently initiated care were less likely to recover. Treatment histories predicted greater well–being only if recovery had been attained. Histories of care help to explain recovery rates and suggest new directions for treatment–seeking theory and research.

Estimating Endogenous Treatment Effects Using Latent Factor Models with and without Instrumental Variables

We provide evidence on the least biased ways to identify causal effects in situations where there are multiple outcomes that all depend on the same endogenous regressor and a reasonable but potentially contaminated instrumental variable that is available. Simulations provide suggestive evidence on the complementarity of instrumental variable (IV) and latent factor methods and how this complementarity depends on the number of outcome variables and the degree of contamination in the IV. We apply the causal inference methods to assess the impact of mental illness on work absenteeism and disability, using the National Comorbidity Survey Replication.

Disentangling Mental Illness Labeling Effects from Treatment Effects on Well-Being

The emerging field of Mad Studies has returned attention to deficiencies of the medical model, refocusing scholars on social causes of mental health problems and on consumers’/survivors’ experiences of labeling and stigma. These themes echo issues addressed in traditional and modified labeling theories. A fundamental labeling premise is that professional categorization as “mentally ill” is a major determinant of individuals’ poorer psychological well-being. However, this relationship has not been tested appropriately because past studies frequently measured formal labeling by a person’s involvement in treatment. Treatment involvement can indicate the receipt of potentially beneficial services or harmful categorization with a stigmatizing label. Independent measures of these constructs in the National Comorbidity Survey-Replication enable reexamining traditional and modified labeling hypotheses for individuals with (N = 1,255) and without (N = 4,172) a recurrent clinical disorder. Supporting labeling theory’s central proposition, formal labeling was linked to more negative affect and disability days in both groups. These relationships were not spurious products of preexisting serious symptoms, refuting a psychiatric explanation. Treatment involvement effects differed noticeably between the groups, underscoring the need to keep treatment and labeling measures distinct.

Abortion Prevalence by Age at First Coitus, Race, Ethnicity, Age, and Education: Evidence from the National Comorbidity Survey Replication

Our specific aim was to report prevalence of abortion by first age of sexual intercourse, by race and ethnic groups, and educational attainment.

Prevalence and Severity of Psychiatric Comorbidities

Chapter 20—The Prevalence and Severity of Psychiatric Comorbidities provides a summary of a landmark study in epidemiology, the The National Comorbidity Survey Replication (NCS-R). This chapter study sought to answer some fundamental questions. How common are comorbid psychiatric conditions? What are the prevalence and severity rates for comorbid anxiety, mood, impulse control, and substance use disorders? Starting with these questions, this chapter describes the basics of the study, including funding, study location, who was studied, how many patients, study design, study intervention, follow-up, endpoints, results, and criticism and limitations. The chapter briefly reviews other relevant studies and information, discusses implications, and concludes with a relevant clinical case.

Further evidence that psychopathology networks have limited replicability and utility: Response to Borsboom et al. (2017) and Steinley et al. (2017)

In our target article, we tested the replicability of four popular psychopathology network estimation methods that aim to reveal causal relationships among symptoms of mental illness. We started with the focal data set from the two foundational psychopathology network papers (i.e., the National Comorbidity Survey–Replication) and identified the National Survey of Mental Health and Wellbeing as a close methodological match for comparison. We compared the psychopathology networks estimated in each dataset—as well as in ten sets of random split-halves within each dataset—with the goal of quantifying the replicability of the network parameters as they are interpreted in the extant psychopathology network literature. We concluded that current psychopathology network methods have limited replicability both within and between samples, and thus have limited utility. Here we respond to the two commentaries on our target article, concluding that Steinley, Hoffman, Brusco and Sher’s (2017) findings—along with other recent developments in the literature—provide further conclusive evidence that psychopathology networks have poor replicability and utility.