This study investigates the associations between self-assessed adverse labor market events (experiencing problems with coworkers, employment changes, financial strain) and health. Longitudinal data are obtained from the National Epidemiological Survey of Alcohol and Related Conditions. Our findings suggest problems with coworkers, employment changes, and financial strain are associated, respectively, with a 3.1 percent (3.3 percent), 0.9 percent (0.2 percent), and 4.5 percent (5.1 percent) reduction in mental health among men (women). Associations are smaller in magnitude and less significant for physical health.

We examine the impact of Axis II personality disorders (PDs) on body weight. PDs are psychiatric conditions that develop early in life from a mixture of genetics and environment, are persistent, and lead to substantial dysfunction for the affected individual. The defining characteristics of PDs conceptually link them with body weight, but the direction of the relationship likely varies across PD type. To investigate these links, we analyze data from Wave II of the National Epidemiological Survey of Alcohol and Related Conditions. We measure body weight with the body mass index (BMI) and a dichotomous indicator for obesity (BMI≥30). We find that women with PDs have significantly higher BMI and are more likely to be obese than otherwise similar women. We find few statistically significant or economically meaningful effects for men. Paranoid, schizotypal, and avoidant PDs demonstrate the strongest adverse impacts on women's body weight while dependent PD may be protective against elevated body weight among men. Findings from unconditional quantile regressions demonstrate a positive gradient between PDs and BMI in that the effects are greater for higher BMI respondents.

Objective

To analyze the associations between Axis II (A2) disorders and two measures of health care utilization with relatively high cost: emergency department (ED) episodes and hospital admissions.

Data source/study setting

Wave I (2001/2002) and Wave II (2004/2005) of the National Longitudinal Survey on Alcohol and Related Conditions (NESARC).

Study design

A national probability sample of adults. Gender-stratified regression analysis adjusted for a range of covariates associated with health care utilization.

Data collection

The target population of the NESARC is the civilian noninstitutionalized population aged 18 years and older residing in the United States. The cumulative survey response rate is 70.2 percent with a response rate of 81 percent (N=43,093) in Wave I and 86.7 percent (N=34,653) in Wave II.

Principal findings

Both men and women with A2 disorders are at elevated risk for ED episodes and hospital admissions. Associations are robust after adjusting for a rich set of confounding factors, including Axis I (clinical) psychiatric disorders. We find evidence of a dose-response relationship, while antisocial and borderline disorders exhibit the strongest associations with both measures of health care utilization.

Conclusions

This study provides the first published estimates of the associations between A2 disorders and high-cost health care utilization in a large, nationally representative survey. The findings underscore the potential implications of these disorders on health care expenditures.

Over the past decade, considerable progress has been made in the control, elimination, and eradication of neglected tropical diseases (NTDs). Despite these advances, most NTD programs have recently experienced important setbacks; for example, NTD interventions were some of the most frequently and severely impacted by service disruptions due to the coronavirus disease 2019 (COVID-19) pandemic. Mathematical modeling can help inform selection of interventions to meet the targets set out in the NTD road map 2021-2030, and such studies should prioritize questions that are relevant for decision-makers, especially those designing, implementing, and evaluating national and subnational programs. In September 2022, the World Health Organization hosted a stakeholder meeting to identify such priority modeling questions across a range of NTDs and to consider how modeling could inform local decision making. Here, we summarize the outputs of the meeting, highlight common themes in the questions being asked, and discuss how quantitative modeling can support programmatic decisions that may accelerate progress towards the 2030 targets.