Using advanced racial and ethnic identity demographics to improve surveillance of work-related conditions in an occupational clinic setting.

BACKGROUND: Although racial and ethnic identities are associated with a multitude of disparate medical outcomes, surveillance of these subpopulations in the occupational clinic setting could benefit enormously from a more detailed and nuanced recognition of racial and ethnic identity. METHODS: The research group designed a brief questionnaire to capture several dimensions of this identity and collected data from patients seen for work-related conditions in four occupational medicine clinics from May 2019 through March 2020. Responses were used to calculate the sensitivity and specificity of extant racial/ethnic identity data within our electronic health records system, and were compared to participants' self-reported industry and occupation, coded according to North American Industry Classification System and Standard Occupational Classification System listings. RESULTS: Our questionnaire permitted collection of data that defined our patients' specific racial/ethnic identity with far greater detail, identified patients with multiple ethnic identities, and elicited their preferred language. Response rate was excellent (94.2%, n = 773). Non-White participants frequently selected a racial/ethnic subcategory (78.1%-92.2%). Using our race/ethnicity data as a referent, the electronic health record (EHR) had a high specificity (>87.1%), widely variable sensitivity (11.8%-82.2%), and poorer response rates (75.1% for race, 82.5% for ethnicity, as compared to 93.8% with our questionnaire). Additional analyses revealed some industries and occupations disproportionately populated by patients of particular racial/ethnic identities. CONCLUSIONS: Our project demonstrates the usefulness of a questionnaire which more effectively identifies racial/ethnic subpopulations in an occupational medicine clinic, permitting far more detailed characterization of their occupations, industries, and diagnoses.

Weight-loss outcomes: a systematic review and meta-analysis of weight-loss clinical trials with a minimum 1-year follow-up.

OBJECTIVE: To assist health professionals who counsel patients with overweight and obesity, a systematic review was undertaken to determine types of weight-loss interventions that contribute to successful outcomes and to define expected weight-loss outcomes from such interventions. DESIGN: A search was conducted for weight-loss-focused randomized clinical trials with >or=1-year follow-up. Eighty studies were identified and are included in the evidence table. OUTCOMES MEASURES: The primary outcomes were a measure of weight loss at 6, 12, 24, 36, and 48 months. Eight types of weight-loss interventions-diet alone, diet and exercise, exercise alone, meal replacements, very-low-energy diets, weight-loss medications (orlistat and sibutramine), and advice alone-were identified. By using simple pooling across studies, subjects mean amount of weight loss at each time point for each intervention was determined. STATISTICAL ANALYSES PERFORMED: Efficacy outcomes were calculated by meta-analysis and provide support for the pooled data. Hedges' gu was combined across studies to obtain an average effect size (and confidence level). RESULTS: A mean weight loss of 5 to 8.5 kg (5% to 9%) was observed during the first 6 months from interventions involving a reduced-energy diet and/or weight-loss medications with weight plateaus at approximately 6 months. In studies extending to 48 months, a mean 3 to 6 kg (3% to 6%) of weight loss was maintained with none of the groups experiencing weight regain to baseline. In contrast, advice-only and exercise-alone groups experienced minimal weight loss at any time point. CONCLUSIONS: Weight-loss interventions utilizing a reduced-energy diet and exercise are associated with moderate weight loss at 6 months. Although there is some regain of weight, weight loss can be maintained. The addition of weight-loss medications somewhat enhances weight-loss maintenance.