Improving the Health and Safety of Transit Workers with Corresponding Impacts on the Bottom Line (2020)

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B-1 A P P E N D I X B Case Study and Benefit-Cost Data Collection There are two parts to each case study: the narrative, with background about the program, and the benefit-cost analysis. The research team compiled short narratives for each case, describing the types of workforce health and safety programs available to transit workers and the general landscape of the agency. If sufficient data were available, a full benefit-cost case study was conducted. The Econometrica team met the goal of producing at least five full case studies from the 14 identified sites, a goal that was dependent on the cooperation and availability of data from the sites. B.1 Case Profile/Narrative For each potential case study location, the research team reached out to the screening survey respondent to schedule an initiation call introducing key team members and roles; explaining the background, scope, and timeframe of the study; collecting narrative information for the case profile; and probing the availability of benefit-cost data. Follow-up interviews were conducted with other key informants from the agency or local union. Information collected included: 1. Program goals (targeted health and safety problems); 2. Targeted worker populations; 3. Program funding; 4. Length of the program; 5. Program type (comprehensive health promotion, smoking cessation, weight management, nutrition, cardiovascular disease prevention, ergonomics, etc.); 6. Specific program activities; 7. Qualitative program benefits (e.g., employee feedback, behavioral changes); 8. Program impact on other operational areas (scheduling, hiring, safety, training, work assignment/work accommodation, procurement, etc.); 9. Existing program benefit-cost analysis (conducted by program staff or external researchers); 10. Participation rates by mode and occupational categories; 11. Program costs (administrative costs, opportunity costs of workers’ time/average wages, incentive payments); and 12. Identification of any quantitative program benefits and availability of data such as: a. Absenteeism/time lost, b. Workers’ compensation claims cost, c. Disability rates, d. Turnover, e. Injuries,

B-2 Improving the Health and Safety of Transit Workers with Corresponding Impacts on the Bottom Line This round of information collection also served as the secondary screening to finalize case selections. The final case study locations were determined by the agency’s cooperation in furnishing the data listed in items 10, 11, and 12. Several locations indicated that they either did not track participation data or would not be able to provide access to individual-level data. B.2 Benefit-Cost Data Collection Depending on findings from the secondary screening call, the research team sent customized lists of data requests to agencies that had promised to share data. Individual-level benefit-cost data collected including the following: 1. Excel files (or tab-delimited files) with downloads of the number of personal days and number of sick days with employee names or employee ID numbers, gender, date of birth, occupational code, and date of hire and departure (if applicable) for 2011 through 2018 (or whatever historical years the organization has) for all employees; 2. Excel files (or tab-delimited files) with downloads of workers’ compensation payments for 2011–2018 with employee names or employee ID numbers for all employees; 3. Excel files (or tab-delimited files) with race with employee names or employee ID numbers for all employees; 4. Employee feedback survey results; 5. Data/reports on healthcare claims from 2011 (starting the year before the wellness efforts began and--if unavailable for 2011--as early as the data were available) through 2018; 6. An analysis of healthcare claims based on prevalence and cost; and 7. The claims data that come in August following nutrition efforts of the last year. All agencies were asked to respond to items 1–3, and requests for additional data (items 4–7) were based on the data the agency said it had available. Four agencies provided more than 75% of the data for items 1–3. One agency provided data for all seven items. The project team asked for and received health condition prevalence data from one agency, and asked for and received data on types of injuries from workers’ compensation data from another agency. Chapter 4 of this report details some of the findings on prevalence rates that agencies requested and/or had available from their insurers. The following agencies provided both narrative information and individual-level data that appeared sufficiently comprehensive for a benefit-cost analysis: • Regional Transit Service (RTS), Rochester, New York; • Transit Authority of River City (TARC), Louisville, Kentucky; • Indianapolis Public Transportation Corporation, Inc. (IndyGo), Indianapolis, Indiana; and • Des Moines Area Regional Transit Agency (DART), Des Moines, Iowa. The LA Metro did not provide individual-level data, so regression modeling was not possible for this location. A fifth case study was conducted based on aggregate data obtained from Los Angeles County Metropolitan Transportation Authority (LACMTA). The results of the case study analysis are included in the main report; however, because the data available from California differed in f. Health status changes (e.g., BMI, smoking rates, etc.), and g. Healthcare claims costs. format from the data provided by the other four agencies, a case study narrative was not developed for inclusion in this appendix or Appendix C.

Case Study and Benefit-Cost Data Collection B-3 Data use agreements were signed with all five agencies, setting forth guidelines for the secure management and protection of individual-level data in accordance with Federal Information Security Management Act (FISMA) requirements and allowing the Econometrica team to possess the received data until the end of its contract with the National Academy of Sciences, at which time Econometrica would provide the agency with a certification of destruction of the data. In the fourth case, data were provided using employee IDs, so all individual-level data remained anonymous.