Authors Matthew W. Courser , Ph.D Stephen R. Shamblen , Ph.D Linda Young, M.A., Knowlton Johnson Ph.D Daniel Carr

Transcription

1 Conducting a Longitudinal Survey With Former Drug Treatment Patients in El Salvador: Using the Research Context to Inform Survey Design and Respondent Tracking November 18, 2011 This research was supported by the U.S. Department of State, Bureau of International Narcotics and Law Enforcement (INL) through contract SAQMPD067D0116 with Alvarez and Associates, Inc.

2 Authors Matthew W. Courser, Ph.D., PIRE Louisville Center Stephen R. Shamblen, Ph.D., PIRE Louisville Center Linda Young, M.A., PIRE Louisville Center Knowlton Johnson, Ph.D., PIRE Louisville Center Daniel Carr, Daniel Carr and Associates

3 Background Drug abuse across El Salvador is a huge and growing problem. Informal network of drug abuse treatment (DAT) centers. Most Salvadoran DAT centers are supported by faith-based groups or small NGOs with little or no formal support. Varied treatment approaches Staff training experiential and informal

4 Background, cont. 2007: the U.S. Department of State s Bureau of International Narcotics and Law Enforcement Affairs (INL) began funding a training program for staff of drug treatment programs and juvenile detention centers. Designed to help ameliorate Salvadoran DAT Center training and support needs. Based on SAMHSA s TAP 21 curriculum.

5 Background, cont. 2008: PIRE funded to evaluate the basic training curriculum and subsequent patient treatment success. Sample of 19 DAT centers in El Salvador. Principal aim: to assess treatment success (i.e., changes in DAT Center patients). Fieldwork managed by Daniel Carr and Associates and FUNDASALVA, a Salvadoran anti-drug foundation.

6 DAT Centers

7 Patients Served by DAT Tended to be low SES 24% Homeless Centers 16% Marginal (extreme poverty - living in urban slum areas) 14% Rural povery (subsistence, high mobility) 44% Lower/working class (primarily urban) 2% Middle class DAT Centers had secondary function as shelters of last resort. SES profile posed challenge for tracking.

8 Focus: Roadmap Not on treatment success outcomes! Study retention between baseline and follow-up interviews Research questions: 1. What was the overall study retention rate between the baseline and follow-up interviews? Did study retention rates vary across the 19 participating DAT centers? 2. What strategies were used to track patients and locate them for the follow-up interview and how were those strategies implemented during fieldwork? 3. Were baseline patient characteristics related to study attrition? 4. Do our results suggest that our follow-up interview data were negatively impacted by non-response error?

9 Evaluation Design Repeated measures design/panel survey with patients Baseline interview: within 0-5 days of completing detox Follow-up interview: 6 months after completing treatment or leaving center Interview focused on drug and alcohol use, risk and protective factors, and criminal behavior. In-person interview methodology In-country field team: 5 FT field interviewers.

10 Instruments/Data Sources Baseline interview: May through November baseline interviews completed 695 total admissions Follow-up interview: Oct 2009 through January 2011 (66 weeks) 448 interviews completed. Long field period required due to length of stay and 6 month follow-up window.

11 Study Retention Overall w1-w2 study retention was 72%. Comparable to U.S. treatment evaluations. For the other 28% of the baseline sample: Eligible but could not be located (11%). Treatment re-entry after November 13, 2009 (7%). Patient relapse (4%). Patient death (2%). Hard refusals to participate in the follow-up interview (2%). Living outside of El Salvador during the field period for the follow-up interview (1%). Patient incarcerated (1%).

12 Study Retention, cont. Variance across Centers 10 of 19 DAT centers had retention rates equal to or greater than 80%. 7 of the 19 DAT Centers had retention rates of between 50% and 66% Maximum study retention rate: 100% Minimum study retention rate: 50%

13 Tracking Information Tracking elements included on baseline interview: Full name (patrilineal and maternal surname), pseudonyms, aliases, nicknames, etc.) Gender, date of birth, and DUI number (unique identification card #) Home address and reference points Telephone and cell phone numbers address (if available) Occupation Workplace addresses and numbers Shelters (names, addresses and phone numbers) Seasonal mobility Recent activity (where the patient stayed the night before entering the DAT center) Hangouts (bars, city parks, marketplaces, etc.) Place of origin Church or religious affiliations and frequency of attendance Future plans after leaving the treatment center Contact information for at least seven family members, relatives, friends and/or others

14 Tracking Strategies, cont. 1. Calls to the personal phone numbers provided by the patient 2. Visits to the home address(es) provided by the patient 3. Phone calls and visits to the homes of family members, relatives, friends, and other contacts 4. Visits to current and previous workplaces 5. Trips to city parks, marketplaces, bars, soccer fields, parking lots, street corners, etc. where people usually hang out 6. Visits to shelter homes, halfway houses, and AA meeting centers 7. Visits to religious and humanitarian organizations (evangelistic ministries, Red Cross and Green Cross) 8. Contacts with community leaders 9. Contacts with other former patients who might know the patient 10. Inquiries in town hall / mayor s office and with local police 11. Inquiries in other DAT centers 12. Continual contact with DAT center staff

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16 Tracking Strategies Active case management techniques Field interviewers met daily with field supervisors to review the sample and to determine next steps for hard to contact former patients. Ongoing follow-up contacts Tracking time (the 72% didn t come cheap): On average,12 hours spent per patient on making follow-up contacts and tracking former patients before the follow-up interview was conducted. for the most difficult to locate patients, the field team spent 54 hours per patient (on average) on follow-up contacts and tracking efforts.

17 What Predicts Study Attrition? Two level hierarchical non-linear model (HNLM) using a logit-link function was run to examine predictors of attrition. Attrition/retention status was regressed on the following predictors: Gender Age Whether the patient was a native Salvadoran, Whether the patient lived in El Salvador for his/her entire life, Whether the patient had less than a high school education, Whether the patient lived with his/her family, Whether the patient lived with a spouse or other relationship partner, patient employment status, Whether the patient had committed a felony in the past month, Whether the patient committed a misdemeanor in the past month, Whether the patient was jailed in the past month.

18 HLNM Model of Study Attrition Stayers Droppers OR % Intercept % Male % At/Above Med. Age (37) % Native Salvadoran % Lived in El Salvador All Life % < High School Educ % Live w/ Family * % Live w/ Partner % Unemployed % Felony in Past Mo * % Misdemeanor in Past Mo % Jailed in Past Mo * p<.01

19 HLNM Results Patients who lived with their families were about half as likely to drop out of the study as those who did not live with their families (OR=.46). Patients who had committed a felony in the past month were almost twice as likely to drop out of the study as those who had not committed a felony in the past month (OR=1.86). Descriptive data suggest that study attrition may be associated with other behaviors such as substance use reported at baseline. We were unable to include those variables in our HNLM models because they were confounded with the (non-uniform) treatment interventions provided to patients by the DAT Centers.

20 Implications for Nonresponse Error Despite success of tracking efforts, our analysis suggests that patient attrition resulted in some nonresponse error. Previous studies have found that the variables that significantly predicted study attrition also are associated with treatment success. Given the research context and serious methodological challenges, not easy to further enhance retention rates.

21 Lessons Learned/Recommendations 1. Importance of matching research design and fieldwork procedures to research context U.S. best practices informed design but adaptation needed for El Salvador. 2. Budgeting adequate time for field staff was key to our success Tracking Building relationships with patients and DAT Center staff Time for working cases 3. Relationships were key to success Little infrastructure or records as we have in U.S. Time spent with DAT Center staff and at DAT Centers helped ensure that DAT Center staff were willing to work with field staff as partners in locating patients DAT Center provided a private, safe place for interview. 4. Retention rates hinged on accuracy of tracking information Collect as much as possible/more is better Limited shelf life and need to continually update. 5. Additional information to consider collecting: Picture or physical description Treatment history over past 12 months (proxy for mobility) How arrived at DAT Center/who brought the patient

22 QUESTIONS AND COMMENTS? Contact: