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Tobacco Control Interventions – Design Trade-Offs

Tobacco Control Interventions – Design Trade-Offs. K. S. (Steve) Brown Department of Statistics and Actuarial Science Health Behaviour Research Group University of Waterloo. What is the Intervention?.

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Tobacco Control Interventions – Design Trade-Offs

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  1. Tobacco Control Interventions – Design Trade-Offs K. S. (Steve) Brown Department of Statistics and Actuarial Science Health Behaviour Research Group University of Waterloo

  2. What is the Intervention? • Comprehensive mix of policies and programs is thought to be most effective way to reduce tobacco use with youth (CDCP, 1999). • Some evidence that school-based programs work best in conjunction with other efforts (e.g. with media activities, by-laws or part of community-wide intervention). • e.g. Florida state-wide intervention emphasized youth leadership in planning and implementing interventions in schools and communities.

  3. Unlike Trials of Therapeutic Agents….. • Many of the tenets of RCT’s do not carry over to community trials • “Intervention” is more than just the agent (e.g. program or policy), but must include provider, setting and context • Hard – and not realistic – to try to control these factors (e.g. provider, setting, context)

  4. Intervention Issues • Multi-faceted mix of programs and policies • Interventions in many settings (schools, stores, media, political jurisdictions) • Interventions need “champion(s)” at the local level • Communities need to be ready to intervene • Sustainability of interventions after the research team leaves is an issue

  5. Intervention Issues • Randomized Trial – communities could pick from menu, but implement several activities simultaneously • Communities must be ready to intervene in more than one area • Hard to attribute “success” to individual components

  6. Intervention Issues • Multiple Baseline – interventions implemented one-at-a-time in different settings • Might be more feasible to have communities tackle only one intervention • Assumes effects are additive – dangerous if interactions are present • Some programs may not work in isolation but may act synergistically with other components

  7. Methodological Issues • End-Points • Measures of Smoking, Process variables • Inclusion/Exclusion Criteria • Blocking/Stratification • Randomization (?) • Blinding • Duration of the study • Longitudinal vs. Repeated Cross-Sectional

  8. Intermediate (Surrogate) End-Points Knowledge, beliefs, values, attitudes, intentions Experimentation Regular Smoking (Weekly Daily) Regular Smoking (Adult) Disease and Death

  9. Process End - Points • Process variables must be measured to assess intervention components • RCT Design assess process and smoking data simultaneously • Multiple Baseline Design intervenes in stages. Not all communities need to measure both process and smoking data

  10. Measures of Smoking, Process Variables • Smoking measures depend on self-report • Measures of process are essential to monitor community activity • Requires measurement of all facets of intervention (e.g. restrictions on sales, enforcement of by-laws, etc.)

  11. Measures of Setting and Context Factors • Need to find reliable measurement systems for setting and contextual factors such as programs, policies and broader environmental variables • Need commitment from communities to continually monitor • Interesting analysis challenges with variables at level of student, school, community, and larger jurisdictions

  12. Inclusion/Exclusion • Inclusion/exclusion criteria are based on community characteristics • Subjects are indirectly recruited • Purpose of research/benefits of research not necessarily a priority for the community • Need willing champions in the communities and researchers willing to work in communities

  13. Blocking/Stratification • Matched pairs RCT requires communities to be matched on characteristics related to outcome (e.g. adult smoking rates, SES, etc.) • Community pairs should include wide variation in expected outcome for generalizability. • Could include pairs in different countries

  14. Blocking/Stratification • Multiple Baseline design uses each community as its own control and corrects for secular effects with staggered start • Fewer communities in each set could limit generalizability • Could choose communities in different countries

  15. Randomization • Typically at level of community • Allows for replication at the level of implementation • Realistic model of how the intervention would be delivered • RCT does require communities to be willing to be randomized • Multiple baseline could randomize within sets of communities to determine order of intervention

  16. Blinding • “Control” groups are often “Usual Care” – i.e. not feasible to change normal practice for the control communities • Difficult to “blind” participants, impossible to “blind” providers • Must recognize the “provider” effect • Difficult to maintain control communities for long trials • Repeated Baseline gives promise of intervention soon to control communities

  17. Duration of Study • Early studies of elementary school based interventions showed differences to the end of Grade 8 that faded by the end of grade 12 without further intervention • Secular trends can make research findings obsolete • Important to study end points after the research team has left • Need to marry research, evaluation and practice

  18. Duration of the Study • Multiple Baseline design requires multiple interventions in multiple communities followed by a comprehensive intervention in several communities to verify the results. • Some interventions may be of shorter duration depending on target (e.g. retailer program vs school-based curriculum).

  19. Duration of Study • RCT requires comprehensive interventions in study communities • For the same total time, the comprehensive intervention could be conducted for a longer time with the RCT • Both designs require sufficient lead time to bring the communities “on board” • Both designs require follow-up to assess sustainability

  20. Longitudinal vs Cross-Sectional Data • Both designs allow for either longitudinal data or repeated cross-sectional data on either individuals or process variables • Repeated cross-sectional data could be available on adolescent smoking rates from existing surveys, but generally insufficient sample sizes would be obtained for smaller communities

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