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Synopsis of lecture for MPH candidates at Universiti Malaya on Friday November 3, 2006 by Professor Omar Hasan Kasule Sr.
1.0 OVERVIEW
A community intervention study targets the whole community and not individuals. It has 3 advantages over individual intervention. It is easier to change the community social environment than to change individual behavior. High-risk lifestyles and behaviors are influenced more by community norms than by individual preferences. Interventions are tested in the actual natural conditions of the community, and cheaper. The Salk vaccine trial carried out in 1954 had 200,000 subjects in the experimental group and a similar number in the control group. The aspirin-myocardial infarction study was a therapeutic intervention that randomized 4524 men to two groups. The intervention group received 1.0 gram of aspirin daily whereas the reference group received a placebo. The Women’s Health Study involved randomization of 40,000 healthy women into two groups to study prevention of cancer and cardiovascular disease. One group received vitamin E and low dose aspirin. The other group received a placebo. The alpha tocopherol and beta carotene cancer prevention trial randomized 19,233 mid-age men who were cigarette smokers.
2.0 DESIGN OF A COMMMUNITY INTERVENTION STUDY
There are basically 4 different study designs. In a single community design, disease incidence is measured before and after intervention. In a 2-community design, one community receives an intervention whereas another one serves as the control. In a one-to-many, the intervention community has several control community. In a many-to-many design there are study with multiple intervention communities and multiple control communities. Allocation of a community to either the intervention or the control group is by randomization. Matching and stratification can also be used in more sophisticated designs. The intervention and the assessment of the outcome may involve the whole community or a sample of the community. Outcome measures may be individual level measures or community level measures.
3.0 COMMUNITY TRIALS: STRENGTHS AND WEAKNESSES
The strength of the community intervention study is that it can evaluate a public health intervention in natural field circumstances. It however suffers from 2 main weaknesses: selection bias and controls getting the intervention. Selection bias is likely to occur when allocation is by community. People in the control community may receive the intervention under study on their own because tight control as occurs in laboratory experimental or animal studies is not possible with humans.
4.0 PROCEDURE OF THE COMMUNITY TRIAL
Rare phenomena and short follow-up periods require larger sample sizes. The intensity, frequency, and duration of the intervention must be adequate. Very short follow-up leads to insufficient data and too long follow-up has high attrition. All procedures must be identical for both areas. Quantitative criteria are best for end-point assessment. Interviews using questionnaires, physical and biochemical parameters, morbidity, and mortality may be used as end-points. Use of morbidity and mortality as end-points is not the best option because of existence of many competing causes of mortality and morbidity. Examination for and recording of the end-point must be blind. The assessment of the end-point may be based on longitudinal change or by repeated cross-sectional surveys. The net change is computed as {(I1 - I0) / I0 } – {(R1 - R0)/ R0} or as (I1/ I0} / {R1 / R0} - 1
5.0 DATA INTERPRETATION
Interpretation of the results may be complicated by secular trends; it is therefore recommended that the study duration be as short as is reasonable. Negative findings could be due to an inadequate intervention effort either not intense enough or not long enough. Negative findings will be found when intervention was against a non-causal factor, the intervention was against a wrong target group, or the sample size was not adequate. End-point assessment may be biased by more diagnostic effort in the intervention group. Clustering must be taken into account in analysis of community intervention data. Community level measures such as means and proportions may be heavily confounded and therefore not reliable.