Improving Population Health Measurement in National Household Surveys: A Simulation Study of the Sample Design of the Comprehensive Survey of Living Conditions of the People on Health and Welfare in Japan

Background: The Comprehensive Survey of Living Conditions of the People on Health and Welfare (CSLC) is a major source of health data in Japan. The CSLC is not strictly based on probabilistic sampling, but instead uses an equal allocation of sample clusters to yield equal standard errors of estimates across prefectures. This study compared the performance of this sample design in measuring population health with that of an alternative probabilistic sampling approach. Methods: A simulation analysis was conducted using hypothetical population data (n = 34 262 865) from which 1000 sample datasets were randomly drawn using 2 sampling methods, namely, a conventional stratified random sampling of a constant number of clusters and an alternative 2-stage cluster sampling of households with probability proportional to size. The root mean squared error was used to measure the accuracy of estimated means of a continuous variable and proportions of its dichotomized variable. Results: The alternative method reduced the variability of estimates in the total population and by strata. It improved further with an increased number of sample clusters in conjunction with a reduced sampling rate of households from selected clusters. Conclusions: The alternative sample design increased the overall accuracy of population estimates of continuous and dichotomous variables from the CSLC. These benefits should be carefully weighed against the costs incurred in traveling to additional clusters in large prefectures. Further simulation research is necessary to investigate the performance of sampling designs for nominal and ordinal response variables.