Two-year follow-up study of elderly residents in S. Paulo, Brazil: methodology and preliminary results

INTRODUCTION: Previous cross-sectional studies have shown a high prevalence of chronic disease and disability among the elderly. Given Brazil’s rapid aging process and the obvious consequences of the growing number of old people with chronic diseases and associated disabilities for the provision of health services, a need was felt for a study that would overcome the limitations of cross-sectional data and shed some light on the main factors determining whether a person will live longer and free of disabling diseases, the so-called successful aging. The methodology of the first follow-up study of elderly residents in Brazil is presented. METHOD: The profile of the initial cohort is compared with previous cross-sectional data and an in-depth analysis of nonresponse is carried out in order to assess the validity of future longitudinal analysis. The EPIDOSO (‘Epidemiologia do Idoso’) Study conducted a two-year follow-up of 1,667 elderly people (65+), living in S. Paulo. The study consisted of two waves, each consisting of household, clinical, and biochemical surveys. RESULTS AND CONCLUSIONS: In general, the initial cohort showed a similar profile to previous cross-sectional samples in S. Paulo. There was a majority of women, mostly widows, living in multigenerational households, and a high prevalence of chronic illnesses, psychiatric disturbances, and physical disabilities. Despite all the difficulties inherent in follow-up studies, there was a fairly low rate of nonresponse to the household survey after two years, which did not actually affect the representation of the cohort at the final household assessment, making unbiased longitudinal analysis possible. Concerning the clinical and blood sampling surveys, the respondents tended to be younger and less disabled than the nonrespondents, limiting the use of the clinical and laboratory data to longitudinal analysis aimed at a healthier cohort. It is worth mentioning that gender, education, family support, and socioeconomic status were not important determinants of nonresponse, as is often the case.

Using register data to assess the impact of response enhancement methods on the risk of nonresponse bias and survey costs

The pursuit of high response rates to minimise the threat of nonresponse bias continues to dominate decisions about resource allocation in survey research. Yet a growing body of research has begun to question this practice. In this study, we use previously unavailable data from a new sampling frame based on population registers to assess the value of different methods designed to increase response rates on the European Social Survey in Switzerland. Using sampling data provides information about both respondents and nonrespondents, making it possible to examine how changes in response rates resulting from the use of different fieldwork methods relate to changes in the composition and representativeness of the responding sample. We compute an R-indicator to assess representativity with respect to the sampling register variables, and find little improvement in the sample composition as response rates increase. We then examine the impact of response rate increases on the risk of nonresponse bias based on Maximal Absolute Bias (MAB), and coefficients of variation between subgroup response rates, alongside the associated costs of different types of fieldwork effort. The results show that increases in response rate help to reduce MAB, while only small but important improvements to sample representativity are gained by varying the type of effort. These findings lend further support to research that has called into question the value of extensive investment in procedures aimed at reaching response rate targets and the need for more tailored fieldwork strategies aimed both at reducing survey costs and minimising the risk of bias.

Effect on trend estimates of the difference between survey respondents and non-respondents: Results from 27 populations in the WHO MONIICA Project

Introduction: In the World Health Organization (WHO) MONICA (multinational MONItoring of trends and determinants in CArdiovascular disease) Project considerable effort was made to obtain basic data on non-respondents to community based surveys of cardiovascular risk factors. The first purpose of this paper is to examine differences in socio-economic and health profiles among respondents and non-respondents. The second purpose is to investigate the effect of non-response on estimates of trends. Methods:Socio-economic and health profile between respondents and non-respondents in the WHO MONICA Project final survey were compared. The potential effect of non-response on the trend estimates between the initial survey and final survey approximately ten years later was investigated using both MONICA data and hypothetical data. Results: In most of the populations, non-respondents were more likely to be single, less well educated, and had poorer lifestyles and health profiles than respondents. As an example of the consequences, temporal trends in prevalence of daily smokers are shown to be overestimated in most populations if they were based only on data from respondents. Conclusions: The socio-economic and health profiles of respondents and non-respondents differed fairly consistently across 27 populations. Hence, the estimators of population trends based on respondent data are likely to be biased. Declining response rates therefore pose a threat to the accuracy of estimates of risk factor trends in many countries.

Participants who left a multiple-wave cohort study had similar baseline characteristics to participants who returned

PURPOSE: Research on determinants of an individual's pattern of response, considered as a profile across time, for cohort studies with multiple waves is limited. In this prospective population-based pregnancy cohort, we investigated baseline characteristics of participants after partitioning them according to their history of response to different interview waves. METHODS: Data are from the Mater-University of Queensland Study of Pregnancy 1981 to 1983 cohort, Brisbane, Australia. Complete baseline information was collected for 7223 of 7535 eligible individuals (95.9%). Follow-up occurred at 6 months, 5 years, and 14 years. Response rates were 93.0%, 72.5%, and 71.8%. Participants were allowed to leave and reenter the study. Participants were categorized as always, intermittent, or never responders. Intermittent responders were categorized further as leavers (responded at least once before leaving the study) or returners (left the study before reentering). RESULTS: Participants who always responded were older, more educated, married, Caucasian, and nonsmokers and had higher incomes. Intermittent responders shared similar baseline characteristics. Relative risk for being an intermittent responder was located between risks for always or never responding. CONCLUSIONS: Participants who left and reentered the study had baseline characteristics similar to participants who responded at least once and then left the study.

Assessing the quality of risk factor survey data: lessons from the WHO MONICA Project

Background and purpose Survey data quality is a combination of the representativeness of the sample, the accuracy and precision of measurements, data processing and management with several subcomponents in each. The purpose of this paper is to show how, in the final risk factor surveys of the WHO MONICA Project, information on data quality were obtained, quantified, and used in the analysis. Methods and results In the WHO MONICA (Multinational MONItoring of trends and determinants in CArdiovascular disease) Project, the information about the data quality components was documented in retrospective quality assessment reports. On the basis of the documented information and the survey data, the quality of each data component was assessed and summarized using quality scores. The quality scores were used in sensitivity testing of the results both by excluding populations with low quality scores and by weighting the data by its quality scores. Conclusions Detailed documentation of all survey procedures with standardized protocols, training, and quality control are steps towards optimizing data quality. Quantifying data quality is a further step. Methods used in the WHO MONICA Project could be adopted to improve quality in other health surveys.