Psychopathology and attrition in the Baltimore ECA 15-Year Follow-up 1981–1996

Westat, RA, Rockville, Maryland 20850-3129, USA.
Social Psychiatry and Psychiatric Epidemiology (Impact Factor: 2.54). 03/1999; 34(2):91-8. DOI: 10.1007/s001270050117
Source: PubMed


Predictors of non-response were investigated in a 15-year follow-up (1981-1996) of 3,481 individuals in a probability sample from the household population of East Baltimore. Demographics (age, sex, race, education, marital status, and unemployment), household factors (living arrangements, household income, household size, and number of children), cultural variables (ancestral ethnicity and foreign language), social variables (social support and networks, committing felony, carrying a weapon, using an alias, and wandering), health factors (physical illness, health insurance, medical assistance, Medicare, receiving disability benefits, social security, and welfare), interviewer's observation, and psychopathologic variables (mental disorders, suicide behavior, comorbidity, and drug use) were collected at baseline in 1981 and in 1982, then linked to follow-up data between 1993 and 1996. A tracing process involving mail, phone, criss-cross directories, motor vehicle administration records, a commercial credit bureau, the state criminal justice system, hospital records, the US National Death Index, and field tracing were used to locate the original sample. A total of 3,066 respondents of the original sample (88.1%) were traced. Non-response was categorized into Sample Mortality (that part of the original sample that died during follow-up), Sample Loss (that part of the original sample that survived but could not be found) and Refusal (that part of the original sample that survived and was found but refused to participate). Stratified analysis and adjusted multiple logistic regression modeling found sample mortality and sample loss were strongly influenced by individual and household variables and by psychopathology. Sample mortality was influenced by specific mental disorders or conditions as mania, drug abuse/dependency, antisocial personality, cognitive impairment, alcohol abuse/dependency, phobia, drug use (except PCP), and comorbidity. Household factors protective against mortality include higher household income, not living as extended members in a married couple family, and living with children in the household. Persons who were unemployed, widowed or single, without high school education, male, and 65 years of age or older were more likely to die. Sample loss was influenced by cognitive impairment, antisocial personality, and cocaine use. Household factors linked to sample loss include living in female-headed families, or non-family households, and living alone. Young nonwhite, divorced/separated, without high school education, and unemployed were also harder to find. Refusal was associated with being white, with incomplete elementary education, living as a spouse in traditional married couple families, or as a child in female-headed families. Psychopathology did not influence refusal.

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    • "Poor relationship quality is an important predictor of mental health problems [15]. However, social networks and support did not predict attrition in a 15-year follow-up study [5], and marital satisfaction and spousal support did not predict attrition in a job satisfaction study [6]. More knowledge is needed about the association between attrition and psychological as well as social factors. "
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    ABSTRACT: Background Attrition is one of the major methodological problems in longitudinal studies. It can deteriorate generalizability of findings if participants who stay in a study differ from those who drop out. The aim of this study was to examine the degree to which attrition leads to biased estimates of means of variables and associations between them. Methods Mothers of 18-month-old children were enrolled in a population-based study in 1993 (N=913) that aimed to examine development in children and their families in the general population. Fifteen years later, 56% of the sample had dropped out. The present study examined predictors of attrition as well as baseline associations between variables among those who stayed and those who dropped out of that study. A Monte Carlo simulation study was also performed. Results Those who had dropped out of the study over 15 years had lower educational level at baseline than those who stayed, but they did not differ regarding baseline psychological and relationship variables. Baseline correlations were the same among those who stayed and those who later dropped out. The simulation study showed that estimates of means became biased even at low attrition rates and only weak dependency between attrition and follow-up variables. Estimates of associations between variables became biased only when attrition was dependent on both baseline and follow-up variables. Attrition rate did not affect estimates of associations between variables. Conclusions Long-term longitudinal studies are valuable for studying associations between risk/protective factors and health outcomes even considering substantial attrition rates.
    BMC Public Health 10/2012; 12(1):918. DOI:10.1186/1471-2458-12-918 · 2.26 Impact Factor
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    • "Many factors have been investigated, though not all factors are consistently found to be significantly associated with non-response [10,11]. However, in most studies, non-responders are more likely to be among the youngest [1-3,12] or oldest participants [6,8,9], to live alone [1-4,6,9,13], to be less educated [1,4,6,8,11-14], unemployed [2,5,9,14] and to have a low income [5,6,11]. Non-responders are more likely to have an unhealthy lifestyle, especially being a smoker [2-4,7,8,11,13]. "
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    ABSTRACT: BACKGROUND: This paper discusses whether baseline demographic, socio-economic, health variables, length of follow-up and method of contacting the participants predict non-response to the invitation for a second assessment of lifestyle factors and body weight in the European multi-center EPIC-PANACEA study. METHODS: Over 500.000 participants from several centers in ten European countries recruited between 1992 and 2000 were contacted 2-11 years later to update data on lifestyle and body weight. Length of follow-up as well as the method of approaching differed between the collaborating study centers. Non-responders were compared with responders using multivariate logistic regression analyses. RESULTS: Overall response for the second assessment was high (81.6%). Compared to postal surveys, centers where the participants completed the questionnaire by phone attained a higher response. Response was also high in centers with a short follow-up period. Non-response was higher in participants who were male (odds ratio 1.09 (confidence interval 1.07; 1.11), aged under 40 years (1.96 (1.90; 2.02), living alone (1.40 (1.37; 1.43), less educated (1.35 (1.12; 1.19), of poorer health (1.33 (1.27; 1.39), reporting an unhealthy lifestyle and who had either a low (<18.5 kg/m2, 1.16 (1.09; 1.23)) or a high BMI (>25, 1.08 (1.06; 1.10); especially ≥30 kg/m2, 1.26 (1.23; 1.29)). CONCLUSIONS: Cohort studies may enhance cohort maintenance by paying particular attention to the subgroups that are most unlikely to respond and by an active recruitment strategy using telephone interviews.
    BMC Medical Research Methodology 09/2012; 24(12):148. DOI:10.1186/1471-2288-12-148 · 2.27 Impact Factor
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    • "Attrition, or drop-out, is largely predicted by the same variables as non-response. Males [13,14], as well as participants with low socio-economic status [13], non-western ethnicity [13,15-17], low academic achievement [3,15,17,18] and physical and mental health problems [13,16-19] are particularly likely to drop-out from longitudinal studies. The observation that non-response is predicted by the same variables as attrition makes it plausible that participants for whom extra recruitment effort was done at inclusion are more likely to drop-out of longitudinal studies than those who were easy-to-recruit at inclusion. "
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    ABSTRACT: Background Extensive recruitment effort at baseline increases representativeness of study populations by decreasing non-response and associated bias. First, it is not known to what extent increased attrition occurs during subsequent measurement waves among subjects who were hard-to-recruit at baseline and what characteristics the hard-to-recruit dropouts have compared to the hard-to-recruit retainers. Second, it is unknown whether characteristics of hard-to-recruit responders in a prospective population based cohort study are similar across age group and survey method. Methods First, we compared first wave (T1) easy-to-recruit with hard-to-recruit responders of the TRacking Adolescents’ Individual Lives Survey (TRAILS), a prospective population based cohort study of Dutch (pre)adolescents (at first wave: n = 2230, mean age = 11.09 (SD 0.56), 50.8% girls), with regard to response rates at subsequent measurement waves. Second, easy-to-recruit and hard-to-recruit participants at the fourth TRAILS measurement wave (n = 1881, mean age = 19.1 (SD 0.60), 52.3% girls) were compared with fourth wave non-responders and earlier stage drop-outs on family composition, socioeconomic position (SEP), intelligence (IQ), education, sociometric status, substance use, and psychopathology. Results First, over 60% of the hard-to-recruit responders at the first wave were retained in the sample eight years later at the fourth measurement wave. Hard-to-recruit dropouts did not differ from hard-to-recruit retainers. Second, extensive recruitment efforts for the web based survey convinced a population of nineteen year olds with similar characteristics as the hard-to-recruit eleven year olds that were persuaded to participate in a school-based survey. Some characteristics associated with being hard-to-recruit (as compared to being easy-to-recruit) were more pronounced among non-responders, resembling the baseline situation (De Winter et al.2005). Conclusions First, extensive recruitment effort at the first assessment wave of a prospective population based cohort study has long lasting positive effects. Second, characteristics of hard-to-recruit responders are largely consistent across age groups and survey methods.
    BMC Medical Research Methodology 07/2012; 12(1):93. DOI:10.1186/1471-2288-12-93 · 2.27 Impact Factor
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