Random Sampling for a Mental Health Survey in a Deprived Multi-Ethnic Area of Berlin.
ABSTRACT The aim of the study was to assess the response to random sampling for a mental health survey in a deprived multi-ethnic area of Berlin, Germany, with a large Turkish-speaking population. A random list from the registration office with 1,000 persons stratified by age and gender was retrieved from the population registry and these persons were contacted using a three-stage design including written information, telephone calls and personal contact at home. A female bilingual interviewer contacted persons with Turkish names. Of the persons on the list, 202 were not living in the area, one was deceased, 502 did not respond. Of the 295 responders, 152 explicitly refused (51.5%) to participate. We retained a sample of 143 participants (48.5%) representing the rate of multi-ethnicity in the area (52.1% migrants in the sample vs. 53.5% in the population). Turkish migrants were over-represented (28.9% in the sample vs. 18.6% in the population). Polish migrants (2.1 vs. 5.3% in the population) and persons from the former Yugoslavia (1.4 vs. 4.8% in the population) were under-represented. Bilingual contact procedures can improve the response rates of the most common migrant populations to random sampling if migrants of the same origin gate the contact. High non-contact and non-response rates for migrant and non-migrant populations in deprived urban areas remain a challenge for obtaining representative random samples.
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ABSTRACT: General psychiatric and forensic psychiatric beds, supported housing and the prison population have been suggested as indicators of institutionalized mental health care. According to the Penrose hypothesis, decreasing psychiatric bed numbers may lead to increasing prison populations. The study aimed to assess indicators of institutionalized mental health care in post-communist countries during the two decades following the political change, and to explore whether the data are consistent with the Penrose hypothesis in that historical context. General psychiatric and forensic psychiatric bed numbers, supported housing capacities and the prison population rates were collected in Azerbaijan, Belarus, Croatia, Czech Republic, East Germany, Hungary, Kazakhstan, Latvia, Poland, Romania, Russia and Slovenia. Percentage change of indicators over the decades 1989-1999, 1999-2009 and the whole period of 1989-2009 and correlations between changes of different indicators were calculated. Between 1989 and 2009, the number of general psychiatric beds was reduced in all countries. The decrease ranged from -11% in Croatia to -51% in East Germany. In 2009, the bed numbers per 100,000 population ranged from 44.7 in Azerbaijan to 134.4 in Latvia. Forensic psychiatric bed numbers and supported housing capacities increased in most countries. From 1989-2009, trends in the prison population ranged from a decrease of -58% in East Germany to an increase of 43% in Belarus and Poland. Trends in different indicators of institutionalised care did not show statistically significant associations. After the political changes in 1989, post-communist countries experienced a substantial reduction in general psychiatric hospital beds, which in some countries may have partly been compensated by an increase in supported housing capacities and more forensic psychiatric beds. Changes in the prison population are inconsistent. The findings do not support the Penrose hypothesis in that historical context as a general rule for most of the countries.PLoS ONE 06/2012; 7(6):e38490. DOI:10.1371/journal.pone.0038490 · 3.53 Impact Factor
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ABSTRACT: Correct identification of ethnicity is central to many epidemiologic analyses. Unfortunately, ethnicity data are often missing. Successful classification typically relies on large databases (n > 500,000 names) of known name-ethnicity associations. We propose an alternative naïve Bayesian strategy that uses substrings of full names. Name and ethnicity data for Malays, Indians, and Chinese were provided by a health and demographic surveillance site operating in Malaysia from 2011-2013. The data comprised a training data set (n = 10,104) and a test data set (n = 9,992). Names were spliced into contiguous 3-letter substrings, and these were used as the basis for the Bayesian analysis. Performance was evaluated on both data sets using Cohen's κ and measures of sensitivity and specificity. There was little difference between the classification performance in the training and test data (κ = 0.93 and 0.94, respectively). For the test data, the sensitivity values for the Malay, Indian, and Chinese names were 0.997, 0.855, and 0.932, respectively, and the specificity values were 0.907, 0.998, and 0.997, respectively. A naïve Bayesian strategy for the classification of ethnicity is promising. It performs at least as well as more sophisticated approaches. The possible application to smaller data sets is particularly appealing. Further research examining other substring lengths and other ethnic groups is warranted.American Journal of Epidemiology 06/2014; DOI:10.1093/aje/kwu129 · 4.98 Impact Factor
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ABSTRACT: The present study aimed to systematically assess the association of socio-economic characteristics and psychological distress in a disadvantaged urban area of a post-Soviet Republic. Psychological distress was assessed in a random sample of 200 persons, aged 18-57, living in a disadvantaged urban area of Kazakhstan using the General Health Questionnaire with 28 items (GHQ-28). Bivariate and multivariate analyses were used to examine the association of social characteristics and psychological distress. Female gender (P < 0.05), living without a partner (P < 0.01), higher age (P < 0.01), unemployment (P < 0.01), and low perceived income (P < 0.05) were associated with psychological distress in multivariate analyses. Non-Kazakh ethnicity (P < 0.05) was linked with psychological distress in bivariate analyses. The educational level was not significantly associated with psychological distress. Women, aged 38-57, living without partner and with low access to financial resources, were at a very high risk of psychological distress. Possibly due to social drift or status inconsistency, higher educational levels were not associated with lower levels of psychological distress in the disadvantaged area.Community Mental Health Journal 05/2013; 50(1). DOI:10.1007/s10597-013-9610-x · 1.03 Impact Factor