Antiretroviral treatment uptake and attrition among HIV-positive patients with tuberculosis in Kibera, Kenya

Using data of human immunodeficiency virus-positive patients with tuberculosis from three primary care clinics in Kibera slums, Nairobi, Kenya, we report on the proportion that started antiretroviral treatment (ART) and attrition (deaths, lost to follow-up and stopped treatment) before and while on ART. Of 427 ART eligible patients, enrolled between January 2004 and December 2008, 70% started ART, 19% were lost to attrition and 11% had not initiated ART. Of those who started ART, 14% were lost to attrition, making a cumulative pre-ART and ART attrition of 33%. ART uptake among patients with TB was relatively good, but programme attrition was high and needs urgent addressing

Sample Attrition in the Presence of Population Attrition

This paper develops a method that accounts for non-ignorable sample attrition in the presence of population attrition for use with a non-representative panel sample. The method is applied to obtain attrition-correcting weights for the native and immigrant samples in the matched Current Population Survey (CPS). Of the two samples, the immigrant sample suffers from sample attrition due to changes in residence as well as population attrition caused by selective return migration. When there is population attrition, the second period cross-section is not representative of the first period population. Therefore, the existing sample attrition-correcting method developed by Hirano, Imbens, Ridder, and Rubin (2001) and Bhattacharya (2008) cannot be applied. We resolve this problem by generating a counterfactual, but representative cross-section prior to applying their method. The counterfactual sample can be obtained by weighting the second period cross-section by one minus the probability of population attrition. We show that the sample attrition and the population attrition processes are separately identified. This is useful because samples usually do not indicate which missing observations are due to sample attrition and which are due to population attrition. The attrition-correcting weights, once obtained, can be used in various studies of immigration using the CPS.

Reducing attrition in panel studies in developing countries.

BACKGROUND: Panel studies offer repeated observations of individuals over time, but the mobility of populations in the developing world often causes attrition in panel studies. Such attrition can cause bias if it is selective but can be reduced by tracking respondents. Tracking in developing countries can be costly and difficult as populations are often highly mobile, infrastructure is poor, structures frequently change, and formal address systems or population records rarely exist. Method In this paper, the attrition and tracking experiences of panel studies in developing countries are reviewed and recommendations made for ensuring effective tracking. Comments Tracking can reduce attrition by up to 45% and is feasible if procedures are locally appropriate, well planned, involve the community, collect as much locating data as possible, and have explicit criteria, and if tracking is done at regular intervals, and interviewers are well trained, supervised, and motivated. CONCLUSION: Attrition is an important issue in panel studies, whilst tracking can be costly it can reduce attrition if effective procedures are used

Can I use a Panel? Panel Conditioning and Attrition Bias in Panel Surveys

Over the past decades there has been an increasing use of panel surveys at the household or individual level, instead of using independent cross-sections. Panel data have important advantages, but there are also two potential drawbacks: attrition bias and panel conditioning effects. Attrition bias can arise if respondents drop out of the panel non-randomly, i.e., when attrition is correlated to a variable of interest. Panel conditioning arises if responses in one wave are in°uenced by participation in the previous wave(s). The experience of the previous interview(s) may affect the answers of respondents in a next interview on the same topic, such that their answers differ systematically from the answers of individuals who are interviewed for the first time. The literature has mainly focused on estimating attrition bias; less is known on panel conditioning effects. In this study we discuss how to disentangle the total bias in panel surveys due to attrition and panel conditioning into a panel conditioning and an attrition effect, and develop a test for panel conditioning allowing for non-random attrition. First, we consider a fully nonparametric approach without any assumptions other than those on the sample design, leading to interval identification of the measures for the attrition and panel conditioning effect. Second, we analyze the proposed measures under additional assumptions concerning the attrition process, making it possible to obtain point estimates and standard errors for both the attrition bias and the panel conditioning effect. We illustrate our method on a variety of questions from two-wave surveys conducted in a Dutch household panel. We found a significant bias due to panel conditioning in knowledge questions, but not in other types of questions. The examples show that the bounds can be informative if the attrition rate is not too high. Point estimates of the panel conditioning effect do not vary a lot with the different assumptions on the attrition process.panel conditioning;attrition bias;measurement error;panel surveys

Cross-Country Income Mobility Comparisons Under Panel Attrition: The Relevance of Weighting Schemes

This paper aims to present an assessment of the effects of panel attrition on income mobility comparisons for some EU-countries by using the European Community Household Panel (ECHP). There are different possibilities of correcting the attrition problem by means of alternative longitudinal weighting schemes. The sensitivity of mobility estimates to these attrition correction procedures is tested in the paper. Our results show that ECHP attrition is characterised by a certain degree of selectivity but only affecting some variables and countries. Different probability models corroborate the existence of a certain non-random attrition. The model chosen to construct the longitudinal weights to correct attrition offers up rather different results than those obtained when Eurostat’s longitudinal weights are used. Although attrition does not seem to have a great effect on aggregated mobility indicators, it does have a decisive effect on decomposition exercises. Finally, the tests conducted on income mobility indicators reveal a certain sensitivity to the weighting system used.Income mobility, attrition, European Community Household Panel.

Teacher Attrition in Charter Schools

This study focuses on the reasons why teachers are leaving charter schools. It is based on analyses of data collected in surveys of charter school employees from around the country from 1997-2006. The authors found that while overall attrition rates fluctuate from year to year and state to state, as many as one in four charter school teachers leave each year- approximately double the traditional public school rate of 11 percent. Moreover, attrition among new teachers in charter schools is close to 40 percent annually. The authors contend that high attrition, "consumes resources of schools, impedes schools' efforts to build professional learning communities and positive, stable school cultures," and recommend that supporters of charters schools focus efforts on reducing teacher attrition

The reasons for attrition: we (still) haven't got a clue

This paper describes a small survey (attrition survey) undertaken to ascertain reasons of attrition and non-response in questionnaire responses from student participants as part of a larger longitudinal survey (longitudinal study). Due to difficulties in retaining participants within the longitudinal study, determining reasons for attrition and non-response became important; mainly. The questionnaire employed in the longitudinal study was developed by the Cambridge Management Institute (CMI) and is widely known as the HEGI instrument or the SPEED network questionnaire. The questionnaire was designed to be completed three times by the participants; one pre-test and two post-tests. Following 18 months of problems of attrition, questions were raised about its value and whether it was suitable to be administered in the environment and setting within which we were using it, in traditional semesters in higher education. Therefore, the subsequent attrition survey was undertaken to look at a number of factors that the authors believed were significant in causing the high rates of attrition and non-response. This data was obtained using a very short questionnaire sent to a proportion of the sample originally part of the longitudinal study. The factors deemed to be of potential significance were plenty and are discussed at detail throughout the paper. Predominantly, issues concerning web-based and paper-based survey methods were also of significance as the former becomes more prevalent but raises the question, how do response rates compare with traditional methods? This was a further area of concern for the authors because a change to the survey mode i.e. distribution and completion method from paper to online was reluctantly introduced as a cost-saving measure. This paper will report the results of the attrition survey in relation to the participants’ responses about reasons for attrition and non-response

Attrition of Households and Individuals in Panel Surveys

Attrition is mostly caused by not contacted or refusing sample members. On one hand it is well-known that reasons to attrite due to non-contact are different from those that are due to refusal. On the other hand does non-contact most probably affect household attrition, while refusal can be effective on both households and individuals. In this article, attrition on both the household and (conditional on household participation) the individual level is analysed in three panel surveys from the Cross National Equivalent File (CNEF): the German Socio- Economic Panel (GSOEP), the British Household Panel Study (BHPS), and the Swiss Household Panel (SHP). To follow households over time we use a common rule in all three surveys. First, we find different attrition magnitudes and patterns both across the surveys and also on the household and the individual level. Second, there is more evidence for reinforced rather than compensated household level selection effects if the individual level is also taken into account.CNEF, individual attrition, household attrition, attrition bias, reference person, household head

Attrition in longitudinal household survey data

Longitudinal household data can have considerable advantages over much more widely used cross-sectional data. The collection of longitudinal data, however, may be difficult and expensive. One problem that has concerned many analysts is that sample attrition may make the interpretation of estimates problematic. Such attrition may be particularly severe in areas where there is considerable mobility because of migration between rural and urban areas. Many analysts share the intuition that attrition is likely to be selective on characteristics such as schooling and that high attrition is likely to bias estimates made from longitudinal data. This paper considers the extent of and implications of attrition for three longitudinal household surveys from Bolivia, Kenya, and South Africa that report very high per-year attrition rates between survey rounds. Our estimates indicate that (1) the means for a number of critical outcome and family background variables differ significantly between attritors and nonattritors; (2) a number of family background variables are significant predictors of attrition; but (3) nevertheless, the coefficient estimates for “standard” family background variables in regressions and probit equations for the majority of the outcome variables considered in all three data sets are not affected significantly by attrition. Therefore, attrition apparently is not a general problem for obtaining consistent estimates of the coefficients of interest for most of these outcomes. These results, which are very similar to results for developed economies, suggest that for these outcome variables—despite suggestions of systematic attrition from univariate comparisons between attritors and nonattritors, multivariate estimates of behavioral relations of interest may not be biased due to attrition.Household surveys Methodology ,