Dataset Information

Choosing a design to fit the situation: how to improve specificity and positive predictive values using Bayesian lot quality assurance sampling.

ABSTRACT:

Background

Lot Quality Assurance Sampling (LQAS) is a provably useful tool for monitoring health programmes. Although LQAS ensures acceptable Producer and Consumer risks, the literature alleges that the method suffers from poor specificity and positive predictive values (PPVs). We suggest that poor LQAS performance is due, in part, to variation in the true underlying distribution. However, until now the role of the underlying distribution in expected performance has not been adequately examined.

Methods

We present Bayesian-LQAS (B-LQAS), an approach to incorporating prior information into the choice of the LQAS sample size and decision rule, and explore its properties through a numerical study. Additionally, we analyse vaccination coverage data from UNICEF's State of the World's Children in 1968-1989 and 2008 to exemplify the performance of LQAS and B-LQAS.

Results

Results of our numerical study show that the choice of LQAS sample size and decision rule is sensitive to the distribution of prior information, as well as to individual beliefs about the importance of correct classification. Application of the B-LQAS approach to the UNICEF data improves specificity and PPV in both time periods (1968-1989 and 2008) with minimal reductions in sensitivity and negative predictive value.

Conclusions

LQAS is shown to be a robust tool that is not necessarily prone to poor specificity and PPV as previously alleged. In situations where prior or historical data are available, B-LQAS can lead to improvements in expected performance.

SUBMITTER: Olives C

PROVIDER: S-EPMC3600627 | biostudies-literature | 2013 Feb

REPOSITORIES: biostudies-literature

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Publications

Choosing a design to fit the situation: how to improve specificity and positive predictive values using Bayesian lot quality assurance sampling.

Olives Casey C Pagano Marcello M

International journal of epidemiology 20130201 1

<h4>Background</h4>Lot Quality Assurance Sampling (LQAS) is a provably useful tool for monitoring health programmes. Although LQAS ensures acceptable Producer and Consumer risks, the literature alleges that the method suffers from poor specificity and positive predictive values (PPVs). We suggest that poor LQAS performance is due, in part, to variation in the true underlying distribution. However, until now the role of the underlying distribution in expected performance has not been adequately e ...[more]

PMID: 23378151

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Project description:BACKGROUND:Cardiovascular diseases (CVDs) are the leading cause of mortality in India. India has rolled out Comprehensive Primary Health Care (CPHC) reforms including population based screening for hypertension and diabetes, facilitated by frontline health workers. Our study assessed blood pressure and blood sugar coverage achieved by frontline workers using Lot Quality Assurance Sampling (LQAS). METHODS:LQAS Supervision Areas were defined as catchments covered by frontline workers in primary health centres in two districts each of Uttar Pradesh and Delhi. In each Area, 19 households for each of four sampling universes (males, females, Above Poverty Line (APL) and Below Poverty Line (BPL)) were visited using probability proportional to size sampling. Following written informed consent procedures, a short questionnaire was administered to individuals aged 30 or older using tablets related to screening for diabetes and hypertension. Using the LQAS hand tally method, coverage across Supervision Areas was determined. RESULTS:A sample of 2052 individuals was surveyed, median ages ranging from 42 to 45?years. Caste affiliation, education levels, and occupation varied by location; the sample was largely married and Hindu. Awareness of and interaction with frontline health workers was reported in Uttar Pradesh and mixed in Delhi. Greater coverage of CVD risk factor screening (especially blood pressure) was seen among females, as compared to males. No clear pattern of inequality was seen by poverty status; some SAs did not have adequate BPL samples. Overall, blood pressure and blood sugar screening coverage by frontline health workers fell short of targeted coverage levels at the aggregate level, but in all sites, at least one area was crossing this threshold level. CONCLUSION:CVD screening coverage levels at this early stage are low. More emphasis may be needed on reaching males. Sex and poverty related inequalities must be addressed by more closely studying the local context and models of service delivery where the threshold of screening is being met. LQAS is a pragmatic method for measuring program inequalities, in resource-constrained settings, although possibly not for spatially segregated population sub-groups.

Project description:BackgroundThe Better Health Outcomes through Mentoring and Assessment (BHOMA) project is a cluster randomized controlled trial aimed at reducing age-standardized mortality rates in three rural districts through involvement of Community Health Workers (CHWs), Traditional Birth Attendants (TBAs), and Neighborhood Health Committees (NHCs). CHWs conduct quarterly surveys on all households using a questionnaire that captures key health events occurring within their catchment population. In order to validate contact with households, we utilize the Lot Quality Assurance Sampling (LQAS) methodology. In this study, we report experiences of applying the LQAS approach to monitor performance of CHWs in Luangwa District.MethodsBetween April 2011 and December 2013, seven health facilities in Luangwa district were enrolled into the BHOMA project. The health facility catchment areas were divided into 33 geographic zones. Quality assurance was performed each quarter by randomly selecting zones representing about 90% of enrolled catchment areas from which 19 households per zone where also randomly identified. The surveys were conducted by CHW supervisors who had been trained on using the LQAS questionnaire. Information collected included household identity number (ID), whether the CHW visited the household, duration of the most recent visit, and what health information was discussed during the CHW visit. The threshold for success was set at 75% household outreach by CHWs in each zone.ResultsThere are 4,616 total households in the 33 zones. This yielded a target of 32,212 household visits by community health workers during the 7 survey rounds. Based on the set cutoff point for passing the surveys (at least 75% households confirmed as visited), only one team of CHWs at Luangwa high school failed to reach the target during round 1 of the surveys; all the teams otherwise registered successful visits in all the surveys.ConclusionsWe have employed the LQAS methodology for assurance that quarterly surveys were successfully done. This methodology proved helpful in identifying poorly performing CHWs and could be useful for evaluating CHW performance in other areas.Trial registrationIdentifier: NCT01942278 . Date of Registration: September 2013.

Dataset Information

Choosing a design to fit the situation: how to improve specificity and positive predictive values using Bayesian lot quality assurance sampling.

Background

Methods

Results

Conclusions

Publications

Choosing a design to fit the situation: how to improve specificity and positive predictive values using Bayesian lot quality assurance sampling.

Similar Datasets

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