Lot Quality Assurance Sampling Survey Research Articles

Prevalence of and factors associated with zero-dose and under-immunized children in selected areas of Bangladesh: Findings from Lot Quality Assurance Sampling Survey

BackgroundIn the era of Gavi’s 5.0 vision of “leaving no one behind with immunization”, childhood routine vaccination in missed communities is considered as a priority concern. Despite having a success story at the national level, low uptake of immunization is still persistent in selected pocket areas of Bangladesh. However, prevalence and the associated factors of zero-dose (ZD) and under-immunization (UI) are still unknown at those geo-pockets of Bangladesh. Thus, the study aims to report and identify the factors associated with ZD and UI in selected geographical locations. MethodsThis study used data from a Lot Quality Assurance Sampling (LQAS) survey where 504 households from 18 clusters of four hard to reach (HTR) and one urban slum were included. Caregivers of children aged 4.5 to 23 months were interviewed. Three outcome variables- ZD, UI and ZD/UI were considered and several related attributes were considered as independent variables. Data were analyzed through bivariate analysis, binary logistic regression and dominance analysis. ResultsOverall, 32% of the children were either ZD (8%) or UI (26%) in the selected areas. The adjusted odds of ZD/UI for urban slum and haor (wetlands) areas were 5.62 and 3.61 respectively considering coastal areas as reference. However, distance of nearest EPI center, availability of EPI card, age of caregivers, education and occupation of mother and number of earning members in household were influential factors for ZD/UI. According to dominance analysis, availability of EPI card can explain the most of the variation of ZD/UI in this study. ConclusionThe study findings highlight the high prevalence ZD/UI in certain geo-pockets of the country. It provided a powerful insight of current situation and associated factors in regards to ZD/UI in the country which will help policy-makers and programme managers in designing programmes to reduce missed communities in Bangladesh.

Postpartum family planning uptake in Uganda: findings from the lot quality assurance sampling survey

BackgroundThe initiation and use of family planning (FP) services within the first 12 months following childbirth, postpartum family planning (PPFP), promotes safe motherhood by reducing unintended pregnancies and ensuring appropriate pregnancy spacing. However, there is a paucity of information on PPFP uptake from community surveys. This study aimed to quantify the reported use of PPFP and identify predictors and barriers to PPFP uptake from a large community survey.MethodsWe analysed data collected from the 2021 Lot Quality Assurance Sampling (LQAS) survey, a cross-sectional community and household survey that covered 68 districts in Uganda. The survey uses small sample sizes to designate health or administrative geographical areas which are assessed to determine whether they achieved the pre-determined target for defined indicators of interest. We abstracted and analysed data collected from mothers of children aged 12 months or younger on reproductive health and FP. PPFP use was defined as the reported use of modern FP by the mother or their partner. Associations were measured using Pearson’s chi-square test at 5% significance. Multivariate logistic regression was performed for variables that were significantly associated with PPFP use to identify the predictors of PPFP.ResultsOverall, 8103 mothers of children aged less than 12 years were included in the analysis; the majority of mothers, 55.8% (4521/8103) were above 24 years while 11.7% (950/8103) were 19 years and under. 98% (7942/8103) of the mothers attended at least one antenatal care (ANC) visit and 86.3% (6997/8103) delivered at a health facility. Only 10% (814/8103) of mothers who participated in the survey reported PPFP use at the time of the survey. Reporting of PPFP use was 5 times higher among mothers of children aged 7–12 months (AOR 4.9; 95%CI 4.1–5.8), 50% higher among mothers with secondary education (AOR 1.5; 95%CI 1.0-2.3), 80% higher among breastfeeding mothers (AOR 1.8; 95%CI 1.3–2.4) and 30% lower among those that didn’t receive a health worker visit within 3 months preceding the survey (AOR 0.7; 95% CI 0.5–0.8). Among 4.6% (372/8103) who stated a reason for non-use of PPFP, the most cited reasons for not using were breastfeeding 43% (161/372), fear of side effects 26.9% (100/372), respondent/partner opposition 17.6% (48/372) and infrequent sex 12.1% (48/372).ConclusionThe analysis showed a low proportion of PPFP uptake among mothers of children under 12 years. Possible barriers included child age, education, a health worker visit, and side effects and perceived benefits of possibly improperly implementing lactation amenorrhea method. Integration of social, community and health services could provide a more holistic approach to improving PPFP uptake.

Providing information for decision-making in the Nigerian Polio Eradication Program, 2016-2020.

Nigeria made a coordinated effort to be certified by the World Health Organization's African Region for interrupting endemic transmission of wild poliovirus type-1 (WPV1) in August 2020 as a response to the resurgence of WPV1 cases in August 2016 after going two years without a case. The NEOC Data Working Group (DWG) was instrumental in providing quality and timely surveillance and campaign information for decision-making in order to interrupt WPV1 transmission and provide data toward documentation of its elimination for regional certification. The polio pre-campaign dashboard was used to assess the level of preparedness for Oral Poliovirus Vaccine (OPV) polio supplementary immunization activities (SIA) at three weeks, two weeks, one week, and three days to the start of each campaign implemented during 2016-2020. The administrative tally sheet, independent monitoring survey, and Lot Quality Assurance Sampling (LQAS) survey data collected and shared from the implementation level were analyzed by the EOC DWG to provide information by person, place, and time. Using a 90% threshold in LQAS surveys defining quality SIAs, the proportion of Local Government Areas (LGAs) in Nigeria's states in which post-SIA LQAS surveys were conducted that met this threshold were assessed over time. The highest level of preparedness attained by 3 days to a polio campaign during August 2016-February 2020 was 95% and the lowest attained was 77%. The admin, independent monitoring, and LQAS data analysis results were given to EOC working groups for assessing the performance and quality of each campaign. Twenty-twenty five percent of LGAs that failed LQAS were identified for repeat vaccination. Further, acute flaccid paralysis and environmental surveillance data and laboratory results were analyzed and shared with NEOC and partners. The government and partners used the information generated by the Data Working Group to take evidence-based action including determining the scope of the polio campaign, intensification of surveillance and routine immunization activities, and special intervention activities. On average, 12% of the 774 LGAs were identified as polio high risk LGAs for intervention using selected surveillance, routine immunization (RI), SIAs, and other relevant data sets. National Emergency Operation Centre Data Working Group provided quality and timely information that supported decision-making processes for the polio program in Nigeria. The quality and timely information enabled the NEOC to make evidence-based and timely decisions that contributed to gap identification and decision-making.

Comparison of the costs and data outputs of conventional cluster sampling and lot quality assurance sampling (LQAS) for assessing the coverage of fortified foods in household surveys

Household surveys are essential for assessing the coverage of public health programmes, including large-scale food fortification (LSFF) programmes in developing countries. For decades, survey implementers have predominantly designed and implemented household-based surveys using conventional cluster sampling, but other sampling approaches, such as lot quality assurance sampling (LQAS), should be considered as an alternative. This study compares the costs and data outputs of conventional cluster sampling and LQAS when used to measure the household-level coverage of a hypothetical LSFF programme. Specifically, four survey scenarios were compared using hypothetical results: conventional cluster sampling to calculate the coverage of fortified foods at the national (scenario A) and regional (scenario B) levels, and LQAS to produce pass/fail results at the national (scenario C) and regional (scenario D) levels. For each scenario, sample sizes were calculated using a target coverage of 25%, 50%, and 75%, and used previous surveys to estimate survey budget costs, which consisted of the costs of administration, field workers, other personnel, materials, and laboratory testing. A national level LQAS survey (scenario C) had the lowest estimated costs (69,424 – 73,462 USD), followed by a national level conventional cluster sampling survey (scenario A) (82,620 – 90, 164 USD). There were higher overall costs and larger cost differences between sampling approaches for surveys designed to yield regional estimates. Here, costs for a conventional cluster sampling survey (scenario B; 212,210 – 251, 470 USD) are more than double those for a LQAS survey (scenario D) (113,060 – 129,540 USD). Sample size is the main driver of survey costs in all scenarios, while costs for field teams (salaries and transportation) and laboratory analyses of food samples vary depending on the scenario and coverage threshold; all other survey costs (e.g., ethical approaval, training & field testing) remain relatively stable across different scenarios and thresholds. While LQAS surveys can be implemented at a lower cost due to smaller sample size requirements, the cost savings are less than expected due to the more dispersed distribution of households. Furthermore, because LQAS are initially designed to yield only pass/fail classification rather than estimates of actual coverage, they may not provide the actionable insights required in routine programme monitoring. When selecting a survey sampling approach, food fortification programme planners must consider what type of results best suit their decision-making needs and available resources. Key words: Coverage, Food Fortification, Sampling, Lot-Quality Assurance Sampling, Cluster-sampling

Model-based small area estimation methods and precise district-level HIV prevalence estimates in Uganda.

BackgroundModel-based small area estimation methods can help generate parameter estimates at the district level, where planned population survey sample sizes are not large enough to support direct estimates of HIV prevalence with adequate precision. We computed district-level HIV prevalence estimates and their 95% confidence intervals for districts in Uganda.MethodsOur analysis used direct survey and model-based estimation methods, including Fay-Herriot (area-level) and Battese-Harter-Fuller (unit-level) small area models. We used regression analysis to assess for consistency in estimating HIV prevalence. We use a ratio analysis of the mean square error and the coefficient of variation of the estimates to evaluate precision. The models were applied to Uganda Population-Based HIV Impact Assessment 2016/2017 data with auxiliary information from the 2016 Lot Quality Assurance Sampling survey and antenatal care data from district health information system datasets for unit-level and area-level models, respectively.ResultsEstimates from the model-based and the direct survey methods were similar. However, direct survey estimates were unstable compared with the model-based estimates. Area-level model estimates were more stable than unit-level model estimates. The correlation between unit-level and direct survey estimates was (β1 = 0.66, r2 = 0.862), and correlation between area-level model and direct survey estimates was (β1 = 0.44, r2 = 0.698). The error associated with the estimates decreased by 37.5% and 33.1% for the unit-level and area-level models, respectively, compared to the direct survey estimates.ConclusionsAlthough the unit-level model estimates were less precise than the area-level model estimates, they were highly correlated with the direct survey estimates and had less standard error associated with estimates than the area-level model. Unit-level models provide more accurate and reliable data to support local decision-making when unit-level auxiliary information is available.

Modeling Poliovirus Surveillance and Immunization Campaign Quality Monitoring Costs for Pakistan and Afghanistan for 2019-2023.

The Global Polio Eradication Initiative (GPEI) Strategic Plan for 2019-2023 includes commitments to monitor the quality of immunization campaigns using lot quality assurance sampling surveys (LQAS) and to support poliovirus surveillance in Pakistan and Afghanistan. We analyzed LQAS and poliovirus surveillance data between 2016 and 2020, which included both acute flaccid paralysis (AFP) case-based detection and the continued expansion of environmental surveillance (ES). Using updated estimates for unit costs, we explore the costs of different options for future poliovirus monitoring and surveillance for Pakistan and Afghanistan. The relative value of the information provided by campaign quality monitoring and surveillance remains uncertain and depends on the design, implementation, and performance of the systems. Prospective immunization campaign quality monitoring (through LQAS) and poliovirus surveillance will require tens of millions of dollars each year for the foreseeable future for Pakistan and Afghanistan. LQAS campaign monitoring as currently implemented in Pakistan and Afghanistan provides limited and potentially misleading information about immunization quality. AFP surveillance in Pakistan and Afghanistan provides the most reliable evidence of transmission, whereas ES provides valuable supplementary information about the extent of transmission in the catchment areas represented at the time of sample collection.

Evaluation of neonatal mortality data completeness and accuracy in Ghana.

Cause-specific mortality data are required to set interventions to reduce neonatal mortality. However, in many developing countries, these data are either lacking or of low quality. We assessed the completeness and accuracy of cause of death (COD) data for neonates in Ghana to assess their usability for monitoring the effectiveness of health system interventions aimed at improving neonatal survival. A lot quality assurance sampling survey was conducted in 20 hospitals in the public sector across four regions of Ghana. Institutional neonatal deaths (IND) occurring from 2014 through 2017 were divided into lots, defined as neonatal deaths occurring in a selected facility in a calendar year. A total of 52 eligible lots were selected: 10 from Ashanti region, and 14 each from Brong Ahafo, Eastern and Volta region. Nine lots were from 2014, 11 from 2015 and 16 each were from 2016 and 2017. The cause of death (COD) of 20 IND per lot were abstracted from admission and discharge (A&D) registers and validated against the COD recorded in death certificates, clinician's notes or neonatal death audit reports for consistency. With the error threshold set at 5%, ≥ 17 correctly matched diagnoses in a sample of 20 deaths would make the lot accurate for COD diagnosis. Completeness of COD data was measured by calculating the proportion of IND that had death certificates completed. Nineteen out of 52 eligible (36.5%) lots had accurate COD diagnoses recorded in their A&D registers. The regional distribution of lots with accurate COD data is as follows: Ashanti (4, 21.2%), Brong Ahafo (7, 36.8%), Eastern (4, 21.1%) and Volta (4, 21.1%). Majority (9, 47.4%) of lots with accurate data were from 2016, followed by 2015 and 2017 with four (21.1%) lots. Two (10.5%) lots had accurate COD data in 2014. Only 22% (239/1040) of sampled IND had completed death certificates. Death certificates were not reliably completed for IND in a sample of health facilities in Ghana from 2014 through 2017. The accuracy of cause-specific mortality data recorded in A&D registers was also below the desired target. Thus, recorded IND data in public sector health facilities in Ghana are not valid enough for decision-making or planning. Periodic data quality assessments can determine the magnitude of the data quality concerns and guide site-specific improvements in mortality data management.

Routine vitamin A supplementation and other high impact interventions in Sierra Leone.

In 2017, transition to routine vitamin A supplementation (VAS) commenced as an integrated reproductive and child health service including vaccinations, Albendazole for deworming, complementary feeding demonstrations, ‘quality’ family planning counselling and provision of modern contraceptives. After 10 months, a lot quality assurance sampling survey evaluated coverage of these interventions. Each of three districts was divided into five supervision areas (lots), and 19 villages were randomly selected in each lot proportional to population size. Households were randomly selected, and a questionnaire was administered to a caregiver of a child 6–11, 12–23 and 24–59 months in each village. Overall, caregivers of 855 children were interviewed, and 19 questionnaires were completed for each age group (6–11, 12–23 and 24–59 months) in each of the five lots in each district. All lots in one district passed the threshold of 80% for VAS and 75% coverage for Albendazole, and two lots failed for either VAS/Albendazole in the other two districts. Overall, weighted VAS coverage for children 6–59 months was 86.9%, and weighted Albendazole coverage for children 12–59 months was 80.9%. Most caregivers (77.2%) knew that complementary feeding should be introduced at 6 months, 44.9% were providing three or more (of six) food groups, 84.9% were aware of family planning and 37.5% were using a modern contraceptive. Integration of reproductive and child health services appears to be a suitable platform for routine VAS and Albendazole whilst improving complementary feeding practices and access to family planning.

Reasons for Missed Children during Lot Quality Assurance Sampling Survey in Birnin Kudu LGA of Jigawa State, Nigeria – Polio Immunization Campaigns 2014

Reasons for Missed Children during Lot Quality Assurance Sampling Survey in Birnin Kudu LGA of Jigawa State, Nigeria – Polio Immunization Campaigns 2014

Trends in newborn umbilical cord care practices in Sokoto and Bauchi States of Nigeria: the where, who, how, what and the ubiquitous role of traditional birth attendants: a lot quality assurance sampling survey

BackgroundNeonatal infections caused by unsafe umbilical cord practices account for the majority of neonatal deaths in Nigeria. We examined the trends in umbilical cord care practices between 2012 and 2015 that coincided with the introduction of chlorhexidine digluconate 7.1% gel in Bauchi and Sokoto States.MethodsWe obtained data from three rounds of lot quality assurance samples (LQAS) surveys conducted in 2012, 2013 and 2015. Households were randomly sampled in each round that totaled 1140 and 1311 households in Bauchi and Sokoto States respectively. Mothers responded to questions on cord care practices in the last delivery. Coverage estimates of practice indicators were obtained for each survey period. Local Government Area (LGA) estimates for each indicator were obtained with α ≤ 5%, and β ≤20% statistical errors and aggregated to State-level estimates with finite sample correction relative to the LGA population.ResultsOver 75 and 80% of deliveries in Bauchi and Sokoto States respectively took place at home. The proportion of deliveries in public facilities reported by mothers ranged from 19% in 2012 to 22.4% in 2015 in Bauchi State and from 12.9 to 13.2% in 2015 in Sokoto State. Approximately 50% of deliveries in Bauchi and more than 80% in Sokoto States were assisted by traditional birth attendants (TBAs) or relatives and friends, with little change in the survey periods. In Bauchi and in Sokoto States, over 75% and over 80% of newborn cords were cut with razor blades underscoring the pervasive role of the TBAs in the immediate postpartum period. Use of chlorhexidine digluconate 7.1% gel for cord dressing significantly increased to the highest level in 2015 in both States. Health workers who attended deliveries in health facilities switched from methylated spirit to chlorhexidine. There were no observable changes in cord care practices among the TBAs.ConclusionUnsafe umbilical cord care practices remained prevalent in Bauchi and Sokoto States of Nigeria, although a recent introduction of chlorhexidine digluconate 7.1% gel positively changed the cord care practices toward safer practices among public health providers. TBAs, friends and relatives played the strongest immediate postpartum roles and mostly retained the unsafe cord care practices such as use of ash, cow dung and hot compress. We recommend that existing TBAs are retrained and refocused to forge stronger links between communities and the primary health centers to increase mothers’ access to skilled birth attendants.

Using lot quality assurance sampling to assess access to water, sanitation and hygiene services in a refugee camp setting in South Sudan: a feasibility study

BackgroundHumanitarian agencies working in refugee camp settings require rapid assessment methods to measure the needs of the populations they serve. Due to the high level of dependency of refugees, agencies need to carry out these assessments. Lot Quality Assurance Sampling (LQAS) is a method commonly used in development settings to assess populations living in a project catchment area to identify their greatest needs. LQAS could be well suited to serve the needs of refugee populations, but it has rarely been used in humanitarian settings. We adapted and implemented an LQAS survey design in Batil refugee camp, South Sudan in May 2013 to measure the added value of using it for sub-camp level assessment.MethodsUsing pre-existing divisions within the camp, we divided the Batil catchment area into six contiguous segments, called ‘supervision areas’ (SA). Six teams of two data collectors randomly selected 19 respondents in each SA, who they interviewed to collect information on water, sanitation, hygiene, and diarrhoea prevalence. These findings were aggregated into a stratified random sample of 114 respondents, and the results were analysed to produce a coverage estimate with 95% confidence interval for the camp and to prioritize SAs within the camp.ResultsThe survey provided coverage estimates on WASH indicators as well as evidence that areas of the camp closer to the main road, to clinics and to the market were better served than areas at the periphery of the camp. This assumption did not hold for all services, however, as sanitation services were uniformly high regardless of location. While it was necessary to adapt the standard LQAS protocol used in low-resource communities, the LQAS model proved to be feasible in a refugee camp setting, and program managers found the results useful at both the catchment area and SA level.ConclusionsThis study, one of the few adaptations of LQAS for a camp setting, shows that it is a feasible method for regular monitoring, with the added value of enabling camp managers to identify and advocate for the least served areas within the camp. Feedback on the results from stakeholders was overwhelmingly positive.

Use of Lot quality assurance sampling surveys to evaluate community health worker performance in rural Zambia: a case of Luangwa district

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.

Use of Lot quality assurance sampling surveys to evaluate community health worker performance in rural Zambia: a case of Luangwa district

Use of Lot quality assurance sampling surveys to evaluate community health worker performance in rural Zambia: a case of Luangwa district

Assessing delivery practices of mothers over time and over space in Uganda, 2003-2012.

BackgroundIt is well known that safe delivery in a health facility reduces the risks of maternal and infant mortality resulting from perinatal complications. What is less understood are the factors associated with safe delivery practices. We investigate factors influencing health facility delivery practices while adjusting for multiple other factors simultaneously, spatial heterogeneity, and trends over time.MethodsWe fitted a logistic regression model to Lot Quality Assurance Sampling (LQAS) data from Uganda in a framework that considered individual-level covariates, geographical features, and variations over five time points. We accounted for all two-covariate interactions and all three-covariate interactions for which two of the covariates already had a significant interaction, were able to quantify uncertainty in outputs using computationally intensive cluster bootstrap methods, and displayed outputs using a geographical information system. Finally, we investigated what information could be predicted about districts at future time-points, before the next LQAS survey is carried out. To do this, we applied the model to project a confidence interval for the district level coverage of health facility delivery at future time points, by using the lower and upper end values of known demographics to construct a confidence range for the prediction and define priority groups.ResultsWe show that ease of access, maternal age and education are strongly associated with delivery in a health facility; after accounting for this, there remains a significant trend towards greater uptake over time. We use this model together with known demographics to formulate a nascent early warning system that identifies candidate districts expected to have low prevalence of facility-based delivery in the immediate future.ConclusionsOur results support the hypothesis that increased development, particularly related to education and access to health facilities, will act to increase facility-based deliveries, a factor associated with reducing perinatal associated mortality. We provide a statistical method for using inexpensive and routinely collected monitoring and evaluation data to answer complex epidemiology and public health questions in a resource-poor setting. We produced a model based on this data that explained the spatial distribution of facility-based delivery in Uganda. Finally, we used this model to make a prediction about the future priority of districts that was validated by monitoring and evaluation data collected in the next year.Electronic supplementary materialThe online version of this article (doi:10.1186/s12982-016-0049-8) contains supplementary material, which is available to authorized users.

Empowering districts to target priorities for improving child health service in Uganda using change management and rapid assessment methods

BackgroundLocal health system managers in low- and middle-income countries have the responsibility to set health priorities and allocate resources accordingly. Although tools exist to aid this process, they are not widely applied for various reasons including non-availability, poor knowledge of the tools, and poor adaptability into the local context. In Uganda, delivery of basic services is devolved to the District Local Governments through the District Health Teams (DHTs). The Community and District Empowerment for Scale-up (CODES) project aims to provide a set of management tools that aid contextualised priority setting, fund allocation, and problem-solving in a systematic way to improve effective coverage and quality of child survival interventions.DesignAlthough the various tools have previously been used at the national level, the project aims to combine them in an integral way for implementation at the district level. These tools include Lot Quality Assurance Sampling (LQAS) surveys to generate local evidence, Bottleneck analysis and Causal analysis as analytical tools, Continuous Quality Improvement, and Community Dialogues based on Citizen Report Cards and U reports. The tools enable identification of gaps, prioritisation of possible solutions, and allocation of resources accordingly. This paper presents some of the tools used by the project in five districts in Uganda during the proof-of-concept phase of the project.ResultsAll five districts were trained and participated in LQAS surveys and readily adopted the tools for priority setting and resource allocation. All districts developed health operational work plans, which were based on the evidence and each of the districts implemented more than three of the priority activities which were included in their work plans.ConclusionsIn the five districts, the CODES project demonstrated that DHTs can adopt and integrate these tools in the planning process by systematically identifying gaps and setting priority interventions for child survival.

Putting the C Back into the ABCs: A Multi-Year, Multi-Region Investigation of Condom Use by Ugandan Youths 2003–2010

A major strategy for preventing transmission of HIV and other STIs is the consistent use of condoms during sexual intercourse. Condom use among youths is particularly important to reduce the number of new cases and the national prevalence. Condom use has been often promoted by the Uganda National AIDS Commission. Although a number of studies have established an association between condom use at one’s sexual debut and future condom use, few studies have explored this association over time, and whether the results are generalizable across multiple locations. This multi time point, multi district study assesses the relationship between sexual debut and condom use and consistent use of condoms thereafter. Uganda has used Lot Quality Assurance Sampling surveys since 2003 to monitor district level HIV programs and improve access to HIV health services. This study includes 4518 sexually active youths interviewed at five time points (2003–2010) in up to 23 districts located across Uganda. Using logistic regression, we measured the association of condom use at first sexual intercourse on recent condom usage, controlling for several factors including: age, sex, education, marital status, age at first intercourse, geographical location, and survey year. The odds of condom use at last intercourse, using a condom at last intercourse with a non-regular partner, and consistently using a condom are, respectively, 9.63 (95%WaldCI = 8.03–11.56), 3.48 (95%WaldCI = 2.27–5.33), and 11.12 (95%WaldCI = 8.95–13.81) times more likely for those individuals using condoms during their sexual debut. These values did not decrease by more than 20% when controlling for potential confounders. The results suggest that HIV prevention programs should encourage condom use among youth during sexual debut. Success with this outcome may have a lasting influence on preventing HIV and other STIs later in life.

Extending cluster lot quality assurance sampling designs for surveillance programs

Lot quality assurance sampling (LQAS) has a long history of applications in industrial quality control. LQAS is frequently used for rapid surveillance in global health settings, with areas classified as poor or acceptable performance on the basis of the binary classification of an indicator. Historically, LQAS surveys have relied on simple random samples from the population; however, implementing two-stage cluster designs for surveillance sampling is often more cost-effective than simple random sampling. By applying survey sampling results to the binary classification procedure, we develop a simple and flexible nonparametric procedure to incorporate clustering effects into the LQAS sample design to appropriately inflate the sample size, accommodating finite numbers of clusters in the population when relevant. We use this framework to then discuss principled selection of survey design parameters in longitudinal surveillance programs. We apply this framework to design surveys to detect rises in malnutrition prevalence in nutrition surveillance programs in Kenya and South Sudan, accounting for clustering within villages. By combining historical information with data from previous surveys, we design surveys to detect spikes in the childhood malnutrition rate.

New tools for evaluating LQAS survey designs

Lot Quality Assurance Sampling (LQAS) surveys have become increasingly popular in global health care applications. Incorporating Bayesian ideas into LQAS survey design, such as using reasonable prior beliefs about the distribution of an indicator, can improve the selection of design parameters and decision rules. In this paper, a joint frequentist and Bayesian framework is proposed for evaluating LQAS classification accuracy and informing survey design parameters. Simple software tools are provided for calculating the positive and negative predictive value of a design with respect to an underlying coverage distribution and the selected design parameters. These tools are illustrated using a data example from two consecutive LQAS surveys measuring Oral Rehydration Solution (ORS) preparation. Using the survey tools, the dependence of classification accuracy on benchmark selection and the width of the ‘grey region’ are clarified in the context of ORS preparation across seven supervision areas. Following the completion of an LQAS survey, estimation of the distribution of coverage across areas facilitates quantifying classification accuracy and can help guide intervention decisions.

Can health workers reliably assess their own work? A test–retest study of bias among data collectors conducting a Lot Quality Assurance Sampling survey in Uganda

Lot Quality Assurance Sampling (LQAS) is a classification method that enables local health staff to assess health programmes for which they are responsible. While LQAS has been favourably reviewed by the World Bank and World Health Organization (WHO), questions remain about whether using local health staff as data collectors can lead to biased data. In this test-retest research, Pallisa Health District in Uganda is subdivided into four administrative units called supervision areas (SA). Data collectors from each SA conducted an LQAS survey. A week later, the data collectors were swapped to a different SA, outside their area of responsibility, to repeat the LQAS survey with the same respondents. The two data sets were analysed for agreement using Cohens' kappa coefficient and disagreements were analysed. Kappa values ranged from 0.19 to 0.97. On average, there was a moderate degree of agreement for knowledge indicators and a substantial level for practice indicators. Respondents were found to be systematically more knowledgeable on retest indicating bias favouring the retest, although no evidence of bias was found for practices indicators. In this initial study, using local health care providers to collect data did not bias data collection. The bias observed in the knowledge indicators is most likely due to the 'practice effect', whereby respondents increased their knowledge as a result of completing the first survey, as no corresponding effect was seen in the practices indicators.

Single, Double and Multiple Sampling Plans: Hypergeometric Distribution

Systematic and standardized approach for monitoring and evaluating disease control programs is needed. The conventional sample survey methods are expensive, time consuming and need a great deal of resources. Lot Quality Assurance Sampling (LQAS) technique, originally a quality control tool in an industry, which was tried successfully for monitoring and evaluating immunization programs, leprosy, tuberculosis, sleeping sickness etc., control programs. Literature review suggests that all, except three, studies employing LQAS survey methodology used single sampling plan and normal approximation method to compute sample size. Hence, there is a need to develop formulae to obtain nomograms on sample sizes as ready reckoner when the variable of interest follows Hypergeometric distribution. The methods for sample size calculation using LQAS methodology under single/double, multiple sampling plans are provided separately for Hypegeometric distribution with/ without considering the Power are developed. Pragmatic solutions for the sample size and the corresponding acceptable number ‘d’ are obtained for diff erent sampling plans such as single, double and multiple sampling plans