Skip to content. Skip to navigation. Lot quality assurance sampling LQAS assesses the quality of a product and can be used to see how health indicators are performing.
According to the World Health Organization , LQAS was initially used to determine whether a batch, or lot, of a product met the desired specifications. Manufacturers took a sample of the product and defined how much risk they were willing to take for not inspecting each item.
Supervision areas are assessed for performance of key health indicators; if the number of successes in an SA is greater than the pre-determined decision rule, then the performance of the relevant health indicator is classified as acceptable. In the second stage, information from several supervision areas are combined and weighted, and an estimate for the entire program area is made for each health indicator.
In certain sampling designs, the estimates for the program area are comparable in accuracy to estimates made from typical cluster sample surveys that are often carried out by NGOs in low- and middle-income countries [ 18 ]. LQAS provides additional value over cluster sampling by providing information about the performance of health indicators within the program area by supervision area , while cluster sampling provides estimates at the overall program catchment area level only.
LQAS therefore allows program managers to identify variation in performance among supervision areas and to prioritize problem-solving in SAs with inadequate performance, rather than assuming performance is the same across the program area. In addition, the survey work for LQAS can be organized at the local level of each SA, and can be carried out concurrent with program activities using the same logistics transport, personnel, etc.
As a result, programming does not need to stop for a period to allow for data collection activities, and survey costs can be shared with the routine costs of program activities. LQAS has been used to assess many development programs globally [ 15 ] such as malaria [ 19 ], malnutrition [ 18 ], and immunization [ 20 ]. There are few studies, however, that discuss the feasibility of using LQAS for measuring health indicators and outcomes in conflict-affected settings.
Deitchler, et al. In another paper, Valadez and colleagues describe the use of LQAS in South Sudan to assess maternal and newborn health indicators [ 22 ]. The many advantages of LQAS made it preferable to the cluster survey method for Medair in West Darfur, where there are multiple challenges including the distance between the communities served by Medair and the tenuous security situation.
Medair managers and supervisors had limited access to the field, and felt that the ability to conduct LQAS alongside usual programmatic work was a particular advantage of LQAS over cluster surveys. The objective of this paper is to describe the process of using the LQAS method in a conflict-affected region as a process to evaluate the effectiveness of primary health programs, and to guide the management of these programs.
This is a secondary data analysis of information collected during routing program management, monitoring, and evaluation of the Medair program. The monitoring and evaluation of primary health programs was carried out from May through December in West Darfur, Sudan. Four separate LQAS assessments were conducted, allowing measurement of key indicators approximately every six months. Medair and a group from Johns Hopkins had agreed to collaborate during these four assessments with the ultimate goal of training Medair staff such that Medair could independently continue assessments after this period without additional technical assistance.
As an initial step of random sampling, the Medair service area was divided into seven supervisory areas. A sampling plan requires estimates of the population in the geographic area of interest, however true population estimates in Darfur are difficult to obtain [ 23 ].
Even without conflict, migration is common among the Darfur population; conflict added further instability to settlements and communities, making the determination of true population numbers a challenge [ 23 ]. Where estimated population information was not available or incomplete, we asked other NGOs for their population estimates of the communities.
We then calculated the cumulative population of the serviced community Table 1. For each round of LQAS, changes to the population listing were made as needed, and a new set of 19 households were selected in each SA. Specifically, we adapted questions that provided information on the following indicators: immunization coverage for diphtheria, pertussis, and tetanus indicator 3. In , the World Health Organization no longer recommended vitamin A supplementation in postpartum women [ 26 ].
However, Medair continued this practice. Finally, the number of women who had received a clean delivery kit prior to their last delivery was also measured because Medair managers felt it was important to assess this indicator, even though it is not a part of the MICS3 or MICS4. The use of questions from MICS4 thus enabled the comparison of key indicators between areas where Medair provided primary health services to areas where Medair did not work.
The adapted questionnaire was translated from English to Sudanese Arabic, the language of study participants. The questionnaire was then back-translated by local translators; back-translations were reviewed by the research team and the local Medair team working in West Darfur.
Additional edits were made through an iterative process to ensure that the translation captured the essence of each question. Pilot testing was done with convenience samples in two communities of IDPs. Interviewers made notes of the capacity of the target population to understand and respond to the questionnaire.
The results of the pilot testing were reviewed jointly by Johns Hopkins investigators and local Medair team members, and final edits were made based on the feedback from interviewers.
The majority of communities could only be accessed by helicopter, and the time in those communities was often restricted to two to three hours. In the first LQAS round, the Medair staff developed a detailed logistic plan that included delineating which staff members were going to specific communities with exact times for flight manifests.
Medair staff worked on the survey separate from their usual work during the first round of data collection so that they could concentrate on learning data collection and management methods without the distraction of their usual work duties. During subsequent LQAS rounds, Medair staff working in each locality conducted interviews with participants under the supervision of research coordinators.
To leverage the advantages of the LQAS methodology, survey work in these later rounds was done alongside the routine tasks of Medair staff on days when the staff visited assigned program communities that overlapped with the LQAS sample selection.
The completed questionnaires were transported by helicopter or car to the main Medair office in El Geneina, the capital of West Darfur, for data entry and analysis. Once staff arrived in a community, they conferred with a community leader or other key informant to develop a map of the community.
Our team also informed community leaders about our planned activities and the purpose of the interviews, and we asked for any input community leaders had as a means of engaging the population. From these discussions, the community was divided into subsections, from which a subsection was randomly selected using a random number table.
If the subsection had more than approximately 30 households, it was further divided and the process of random selection was repeated until an area was chosen with approximately 30 households to facilitate the process of randomly selecting a household [ 17 ]. The households were then numbered, and one was randomly chosen using a random number table.
For large communities where more than one sampling unit was located, the process of selecting a household was repeated. A parallel sampling method was used to administer the questionnaire [ 29 ].
In each randomly selected household, women between the ages of 15 and 49 who had a live birth in the 2 years prior to the interview were enrolled.
If a household did not have a woman who met the inclusion criteria, surveyors went to the next nearest household as measured from the front door of one dwelling to the front door of the next. Questions in the survey were grouped depending on the age group of children to which they referred.
For example, immunization questions required children that were at least 1 year of age while questions on MNCH indicators referred to children younger than 1 year. Thus, staff asked appropriate question groups depending on the age of children in a household. If a qualifying household did not have children from the age group required for a group of questions, the next closest households were screened until children from each age group were included in the survey.
As a result, more than 19 participant households were required in each SA to complete the different set of questions. Verbal consent was obtained from all participants from a standardized consent script that was read to the participants by the surveyors.
Medair managers selected staff members to be trained in LQAS survey techniques, which took place over a three-day period of class work and one day of hands-on practice in a nearby community. Staff who showed a particular aptitude for the survey techniques were designated as survey supervisors. During the survey, supervisors oversaw the household selection process and reviewed completed paper questionnaires to assure completeness prior to leaving the community.
Medair staff entered responses from the paper questionnaire into a database that was created in Epi Info version 3. The classification was done by comparing the number of positive responses to a pre-determined decision rule value that had been calculated using LQAS sampling rules [ 17 ].
If the number of positive responses did not exceed the decision rule value, the performance of the health indicator in an SA was classified as inadequate; otherwise, the performance of the indicator was classified as adequate. Next, the responses from all SAs in the program area were pooled. Doing so allowed the project to classify SAs as either adequate or inadequate for each indicator, as determined by baseline figures. We weighted the data by the population size of each SA and adjusted the standard errors for clustering at the SA level.
The average ages of participants in the four rounds were similar, with a range in mean age of The most common level of education was no schooling, with approximately two-thirds of women reporting no formal education. The second most common level of education was a primary education, which Almost all of the women were married a range of Several indicators for maternal, newborn, and child health showed improvement from the first to last LQAS rounds when calculated at the program level Tables 3 and 4.
Tetanus toxoid vaccination coverage for neonates increased throughout the assessment period from The only time an SA was classified as inadequate for tetanus toxoid coverage was SA7 during the first round. The coverage of births attended by a skilled health professional also improved significantly from LQAS is a statistical methodology used for data collection in health and other programs by several organizations, and it originated in manufacturing and factory production.
While LQAS is perceived as fairly quick to carry out, there are ramifications that come with implementing such a quick program. If using complex surveys, for example, this can increase the amount of time it takes to train interviewers, the amount of time interviewers will spend in the field collecting data, and also the time needed to analyze data.
Some of the health outcomes analyzed in both programs include those related to water, sanitation and hygiene; breastfeeding, nutrition and food security; and vaccination and immunization. Information gathered on these health outcomes can then be used to prioritize project areas. We recommend upgrading your browser with one of the following to properly view our website:. Please note that this is not an exhaustive list of browsers.
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