Dataset Information

Adapting Lot Quality Assurance Sampling to accommodate imperfect diagnostic tests: application to COVID-19 serosurveillance in Haiti.

ABSTRACT:

Background

Lot Quality Assurance Sampling (LQAS), a tool used for monitoring health indicators in low resource settings resulting in "high" or "low" classifications, assumes that determination of the trait of interest is perfect. This is often not true for diagnostic tests, with imperfect sensitivity and specificity. Here, we develop Lot Quality Assurance Sampling for Imperfect Tests (LQAS-IMP) to address this issue and apply it to a COVID-19 serosurveillance study design in Haiti.

Methods

We first derive a modified procedure, LQAS-IMP, that accounts for the sensitivity and specificity of a diagnostic test to yield correct classification errors. We then apply the novel LQAS-IMP to design an LQAS system to classify prevalence of SARS-CoV-2 antibodies among healthcare workers at eleven Zanmia Lasante health facilities in Haiti. Finally, we show the performance of the LQAS-IMP procedure in a simulation study.

Results

We found that when an imperfect diagnostic test is used, the classification errors in the standard LQAS procedure are larger than specified. In the modified LQAS-IMP procedure, classification errors are consistent with the specified maximum classification error. We then utilized the LQAS-IMP procedure to define valid systems for sampling at eleven hospitals in Haiti.

Conclusion

The LQAS-IMP procedure accounts for imperfect sensitivity and specificity in system design; if the accuracy of a test is known, the use of LQAS-IMP extends LQAS to applications for indicators that are based on laboratory tests, such as SARS-CoV-2 antibodies.

SUBMITTER: Fulcher IR

PROVIDER: S-EPMC9707425 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Publications

Adapting Lot Quality Assurance Sampling to accommodate imperfect diagnostic tests: application to COVID-19 serosurveillance in Haiti.

Fulcher Isabel R IR Clisbee Mary M Lambert Wesler W Leandre Fernet Renand FR Hedt-Gauthier Bethany B

BMC public health 20221129 1

<h4>Background</h4>Lot Quality Assurance Sampling (LQAS), a tool used for monitoring health indicators in low resource settings resulting in "high" or "low" classifications, assumes that determination of the trait of interest is perfect. This is often not true for diagnostic tests, with imperfect sensitivity and specificity. Here, we develop Lot Quality Assurance Sampling for Imperfect Tests (LQAS-IMP) to address this issue and apply it to a COVID-19 serosurveillance study design in Haiti.<h4>Me ...[more]

PMID: 36447195

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Project description:BackgroundMonitoring and evaluation (M&E) is a key component of large-scale neglected tropical diseases (NTD) control programs. Diagnostic tests deployed in these M&E surveys are often imperfect, and it remains unclear how this affects the population-based program decision-making.MethodologyWe developed a 2-stage lot quality assurance sampling (LQAS) framework for decision-making that allows for both imperfect diagnostics and spatial heterogeneity of infections. We applied the framework to M&E of soil-transmitted helminth control programs as a case study. For this, we explored the impact of the diagnostic performance (sensitivity and specificity), spatial heterogeneity (intra-cluster correlation), and survey design on program decision-making around the prevalence decisions thresholds recommended by WHO (2%, 10%, 20% and 50%) and the associated total survey costs.Principal findingsThe survey design currently recommended by WHO (5 clusters and 50 subjects per cluster) may lead to incorrect program decisions around the 2% and 10% prevalence thresholds, even when perfect diagnostic tests are deployed. To reduce the risk of incorrect decisions around the 2% prevalence threshold, including more clusters (≥10) and deploying highly specific diagnostic methods (≥98%) are the most-cost saving strategies when spatial heterogeneity is moderate-to-high (intra-cluster correlation >0.017). The higher cost and lower throughput of improved diagnostic tests are compensated by lower required sample sizes, though only when the cost per test is <6.50 US$ and sample throughput is ≥3 per hour.Conclusion/significanceOur framework provides a means to assess and update M&E guidelines and guide product development choices for NTD. Using soil-transmitted helminths as a case study, we show that current M&E guidelines may severely fall short, particularly in low-endemic and post-control settings. Furthermore, specificity rather than sensitivity is a critical parameter to consider. When the geographical distribution of an NTD within a district is highly heterogeneous, sampling more clusters (≥10) may be required.

Project description:BackgroundWorldwide, vaccine-preventable diseases have been a significant cause of mortality in the under-5 age group. To reduce the disease burden, new vaccines are being introduced in every country's immunization programmes. For this to happen, high vaccination coverage is necessary. However, rapidly identifying the areas that fail to reach the expected coverage becomes cumbersome. During recent years, lot quality assurance sampling (LQAS) has been widely used in evaluating immunization coverage across the globe. The present study aims to pilot this approach for field monitoring of a new vaccine against routine concurrent field monitoring in one of the North-Eastern states of India.MethodologyFor LQAS, a community-based cross-sectional study was undertaken among 55 children aged 0-23 months in all 5 Primary health centres (lots) of Medziphema block, Dimapur, Nagaland. The total sample size for LQAS was calculated based on α = 5, β = 90 using Lemeshow and Taber-LQAS table with a target level of immunization defined as 90 % and the lower limit set to 55 %. For the concurrent field monitoring, a sample of 30 children in the same age group was selected through random sampling. Pre-designed, pre-tested questionnaire for the caregivers, scripted on a digital tool was employed with verification of immunization card and caregiver's recall. Data was analyzed using SPSS software version 25.0.ResultsThe study found a slight difference in the percentage of children age-appropriately vaccinated for PCV (as per the schedule) in concurrent field monitoring (93.3 %) and LQAS (90.9 %). However, no statistically significant difference was found in comparing the immunization coverage using both methodologies (p > 0.05).ConclusionThe study findings encourage that LQAS can be considered for monitoring the immunization coverage of a newly introduced vaccine. It offers the added advantage of identifying poor/low-performing pockets that require focused attention.

Project description:BackgroundCardiovascular 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).MethodsLQAS 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.ResultsA 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.ConclusionCVD 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.

Dataset Information

Adapting Lot Quality Assurance Sampling to accommodate imperfect diagnostic tests: application to COVID-19 serosurveillance in Haiti.

Background

Methods

Results

Conclusion

Publications

Adapting Lot Quality Assurance Sampling to accommodate imperfect diagnostic tests: application to COVID-19 serosurveillance in Haiti.

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