Modeling poliovirus surveillance and immunization campaign quality monitoring costs for Pakistan and Afghanistan for 2019-2023

Background 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. Methods 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. Results 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. Conclusions 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.

Mixed Methods Lot Quality Assurance Sampling: A novel, rapid methodology to inform equity focused maternal health programming in rural Rajasthan, India

India has experienced a significant increase in facility-based delivery (FBD) coverage and reduction in maternal mortality. Nevertheless, India continues to have high levels of maternal health inequity. Improving equity requires data collection methods that can produce a better contextual understanding of how vulnerable populations access and interact with the health care system at a local level. While large population-level surveys are valuable, they are resource intensive and often lack the contextual specificity and timeliness to be useful for local health programming. Qualitative methods can be resource intensive and may lack generalizability. We describe an innovative mixed-methods application of Large Country-Lot Quality Assurance Sampling (LC-LQAS) that provides local coverage data and qualitative insights for both FBD and antenatal care (ANC) in a low-cost and timely manner that is useful for health care providers working in specific contexts. LC-LQAS is a version of LQAS that combines LQAS for local level classification with multistage cluster sampling to obtain precise regional or national coverage estimates. We integrated qualitative questions to uncover mothers’ experiences accessing maternal health care in the rural district of Sri Ganganagar, Rajasthan, India. We interviewed 313 recently delivered, low-income women in 18 subdistricts. All respondents participated in both qualitative and quantitative components. All subdistricts were classified as having high FBD coverage with the upper threshold set at 85%, suggesting that improved coverage has extended to vulnerable women. However, only two subdistricts were classified as high ANC coverage with the upper threshold set at 40%. Qualitative data revealed a severe lack of agency among respondents and that household norms of care seeking influenced uptake of ANC and FBD. We additionally report on implementation outcomes (acceptability, feasibility, appropriateness, effectiveness, fidelity, and cost) and how study results informed the programs of a local health non-profit.

Carencias y variabilidad en la calidad de la atención a neonatos hospitalizados en México. Estudio transversal en 28 hospitales públicos

Objetivo. Evaluar la calidad de la atención a neonatos con indicadores de proceso, en patologías seleccionadas. Ma­terial y métodos. Evaluación multicéntrica, transversal de nueve indicadores en 28 hospitales de 11 entidades de México. Se utilizó Lot Quality Assurance Sampling (LQAS) para estándares de calidad y muestra por hospital. Casos seleccio­nados al azar del Subsistema Automatizado de Egresos Hos­pitalarios. Se clasifican hospitales como “cumplimiento con estándar”/“no cumplimiento” por indicador y, cumplimiento con IC95% exacto binomial, regional y nacional, según mues­treo estratificado no proporcional. Resultados. Ningún indicador cumple el estándar de 75% en hospitales, con 0 a 19 hospitales que cumplen, según indicador. Excepto la iden­tificación oportuna de asfixia perinatal e inicio de antibiótico correcto en sospecha de sepsis temprana, el cumplimiento es <50% en todos los demás indicadores. Conclusiones. La calidad de la atención a neonatos en hospitales es heterogé­nea y deficiente. Se proponen indicadores para monitorizar iniciativas de mejora.

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

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 in Haiti.Results: As part of the standard LQAS procedure, the user specifies allowable classification errors for the system, which is defined by a sample size and decision rule. We show that when an imperfect diagnostic test is used, the classification errors are larger than specified. We derive a modified procedure, LQAS-IMP, that accounts for the sensitivity and specificity of a diagnostic test to yield correct classification errors. We apply our methods to create a sampling scheme at Zanmi Lasante health facilities in Haiti to assess the prior circulation of COVID-19 among healthcare workers (HCWs) using a limited number of antibody tests. Conclusions: 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 COVID-19 antibodies.

Assessing cardiovascular disease risk factor screening inequalities in India using Lot Quality Assurance Sampling

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.

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

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 in Haiti. Development: As part of the standard LQAS procedure, the user specifies allowable classification errors for the system, which is defined by a sample size and decision rule. We show that when an imperfect diagnostic test is used, the classification errors are larger than specified. We derive a modified procedure, LQAS-IMP, that accounts for the sensitivity and specificity of a diagnostic test to yield correct classification errors. Application: At Zanmi Lasante health facilities in Haiti, the goal was to assess the prior circulation of COVID-19 among healthcare workers (HCWs) using a limited number of antibody tests. As the COVID-19 antibody tests were known to have imperfect diagnostic accuracy, we used the LQAS-IMP procedure to define valid systems for sampling at eleven hospitals in Haiti. Conclusions: 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 COVID-19 antibodies.

Implementation of Lot Quality Assurance Sampling Using Mobile Data Collection Tools to Assess Vaccination Coverage After Synchronized Polio Vaccination in the Province of Camiguin, Philippines

ABSTRACTObjectivesThe study assessed immunization coverage after one round of synchronized Polio vaccination in Camiguin, Philippines. It included classifying the coverage level, identifying the level of awareness and source of information for the campaign, describing the reasons for non-vaccination, and pilot a mobile data collection platform.MethodsLot Quality Assurance Sampling (LQAS) is a household survey using a multi-stage clustered technique. Sixty respondents per municipality, divided into 6 clusters, with one barangay considered as one cluster. Barangays were selected with probability proportional to size. Households were taken by systematic sampling. One child was randomly selected if there were multiple children in a household. Data collection was done using KoBo Toolbox.ResultsThe municipalities of Mahinog and Sagay had two unvaccinated children each. Guinsiliban and Mambajao only had one unvaccinated child each. Catarman did not have any unvaccinated child. The reasons for non-vaccination were the lack of means of validation, fear of side effects, and the absence of a caregiver at the time of vaccination. The most common sources of information were health workers and television.DiscussionCoverage for all five municipalities of the province passed the decision value. The main reason for non-vaccination was the lack of means of validation, which emphasizes the need for high quality finger marking and the provision of vaccination cards. Only Mahinog did not pass the threshold for campaign awareness. LQAS is useful for validating areas with concerns on the set target population and administrative coverage. Mobile data collection through KoBo ToolBox is a useful method for field use. It is easily adaptable, user-friendly, and allows for immediate data validation and analysis.