Evaluation of the implementation of a community health worker-led COVID-19 contact tracing intervention in Chiapas, Mexico, from March 2020 to December 2021.

BACKGROUND: Mexico is one of the countries with the greatest excess death due to COVID-19. Chiapas, the poorest state in the country, has been particularly affected. Faced with an exacerbated shortage of health professionals, medical supplies, and infrastructure to respond to the pandemic, the non-governmental organization Compañeros En Salud (CES) implemented a COVID-19 infection prevention and control program to limit the impact of the pandemic in the region. We evaluated CES's implementation of a community health worker (CHW)-led contact tracing intervention in eight rural communities in Chiapas. METHODS: Our retrospective observational study used operational data collected during the contract tracing intervention from March 2020 to December 2021. We evaluated three outcomes: contact tracing coverage, defined as the proportion of named contacts that were located by CHWs, successful completion of contact tracing, and incidence of suspected COVID-19 among contacts. We described how these outcomes changed over time as the intervention evolved. In addition, we assessed associations between these three main outcomes and demographic characteristics of contacts and intervention period (pre vs. post March 2021) using univariate and multivariate logistic regression. RESULTS: From a roster of 2,177 named contacts, 1,187 (54.5%) received at least one home visit by a CHW and 560 (25.7%) had successful completion of contact tracing according to intervention guidelines. Of 560 contacts with complete contact tracing, 93 (16.6%) became suspected COVID-19 cases. We observed significant associations between sex and coverage (p = 0.006), sex and complete contact tracing (p = 0.049), community of residence and both coverage and complete contact tracing (p < 0.001), and intervention period and both coverage and complete contact tracing (p < 0.001). CONCLUSIONS: Our analysis highlights the promises and the challenges of implementing CHW-led COVID-19 contact tracing programs. To optimize implementation, we recommend using digital tools for data collection with a human-centered design, conducting regular data quality assessments, providing CHWs with sufficient technical knowledge of the data collection system, supervising CHWs to ensure contact tracing guidelines are followed, involving communities in the design and implementation of the intervention, and addressing community member needs and concerns surrounding stigmatization arising from lack of privacy.

Adapting Lot Quality Assurance Sampling to accommodate imperfect diagnostic 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 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.

Childhood immunization during the COVID-19 pandemic: experiences in Haiti, Lesotho, Liberia and Malawi.

OBJECTIVE: To examine changes in vaccination of children younger than 1 year during the coronavirus disease 2019 (COVID-19) pandemic (March 2020-August 2021) in Haiti, Lesotho, Liberia and Malawi. METHODS: We used data from health management information systems on vaccination of children aged 12 months or younger in districts supported by Partners In Health. We used data from January 2016 to February 2020 and a linear model with negative binomial distribution to estimate the expected immunization counts for March 2020-August 2021 with 95% prediction intervals, assuming no pandemic. We compared these expected levels with observed values and estimated the immunization deficits or excesses during the pandemic months. FINDINGS: Baseline vaccination counts varied substantially by country, with Lesotho having the lowest count and Haiti the highest. We observed declines in vaccination administration early in the COVID-19 pandemic in Haiti, Lesotho and Liberia. Continued declines largely corresponded to high rates of COVID-19 infection and discrete stock-outs. By August 2021, vaccination levels had returned to close to or above expected levels in Haiti, Liberia and Lesotho; in Malawi levels remained below expected. CONCLUSION: Patterns of childhood immunization coverage varied by country over the course of the pandemic, with significantly lower than expected vaccination levels seen in one country during subsequent COVID-19 waves. Governments and health-care stakeholders should monitor vaccine coverage closely and consider interventions, such as community outreach, to avoid or combat the disruptions in childhood vaccination.

Disruptions in maternal health service use during the COVID-19 pandemic in 2020: experiences from 37 health facilities in low-income and middle-income countries.

The COVID-19 pandemic has heterogeneously affected use of basic health services worldwide, with disruptions in some countries beginning in the early stages of the emergency in March 2020. These disruptions have occurred on both the supply and demand sides of healthcare, and have often been related to resource shortages to provide care and lower patient turnout associated with mobility restrictions and fear of contracting COVID-19 at facilities. In this paper, we assess the impact of the COVID-19 pandemic on the use of maternal health services using a time series modelling approach developed to monitor health service use during the pandemic using routinely collected health information systems data. We focus on data from 37 non-governmental organisation-supported health facilities in Haiti, Lesotho, Liberia, Malawi, Mexico and Sierra Leone. Overall, our analyses indicate significant declines in first antenatal care visits in Haiti (18% drop) and Sierra Leone (32% drop) and facility-based deliveries in all countries except Malawi from March to December 2020. Different strategies were adopted to maintain continuity of maternal health services, including communication campaigns, continuity of community health worker services, human resource capacity building to ensure compliance with international and national guidelines for front-line health workers, adapting spaces for safe distancing and ensuring the availability of personal protective equipment. We employ a local lens, providing prepandemic context and reporting results and strategies by country, to highlight the importance of developing context-specific interventions to design effective mitigation strategies.

Auto-G-Computation of Causal Effects on a Network.

Methods for inferring average causal effects have traditionally relied on two key assumptions: (i) the intervention received by one unit cannot causally influence the outcome of another; and (ii) units can be organized into nonoverlapping groups such that outcomes of units in separate groups are independent. In this article, we develop new statistical methods for causal inference based on a single realization of a network of connected units for which neither assumption (i) nor (ii) holds. The proposed approach allows both for arbitrary forms of interference, whereby the outcome of a unit may depend on interventions received by other units with whom a network path through connected units exists; and long range dependence, whereby outcomes for any two units likewise connected by a path in the network may be dependent. Under network versions of consistency and no unobserved confounding, inference is made tractable by an assumption that the networks outcome, treatment and covariate vectors are a single realization of a certain chain graph model. This assumption allows inferences about various network causal effects via the auto-g-computation algorithm, a network generalization of Robins' well-known g-computation algorithm previously described for causal inference under assumptions (i) and (ii). Supplementary materials for this article are available online.

Pain and Quality of Life after Laparoscopic Excision of Endometriosis.

STUDY OBJECTIVE: To evaluate the long-term impact of laparoscopic excision of endometriosis on quality of life through pain reduction as measured by the Endometriosis Health Profile-30 (EHP-30) in uterine-sparing (preservation of the uterus and at least 1 ovary) and nonuterine-sparing (removal of the uterus) surgery. DESIGN: Cohort study. SETTING: Academic medical center. PATIENTS: Sixty-one women who had undergone laparoscopic excision of endometriosis for pelvic pain were enrolled in a tissue-procurement study. INTERVENTIONS: Patients who had previously completed an EHP-30 preoperatively and at 4 weeks postoperatively were mailed a copy of the EHP-30 2.6 to 6.8 years after their index surgery. MEASUREMENTS AND MAIN RESULTS: The primary outcome was quality of life as measured by changes in the EHP-30 scores before their index surgery and those measured weeks and years later. The secondary outcome was a comparison of the EHP-30 scores between patients who underwent excision of endometriosis alone and those who underwent excision of endometriosis with hysterectomy +/- oophorectomy. From 2011 to 2015, 61 women underwent laparoscopic excision of endometriosis for pelvic pain. Forty-six of the 61 patients completed the EHP-30 for a response rate of 75%. The patients demonstrated significant improvement in all 5 scales of the EHP-30 (pain, control and powerlessness, emotional well-being, social support, and self-image) at 4 weeks postoperatively (p <.001), which persisted for up to 6.8 years in follow-up (p <.001) when compared with their baseline scores. The improvement in EHP-30 scores did not differ by American Society for Reproductive Medicine staging or index surgery. Definitive surgery (total laparoscopic hysterectomy/bilateral salpingo-oophorectomy) was not associated with improved outcomes when compared with uterine-sparing surgery. CONCLUSION: Laparoscopic excision of endometriosis offers improvement in all quality-of-life domains as measured by the EHP-30, including a reduction in pain, an effect that may persist for up to 6.8 years. These findings suggest that laparoscopic excision of endometriosis with uterine preservation can be considered as an option for discussion during counseling for treatment of endometriosis.