Inclusion of pregnant women in COVID-19 treatment trials: a review and global call to action.

Inclusion of pregnant women in COVID-19 clinical trials would allow evaluation of effective therapies that might improve maternal health, pregnancy, and birth outcomes, and avoid the delay of developing treatment recommendations for pregnant women. We explored the inclusion of pregnant women in treatment trials of COVID-19 by reviewing ten international clinical trial registries at two timepoints in 2020. We identified 155 COVID-19 treatment studies of non-biological drugs for the April 7-10, 2020 timepoint, of which 124 (80%) specifically excluded pregnant women. The same registry search for the July 10-15, 2020 timepoint, yielded 722 treatment studies, of which 538 (75%) specifically excluded pregnant women. We then focused on studies that included at least one of six drugs (remdesivir, lopinavir-ritonavir, interferon beta, corticosteroids, chloroquine and hydroxychloroquine, and ivermectin) under evaluation for COVID-19. Of 176 such studies, 130 (74%) listed pregnancy as an exclusion criterion. Of 35 studies that evaluated high-dose vitamin treatment for COVID-19, 27 (77%) excluded pregnant women. Despite the surge in treatment studies for COVID-19, the proportion excluding pregnant women remains consistent. Exclusion was not well justified as many of the treatments being evaluated have no or low safety concerns during pregnancy. Inclusion of pregnant women in clinical treatment trials is urgently needed to identify effective COVID-19 treatment for this population.

Retention-in-care in the PMTCT cascade: definitions matter! Analyses from the INSPIRE projects in Malawi, Nigeria and Zimbabwe.

INTRODUCTION: Definitions of retention-in-care in Prevention of Mother-to-Child Transmission of HIV (PMTCT) vary substantially between studies and programmes. Some definitions are based on visits missed/made, others on a minimum total number of visits, or attendance at a final clinic visit at a specific time. An agreed definition could contribute to developing evidence-based interventions for improving retention-in-care. In this paper, we estimated retention-in-care rates according to different definitions, and we quantified and visualized the degree of agreement between definitions. METHODS: We calculated retention in care rates using nine definitions in the six INSPIRE PMTCT intervention studies, conducted in three sub-Saharan African countries between 2013 and 2017. With data from one of the studies (E4E), we estimated the agreement between definitions using Gwet's agreement coefficient (AC1) and concordance. We calculated positive predictive values (PPV) and negative predictive values (NPV) for all definitions considering successively each definition as the reference standard. Finally, we used a Multiple Correspondence Analysis (MCA) to examine clustering of the way different definitions handle retention-in-care. RESULTS: Retention-in-care rates among 5107 women ranged from 30% to 76% in the complete dataset with Gwet's AC1 being 0.56 [0.53; 0.59] indicating a moderate agreement between all definitions together. Two pairs of definitions with high inner concordance and agreement had either very high PPV or very high NPV, and appeared distinct from the other five definitions on the MCA figures. These pairs of definitions were also the ones resulting in the lowest and highest estimates of retention-in-care. The simplest definition, that only required a final clinic visit to classify women as retained in care, and classified 55% of women as retained in care, had a PPV ranging from 0.7 to 1 and a NPV ranging from 0.69 to 0.98 when excluding the two pairs afore-mentioned; it resulted in a moderate to substantial agreement and a 70% to 90% concordance with all other definitions. CONCLUSIONS: Our study highlights the variability of definitions in estimating retention-in-care. Some definitions are very stringent which may be required in some instances. A simple indicator such as attendance at a single time point may be sufficient for programme planning and evaluation.

Building Health System Capacity Through Implementation Research: Experience of INSPIRE-A Multi-country PMTCT Implementation Research Project.

BACKGROUND: The INSPIRE-Integrating and Scaling Up PMTCT through Implementation REsearch-initiative was established as a model partnership of national prevention of mother-to-child transmission of HIV (PMTCT) implementation research in 3 high HIV burden countries-Malawi, Nigeria, and Zimbabwe. INSPIRE aimed to link local research groups with Ministries of Health (MOH), build local research capacity, and demonstrate that implementation research may contribute to improving health care delivery and respond to program challenges. METHODOLOGY: We used a mixed methods approach to review capacity building activities, as experienced by health care workers, researchers, and trainers conducted in the 6 INSPIRE projects before and during study implementation. RESULTS: Between 2011 and 2016, over 3400 health care workers, research team members, and community members participated in INSPIRE activities. This included research prioritization exercises, proposal development, good clinical practice and research ethics training, data management and analysis workshops, and manuscript development. Health care workers in clinics and district health offices acknowledged the value of hosting implementation research projects and how the quality of services improved. Research teams acknowledged the opportunities that projects provided for personal development and the value of participating in a multicountry research network. DISCUSSION: INSPIRE provided an opportunity for African-led research in which researchers worked closely with national MOH to identify priority research questions and implement studies. Close partnerships between research teams and local implementers facilitated project responsiveness to local program issues. Consequently, processes and training needed for study implementation also improved local program management and service delivery. Additional benefits included improved data management, publications, and career development.