Comprehensive profiling of social mixing patterns in resource poor countries: a mixed methods research protocol

Background

Low-and-middle-income countries (LMICs) bear a disproportionate burden of communicable diseases. Social interaction data inform infectious disease models and disease prevention strategies. The variations in demographics and contact patterns across ages, cultures, and locations significantly impact infectious disease dynamics and pathogen transmission. LMICs lack sufficient social interaction data for infectious disease modeling.

Methods

To address this gap, we will collect qualitative and quantitative data from eight study sites (encompassing both rural and urban settings) across Guatemala, India, Pakistan, and Mozambique. We will conduct focus group discussions and cognitive interviews to assess the feasibility and acceptability of our data collection tools at each site. Thematic and rapid analyses will help to identify key themes and categories through coding, guiding the design of quantitative data collection tools (enrollment survey, contact diaries, exit survey, and wearable proximity sensors) and the implementation of study procedures.We will create three age-specific contact matrices (physical, nonphysical, and both) at each study site using data from standardized contact diaries to characterize the patterns of social mixing. Regression analysis will be conducted to identify key drivers of contacts. We will comprehensively profile the frequency, duration, and intensity of infants' interactions with household members using high resolution data from the proximity sensors and calculating infants' proximity score (fraction of time spent by each household member in proximity with the infant, over the total infant contact time) for each household member.

Discussion

Our qualitative data yielded insights into the perceptions and acceptability of contact diaries and wearable proximity sensors for collecting social mixing data in LMICs. The quantitative data will allow a more accurate representation of human interactions that lead to the transmission of pathogens through close contact in LMICs. Our findings will provide more appropriate social mixing data for parameterizing mathematical models of LMIC populations. Our study tools could be adapted for other studies.

The development and implementation of a community engagement strategy to improve maternal health in southern Mozambique

Delays to seek medical help can contribute to maternal deaths particularly in community settings at home or on the road to a health facility. Community engagement (CE) can improve care-seeking behaviours and complements community-based interventions strengthening maternal health. The purpose of this paper is to describe the process undertaken to develop and implement a large-scale community engagement strategy in rural southern Mozambique. The CE strategy was developed within the context of the "Community-Level Interventions for Pre-eclampsia" (NCT01911494) conducted between 2015-2017 in southern Mozambique. Key CE messages included pregnancy complications and their warning signs, including pre-eclampsia and eclampsia, as well as emergency readiness, birth preparedness, decision-making mechanisms, transport options and information about the trial. CE meeting logs were used to record quantitative and qualitative information on demographic data and feedback. Quantitative data was analyzed using RStudio (RStudio Inc, Boston, United States) and community feedback was qualitatively analyzed on NVivo12 (QSR International, Melbourne, Australia). CE activities reached 19,169 participants during 4,239 meetings. CE activities were reported to be well received by community members though there was a relatively lower participation of men (3565 /18.6%). The use of recognized local leaders and personnel, such as community leaders, nurses and community health workers, allowed for greater acceptance of CE activities and maximized coverage of health messages in the community setting. Our CE strategy was effective in integrating maternal health promoting activities in routine care of community health workers and nurses in the area. Understanding district differences, engaging husbands, partners, mothers-in-law and community-level decision-makers to build local support for maternal health and flexibility to tailor messages to local needs were important in developing sustainable forms of CE. Better strategies are needed to effectively engage men in maternal health promotion who were less available due to working outside of the home or neighbourhoods.

Community-level interventions for pre-eclampsia (CLIP) in Mozambique: A cluster randomised controlled trial

OBJECTIVES

Pregnancy hypertension is the third leading cause of maternal mortality in Mozambique and contributes significantly to fetal and neonatal mortality. The objective of this trial was to assess whether task-sharing care might reduce adverse pregnancy outcomes related to delays in triage, transport, and treatment.

STUDY DESIGN

The Mozambique Community-Level Interventions for Pre-eclampsia (CLIP) cluster randomised controlled trial (NCT01911494) recruited pregnant women in 12 administrative posts (clusters) in Maputo and Gaza Provinces. The CLIP intervention (6 clusters) consisted of community engagement, community health worker-provided mobile health-guided clinical assessment, initial treatment, and referral to facility either urgently (<4hrs) or non-urgently (<24hrs), dependent on algorithm-defined risk. Treatment effect was estimated by multi-level logistic regression modelling, adjusted for prognostically-significant baseline variables. Predefined secondary analyses included safety and evaluation of the intensity of CLIP contacts.

MAIN OUTCOME MEASURES

20% reduction in composite of maternal, fetal, and newborn mortality and major morbidity.

RESULTS

15,013 women (15,123 pregnancies) were recruited in intervention (N = 7930; 2·0% loss to follow-up (LTFU)) and control (N = 7190; 2·8% LTFU) clusters. The primary outcome did not differ between intervention and control clusters (adjusted odds ratio (aOR) 1·31, 95% confidence interval (CI) [0·70, 2·48]; p = 0·40). Compared with intervention arm women without CLIP contacts, those with ≥8 contacts experienced fewer primary outcomes (aOR 0·79 (95% CI 0·63, 0·99); p = 0·041), primarily due to improved maternal outcomes (aOR 0·72 (95% CI 0·53, 0·97); p = 0·033).

INTERPRETATION

As generally implemented, the CLIP intervention did not improve pregnancy outcomes; community implementation of the WHO eight contact model may be beneficial.

FUNDING

The University of British Columbia (PRE-EMPT), a grantee of the Bill & Melinda Gates Foundation (OPP1017337).

The PRECISE (PREgnancy Care Integrating translational Science, Everywhere) database: open-access data collection in maternal and newborn health

In less-resourced settings, adverse pregnancy outcome rates are unacceptably high. To effect improvement, we need accurate epidemiological data about rates of death and morbidity, as well as social determinants of health and processes of care, and from each country (or region) to contextualise strategies. The PRECISE database is a unique core infrastructure of a generic, unified data collection platform. It is built on previous work in data harmonisation, outcome and data field standardisation, open-access software (District Health Information System 2 and the Baobab Laboratory Information Management System), and clinical research networks. The database contains globally-recommended indicators included in Health Management Information System recording and reporting forms. It comprises key outcomes (maternal and perinatal death), life-saving interventions (Human Immunodeficiency Virus testing, blood pressure measurement, iron therapy, uterotonic use after delivery, postpartum maternal assessment within 48 h of birth, and newborn resuscitation, immediate skin-to-skin contact, and immediate drying), and an additional 17 core administrative variables for the mother and babies. In addition, the database has a suite of additional modules for 'deep phenotyping' based on established tools. These include social determinants of health (including socioeconomic status, nutrition and the environment), maternal co-morbidities, mental health, violence against women and health systems. The database has the potential to enable future high-quality epidemiological research integrated with clinical care and discovery bioscience.