Currently, 40% of HIV-infected women enrolled in national prevention of mother-to-child transmission (PMTCT) program in India are loss to follow-up (LTF) before they can receive single dose Nevirapine. To date no study from India has examined the reasons for inadequate utilization of PMTCT services. This study sought to examine the socio-demographic factors associated with LTF of HIV-infected women enrolled during 2002-2008 in a large-scale private sector PMTCT program in Maharashtra, India. Data on HIV-infected women who were enrolled during pregnancy (N=734) and who reported live birth (N=770) were used to analyze factors associated with LTF before delivery and after delivery, respectively. Univariate and multivariate analyses were conducted to estimate the associations between being LTF and socio-demographic factors using generalized linear models. Eighty (10.9%) women were LTF before delivery and 151 (19.6%) women were LTF after delivery. Women with less than graduate level education (RR = 6.32), from a poor family (RR = 1.61), who were registered after 20 weeks of pregnancy (RR = 2.02) and whose partners were HIV non-infected or with unknown HIV status (RR = 2.69) were more likely to be LTF before delivery. Similarly, the significant factors for LTF after delivery were less than graduate level education (RR = 1.82), poor family (RR = 1.42), and registration after 20 weeks of pregnancy (RR = 1.75). This study highlights the need for innovative and effective counseling techniques for less educated women, economic empowerment of women, better strategies to increase uptake of partner's HIV testing, and early registration of women in the program for preventing LTF in PMTCT programs. This need for innovative counseling techniques is even greater for PMTCT programs in the public health sector as the women accessing care in the public sector are likely to be less educated and economically more deprived.

BackgroundIn spite of effective strategies to eliminate mother-to-child-transmission of HIV, the implementation of such strategies remains a major challenge in developing countries. In India, programs for the prevention of mother-to-child transmission (PMTCT) have been scaled up widely since 2005. However, these programs reach only a small percentage of pregnant women, and their overall effectiveness is low. Evidence-based program planning and implementation could significantly improve their effectiveness. This study sought to systematically retrieve, thematically categorize and review published research on PMTCT of HIV in India, focusing on research related to the provision and/or utilization of the cascade of services provided in a PMTCT program, in order to direct further research to enhance program implementation and effectiveness.MethodsA systematic search using MEDLINE, US National Library of Medicine Gateway system (PubMed) and ISI Web of Knowledge resulted in 1,944 abstracts, of which 167 met our inclusion criteria.ResultsA huge share of the empirical literature on PMTCT in India (N = 134) deals with epidemiological studies (N = 60). The 46 papers related to utilization/provision of the cascade of PMTCT services were mostly from the four high HIV prevalence states in southern India and from the public sector. Studies on experiences of implementing a PMTCT program (N = 20) show high rates of drop out of women in the cascade particularly prior to receiving ARV. Studies on individual components of the cascade (N = 26) show that HIV counseling and testing is acceptable and feasible. Literature on other components of the cascade - such as pregnant women’s access to ANC care, HIV infected women’s immunological assessment using CD4 testing, repeat HIV testing among pregnant women, early infant diagnosis and factors related to linking HIV infected women and children to postnatal care – is lacking.ConclusionsWhile the scale of the Indian PMTCT program is large, comprehensive understanding of the context-driven factors affecting its efficiency is lacking. Systematic and more focused public health research output is needed on the issues related to reduction of drop outs of women in the cascade, role of PMTCT programs in improving maternal and child health indicators and role of private sector in delivering PMTCT services.

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The COVID-19 epidemic created, at the time of writing the paper, highly unusual and uncertain socio-economic conditions. The world economy was severely impacted and business-as-usual activities severely disrupted. The situation presented the necessity to make a trade-off between individual health and safety on one hand and socio-economic progress on the other. Based on the current understanding of the epidemiological characteristics of COVID-19, a broad set of control measures has emerged along dimensions such as restricting people's movements, high-volume testing, contract tracing, use of face masks, and enforcement of social-distancing. However, these interventions have their own limitations and varying level of efficacy depending on factors such as the population density and the socio-economic characteristics of the area. To help tailor the intervention, we develop a configurable, fine-grained agent-based simulation model that serves as a virtual representation, i.e., a digital twin of a diverse and heterogeneous area such as a city. In this paper, to illustrate our techniques, we focus our attention on the Indian city of Pune in the western state of Maharashtra. We use the digital twin to simulate various what-if scenarios of interest to (1) predict the spread of the virus; (2) understand the effectiveness of candidate interventions; and(3) predict the consequences of introduction of interventions possibly leading to trade-offs between public health, citizen comfort, and economy. Our model is configured for the specific city of interest and used as an in-silico experimentation aid to predict the trajectory of active infections, mortality rate, load on hospital, and quarantine facility centers for the candidate interventions. The key contributions of this paper are: (1) a novel agent-based model that seamlessly captures people, place, and movement characteristics of the city, COVID-19 virus characteristics, and primitive set of candidate interventions, and(2) a simulation-driven approach to determine the exact intervention that needs to be applied under a given set of circumstances. Although the analysis presented in the paper is highly specific to COVID-19, our tools are generic enough to serve as a template for modeling the impact of future pandemics and formulating bespoke intervention strategies.

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