Learnings From the Implementation of an Electronic Human Resource Management System for the Health Workforce in Uttar Pradesh, India.

The state of Uttar Pradesh (UP), India, has one of the largest single public health systems globally, serving about 235 million people through more than 30,000 public health facilities with approximately 160,000 health personnel. Yet, the UP health system has a shortfall of public health facilities to meet the population's needs, a shortage of clinical and nonclinical health personnel, inequitable distribution of existing health personnel, and low utilization of public health facilities. A robust and effective electronic human resource management system (eHRMS) that provides real-time information about the lifecycle of all health professionals in UP may aid in improving the health workforce, resulting in better health services and improved health outcomes. The Government of UP rolled out Manav Sampada, a comprehensive eHRMS that complied with global norms and requirements. We describe the implementation of Manav Sampada at scale and elaborate on key learnings and adoption strategies. Manav Sampada was based on key principles of integration and data-sharing with other digital systems, included functional components, a minimum dataset, used a lifecycle-based approach, and a workflow-based system, all of which acted to improve human resource data quality. The eHRMS emerged as a valuable tool for key stakeholders in reviewing worker performance, identifying skill-building needs, and allocating resources for training, leading to improved availability and equity in the distribution of a few critical cadres. The eHRMS in UP is well positioned to become an integral part of the Ayushman Bharat Digital Mission, the backbone of India's integrated digital health infrastructure. Linking eHRMS to a planned beneficiary-centric unitized health service delivery system (capturing information at the individual level rather than the aggregate level) will enable the measurement of service delivery and quality, leading to improved workforce management.

Understanding drivers of family planning in rural northern India: An integrated mixed-methods approach.

BACKGROUND: Family planning is a key means to achieving many of the Sustainable Development Goals. Around the world, governments and partners have prioritized investments to increase access to and uptake of family planning methods. In Uttar Pradesh, India, the government and its partners have made significant efforts to increase awareness, supply, and access to modern contraceptives. Despite progress, uptake remains stubbornly low. This calls for systematic research into understanding the 'why'-why people are or aren't using modern methods, what drives their decisions, and who influences them. METHODS: We use a mixed-methods approach, analyzing three existing quantitative data sets to identify trends and geographic variation, gaps and contextual factors associated with family planning uptake and collecting new qualitative data through in-depth immersion interviews, journey mapping, and decision games to understand systemic and individual-level barriers to family planning use, household decision making patterns and community level barriers. RESULTS: We find that reasons for adoption of family planning are complex-while access and awareness are critical, they are not sufficient for increasing uptake of modern methods. Although awareness is necessary for uptake, we found a steep drop-off (59%) between high awareness of modern contraceptive methods and its intention to use, and an additional but smaller drop-off from intention to actual use (9%). While perceived access, age, education and other demographic variables partially predict modern contraceptive intention to use, the qualitative data shows that other behavioral drivers including household decision making dynamics, shame to obtain modern contraceptives, and high-risk perception around side-effects also contribute to low intention to use modern contraceptives. The data also reveals that strong norms and financial considerations by couples are the driving force behind the decision to use and when to use family planning methods. CONCLUSION: The finding stresses the need to shift focus towards building intention, in addition to ensuring access of trained staff, and commodities drugs and equipment, and building capacities of health care providers.

Bringing Greater Precision to Interactions Between Community Health Workers and Households to Improve Maternal and Newborn Health Outcomes in India.

INTRODUCTION: Community health workers (CHWs) play a key role in the health of mothers and newborns in low- and middle-income countries. However, it remains unclear by what actions and messages CHWs enable good outcomes and respectful care. METHODS: We collected a uniquely linked set of questions on behaviors, beliefs, and care pathways from recently delivered women (n=5,469), their husbands (n=3,064), mothers-in-law (n=3,626), and CHWs (accredited social health activists; n=1,052) in Uttar Pradesh, India. We used logistic regression to study associations between CHW actions and household behaviors during antenatal, delivery, and postnatal periods. RESULTS: Pregnant women who were visited earlier in pregnancy and who received multiple visits were more likely to perform recommended health behaviors including attending multiple checkups, consuming iron and folic acid tablets, and delivering in a health facility (ID), compared to women visited later or receiving fewer visits, respectively. Counseling the woman was associated with higher likelihood of attending 3+ checkups and consuming 100+ iron and folic acid tablets, whereas counseling the husband and mother-in-law was associated with higher rates of ID. Certain behavior change messages, such as the danger of complications, were associated with more checkups and ID, but were only used by 50%-80% of CHWs. During delivery, 57% of women had the CHW present, and their presence was associated with respectful care, early initiation of breastfeeding, and exclusive breastfeeding, but not with delayed bathing or clean cord care. The newborn was less likely to receive delayed bathing if the CHW incorrectly believed that newborns could be bathed soon after birth (which is believed by 30% of CHWs). CHW presence was associated with health behaviors more strongly for home than facility deliveries. Home visits after delivery were associated with higher rates of clean cord care and exclusive breastfeeding. Counseling the mother-in-law (but not the husband or woman) was associated with exclusive breastfeeding. CONCLUSION: We identified potential ways in which CHW impact could be improved, specifically by emphasizing the importance of home visits, which household members are targeted during these visits, and what messages are shared. Achieving this change will require training CHWs in counseling and behavior change and providing supervision and modern tools such as apps that can help the CHW keep track of her beneficiaries, suggest behavior change strategies, and direct attention to households that stand to gain the most from support.

Closing the gap on institutional delivery in northern India: a case study of how integrated machine learning approaches can enable precision public health.

INTRODUCTION: Meeting ambitious global health goals with limited resources requires a precision public health (PxPH) approach. Here we describe how integrating data collection optimisation, traditional analytics and causal artificial intelligence/machine learning (ML) can be used in a use case for increasing hospital deliveries of newborns in Uttar Pradesh, India. METHODS: Using a systematic behavioural framework we designed a large-scale survey on perceptual, interpersonal and structural drivers of women's behaviour around childbirth (n=5613). Multivariate logistic regression identified factors associated with institutional delivery (ID). Causal ML determined the cause-and-effect ordering of these factors. Variance decomposition was used to parse sources of variation in delivery location, and a supervised learning algorithm was used to distinguish population subgroups. RESULTS: Among the factors found associated with ID, the causal model showed that having a delivery plan (OR=6.1, 95% CI 6.0 to 6.3), believing the hospital is safer than home (OR=5.4, 95% CI 5.1 to 5.6) and awareness of financial incentives were direct causes of ID (OR=3.4, 95% CI 3.3 to 3.5). Distance to the hospital, borrowing delivery money and the primary decision-maker were not causal. Individual-level factors contributed 69% of variance in delivery location. The segmentation analysis showed four distinct subgroups differentiated by ID risk perception, parity and planning. CONCLUSION: These findings generate a holistic picture of the drivers and barriers to ID in Uttar Pradesh and suggest distinct intervention points for different women. This demonstrates data optimised to identify key behavioural drivers, coupled with traditional and ML analytics, can help design a PxPH approach that maximise the impact of limited resources.

Facilities are substantially more influential than care providers in the quality of delivery care received: a variance decomposition and clustering analysis in Kenya, Malawi and India.

INTRODUCTION: Improving the quality of care during childbirth is essential for reducing neonatal and maternal mortality. One barrier to improving quality of care is understanding the appropriate level to target interventions. We examine quality of care data during labour and delivery from multiple countries to assess whether quality varies primarily from nurse to nurse within the same facility, or primarily between facilities. METHODS: To assess the relative contributions of nurses and facilities to variance in quality of care, we performed a variance decomposition analysis using a linear mixed effect model on two data sources: (1) the number of vital signs assessed for women in labour from a study of nurse practices in Uttar Pradesh, India; 2) broad-scale indices of respectful and competent care generated from Service Provision Assessments in Kenya and Malawi. We used unsupervised clustering, a data mining technique that groups objects together based on similar characteristics, to identify groups of facilities that displayed distinct patterns of vital signs assessment behaviour. RESULTS: We found 3-10 times more variance in quality of care was explained by the facility where a patient received care than by the nurse who provided it. The unsupervised clustering analysis revealed groups of facilities with highly distinct patterns of vital signs assessment, even when overall rates of vital signs assessments were similar (eg, some facilities consistently test fetal heart rate, but not other vitals, others only blood pressure). CONCLUSION: Facilities within a region can vary substantially in the quality of care they provide to women in labour, but within a facility, nurses tend to provide similar care. This holds true both for care that can be influenced by equipment availability and technical training (eg, vital signs assessment), as well as cultural aspects (eg, respectful care).

The contribution of district prioritization on maternal and newborn health interventions coverage in rural India.

BACKGROUND: In 2001, India prioritized eight most socioeconomically disadvantaged states known as Empowered Action Group (EAG) states and in 2013, it prioritized 190 of the 718 as high priority districts (HPDs) to accelerate the decline in maternal and newborn mortality. This paper assesses whether the HPDs achieved a greater coverage of maternal and newborn health interventions than the non-HPDs and HPDs in EAG states achieved greater coverage than those in non-EAG states. METHODS: We used data from the Sample Registration System to assess rural neonatal mortality trends in EAG states and all India. We computed a co-coverage index based on seven maternal and newborn health interventions from the 2015/16 National Family Health Survey. Difference in differences (DID) analyses were used to examine the contribution of district prioritization, considering the HPDs and the illiterate as treatment groups and 2013 as the time cut-off for the pre- and post-treatment. RESULTS: Neonatal mortality declined in rural India from 36 to 27 per 1000 live births during 2010-2016 at 4.5% per year. Four EAG states experienced faster rates of decline than the national rate. From 2013, the co-coverage index increased significantly more in the HPDs compared to non-HPDs (DID = 0.11, P ≤ 0.005). The district prioritization effect on co-coverage was statistically significant in only EAG states (DID = 0.13, P ≤ 0.05). The coverage gains for illiterate mothers were greater than for literate mothers, especially in the HPDs. CONCLUSIONS: The district prioritization in India is associated with greater improvements in the coverage of maternal and newborn health services in EAG states and the HPDs, including reductions in inequalities within those states and districts. There are however still large gaps between states and districts and within districts by the mother's literacy status that need further prioritization to make progress towards the SDG targets by 2030.

Fertility and family planning in Uttar Pradesh, India: major progress and persistent gaps.

BACKGROUND: Uttar Pradesh (UP) is the most populous state in India with historically high levels of fertility rates than the national average. Though fertility levels in UP declined considerably in recent decades, the current level is well above the government's target of 2.1. DATA AND METHODS: Fertility and family planning data obtained from the different rounds of Sample Registration System (SRS) and the National Family Health Survey (NFHS). We analyzed fertility and family planning trends in India and UP, including differences in methods mix, using SRS (1971-2016) and NFHS (1992-2016). Bivariate and multivariate regression analyses were used. RESULTS: From 2000, while the total fertility rate (TFR) declined in UP, it is still well above the national level in 2015-16 (2.7 vs 2.18, respectively). The demand for family planning satisfied increased from 52 to 72% during 1998-99 to 2015-16 in UP, compared to an increase from 75 to 81% in India. Traditional methods play a much greater role in UP than in India (22 and 9% of the demand satisfied respectively), while use of sterilization was relatively low in UP when compared to the national averages (18.0 and 36.3% of current married women 15-49 years in UP and India, respectively in 2015-16). Within UP, district fertility ranged from 1.6 to 4.4, with higher fertility concentrated in districts with low female schooling, predominantly located in north-central UP. Fertility declines were largest in districts with high fertility in the late nineties (B = 7.33, p < .001). Among currently married women, use of traditional methods increased and accounted for almost one-third of users in 2015-16. Use of sterilization declined but remained the primary method (ranging from 33 to 41% of users in high and low fertility districts respectively) while condom use increased from 17 and 16% in 1998-99 to 23 and 25% in 2015-16 in low and high fertility districts respectively. CONCLUSIONS AND IMPLICATIONS: Greater reliance on traditional methods and condoms coupled with relatively low demand for modern contraception suggest inadequate access to modern contraceptives, especially in district with high fertility rates. Family planning activities need to be appropriately scaled according to need and geography to ensure the achievement of state-level improvements in family planning programs and fertility outcomes.