Background Rural India has a severe shortage of human resources for health (HRH). The National Rural Health Mission (NRHM) deploys HRH in the rural public health system to tackle shortages. Sanctioning under NRHM does not account for workload resulting in inadequate and inequitable HRH allocation. The Workforce Indicators of Staffing Needs (WISN) approach can identify shortages and inform appropriate sanctioning norms. India currently lacks nationally relevant WISN estimates. We used existing data and modelling techniques to synthesize such estimates. Methods We conducted a retrospective analysis of existing survey data for 93 facilities from 5 states over 8 years to create WISN calculations for HRH cadres at primary and community health centres (PHCs and CHCs) in rural areas. We modelled nationally representative average WISN-based requirements for specialist doctors at CHCs, general doctors and nurses at PHCs and CHCs. For 2019, we calculated national and state-level overall and per-centre WISN differences and ratios to depict shortage and workload pressure. We checked correlations between WISN ratios for cadres at a given centre-type to assess joint workload pressure. We evaluated the gaps between WISN-based requirements and sanctioned posts to investigate suboptimal sanctioning through concordance analysis and difference comparisons. Results In 2019, at the national-level, WISN differences depicted workforce shortages for all considered HRH cadres. WISN ratios showed that nurses at PHCs and CHCs, and all specialist doctors at CHCs had very high workload pressure. States with more workload on PHC-doctors also had more workload on PHC-nurses depicting an augmenting or compounding effect on workload pressure across cadres. A similar result was seen for CHC-specialist pairs—physicians and surgeons, physicians and paediatricians, and paediatricians and obstetricians–gynaecologists. We found poor concordance between current sanctioning norms and WISN-based requirements with all cadres facing under-sanctioning. We also present across-state variations in workforce problems, workload pressure and sanctioning problems. Conclusion We demonstrate the use of WISN calculations based on available data and modelling techniques for national-level estimation. Our findings suggest prioritising nurses and specialists in the rural public health system and updating the existing sanctioning norms based on workload assessments. Workload-based rural HRH deployment can ensure adequate availability and optimal distribution.
BACKGROUND Rural India has a severe shortage of human resources for health (HRH). The National Rural Health Mission (NRHM) deploys HRH in the rural public health system to tackle shortages. Sanctioning under NRHM does not account for workload resulting in inadequate and inequitable HRH allocation. The Workforce Indicators of Staffing Needs (WISN) approach can identify shortages and inform appropriate sanctioning norms. India currently lacks nationally-relevant WISN estimates. We used existing data and modelling techniques to synthesize such estimates. METHODS We conducted a retrospective analysis of existing survey data for 93 facilities from 5 states over 8 years to create WISN calculations for HRH cadres at primary and community health centres (PHCs and CHCs) in rural areas. We modelled nationally-representative average WISN-based requirements for specialist doctors at CHCs, general doctors and nurses at PHCs and CHCs. For 2019, we calculated national and state-level overall and per-centre WISN differences and ratios to depict shortage and workload pressure. We checked correlations between WISN ratios for cadres at a given centre-type to assess joint workload pressure. We evaluated the gaps between WISN-based requirements and sanctioned posts to investigate suboptimal sanctioning through concordance analysis and difference comparisons. RESULTS In 2019, at the national-level, WISN differences depicted workforce shortages for all considered HRH cadres. WISN ratios showed that nurses at PHCs and CHCs, and all specialist doctors at CHCs had very high workload pressure. States with more workload on PHC-doctors also had more workload on PHC-nurses depicting an augmenting or compounding effect on workload pressure across cadres. A similar result was seen for CHC-specialist pairs - physicians and surgeons, physicians and paediatricians, and paediatricians and obstetricians-gynaecologists. We found poor concordance between current sanctioning norms and WISN-based requirements with all cadres facing under-sanctioning. We also present across-state variations in workforce problems, workload pressure and sanctioning problems. CONCLUSION We demonstrate the use of WISN calculations based on available data and modelling techniques for national-level estimation. Our findings suggest prioritising nurses and specialists in the rural public health system and updating the existing sanctioning norms based on workload assessments. Workload-based rural HRH deployment can ensure adequate availability and optimal distribution.
India was one of the most vulnerable countries to the COVID-19 pandemic considering the high transmissibility of the virus, exploding population, and fragile healthcare infrastructure. As an early counter, India implemented a country-wide lockdown and we aimed to study the impact of 4 lockdowns and 2 unlock phases on 6 outcomes: case growth, death count, effective reproduction number, mobility, hospitalization, and infection growth by two methods: interrupted time series (ITR) analysis and Bayesian causal impact analysis (BCIA) for nationals and sub-national levels. We observed that the effects are heterogeneous across outcomes and phases. For example, ITR revealed the effect to be significant for all the outcomes across all phases except for case growth in phase 1. BCIA revealed that the causal effect of all four lockdown phases was positive for deaths. At the state level, Maharashtra benefited from the lockdown in comparison to Tripura. Effects of lockdown phases 3 and 4 on death count were correlated (R=0.70, p<0.05) depicting the 'extended impact' of phase-wise interventions. We observed the highest impact on mobility followed by hospitalization, infection growth, effective reproduction number, case growth, and death count. For optimal impact, lockdown needs to be implemented at the sub-national level considering various demographic variations between states.
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