Assessment of variation in cesarean delivery rates between public and private health facilities in India from 2005 to 2016

Importance: The rates of cesarean deliveries have more than doubled in India, from 8% of deliveries in 2005 to 17% of deliveries in 2016. The World Health Organization recommends that cesarean deliveries should not exceed 10% to 15% of all deliveries in any country. An understanding of the association of private and public facilities with the increase in cesarean delivery rates in India is needed. Objective: To assess the association of public vs private sector health care facilities with cesarean delivery rates in India and to estimate the potential cost savings if private sector facilities followed World Health Organization recommendation for cesarean deliveries. Design, Setting, and Participants: This cross-sectional study used institutional delivery data from the representative National Family Health Survey (NFHS) in India, including data from the NFHS-1 (1992-1993), the NFHS-3 (2005-2006), and the NFHS-4 (2015-2016). The NFHS-3 and NFHS-4 provided data on 22 647 deliveries and 195 366 deliveries, respectively. The NHFS-4 was the first survey to provide data on out-of-pocket expenditures for delivery by facility type, allowing for a comparison of cesarean deliveries and costs between public and private facilities. The primary sample comprised all pregnant women who delivered infants in public and private institutional facilities in India and who were included the NFHS-3 and the NFHS-4; data on pregnant women who were included in the NFHS-1 were used for comparison. The study's findings were analyzed through geographic mapping, data tabulation, funnel plots, multivariate logistic regression analyses, and potential cost-savings scenario analyses. Data were analyzed from June to December 2019. Main Outcomes and Measures: The main outcome was the rate of cesarean deliveries by facility type (public vs private) and by participant socioeconomic, demographic, and health characteristics. Secondary outcomes were the potential number of avoidable cesarean deliveries and the potential cost savings if private sector facilities followed the World Health Organization recommendations for cesarean deliveries. Results: In the NFHS-3, 22 610 total births occurred at institutional facilities. Of those, 2178 births (15.2%) were cesarean deliveries in public facilities, and 3200 births (27.9%) were cesarean deliveries in private facilities. Of 195 366 total institutional births in the NFHS-4, 15 165 births (11.9%) were cesarean deliveries in public facilities, and 20 506 births (40.9%) were cesarean deliveries in private facilities. The cesarean delivery rate in public health facilities increased from 7.2% in the NFHS-1 to 11.9% in the NFHS-4, whereas in private health facilities, the rate increased from 12.3% to 40.9% during the same period. A substantial increase was found in cesarean delivery rates between the NFHS-3 (2005-2006) and the NFHS-4 (2015-2016), with 22 states exceeding the World Health Organization's upper threshold of 15% in the NFHS-4. The odds ratio for cesarean deliveries in private facilities compared with public facilities increased from 1.62 (95% CI, 1.49-1.76) in the NFHS-3 to 4.17 (95% CI, 4.04-4.30) in the NFHS-4. The number of avoidable cesarean deliveries would have been 1.83 million, with a potential cost savings of $320.60 million, if private sector facilities in India had followed the 15% threshold for cesarean delivery rates recommended by the World Health Organization. Conclusions and Relevance: In this study, private sector health facilities were associated with a substantial increase in cesarean deliveries in India. Further research is needed to assess the factors underlying the increase in cesarean deliveries in private sector facilities

Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980-2017: a systematic analysis for the Global Burden of Disease Study 2017.

BACKGROUND: Global development goals increasingly rely on country-specific estimates for benchmarking a nation's progress. To meet this need, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2016 estimated global, regional, national, and, for selected locations, subnational cause-specific mortality beginning in the year 1980. Here we report an update to that study, making use of newly available data and improved methods. GBD 2017 provides a comprehensive assessment of cause-specific mortality for 282 causes in 195 countries and territories from 1980 to 2017. METHODS: The causes of death database is composed of vital registration (VR), verbal autopsy (VA), registry, survey, police, and surveillance data. GBD 2017 added ten VA studies, 127 country-years of VR data, 502 cancer-registry country-years, and an additional surveillance country-year. Expansions of the GBD cause of death hierarchy resulted in 18 additional causes estimated for GBD 2017. Newly available data led to subnational estimates for five additional countries-Ethiopia, Iran, New Zealand, Norway, and Russia. Deaths assigned International Classification of Diseases (ICD) codes for non-specific, implausible, or intermediate causes of death were reassigned to underlying causes by redistribution algorithms that were incorporated into uncertainty estimation. We used statistical modelling tools developed for GBD, including the Cause of Death Ensemble model (CODEm), to generate cause fractions and cause-specific death rates for each location, year, age, and sex. Instead of using UN estimates as in previous versions, GBD 2017 independently estimated population size and fertility rate for all locations. Years of life lost (YLLs) were then calculated as the sum of each death multiplied by the standard life expectancy at each age. All rates reported here are age-standardised

The burden of infectious and cardiovascular diseases in India from 2004 to 2014

OBJECTIVES In India, both communicable and non-communicable diseases have been argued to disproportionately affect certain socioeconomic strata of the population. Using the 60th (2004) and 71st (2014) rounds of the National Sample Survey, this study assessed the balance between infectious diseases and cardiovascular diseases (CVD) from 2004 to 2014, as well as changes in the disease burden in various socioeconomic and demographic subpopulations. METHODS Prevalence rates, hospitalization rates, case fatality rates, and share of in-patients deaths were estimated to compare the disease burdens at these time points. Logistic regression and multivariate decomposition were used to evaluate changes in disease burden across various socio-demographic and socioeconomic groups. RESULTS Evidence of stagnation in the infectious disease burden and rapid increase in the CVD burden was observed. Along with the drastic increase in case fatality rate, share of in-patients deaths became more skewed towards CVD from 2004 to 2014. Logistic regression analysis demonstrated a significant shift of the chance of succumbing to CVD from the privileged class, comprising non-Scheduled Castes and Tribes, more highly educated individuals, and households with higher monthly expenditures, towards the underprivileged population. Decomposition indicated that a change in the probability of suffering from CVD among the subcategories of age, social groups, educational status, and monthly household expenditures contributed to the increase in CVD prevalence more than compositional changes of the population from 2004 to 2014. CONCLUSIONS This study provides evidence of the ongoing tendency of CVD to occur in older population segments, and also confirms the theory of diffusion, according to which an increased probability of suffering from CVD has trickled down the socioeconomic gradient

Debugging assertion failures in software controllers using a reference model

Model based frameworks like Simulink/Stateflow for developing software control algorithms advocate the analysis of an abstract discrete state model of the controller before developing the actual code for the implementation. Though some of the existing tools support automatic code generation from the model, in actual practice the code is developed manually with the model as a reference, and independently validated against the safety requirements. If the safety properties can be guaranteed by the code then we reach verification closure, but if this is not the case, then we must debug the actual source of error in the code. We propose an approach for using the abstract model as a reference in this debugging task

Asset and consumption gradient of health estimates in India: Implications for survey and public health research

The wealth index based on household assets and amenities is been increasingly used to explain economic variations of health outcomes in the developing countries. While the variables used to compute the wealth index are easy to collect and time- and cost-effective, the wealth index tends to have an urban bias, uses arbitrary weighting, does not provide per capita measures and is a poor measure of inequality. We used micro data from two of the large-scale population-based surveys, the Longitudinal Ageing Study in India, 2017–18 and the India Human Development Survey, 2011–12 that covered over 42,000 households each and collected data on household consumption, assets and amenities in India. We examined the variations and inequality in health estimates by consumption per capita and asset-based measures in India. Descriptive statistics, logistic regression model, concentration index, and concentration curve were used in the analyses. | We found a weak association between monthly per capita consumption expenditure (MPCE) and wealth index in both the surveys. Some of the health conditions such as hypertension, cataract, refractive error, and diabetes tended to be underestimated in the bottom 40% of the population when economic well-being was measured using the wealth index compared to consumption. Socio-economic inequality in health outcome, inpatient and outpatient health services were underestimated when measured using the wealth index than when measured using MPCE. | We conclude that economic gradients of health by consumption and wealth index are inconsistent and that per capita consumption predicts health estimates better than the wealth index. It is recommended that public health research using population-based surveys that provide data on consumption and wealth index use per capita consumption to explain economic variations in health and health care utilization. We also suggest that the future rounds of the health surveys of National Sample Survey and the National Family and Health Surveys include an abridged version of the consumption schedule to predict better economic variations in health and health care utilization in India

PERCEPTION OF QUALIFIED MEDICAL PRACTITIONERS TOWARD PRESCRIBING IN INTERNATIONAL NON-PROPRIETARY (GENERIC) NAMES – AN OBSERVATIONAL, CROSS-SECTIONAL STUDY AT A TERTIARY CARE HOSPITAL IN INDIA

Objectives: The study was conducted to assess the perception and practice of medical practitioners, working in tertiary care, and teaching institutions in Eastern India, regarding the use of generic (non-proprietary) names while prescribing. The study tried to assess their perception toward using drugs from the National List of Essential Medicine (NLEM), as well. Methods: An observational, cross-sectional study was conducted. Medical practitioners attached to the institution were considered for the study and those who gave voluntarily consent were included. Hundred participants were interviewed based on convenient random sampling. They were provided with the study questionnaire and the responses were analyzed using Microsoft Excel 2007 using charts and tables. Results: Majority (43/100) did not feel that generic medicines are as effective as reputed brands, while 32 felt they are of equally effective. About 45% (45/99) felt generics to be equally safe as and 24% (24/99) did not feel so. About 86% considered generics to be cheaper. About 56% did not prefer to substitute with generics in all conditions. About 73% had doubts regarding the quality of production of generics. The decision to use generics was mostly influenced by the lower cost (73%) and by administrative pressure (53%). About 58% felt that the NLEM does not contain all the medicines they would require in practice. About 94% agreed to prescribe more in generics if the quality may be ensured. Conclusion: Awareness of the NLEM and about generics needs to be improved. Authorities need to ensure the quality of generics and assure the prescribers about it

Survey implementation process and interviewer effects on skipping sequence of maternal and child health indicators from National Family Health Survey: An application of cross-classified multilevel model

Implementing a large-scale survey involves a string of intricate procedures exposed to numerous types of survey errors. Uniform and systematic training protocols, comprehensive survey manuals, and multilayer supervision during survey implementation help reduce survey errors, providing a consistent fieldwork environment that should not result in any variation in the quality of data collected across interviewers and teams. With this background, the present study attempts to delineate the effect of field investigator (FI) teams and survey implementation design on the selected outcomes. Data on four of the bigger Empowered Action Group (EAG) states of India, namely Uttar Pradesh, Madhya Pradesh, Bihar, and Rajasthan, were obtained from the fourth round of the National Family Health Survey (NFHS-4) for analysis. A fixed-effect binary logistic regression model was used to assess the effect of FI teams and survey implementation design on the selected outcomes. To study the variation in the outcome variables at the interviewer level, a cross-classified multilevel model was used. Since one interviewer had worked in more than one primary sampling unit (PSU) & district and did not follow a perfect hierarchical structure, the cross-classified multilevel model was deemed suitable. In addition, since NFHS-4 used a two-stage stratified sampling design, two-level weights were adjusted for the models to compute unbiased estimates. This study demonstrated the presence of interviewer-level variation in the selected outcomes at both inter- and intra-field agencies across the selected states. The interviewer-level intra-class correlation coefficient (ICC) for women who had not availed antenatal care (ANC) was the highest for eastern Madhya Pradesh (0.23) and central Uttar Pradesh (0.20). For ‘immunisation card not seen’, Rajasthan (0.16) and western Uttar Pradesh (0.13) had higher interviewer-level ICC. Interviewer-level variations were insignificant for women who gave birth at home across all regions of Uttar Pradesh. Eastern Madhya Pradesh, Rajasthan, and Bihar showed higher interviewer-level variation across the selected outcomes, underlining the critical role of agencies and skilled interviewers in different survey implementation designs. The analysis highlights non-uniform adherence to survey protocols, which implies that not all interviewers and agencies performed in a similar manner in the field. This study recommends a refined mechanism for field implementation and supervision, including focused training on the challenges faced by FIs, random vigilance, and morale building. In addition, examining interviewer-level characteristics, field challenges, and field agency effects may also highlight the roots of interviewer-level variation in the data. However, based on the interviewer\u27s performance in the field, the present study offers an intriguing insight into interviewer-level variations in the quality of data

Quality of anthropometric data in India\u27s National Family Health Survey: Disentangling interviewer and area effect using a cross-classified multilevel model

India has adopted a target-based approach to reduce the scourge of child malnourishment. Because the monitoring and evaluation required by this approach relies primarily on large-scale data, a data quality assessment is essential. As field teams are the primary mode of data collection in large-scale surveys, this study attempts to understand their contribution to variations in child anthropometric measures. This research can help disentangle the confounding effects of regions/districts and field teams on the quality of child anthropometric data. The anthropometric z-scores of 2,25,002 children below five years were obtained from the fourth round of India\u27s National Family and Health Survey (NFHS-4), 2015–16. Unadjusted and adjusted standard deviations (SD) of the anthropometric measures were estimated to assess the variations in measurements. In addition, a cross-classified multilevel model (CCMM) approach was adopted to estimate the contribution of geographical regions/districts and teams to variations in anthropometric measures. The unadjusted SDs of the measures of stunting, wasting, and underweight were 1.7, 1.4, and 1.2, respectively. The SD of stunting was above the World Health Organisation threshold (0.8–1.2), as well as the Demographic and Health Survey mark. After adjusting for team-level characteristics, the SDs of all three measures reduced marginally, indicating that team-level workload had a marginal but significant role in explaining the variations in anthropometric z-scores. The CCMM showed that the maximum contribution to variations in anthropometric z-scores came from community-level (Primary Sampling Unit (PSU)) characteristics. Team-level characteristics had a higher contribution to variations in anthropometric z-scores than district-level attributes. Variations in measurement were higher for child height than weight. The present study decomposes the effects of district- and team-level factors and highlights the nuances of introducing teams as a level of analysis in multilevel modelling. Population size, density, and terrain variations between PSUs should be considered when allocating field teams in large-scale surveys