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Efficiency analysis in the management of COVID-19 pandemic in India based on data envelopment analysis

ABSTRACT: Purpose: This article measured the performance of 32 states and union territories (UTs) of India against COVID-19 disease using efficiency score which was calculated by data envelopment analysis (DEA) and compared the efficiency score with the different models which are used in many articles to evaluate the efficiency of healthcare system. Here the input parameters are taken as public health expenditure in a million, number of hospitals, number of hospital beds, percentage of health workers, population density, and number of infected, and output parameters divided into good and bad categories such as the number of recovered are taken as good output. The number of death is taken as bad outputs. The modified undesirable output model is used to calculate efficiency score and compared the efficiency score with Charnes, Cooper, and Rhodes (CCR) and Banker, Charnes, and Cooper (BCC) models. Finally, the states & UTs are completely ranked with the help of efficiency score and Maximal Balance Index, and evaluated benchmarking for each states & UTs. Data Source: Secondary data were collected from Census 2011 and the Ministry of health & family welfare, Government of India on 32 stats & UTs (NHAC, 2018; NHP, 2019; COVID19India, 2021). Results: According to Undesirable model results, 16 (50%) of 32 Indian states & UTs s were found to be efficient. Among the efficient DMUs, Chandigarh is the most efficient unit and Meghalaya is the most inefficient unit. Rajasthan was the most referenced state for inefficient states. Limitation: The efficiency score is affected by changing the number of inputs and outputs. The lack of more effective parameters are used to evaluate performance and enable qualitative variable comparison.

SUBMITTER: Mohanta K

PROVIDER: S-EPMC8556177 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundIn order to measure and analyse the technical efficiency of district hospitals in Ghana, the specific objectives of this study were to (a) estimate the relative technical and scale efficiency of government, mission, private and quasi-government district hospitals in Ghana in 2005; (b) estimate the magnitudes of output increases and/or input reductions that would have been required to make relatively inefficient hospitals more efficient; and (c) use Tobit regression analysis to estimate the impact of ownership on hospital efficiency.MethodsIn the first stage, we used data envelopment analysis (DEA) to estimate the efficiency of 128 hospitals comprising of 73 government hospitals, 42 mission hospitals, 7 quasi-government hospitals and 6 private hospitals. In the second stage, the estimated DEA efficiency scores are regressed against hospital ownership variable using a Tobit model. This was a retrospective study.ResultsIn our DEA analysis, using the variable returns to scale model, out of 128 district hospitals, 31 (24.0%) were 100% efficient, 25 (19.5%) were very close to being efficient with efficiency scores ranging from 70% to 99.9% and 71 (56.2%) had efficiency scores below 50%. The lowest-performing hospitals had efficiency scores ranging from 21% to 30%.Quasi-government hospitals had the highest mean efficiency score (83.9%) followed by public hospitals (70.4%), mission hospitals (68.6%) and private hospitals (55.8%). However, public hospitals also got the lowest mean technical efficiency scores (27.4%), implying they have some of the most inefficient hospitals.Regarding regional performance, Northern region hospitals had the highest mean efficiency score (83.0%) and Volta Region hospitals had the lowest mean score (43.0%).From our Tobit regression, we found out that while quasi-government ownership is positively associated with hospital technical efficiency, private ownership negatively affects hospital efficiency.ConclusionsIt would be prudent for policy-makers to examine the least efficient hospitals to correct widespread inefficiency. This would include reconsidering the number of hospitals and their distribution, improving efficiency and reducing duplication by closing or scaling down hospitals with efficiency scores below a certain threshold. For private hospitals with inefficiency related to large size, there is a need to break down such hospitals into manageable sizes.

Project description:BackgroundAssociation between human mobility and disease transmission has been established for COVID-19, but quantifying the levels of mobility over large geographical areas is difficult. Google has released Community Mobility Reports (CMRs) containing data about the movement of people, collated from mobile devices.ObjectiveThe aim of this study is to explore the use of CMRs to assess the role of mobility in spreading COVID-19 infection in India.MethodsIn this ecological study, we analyzed CMRs to determine human mobility between March and October 2020. The data were compared for the phases before the lockdown (between March 14 and 25, 2020), during lockdown (March 25-June 7, 2020), and after the lockdown (June 8-October 15, 2020) with the reference periods (ie, January 3-February 6, 2020). Another data set depicting the burden of COVID-19 as per various disease severity indicators was derived from a crowdsourced API. The relationship between the two data sets was investigated using the Kendall tau correlation to depict the correlation between mobility and disease severity.ResultsAt the national level, mobility decreased from -38% to -77% for all areas but residential (which showed an increase of 24.6%) during the lockdown compared to the reference period. At the beginning of the unlock phase, the state of Sikkim (minimum cases: 7) with a -60% reduction in mobility depicted more mobility compared to -82% in Maharashtra (maximum cases: 1.59 million). Residential mobility was negatively correlated (-0.05 to -0.91) with all other measures of mobility. The magnitude of the correlations for intramobility indicators was comparatively low for the lockdown phase (correlation ≥0.5 for 12 indicators) compared to the other phases (correlation ≥0.5 for 45 and 18 indicators in the prelockdown and unlock phases, respectively). A high correlation coefficient between epidemiological and mobility indicators was observed for the lockdown and unlock phases compared to the prelockdown phase.ConclusionsMobile-based open-source mobility data can be used to assess the effectiveness of social distancing in mitigating disease spread. CMR data depicted an association between mobility and disease severity, and we suggest using this technique to supplement future COVID-19 surveillance.

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Efficiency analysis in the management of COVID-19 pandemic in India based on data envelopment analysis

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