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Background: The purpose of this study is to compute surgical total factor productivity with Malmquist index, and to evaluate the effects of states of emergency against the novel coronavirus disease 2019 (COVID-19) pandemic on its productivity change. We hypothesized that the states of emergency significantly reduced surgical total factor productivity in Japan.Methods: The authors collected data from all the surgical procedures performed in Teikyo University Hospital from April 1 through September 30 in 2019-21. Non-radial and non-oriented Malmquist model under the variable returns-to-scale assumptions was employed. The decision making unit (DMU) was defined as a surgical specialty department. Inputs were defined as (1) the number of medical doctors who assisted surgery, and (2) the duration of surgical operation from skin incision to closure. The output was defined as the surgical fee for each surgery. The study period was divided into fifty-one ten- (or eleven-) day periods. We added all the inputs and outputs of the surgical procedures for each DMU during these study periods, and computed its Malmquist index, efficiency change and technical change.Results: Seven thousand nine hundred and thirty-one surgical procedures were analyzed. The overall productivity and efficiency progressed significantly both during states of emergency and during no states of emergency. Our subgroup analysis demonstrated that there were no surgical specialties that had significantly different productivity, efficiency or technical changes between states of emergency and no states of emergency.Conclusions: We demonstrated that the surgical productivity did not suffer despite the states of emergency against the COVID-19 pandemic in Japan. [ABSTRACT FROM AUTHOR]

Background: A simple indicator of technical efficiency, such as productivity of health workers, measured using routine health facility data, can be a practical approach that can inform initiatives to improve efficiency in low- and middle-income countries. This paper presents a proof of concept of using routine information from primary healthcare (PHC) facilities to measure health workers' productivity and its application in three regions of Ethiopia.Methods: In four steps, we constructed a productivity measure of the health workforce of Health Centers (HCs) and demonstrated its practical application: (1) developing an analytical dataset using secondary data from health management information systems (HMIS) and human resource information system (HRIS); (2) principal component analysis and factor analysis to estimate a summary measure of output from five indicators (annual service volume of outpatient visits, family planning, first antenatal care visits, facility-based deliveries by skilled birth attendants, and children [< 1 year] with three pentavalent vaccines); (3) calculating a productivity score by combining the summary measure of outputs and the total number of health workers (input), and (4) implementing regression models to identify the determinant of productivity and ranking HCs based on their adjusted productivity score.Results: We developed an analytical dataset of 1128 HCs; however, significant missing values and outliers were reported in the data. The principal component and factor scores developed from the five output measures were highly consistent (correlation coefficient = 0.98). We considered the factor score as the summary measure of outputs for estimating productivity. A very weak association was observed between the summary measure of output and the total number of staff. The result also highlighted a large variability in productivity across similar health facilities in Ethiopia, represented by the significant dispersion in summary measure of output occurring at similar levels of the health workers.Conclusions: We successfully demonstrated the analytical steps to estimate health worker productivity and its practical application using HMIS and HRIS. The methodology presented in this study can be readily applied in low- and middle-income countries using widely available data-such as DHIS2-that will allow further explorations to understand the causes of technical inefficiencies in the health system. [ABSTRACT FROM AUTHOR]