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Background: Nonpharmacological interventions, such as personal protective equipment for example, surgical masks and respirators, and maintenance of hand hygiene along with COVID-19 vaccines have been recommended to reduce viral transmission in the community and health care settings. There is evidence from the literature that surgical and N95 masks may reduce the initial degree of exposure to the virus. A limited research that has studied the cost-effective analysis of surgical masks and N95 masks among health care workers in the prevention of COVID-19 in India. The objective of this study was to estimate the cost-effectiveness of N95 and surgical mask compared to wearing no mask in public hospital settings for preventing COVID-19 infection among Health care workers (HCWs) from the health care provider's perspective. Methods: A deterministic baseline model, without any mask use, based on Eikenberry et al was used to form the foundation for parameter estimation and to estimate transmission rates among HCWs. Information on mask efficacy, including the overall filtering efficiency of a mask and clinical efficiency, in terms of either inward efficiency(ei) or outward efficiency(e0), was obtained from published literature. Hospitalized HCWs were assumed to be in one of the disease states i.e., mild, moderate, severe, or critical. A total of 10,000 HCWs was considered as representative of the size of a tertiary care institution HCW population. The utility values for the mild, moderate and severe model health states were sourced from the primary data collection on quality-of-life of HCWs COVID-19 survivors. The utility scores for mild, moderate, and severe COVID-19 conditions were 0.88, 0.738 and 0.58, respectively. The cost of treatment for mild sickness (6,500 INR per day), moderate sickness (10,000 INR per day), severe (require ICU facility without ventilation, 15,000 INR per day), and critical (require ICU facility with ventilation per day, 18,000 INR) per day as per government and private COVID-19 treatment costs and capping were considered. One way sensitivity analyses were performed to identify the model inputs which had the largest impact on model results. Results: The use of N95 masks compared to using no mask is cost-saving of $1,454,632 (INR 0.106 billion) per 10,000 HCWs in a year. The use of N95 masks compared to using surgical masks is cost-saving of $63,919 (INR 0.005 billion) per 10,000 HCWs in a year. the use of surgical masks compared to using no mask is cost-saving of $1,390,713 (INR 0.102 billion) per 10,000 HCWs in a year. The uncertainty analysis showed that considering fixed transmission rate (1.7), adoption of mask efficiency as 20%, 50% and 80% reduces the cumulative relative mortality to 41%, 79% and 94% respectively. On considering ei = e0 (99%) for N95 and surgical mask with ei = e0 (90%) the cumulative relative mortality was reduced by 97% and the use of N95 masks compared to using surgical masks is cost-saving of $24,361 (INR 0.002 billion) per 10,000 HCWs in a year. Discussion: Both considered interventions were dominant compared to no mask based on the model estimates. N95 masks were also dominant compared to surgical masks. [ABSTRACT FROM AUTHOR]

Background: A national study, 'Costing of healthcare services in India' (CHSI) aimed at generating reliable healthcare cost estimates for health technology assessment and price-setting is being undertaken in India. CHSI sampled 52 public and 40 private hospitals in 13 states and used a mixed micro-costing approach. This paper aims to outline the process, challenges and critical lessons of cost data collection to feed methodological and quality improvement of data collection. Methods: An exploratory survey with 3 components–an online semi-structured questionnaire, group discussion and review of monitoring data, was conducted amongst CHSI data collection teams. There were qualitative and quantitative components. Difficulty in obtaining individual data was rated on a Likert scale. Results: Mean time taken to complete cost data collection in one department/speciality was 7.86(±0.51) months, majority of which was spent on data entry and data issues resolution. Data collection was most difficult for determination of equipment usage (mean difficulty score 6.59±0.52), consumables prices (6.09±0.58), equipment price(6.05±0.72), and furniture price(5.64±0.68). Human resources, drugs & consumables contributed to 78% of total cost and 31% of data collection time. However, furniture, overheads and equipment consumed 51% of time contributing only 9% of total cost. Seeking multiple permissions, absence of electronic records, multiple sources of data were key challenges causing delays. Conclusions: Micro-costing is time and resource intensive. Addressing key issues prior to data collection would ease the process of data collection, improve quality of estimates and aid priority setting. Electronic health records and availability of national cost data base would facilitate conducting costing studies. [ABSTRACT FROM AUTHOR]