ABSTRACT
The Covid-19 pandemic had a devastating impact on the world of work in India. It resulted in unprecedented job and income losses, reinforcing the adverse labour market situation of women. There was a significant contraction in demand as the crisis unfolded, and the effects of this contraction was exacerbated by an increase in the demand for unpaid care work as a result of business and school closures, and return migration. Return migration of working-age men pulled women out of employment, particularly in rural areas. Job and income losses and lower household income pushed women towards employment as a survival strategy to supplement family income, a strategy captured in the term "added-worker effect." We postulate that the net result of these competing forces on women's employment is ambiguous and investigate the pattern of rural women's labour supply during the Covid-19 pandemic. The paper uses a mixed methods approach. Factors that played a determining role in changes to the labour force status of women-broadly classified through focus group discussions with women in selected rural areas-were further probed through econometric analysis. The main factors were loss of employment by members of the household, changes in household size during the period as migrants returned home, shifts in the time women spent on unpaid care work, and the number of young children in a household. The impact of these factors on entry into and exit from employment were estimated for both women and men in rural and urban India, using data from the Centre for Monitoring the Indian Economy (CMIE) Consumer Pyramids Household Survey (CPHS). The findings show complex coping strategies at the household level where women's work-paid and unpaid-plays a critical role.
ABSTRACT
Objectives: Remdesivir (RDV) is the only antiviral treatment conditionally approved to treat COVID-19 hospitalized patients (1). In Mexico, RDV received an Emergency Use Authorization on March 2021 for hospitalized adults with a confirmed diagnosis of COVID-19 (2). This study assessed to conduct a CEA of the use of RDV in hospitalized patients compared to the standard of care (SoC) in the Mexican context. Methods: A combined decision tree with a Markov model was used to perform a CEA of treated hospitalized adults for COVID-19 with RDV. In the first stage, patients were followed for 28 days during the hospital stay with a decision tree that consisted of three nodes: 1) the decision between SoC or RDV;2) whether patients received supplementary oxygen or not during the hospital stay, and 3) whether they remain alive or died. After the 28-days follow-up, in a second stage, patients who remain alive entered on a Markov model with two mutually exclusive health states (alive or dead) with one-year cycles. Probabilities, resource utilization, and costs (e.g., cost of hospitalization, monitoring tests, complementary medicines) were extracted from public governmental sources (3–6). Health outcomes (measured as Life-Years Gained -LYG) and costs were discounted following national guidelines (7). Deterministic and probabilistic sensitivity analyses were conducted. Results: A thousand adults were modeled. In the base case, RDV provided 0.7 more LYG and generated savings for 1,722 USD compared to SoC (0.91 LYG and -2,038 USD in probabilistic analysis). Thus, RDV is a dominant intervention to treat COVID-19. Results were consistent for all sensitivity scenarios but only for all patients with severe COVID-19 (ICER 2,386 USD), remaining as a cost-effective option. Conclusions: Given the potential clinical benefits of RDV, the model estimated that RDV represents a cost-saving option to the Mexican health system for hospitalized patients with COVID-19.