The dataset consists of quantitative data derived mainly from international datasets (ILO, WHO), supplemented by data from national datasets and modelled data to complete missing values. It shows the statistical data we collated and used to calculate estimates of Covid-19 deaths among migrant health care workers and includes details on how missing information was imputed. It includes spreadsheet estimates for India, Nigeria, Mexico, and the UK for excess and reported Covid-19 deaths amongst foreign-born workers and for all workers in the human health and social work sector and in three specific health occupations: doctors, nurses, and midwives. For each group the spreadsheets provide a basic estimate and an age-sex standardised estimate.This project aims to give proper attention to the place of migrant workers in health care systems during the Covid-19 pandemic. Migrant workers are of substantial and growing significance in many countries' health and care systems and are key to realising the global goal of universal health care, so it is vital that we understand much better how Covid-19 is impacting on them. The project's overarching research questions are, in the relation to Covid-19, what risks do migrant health care workers experience, what are the pressures on resilient and sustainable health care workforces, and how are stakeholders responding to these risks and pressures? We develop a research method to estimate Covid-19 migrant health care worker mortality rates and trial the method, undertaking statistical analysis and modelling using quantitative data drawn from WHO and OECD data and other demographic and bio-statistical data as available. In addition to strengthening the methodological techniques and empirical evidence base on the risks of Covid-19 infection and death among migrant health care workers our project also tracks, through documentary analysis, collective responses to such risks and challenges to sustainable health workforces for universal health coverage. This project is attuned to the urgent need for high quality data and for 'real world' solutions-focused Covid-19 research forged from collaboration. We are focused on the immediate application of proof-of concept findings to a rapidly-evolving global health crisis.

This project relied entirely on freely-available international statistical data that has already been quality checked by reputable organisations prior to being released to the public. We drew on three types of source. First, international datasets such as National Healthcare Workforce Accounts (NHWA, WHO), human health and social work sector labour force data (ISIC Q, ILO), WHO's Covid-19 dashboard, and estimates of excess deaths produced by various academics and research organisations (e.g. Johns Hopkins Center for Systems Science and Engineering). Second, best-available national statistical surveys (e.g. Annual Population Survey, UK Office for National Statistics) were used where data needed was not available in international datasets. Third, in the absence of a reported value from an extant dataset, we imputed missing data using best models. This latter method was used to calculate the proportion of foreign-born health workers among health workforces.