Health Inequalities: To What Extent are Decision-Makers and Economic Evaluations on the Same Page? An English Case Study.

Economic evaluations have increasingly sought to understand how funding decisions within care sectors impact health inequalities. However, there is a disconnect between the methods used by researchers (e.g., within universities) and analysts (e.g., within publicly funded commissioning agencies), compared to evidence needs of decision makers in regard to how health inequalities are accounted for and presented. Our objective is to explore how health inequality is defined and quantified in different contexts. We focus on how specific approaches have developed, what similarities and differences have emerged, and consider how disconnects can be bridged. We explore existing methodological research regarding the incorporation of inequality considerations into economic evaluation in order to understand current best practice. In parallel, we explore how localised decision makers incorporate inequality considerations into their commissioning processes. We use the English care setting as a case study, from which we make inference as how local commissioning has evolved internationally. We summarise the recent development of distributional cost-effectiveness analysis in the economic evaluation literature: a method that makes explicit the trade-off between efficiency and equity. In the parallel decision-making setting, while the alleviation of health inequality is regularly the focus of remits, few details have been formalised regarding its definition or quantification. While data development has facilitated the reporting and comparison of metrics of inequality to inform commissioning decisions, these tend to focus on measures of care utilisation and behaviour rather than measures of health. While both researchers and publicly funded commissioning agencies are increasingly putting the identification of health inequalities at the core of their actions, little consideration has been given to ensuring that they are approaching the problem in a consistent way. The extent to which researchers and commissioning agencies can collaborate on best practice has important implications for how successful policy is in addressing health inequalities.

Comparing the COVID-19 pandemic in space and over time in Europe, using numbers of deaths, crude rates and adjusted mortality trend ratios.

Comparison of COVID-19 trends in space and over time is essential to monitor the pandemic and to indirectly evaluate non-pharmacological policies aimed at reducing the burden of disease. Given the specific age- and sex- distribution of COVID-19 mortality, the underlying sex- and age-distribution of populations need to be accounted for. The aim of this paper is to present a method for monitoring trends of COVID-19 using adjusted mortality trend ratios (AMTRs). Age- and sex-mortality distribution of a reference European population (N = 14,086) was used to calculate age- and sex-specific mortality rates. These were applied to each country to calculate the expected deaths. Adjusted Mortality Trend Ratios (AMTRs) with 95% confidence intervals (C.I.) were calculated for selected European countries on a daily basis from 17th March 2020 to 29th April 2021 by dividing observed cumulative mortality, by expected mortality, times the crude mortality of the reference population. These estimated the sex- and age-adjusted mortality for COVID-19 per million population in each country. United Kingdom experienced the highest number of COVID-19 related death in Europe. Crude mortality rates were highest Hungary, Czech Republic, and Luxembourg. Accounting for the age-and sex-distribution of the underlying populations with AMTRs for each European country, four different patterns were identified: countries which experienced a two-wave pandemic, countries with almost undetectable first wave, but with either a fast or a slow increase of mortality during the second wave; countries with consistently low rates throughout the period. AMTRs were highest in Eastern European countries (Hungary, Czech Republic, Slovakia, and Poland). Our methods allow a fair comparison of mortality in space and over time. These might be of use to indirectly estimating the efficacy of non-pharmacological health policies. The authors urge the World Health Organisation, given the absence of age and sex-specific mortality data for direct standardisation, to adopt this method to estimate the comparative mortality from COVID-19 pandemic worldwide.