The underlying factors of excess mortality in 2020: a cross-country analysis of pre-pandemic healthcare conditions and strategies to cope with Covid-19

Abstract

Background Government responses to the pandemic varied in terms of timing, duration, and stringency, seeking to protect healthcare systems, whose pre-pandemic state varied significantly. Therefore, the severity of Covid-19 and, thus, excess mortality have been unequal across counties. This paper explores the geography of excess mortality and its underlying factors in 2020, highlighting the effects of health policies pre-pandemic and strategies devised by governments to cope with Covid-19. Methods Excess mortality is estimated for 79 high, medium and low-income countries. The factors of excess mortality are examined employing median quantile regression analysis. Results Health privatization, healthcare underfunding, and late implementation of containment and mitigation strategies were powerful drivers of excess mortality. By contrast, the results suggest a negative association of excess mortality with health expenditure, number of doctors and hospital beds, share of population covered by health insurance and test and trace capacity. Conclusions The evidence highlights the importance of sufficiently funded healthcare systems with universal access and strong primary healthcare in the battle against the pandemic. An early response to Covid-19, including borders’ controls and a strong test and trace capacity, could improve epidemiological surveillance and minimize excess mortality, with stringent and lengthy lockdowns not providing a significant benefit.

Sociodemographic inequalities and excess non-COVID-19 mortality during the COVID-19 pandemic: A data-driven analysis of 1,069,174 death certificates in Mexico

“…Our results support the hypothesis that groups with sociodemographic vulnerabilities experienced the highest impact of excess mortality attributable to hospital saturation, with this impact having an unequal distribution within Mexico. In the European region, countries with high excess mortality, such as Bulgaria, Russia, and Serbia were impacted by diverse social barriers with difficulties to full adhere to social isolation policies (30,31). Two recent reports in England revealed that communities with high-density of care homes, with a high proportion of residents on income support, overcrowding conditions, and ethnic minorities were at higher risk of excess mortality and years of life lost due to the COVID-19 pandemic (32,33).…”

“…Two recent reports in England revealed that communities with high-density of care homes, with a high proportion of residents on income support, overcrowding conditions, and ethnic minorities were at higher risk of excess mortality and years of life lost due to the COVID-19 pandemic (32,33). In Latin American countries, the impact of socioeconomic disparities was sharp mainly due to countries experiencing diverse social barriers to sustain lockdown mandates driven by low stipend support, a high proportion of their population working in informal conditions, and uncovered health care basic needs even within healthcare workers personnel (3,30,34). Nevertheless, this evidence and the comparison between countries should be interpreted with caution given the variation in COVID-19 dynamics, within-country gradients of sociodemographic inequalities, and the profiles of high-risk comorbidities.…”

The influence of democracy, governance and government policies on the COVID‐19 pandemic mortality

“…Despite this, the general correlation still persists. Kapitsinis (2021) analyzed state responses in 79 countries worldwide. In his analysis of to the pandemic, he looked at the mortality rates within the state. Privatization of health care, underfunding of health care and delayed implementation of strategies to contain and mitigate the coronary pandemic were the main causes of excess mortality.…”

Assessing the effectiveness of public health interventions for Covid‐19 in Greece and Cyprus

“…Because change will not happen instantaneously, we empirically estimate NPI effectiveness by including various lags in our model: t + 1 (2), t + 2 (4), and t + 3 (6 weeks) after their introduction. While Covid‐19 deaths or excess mortality are important aspects of the debate (e.g., Kapitsinis, 2021 ; Mendez‐Brito et al, 2021 ), we opted not to use those figures. The main concern is that there is no theoretical reason why deaths should be more directly affected by NPIs than cases.…”