An Ecological Study of the Determinants of Differences in 2009 Pandemic Influenza Mortality Rates between Countries in Europe

Abstract

BackgroundPandemic A (H1N1) 2009 mortality rates varied widely from one country to another. Our aim was to identify potential socioeconomic determinants of pandemic mortality and explain between-country variation.MethodologyBased on data from a total of 30 European countries, we applied random-effects Poisson regression models to study the relationship between pandemic mortality rates (May 2009 to May 2010) and a set of representative environmental, health care-associated, economic and demographic country-level parameters. The study was completed by June 2010.Principal FindingsMost regression approaches indicated a consistent, statistically significant inverse association between pandemic influenza-related mortality and per capita government expenditure on health. The findings were similar in univariable [coefficient: –0.00028, 95% Confidence Interval (CI): –0.00046, –0.00010, p = 0.002] and multivariable analyses (including all covariates, coefficient: –0.00107, 95% CI: –0.00196, –0.00018, p = 0.018). The estimate was barely insignificant when the multivariable model included only significant covariates from the univariate step (coefficient: –0.00046, 95% CI: –0.00095, 0.00003, p = 0.063).ConclusionsOur findings imply a significant inverse association between public spending on health and pandemic influenza mortality. In an attempt to interpret the estimated coefficient (–0.00028) for the per capita government expenditure on health, we observed that a rise of 100 international dollars was associated with a reduction in the pandemic influenza mortality rate by approximately 2.8%. However, further work needs to be done to unravel the mechanisms by which reduced government spending on health may have affected the 2009 pandemic influenza mortality.

An ecological study of socioeconomic predictors in detection of COVID-19 cases across neighborhoods in New York City

“…Multiple regression models were built using a method adjusted from Nikolopoulos et 136 al. [31]. In the univariable models, we considered each predictor variable separately (i.e., 137 one model per variable).…”

“…and removing the least significant predictor until we were left with a model containing 142 only significant predictors [31]. In all cases, the expected number of detected COVID-19 143 cases in ZCTA i, λ i , was represented by Eq 3:…”

Subjects Hospitalized With the 2009 Pandemic Influenza A (H1N1) Virus in a Respiratory Infection Unit: Clinical Factors Correlating With ICU Admission

“…Among these cases, 294 were admitted to ICUs, and 149 died from causes related to 2009 influenza A (H1N1). [2][3][4][5][6][7] Since that period, influenza epidemics have resulted in an estimated 3-5 million cases of severe illness and 250,000 -500,000 deaths each year. 1,8,9 The risk of complications from influenza, including lower respiratory tract infection, hospital admissions, and death, depends on varying determinants such as age and type of comorbidity that may be present.…”

Country-level Determinants of the Severity of the First Global Wave of the COVID-19 Pandemic: An Ecological Study

“…Nikolopoulos et al reported a negative association between health expenditure and death rates from the 2009 influenza pandemic in 30 European countries. 21 Several other previous studies have also reported associations between airline travel and spread of the H1N1 influenza virus infection. 32 Comorbidities may account for differences in mortality rates across countries.…”

“…Whilst previous ecological studies of other epidemics have utilised case or death counts as outcome, 21 these variables may be prone to bias due to variations in country level control measures including different testing strategies, 22 variations in population movement controls and differences in secondary attack rates within community cohorts 23 . The mean mortality rate was thus chosen as outcome instead, since it is independent of these highly variable parameters and may thus represent a more reliable indicator of the country-level severity of the COVID-19 pandemic Mean mortality rate was defined as the slope of the mean mortality curve (Figure 1), measured from the first day when more than 2 COVID-19 deaths were reported until either the mortality curve reached a peak value or the 1 st of May 2020, whichever occurred first.…”