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1.
Health Aff (Millwood) ; 41(4): 474-486, 2022 Apr.
Article in English | MEDLINE | ID: covidwho-1765548

ABSTRACT

Although considerable uncertainty remains, the COVID-19 pandemic and public health emergency are expected to continue to influence the near-term outlook for national health spending and enrollment. National health spending growth is expected to have decelerated from 9.7 percent in 2020 to 4.2 percent in 2021 as federal supplemental funding was expected to decline substantially relative to 2020. Through 2024 health care use is expected to normalize after the declines observed in 2020, health insurance enrollments are assumed to evolve toward their prepandemic distributions, and the remaining federal supplemental funding is expected to wane. Economic growth is expected to outpace health spending growth for much of this period, leading the projected health share of gross domestic product (GDP) to decline from 19.7 percent in 2020 to just over 18 percent over the course of 2022-24. For 2025-30, factors that typically drive changes in health spending and enrollment, such as economic, demographic, and health-specific factors, are again expected to primarily influence trends in the health sector. By 2030 the health spending share of GDP is projected to reach 19.6 percent.


Subject(s)
COVID-19 , Health Expenditures , Forecasting , Gross Domestic Product , Humans , Insurance, Health , Pandemics , United States/epidemiology
2.
Int J Environ Res Public Health ; 19(4)2022 02 10.
Article in English | MEDLINE | ID: covidwho-1715303

ABSTRACT

This paper assesses the convergence process in the health care expenditure for selected European Union (EU) countries over the past 50 years. As a novel contribution, we use bound unit root tests and, for robustness purposes, a series of tests for strict stationarity to provide new insights about the convergence process. We make a comparison between public and private health expenditure per capita and as a percentage of the gross domestic product (GDP), with a focus on six EU countries with different health care systems in place. When we consider the health expenditure per capita, we report mixed findings. We show that the spread from the group average is stationary in the cases of Finland and Portugal when the overall and public expenditure is considered. In terms of private expenditure, the convergence process is noticed only for Austria. For all other countries included in our sample, we document a non-stationary process, indicating a lack of convergence. This result is robust to the different tests we use. However, when we assess the convergence in terms of the health-expenditure-to-GDP ratio, the convergence process is recorded for Austria only. The robustness check we performed using strict stationarity tests partially confirmed the mixed results we obtained. Therefore, our findings highlight the heterogeneity of the EU health care systems and the need for identification of common solutions to the EU health care systems' problems in order to enhance their convergence processes.


Subject(s)
Delivery of Health Care , Health Expenditures , Austria , European Union , Gross Domestic Product
3.
PLoS One ; 17(2): e0263245, 2022.
Article in English | MEDLINE | ID: covidwho-1708180

ABSTRACT

In low- and middle-income countries (LMICs), economic downturns can lead to increased child mortality by affecting dietary, environmental, and care-seeking factors. This study estimates the potential loss of life in children under five years old attributable to economic downturns in 2020. We used a multi-level, mixed effects model to estimate the relationship between gross domestic product (GDP) per capita and under-5 mortality rates (U5MRs) specific to each of 129 LMICs. Public data were retrieved from the World Bank World Development Indicators database and the United Nations World Populations Prospects estimates for the years 1990-2020. Country-specific regression coefficients on the relationship between child mortality and GDP were used to estimate the impact on U5MR of reductions in GDP per capita of 5%, 10%, and 15%. A 5% reduction in GDP per capita in 2020 was estimated to cause an additional 282,996 deaths in children under 5 in 2020. At 10% and 15%, recessions led to higher losses of under-5 lives, increasing to 585,802 and 911,026 additional deaths, respectively. Nearly half of all the potential under-5 lives lost in LMICs were estimated to occur in Sub-Saharan Africa. Because most of these deaths will likely be due to nutrition and environmental factors amenable to intervention, countries should ensure continued investments in food supplementation, growth monitoring, and comprehensive primary health care to mitigate potential burdens.


Subject(s)
Child Mortality/trends , Developing Countries , Gross Domestic Product/trends , Africa South of the Sahara , Child, Preschool , Dietary Supplements , Environment , Female , Humans , Infant , Infant, Newborn , Male , Poverty , Primary Health Care , Regression Analysis , Uncertainty
5.
J Infect Public Health ; 15(2): 255-260, 2022 Feb.
Article in English | MEDLINE | ID: covidwho-1693254

ABSTRACT

BACKGROUND: The spread of COVID-19 depends on a lot of social and economic factors. THE AIM: to study the influence of country's gross domestic product, population prevalence of overweight/ obesity, NCD mortality, and vaccination on COVID-19 morbidity and mortality rates. METHODS: A cross-sectional study with two phases: correlation-regression interrelations in 1) all world countries; 2) all world non-island countries. The study includes the following data from 218 world countries: COVID-19 morbidity/mortality rates, GDP per capita, the prevalence of overweight/ obesity, NCD mortality among adults (both sexes), people fully vaccinated against COVID-19. RESULTS: An average percentage of the prevalence of overweight among adults in world countries by 2019 was 47.31 ± 15.99%, obesity 18.34 ± 9.64%, while the prevalence by 2016 were 39% and 13%, respectively. Overweight and obesity among adults during three years grew by 21.2% and 40.8%, respectively. Data from the world countries provide significant correlations (p < 0.0001) between COVID-19 morbidity, and: GDP (r = 0.517), overweight (r = 0.54), obesity (r = 0.528), NCD mortality (r = 0.537); COVID-19 mortality, and: GDP (r = 0.344), overweight (r = 0.514), obesity (r = 0.489), NCD mortality (r = 0.611); GDP, and: overweight (r = 0.507), obesity (r = 0.523), NCD mortality (r = 0.35), fully vaccinated people (r = 0.754). An increase in fully vaccinated people, from 3% to 30% of world population, decreases new confirmed COVID-19 cases, although the dependence was not significant (p = 0.07). Data from non-island world countries provides more highly significant correlations (p < 0.0001) between COVID-19 morbidity, and: GDP (r = 0.616), overweight (r = 0.581), obesity (r = 0.583); COVID-19 mortality, and: GDP (r = 0.43), overweight (r = 0.556), obesity (r = 0.539); GDP, and: overweight (r = 0.601), obesity (r = 0.633). The differences of correlation coefficients between data of 176 world countries and data of 143 world non-island countries were not significant (Z-scores<1.29; p > 0.05). CONCLUSION: The study provides evidence of a significant impact of overweight/obesity prevalence on the increase in COVID-19 morbidity/mortality. Countries with higher GDP have a high overweight/obesity prevalence and possibility to get vaccinated.


Subject(s)
COVID-19 , Noncommunicable Diseases , Adult , Cross-Sectional Studies , Female , Global Health , Gross Domestic Product , Humans , Male , Obesity/epidemiology , Overweight/epidemiology , Prevalence , SARS-CoV-2 , Vaccination
7.
Epidemiol Infect ; 150: e1, 2021 11 16.
Article in English | MEDLINE | ID: covidwho-1616902

ABSTRACT

This paper demonstrates how the combustion of fossil fuels for transport purpose might cause health implications. Based on an original case study [i.e. the Hubei province in China, the epicentre of the coronavirus disease-2019 (COVID-19) pandemic], we collected data on atmospheric pollutants (PM2.5, PM10 and CO2) and economic growth (GDP), along with daily series on COVID-19 indicators (cases, resuscitations and deaths). Then, we adopted an innovative Machine Learning approach, applying a new image Neural Networks model to investigate the causal relationships among economic, atmospheric and COVID-19 indicators. Empirical findings emphasise that any change in economic activity is found to substantially affect the dynamic levels of PM2.5, PM10 and CO2 which, in turn, generates significant variations in the spread of the COVID-19 epidemic and its associated lethality. As a robustness check, the conduction of an optimisation algorithm further corroborates previous results.


Subject(s)
Air Pollutants/adverse effects , Air Pollution/adverse effects , COVID-19/mortality , Fossil Fuels/adverse effects , Gross Domestic Product/statistics & numerical data , Neural Networks, Computer , Carbon Dioxide/adverse effects , China/epidemiology , Economic Development/statistics & numerical data , Humans , Particulate Matter/adverse effects
8.
Am J Public Health ; 111(11): 1947-1949, 2021 11.
Article in English | MEDLINE | ID: covidwho-1613421
9.
Sci Rep ; 11(1): 23739, 2021 12 09.
Article in English | MEDLINE | ID: covidwho-1565735

ABSTRACT

This article examines the main factors affecting COVID-19 lethality across 16 European Countries with a focus on the role of health system characteristics during the first phase of the diffusion of the virus. Specifically, we investigate the leading causes of lethality at 10, 20, 30, 40 days in the first hit of the pandemic. Using a random forest regression (ML), with lethality as outcome variable, we show that the percentage of people older than 65 years (with two or more chronic diseases) is the main predictor variable of lethality by COVID-19, followed by the number of hospital intensive care unit beds, investments in healthcare spending compared to GDP, number of nurses and doctors. Moreover, the variable of general practitioners has little but significant predicting quality. These findings contribute to provide evidence for the prediction of lethality caused by COVID-19 in Europe and open the discussion on health policy and management of health care and ICU beds during a severe epidemic.


Subject(s)
COVID-19/mortality , Community Health Planning , Health Facilities , Health Services Accessibility , Health Systems Plans , Age Factors , Europe/epidemiology , Gross Domestic Product , Health Policy , Humans , Intensive Care Units , Pandemics , SARS-CoV-2
10.
BMJ Glob Health ; 6(12)2021 12.
Article in English | MEDLINE | ID: covidwho-1550947

ABSTRACT

OBJECTIVES: COVID-19 has altered health sector capacity in low-income and middle-income countries (LMICs). Cost data to inform evidence-based priority setting are urgently needed. Consequently, in this paper, we calculate the full economic health sector costs of COVID-19 clinical management in 79 LMICs under different epidemiological scenarios. METHODS: We used country-specific epidemiological projections from a dynamic transmission model to determine number of cases, hospitalisations and deaths over 1 year under four mitigation scenarios. We defined the health sector response for three base LMICs through guidelines and expert opinion. We calculated costs through local resource use and price data and extrapolated costs across 79 LMICs. Lastly, we compared cost estimates against gross domestic product (GDP) and total annual health expenditure in 76 LMICs. RESULTS: COVID-19 clinical management costs vary greatly by country, ranging between <0.1%-12% of GDP and 0.4%-223% of total annual health expenditure (excluding out-of-pocket payments). Without mitigation policies, COVID-19 clinical management costs per capita range from US$43.39 to US$75.57; in 22 of 76 LMICs, these costs would surpass total annual health expenditure. In a scenario of stringent social distancing, costs per capita fall to US$1.10-US$1.32. CONCLUSIONS: We present the first dataset of COVID-19 clinical management costs across LMICs. These costs can be used to inform decision-making on priority setting. Our results show that COVID-19 clinical management costs in LMICs are substantial, even in scenarios of moderate social distancing. Low-income countries are particularly vulnerable and some will struggle to cope with almost any epidemiological scenario. The choices facing LMICs are likely to remain stark and emergency financial support will be needed.


Subject(s)
COVID-19 , Developing Countries , Gross Domestic Product , Humans , Policy , SARS-CoV-2
11.
Inquiry ; 58: 469580211060184, 2021.
Article in English | MEDLINE | ID: covidwho-1538023

ABSTRACT

The present study aimed to identify the factors associated with the distribution of the first doses of the COVID-19 vaccine. In this study, we used 9 variables: human development index (HDI), gross domestic product (GDP per capita), Gini index, population density, extreme poverty, life expectancy, COVID cases, COVID deaths, and reproduction rate. The time period was until February 1, 2021. The variable of interest was the sum of the days after the vaccine arrived in the countries. Pearson's correlation coefficients were calculated, and t-test was performed between the groups that received and did not receive the immunizer, and finally, a stepwise linear regression model was used. 58 (30.4%) of the 191 countries received the SARS-CoV-2 vaccine. The countries that received the most doses were the United States, China, the United Kingdom, and Israel. Vaccine access in days showed a positive Pearson correlation HDI, GDP, life expectancy, COVID-19 cases, deaths, and reproduction rate. Human development level, COVID-19 deaths, GDP per capita, and population density are able to explain almost 50% of the speed of access to immunizers. Countries with higher HDI and per capita income obtained priority access.


Subject(s)
COVID-19 Vaccines , COVID-19 , Gross Domestic Product , Humans , Income , SARS-CoV-2
12.
Sci Rep ; 11(1): 22914, 2021 11 25.
Article in English | MEDLINE | ID: covidwho-1537336

ABSTRACT

The COVID-19 pandemic has spurred controversies related to whether countries manipulate reported data for political gains. We study the association between accuracy of reported COVID-19 data and developmental indicators. We use the Newcomb-Benford law (NBL) to gauge data accuracy. We run an OLS regression of an index constructed from developmental indicators (democracy level, gross domestic product per capita, healthcare expenditures, and universal healthcare coverage) on goodness-of-fit measures to the NBL. We find that countries with higher values of the developmental index are less likely to deviate from the Newcomb-Benford law. The relationship holds for the cumulative number of reported deaths and total cases but is more pronounced for the death toll. The findings are robust for second-digit tests and for a sub-sample of countries with regional data. The NBL provides a first screening for potential data manipulation during pandemics. Our study indicates that data from autocratic regimes and less developed countries should be treated with more caution. The paper further highlights the importance of independent surveillance data verification projects.


Subject(s)
COVID-19/economics , COVID-19/epidemiology , Disease Notification/statistics & numerical data , Data Accuracy , Data Collection/trends , Delivery of Health Care , Developed Countries/economics , Developing Countries/economics , Gross Domestic Product , Humans , Models, Statistical , Pandemics , SARS-CoV-2 , Universal Health Insurance
15.
Lancet ; 398(10308): 1317-1343, 2021 10 09.
Article in English | MEDLINE | ID: covidwho-1433921

ABSTRACT

BACKGROUND: The rapid spread of COVID-19 renewed the focus on how health systems across the globe are financed, especially during public health emergencies. Development assistance is an important source of health financing in many low-income countries, yet little is known about how much of this funding was disbursed for COVID-19. We aimed to put development assistance for health for COVID-19 in the context of broader trends in global health financing, and to estimate total health spending from 1995 to 2050 and development assistance for COVID-19 in 2020. METHODS: We estimated domestic health spending and development assistance for health to generate total health-sector spending estimates for 204 countries and territories. We leveraged data from the WHO Global Health Expenditure Database to produce estimates of domestic health spending. To generate estimates for development assistance for health, we relied on project-level disbursement data from the major international development agencies' online databases and annual financial statements and reports for information on income sources. To adjust our estimates for 2020 to include disbursements related to COVID-19, we extracted project data on commitments and disbursements from a broader set of databases (because not all of the data sources used to estimate the historical series extend to 2020), including the UN Office of Humanitarian Assistance Financial Tracking Service and the International Aid Transparency Initiative. We reported all the historic and future spending estimates in inflation-adjusted 2020 US$, 2020 US$ per capita, purchasing-power parity-adjusted US$ per capita, and as a proportion of gross domestic product. We used various models to generate future health spending to 2050. FINDINGS: In 2019, health spending globally reached $8·8 trillion (95% uncertainty interval [UI] 8·7-8·8) or $1132 (1119-1143) per person. Spending on health varied within and across income groups and geographical regions. Of this total, $40·4 billion (0·5%, 95% UI 0·5-0·5) was development assistance for health provided to low-income and middle-income countries, which made up 24·6% (UI 24·0-25·1) of total spending in low-income countries. We estimate that $54·8 billion in development assistance for health was disbursed in 2020. Of this, $13·7 billion was targeted toward the COVID-19 health response. $12·3 billion was newly committed and $1·4 billion was repurposed from existing health projects. $3·1 billion (22·4%) of the funds focused on country-level coordination and $2·4 billion (17·9%) was for supply chain and logistics. Only $714·4 million (7·7%) of COVID-19 development assistance for health went to Latin America, despite this region reporting 34·3% of total recorded COVID-19 deaths in low-income or middle-income countries in 2020. Spending on health is expected to rise to $1519 (1448-1591) per person in 2050, although spending across countries is expected to remain varied. INTERPRETATION: Global health spending is expected to continue to grow, but remain unequally distributed between countries. We estimate that development organisations substantially increased the amount of development assistance for health provided in 2020. Continued efforts are needed to raise sufficient resources to mitigate the pandemic for the most vulnerable, and to help curtail the pandemic for all. FUNDING: Bill & Melinda Gates Foundation.


Subject(s)
COVID-19/prevention & control , Developing Countries/economics , Economic Development , Healthcare Financing , International Agencies/economics , COVID-19/economics , COVID-19/epidemiology , Financing, Government/economics , Financing, Government/organization & administration , Global Health/economics , Government Programs/economics , Government Programs/organization & administration , Government Programs/statistics & numerical data , Government Programs/trends , Gross Domestic Product , Health Expenditures/statistics & numerical data , Health Expenditures/trends , Humans , International Agencies/organization & administration , International Cooperation
16.
Infect Genet Evol ; 95: 105081, 2021 11.
Article in English | MEDLINE | ID: covidwho-1401709

ABSTRACT

Coronavirus disease 2019 (COVID-19) has harshly impacted Italy since its arrival in February 2020. In particular, provinces in Italy's Central and Northern macroregions have dealt with disproportionately greater case prevalence and mortality rates than those in the South. In this paper, we compare the morbidity and mortality dynamics of 16th and 17th century Plague outbreaks with those of the ongoing COVID-19 pandemic across Italian regions. We also include data on infectious respiratory diseases which are presently endemic to Italy in order to analyze the regional differences between epidemic and endemic disease. A Growth Curve Analysis allowed for the estimation of time-related intercepts and slopes across the 16th and 17th centuries. Those statistical parameters were later incorporated as criterion variables in multiple General Linear Models. These statistical examinations determined that the Northern macroregion had a higher intercept than the Southern macroregion. This indicated that provinces located in Northern Italy had historically experienced higher plague mortalities than Southern polities. The analyses also revealed that this geographical differential in morbidity and mortality persists to this day, as the Northern macroregion has experienced a substantially higher COVID-19 mortality than the Southern macroregion. These results are consistent with previously published analyses. The only other stable and significant predictor of epidemic disease mortality was foreign urban potential, a measure of the degree of interconnectedness between 16th and 17th century Italian cities. Foreign urban potential was negatively associated with plague slope and positively associated with plague intercept, COVID-19 mortality, GDP per capita, and immigration per capita. Its substantial contribution in predicting both past and present outcomes provides a temporal continuity not seen in any other measure tested here. Overall, this study provides compelling evidence that temporally stable geographical factors, impacting both historical and current foreign pathogen spread above and beyond other hypothesized predictors, underlie the disproportionate impact COVID-19 has had throughout Central and Northern Italian provinces.


Subject(s)
COVID-19/epidemiology , Endemic Diseases/history , Models, Statistical , Pandemics , Plague/epidemiology , COVID-19/history , COVID-19/mortality , Cities , Emigrants and Immigrants/statistics & numerical data , Geography , Gross Domestic Product , History, 16th Century , History, 17th Century , History, 21st Century , Humans , Italy/epidemiology , Plague/history , Plague/mortality , Prevalence , Survival Analysis
17.
Sci Rep ; 11(1): 17744, 2021 09 07.
Article in English | MEDLINE | ID: covidwho-1397902

ABSTRACT

A simple method is utilised to study and compare COVID-19 infection dynamics between countries based on curve fitting to publicly shared data of confirmed COVID-19 infections. The method was tested using data from 80 countries from 6 continents. We found that Johnson cumulative density functions (CDFs) were extremely well fitted to the data (R2 > 0.99) and that Johnson CDFs were much better fitted to the tails of the data than either the commonly used normal or lognormal CDFs. Fitted Johnson CDFs can be used to obtain basic parameters of the infection wave, such as the percentage of the population infected during an infection wave, the days of the start, peak and end of the infection wave, and the duration of the wave's increase and decrease. These parameters can be easily interpreted biologically and used both for describing infection wave dynamics and in further statistical analysis. The usefulness of the parameters obtained was analysed with respect to the relation between the gross domestic product (GDP) per capita, the population density, the percentage of the population infected during an infection wave, the starting day and the duration of the infection wave in the 80 countries. We found that all the above parameters were significantly associated with GDP per capita, but only the percentage of the population infected was significantly associated with population density. If used with caution, this method has a limited ability to predict the future trajectory and parameters of an ongoing infection wave.


Subject(s)
COVID-19/epidemiology , Forecasting/methods , Models, Statistical , Pandemics/statistics & numerical data , Data Interpretation, Statistical , Feasibility Studies , Global Burden of Disease , Gross Domestic Product/statistics & numerical data , Humans , Normal Distribution , Population Density
18.
Int J Environ Res Public Health ; 18(10)2021 05 14.
Article in English | MEDLINE | ID: covidwho-1389364

ABSTRACT

The rapid, unexpected, and large-scale expansion of the SARS-CoV-19 pandemic has led to a global health and economy crisis. However, although the crisis itself is a worldwide phenomenon, there have been considerable differences between respective countries in terms of SARS-CoV-19 morbidities and fatalities as well as the GDP impact. The object of this paper was to study the influence of the SARS-CoV-19 pandemic on global gross domestic product. We analyzed data relating to 176 countries in the 11-month period from February 2020 to December 2020. We employed SARS-CoV-19 morbidity and fatality rates reported by different countries as proxies for the development of the pandemic. The analysis employed in our study was based on moving median and quartiles, Kendall tau-b coefficients, and multi-segment piecewise-linear approximation with Theil-Sen trend lines. In the study, we empirically confirmed and measured the negative impact of the SARS-CoV-19 pandemic on the respective national economies. The relationship between the pandemic and the economy is not uniform and depends on the extent of the pandemic's development. The more intense the pandemic, the more adaptive the economies of specific countries become.


Subject(s)
COVID-19 , SARS Virus , Gross Domestic Product , Humans , Pandemics , SARS-CoV-2
19.
Am J Trop Med Hyg ; 105(4): 896-902, 2021 08 30.
Article in English | MEDLINE | ID: covidwho-1378472

ABSTRACT

Health events emerge from host, community, environment, and pathogen factors-forecasting epidemics is a complex task. We describe an exploratory analysis to identify economic risk factors that could aid epidemic risk assessment. A line list was constructed using the World Health Organization Disease Outbreak News (2016-2018) and economic indicators from the World Bank. Poisson regression employing forward imputations was used to establish relationships with the frequency with which countries reported public health events. Economic indicators demonstrated strong performance appropriate for further assessment in surveillance programming. In our analysis, three economic indicators were significantly associated to event reporting: how much the country's urban population changed, its average forest area, and a novel economic indicator we developed that assessed how much the gross domestic product changed per capita. Other economic indicators performed less well: changes in total, female, urban, and rural population sizes; population density; net migration; change in per cent forest area; total forest area; and another novel indicator, change in percent of trade as a fraction of the total economy. We then undertook a further analysis of the start of the current COVID-19 pandemic that revealed similar associations, but confounding by global disease burden is likely. Continued development of forecasting approaches capturing information relevant to whole-of-society factors (e.g., economic factors as assessed in our study) could improve the risk management process through earlier hazard identification and inform strategic decision processes in multisectoral strategies to preventing, detecting, and responding to pandemic-threat events.


Subject(s)
Disease Outbreaks , Economic Factors , Epidemics , Forests , Gross Domestic Product , Urban Population , Humans , Models, Statistical , Probability , Risk Factors , World Health Organization
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