scholarly journals The Effect of GDP and Distance on Timing of COVID-19 Spread in Chinese Provinces in 2020

2020 ◽  
Author(s):  
Alice Kuan ◽  
Mingxin Chen ◽  
David Bishai
Keyword(s):  
Proportional Hazards ◽  
Cox Proportional Hazards ◽  
Gdp Per Capita ◽  
Critical Factors ◽  
Explanatory Variables ◽  
Contagious Diseases ◽  
Per Capita ◽  
Travel Policy ◽  
Socioeconomic Data ◽  
Province Level

The geographical spread of COVID-19 across China's provinces provides the opportunity for retrospective analysis on contributors to the timing of the spread. Highly contagious diseases need to be seeded into populations and we hypothesized that greater distance from the epicenter in Wuhan, as well as higher province-level GDP per capita, would delay the time until a province detected COVID-19 cases. To test this hypothesis, we used province-level socioeconomic data such as GDP per capita and percentage of the population aged over 65, distance from the Wuhan epicenter, and health systems capacity in a Cox proportional hazards analysis of the determinants of each province's time until epidemic start. The start was defined by the number of days it took for each province to reach thresholds of 3, 5, 10, or 100 cases. We controlled for the number of hospital beds and physicians as these could influence the speed of case detection. Surprisingly, none of the explanatory variables had a statistically significant effect on the time it took for each province to get its first cases; the timing of COVID-19 spread appears to have been random with respect to distance, GDP, demography, and the strength of the health system. Looking to other factors, such as travel, policy, and lockdown measures, could provide additional insights on realizing most critical factors in the timing of spread.

scholarly journals Impact of Immigration Status on Cancer Outcomes in Ontario, Canada

2017 ◽  
Vol 13 (7) ◽  
pp. e602-e612 ◽  
Author(s):  
Matthew C. Cheung ◽  
Craig C. Earle ◽  
Hadas D. Fischer ◽  
Ximena Camacho ◽  
Ning Liu ◽  
...  
Keyword(s):  
Proportional Hazards ◽  
Hematologic Malignancies ◽  
Cox Proportional Hazards ◽  
Administrative Databases ◽  
Healthy Immigrant Effect ◽  
Recent Immigrants ◽  
Explanatory Variables ◽  
Specific Mortality ◽  
Cancer Specific Mortality ◽  
The Impact

Background: Prior studies have documented inferior health outcomes in vulnerable populations, including racial minorities and those with disadvantaged socioeconomic status. The impact of immigration on cancer-related outcomes is less clear. Methods: Administrative databases were linked to create a cohort of incident cancer cases (colorectal, lung, prostate, head and neck, breast, and hematologic malignancies) from 2000 to 2012 in Ontario, Canada. Cancer patients who immigrated to Canada (from 1985 onward) were compared with those who were Canadian born (or immigrated before 1985). Patients were followed from diagnosis until death (cancer-specific or all-cause). Cox proportional hazards models were estimated to determine the impact of immigration on mortality after adjusting for explanatory variables. Additional adjusted models studied the relationship of time since immigration and cancer-specific and overall mortality. Results: From 2000 to 2012, 11,485 cancer cases were diagnosed in recent immigrants (0 to 10 years in Canada), 17,844 cases in nonrecent immigrants (11 to 25 years), and 416,118 cases in nonimmigrants. After adjustment, the hazard of mortality was lower for recent immigrants (hazard ratio [HR], 0.843; 95% CI, 0.814 to 0.873) and nonrecent immigrants (HR, 0.902; 95% CI, 0.876 to 0.928) compared with nonimmigrants. Cancer-specific mortality was also lower for recent immigrants (HR, 0.857; 95% CI, 0.823 to 0.893) and nonrecent immigrants (HR, 0.907; 95% CI, 0.875 to 0.94). Among immigrants, each year from the original landing was associated with increased mortality (HR, 1.004; 95% CI, 1.000 to 1.009) and a trend to increased cancer-specific mortality (HR, 1.005; 95% CI, 0.999 to 1.010). Conclusion: Immigrants demonstrate a healthy immigrant effect, with lower cancer-specific mortality compared with Canadian-born individuals. This benefit seems to diminish over time, as the survival of immigrants from common cancers potentially converges with the Canadian norm.

scholarly journals Computing the Hazard Ratios Associated With Explanatory Variables Using Machine Learning Models of Survival Data

2021 ◽  
pp. 364-378
Author(s):  
Sameer Sundrani ◽  
James Lu
Keyword(s):  
Machine Learning ◽  
Survival Data ◽  
Proportional Hazards ◽  
Cox Proportional Hazards ◽  
Training Data ◽  
Data Sets ◽  
Data Set ◽  
Explanatory Variables ◽  
Cancer Data ◽  
Pan Cancer

PURPOSE The application of Cox proportional hazards (CoxPH) models to survival data and the derivation of hazard ratio (HR) are well established. Although nonlinear, tree-based machine learning (ML) models have been developed and applied to the survival analysis, no methodology exists for computing HRs associated with explanatory variables from such models. We describe a novel way to compute HRs from tree-based ML models using the SHapley Additive exPlanation values, which is a locally accurate and consistent methodology to quantify explanatory variables’ contribution to predictions. METHODS We used three sets of publicly available survival data consisting of patients with colon, breast, or pan cancer and compared the performance of CoxPH with the state-of-the-art ML model, XGBoost. To compute the HR for explanatory variables from the XGBoost model, the SHapley Additive exPlanation values were exponentiated and the ratio of the means over the two subgroups was calculated. The CI was computed via bootstrapping the training data and generating the ML model 1,000 times. Across the three data sets, we systematically compared HRs for all explanatory variables. Open-source libraries in Python and R were used in the analyses. RESULTS For the colon and breast cancer data sets, the performance of CoxPH and XGBoost was comparable, and we showed good consistency in the computed HRs. In the pan-cancer data set, we showed agreement in most variables but also an opposite finding in two of the explanatory variables between the CoxPH and XGBoost result. Subsequent Kaplan-Meier plots supported the finding of the XGBoost model. CONCLUSION Enabling the derivation of HR from ML models can help to improve the identification of risk factors from complex survival data sets and to enhance the prediction of clinical trial outcomes.

scholarly journals Government Spending and Real Exchange Rate Case of Albania

2013 ◽  
Vol 2 (4) ◽  
pp. 303 ◽  
Author(s):  
Edmira Cakrani ◽  
Pranvera Resulaj ◽  
Luciana Koprencka (Kabello)
Keyword(s):  
Exchange Rate ◽  
Real Exchange Rate ◽  
Unit Root ◽  
Direct Investment ◽  
Gdp Per Capita ◽  
Explanatory Variables ◽  
Real Gdp ◽  
Real Gdp Per Capita ◽  
Per Capita ◽  
The Impact

Various studies have found that governmentspending can lead to overestimation orunderestimation of the real exchange rate, depending on the composition of theseexpenditures. The purpose of this paper is toassess the impact of government spendingon real exchange rate in Albania. In this paper is used a log liner model with quarterlydata. Other explanatory variables in this model are: foreign direct investment, remittances,real GDP per capita, openness. Variables are tested for unit root and cointegration. Theresults indicate that government spendingis associated with overvaluation of realexchange rate in Albania.JEL Classification: E62; F31Various studies have found that governmentspending can lead to overestimation orunderestimation of the real exchange rate, depending on the composition of theseexpenditures. The purpose of this paper is toassess the impact of government spendingon real exchange rate in Albania. In this paper is used a log liner model with quarterlydata. Other explanatory variables in this model are: foreign direct investment, remittances,real GDP per capita, openness. Variables are tested for unit root and cointegration. Theresults indicate that government spendingis associated with overvaluation of realexchange rate in Albania.

scholarly journals DETERMINANTES DEL AUMENTO O DISMINUCIÓN DE EMPRESAS: ANÁLISIS POR ENTIDAD FEDERATIVA Y ESTRATO DE EDAD

2018 ◽  
Vol 37 (1) ◽  
Author(s):  
Rolando I. Valdez ◽  
Eder J. Noda-Ramírez
Keyword(s):  
Panel Data ◽  
Interest Rate ◽  
Gdp Per Capita ◽  
Panel Data Models ◽  
Ceteris Paribus ◽  
Explanatory Variables ◽  
Social Insecurity ◽  
Per Capita ◽  
The Interest Rate ◽  
Number Of Firms

En este trabajo, se pone a prueba la hipótesis de que los mismos factores afectan de manera distinta el aumento o disminución de empresas, según su edad. Para ello, se usan dos modelos de datos panel, cuya variable dependiente es el estrato de edad de la empresa: recién nacida, joven, adulta y mayor. En total, se estiman ocho ecuaciones utilizando variables explicativas de tipo económico y social. Entre los resultados más importantes destaca que el PIB, PIB per cápita, la TIIE, la Tasa de interés bancaria y la liquidez de la economía ejercen el mismo efecto, ceteris paribus, sobre la cantidad de empresas, independientemente de su edad. No obstante, la migración y la inseguridad afectan solo a las empresas recién nacidas y a las jóvenes. Abstract In this present study, the hypothesis that is tested is that the same factors diversely affect the ups and downs in the number of firms, taking into consideration their age. To prove this, there are two specific panel data models, whose dependent variable is the firm’s age stratum: infant, young, adult and elderly. Overall, eight equations are estimated, taking into account economic and social explanatory variables as well. The main results highlight that gdp, gdp per cápita, the interest rate, the banking interest rate and economic liquidity equally impact, ceteris paribus, the number of firms, independent to their age. However, migration and social insecurity impact only infant firms as well as young firms.

Reducing Inequality in Developing Countries Through Microfinance

2019 ◽  
pp. 281-301 ◽  
Author(s):  
Richardson Kojo Edeme ◽  
Chigozie Nelson Nkalu
Keyword(s):  
Developing Countries ◽  
Poverty Reduction ◽  
Impact Analysis ◽  
Linear Connection ◽  
Gdp Per Capita ◽  
Positive Sign ◽  
Explanatory Variables ◽  
Per Capita ◽  
Macro Variables ◽  
The Impact

Even though microfinance is expected to significantly affect macro variables such as inequality, poverty, and human development, there has not been enough empirical study on the impact analysis at the macro level, such as the effect of microfinance on inequality, especially in developing countries of Africa. This chapter, therefore, provides a detailed empirical analysis of the correlation between microfinance and inequality in West Africa sub-region. The correlation coefficient shows that although there is a positive linear connection between the possibilities of microfinance to reduce inequality; it has not contributed significantly to poverty reduction with the independent variables. The findings further suggest that the most robust explanatory variables for inequality reduction are GDP per capita and democracy which are invariably significant with positive sign. Taken together, these findings reinforce the intuition that greater democracy and provision and expansion of financial infrastructures especially in backward countries of the region are necessary for microfinance to thrive and contribute abundantly to inequality reduction.

Age at Exposure to Parental Suicide and the Subsequent Risk of Suicide in Young People

Crisis ◽  
2018 ◽  
Vol 39 (1) ◽  
pp. 27-36 ◽  
Author(s):  
Kuan-Ying Lee ◽  
Chung-Yi Li ◽  
Kun-Chia Chang ◽  
Tsung-Hsueh Lu ◽  
Ying-Yeh Chen
Keyword(s):  
Young People ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Birth Registry ◽  
Data Set ◽  
Parental Suicide ◽  
The Impact ◽  
Age At Exposure

Abstract. Background: We investigated the age at exposure to parental suicide and the risk of subsequent suicide completion in young people. The impact of parental and offspring sex was also examined. Method: Using a cohort study design, we linked Taiwan's Birth Registry (1978–1997) with Taiwan's Death Registry (1985–2009) and identified 40,249 children who had experienced maternal suicide (n = 14,431), paternal suicide (n = 26,887), or the suicide of both parents (n = 281). Each exposed child was matched to 10 children of the same sex and birth year whose parents were still alive. This yielded a total of 398,081 children for our non-exposed cohort. A Cox proportional hazards model was used to compare the suicide risk of the exposed and non-exposed groups. Results: Compared with the non-exposed group, offspring who were exposed to parental suicide were 3.91 times (95% confidence interval [CI] = 3.10–4.92 more likely to die by suicide after adjusting for baseline characteristics. The risk of suicide seemed to be lower in older male offspring (HR = 3.94, 95% CI = 2.57–6.06), but higher in older female offspring (HR = 5.30, 95% CI = 3.05–9.22). Stratified analyses based on parental sex revealed similar patterns as the combined analysis. Limitations: As only register-­based data were used, we were not able to explore the impact of variables not contained in the data set, such as the role of mental illness. Conclusion: Our findings suggest a prominent elevation in the risk of suicide among offspring who lost their parents to suicide. The risk elevation differed according to the sex of the afflicted offspring as well as to their age at exposure.

Searching for new Sources of growth. Are the Developing Countries Catching up with the Developed ones?

2015 ◽  
pp. 30-53
Author(s):  
V. Popov
Keyword(s):  
Developing Countries ◽  
Western Europe ◽  
Gdp Per Capita ◽  
The West ◽  
Catching Up ◽  
The Us ◽  
The World ◽  
Economic Divergence ◽  
Per Capita ◽  
Relative Standing

This paper examines the trajectory of growth in the Global South. Before the 1500s all countries were roughly at the same level of development, but from the 1500s Western countries started to grow faster than the rest of the world and PPP GDP per capita by 1950 in the US, the richest Western nation, was nearly 5 times higher than the world average and 2 times higher than in Western Europe. Since 1950 this ratio stabilized - not only Western Europe and Japan improved their relative standing in per capita income versus the US, but also East Asia, South Asia and some developing countries in other regions started to bridge the gap with the West. After nearly half of the millennium of growing economic divergence, the world seems to have entered the era of convergence. The factors behind these trends are analyzed; implications for the future and possible scenarios are considered.

Atlas of economic complexity, Russian regional pages

2018 ◽  
pp. 71-91 ◽  
Author(s):  
I. L. Lyubimov ◽  
M. V. Lysyuk ◽  
M. A. Gvozdeva
Keyword(s):  
Value Added ◽  
Growth Strategy ◽  
Gdp Per Capita ◽  
Export Diversification ◽  
Russian Regions ◽  
Economic Complexity ◽  
Diversification Strategies ◽  
Central Regions ◽  
Per Capita ◽  
Regions Of Russia

Well-established results indicate that export diversification might be a better growth strategy for an emerging economy as long as its GDP per capita level is smaller than an empirically defined threshold. As average incomes in Russian regions are likely to be far below the threshold, it might be important to estimate their diversification potential. The paper discusses the Atlas of economic complexity for Russian regions created to visualize regional export baskets, to estimate their complexity and evaluate regional export potential. The paper’s results are consistent with previous findings: the complexity of export is substantially higher and diversification potential is larger in western and central regions of Russia. Their export potential might become larger if western and central regions, first, try to join global value added chains and second, cooperate and develop joint diversification strategies. Northern and eastern regions are by contrast much less complex and their diversification potential is small.

The Reproduction Vector of the Russian Economy: 1999-2007

2008 ◽  
pp. 94-109 ◽  
Author(s):  
D. Sorokin
Keyword(s):  
Economic Policy ◽  
Human Capital ◽  
Growth Rates ◽  
Developed Countries ◽  
Gdp Per Capita ◽  
Real Sector ◽  
Russian Economy ◽  
Per Capita ◽  
The Developed Countries

The problem of the Russian economy’s growth rates is considered in the article in the context of Russia’s backwardness regarding GDP per capita in comparison with the developed countries. The author stresses the urgency of modernization of the real sector of the economy and the recovery of the country’s human capital. For reaching these goals short- or mid-term programs are not sufficient. Economic policy needs a long-term (15-20 years) strategy, otherwise Russia will be condemned to economic inertia and multiplying structural disproportions.

The monetary value of disability-adjusted-life-years lost in Kenya in 2017.pdf

2019 ◽  
Author(s):  
Joses Kirigia ◽  
Rose Nabi Deborah Karimi Muthuri
Keyword(s):  
New York ◽  
International Monetary Fund ◽  
Health Expenditure ◽  
Analytical Framework ◽  
World Health ◽  
Gdp Per Capita ◽  
Current Health ◽  
Monetary Value ◽  
Life Years ◽  
Per Capita

<div>A variant of human capital (or net output) analytical framework was applied to monetarily value DALYs lost from 166 diseases and injuries. The monetary value of each of the 166 diseases (or injuries) was obtained through multiplication of the net 2019 GDP per capita for Kenya by the number of DALYs lost from each specific cause. Where net GDP per capita was calculated by subtracting current health expenditure from the GDP per capita. </div><div> </div><p>The DALYs data for the 166 causes were from IHME (Global Burden of Disease Collaborative Network, 2018), GDP per capita data from the International Monetary Fund world economic outlook database (International Monetary Fund, 2019), and the current health expenditure per person data from the WHO Global Health Expenditure Database (World Health Organization, 2019b). A model consisting of fourteen equations was calculated with Excel Software developed by Microsoft (New York).</p><p> </p>

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