Health Care Expenditure and GDP in African Countries: Evidence from Semiparametric Estimation with Panel Data

Lv, Zhike; Zhu, Huiming

doi:10.1155/2014/905747

Cited by 20 publications

(19 citation statements)

References 28 publications

(36 reference statements)

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“…( Fuchs, 2013 ) concluded that the rate of growth in health care expenditures appears to have been substantially related to the growth of the GDP. ( Lorenzoni, Belloni, & Sassi, 2014 ), ( Rtveladze et al, 2014) , ( Ozturk & Topcu, 2014 ), ( Lv & Zhu, 2014 ), and ( Pascual & Gonzaacute, 2014 ) demonstrated the relationship between health expenditures and economic growth in OECD countries and other regions in the world. The findings of these articles indicate that GDP is the most important factor determining health care expenditures of countries.…”

Section: Introductionmentioning

confidence: 97%

Health Care Expenditure and GDP in Oil Exporting Countries: Evidence From OPEC Data, 1995-2012

Fazaeli

Ghaderi

Salehi

2015

GJHS

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Background:There is a large body of literature examining income in relation to health expenditures. The share of expenditures in health sector from GDP in developed countries is often larger than in non-developed countries, suggesting that as the level of economic growth increases, health spending increase, too.Objectives:This paper estimates long-run relationships between health expenditures and GDP based on panel data of a sample of 12 countries of the Organization of the Petroleum Exporting Countries (OPEC), using data for the period 1995-2012.Patients & Methods:We use panel data unit root tests, cointegration analysis and ECM model to find long-run and short-run relation. This study examines whether health is a luxury or a necessity for OPEC countries within a unit root and cointegration framework.Results:Panel data analysis indicates that health expenditures and GDP are co-integrated and have Engle and Granger causality. In addition, in oil countries that have oil export income, the share of government expenditures in the health sector is often greater than in private health expenditures similar developed countries.Conclusions:The findings verify that health care is not a luxury good and income has a robust relationship to health expenditures in OPEC countries.

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Section: Introductionmentioning

confidence: 97%

Health Care Expenditure and GDP in Oil Exporting Countries: Evidence From OPEC Data, 1995-2012

Fazaeli

Ghaderi

Salehi

2015

GJHS

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“…Most of the studies on determinants of healthcare expenditure have used demand function approaches to analyse the factors explaining healthcare expenditures, where healthcare expenditures are hypothesized to be a function of income (Newhouse, 1977) and a selection of non‐income variables such as population density (Crivelli et al, 2006; Hosoya, 2014), population's age structure (Barkat, Mrabet, & Alsamara, 2016; Okunade, Karakus, & Okeke, 2004), foreign aid (Barkat et al, 2016; Murthy & Okunade, 2009) and political stability (Biadgilign et al, 2019; Imoughele & Ismaila, 2013). While some models have applied either cross‐section regression (Murthy & Okunade, 2009) or time series regression (Tajudeen, Tajudeen, & Dauda, 2018), most studies have employed panel data models in both developed countries (Baltagi & Moscone, 2010; Murthy & Okunade, 2016) and developing countries (Barkat et al, 2016; Barkat, Sbia, & Maouchi, 2019; Lv & Zhu, 2014).…”

Section: Literature Reviewmentioning

confidence: 99%

Spillover effects in public healthcare expenditure in Sub‐Saharan Africa: A spatial panel analysis

Jeetoo

2020

African Development Review

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While previous studies have neglected the spatial interaction effects in modelling healthcare expenditure across countries, this study provides the first thorough spatial analysis of public healthcare expenditures between countries so far. It investigates the determinants of public healthcare expenditure in Sub-Saharan Africa (SSA) using a balanced panel of 43 SSA countries over the period 2000 to 2015. Both spatial interdependence and individual heterogeneity are accounted for through the application of the spatial autoregressive model so as to avoid potential bias and inefficiencies in parameter estimates. In addition to statistically significant effects of income, population density, official development assistance, and political stability, the results indicate the existence of positive spatial dependence in public healthcare expenditure in SSA. This suggests that governments in SSA countries tend to base their decision to allocate healthcare expenditure on that of nearby countries. Moreover, the results reveal significant impacts of population density and official development assistance of neighbouring countries on a country's level of public healthcare expenditure.

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“…Angola spends 3.4% of their GDP on healthcare expenditure. Republic of Congo spends 2.5%; Eritrea spends 2.6%, and Kuwait spends 2.7% of their GDP on health compared to the Unites States which spends 17.9% of their GDP [ 1 ]. Developing countries struggle under the burden of human immunodeficiency virus (HIV), tuberculosis and malaria [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning to Improve the Effectiveness of ANRS in Predicting HIV Drug Resistance

Singh

2017

Healthc Inform Res

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ObjectivesHuman immunodeficiency virus infection and acquired immune deficiency syndrome (HIV/AIDS) is one of the major burdens of disease in developing countries, and the standard-of-care treatment includes prescribing antiretroviral drugs. However, antiretroviral drug resistance is inevitable due to selective pressure associated with the high mutation rate of HIV. Determining antiretroviral resistance can be done by phenotypic laboratory tests or by computer-based interpretation algorithms. Computer-based algorithms have been shown to have many advantages over laboratory tests. The ANRS (Agence Nationale de Recherches sur le SIDA) is regarded as a gold standard in interpreting HIV drug resistance using mutations in genomes. The aim of this study was to improve the prediction of the ANRS gold standard in predicting HIV drug resistance.MethodsA genome sequence and HIV drug resistance measures were obtained from the Stanford HIV database (http://hivdb.stanford.edu/). Feature selection was used to determine the most important mutations associated with resistance prediction. These mutations were added to the ANRS rules, and the difference in the prediction ability was measured.ResultsThis study uncovered important mutations that were not associated with the original ANRS rules. On average, the ANRS algorithm was improved by 79% ± 6.6%. The positive predictive value improved by 28%, and the negative predicative value improved by 10%.ConclusionsThe study shows that there is a significant improvement in the prediction ability of ANRS gold standard.

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Health Care Expenditure and GDP in African Countries: Evidence from Semiparametric Estimation with Panel Data

Cited by 20 publications

References 28 publications

Health Care Expenditure and GDP in Oil Exporting Countries: Evidence From OPEC Data, 1995-2012

Health Care Expenditure and GDP in Oil Exporting Countries: Evidence From OPEC Data, 1995-2012

Spillover effects in public healthcare expenditure in Sub‐Saharan Africa: A spatial panel analysis

Machine Learning to Improve the Effectiveness of ANRS in Predicting HIV Drug Resistance

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