Journals / NİŞANTAŞI ÜNİVERSİTESİ SOSYAL BİLİMLER DERGİSİ / 2026 / Cilt: 13 Sayı: 2

DETERMINANTS OF LIFE EXPECTANCY AT BIRTH IN OECD COUNTRIES: AN EMPIRICAL ANALYSIS OF ECONOMIC, HEALTH, AND ENVIRONMENTAL FACTORS

OECD ÜLKELERİNDE DOĞUMDA BEKLENEN YAŞAM SÜRESİNİN BELİRLEYİCİLERİ: EKONOMİK, SAĞLIK VE ÇEVRESEL FAKTÖRLERİN AMPİRİK ANALİZİ

Mustafa Uzan(SÜLEYMAN DEMİREL ÜNİVERSİTESİ, SOSYAL BİLİMLER ENSTİTÜSÜ)0000-0002-8515-5639
Adnan Karaıbrahımoglu(SÜLEYMAN DEMİREL ÜNİVERSİTESİ, TIP FAKÜLTESİ)0000-0002-8277-0281
Pages
1153–1167
Publication Date
2025-12-31
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Policymakers should prioritize systemic efficiency, preventive services, and universal access to primary care.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study aims to identify the key economic, healthcare system, and environmental determinants of life expectancy at birth (LEB) across OECD countries.
📊
Key Findings & Data:It focuses on GDP per capita, public health expenditure per capita, health expenditure as a share of GDP, percentage of population exposed to air pollution (PM2.5), and the proportion of population aged 65 and over.
💡
Conclusion & Impact:Policymakers should prioritize systemic efficiency, preventive services, and universal access to primary care.
🔬Design:Araştırma Makalesi
📍Scope:2021 Verileri
⚙️Variables:doğumda beklenen yaşam süresi · sağlık harcamaları · hava kirliliği

Abstract

This study aims to identify the key economic, healthcare system, and environmental determinants of life expectancy at birth (LEB) across OECD countries. It focuses on GDP per capita, public health expenditure per capita, health expenditure as a share of GDP, percentage of population exposed to air pollution (PM2.5), and the proportion of population aged 65 and over. A cross-sectional design was adopted using 2021 data from OECD and World Bank databases. Data from 38 countries were analyzed using IBM SPSS Statistics 26. Descriptive statistics and Pearson correlations were first calculated, followed by multiple linear and binary logistic regression analyses. GDP per capita showed a statistically significant positive effect on LEB (p<0.01). Surprisingly, public health expenditure per capita had a negative and significant impact (p<0.05), suggesting possible inefficiencies in resource allocation. Health expenditure as a share of GDP was positively associated with LEB, though its effect was marginally significant. Air pollution and the proportion of elderly population were not statistically significant. Life expectancy is shaped not only by the amount of spending but by how effectively and equitably resources are utilized. Economic prosperity remains a key driver of improved health outcomes. Policymakers should prioritize systemic efficiency, preventive services, and universal access to primary care.

Özet

Bu çalışma, OECD ülkelerinde doğumda beklenen yaşam süresini (DBYS) etkileyen başlıca ekonomik, sağlık sistemi ve çevresel faktörleri belirlemeyi amaçlamaktadır. Analiz, kişi başına gayri safi yurtiçi hasıla (GSYİH), kamu sağlık harcaması, sağlık harcamalarının GSYİH içindeki oranı, hava kirliliğine maruz kalan nüfusun oran (PM2.5) ve 65 yaş üstü nüfus oranı değişkenlerine odaklanmaktadır. Araştırmada kesitsel analiz deseni benimsenmiş ve 2021 yılına ait OECD ve Dünya Bankası verileri kullanılmıştır. 38 ülkeye ait veriler SPSS 26 programı aracılığıyla analiz edilmiştir. Öncelikle betimleyici istatistikler ve Pearson korelasyonları hesaplanmış; ardından çoklu doğrusal regresyon ve ikili lojistik regresyon analizleri gerçekleştirilmiştir. Kişi başına düşen gelir düzeyinin DBYS üzerinde pozitif ve anlamlı bir etkisi saptanmıştır (p<0.01). Kamu kişi başı sağlık harcamasının ise beklenmedik şekilde negatif yönlü ve anlamlı etkisi bulunmuştur (p<0.05). Sağlık harcamalarının GSYİH oranı pozitif ancak anlamlılık sınırında kalmıştır. Hava kirliliği ve yaşlı nüfus oranı değişkenlerinin etkileri anlamlı bulunmamıştır. Yaşam süresi yalnızca harcama miktarıyla değil, sağlık sistemlerinin yapısı, hizmet kapsayıcılığı ve kaynak verimliliğiyle şekillenmektedir. Ekonomik refah, yaşam süresini artıran temel belirleyici olarak öne çıkmaktadır. Politika yapıcıların sağlık sistemlerinin organizasyonel etkinliğine ve önleyici sağlık hizmetlerine odaklanmaları gerekmektedir.

🔖

Cite This Article

Uzan, M., Karaıbrahımoglu, A. (2025). DETERMINANTS OF LIFE EXPECTANCY AT BIRTH IN OECD COUNTRIES: AN EMPIRICAL ANALYSIS OF ECONOMIC, HEALTH, AND ENVIRONMENTAL FACTORS. NİŞANTAŞI ÜNİVERSİTESİ SOSYAL BİLİMLER DERGİSİ, Cilt: 13 Sayı: 2, 1153–1167. https://doi.org/10.52122/nisantasisbd.1748907

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (41)

  1. Aburto, J. M., Schöley, J., Kashnitsky, I., Zhang, L., Rahal, C., Missov, T. I., Mills, M. C., Dowd, J. B., & Kashyap, R. (2022). Quantifying impacts of the COVID-19 pandemic through life-expectancy losses: a population-level study of 29 countries. International journal of epidemiology, 51(1), 63–74. https://doi.org/10.1093/ije/dyab207.
  2. Anderson, G. F., & Hussey, P. S. (2001). Comparing health system performance in OECD countries. Health Affairs, 20(3), 219-232. https://doi.org/10.1377/hlthaff.20.3.219.
  3. Burnett, R., Chen, H., Szyszkowicz, M., Fann, N., Hubbell, B., Pope, C. A.,. & Spadaro, J. V. (2018). Global estimates of mortality associated with long-term exposure to outdoor fine particulate matter. Proceedings of the National Academy of Sciences, 115(38), 9592-9597. https://doi.org/10.1073/pnas.1803222115.
  4. Cohen, A. J., Brauer, M., Burnett, R., Anderson, H. R., Frostad, J., Estep, K.,. & Forouzanfar, M. H. (2017). Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015. The Lancet, 389(10082), 1907-1918. https://doi.org/10.1016/S0140-6736(17)30505-6.
  5. Cutler, D., Deaton, A., & Lleras-Muney, A. (2006). The determinants of mortality. Journal of Economic Perspectives, 20(3), 97–120. https://doi.org/10.1257/jep.20.3.97.
  6. Deaton, A. (2003). Health, inequality, and economic development. Journal of Economic Literature, 41(1), 113-158. https://doi.org/10.1257/002205103321544710.
  7. Enroth, L., Jasilionis, D., Németh, L., Strand, B., Tanjung, I., Sundberg, L., Fors, S., Jylhä, M., & Brønnum-Hansen, H. (2022). Changes in socioeconomic differentials in old age life expectancy in four Nordic countries: the impact of educational expansion and education-specific mortality. European Journal of Ageing, 19, 161-173. https://doi.org/10.1007/s10433-022-00698-y.
  8. Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). Sage.
  9. Filmer, D., & Pritchett, L. (1999). The impact of public spending on health: does money matter? Social Science & Medicine, 49(10), 1309-1323. https://doi.org/10.1016/S0277-9536(99)00150-1.
  10. GBD 2019 Risk Factors Collaborators. (2020). Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. The Lancet, 396(10258), 1223–1249. https://doi.org/10.1016/S0140-6736(20)30752-2.
  11. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Pearson.
  12. Hitiris, T., & Posnett, J. (1992). The determinants and effects of health expenditure in developed countries. Journal of Health Economics, 11(2), 173-181. https://doi.org/10.1016/0167-6296(92)90033-W.
  13. Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied Logistic Regression (3rd ed.). Wiley.
  14. Islam, N., Shkolnikov, V. M., Acosta, R. J., Klimkin, I., Kawachi, I., Irizarry, R. A., & Abraido-Lanza, A. F. (2021). Excess deaths associated with COVID-19 pandemic in 2020: age and sex disaggregated time series analysis. BMJ, 373, n1137. https://doi.org/10.1136/bmj.n1137.
  15. Joumard, I., André, C., Nicq, C., & Chatal, O. (2010). Health status determinants: lifestyle, environment, health care resources and efficiency. OECD Economics Department Working Papers, No. 627. OECD Publishing. https://doi.org/10.1787/5kmd8st4wzwl-en.
  16. Kleinbaum, D. G., & Klein, M. (2010). Logistic Regression: A Self-Learning Text (3rd ed.). Springer.
  17. Kontis, V., Bennett, J., Mathers, C., Li, G., Foreman, K., & Ezzati, M. (2017). Future life expectancy in 35 industrialised countries: projections with a Bayesian model ensemble. Lancet (London, England), 389, 1323 - 1335. https://doi.org/10.1016/S0140-6736(16)32381-9.
  18. Mackenbach, J. P., Stirbu, I., Roskam, A. J. R., Schaap, M. M., Menvielle, G., Leinsalu, M., & Kunst, A. E. (2013). Socioeconomic inequalities in health in 22 European countries. New England Journal of Medicine, 358(23), 2468-2481. https://doi.org/10.1056/NEJMsa0707519.
  19. Marmot, M. (2020). The Health Gap: The Challenge of an Unequal World. Bloomsbury Publishing.
  20. Menard, S. (2002). Applied Logistic Regression Analysis (2nd ed.). Sage.
  21. Mishra, V. (2020). India’s Projected Aged Population (65+), Projected Life Expectancy at Birth and Insecurities Faced by Aged Population. Ageing International, 45, 72-84. https://doi.org/10.1007/S12126-019-09350-0.
  22. Newhouse, J. P. (1992). Medical care costs: how much welfare loss? Journal of Economic Perspectives, 6(3), 3-21. https://doi.org/10.1257/jep.6.3.3.
  23. Nixon, J., & Ulmann, P. (2006). The relationship between health care expenditure and health outcomes: Evidence and caveats for a causal link. European Journal of Health Economics, 7(1), 7-18. https://doi.org/10.1007/s10198-005-0336-8.
  24. O'Brien, R. M. (2007). A caution regarding rules of thumb for variance inflation factors. Quality & Quantity, 41(5), 673–690. https://doi.org/10.1007/s11135-006-9018-6.
  25. OECD. (2016). The Economic Consequences of Outdoor Air Pollution. OECD Publishing. https://doi.org/10.1787/9789264257474-en. Erişim Tarihi. 25.04.2025
  26. OECD. (2019). Health at a Glance 2019: OECD Indicators. OECD Publishing. https://doi.org/10.1787/4dd50c09-en Erişim Tarihi. 25.04.2025
  27. OECD (2021), Health at a Glance 2021: OECD Indicators, OECD Publishing, Paris, https://doi.org/10.1787/ae3016b9-en. Erişim Tarihi. 25.04.2025
  28. OECD. (2023a). Life expectancy at birth (indicator). https://doi.org/10.1787/27e0fc9d-en Erişim Tarihi. 25.04.2025
  29. OECD (2023b). Health at a Glance 2023: OECD Indicators. OECD Publishing, Paris, https://doi.org/10.1787/7a7afb35-en. Erişim Tarihi. 25.04.2025
  30. Pampel, F. C. (2000). Logistic Regression: A Primer. Sage.
  31. Pega, F., Carter, K., Blakely, T., & Atkinson, J. (2021). Health effects of income security policies: a systematic review and meta-analysis. The Lancet Public Health, 6(3), e139–e148. https://doi.org/10.1016/S2468-2667(20)30210-9
  32. Pope, C. A., Ezzati, M., & Dockery, D. W. (2009). Fine-particulate air pollution and life expectancy in the United States. New England Journal of Medicine, 360(4), 376-386. https://doi.org/10.1056/NEJMsa0805646.
  33. Preston, S. H. (1975). The changing relation between mortality and level of economic development. Population Studies, 29(2), 231-248. https://doi.org/10.1080/00324728.1975.10410201.
  34. Raleigh, V. S. (2019). Trends in life expectancy in EU and other OECD countries: Why are improvements slowing? OECD Health Working Papers, No. 108. OECD Publishing. https://doi.org/10.1787/223159ab-en.
  35. Tıraş, H. H., & Özbek, S. (2020). OECD ülkelerinde doğuşta yaşam beklentisinin belirleyicilerinin ekonometrik analizi. Business & Management Studies: An International Journal, 8(3), 2893–2923. https://doi.org/10.15295/bmij.v8i3.1542.
  36. Trias-Llimós, S., Riffe, T., & Bilal, U. (2023). Monitoring life expectancy levels during the COVID-19 pandemic: The impact on population health. BMJ Global Health, 8, e011681. https://doi.org/10.1136/bmjgh-2022-011681.
  37. Vollset, S. E., Goren, E., Yuan, C. W., Cao, J., Smith, A. E., Hsiao, T.,. & Murray, C. J. L. (2020). Fertility, mortality, migration, and population scenarios for 195 countries and territories from 2017 to 2100: a forecasting analysis for the Global Burden of Disease Study. The Lancet, 396(10258), 1285–1306. https://doi.org/10.1016/S0140-6736(20)30677-2.
  38. World Health Organization (WHO). (2021). WHO global air quality guidelines: particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide. World Health Organization. https://apps.who.int/iris/handle/10665/345329.
  39. Burnett, R., Chen, H., Szyszkowicz, M., Fann, N., Hubbell, B., Pope, C. A.,. & Spadaro, J. V. (2018). Global estimates of mortality associated with long-term exposure to outdoor fine particulate matter. Proceedings of the National Academy of Sciences, 115(38), 9592-9597. https://doi.org/10.1073/pnas.1803222115.
  40. Cohen, A. J., Brauer, M., Burnett, R., Anderson, H. R., Frostad, J., Estep, K.,. & Forouzanfar, M. H. (2017). Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015. The Lancet, 389(10082), 1907-1918. https://doi.org/10.1016/S0140-6736(17)30505-6.
  41. Vollset, S. E., Goren, E., Yuan, C. W., Cao, J., Smith, A. E., Hsiao, T.,. & Murray, C. J. L. (2020). Fertility, mortality, migration, and population scenarios for 195 countries and territories from 2017 to 2100: a forecasting analysis for the Global Burden of Disease Study. The Lancet, 396(10258), 1285–1306. https://doi.org/10.1016/S0140-6736(20)30677-2.