IDEAS home Printed from https://ideas.repec.org/a/dem/demres/v40y2019i15.html

Lung cancer mortality in historical context. How stable are spatial patterns of smoking over time?

Author

Listed:
  • Krzysztof Czaderny

    (Uniwersytet Ekonomiczny w Krakowie)

Abstract

Background: The lands of the Recovered Territories were acquired by Poland in 1945 and populated by almost 5 million Polish settlers mostly from central provinces in the country and from ex-Polish territories in the east. Transgenerational persistence of informal institutions provides a context for the study. Objective: Persistence of the spatial variation of age-adjusted lung cancer mortality and their causes were evaluated. The high smoking prevalence in the Recovered Territories was hypothesised to be related to the age of the local communities and persistence of informal institutions. Methods: A spatial scan statistic was used to detect clusters of elevated lung cancer mortality. A two stages least squares regression model with heteroscedasticity and autocorrelation consistent standard errors was fitted to identify determinants of a spatial clustering of lung cancer mortality. Results: A strong west-to-east trend of lung cancer mortality is consistent with spatial patterns of tobacco use in interwar, post-war, and current Poland. Age-adjusted lung cancer mortality was contrastingly high in 1980–1984 in Masuria, West Pomerania, and Silesia. Tobacco prevalence and unbalanced dietary patterns were found to be associated with a spatial clustering of lung cancer mortality in the country. Conclusions: Migrant communities of the Recovered Territories were more likely to take up and continue smoking. This effect was enhanced by the young age of communities and population, as well as by high urbanisation and availability of employment outside agriculture in the Recovered Territories. The identified spatial pattern is consistent with elevated frequency of other socially condemned behaviours in the Recovered Territories, including crime, nonmarital births, and divorce. Contribution: Phantom borders reflecting different historical legacies can structure health-related behaviours. Spatial distribution of lung cancer mortality is persistent over time, particularly in women.

Suggested Citation

  • Krzysztof Czaderny, 2019. "Lung cancer mortality in historical context. How stable are spatial patterns of smoking over time?," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 40(15), pages 395-416.
    • Handle: RePEc:dem:demres:v:40:y:2019:i:15
    DOI: 10.4054/DemRes.2019.40.15
    as

    Download full text from publisher

    File URL: https://www.demographic-research.org/volumes/vol40/15/40-15.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.4054/DemRes.2019.40.15?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Kelejian, Harry H. & Prucha, Ingmar R., 2007. "HAC estimation in a spatial framework," Journal of Econometrics, Elsevier, vol. 140(1), pages 131-154, September.
    2. Alberto Bisin & Thierry Verdier, 2000. ""Beyond the Melting Pot": Cultural Transmission, Marriage, and the Evolution of Ethnic and Religious Traits," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 115(3), pages 955-988.
    3. Christina Westphal, 2016. "Healthy Migrants? Health Selection of Internal Migrants in Germany," European Journal of Population, Springer;European Association for Population Studies, vol. 32(5), pages 703-730, December.
    4. Conley, T. G., 1999. "GMM estimation with cross sectional dependence," Journal of Econometrics, Elsevier, vol. 92(1), pages 1-45, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Frank Davenport, 2017. "Estimating standard errors in spatial panel models with time varying spatial correlation," Papers in Regional Science, Wiley Blackwell, vol. 96, pages 155-177, March.
    2. Yencha, Christopher, 2023. "Spatial heterogeneity and non-fungible token sales: Evidence from Decentraland LAND sales," Finance Research Letters, Elsevier, vol. 58(PA).
    3. Lee, Jungyoon & Robinson, Peter M., 2016. "Series estimation under cross-sectional dependence," Journal of Econometrics, Elsevier, vol. 190(1), pages 1-17.
    4. Jungyoon Lee & Peter Robinson, 2016. "Series estimation under cross-sectional dependence," LSE Research Online Documents on Economics 63380, London School of Economics and Political Science, LSE Library.
    5. Pakel, Cavit, 2019. "Bias reduction in nonlinear and dynamic panels in the presence of cross-section dependence," Journal of Econometrics, Elsevier, vol. 213(2), pages 459-492.
    6. Yun, Seong Do & Gramig, Benjamin M & Delgado, Michael S. & Florax, Raymond J.G.M., 2015. "Does Spatial Correlation Matter in Econometric Models of Crop Yield Response and Weather?," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205465, Agricultural and Applied Economics Association.
    7. Oded Galor & Ömer Özak, 2016. "The Agricultural Origins of Time Preference," American Economic Review, American Economic Association, vol. 106(10), pages 3064-3103, October.
    8. Lambert, Dayton M. & Florax, Raymond J.G.M. & Cho, Seong-Hoon, 2008. "Bandwidth Selection For Spatial Hac And Other Robust Covariance Estimators," Working papers 44258, Purdue University, Department of Agricultural Economics.
    9. Weining Wang & Jeffrey M. Wooldridge & Mengshan Xu & Cuicui Lu & Chaowen Zheng, 2025. "Using generalized estimating equations to estimate nonlinear models with spatial data," Econometric Reviews, Taylor & Francis Journals, vol. 44(2), pages 214-242, February.
    10. repec:asg:wpaper:1045 is not listed on IDEAS
    11. Natalia Bailey & Sean Holly & M. Hashem Pesaran, 2016. "A Two‐Stage Approach to Spatio‐Temporal Analysis with Strong and Weak Cross‐Sectional Dependence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(1), pages 249-280, January.
    12. Michael P. Leung, 2019. "Inference in Models of Discrete Choice with Social Interactions Using Network Data," Papers 1911.07106, arXiv.org.
    13. Dayton M. Lambert & Kevin T. McNamara, 2009. "Location determinants of food manufacturers in the United States, 2000–2004: are nonmetropolitan counties competitive?," Agricultural Economics, International Association of Agricultural Economists, vol. 40(6), pages 617-630, November.
    14. Gupta, Abhimanyu, 2018. "Autoregressive spatial spectral estimates," Journal of Econometrics, Elsevier, vol. 203(1), pages 80-95.
    15. Oded Galor & Ömer Özak & Assaf Sarid, 2018. "Geographical Roots of the Coevolution of Cultural and Linguistic Traits," NBER Working Papers 25289, National Bureau of Economic Research, Inc.
    16. Kojevnikov, Denis & Marmer, Vadim & Song, Kyungchul, 2021. "Limit theorems for network dependent random variables," Journal of Econometrics, Elsevier, vol. 222(2), pages 882-908.
    17. Yixiao Sun & Min Seong Kim, 2015. "Asymptotic F-Test in a GMM Framework with Cross-Sectional Dependence," The Review of Economics and Statistics, MIT Press, vol. 97(1), pages 210-233, March.
    18. Rabovič, Renata & Čížek, Pavel, 2023. "Estimation of spatial sample selection models: A partial maximum likelihood approach," Journal of Econometrics, Elsevier, vol. 232(1), pages 214-243.
    19. Min Seong Kim, 2021. "Robust Inference for Diffusion-Index Forecasts with Cross-Sectionally Dependent Data," Working papers 2021-04, University of Connecticut, Department of Economics.
    20. Kim, Min Seong & Sun, Yixiao, 2013. "Heteroskedasticity and spatiotemporal dependence robust inference for linear panel models with fixed effects," Journal of Econometrics, Elsevier, vol. 177(1), pages 85-108.
    21. Vincent Boucher & Yann Bramoullé, 2020. "Binary Outcomes and Linear Interactions," AMSE Working Papers 2038, Aix-Marseille School of Economics, France.

    More about this item

    Keywords

    ;
    ;
    ;

    JEL classification:

    • J1 - Labor and Demographic Economics - - Demographic Economics
    • Z0 - Other Special Topics - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:dem:demres:v:40:y:2019:i:15. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Editorial Office (email available below). General contact details of provider: https://www.demogr.mpg.de/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.