Analyzing a Statistical Method of Estimating Respiratory Deaths based on the Thailand Verbal Autopsy study

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Pradthana Minsan

Abstract

     The archives record of the causes of death in Thailand using the death registration (DR) system is important source of mortality data. Over the past two decades, Thailand has presented many formats to enhance civil registration and vial statistics systems. More than 30% of death unregistered and about 40% of deaths registered have given the cause of death as “ill-defined”. The aim of this study was to propose a statistical model to estimate percentages of respiratory deaths in Thailand based on a sample of 9,644 deaths from the 2005 Verbal Autopsy (VA) study. Logistic regression was used to predict respiratory deaths classified by three factors, province, gender-age group and cause of each death. The receiver operating characteristic (ROC) curve was used to assess accuracy of the model prediction and the area under the ROC curve measures discrimination model which has ability to correctly predict those with and without respiratory deaths. Province, gender-age group and cause of death were statistically significant associated with respiratory deaths. The area under ROC curve was 0.7 with a false positive rate of 5.52% and sensitivity of 39.2%. Moreover, the results revealed that the under-reporting of respiratory deaths were those registered as tuberculosis, septicemia and other CVD cases. In conclusions, the logistic model in this study can be used for estimating the respiratory deaths from the DR database in Thailand or the DR system in other countries that are under-reporting the death rate.

References

Bureau of Policy and Strategy, Ministry of Public Health (Thailand). (2007). Public Health statistics A.D.2007. Retrieved from http://bps.moph.go.th/
new_bps/sites/default/files/statistic-50.pdf

Bureau of Policy and Strategy, Ministry of Public Health (Thailand). (2014). Public Health statistics A.D. 2014. Retrieved from http://bps.moph.go.th/
new_bps/sites/default/files/health_statistics2557.pdf

Byass, P. (2010). Integrated multisource estimates of mortality for Thailand in 2005. Population health metrics, 8(1), 10. https://doi.org/10.1186/1478-7954-8-10

Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (Eds) (2013). Applied Logistic Regression. New York: Wiley.

Jamrozik, E., & Musk, A. W. (2011). Respiratory health issues in the Asia–Pacific region: An overview. Respirology, 16(1), 3-12. http://dx.doi.
org/10.1111/j.1440-1843.2010.01844.x

Kongchouy, N., & Sampantarak, U. (2010). Confidence intervals for adjusted proportions using logistic regression. Modern Applied Science, 4(6), 2.

Lumley, T. (2010). Complex Surveys: A Guide to Analysis Using R. Wiley.

Mathers, C. D., Ma Fat, D., Inoue, M., Rao, C., & Lopez, A. D. (2005). Counting the dead and what they died from: an assessment of the global status of cause of death data. Bulletin of the world health organization, 83(3), 171-177c.

McNeil, D. (1996). Epidemiological Research Methods. New York: Wiley.

Polprasert, W., Rao, C., Adair, T., Pattaraarchachai, J., Porapakkham, Y., & Lopez, A. D. (2010). Cause-of-death ascertainment for deaths that occur outside hospitals in Thailand: application of verbal autopsy methods. Population Health Metrics, 8(1), 13. https://doi.org/10.1186/1478-7954-8-13

Porapakkham, Y., Rao, C., Pattaraarchachai, J., Polprasert, W., Vos, T., Adair, T., & Lopez, A. D. (2010). Estimated causes of death in Thailand, 2005: implications for health policy. Population Health Metrics, 8(1), 14. https://doi.org/10.1186/1478-7954-8-14

Rao, C., Porapakkham, Y., Pattaraarchachai, J., Polprasert, W., Swampunyalert, N., & Lopez, A. D. (2010). Verifying causes of death in Thailand: rationale and methods for empirical investigation. Population health metrics, 8(1), 11. https://doi.org/10.1186/1478-7954-8-11

Strategy and Planning Division, Ministry of Public Health (Thailand). (2005). Morbidity Report A.D. 2005. Retrieved from http://bps.moph.go.th/new_
bps/%E0%B8%AA%E0%B8%A3%E0%B8%B8%E0%B8%9B%E0%B8%A3%E0%B8%B2%E0%B8%A2%E0%B8%87%E0%B8%B2%E0%B8%99%E0%B8%81%E0%B8%B2%E0%B8%A3%E0%B8%9B%E0%B9%88%E0%B8%A7%E0%B8%A2

Strategy and Planning Division, Ministry of Public Health (Thailand). (2013). Morbidity Report A.D. 2013. Retrieved from http://bps.moph.go.th/new_
bps/%E0%B8%AA%E0%B8%A3%E0%B8%B8%E0%B8%9B%E0%B8%A3%E0%B8%B2%E0%B8%A2%E0%B8%87%E0%B8%B2%E0%B8%99%E0%B8%81%E0%B8%B2%E0%B8%A3%E0%B8%9B%E0%B9%88%E0%B8%A7%E0%B8%A2

Tangcharoensathien, V., Faramnuayphol, P., Teokul, W., Bundhamcharoen, K., & Wibulpholprasert, S. (2006). A critical assessment of mortality statistics in Thailand: potential for improvements. Bulletin of the World Health Organization, 84(3), 233-238.

Tongkumchum, P., & McNeil, D. (2009). Confidence intervals using contrasts for regression model. Songklanakarin Journal of Science and Technology, 31(2), 151-156.

Waeto, S., Pipatjaturon, N., Tongkumchum, P., Choonpradub, C., Saelim, R., & Makaje, N. (2013). Estimating liver cancer deaths in Thailand based on verbal autopsy study. Journal of research in health sciences, 14(1), 18-22.

World Health Organization. (2004). International Statistical Classification of Diseases and Health Related Problems Tenth Revision Volume 1 (2nd ed., pp. 977-1066). World Health Organization, Geneva, Switzerland.

Keywords
ICD-10; Mortality; Logistic Regression; Confidence Intervals; ROC curve
Section
Research Articles

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How to Cite
MINSAN, Pradthana. Analyzing a Statistical Method of Estimating Respiratory Deaths based on the Thailand Verbal Autopsy study. Naresuan University Journal: Science and Technology (NUJST), [S.l.], v. 26, n. 1, p. 32-39, mar. 2018. ISSN 2539-553X. Available at: <http://www.journal.nu.ac.th/NUJST/article/view/1563>. Date accessed: 24 may 2018.