Estimating Lung Cancer Deaths in Thailand based on the 2005 Verbal Autopsy Study

Transcription

1 Estimating Lung Cancer Deaths in Thailand based on the 2005 Verbal Autopsy Study Nattakit Pipatjaturon 1 and Phattrawan Tongkumchum 2 1 Office of Disease Prevention & Control, Phitsanulok, Thailand 2 Department of Mathematics and Computer Science, PSU, Pattani Campus Thailand

2 Background 1 The quality of mortality statistics in Thailand is low (Mathers et al., 2005). About 40% of deaths are registered with unknown or nonspecific causes in 2005 (Rao et al., 2010).

3 Objective 2 To improve the quality of lung cancer death registration in 2005 To estimate lung cancer deaths in 2005

4 VA data in VA Data structure 9,644 cases from 28 districts in 9 provinces 9,495 cases for aged 5+

5 VR data in 2005 The database provided information on case by case registered deaths province, sex, age, location of death and, cause of deaths are provided. VR Data structure 4

6 Path diagram 5 Outcomes: Deaths from lung cancer (C30-39). determinants outcome (a) province (b) sex & age-group Lung cancer death? (c) reported ICD-10 cause group (d) location (in/outside hospital)

7 Logistic regression model 6 This model formulates the logit of the probability P of death due to the specified cause group as an additive linear function of the three determinant factors as follows: logit(p) = log(p)-log(1-p) = constant + factor(province) + factor(sex-age group) + factor(vr cause-location group) The province factor has 9 levels corresponding to the 9 provinces in the VA sample. The number of levels in the sex-age group factor depends on the age distribution of deaths from the selected outcome. For lung cancer these are (5-29, 30-39,, 70-79, 80+). Similarly, the number of levels in the VR cause-location factor will vary according to the number of such reported cause groups that affect the outcome cause group.

8 For our cause groups these predictor groups are shown below. Lung cancer has 5 groups, including other groups combined

9 Model results 8 Logistic Regression Model factor p-value Province Sex-age < VR cause <

10 Triangulation method To use the model to predict results for provinces outside the VA study, their coefficients are estimated, based on the latitude and longitude of their central points. To do this, triangles were drawn linking the 9 VA provinces. These triangles were set at planes, like roofs on poles with heights corresponding to their model coefficient values at the vertices of the triangles. For each triangle, values (a, b, c) are obtained by solving three equations as follows: a+(longitude(prov 1 ) b)+(latitude(prov 1 ) c) = coef(prov 1 ) (1) a+(longitude(prov 2 ) b)+(latitude(prov 2 ) c) = coef(prov 2 ) (2) a+(longitude(prov 3 ) b)+(latitude(prov 3 ) c) = coef(prov 3 ) (3) The coefficient for any province j within a triangle is now given by coef(prov j ) = a+(longitude(prov j ) b)+(latitude(prov j ) c) Coefficients for provinces outside triangles are obtained similarly by extrapolation. The next slide illustrates this method for lung cancer deaths. 9

11 The coefficients from the logistic regression model for the nine provinces are plotted (in black) on the map. Values at other places (in blue) are averages of coefficients from nearby provinces. The thematic map on the right graphs values interpolated for all 76 provinces. It suggests that highest province coefficients occurred in the north of Thailand. Lung cancer

12 11 Lung cancer 2005 Estimating mortality rate of lung cancer (per 100,000 population) in 2005 Region Male Female Total Bangkok Central North Northeast South The map shows the percent of lung cancer deaths from all deaths. It suggests that highest percent of lung cancer deaths occurred in the north of Thailand. The Table shows the mortality rate that highest in the north region.

13 Estimated deaths in the target population: 2005 An area plot shows age distributions of lung cancer reported deaths in Thailand s 2005 population after adjusting for misreporting based on the model. VA/VR inflation factors (IF) show that deaths in lung cancer cause groups were substantially under-reported. 12

14 Conclusions 13 Lung cancer deaths were estimated based on the VA data. Logistic regression model and triangulation method were used to interpolate all 76 province coefficients. Thematic map of these Interpolated province coefficients suggest that lung cancer deaths have different regional patterns. However no significant difference in variation among 9 province. Lung cancer deaths in 2005 were under-reported. These methods can apply to estimate other causes and further years. Reliable estimation on lung cancer burden can provide essential guidance for the public health authorities of cancer prevention and control.

15 Acknowledgements 14 We are extremely grateful to Prof.Don McNeil for guidance, support and assistance. Finally, we thank Bureau of Policy and Strategy, Ministry of Public Health Thailand for providing the data.

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