Author(s): AM Rangoli and AS Talawar

Abstract: The analysis of competing risk data is very important to understand the change in the effect of one cause over another. Here, we are considering competing risks analyses using life table methods. Initially, we have constructed an abridged life table for the year 2019, Indian male and female population. And observed that females have a longer life expectancy than males. Also, we have constructed multiple decrement and cause-eliminated life tables for the different causes (malaria, chronic respiratory disease, total cancers, lip and oral cavity cancer, tuberculosis, HIV, and CVD). If we eliminate CVD as a cause of death, then one can get an additional 4.9 years of life. We have also estimated crude, net, and partial crude probabilities. Here we have observed that malaria affects more infants and children's, whereas HIV affects more to adults, and chronic respiratory disease and CVD affects older peoples. For malaria, the crude probability of dying is 0.000188, and the net probability of dying is 0.029811. From this, we can say that in the absence of competing risks, the net probability is high. Hence, it is necessary to study the competing risk approach to understand the effect of one cause in the presence or absence of competing risks. And the gain in life expectancy is also estimated; from that, we can say that the contribution of life expectancy at birth for malaria is from infants and children's, and for total cancers, chronic respiratory disease, and CVD, it is from older ages.

DOI: 10.22271/maths.2023.v8.i6a.1401

Pages: 41-52 | Views: 131 | Downloads: 24

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