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Nurin Haniah Asmuni
Universiti Teknologi MARA

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Healthy life expectancy vs health expenditure by sullivan method in Malaysia Muhammad Hakeem Omar; Nurin Haniah Asmuni; Sharifah Nazatul Shima
Indonesian Journal of Electrical Engineering and Computer Science Vol 14, No 1: April 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v14.i1.pp402-406

Abstract

The improvement of mortality rates in many countries over the world has a major impact on cost associated with living longer due to mortality and morbidity risk. In particular, the trend in life expectancy of Malaysian population has steadily increased for many years where in 2017, Malaysian are expected to live up to 74.8 years compared to 74.3 years in 2011. Life expectancy can be defined as the average period of a person may expect to live, while the definition of disability-free life expectancy is the average number of years a person is expected to live without health disability.  If a person takes a good care and services through the advancement of medical technology, it may expend the period of life expectancy for a person. Thus, longevity may have a positive relationship with health expenditure. United State for instance spends more on health across years, however United State becomes the outlier as compared to other countries with higher percentage of increase in life expectancy per dollar spent on health expenditure. Disability or disability-free life expectancy can rise at certain degree among Malaysian. The general public do not know whether longevity will expose a person to a greater period spend in disability state or not. Therefore, this paper presents healthy life expectancy vs. health expenditure by Sullivan method in Malaysia to provide further understanding of morbidity rate for Malaysian population due to longevity. This paper calculates the disability-free life expectancy for Malaysian population which then will be used in country comparison. Relationship between disability-free life expectancy and health expenditure will be studied. Sullivan method will be applied in the calculation by using a period life table based on age and gender groups.
Lung cancer transition rate by stages using discrete time markov model Muhammad Hakeem Omar; Syazreen Niza Shair; Nurin Haniah Asmuni
Indonesian Journal of Electrical Engineering and Computer Science Vol 18, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v18.i3.pp1295-1302

Abstract

Morbidity risk is linked to the health status or a disease within a population. Morbidity risk is the risk of illness associated with health status or disease within a population. Cancer is one of the prime causes of both morbidity and mortality in majority of countries worldwide. In year 2016, the probability of Malaysian diagnosed with cancer before they reach age 75 is one over four. It has been reported that lung cancer has the highest deaths and it increased by 16.03% from 2012 to 2016. Malaysian National Cancer Registry reported that in year 2007 until 2011, 69.9% of lung cancer are men and the remaining 30.1% are women. Chinese become the dominant lung cancer cases representing 51.04% of total lung cancer patients in Malaysia followed by Malay, 44.81% and Indian, 4.16%. Treatments for lung cancer patients may vary by cancer stages. If cancer were just spread in one place, doctor may recommend a local treatment to get rid of cancer completely. Whereas, if a cancer has spread throughout the whole body, more comprehensive treatments may be needed. Therefore, knowing the probability of transition rate between cancer stages is important for healthcare cost effectiveness evaluation and expected cost calculation. This paper aims to estimate lung cancer transition rate by stages using the Functional Markov model. The Lung cancer transition rate will be calculated based on discrete time on a yearly basis. As a result, the probability of a Lung cancer patient recovering or deteriorating can be estimated.
Co-Authors Muhammad Hakeem Omar Sharifah Nazatul Shima Syazreen Niza Shair