Gender and Age Disparities in Causes of Deaths What's behind the male/female gap in cause of deaths? Is it attributed by merely biological characteristic of being a male and a female? To what extent geographic location of the people living in a certain area and their socio-economics status affect their health outcomes? Gender disparities in mortality and longevity in life vary by region. It was observed through years that men lives shorter than women. In the Philippines, the highest life expectancy in 2010 was reported in National Capital Region with life expectancy of 68.96 and 74.87 while the lowest was in Autonomous Region of Muslim Mindanao with 60.43 and 65.53 for males and females respectively. However, these statistics does not cover depth reason of gender and age disparities in terms of health inequalities. The health of the population can be assessed in the behaviour of the cause-specific mortality rates in a certain area or country across different ages by paying attention on the number of people who die in a specific cause along with the socio-economicdemographic factors in a given time. The wide gap in life expectancy between males and female is an evidence of the existing nature of social inequalities across region in the Philippines. Mortality outcomes as a contributing factor of longevity in life draw much attention in health and economic issues and policies. This paper will provide insights through mortality information in the existences of social and economic in the Philippines. Objective of this research: This research paper aims to address questions on some neglected aspect of gender natured vulnerability of dying by showing the age and gender disparities due to selected cause of deaths. This will also identify the contributing factors of the high statistics of mortality of a specified cause of death. Through this study, we can provide useful information that can help advance the status of the women when it comes to health inequality.
Review of Related Literature:
Numerous literatures in the field of health economics focuses on relationship of life expectancy to economic development but few studies put too much attention on the factors affecting longevity of human life because of the established facts that life expectancy is attributed by genetic factors and environmental factors. Acemoglu and Simon (2006, p.2) discussed that “The effects of the international epidemiological transition on a country’s life expectancy were related to the extent to which its population was initially (circa 1940) affected by various specific diseases, for example, tuberculosis, malaria, and pneumonia, and to the timing of the various health interventions.” This paper tries to investigate some other factors that affects outcome of deaths that tend to be ignored because we are looking at the broader perspective. Sen, A (1998) mentioned that “It is certainly true that mortality rates are affected by poverty and economic deprivation. Personal income is unquestionably a basic determinant of survival and death, and more generally of the quality of life of a person. Nevertheless, income is only one variable among many that affect our chances of enjoying life, and some of the other variables are also influenceable by economic policy.” In this study, I equally put much attention age disparity of dying. I also includes the literacy rate as contributing factors in this research. Numerous journals backed up my assumption that people at certain age are vulnerable in a particular disease or externalities that would increase their likelihood of dying at particular cause. Literacy rate can be proximate variable to the vulnerability of the externalities. There are some journals research however, that provides use another dimension of thinking that the extent of the individual valued their life and their preference to live can affect their likelihood of dying. “A major hypothesis about what causes the apparent convergence in health differentials in later life is selective survivorship. The selective survivorship thesis is rooted in the racial mortality cross-over debate where research finds that the survival
advantage enjoyed by whites becomes a mortality disadvantage at the oldest ages. Beckett, M. (2000.p .106-119).
Data Requirement: The data on mortality utilized in the model is taken from the 2010 Vital Registry of Deaths compiled by the Philippine Statistics Authority. The data used is the registry of the 488, 265 individual records of deaths disaggregated by Region of Occurrence. Socio-economic and health characteristics like age, sex, marital status, cause of death, occupation, usual residence and place of occurrence of the event from the death register are also provided in the data and exploited in this study. The cause of deaths in the individual records are coded using International Classification of Diseases 10th Revision.” The International Classification of Diseases (ICD) is the standard diagnostic tool for epidemiology, health management and clinical purposes. This includes the analysis of the general health situation of population groups. It is used to monitor the incidence and prevalence of diseases and other health problems, proving a picture of the general health situation of countries and populations.” (Retrieved from http://www.who.int/classifications/icd/en/). Mortality outcomes from the registry are categorized by selected causes of deaths. About nine major causes of death are identified and categorized in this study. The Selection of the causes of death is based on the consistency of the rankings across period of time. Below is the list of the identified causes of death that I put attention in this study. Cause of Mortality (1) Disease of the Heart (2) Cerebro-Vascular Disease (3) Cancer (4) Pneumonia (5) Tuberculosis (6) Chronic Lower Respiratory Diseases (7) Diabetes Mellitus (8) Nephritis, Nephrotic Syndrome and Nephrosis (9) Assault
WHO (ICD -10 Code) I00-I51 I60-I69 C00-C95 J12-J18 A15-A19, B90 J40-J47 E10-E14 N00-N27 X85-Y09
The sex disaggregated data on Literacy Rate that is also one of the important variables to analyze in this research. The data in Literacy Rate is taken from the 2008 Functional Literacy and Mass Media Survey. This paper also considered to include data on Life Expectancy by region. Life Expectancy is based from the 2010 Census-Based Population Projection baseline Life Expectancy. Life expectancies by region are disaggregated by sex. Limits: This paper suspected that mortality differentials were due to misreporting of causes of death at older ages. Since data utilized in this study was taken from the registry of death it is possible to have errors also in completeness of registration that possible affect the results. The research also did not consider to omit observations that might have bias effect on the likelihood of dying. For example, deaths due to certain conditions originating in the perinatal period are biased because this cause of mortality is exclusively for women. Same with malignant neoplasm or cancer-related cause of mortality such as prostate cancer that are exclusively related to men. However if the net difference in rate of the gender bias – that is, all-cause specific mortality rate that exclusively for women minus all-cause mortality rate for man is zero or close to zero at all ages in all regions, this bias can be minimized. There is possibility of spurious relationship between the variables identified in the model. For example, marital status of the person. Result shows significant impact of marital status to the likelihood of dying at particular cause. Spurious relationship between the dependent variable can be suspected for a false indication of causality. Factors such as occupation and educational attainment was not included in the model. Education might have a positive impact in mortality. Occupation sometimes give a positive or an adverse effect on the mortality status.
Methodology: In selecting model for this study, data elements and attributes are scrutinized carefully. Because of the independent variable are categorized in many 10 categrories, the most appropriate model to use is the multinomial logistic regression.
Doing an OLS
regression is possible but this would be would be less suitable because of the violation of the assumption of independent, identically distributed errors. D Cancer: U1 1 x 1 D
Heart Disease: U 2
2 x 2
D TB : U 3 3 x 3 . . .
Using the 2010 Philippines Mortality Data, I examined the cross
sectional relationship age, sex and other factors to deaths due to particular causes. The study employs a multinomial logistic regression. The model is written as follows.
PCancer P(U1 U 2 , U1 U 3 ,...U1 U n ) P( x1 1 x 2 2 ), x1 1 x 3 3 ),..., x1 1 x n n )
The basic assumption of the model is the error terms are independent and follow type I extreme value distribution. Then, the probability that a person die at a given cause is written as:
1 e e x1
x ( 1 2 )
1 ... e x ( 1 n )
e x1 e x2 ... e x n
This research will estimate a model to determine the extent of gender gap in longevity of life which is causes of death occurrences clustering for the regions.
e 0 x1 1 e 0 1 ( nwifeinc) 2 (exp er ) 3 (exp ersq) 4 ( kidslt6) 5 ( kidsge6) other factors such as marital status, region, age and cause of death. Interpreation of results: The report from which these data shows that the widening of relative inequalities in mortality can also be demonstrated by gender and age disparities. It was observed that men are more likely to die in some selected causes compared to women and viceversa. For Group 1 The coefficients tell us that the higher the age and literacy rate of married males increase the likelihood of dying in heart disease than dying due to other unpopular causes of death that are not listed in the selected causes above.
older people with high literacy rate males who are married increase married males are with increasing male and married marrielower temperatures and higher dates increase the likelihood that you will have one or two distress incidents as opposed to none. We see the same thing in group 3, but the effects are even larger. In general, results shows that sex, age, marital status and literacy rate significantly affect likelihood of dying from a particular cause. This is evidently seen in the social environment in the Philippines where those with low literacy rate are more expose to the likelihood of dying
from assault. It is interesting to note that those who are with hight literacy rates are most likely to die of heart disease, CLRD. Some coefficient does not give large gains and not substantially affect the in the likelihood of dying on a particular cause although resulting coefficients are statistically significant. Interpretation of results:
The data reveals that there is widening of relative inequalities in mortality across age and marital status and varies signinficanty by causes of deaths. The results demonstrated that gender gap varies by selected causes of mortality. Result reveals that the gender gap 5.81, that is, the regional life expectancy at birth for females is 5.81 years higher than males, holding the same regional characteristics This paper extends its analysis in examining the mortality rates of men and women especially in early adulthood.
In this article we address the specific vulnerability of girls and women with respect to mortality from natural disasters and their aftermath. Biological and physiological differences between the sexes are unlikely to explain large-scale gender differences in mortality rates. Social norms and role behaviors provide some further explanation, but what is likely to matter most is the everyday socioeconomic status of women.
References: Acemoglu, Daron, and Simon Johnson. Disease and development: the effect of life expectancy on economic growth. No. w12269. National Bureau of Economic Research, 2006. Sen, A. (1998). Mortality as an indicator of economic success and failure.The Economic Journal, 108(446), 1-25. International Classification of Diseases (ICD), retrieved from: http://www.who.int/entity/classifications/icd/en/
Beckett, M. (2000). Converging health inequalities in later life-an artifact of mortality selection?. Journal of Health and Social Behavior, 106-119. Meara, E. R., Richards, S., & Cutler, D. M. (2008). The gap gets bigger: changes in mortality and life expectancy, by education, 1981–2000. Health Affairs, 27(2), 350-360.
Wilson, M., & Daly, M. (1997). Life expectancy, economic inequality, homicide, and reproductive timing in Chicago neighbourhoods. BMJ: British Medical Journal, 314(7089), 1271.
Methods: 113
2 Complications of medical and surgical care
4944
3 Event of undetermined intent
2123
4 Intentional self-harm
63
5 Legal intervention and
operations of war 8317
6 Other external causes of accidental injury
28
7 Sequelae of external causes of morbidity and mortality
8423
Source
8 Transport accidents
SS
df
MS
Model Residual
181864.366 10 44246.803 36312
18186.4366 1.21851738
Total
226111.169 36322
6.22518497
LifeExp
Coef.
MarrFem MarrMale SingFem dmonth Age Assault transport unknownintent selfharm accinjury _cons
5.683698 -.1179813 5.739525 .0012538 -.0016998 .0289798 -.0234099 .1975701 .2561989 -.0492171 66.64226
Std. Err. .0250682 .0139813 .0195778 .0016955 .0003257 .0779787 .0782393 .0788688 .0809584 .0782561 .0791516
Number of obs F( 10, 36312) Prob > F R-squared Adj R-squared Root MSE
t 226.73 -8.44 293.17 0.74 -5.22 0.37 -0.30 2.51 3.16 -0.63 841.96
P>|t| 0.000 0.000 0.000 0.460 0.000 0.710 0.765 0.012 0.002 0.529 0.000
= 36323 =14925.05 = 0.0000 = 0.8043 = 0.8043 = 1.1039
[95% Conf. Interval] 5.634563 -.1453851 5.701152 -.0020695 -.0023382 -.1238608 -.1767613 .0429849 .0975181 -.2026015 66.48712
5.732832 -.0905774 5.777898 .004577 -.0010615 .1818203 .1299415 .3521553 .4148796 .1041672 66.7974
Impact of External Cause of Death on Life expectancy What is the effect of premature mortality on life expectancy?
mproving health around the world today is an important social objective, which has obvious direct payoff s in terms of longer and better lives for millions. 1 There is also a growing consensus that improving health can have equally large indirect payo ff s through accelerating economic growth. 2 For example, Gallup and Sach
death. Gallup, J. L., Sachs, J. D., & Mellinger, A. D. (1999). Geography and economic development. International regional science review, 22(2), 179-232. Neumayer, E., & Plümper, T. (2007). The gendered nature of natural disasters: The impact of catastrophic events on the gender gap in life expectancy, 1981–2002. Annals of the Association of American Geographers, 97(3), 551-566.
In examining gender differences in the effects of education and income on the risk of mortality, individual and spousal indicators of these constructs were used. The effects of
spousal measures are, of course, relevant only to those who are married.
Basic literacy or simple literacy refers to the ability of a person to read and write with understanding a simple message in language or dialect. In this survey, basic literacy status of an individual was determined based on the respondents answer to the question “Can ____ read and write a simple message in any language or dialect?” Functionally literate refers to a person who can read, write and compute or one who can read, write, compute and comprehend.
