In one of my courses on Health Insurance which I used to offer in the second year of PGP at IIM Ahmedabad, we used to discuss one interesting reading on "risk selection and risk adjustment" (see the reference below). The principal theme was using information about age, gender and health status to predict outcomes in health insurance and thereby pricing and approaches of predictive modeling.
In this particular reference, we had this great graph presenting actual spending by age and gender in the US context in 2009. Each point in the graph below presented one-year average health spending per capita as per age and gender. The spending in very early years (babies) are higher, and also women have higher medical expenses during their reproductive age group than males at the same reference age group. The per capita spending on health is more than double for females in age groups ranging between 25 and 35 years. This non-linear pattern can also be captured through modeling process.
The data and graph presented above also introduces us to understand why there is need for gender-based HCF policies in any economic system. In India HCF to a large extent has tried to address this issues and programmes such as NRHM, now NHM, allocated significant resources to meet reproductive and child health (RCH) challenges.
However, we are not sure whether the same is true for health insurance claims settlement. Though we do not have much information on gender based health insurance claim settlements, the data provided by RG Jeevandayee Arogya Yojana in Maharashtra provide some insights in gender based claim settlement for populations belonging to lower income groups. Here the gender disparity in claims looks quite obvious. The number of claims for females is 28% less than that of males and in terms of the amount of claims, it is 39% less. This difference is not explained by sex-ratio which is 929 females per 1000 males in Maharashtra. The RGJAY insurance scheme is also enrolled on family basis in 35 districts of the state and therefore has no enrollment bias towards males. There are 491 hospitals in the network.
The study of Insurance Information Bureau of India (IIBI) also indicate that for the year 2013-14, the data received from all insurers indicate that amount of claims paid to males is 72% and that to females is 28%.
As gender has significant influence on how people will respond in a complex and context-specific situations, gender should be at the core of health financing policies. Some important learnings from this are:
Ellis R P and T J Layton (2014). Risk Selection and Risk Adjustment, in Encyclopedia of Health Economics, ed. A J Culyer, Volume 3 pp. 289-297, Elsevier Inc.
In this particular reference, we had this great graph presenting actual spending by age and gender in the US context in 2009. Each point in the graph below presented one-year average health spending per capita as per age and gender. The spending in very early years (babies) are higher, and also women have higher medical expenses during their reproductive age group than males at the same reference age group. The per capita spending on health is more than double for females in age groups ranging between 25 and 35 years. This non-linear pattern can also be captured through modeling process.
Source: Ellis and Layton (2014)
The data and graph presented above also introduces us to understand why there is need for gender-based HCF policies in any economic system. In India HCF to a large extent has tried to address this issues and programmes such as NRHM, now NHM, allocated significant resources to meet reproductive and child health (RCH) challenges.
However, we are not sure whether the same is true for health insurance claims settlement. Though we do not have much information on gender based health insurance claim settlements, the data provided by RG Jeevandayee Arogya Yojana in Maharashtra provide some insights in gender based claim settlement for populations belonging to lower income groups. Here the gender disparity in claims looks quite obvious. The number of claims for females is 28% less than that of males and in terms of the amount of claims, it is 39% less. This difference is not explained by sex-ratio which is 929 females per 1000 males in Maharashtra. The RGJAY insurance scheme is also enrolled on family basis in 35 districts of the state and therefore has no enrollment bias towards males. There are 491 hospitals in the network.
The study of Insurance Information Bureau of India (IIBI) also indicate that for the year 2013-14, the data received from all insurers indicate that amount of claims paid to males is 72% and that to females is 28%.
Gender Disparity of Claims in
RG Jeevandayee Arogya Yojana (RGJAY)
Source: https://www.jeevandayee.gov.in/RGJAY/FrontServlet?requestType=
CommonRH&actionVal=HomepageStats&pageName=5 (accessed on 9 March 2017)
As gender has significant influence on how people will respond in a complex and context-specific situations, gender should be at the core of health financing policies. Some important learnings from this are:
- health finance is not gender neutral and therefore health care financing (HCF) policies need to be considerate of this fact,
- gender can be a key to stratification to assess risks and therefore developing gender-based health financing policies,
- gender affects how we live, work and relate to each other at all levels, including in relation to the health finance,
- household decision-making and health seeking behaviour and health financing policies around gender will be critical as it affects access to and utilization of health services.
Ellis R P and T J Layton (2014). Risk Selection and Risk Adjustment, in Encyclopedia of Health Economics, ed. A J Culyer, Volume 3 pp. 289-297, Elsevier Inc.
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