A Study Conducted In Mumbai Health And Social Care Essay

"The human race has had long experience and a fine tradition in surviving adversity, but we now face a task for which we have little experience, the task of surviving prosperity"

-Alan Gregg


Coronary heart disease (CHD) is a type of cardiovascular disease which falls in the category of non communicable or chronic diseases. The World Health organization (WHO) defines cardiovascular diseases (CVDs), as a group of disorders of the heart and blood vessels, encompassing the following set of diseases:

Coronary heart disease or Ischaemic heart disease (IHD ), for instance heart attack

Cerebrovascular disease

Peripheral arterial disease

Rheumatic heart disease Congenital heart disease

Deep vein thrombosis and pulmonary embolism

CHD occurs when the arteries of the heart that normally provide blood and oxygen to the heart are narrowed or even completely blocked. It manifests itself in the following problems like Angina, Acute coronary syndrome (ACS), acute myocardial infarction (AMI), and shortness of breath amongst other symptoms and is characterized by its long latent period, slow progression and non spontaneous cure; in fact it is rarely cured completely.

Angina is exertional chest pain, pressure, or discomfort caused by blockages in one or more of the heart arteries, which reduces the flow of blood.

Acute coronary syndromes (ACS), otherwise known as heart attacks, occur when a blockage occurs suddenly. ACS encompasses acute myocardial infarction (AMI) with and without ST-segment elevation and unstable angina. It is a major cause of death in CAD.

WHO (2011), has classified CVD risk factors [1] under the following heads, the same are shared by CHD also.

Behavioral risk factors

tobacco use

physical inactivity

unhealthy diet (rich in salt, fat and calories)

harmful use of alcohol

Metabolic risk factors

raised blood pressure (hypertension)

raised blood sugar (diabetes)

raised blood lipids (e.g. cholesterol)

overweight and obesity

Other risk factors

poverty and low educational status

advancing age

inherited (genetic) disposition

psychological factors (e.g. stress, depression)

Global Scenario

Countries across the globe are witnessing a rapid epidemiological transition with the shift in causes of death and illness from infectious diseases and malnutrition to chronic, non communicable diseases such as cancer and heart diseases. As a result non communicable diseases are today a leading global killer (WHO, 2012).

In 2008 chronic diseases mainly cardiovascular diseases, diabetes, cancers and chronic respiratory diseases caused 36 million or 63% deaths out of 57 million global deaths. Among these the contribution of CVD was the highest, claiming 17.3 million or 30% of deaths globally (WHO, 2011).

Taking together mortality and morbidity figures, in 2008 CVD led to 151 million DALYs [2] (representing 10% of all DALYs in that year).

Contrary to the popular belief that chronic diseases afflict mainly the rich world populations, the current evidence reveals a different story. According to the WHO estimates, in 2008 roughly four out of five NCD deaths occurred in low and middle income countries. Over 80% of the CVD deaths today are concentrated in the developing countries, affecting both men and women equally (WHO, 2011; Shah & Mathur, 2010).

These trends are clearly depicted in the figure made below:

CVD compared with other causes of death

Source: WHO (as cited in Gaziano, 2005)

In fact due to the epidemiological transition, CVD is the leading cause of death in all World Bank developing regions, barring Sub Saharan Africa Mathers et al. (as cited in Gaziano, 2005). Among people below the age of 70, CVDs were responsible for the largest proportion (39%) of NCD deaths (WHO, 2011).

It not only affects high proportion of working age adults (35 – 65 years), in fact its impact is felt much earlier in the developing world as compared to the developed world (WHO, 2011; Gersh et al. 2010; Berkowitz, 2009) . On an average, CVD affects low and middle income countries ten to fifteen years earlier than high income countries, reducing their workforce capacity and potential economic growth (Leeder, Raymond, Greenberg, Liu, & Esson, 2004)

Further evidence reveals that even within the developing countries, it is the Indian subcontinent (including India, Pakistan, Bangladesh, Sri Lanka and Nepal) that has the highest rates of CVD globally (Ali, Narayan, & Tandon, 2010; Goyal & Yusuf, 2006). Infact heart diseases are rising in Asian Indians 5 –10 years earlier than in other populations (Shah & Mathur, 2010).

Future projections by WHO suggest that by 2030, about 23.6 million people will die from CVDs. Hence, CVDs would continue to remain the single largest cause of death, with the largest increase in number of deaths taking place in the South-East Asia Region.

Further looking at the distribution of global CVD death by cause, coronary heart disease or ischaemic heart disease remains the number one killer (Ali, Narayan, & Tandon, 2010). Over 82% of the mortality burden is caused by CHD, stroke (both hemorrhagic and ischaemic), hypertensive heart disease or congestive heart failure (CHF).

These not only cause human life and suffering but also come with heavy financial costs, future projection reveal that over the next 20 years, NCDs will cost more than US$ 30 trillion, representing 48% of global GDP in 2010,which would push millions of people below the poverty line ( Bloom et al., 2011).

Indian Scenario

Mortality data from Global Burden of Disease Study reveals that cardiovascular diseases especially coronary heart diseases are an important cause of death in India. In fact CVD has reached epidemic proportions in India (Gupta & Gupta 2009)

CVD causes 1.7 – 2.0 million annual deaths in India (Gupta, Guptha, Joshi, & Xavier, 2011). Current CAD trends indicate that for India the concern is not only the high burden of it, but the fact that CAD largely affects its productive workforce (35–65 years), very young. The mean age for first presentation of acute myocardial infarction in Indians was found to be 53 years (Sharma & Ganguly, 2005). Facts reveal that in India about 50 percent of CHD related deaths occur in people younger than 70 years compared with only 22 per cent in the West (Shah & Mathur, 2010).

CHD, which manifests at a younger age, can have devastating consequences for an individual, the family, and society. Further epidemiological studies performed over the past 50 years reveal that it is increasing in both urban and rural areas. The adult prevalence has increased in urban areas from about 2% in 1960 to 6.5% in 1970, 7.0% in 1980, 9.7% in 1990 and 10.5% in 2000; while in rural areas, it increased from 2% in 1970, to 2.5% in 1980, 4% in 1990, and 4.5% in 2000. In terms of absolute numbers this translates into 30 million CHD patients in the country (Gupta, 2008)

According to Leeder et al. (2004) deaths due to CVD, in the age group of 35 to 64 years, resulted in 9.2 million potentially productive years of life being lost in 2000 (570% more than the corresponding figure for the U.S.) and are expected to rise to a loss of 17.9 million years in 2030 (940% more than the projected figure for the U.S.)

According to WHO (2005), India stands to lose approximately 237 billion USD due to heart disease, stroke, and diabetes.

Historical experience of the developed world suggests that CVD epidemic initially commences in the upper class gentry, due to the modern lifestyles characterized by diets rich in fat and calories, sedentariness, and smoking. However, overtime the risk permeates across the social spectrum, affecting all classes. Since the higher classes are in a better position to respond to the knowledge of risk facts and the message of prevention, CVD rates start to decline in them, while becoming increasingly more evident among the lower social classes (Reddy & Yusuf, 1998).

A similar pattern has also been observed in case of India. A chronological overview of literature on CVD reveals, most studies conducted in 60’s and 70’s focused on high income populations and upper socioeconomic classes, as high CVD risk segments of the society, ignoring its impact on people from the low socio economic classes for long (Jeemon & Reddy, 2010; Sarvotham & Berry, 1968; Mathur, 1960). Putting in the words of Mathur (1960);

"It seems clear from the studies on patients and the general population that coronary heart disease has a predilection for the privileged class of society in great contradistinction to individuals of low-socioeconomic group, who enjoy relative immunity from this disease."

A complete reversal of this trend has been highlighted in various studies since then. Gupta, Gupta & Ahluwalia (1994) found an inverse relationship between the level of education and prevalence of CHD and its risk factors. Since education is an important measure of socio economic status, the results are in conformity with the trends.

In fact Gupta & Gupta (2009), suggest that being illiterate or poor to be an independent risk factor for acute myocardial infarction. As many of the standard CHD risk factors such as smoking, tobacco use, low physical activity, high dietary fat intake, uncontrolled hypertension uncontrolled hypertension amongst factors are very common among individuals with low socio economic status.

Xavier et al. (2008) in their study aimed at documenting the characteristics’, treatments and outcomes of patients with ACS who were admitted to hospitals in India, found majority of the patients, were from lower middle and poor social classes.

Based on literature across studies, it would not be wrong to say that the social class gradient in cardiovascular event rates among Indians has reversed with evidence of excess CVD among lower socio economic groups.

Further CVD has largely been an urban phenomenon and only recently reports indicate a rise in rural populations (Shah & Mathur, 2010; ICMR & MRC, 2009; Mohan et al. 2001). However from this it cannot be inferred that the disease had been completely absent in rural India, may be due to lack of awareness people ignored the symptoms and did not seek the relevant treatment or the actual cause of death did not get reported.

Amongst the studies which emphasize on the urban nature of the disease, one study simultaneously compared the risk of CVDs among rural and urban populations. It suggested that effective urbanization and modernization influences CVD risk factors, hence confirmed the role of habitat (rural vs. urban) in making people susceptible to CVDs (Das, Pal, & Ghosh, 2008).

Chadha, Gopinath & Shekhawat (1997) explained this variation through differences in lifestyles. As rural men and women work in agriculture, involving heavy physical activity, while most urban men and women have sedentary habits, hence the latter are at a larger risk of CVDs.

A cohort study conducted in Delhi reported high incident rates of obesity; hypertension and diabetes among young, urban Indian cohort (mean age 29 – 36), reflecting on the high future CVD burden that this population carries (Huffman, et al., 2011). Although the study was based in Delhi focusing on upper and middle socio economic classes, it provides key insights for other settings also. Increased caloric intake (particularly of energy-dense foods) and decreased physical activity associated with increased urbanization and socioeconomic development were suggested to be the main reasons for CVD risk factor increase.

Although the focus of this study is on an urban section, however this does not undermine the fact that CVD poses a severe threat even in rural India. As revealed by a study conducted in 45 rural villages of Andhra Pradesh in 2003 – 2004, which noted diseases of the circulatory system, IHD and stroke to be the leading causes of mortality, causing 32% of all deaths, outranking infectious diseases which were responsible for 13%. Although the study was restricted to Andhra Pradesh but provides new insights into rapid progression of epidemiological transition in rural India (Joshi, et al., 2006).

It is essential to note that even the figures revealed by various studies, confirming the growing burden of CVD are in reality an understatement of the real burden that underlies this pandemic, as till date India lacks a comprehensive cardiovascular disease and risk factor surveillance system and there exist irregularities in completion of death certificates.

Economic Impact of Cardiovascular Diseases

Disease and ill health not only cause human suffering and loss of life but also come with huge financial costs to individuals, society and the economy as a whole. Impact of chronic diseases like CVD can be looked from two perspectives;

Macroeconomic view: where by causing premature deaths and morbidity, it reduces life expectancy, thereby depleting the quality and quantity of countries labor force, ultimately affecting economic productivity, hence leading to lower national output\income.

Microeconomic view: where at household level high cost treatment and prolonged nature of the disease challenges household income and saving, along with distorting their investment activities, for instance schooling of children thereby propagating the spiral of ill health and poverty.

As a result of the growing epidemic developing countries are facing huge economic challenges. For instance 2% to 3% of South Africa’s gross national income, or roughly 25% of aggregate healthcare expenditures, was devoted to the direct treatment of CVD (Gaziano, 2005).

A glance at the current expenditure levels of the developed countries provides an indication of possible future expenditure in developing countries. For instance, the United States spent an estimated $368 billion in relation to direct and indirect costs of CVD in 2004 (Gaziano, 2005)

Further CVD cost the EU health care systems just under USD 260 billion, representing a cost per capita of more than USD 500 per annum, which accounts for 10% of the health care expenditure across the EU. Besides this figure is an underestimation as it only includes the direct cost of health care (Berkowitz, 2009).

Countries like China, India and the Russian Federation stand to lose on average between $23 - $53 billion annually due to deaths from heart disease, stroke and diabetes over 2005 - 2015. These annual losses are compared to Canada ($0.9 billion), the United Kingdom ($3.4 billion), Pakistan ($3.5 billion) and Brazil ($5.1 billion). In absolute terms, GDP losses are estimated to be highest for the most populous countries, namely India and China (Abegunde & Stanciole, 2006)

Similar studies done across the globe suggest massive direct and indirect costs associated with CVD’s and other chronic conditions (Patra et al. 2007; Leal et al. 2006; Liu et al 2002).

At the micro level various studies on CVD or NCD indicate increased incidence of catastrophic health expenditure (CHE) due to these conditions. As IHD, stroke or heart failure are leading drivers of cost through hospitalizations and require necessary followed up care in addition to lost productivity from premature mortality (Bloom et al., 2011)

In a study conducted in Burkina Faso odds of experiencing catastrophic financial consequences increased from 3.3 to 7.8 when a household member has chronic illness. In the same paper Saksena, Xu, & Evans (2011) made a reference to a study conducted in two districts of china, where in a sample of 6157 households, 14- 21 % were found to suffer from financial catastrophe due to expenditures on chronic medical conditions even after insurance reimbursements.

In a study conducted in Vietnam with a sample size of 800, it was observed that households with at least one member with a chronic disease had CHE and impoverishment rates as high as 14.6% and 7.6% respectively (Minh & Tran, 2012).

Heart diseases like CAD are chronic conditions and involve catastrophically high cost of care which drives individuals and their families below poverty line if they are not supported by public funding (Daivadanam, Thankappan, & Sarma, 2012)

In India there has been paucity of systematic efforts to document and quantify the economic and social impact of the growing epidemic of CVDs. Most of the studies are regional in nature and mainly concentrated around Delhi and some down south (Ahmad & Bhopal, 2005).

The first nationally representative study on health financing related to non communicable diseases based on National Sample Survey Organization (NSSO) data from 1995-96 and 2004 covering nearly 200 thousand households revealed that hospitalization with CVD results in 12% higher odds of incurring CHE and 37% greater odds for falling into poverty. The study also emphasized the role of private providers in providing health services related to NCDs. Taking India’s per capita income of 2004, INR 25,320 the study concluded that a single hospital stay for cancer or heart disease obtained from private facilities would account for anywhere between 80 to 90 % of households income. Even if care was sought from public facilities, the out of pocket expenses would have still amounted for 40 to 50 % of households’ per capita income (Engelgau, Karan, & Mahal, 2012).

In a regional study conducted in Thiruvananthapuram district, Kerala with a sample of 210 ACS patients, 84% of the patients were found to suffer CHE, with threshold taken to be exceeding 40% of household’s ability to pay.

The study also revealed that participants belonging to low socio – economic status (SES) were 15 times, whose jobs were adversely affected were 7 times, who had no health security were six times and who underwent any intervention were three times more likely to have CHE compared to their counterparts. The majority (41%) of the participants took loans, while 14% drew on their saving and only 8% covered these health expenses through insurance, remaining used a combination of above (Daivadanam, Thankappan, & Sarma, 2012)

In a study conducted in India the odds of incurring catastrophic expenses due to hospitalization were found to be 30% greater in case of CVD as compared to the same for communicable diseases (Saksena, Xu, & Evans, 2011)

Huffman et al. (2011) surveyed 1657 hospitalized CVD patients from Argentina, China, India and Tanzania to measure the impact of CVD hospitalization on households, found that CVD patients across these countries had to bear significant financial burden.

In majority of the studies hospitalization or inpatient care was the major driver of cost. However a study conducted in Bangalore provided evidence that since health care in India is primarily financed through out of pocket payments (OOP), even outpatient care ( medication, diagnostics) in case of chronic diseases pushes people into poverty (Bhojani, et al., 2012).

Along with high CVD related costs, many participants report decreased income, poorer perceived health (including emotional problems), lower functional and productivity capacity and variable household effects, all of which exacerbate their financial instability.

Payment Mechanisms

Health systems of many countries rely on patients out of pocket payments as a source of financing their activities. In a survey conducted in 89 countries covering 89% of the world’s population, Xu et al. (2007), observed that 150 million people each year suffer financial catastrophe, and 100 million are pushed below the poverty line because of payments made towards health services. Ninety percent of these people live in low income countries.

National health account, 2004 – 05 reveal that healthcare system in India is mainly catered by the private sector, mainly out of pocket household spending. In a study conducted by Huffman et al. (2011) as mentioned above India had the highest 15 month out of pocket CVD expenditures as compared to other LMIC’s. Over all CHE was common, regressive [3] and closely associated with age (55 years), lack of private/social health insurance and stroke. Distress financing was also common in case of India and closely associated with rural status, less than secondary school education, absence of private/social health insurance.

Rao, Bhatnagar, & Murphy (2011) in their study based on NSSO 60th round data also found OOP payments for hospital treatment in case of CVDs and diabetes to claim a large share of annual household expenditures, share for CVD being 30% , which in case of poor was mainly financed through borrowings.

Berki (1986) puts forth the idea that most of the studies look at the impact of illness for a limited time period, disregarding the reoccurrence of catastrophic even over time. In this respect irrespective of the threshold we take, 12 to 15 percent of the families are likely to face such consequences again, causing them to liquidate their assets, thereby making them spend more than 100 percent of their income on medical care.

Study conducted by Huffman et al. (2011) discussed above revealed that in India alone distress financing involving risky financial activities such as borrowing loans and selling assets was present in greater than 40% of CVD patients.

Besides death especially of a parent often means a permanent loss of income and often displaces other consumption and investment activities of the household. For instance to supplement household income and reduce spending on other activities (e.g. educational expenses) children are often removed from school and engaged in productive labor (Govender, Ghaffa, & Nishtar, 2007 )

Cost of Illness

Various approaches have been used to capture the economic burden of diseases. The Global Economic burden of Non communicable disease report (2011) looks into the following three approaches;

The Cost of illness method

Macroeconomic simulation

The value of a statistical life.

Amongst these the cost of illness method is most popularly used to capture the microeconomic impact of diseases on households, while other focus on the economic impact at the global level. Since it aligns with the study objectives, it has been discussed below.

Cost of Illness Method

The objective of cost of illness studies is to estimate direct cost attributable to illness and indirect costs attributable to productivity losses due to morbidity or mortality.

Direct costs are defined as the value of goods and services for which payment is made and resources used in treatment, care, and rehabilitation related to chronic disease while indirect costs refer to the value of economic output lost or loss of production because of illness, disability, or premature death (Patra et al., 2007).

Xu et al. (2007) in his study however excluded lost production due to illness as they considered it to be a measure of impact of illness on household income rather than financial impact of seeking care.

In simple terms direct cost refers to the visible costs associated with diagnosis, treatment, and care. These include personal medical care costs such as payment towards doctor’s consultation, laboratory tests, medication purchases, hospital bills or personal non-medical costs such as the cost of transportation to a health provider. While indirect refers to the invisible costs related to disease but not involved in treatment. This includes the impact of illness on the ability of either patients or their caretakers (in taking patient to the hospital and hence not being able to go for work) to work often, expressed as wage loss.

The following table lists down the key points that need to be kept in mind while applying the cost of illness method:

Time Frame

First year after diagnosis

One year only

Unit of Analysis

New cases of illness in a year

All cases of illness in a year

Events of illness

Direct Costs

Personal medical care costs

Non Personal medical care costs

Indirect Costs

Lost income

Due to mortality

Due to disability and seeking care


Non Personal costs

Source: Adapted from Bloom et al., 2011

Information on these costs helps to understand how these costs are financed and how the economic burden on households would vary with income class. Since formal insurance is limited and firms pay for only a small portion of total health spending in India, these costs can cause severe financial hardships to households, particularly for people with lower socio economic status.

Proponents of cost of illness studies argue that these facilitate comparisons between burdens of different diseases, thereby facilitating policy makers to prioritize scarce research funds to areas with the highest burden. Besides if such studies are performed at regular intervals they can be useful in measuring the impact of health policy decisions (Leal et al., 2006).

Catastrophic Health Expenditures

Out of pocket payments cause households to incur catastrophic expenditures, hence can lead to impoverishment for many (WHO). Now the level at which health care spending becomes catastrophic depends on the households capacity to pay. For a poor household, a meager medicine bill can mean a financial catastrophe, forcing him to cut down expenditure of basic necessities like food, shelter, children’s education etc., while for a rich household even a high cost surgical procedure may not have such consequences.

Besides payment mechanisms play a very important role in determining whether the expenditure becomes catastrophic or not. Wyszewianski (1986) describes a situation to be financially catastrophic when health expenditure becomes large relative to patients ability to pay, as determined by the extent of third party coverage and other resources available to pay for care.

Berki (1986) takes a societal view in defining catastrophic payment, argues that a disease may have catastrophic financial implications if it results in high productivity losses (indirect costs of illness), due to premature mortality and high morbidity in the working age population.

It is essential to realize that spending on health care is an investment, but then this should not become large enough that individuals have to compromise on the basic necessities of life or accepted standards of living. Based on the similar idea Wagstaff and Doorslaer (2002) explain the term catastrophic by using two approaches, namely;

The first approach sets the threshold as a proportion of income, the idea being that households do not spend more than a pre specified fraction 'z' of their income on health care, to ensure that they at least have (1 - z ) of their income to spend on things other than health

The other approach sets the minimum in terms of an absolute level of income, and the concern is to ensure that spending on health care does not push households into poverty or further into it if they are already there.

The former approach suffers an inherent limitation, of being insensitive to the hardship that payments cause. As no matter what threshold we take, someone or the other would be crossing it at varies points in time and across societies. Thus the key idea is not to capture how many people are crossing this threshold but to capture the fact that represent a threat to a household’s ability to purchase other goods and services that may, like health care, make a difference to its members’ ability to survive and flourish as a human beings or in other words to capture the level of impoverishment (Wagstaff and Doorslaer 2002).

Thus how much is left for the household post health care payment, determines the extent of catastrophe which differs from rich to poor households.

Xu et al., (2007) assert that although there is no consensus regarding the specific threshold for defining financial catastrophe; however in their study they defined health care payments as catastrophic if they exceeded 40% of a household’s capacity to pay in any year. Where, capacity to pay was defined as household total spending minus subsistence spending (food expenditure).

While in a study conducted in India discussed earlier, out of pocket spending exceeding 30% of non-subsistence spending where non-subsistence was based on the poverty line data from the Indian Planning Commission, was considered catastrophic (Saksena, Xu, & Evans, 2011).

The World Health Organization recommends the use of non-food expenditure as the measure of a household’s capacity to pay, and this serves as the denominator for assessing catastrophe (WHO 2000). Use of non-food expenditure is considered appropriate as food is seen as a basic necessity and constitutes a major share of household expenditure.

Although fixed threshold levels are commonly used to examine financial catastrophe, Onoka et al. (2011) suggest that taking variable thresholds depicts reality better, they explained this with the help of the following example; considering a variable threshold level of 5% for the poorest, the richest household who are spending several times as much would need to spend about 30% of their total consumption expenditure on health before having the potential of being tipped into poverty. If, on the other hand, they are prone to poverty at health expenditure level of 40% of their non-food expenditure, it will be quite realistic to suppose that the poorest groups, whose non-food expenditure is about 5% of that of the richest, would only need to spend a small fraction of their available cash before being thrown into poverty. Thus, use of variable threshold levels gauges the economic burden of illness more intensively.

Pal (2010) used a very different approach, the ‘Engle curve’ [4] approach or ‘deprivation point’ criteria to explain catastrophic spending. According to this approach, health expenditure that reduces the consumption of necessities below the required level is considered as catastrophic. Here a distinction is made between necessary and unnecessary consumption.

From the discussion so far, it can be argued that health care is expensive and in the absence of any insurance cover can cause households with severe and immediate medical needs to expend a large fraction of their household budget on health care. Such spending is often accommodated by cutting back on consumption of other goods and services, by accumulating debt, by running down savings or by selling assets. No matter what financing strategy households adopt, many suffer a cost that may be labeled as "catastrophic", which is a major cause of concern in health care financing system of any country.

Medical Poverty Trap

As discussed before, the catastrophic approach suffers an inherent limitation of being insensitive to the hardships caused to people from health care expenditures, to overcome this, an alternative approach of impoverishment is developed, the core idea being that no one ought to be pushed into poverty or further into poverty because of health care expenses (Wagstaff & Doorslaer, 2003).

Jacobs et al. (2011) suggests that in low income countries, health care and related expenditures feature as proximate causes of impoverishment. Similar finding were obtained by Russell (2004) in his study which documented the economic impact of illness for households in developing countries, where he defined impoverishment as the process of household asset depletion and income loss that causes consumption levels to fall below the minimum needs.

Russell also reflected on the urgent need to develop financial risk protection mechanisms, against high direct treatment costs for serious illnesses, for instance, through efforts to expand coverage of tax or insurance based financing systems that protect households from catastrophic payments at the time of illness.

In the context of India , Berman et al. (2010) suggests that high share of private healthcare spending, which is mainly out of pocket spending is an important cause of impoverishment. Using NSSO’s 60th national morbidity and healthcare survey data the study estimated that around 63.22 million individuals or 11.88 million households were pushed to below poverty line due to healthcare expenditures in 2004. These estimates were however, inclusive of the coping mechanisms. In the absence of this correction the impoverishment rates would have been around 73.9 million or 13.9 million individuals or households respectively.

These studies reflect on the fact, that there exist an intricate relationship between ill health and poverty. Wagstaff (2002) defines it in terms of a vicious cycle, where on one hand poverty breeds ill health while on other ill health maintains poverty. He explains this with the help of the following circuit:

Cycle of health and poverty

Diminished Income

Loss of wages

Costs of health care

Greater vulnerability to catastrophic illness

Poor health outcomes

Ill health


High Fertility



Characteristics of the poor

Inadequate service utilization

Unhealthy sanitary and dietary practice

Caused By

Lack of income knowledge

Poverty in community social norms, weak institutions and infrastructure, bad environment

Poor health provision, inaccessible, lacks key inputs, irrelevant services, low quality

Excluded from health finance system – limited insurance, copayments

The same phenomenon has been explained by Whitehead et al. (2001), in terms of a ‘medical poverty trap’, defined as situation where out of pocket costs for public and private health care services drive many families into poverty and increase the poverty of those who are already poor.

Chowdhury in his paper titled "Health Shocks and the Urban Poor: A Case study of Slums in Delhi" suggest that there are two major pathways through which illness leads to poverty;

The first is through the death or disability of the breadwinner of the household. This reduces future income generation and may jeopardize basic household consumption. Depleted of wealth, the household may invest less on children’s education creating a vicious intergenerational poverty cycle.

The second pathway is through prohibitive treatment cost. When a member falls ill, the household faces several different costs (treatment cost, transportation cost, opportunity cost of care giving etc.) and takes recourse to diverse strategies to finance the same. These coping strategies very often turn out to be potential poverty traps.

This often leads to untreated morbidity, incomplete treatment or treatment at the cost of financial and social wellbeing, thereby leading to long term impoverishment.

Conceptual Framework

Individual seeks treatment

CHD is clinically diagnosed

Private Hospital

Public Hospital

Incurs Cost

Indirect Costs

Direct Costs

Adopts coping mechanisms (Informal loans, asset sales, mortgage of property, insurance etc)

Impact on household economy (e.g. children’s education)

Modified and adapted from Russell (2004)

Catastrophic Expenditure

Impoverishment or Medical poverty trap


Coronary heart disease (CHD) is a major cause of death and disability worldwide and has reached epidemic proportions in India also. Since CHD is a chronic disease with expensive treatment options requiring lifelong medication, it increases the incidence of catastrophic spending. Thereby, challenging the economy of various households, who are forced to resort to distress financing options, which increases their likelihood of impoverishment or pushes many into the medical poverty trap.


Despite the fact that CHD has reached epidemic proportions in India, there has been paucity of systematic efforts to document and quantify the economic and social impact of it. Most of the studies are concentrated in and around Delhi or a few in the southern part of the country. No such study has been undertaken for Mumbai so far

In a urban hub like Mumbai lack of such a study is a significant knowledge, As many of the standard CHD risk factors such as smoking, tobacco use, low physical activity, high dietary fat intake, uncontrolled hypertension etc., are very common in Mumbai

Besides this study would be a modest attempt to reflect on the need to prioritize scarce research funds to this area which has so far been ignored in comparison to communicable diseases and malnutrition, to ensure that the required infrastructure and financial risk protection mechanisms are in place to protect households.


Coronary heart disease has an economic impact on the urban population of Mumbai


To find out the direct and indirect costs associated with CHD among urban population in Mumbai.

To find out the proportion of CHD patients, experiencing catastrophic health expenditure in Mumbai.

To find out the various coping mechanisms adopted by households and their impact on the household economy.

To find out the extent to which CHD leads to impoverishment.


What are the direct costs associated with CHD?

What are the indirect costs associated with CHD?

What is the extent of catastrophic health care expenditure among patients undergoing treatment for CHD?

What are the financial coping mechanisms adopted by households to deal with CHD?

What is the impact of this expenditure on the household economy and impoverishment?


RQ3: CHD causes CHE among patients undergoing treatment for CHD in Mumbai

RQ5: CHD leads to impoverishment of households undertaking treatment for CHD in Mumbai.


Research Design

This is an explanatory study about the expenditure and coping mechanism adopted by the urban population suffering from CHD in Mumbai. The study is explanatory in nature because the investigator not only intends to find out the costs of illness but also looks into how households are bearing these costs.


People undergoing treatment for CHD in Mumbai

Sample Size

30% of patients diagnosed with CHD between 1st May 2011 to 1st May 2012, who are undertaking treatment for the same in the selected hospitals.

Sampling Technique

Random Sampling: Once the list of patients diagnosed with CHD during the aforementioned time period, is obtained, the investigator would randomly select patients for the study purpose.



This study deals with individuals who satisfy the following conditions:

Individuals suffering from CHD which includes ACS, AMI, unstable angina and undergoing treatment from the chosen hospital with or without hospitalization

Individuals diagnosed with CHD between 1st May 2011 to 1st May 2012, to ensure that only those patients are included who have completed at least one year of treatment

Individuals in the age group 25 – 65 years

Treatment is classified into:

Surgical (coronary artery bypass or heart transplant)

Non surgical (coronary angioplasty, using stents)


Only hospitals which have a cardiac catheterization lab would be contacted

The investigator would ensure that at least one government and one private hospital is included in the list of participating hospitals


The study is an amalgamation of both primary and secondary level data.

At the first stage hospitals would be contacted and the required permissions would be taken to allow the investigator to interact with the patient or their primary care takers during cardiac OPDs to take their consent for participation

In the hospitals which consent for the study, pilot testing of the interview schedule would be done and relevant modifications would be made if necessary

For data collection, random sampling technique would be adopted and the chosen patients would be explained the purpose of the study. Those who agree to participate would be interviewed either in OPD or at a mutually convenient location


Interview Method

Data collection tool

Semi structured interview schedule


Direct costs: Comprises of all visible costs associated with diagnosis, treatment, and care. These include;

Consultation fees

Charges for diagnostic tests

Medicine charges

Hospitalization charges ( admission charges, bed charges, intervention charges and other administrative expenses)

Expenses of hiring external caretaker for the patient.

Indirect refers to the invisible costs related to disease but not involved in treatment.

expenditure on travelling

purchases of food and other utilities,

Expenses on dietary modification post diagnosis

wage loss due to illness.

All these costs would be calculated for both the patient and the caretaker.

Catastrophic health expenditure: Wyszewianski (1986) describes a situation to be financially catastrophic when health expenditure becomes large relative to patients ability to pay, as determined by the extent of third party coverage and other resources available to pay for care.

For the study purpose CHE is present if out of pocket expenditure is more than 40% of the household’s capacity to pay. Ability to pay refers to non subsistence expenditure which is difference between the total household consumption expenditure and their subsistence expenditure.

For each patient the two cost would be added to arrive at a figure of total cost and if these after taking into account reimbursements , if any as a fraction of households non food expenditure exceed 40% it would be termed as catastrophic.