28 Morbidity: Basic concepts and measures

Nupur Mahajan and Gautam Kshatriya

epgp books

 

 

 

Contents:

    Morbidity: Concepts and Definition

Reproductive morbidity

  • Determinants of reproductive morbidity Models of morbidity analysis

The Medical Model

  • The Functional Model
  • The Psychological Model
  • The Legal Model
  • The Biopsychosocial Model

Measurement of morbidity

Morbidity versus mortality

 

Learning Objectives:

  • To define morbidity and reproductive morbidity
  • To understand the models of morbidity analysis
  • To determine different measures of morbidity analysis
  • To distinguish between morbidity and mortality

    Morbidity: Concept and definition

 

Morbidity refers to the level of sickness and disability present in a population. The term “morbidity” and the root term “morbid” is derived from word “morbus” from Latin language which means disease. Though morbidity has a specific meaning in epidemiology, it is used in various other ways in the scientific community.

 

The concept of morbidity has been the area of interest to human societies throughout history as people have tried very hard to understand sickness and death. Demographers by tradition focused on the study of mortality which is considered to be the final result of morbidity, and only recently has the emphasis shifted more towards morbidity. As stated that morbidity leads to mortality in most cases, it plays a greater role in shaping the nature of the society, therefore, the interest in morbidity studies have increased many folds. In fact, the current concerns over disparities in health status among the elite and poor have attracted the attention of demographers and public health practitioners into this perspective.

 

Morbidity may be used to refer to a person or a group. Morbidity as an individual’s identity may refer to the health status of an individual, whereas while highlighting morbidity in group, it may be the health status of the specific group or population. Demographers are almost exclusively interested in morbidity associated with populations and very rarely study the morbidity of individuals, thus there is difference between individual (clinical) morbidity and group (epidemiological) morbidity. However, the situation where population level data is not available, the indentified health status of the individuals is considered.

 

Morbidity can be measured objectively through clinical tests or subjectively through self-reports by individuals. It is important to calculate the morbidity level for the total population under study, however, estimating the morbidity levels of the subsets within the population is equally necessary to understand the pattern of morbidity in that population.

 

Reproductive morbidity

 

Reproductive morbidity encompasses on the obstetric morbidity which include conditions during pregnancy, delivery and the post-partum period while gynaecological morbidity includes the conditions of the reproductive tract which are not associated with pregnancy such as reproductive-tract infections, cervical cell changes, prolapse and infertility. Additionally, reproductive morbidity is also considered to cover related morbidity concerns such as conditions of urinary-tract infections, anaemia, high blood pressure, obesity and syphilis as a systemic condition.

Figure 1. Conditions constituting of obstetric morbidity.

 

Obstetric morbidity conditions are of public health interest because they are common and may have serious implications for maternal and child health.

 

Figure 2: list of gynaecological and related morbidity conditions.

 

Determinants of reproductive morbidity

 

The first and realistic step towards policy making aimed at alleviating conditions of reproductive morbidity is to understand the determinants and their mechanisms for production of level of ill-health. In analysing the determinants of varied condition of ill-health in population groups, the approach to categorize the determinants on the basis of their mode of operation or distance from the outcome of ill-health is considered. This approach originates from the research on the determinants of fertility in the work of Davis and Blake (1956) and also, in the works of Bongaarts (1978). The introduction of this approach in health sector was through the works of Mosley and Chen (1984) in which they attempted to synthesize a model of medical and socioeconomic determinants for child survival. This approach categorizes determinants into two categories: ‘intermediate variables’ which have biological link to the outcome variable of interest and the ‘background variables’ which operate through the intermediate variables. Intermediate variables, being more direct in their effect have also been referred to as proximate determinants. There variables are most open to medical interventions. The background variables signify the social context of ill-health.

Figure 3. Determinants of reproductive morbidity (Source: Zurayk et al., 1993)

 

The medical-risk factors are the general health condition or the vulnerability status of a woman. Vulnerability is affected by exposure to nutritional, infectious and other morbidities. These are correlated with reproductive morbidity in a collective and interactive manner (Winikoff 1987). The vulnerability status can be measured through biological data which is provided by the level of anaemia and anthropometric indicators, or through medical history information which consists of cumulative reproductive health experience and general health conditions such as hypertension and diabetes. However, measuring these factors in the field set up may not be easy.

 

Moving one step in reverse, the block of intermediate factors is shown which include variables such as woman’s childbearing pattern, use of health services and health-related practices which have an effect on the susceptibility. A woman’s childbearing pattern depends upon her age at time of childbearing, the number of pregnancies and births, and the space between consecutive births. The prevalence of reproductive morbidity is higher in the early and late childbearing years and it increases with the number of pregnancies and births, and with shorter birth intervals as indicated by the studies (Dixon-Mueller and Wasserheit 1991). The degree of a woman’s use of health services during pregnancy, at the time of delivery and in post-partum period becomes an essential aspect to avoid majority of complications and health related problems associated with childbearing. Use of services by women for gynaecological and general health care is equally important for controlling reproductive morbidity. This depends heavily on her perception of requirement of facilities and health services, and also on the availability, accessibility and, quality of these services.

 

The likelihood of a women’s suffering from reproductive morbidity is affected on a large scale by the health related behaviours, especially during pregnancy. A woman’s diet, her workload especially physical work, and her personal hygiene practices are important factors. Also very significant aspects regarding reproductive-tract infections are woman’s sexual activity, or the partner’s sexual health activities.

 

A final step in the backward direction list the background variables which comprise of the personal resources of the woman, like education, urban or rural origin, and work experience. Household resources indicating personal resources of other members of the household, mainly the woman’s husband, housing conditions and amenities available are also some of the important background determinants. Accessibility to health services and individuals in association with health institutions, and some sort of support network that women can resort to for health-related matters are the community resources. Then there are the social institution resources, which are represented by the dominant values linked with reproduction and reproductive health care in the community. The direction of influence from the determinants to reproductive morbidity conditions is followed to improve upon reproductive morbidity. The utility of the framework developed for a policy in improvising reproductive health conditions of women depends on its capacity to yield a broad diagnosis of the problem when it is used in community settings. Application of the framework involves a measurement challenge which makes it difficult for arriving at field indicators which effectively represent the enormity of reproductive morbidity at community level. Furthermore, capturing the social context in its relation to health is another challenge of a conceptual as well as operational nature.

 

Models of morbidity analysis

 

In order to examine the nature of sickness and disability among various population groups, a lot of concepts must be understood by the researchers. Some of these concepts are derived from medical science whereas some of these have social science roots of origin. Among these concepts, many are defined precisely and accurately while the others are more subjective and have unstructured definitions which may be situational in nature, at times. Following are the key concepts and models used in morbidity analysis.

 

The Medical Model

 

The medical model had its origin in the establishment of germ theory as the foundation for modern scientific medicine. This view highlights the existence of evidently recognized clinical symptoms, reflecting the certainty that illness is responsible for the existence of biological pathology. Thus illness is considered as a state which involves the presence of distinct symptoms and health is regarded as a negative residual condition reflecting the absence of symptoms.

 

The study by Freund and McGuire in 1999 highlighted that the medical model believes in a dichotomy between the mind and body, where the diseases are supposed to be positioned within the body. The philosophical foundation for this dichotomy between the mind and body are traced to Descartes’ division of the person into two parts the mind and the body. However, the practical foundation is probably lying in medicine’s shift to a focus on clinical observation toward the end of the eighteenth century and on the pathological anatomy from the beginning in the nineteenth century. This notion indicates that body can be understood and treated as a separate identity from other aspects of the person who inhabits it (Hahn and Kleinman 1983). The body according to the medical perceptive is considered as docile and can be used for purpose of observation, manipulation, transformation and improvement by the physicians.

 

The medical model also believes that illness can be reduced to the disordered biochemical and neurophysiologic functions. The medical model does not consider external factors which may affect the health. Due to its scientific basis and its significance in addressing to certain types of diseases and disorders, the medical model has been widely used and accepted. However, it has been criticised as it focuses mainly on the acute diseases and not on chronic diseases.

 

The Functional Model

 

A second approach to define health and illness is the functional model. This model emphasises that health and illness is a sign of level of social normality rather than physical normality of an individual (Parsons 1972).

 

The functional model is deep rooted to give conceptualizations of health and illness rather than professional ones. In this approach, the diagnosis of a disorder is made by a social group based on societal criteria rather than the clinical criteria. The treatment is then done in order to restore an affected individual’s social normality. The individual in this approach is considered as healthy and cured when that individual is able to resume with the social functioning normally and not when the clinical signs of the disorder disappear.

 

For example: An alcoholic, who has been addicted to alcohol for years but is able to do his job efficiently and is maintaining his family relationships well, will be considered “ill” under the medical model because due to alcohol his body functions are disorganized and functioning improperly, but according to the functional model he is considered “fit” as he is able to fulfil the societal criteria well. On the contrary, a person with chronic back pain may be considered sick according to the functional model, as the back pain may resist him from performing his social roles adequately.

 

The Psychological Model

 

This model is also referred to as the stress model. This is till now the most subjective one out of the three models of health and illness. In this model, the determination of health and illness is done by the individuals themselves. Therefore, the basis of this model is self-evaluation of health and illness by the individuals. So, if the person feels fit, he is considered fit and when he feels sick, he is considered as sick. Furthermore, the affected individual will only determine whether they have been cured of their illness or not. This approach emphasises on the significance of stress an individual takes which leads to production of sickness. According to this approach, majority of the physical illness is a reaction to the stress which the individual goes through.

 

The Legal Model

 

The final model that is the legal model applies to communicable diseases and mental illness. The legal model is applied in the cases where the capability of the individual is in question. For example: during the spread of the ebola virus in 2014, the importance of communicable disease control was a matter of grave concern. This led to formulation of a legal definition of morbidity. Public health authorities have a major role to play in the scenarios for controlling contagious diseases and these authorities are responsible for declaring health emergencies which may lead to restrictions in travel and other social interactions, all for repressing the cause and effect of the communicable diseases.

 

The Biopsychosocial Model

 

George L. Engel in his work explained that many factors interact together to give rise to health and ill-health conditions, and gave a model called the biopyschological model of disease in 1977. This model takes into consideration some factors which contribute to health and illness of an individual and argues that taking only the biological factors to explain health and illness is not sufficient. It is important to understand the psychological and social factors along with the biological factors. The understanding of the interaction of all these factors will help in better understanding of the causes, manifestation, course and outcome of health and disease. According to this model proposed by Engel, ill-health is not caused individually by the effect of biological or social or psychological factors but the combined effect of all these factors is responsible for disorientation of health of a person.

 

Measurement of morbidity

 

As explained in the earlier section, morbidity refers to the diseases, illness, injuries, and disabilities in a population. It refers to the number of people in a population who are ill. Morbidity measures can be used to describe the period of illness which the ill people experience or the duration of these diseases. Data procured from populations on the frequency and distribution of illness can help in controlling spread of these diseases and may also lead to identification of the possible causes of these diseases.

 

Surveillance systems and sample surveys are the major methods for obtaining morbidity data. However, these methods are very costly and require a greater manpower for efficient data collection; therefore, they are used selectively in developing countries only to gather data on health problems of extreme importance. Measures of morbidity frequency characterize the number of persons in a population who become ill (incidence) or are ill at a given time (prevalence). Commonly used measures are listed

Figure 4: Frequently used morbidity measures

 

Incidence refers to the occurrence of new cases of disease in a population over a specified period of time. However, some epidemiologists use incidence as the number of new cases in a community, some other consider incidence to be the number of new cases per unit of population.

 

Incidence proportion and incidence rate are two commonly used types of incidence measures.

 

Incidence proportion or risk

 

Attack rate or probability of developing disease or cumulative incidence is used interchangeably for incidence proportion or risk.

 

Incidence proportion is referred to as the proportion of population which was disease free initially and developed disease, became injured, or died during a specified period of time. Incidence proportion is a proportion because the persons in the numerator, those who develop disease, are all included in the denominator as part of the entire population.

 

Method for calculating incidence proportion (risk)

 

Number of new cases of disease or injury during a specified time

size of population at the start of period

 

Example: In the study among diabetics, 100 of the 189 diabetic men died during the 13-year follow-up period. Calculate the risk of death for these men.

 

Numerator      =       100                  deaths                  among                  the                  diabetic                  men

Denominator = 189 diabetic men

Risk = (100 ⁄ 189) × 100 = 52.9%

 

Secondary attack rate

 

In the epidemic situation, the term attack rate is frequently used as a synonym for incidence risk. It is the risk of getting the disease during a specified period. Overall attack rate, food-specific attack rate, secondary attack rate, etc. can be calculated.

 

Overall attack rate is the ratio of total number of new cases and the total population.

 

A food-specific attack rate is the number of persons who became ill by eating a specific food item divided by the total number of persons who ate that food.

 

A secondary attack rate is calculated to understand and note the difference between community transmission of illness versus transmission of illness in a household, or other closed population. It is calculated as:

 

Number of cases among contacts of primary cases  x 10n

Total number of contacts

 

Calculating Secondary Attack Rates

 

Consider an outbreak of shigellosis in which 18 persons in 18 separate households became ill. If the population of the community was 1,000, then overall attack rate will be 18 ⁄ 1,000 × 100% = 1.8%. One incubation period later, 17 persons in the same households as these “primary” cases developed shigellosis. If the 18 households included 86 persons, calculate the secondary attack rate.

 

Secondary attack rate = (17 ⁄ (86 − 18)) × 100% = (17 ⁄ 68) × 100% = 25.0%

 

Incidence rate or person-time rate

 

Incidence rate or person-time rate is generally calculated from a long-term cohort follow-up study, wherein the enrolled individuals are followed over a period of time and the occurrence of new cases of disease is noted. In this procedure, each person is observed from a specific start time until one of these four aspects are reached i.e. onset of disease, death, drop out of the study or the end of the study. Similar to incidence proportion, the numerator of incidence rate is the number of new cases which are identified during the period of observation. The denominator is the sum of the time each person was observed, totalled for all persons. The denominator, thus, represents total time the population was at risk of and was being observed for disease. Thus, the incidence rate is the ratio of the number of cases to the total time the population is at risk of disease.

 

Incidence rate=

Number of new cases of disease or injury during specified period

Time each person was observed, totalled for all persons

 

In a long-term follow-up study of morbidity, each study participant may be observed for several years continuously. One person followed for 6 years without any development of disease is said to contribute 6 person-years of follow-up.

 

The denominator of the person-time rate is the sum of all of the person-years for each study participant. So, if someone dropped out of the follow up in year 3 or if someone is diagnosed with a disease in year 3, then each person will contribute 2.5 years of disease-free follow-up to the denominator.

 

An incidence rate describes the magnitude of disease in terms of time. It gives an estimate of how quickly the disease occurred in a population. Due to its person-time property, it is advantageous than incidence proportion, as person-time is calculated for each participant, it accommodates a person being part of and leaving the study.

 

However, person-time has one main drawback. It assumes the probability of disease during the study time to be constant, so if 20 people were followed for one year that equals for one person followed for 20 years. But this assumption is considered invalid as the risk of many chronic diseases increase with age.

 

Example C: In 2003, 44,232 new cases of acquired immunodeficiency syndrome (AIDS) were reported in the United States. The estimated mid-year population of the U.S. in 2003 was approximately 290,809,777. Calculate the incidence rate of AIDS in 2003.

Numerator = 44,232 new cases of AIDS
Denominator = 290,809,777 estimated mid-year population
10n = 100,000
Incidence rate = (44,232 ⁄ 290,809,777) × 100,000
= 15.21 new cases of AIDS per 100,000 population

 

Prevalence

 

Prevalence also referred to as the prevalence rate is the proportion of individuals in a population who suffer from a particular disease at a specified point in time. Prevalence differs from incidence as prevalence includes all cases, both new and the pre-existing ones, in the population at a specified time, but incidence only takes into consideration the new cases.

 

Point prevalence refers to the prevalence which is measured at a particular point of time. It is the proportion of individuals with a particular disease on a particular date.

 

Period prevalence refers to prevalence measured over a specific interval of time. It is the proportion of people with a particular disease at any time during the specified interval.

 

Method for calculating prevalence of disease

 

All new and pre-existing cases during a given time period x 10n

Population during the same time period

 

Method for calculating prevalence of an attribute

 

Persons having a particular attribute during a given time period x 10n

Population during the same time period

 

The value of 10 n is usually 1 or 100 for a common attribute. For rare attributes and for most diseases, the value of 10 n might be as high as 1,000, 100,000, or even 1,000,000.

 

Example: In a survey of 1,150 women who gave birth in Rewa in 2002, a total of 468 reported taking a multivitamin at least 4 times a week during the month before becoming pregnant. Calculate the prevalence of frequent multivitamin use in this group.

 

Numerator               =                                     468                                     multivitamin                                      users

Denominator = 1,150 women

Prevalence = (468 ⁄ 1,150) × 100 = 0.407 × 100 = 40.7%

 

Prevalence and incidence are frequently confused as being same. However, prevalence refers to proportion of persons who are having a condition at or during a particular time period, while incidence refers to the proportion or rate of people who have developed a condition during a particular time period. Thus, prevalence includes new and pre-existing cases whereas incidence includes new cases only. The difference is in their numerators.

 

Numerator of incidence = new cases that occurred during a given time period

 

Numerator of prevalence = all cases present during a given time period

 

The numerator of an incidence proportion or rate consists of people who became ill during the specified time interval while the numerator for prevalence takes all the individuals who were ill from a specified cause during the specified interval irrespective of the time of the beginning of the illness.

 

Incidence as well as duration of illness is the basis of prevalence. High prevalence of a disease within a population reflects high incidence and/or long-standing survival without cure. On the other hand, low prevalence will indicate low incidence and a fatal process of recovery from disease.

 

Prevalence rather than incidence is often measured for chronic diseases such as diabetes or osteoarthritis because these diseases have later age of onset and have longer duration of surfacing as an ailment.

 

Morbidity versus Mortality

 

Morbidity refers to the detrimental state of an individual, while mortality refers to the state of being mortal. Both these concepts are applicable at the individual level or at population level. For instance, morbidity rate estimates the incidence of a disease in a population and/or geographic location in a single year while mortality rate will give the rate of death in that population.

 

The word morbid is associated with sickness, illness and disease. Morbidity, as a concept, can be applied on to an individual entity (e.g., someone with cardiovascular disease) or to a population in particular or as a whole in the form of a morbidity rate (e.g., the incidence of malaria in a population). There is also a concept called co-morbidity which is when two or more illnesses affect an individual at the same time. For example: Dysentery is co-morbid with seasonal flu.

 

The rate of morbidity varies depending on the type of disease. Diseases which are highly contagious may lead to high morbidity rate in a population while diseases which are non-contagious may affect individuals only. Morbidity rates facilitate doctors, public health officers, and scientists to calculate the disease risk and make recommendations in public health matters consequently.

 

Crude Death Rate (CDR) is the total number of deaths in a year, per 1,000 individuals. This measure of mortality may be used to document how many people have died across the globe in a particular year. Crude Death Rate is very often paired with Crude Birth Rate (CBR) which estimates the number of people born in a year. This pairing helps in keeping an estimate on the total living human population as well as the population of dead in the world. There are various kinds of mortality rates to estimate the rate of human deaths in the world, as the death rate may be affected by economic well-being, the incidence of illness in that particular population, age, gender, etc. Some of the mortality measures are:

 

Maternal Mortality Rate (MMR) which is the annual number of female deaths per 100,000 live births caused due to complication in pregnancy or during the period of child bearing.

 

Infant Mortality Rate (IMR) is calculated as the number of deaths of children (aged less than one year) per 1,000 live births.

 

Age-specific Mortality Rate is an estimate of total number of deaths in a particular age group. It is a ratio of number of deaths of people in a specific age group to the total number of people in that age group.

 

These mortality rates indicate towards the health of the entire world and give an insight into the global health and well-being of populations across the globe.

 

Morbidity is estimated to determine the severity of disease and the need for proper and systematic medical intervention. It can also be used to predict disease risk and make comparisons of patient illness and the outcomes in various hospitals and medical set-ups. Standardized disease classification systems, such as the APACHE II, SAPS II, and Glasgow Coma Scale are some of the many standardised disease classification systems which have enabled doctors throughout the world to offer science-based care to the patients.

 

Similar to morbidity, mortality can also be scored or predicted with the help of scoring systems like SAPS III, PIM2, and SOFA which suggest ways for predicting the mortality of a patient in intensive care in a realistic manner. Scoring and predicting mortality facilitate hospitals and care units in improving the treatment from year to year.

 

Due to poor reporting standards in less developed and developing counties, gathering credible and reliable statistical data for mortality and morbidity is a tedious task. However, it is advisable to collect information associated with morbidity and mortality, as this may lead to improvement in life quality all over the globe.

 

By understanding the concept of morbidity and mortality, one can possibly understand the mechanism of widespread of diseases and differences in the morbidity and mortality rates of the same disease. It is very much possible that a disease which is widespread and is having a high morbidity rate may have a low mortality rate, and the converse may also be true. These rates may vary and change time to time due to environmental changes and advancement in the medicine sector. For examples, HIV/AIDS was widespread in during the years 1980s to 1990s with a very high mortality rate, but, currently, in areas which have started coming up with HIV awareness and prevention programmes, are having medical facilities to curb the spread of this virus show a significant decrease in morbidity and mortality rates. Whereas in places where medical facilities remain scarce and the awareness programmes have not been very fruitful, the spread of HIV still remains are cause of great concern.

 

Data related to diseases, causes of death, mortality and morbidity are frequently complied and is available online for free for the people throughout the world. These statistics, fact, figures and information are complied by various organizations such as United Nations (UN), World Health Organization (WHO), Centre for Disease Control and Prevention (CDC), and Vital Statistics by Census of India, etc.

 

These organizations also publish reports and maintain databases for analysing the rate of changes in morbidity and mortality. The Centre for Disease Control and Prevention in United States publishes a periodical on morbidity and mortality under the name Weekly Morbidity and Mortality Report, the World Health Organization in Europe has a European Hospital Morbidity Database, and the data on morbidity in Australia can be found in its national hospital morbidity database.

 

A Human Mortality Database was developed in the early 2000s by the University of California, University of Barkley’s Department of Demography and Germany’s Max Planck Institute for Demographic Research. This open database provides mortality rates and other population data for as many as thirty seven countries across the world.

 

Summary

 

From the above module, it is clear that morbidity is very different from mortality, although the two terms are often used together or interchangeably. Morbidity is an estimate of the level of sickness and disability in a population and it has helped the researchers to understand the concept of sickness and death. Morbidity can be measured objectively as well as subjectively. Calculation of morbidity level in the total population and estimation of morbidity in the subsets of the population are important to study the pattern of morbidity in a population. A few models have been discussed above which help in morbidity analysis in different scenarios and situations. The morbidity measures are used to describe the period of illness and the duration of these diseases. It is important to measure morbidity because the assessment helps the demographers to understand the distribution and frequency of illness, disease which further help in designing health programmes. It also leads to identification of the cause of occurrence of diseases. Certain methods are used by the developed countries to obtain morbidity data, however, these methods are costly and cannot be practiced in countries with limited sources in terms of economy as well as manpower.

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References

  • Bongaarts, J. 1978. A framework for analyzing the proximate determinants of fertility. Population Development Review 4,1:105–132.
  • Davis, K. and J. Blake. 1956. Social structure and fertility: an analytical framework. Economic Development and Cultural Change 4:211–235.
  • Dixon-Mueller, R. and J. Wasserheit. 1991. The Culture of Silence: Reproductive tract infections among Women in the Third World. New York: International Women’s Health Coalition.
  • Engle, G. L. (1977). The need for a new medical model: A challenge for biomedicine. Science, 196, 129–135.
  • Freund, P. E. S., & McGuire, M. B. (1999). Health, illness and the social body (3rd ed.). Englewood Cliffs, NJ: Prentice Hall.
  • Hahn, R. A., & Kleinman, A. (1983). Biomedical practice and anthropological theory: Frameworks and directions. Annual Review of Anthropology, 12, 305–333.
  • Mosley, W. and L. Chen. 1984. An analytical framework for the study of child survival in developing countries. In Child Survival: Strategies for Research, ed. W. Mosley and L. Chen. Population Development Review (Supplement), 10:25–48.
  • Parsons, T. (1972). Definitions of health and illness in the light of American values and social structure. In E. G. Jaco (Ed.), Patients, physicians, and illness (pp. 107–127). New York: MacMillan.
  • Winikoff, B. 1987. Women’s health: an alternative perspective for choosing health interventions. Paper presented at First Annual Meeting of Community Epidemiology/Health Management Network, Dhaka.
  • Zurayk, H., Khattab, H., Younis, N., El-Mouelhy, M., & Fadle, M. (1993). Concepts and measures of reproductive morbidity. Health Transition Review, 17-40.

    Suggested Readings

  • Larson, J. S. (1991). The Measurement of Health: Concepts and Indicators (Vol. 31). United States: Greenwood Publishing Group.
  • Taylor, F. K. (1979). The Concepts of Illness, Disease and Morbus. United Kingdom: Cambridge University Press.