Introduction

Morbidity as a state of ill health, has been increasingly recognised as a measurable indicator of wellbeing, and is said to be more difficult to measure than death and infant mortality rates (WHO 2001). Morbidity measures are of two types, i.e., self-perceived and observed. While self-perceived morbidity refers to measure that is perceived and reported by an individual usually in response to inquiries regarding illness, observed morbidity, in contrast, is assessed through an independent observer employing specific method that can be repeated with some degree of consistency. Jn other words, self-perceived morbidity depends upon an individual’s perception of illness whereas observed morbidity is influenced by the standards of abnormality as assessed by a trained observer (Government of India 2005; Murthy and Sastry 2005).

As observed, morbidity levels are very high and due to epidemiological transition (Mercer 1990; Albala 1995; Panikar. 1999). Epidemiological transition reflects changes in the causes of death from infectious diseases to non-communicable diseases (Caldwell 1990; McNamera 1982; cited in Panikar 1999). However, the causal mechanism of demographic changes and consequences are unclear (Omran and Ross 1982). Three fundamental changes take place during epidemiological transition; i) mortality decline due to infectious diseases, injuries, and mental illness; ii) shift of the burden of death and diseases from younger to older groups; and iii) change in the health profile from ‘dominated by death’ to ‘dominated by morbidity’. Epidemiological transition, therefore, implies change in the morbidity profile from state of acute, infectious and parasitic diseases like plague, smallpox and cholera to non-communicable, degenerative and chronic diseases like cardiovascular, cancer, diabetes, neoplasms, etc (Mercer 1990; Albala 1995; Prata 1992; Crews 1987; Reis 1978; cited in Panikar 1999). India is observed to have entered the third phase of the epidemiological transition characterized by lower death rates due to communicable diseases to higher deaths due to non-communicable diseases. This is said to have been responsible for higher morbidity rate due to non-communicable diseases (Panikar 1984, cited in Panikar 1999). Over the years, as a consequence of the expansion in the health care network and control of communi­cable diseases as well as improvement in the non-medical components of health like nutrition, education and water supply, there has been a steady decline in the death rate from 23 during 1951-61 to 8 in 2001 in India (Sample Registration System 2002). This has obviously resulted in the improvement of life expectancy at birth. While mortality is declining, morbidity among people is increasing. According to a study conducted on ‘global burden of disease’, ill-health accounted for 10 percent of the global disease burden (Murray and Lopez 1996). Another study conducted by NCAER has estimated a morbidity prevalence rate (MPR) of 104 for rural and 101 for urban areas per 1,000 population during a reference period of 30 days prior to the date of survey (Shariff ] 995). It is important to note that sex and age of the individuals have shown significant association with morbidity. The results of NCAER study have also highlighted the extremely high levels of morbidity prevalence among very young (0-4 years) and very old. Further, the study has suggested that most of the males had an advantage in morbidity in the 15-34 and 35-59 age categories, thus pointingto high reproductive morbidity among Indian women. A higher natural resistance of females to morbidity in the younger ages is apparent which gets converted to high level of risks in subsequent ages, mostly emerging out of the socio-behavioural factors (NCAER 1992).

There are several studies conducted on morbidity in India. These studies have focussed on individual causes of morbidity and their remedial measures (Shariff 1995; NCAER 1992; Duraisamy 2001). Some of the studies have documented disease-wise morbi-dity condition of people. They have found that age, birth place, perception about nutritional status have significant influence on morbidity. However, studies conducted on morbidity pattern and nature of illness is scarce. Secondly, studies on factors responsible for increasing morbidity and cost of treatment are almost non-existent. Concerted efforts are, therefore, needed to provide necessary information to health planners and policy makers in order to design appropriate strategies to bring about improvement in health among people. Therefore, the present study has undertaken to highlight the factors responsible for increasing morbidity condition and cost of illness in Karnataka State.

Objectives The objectives of the study are:

  • to examine the prevalence of morbidity pattern and type of illness;
  • to highlight the type of health care used and cost of treatment for the illness; and
  • to understand the factors responsible for increasing morbidity condition.

Data

Data for the present study have been taken from the National Sample Survey (NSS) 60th round, conducted during January to June, 2004. For this survey, information from 3,365 households and 16,986 individuals were collected for scheduled castes, scheduled tribes and others in Karnataka. The prevalence of morbidity conditions and cost of treatment among household members was measured more directly on a large scale in this survey.

Methodology

The NSS 60th round collected information on the prevalence of diseases like cardiovascular, diabetes, eye ailments, gastro­intestinal and febrile illnesses, cancer and other tumours, kidney urinary, asthma, tuberculosis, malaria, jaundice and physical disabilities. NSS 60th round also collected data on the duration of illness and hospitalization during last 365 days and expenditure incurred for the illness. In this study, data have been analysed to examine gender differentials in the prevalence of short-term and long-term morbidity. The socio-economic and demographic characteristics have also been analysed to examine whether the socio-economic variables have any effect on health conditions. In addition, gender differentials in the health seeking behaviour and expenditure on treatment of illness have been analysed to assess the extent of direct and indirect costs incurred during illness for tribal population.

Morbidity Pattern in India

Morbidity data collected by the National Sample Survey Organisation from 17th round to 60th round have been presented in Table-1. The data show that morbidity rate had increased over time both in rural and urban areas. The number of ailing persons was highest in 1961-62 but declined in 1973-74. The rate of decline was high in urban areas than in rural areas. The 42nd and 52nd rounds show that there was an increase in the number of ailing persons. Data show that morbidity reporting was slightly higher in rural areas than in-urban areas. However, the rate of increase in morbidity reporting in urban areas from 42nd and 52nd round was higher compared to rural areas. This view has been supported by some of the micro level studies as well. A study conducted in Jalgaon district of Madhya Pradesh observed that within urban areas the slum people had higher morbidity because of unhygienic conditions in which they lived and their low levels of awareness and preventive care (Duggal and Arun 1989). Sunder and Sharma (2002) have estimated monthly morbidity prevalence rate for Delhi and Chennai city. According to them, it was 104 episodes per 1,000 population in Delhi and 83 episodes per 1,000 population in Chennai. The National Sample Survey 60th round data presented in table-2 clearly indicate that tribal people hospitalisation due to morbidity is lesser than non-tribal hospitalisation. However, in some states morbidity for tribals is more or less similar.

Table-1: Morbidity Prevalence Rate for General population (Per 1,000 Population) in India

Rural Urban
Male Female Total Male Female Total
NSS 1961-62 (30 days) PAP 139 123 132 133 128 131
NSS 1973-74 (15 days) PR 47 40 43 43 41 4?
NSS 1986-87 (30 days) PAP 64 63 64 30 33 31
NSS 1995-96 (15 days) PR 54 58 56 52 58 55
NSS 2004 (15 days) PR 88.0 94.5 91.2 95.1 1 10.3 102.5

PAP = Proportion of ailing persons; PR = Prevalence Rate
Source: NSSO 2006; NSSO1998.

Table-2: Hospitalisation and Morbidity Level among Tribes and Non-Tribes By different States in India

 

 

 

States Hospitalisation during last 365 days Morbidity during last 15 days
Tribes Non-tribes Tribes Non-tribes
Arunachal Pradesh 56.39 68.45 63.23 37.58
Andhra Pradesh 97.11 92.85 97.94 125.02
Assam 50.29 64.51 55.44 80.28
Bihar 47.61 60.96 57.14 56.30
Chandigarh 100 87.46 100 102.50
Chhattisgarh 39.38 72.51 53.98 87.41
Gujarat 73.63 81.84 63.33 88.17
Haryana 111.11 7523 111.11 100.82
Himachal Pradesh 69.30 84.03 84.15 104.93
Jammu and Kashmir 83.33 69.27 111.11 75.95
Jarkhand 35.89 56.30 28.71 38.53
Karnataka 71.42 81.34 66.32 77.35
Goa 0 92.73 375 97.20
Kerala 106.91 110.90 163.52 241.61
Mandhya Pradesh 52.95 72.86 60.05 72.03
Maharashtra 78.57 88.22 86.38 119.45
Meghalaya 48.65 80.50 53.97 44.49
Nagaland 78.32 104.34 60.07 52.17
Manipur 55.04 70.14 15.04 34.70
Orissa 71.07 79.06 61.62 74.82
Punjab 58.82 74.44 0 131.65
Rajasthan 66.05 68.07 45.47 77.83
Sikhim 66.13 77.05 59.52 51.36
Tamil nadu 89.10 103.23 19.80 111.90
Uttar Pradesh 70.46 63.85 107.38 110.06
Wb 73.02 84.03 63.62 131.ll
Uttaranchal 55.55 78.54 83.33 84.67
Mizoram 69.88 140 23.54 0
Tripura 89.02 95.68 120.13 124.38
Pondichery 71.42 105.44 178.57 191.87
Lashadeep 73.68 166.66 134.73 333.33
damab&diu 72.46 83.45 0 44.00
nagar haveli 8 ,.58 95.08 23.012 29.50
Delhi 0 43.57 0 21.97
Andaban 50 105.77 40.90 64.64
India 62.85 77.48 55.84 100.40

Source: NSSO 2006.

Tribal Morbidity Prevalence in Karnataka

Based on NSS 60th round data, the present study has worked out the disease prevalence rate by sex and background variables for tribes and the same have been presented in Table-3. The data show that morbidity for females was less than males by any category of socio-economic groups of the populations. Lt is also mentioned that perceived morbidity was expected to be higher among higher socio-economic categories. However, the situation was different in urban areas. In urban areas, females had higher morbidity prevalence than among males. For example, in urban areas, the reported prevalence rate of illness worked out to be 76.9 per 1,000 population for males as against 79.6 for females. On the other hand, in rural areas, the reported prevalence rate worked out to be 80.1 for males and 70.5 for females per 1,000 population. The prevalence rate of illness by background variables like education, marital status, work status and landholding size show an interesting picture. For instance, illiterates, those who lived in pucca houses, widowed or divorced persons and people who did not work had higher prevalence rate than other categories. Regarding hospitalisation, during the last 365 days males were frequently hospitalised than females and this was true for both rural and urban areas and also with respect to other socio-economic variables.

Table-3: Tribal Morbidity (Per 1,000 population) Prevalence by Sex and Background Variables

Background Variables Hospitalisation Last 365 days Morbidity During During Last 15 days
Male Female Total Male Female Total
Rural 85.7 76.4 81.0 80.1 70.5 75.3
Urban 85.0 75.3 80.2 76.9 79.6 78.2
Illiterate 87.7 76.9 81.4 102.3 93.9 97.4
Literate 84.2 75.1 80.2 66.9 57.3 62.7
Pucca house 89.1 79.8 84.5 81.6 82.3 82.0
Semi Pucca house 81.0 70.2 75.6 75.2 65.8 70.5
Kutcha house others 66.7 68.2 67.5 63.9 36.7 49.9
Less than 1 hectare 87.0 76,7 81.8 77.5 76.8 77.2
1-4 hectares 77.7 71.7 74.7 79.6 65.8 72.8
Above 5 hectares 87.6 79.8 . 83.8 92.1 70.4 81.5
Never-married 56.2 43.1 50.5 44.7 36.2 41.0
Currently married 115.2 91.7 103.6 110.4 71.4 90.6
Widowed/divorced/ separated 169.6 124.5 130.7 263.2 210.0 217.3
Working 86.2 78.1 83.8 60.6 50.5 57.5
Non-v/orking 84.2 75.2 78.5 103.0 82.6 90.0
Total 85.36 75.94 80.65 78.65 74.53 76.59

Data provided in Table-4 provide very good inferences in respect of 0-14, 15-34, 35-59 and 60+ age groups in each category of socio-economic groups. For instance, during 15 days reference period, child morbidity was higher than 15-34 age groups. But when it concerned to hospitalisation, the trend was different. That means children were more subject to short-term illnesses than 15-34,35-59 and 60+ age groups. The age-wise morbidity pattern in both rural and urban areas was more or less similar with respect to 60+ year age group having the highest prevalence rate, followed by 35-59 age groups. On the other hand, reported prevalence rate among illiterates, landless and non-working people by age group show a different pattern.

TabIe-4 : Tribal Morbidity Prevalence Rate (Per 1,000 population) by Age Groups and Background Variables

 

 

 

 

Background Variables

 

 

Morbidity During
Last 15 Days
Hospitalization During
Last 365 Days
Broad Age Groups Broad Age Groups
0-14 15-34 35-59 60+ 0-14 15-34 35-59 60+
Male 51.0 30.9 82.1 350.6 50.2 61.9 126.31 85.0
Female 39.9 31.3 86.8 322.8 36.3 67.2 114.71 25.7
Rural 44.3 30.9 83.9 332.1 46.3 62.7 125.2 148.0
Urban 47.3 31.4 85.3 342.4 39.3 66.6 114.1 164.8
Illiterate 65.0 37.9 76.1 313.9 47.2 63.4 113.6 124.1
Literate 27.9 29.1 92.3 373.7 39.9 64.9 126.3 206.5
Working 21.7 26.8 64.4 250.0 7.2 59.3 110.3 121.8
Non-working 46.2 37.0 125.9 376.8 44.4 71.6 140.6 170.9
Never married4 5.6 30.4 82.2 500.0 43.4 62.6 68.5 125.0
Currently married 0.0 31.9 84.1 335.3 0.0 67.0 121.3 160.6
Widowed / divorced /separated 25.3 88.0 336.8 38.0 120.0 149.7
Less than 1 hectare 44.6 34.1 84.8 339.7 45.1 66.8 122.4 150.3
1 -4 hectares 44.7 20.6 78.8 337.9 37.1 60.7 108.8 151.7
Above 5 hectares 63.7 22.2 101.4 295.5 39.8 44.4 129.0 238.6
Pucca houses 50.2 30.8 91.9 348.3 41.2 69.6 120.6 170.6
Semi-pucca houses 40.0 32.6 76.7 323.8 43.6 55.6 123.8 136.5
Kutcha houses others 35.0 24.2 39.5 254.2 66.1 56.5 84.7 67.8

Data on prevalence rate for those who were hospitalised during last 365 days show more or less a similar pattern. Sex differentials in hospitalization for illness were higher in the case of children as compared to the adult population. This sex differential in hospitalization does not necessarily reflect a better health status of the females. This probably indicates the extent of under reporting of illness among female adults and girls. Also, it is a question of perception. In the case of women, even if they were suffering from illness, they perhaps did not consider themselves ill since they could not afford to take-off from their domestic chores. In the case of males, since they were the main breadwinners, medical aid was probably sought more often. This phenomenon has been highlighted in other studies as well. A study conducted by NCAER (1992) has shown that male children were looked after better compared to female children. Since the households were asked to report illnesses that needed medical care, it is not surprising that the reported number of cases were fewer for women and female children as compared to their male counterparts (NCAER 1992). Another study conducted in Jalgaon district of Madhya Pradesh has brought out that the perception of illness depended on purchasing power.and income level of the people (Duggal and Am in 1989). Duggal and Amin (1989) have suggested that people belonging to lower income class were more susceptible to various illnesses perhaps due to poor sanitary and hygienic conditions and lower nutritional status. The major factor responsible for high incidence of illness was that large number of people fall into the categories of below the poverty line.

Nature of Illness

The NSS 60th round collected information on the nature of illness suffered and hospitalization of individuals during the reference period. Among the illnesses suffered by the individual febrile illness was the most common disease, followed by cardio­vascular disease, diabetes, joints and bones disorder, respiratory infections, other disabilities, gastrointestinal infections and gynaecological problems. Data presented in Table-5 show that among the various types of diseases suffered by the household members during the reference period, ‘febrile’ illness seems to be the most common ailment as this accounted for 15 percent of the reported illness cases. Both rural and urban areas exhibited some­what similar pattern among adults and children of either sex.

Generally, as the age advances more and more people suffer from different types of diseases. Some of the diseases like cardiovascular disease, asthma, diabetes, joints and bones disorder, eye ailments, etc., are commonly reported among old people. An interesting picture emerges when we look at the data on morbidity rate by sex and type of disease. It shows that morbidity was higher for males than among females. Some of the diseases like asthma, respiratory problems, kidney and prostate disorders, diabetes and skin diseases were higher among males, whereas gynecological problems, joints and bones disorder, neurological/ psychiatric problems and other physical disabilities were common among females. However, cardiovascular disease, cancer and eye-ailment were common among both males and females.

TabIe-5: Tribal Morbidity Prevalence by Residence, Age Group and Nature of Ailments (Per 1,000 Population)

Nature of Ailments Rural Urban Age Group Total
Male Female Male Female 0-14 15-34 35-59 60+
Gastro intestinal 4.72 3.38 1.56 1.86 3.15 1.65 3.46 6.54 3.00
Jaundice 021 0.21 1.30 0.00 0.20 0.33 0.69 0.65 0.41
Cardiovas cular diseases 6.01 5.28 14.60 17.03 0.00 0.17 1129 80.44 10.18
Respiratory ailments 2.79 2.74 2.61 1.60 3.35 0.83 2.99 4.58 2.47
Tuberculosis 1.93 1.90 0.52 0.27 0.20 0.66 2.76 2.62 1.24
Asthma 7.09 2.96 1.82 1.60 0.20 0.17 3.46 28.12 3.53
Joints & bones disorder 9.45 7.60 6.78 10.11 0.00 1.65 6.45 69.33 8.48
Prostate disorder 1.72 0.21 2.09 0.53 0.20 0.50 1.84 4.58 1.12
Gynecological disorder 0.00 2.96 0.00 1.06 0.00 1.82 1.38 0.65 1.06
Neurological disorder 3.65 1.90 4.17 3.99 0.99 1.99 4.61 13.08 3.36
Eye ailments 4.72 5.91 2.35 4.79 0.39 0.17 1.15 45.13 4.53
Skin diseases 2.36 1.06 0.52 0.80 1.58 0.50 0.46 5.23 1.24
Diabetes 3.01 2.74 10.69 12.24 0.20 0.17 8.75 48.40 6.71
Anaemia 0.21 0.42 0.00 0.80 0.00 0.66 0.23 0.65 0.35
STD 0.21 0.00 0.00 0.00 0.00 0.00 0.23 0.00 0.06
Febrile illness 15.46 16.68 1225 12.78 21.69 9.60 12.44 15.70 14.48
Tetanus 0.21 0.00 0.00 0.00 0.00 0.00 0.00 0.65 0.06
Disabilities 11.60 7.18 8.86 8.78 1.58 3.14 8.52 59.52 9.13
Cancer 0.64 0.84 0.78 0.80 0.00 0.50 1.61 1.96 0.77
Others 11.60 12.46 14.60 14.11 11.83 6.78 14.51 37.93 13.07

The National Family Health Survey 1998-99 (NFHS-2) and National Family Health Survey 2005-06 (NFHS-3) have collected information on health seeking behaviour of people and risk involved leading to different diseases. The data have caliculated for tribes and non-tribes and presented in Table-6. It is indicated that non-tribal morbidity is more or less similar to NSS 60th round data ie. Tribal morbidity is lesser than non-tribal morbidity even for different diseases.

Table-6: Prevalence rate of various diseases for tribes and non-tribes in Karnataka (Per 1000 Population)

Diseases   NFHS-3 NFHS-2
Tribes Non-tribes Tribes Non-tribes
Diabetes Male 5.0 7.0
Female 6.0 14.0
Total 5.0 10.0
Asthma Male 8.0 13.0
Female 6.0 9.0
Total 7.0 11.0 3.1 2.6
Thyroid Male 5.0 8.0
Female NA 3.0
Total 5.0 5.0
Tuberculosis 101 0.5