Disability divides in : Evidence from 2011 Census

Abstract

Background: Understanding disability divide in India has enormous relevance to public health policies and programs.

Objectives: Present study aims to quantify disability divide in India by gender, region (rural-urban, states and districts) and social groups. It also examines the association between disability prevalence and demographic and socio-economic characteristics of the districts of India.

Methods: Age-standardized disability prevalence rate (ASDPR)s were calculated applying WHO’s standardized age sex weights on 2011 Census disability data. Multiple regression was carried out to examine the association between disability prevalence rate and demographic and socio-economic characteristics of 640 districts of India.

Results: The size of seriously disabled persons in India is as enormous as 26.8 million. ASDPR varies substantially from a minimum of 1.01 % to the maximum of 6.14% among the districts of India. ASDPR is higher among females, rural and ST/SC population. Regression model established that disability rate in districts increases with increase in the percent of urbanites, elderly, scheduled tribes and the population living in dilapidated housing condition. On the other hand, disability rate decreases with the increase of female literacy, main workers, and people having safe drinking water.

Conclusion: The burden of disability disproportionately falls on geographic regions and socio-economic groups in India. Our findings demand the need of region or group specific design of public health policy in India. Additionally, Indian census definition of disability should be modified to generate internationally comparable estimates of disability rate.

Words: 235

Key words: Disability; India, Socio-economic; Census; rural-urban

Introduction

Health inequality literature has recognized disability as an emerging concern1–3. Studies conducted in multi- country set up shows that people with disability face greater social and economic discrimination at each phase of their life even at the presence of numerous societal and governmental supports4–6. People with disabilities continue to experience the barrier to health services, which leads poorer health outcomes7. The situation is gloomier in developing countries where exists a vicious cycle between disability, education and economic status of the disabled population8-10. Research demonstrated that people with disability have poor schooling, poor utilization of health care services and poor access to the employment market. Finally, people with disability are subject to the strong social stigma with community and families11. Nevertheless, studies based on the disability status of developing countries are very limited and needs greater attention from health researchers, policy makers and other important stakeholders. The present study is a sincere effort to fill up the gap in the literature in developing countries by understanding demographic, socio-economic and spatial disparity in disability prevalence in Indian districts.

A summery of disability data and related studies in India

The limited available studies are addressing disability in India mainly revolve around the three principal issues on disability viz. A) Measurement of disability data in census and other surveys12–15 B) differentials, determinants and impact of disability in general10, 12, 16–20 and C) the pattern of disability among older adults’ health 14, 21–25.

Studies addressing the measurement of disability in India critically evaluated both the type and nature of disability statistics provided by two main official sources, popularly known as the and National Sample Survey Organization (NSSO). Both of these sources provide a non-comparable magnitude and prevalence rate of disability, a resultant factor of different study design, disability definition and type of disability12. While the census is a gigantic task of the enumerating entire population, NSSO is based on a nationally representative stratified sample. Secondly, there exists a fundamental difference in disability definition in these two sources. Census defines disability more as a medical term whereas NSSO defines disability as a measure of activity limitation in daily life. Finally, the definition of each type of disability (visual, hearing, locomotors etc.) varies from one source to another. Nevertheless the official estimates by census and NSSO (2.21 percent in Census 2011 & 1.8 percent in NSSO 2008) are much lower than alternative estimates (of at least 5-8 percent) based on better methods and more inclusive definitions of disability15.

The question canvassed in Census 2011 was “Is this person mentally or physically disabled?” If the answer to this question is “yes’, a list of eight different types of disability is provided viz. Disability 1) in seeing, 2) in hearing, 3) in speech 4) in movement 5) mental retardation 6) mental illness 7) any other and 8) multiple disability to document the types of disability. Census 2011 manual gives a detailed description of disability investigation (Office of Registrar General and Census Commissioner 2011). Interestingly the census definition of disability doesn’t strictly follow any of the conceptual models of disability defined by previous studies26–28 but may be closer to medical model of serious disability15. There exist three different approaches to understanding disability viz. the medical model, the social model and the International Classification of Functioning, Disability and Health (ICF) model26, 29. While medical model relates disability intrinsic to body or mind and doesn’t entirely depend on limitations in daily life activities, the social model consider disability as result of society’s failure to address the needs of persons with impairment12, 30. WHO’s ICF model of disability provides a coherent conceptualization of disability since it combines disability due to impairment, activity limitations and participation restrictions12, 27, 31. Most of literature addressing disability in developing countries essentially followed the ICF model. For instance, the Global Burden of Disease study defines disability as “short- or long-term health loss, other than death, such as chronic respiratory disease, diabetes, cardiovascular diseases, and mental or behavioral disorders” 32 . Unlike these studies, Indian census completely overlooked disability in daily activities/participation and defined disability closer to the medical model and hence provided minimum prevalence estimates.

Nevertheless studies in other countries found that simply asking whether or not people have a disability (and what type) yields lower bound estimates of disability prevalence, with a strong bias towards more serious disabilities15. Analysis of 14 household surveys from 13 developing countries suggest about 1-2 percent of people with disabilities10. This study found that the households having functionally disabled people due to old age (who are other-wise not disable) will not report them as “disabled” in census type of question15. Consequently, official estimates of disability in India can be considered as a reliable estimate of serious disabilities15.

A review of studies addressing differentials and determinants of disability demonstrate the gender dimension of disability prevalence in India16. While the male disadvantage in disability was more visible in “locomotor” and “hearing” disability, the female disadvantage was more pronounced in “visual disability” 16. Among all types of disability, locomotor disability is found to be the most prevalent type of disability18. A study on disability burden in an Indian village shows that disability rate was higher among the older population and females (Singh 2008)19. Only a small fraction of disabled people in India received government assistance 17, 19. A global study on disability burden by Salomon et al20 found that Indian women have longer life expectancy and healthy life expectancy than Indian men in 1990 & 2010. Disabled adults typically live in poorer than average households and a vicious cycle of low schooling attainment and subsequent poverty is observed in developing countries10, 33–34. They face discrimination in employment in the form of differential access to employment12. Additionally, parents with disabled children need to spend more on their children compared to the parent with normal children17.

The third group of studies, based primarily on household surveys, concentrates on the changing pattern of chronic diseases and its relationship with disability and quality of life among older adults in India14, 21–25. These studies attempted to answer the question whether health transition from infectious disease to chronic disease leads higher level of disability in later life. A recent study on India revealed that the increase in life expectancy does not inevitably leads a better quality especially among women25. Disability prevalence is higher among the oldest-old age group, women, people residing in the rural area and people belonging to the household with lower wealth quintile. Also, a close association between non-communicable and functional disability was observed in most cases indicating poor quality of life among Indian elderly.

Research questions and objectives

The above discussion clearly conveys that available studies still silent on a few basic research questions on disability burden in India. For example, how is the burden of disability divided across small geographical units in India? What level of disparity prevailed in disability between and within states? How disability burden varies from young to old age? Is there any gender dimension in disability and if yes, how it interacts with space? Which social group in India experiences the highest level of disability? What are the demographic and socio-economic determinants of disability in India? The Census data gives a unique opportunity to understand disability divide in India by providing disability information in lowest administrative (or geographical) units. Furthermore, very few comprehensive efforts are carried out to understand the role of demographic, socio-economic and health care variables in the burden of disability in India.

Using district-level census information, the present study examines if the age composition of the population is controlled, how burden of disability is divided across space, in particular across types of residence, states and districts of India. By this, it aims to identify the critical districts and social groups with respect to disability prevalence for providing crucial input for policy and intervention. Secondly it measures the burden of disability by socio-economic subgroups in 640 Indian districts using the most recent census data.

Methods

Data overview The present study predominantly based on the census of India 2011 data on disability, demographic and socio- economic conditions of Indian subpopulations. The 15th Indian census, conducted by Office of Registrar General and Census Commissioner (ORGCC), was mammoth task with 2.7 million officials visit all households in 7935 and 640867 villages spreading over 35 states and union territories of India36. Census collected wide range demographic and socio-economic indicators through the population enumeration schedule. The table C-20 titled as “Disabled by age group and type of disability” presented disability information at district and sub-district level. . ORGCC took special efforts to improve the coverage of disability by intensive training to census functionaries on one side and a wide publicity through electronic and print media to sensitize counting of disables on the other side. A detailed description of disability information can be found elsewhere37.

Variable definitions Outcome variable

We calculated the outcome variable percent disabled for 640 districts in 35 states and union territories of India. We divided the number people with any kind disability in a district by the total population in that district to obtain percent disabled.

Exposure Variables

We assessed the variation in disability prevalence rate by a couple of demographic, socio-economic, health status and health care variables. Since the unit of analysis is district, we measure all the exposure variables at the district level. The previous literature suggests that the demographic variables such age and gender play an imperative role in the disability status38–40. Therefore, we include percent of the female population and percent of people above age 60 in the multivariate analysis. Disadvantaged social groups popularly known as scheduled castes (SCs) and scheduled tribes (STs) are found to have poorer health outcomes in India41. The Constitution of India recognized the SCs and the STs as the socially disadvantaged groups in India. This recognition is based on the ethnicity of the person and on the constitution of India that has given these social groups special status for upward socio-economic mobility. A detailed description of composition and meaning of these groups is available in a study by Desai and Kulkarni42. We used percent SCs and STs in the districts to examine their association with disability prevalence in India. Female literacy is always found to be a strong predictor of health outcomes in India and developing countries41. Hence, we include the percent of female literate in the districts as an exposure variable. Since the Census of India doesn’t provide direct information on income or expenditure of the populations, we used two variables viz. percent of the urban population and percent of the main workers at district level to capture the economic status of the people belonging to the districts. Additionally a set of household amenities variables viz. percent of people having safe drinking water, percent of people with dilapidated house condition, percent of people with no toilet facility, percent of household having 2 or more dwelling room, percent of household using clean fuel for cooking, percent of household availing banking services were considered as a proxy variable of economic status of the people belonging to a district. Safe water is defined as tape water from treated source, covered well, hand pump and tube well. Similarly dilapidated house condition is considered as houses, which show sign of decay or those breaking down, and required major repairs and are far from being in condition that can be restored or repaired43.

Statistical analysis

Since disability status is strongly age dependent, comparison of crude disability rate will be misleading if underlying age composition differs in the populations being compared. Age composition of Indian subpopulation varies considerably at the state and district level. We, therefore, calculated age-standardized disability prevalence rate (ASDPR) applying WHO’s standardized age sex weights (Ahmad et al 2001) to examine the disparity in disability prevalence across population subgroups.

Descriptive analysis of the outcome and exposure variables for 640 districts of India was carried out. Next multiple linear regression was performed in STATA S.E. 12.0 to assess factors affecting disability prevalence. ArcGIS software was used to draw a district level map of disability prevalence.

Results

Table 1 presents the absolute number of disabled persons by age, sex and types of residence in India as counted in 2011 census. The figures in parenthesis show the percent share of each age group to the total disabled persons. The size of seriously disabled persons in India is as enormous as 26.8 million. The absolute number of the disabled male is higher than the female, and about 70% of total disabled persons live in rural India. The surplus of male disabled persons over female disabled reduces as age increases. It is noteworthy that the percent share of aged 60 and above in case of female is substantially higher than male compared to the other age groups (18.61 % among male against 23.03% among female in total India etc.).

Table 2 illustrates age-standardized disability prevalence rate for Indian subpopulations categorized by type of residence, social groups and 35 states and union territories. Clearly ASDPR is marginally higher among male than female. Consistent with Table 1, disability is more extant among rural Indian than urban Indian even after freezing the age structure of the both populations. The socio-economically disadvantaged population (SCs & STs) reported higher disability rate than the other population. Finally, considerable disparity in ASDPR is observed among states (Male: 1.21 % in Daman & Diu against 3.61% in ; Female: 1.15 % in against 3.68 % in Sikkim).

Figure 1 unveils interstate and intrastate variation in ASDPR by sex by displaying a district level map. It is evident that ASDPR varies from a minimum of about 1.01 % to the maximum of about 6.14% indicating much larger inter-district district disparity in ASDPR than inter-state state disparity (Male: 1.02 % in Daman & Diu against 6.25 % in Anantnag; Female: 1.03 % in Dadra and Nagar Haveli against 6.03% in Anantnag). Also, a clear geographical clustering in disability is observed in the central and east India.

Table 3 describes the descriptive statistics of outcome and exposure variables for 640 districts of India. The average value of the outcome variable (percent disabled) is 2.15% and varies between 0.76% and 4.51%. All socio-economic and demographic variables widely dispersed across districts. While the percent of ST ranges from a minimum of 0.00 % to the maximum 98.58 %; percent of SC ranges from 0.00% to 50.17%. On average, district level female literacy is about 55.24% (varies between 24.25% and 88.62%). We found districts completely with 100.00% rural population as well as districts with 100.00% urban population. On average Indian districts has 70.68 percent of the population with safe drinking water; 5.03% of the population with dilapidated housing condition and 53.63% of the population with no toilet facility within the premise. There exists significant variation in the entire household facilities mentioned above.

Table 4 presents the results of linear regression model assessing demographic and socio-economic determinants of disability prevalence in India. The two demographic exposure variables viz. percent of female and percent people more than age 60 are significantly associated with disability prevalence in the districts of India. While percent of female population is negatively associated [β=-0.060, CI (-0.090, -0.030) & P <0.001], percent of people more than age 60 are positively associated with disability prevalence [β=0.155, CI (0.122, 0.186) & P <0.001]. With the increase in the percent of ST population, disability prevalence also increases, and this association is statistically significant [β= 0.003, CI (0.000, 0.004) & P <0.005]. There is no significant association between the percent of SC population and disability prevalence. The percent of female literacy and main workers are found to be negatively associated with disability prevalence [β=-0.013, CI (-0.019, -0.007) & P <0.001] and [β=-0.013, CI (- 0.017, -0.008) & P <0.001] respectively. As percent of urban population increases, disability prevalence also increases [β=0.008, CI (0.004, 0.012) & P <0.001]. Among household related variables, percent of the household having safe drinking water and percent of household with the dilapidated condition are found to statistically significant association with disability status. As the percent of household with safe drinking water increases, disability prevalence decreases [β=-0.003, CI (-0.005, -0.000) & P <0.005]. On the other hand, increasing the percent of household with dilapidated condition leads increasing percent of disabled [β=0.040, CI (0.023, 0.056) & P <0.001]. We don't observe statistically significant association between disability prevalence rates and the rest of the household amenities variables (percent of household with no toilet facility, percent of household having 2 or more dwelling room, percent of household using clean fuel for cooking, percent of household availing banking services).

Discussion

The present study quantified disability divides in India across age, sex, geographical and administrative units ( the type of residence, states, and districts), and social groups (scheduled tribe, scheduled caste and others). Further, it revealed the nexus between poor socio-economic status and serious disability in the context of an emerging country as India. This study is a significant addition to the existing body of literature in India since to the best our knowledge there is no analogous systematic study in India to understand disparity in disability prevalence by demographic, socio-economic and geographic characteristics.

The study used census information on disability in India based on a simple question whether a person is mentally or physically disabled or not. By this definition, census information generates minimum but reliable estimates of serious disability rate. In spite, we observed the existence of enormous size of disabled persons in India. We also recognized that the burden of disability falls disproportionately on geographic regions and social-economic groups in India.

In agreement with previous studies in developed and developing countries, the current study also found a strong association between disability and demographic and socio-economic characteristics of the population under study. Districts are having a higher percent of old people (above age 60) also have the higher percent of disabled people indicating age as an important determinant of disability. This is consistent with the conclusions of previous studies15, 21. In India, socio-economically disadvantaged ST and SC groups usually have poorer health outcomes than the rest of the population41. This analysis further confirms that disability burden is higher among these disadvantaged social groups. Regression result also shows that the net effect of having higher percent of ST population in a district leads higher prevalence of disability in that district indicating districts having higher percentage of deprived population also have higher likelihood of disabled population. A similar observation can be on the rural-urban differential in disability in India. A positive association between the percent of the urban population and percent of disabled is found at the district level after controlling other exposure variables.

Female literacy matters a lot when it comes to any health outcomes in India and other developing countries41. Disability status is also not an exception to this as clear from the multiple regression results. Districts are having a higher percent of households with poorer economic conditions also have the higher prevalence of disability rate after eliminating the role of demographic and social variables.

One important finding of the present study stands contrast to the previous findings observed in India and other countries. Contrary to previous findings, the present study found that men carry higher burden of disability than women both in terms of absolute size of disabled persons and disability prevalence rate. However numerous empirical evidences based on multi-country setup shows that women have higher disability prevalence rates than men although women survive longer than men39, 45–46. There might be two possible explanations for this contradictory finding. In the first place, we may believe that relatively lower female disability inherited due to the kind of disability information we are using in the present analysis. Most of the previous studies reporting higher female disability are largely based on reported disability on activities of daily limitations (ADLs) or functional assessment. However, the present study is based on disability information closer to the medical definition. Secondly, even after serious efforts by census authority in India to count each and every disabled individual, we cannot totally reject the hypothesis that a fraction of disabled women are under enumerated in census due to the presence of stigma towards disability as well as negligence towards a specific gender. Although we have not found any study addressing female under-enumeration in census 2011, a few attempts on previous censuses conveyed that females especially marginalized females (widows, elderly etc.) were under enumerated in 1991 census47. Further research should be conducted to explore this particular concern.

Limitation The major limitation of the present study is that we cannot compare the magnitude of disability prevalence with the other studies based on ICF definition. Therefore, results should be interpreted with caution. Secondly we are using reported data on disability (rather than medically certified data) from the Census of India, which is criticized for age misreporting48. We minimized this problem by presenting the results by age group rather than single year age. Similarly, the differential pattern of reporting disability across regions or population subgroups may affect the results. Finally, we assessed the relationship between disability prevalence and socio-economic variables at aggregated level, not at an individual level. Hence, the inference drawn from regression analysis holds only for districts, not for individuals. Otherwise, ecological fallacy may arise because the process of aggregating data may conceal the variations that are not visible at the district level. Nevertheless, the present analysis provides thorough insight in the disability divide in India based on a very recent data on a complete enumeration of the disabled population.

Conclusion The findings of the present study have enormous relevance in public health policies and programs in India. A wide geographical and socio-economic disparity in disability prevalence demands the need of region or group specific design of public health policy along with crucial governmental effort for improving socio-economic conditions of the underprivileged section of the population. Since older people have higher chance of being disabled and transition towards ageing has already started in several states, the need of disability care and the associated direct and indirect results on the family members should be taken care in the present and future public health policies. Besides further research should be taken up to understand the status of disability care, differential health care utilization by disabled and non-disabled population, etc. Finally, the present study strongly suggests a modification of the census definition of disability as per WHO’s definition to produce internationally comparable results.

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Table 1: Absolute number of disabled persons by age, sex and type of residence in India 2011

Rural Urban Total Age Male Female Male Female Male Female 1270819 1055345 501130 419577 1771949 1474922 0-9 (12.2) (12.83) (10.94) (11.65) (11.82) (12.47) 7033331 5087384 3391010 2537966 10424341 7625350 10-59 (67.57) (61.86) (74.07) (70.48) (69.55) (64.48) 60 and 2104018 2081024 685894 643059 2789912 2724083 above (20.21) (25.30) (14.98) (17.85) (18.61) (23.03) 10408168 8223753 4578034 3600602 14986202 11824355 Total (100.00) (100.00) (100.00) (100.00) (100.00) (100.00)

Table 2: Age Standardized disability prevalence rate for Indian subpopulation

Male Female States Male Female India 2.60 2.16 3.02 2.38 Type of residence 3.40 2.96 Rural 2.66 2.20 Punjab 2.66 2.11 Urban 2.46 2.07 3.00 2.67 Social Groups Tamil Nadu 1.83 1.45

Scheduled Caste 2.98 2.44 2.46 2.03 Scheduled Tribe 2.58 2.27 2.53 2.12 All others 1.92 1.59 2.20 1.81 States 2.39 2.20 3.04 2.56 2.56 2.54 1.88 1.77 2.20 1.94 2.72 2.15 1.85 1.71 3.07 2.67 1.75 1.55 Delhi 1.76 1.45 2.07 1.97 2.10 1.75 Sikkim 3.61 3.68 2.58 2.11 2.05 1.75 2.60 2.08 Andaman & 2.20 1.85 & Kashmir 3.54 3.08 2.72 2.66 2.89 2.46 Chandigarh 1.67 1.42 2.42 2.02 Puducherry 2.79 2.21 Kerala 2.44 1.99 Dadra & nagar haveli 1.28 1.16 Madhya Pradesh 2.65 2.10 Daman & diu 1.21 1.16

Table 3: Descriptive statistics of outcome and exposure variables for 640 districts of India

Variables Minimum Maximum Mean SD Outcome variable

Percent disabled 0.76 4.51 2.15 0.57 Demographic variables

Percent of people more than age 60 2.46 17.82 8.34 2.06 Percent female population 34.79 54.22 48.55 1.64 Socio-economic variables

Percent ST 0.00 98.58 17.70 26.97 Percent SC 0.00 50.17 14.86 9.13 Percent female literacy 24.25 88.62 55.24 12.41 Percent of urban population 0.00 100.00 26.40 21.11 Percent of main workers 30.65 96.40 73.28 12.65 Percent of household having safe drinking water 8.60 99.60 70.68 19.95 Percent of household with dilapidated condition 0.20 17.70 5.03 3.12 Percent of HH having 2 or more dwelling room 11.90 96.50 62.72 15.45 Percent of HH using clean fuel for cooking 0.70 92.40 25.52 20.05 Percent of households availing banking services 10.50 93.90 58.01 16.97 Percent of household with no toilet facility within the premises 1.10 94.40 53.63 26.30

Tables 4: Results of linear regression model assessing demographic and socio-economic determinants of disability prevalence in India, 2011

Variables β p-values [95% Conf. Interval]

Demographic % Of female -0.060 0.000 (-0.090, -0.030) % Of people more than age 60 0.155 0.000 (0.122, 0.186)

Socio Economic % Of ST population 0.003 0.030 (0.000, 0.004) % Of SC population -0.002 0.446 (-0.008, 0.003) % Of female literacy -0.013 0.000 (-0.019, -0.007) % Of urban population 0.008 0.000 (0.004, 0.012) % Of main workers -0.013 0.000 (-0.017, -0.008) % Of household having safe drinking water -0.003 0.015 (-0.005, -0.000) % Of household with dilapidated condition 0.040 0.000 (0.023, 0.056) % Of household with no toilet facility 0.001 0.673 (-0.002, 0.003) % Of household having 2 or more dwelling room 0.001 0.451 (-0.001, 0.004) % Of household using clean fuel for cooking 0.001 0.799 (-0.004, 0.005) % Of household availing banking services 0.001 0.467 (-0.001, 0.004) R2 0.2024

Figure 1: A district level map of age standardized disability prevalence rate by sex, India, 2011