“Health divide” between indigenous and non-indigenous populations in Kerala, India: Population based study

Large inequalities in health exist between indigenous and non-indigenous populations worldwide [1]. This “health divide” has also been demonstrated in India [2], where indigenous groups are officially classified as Scheduled Tribes. Compared to national averages, Scheduled Tribes have higher mortality rates [3] and experience a greater prevalence of tuberculosis [4] and undernutrition [5], including high anaemia levels [6, 7]. These groups are also exposed to higher risks of inadequate food intake [8], poor hygiene [9], and tobacco and alcohol consumption [10], as well as lower access to health care [11‐13].

Increasing social inequalities, the development of the private health sector, barriers to access to care among the poor [14, 15] and the deterioration in the quality of the public health sector are all factors that may increase the differential vulnerability of tribal populations. However, Scheduled Tribes in India are demographically, culturally and economically heterogeneous, varying widely in terms of their population size, language, and the nature of their interactions with the rest of society [16, 17]. Given that exclusion from social and economic opportunities varies in nature and magnitude [18, 19], inequalities in health outcomes between tribal groups are likely. Yet most of the evidence on the health of Scheduled Tribes is available either at the aggregated level, failing to account for the diversity among Scheduled Tribe groups, or focuses on the health of a specific Scheduled Tribe, making it difficult to analyse inter-tribal inequalities in health.

Broad social and economic changes have taken place in India in the past 20 years, particularly with respect to affirmative action policies intended to benefit Scheduled Tribe members [20]. Land tenancy reforms initially showed impressive effects in the 1970s and 1980s in states such as Kerala, although progress has slowed in recent years [21]. Recent evidence suggests that Scheduled Tribes continue to be highly marginalised, remaining virtually landless, perceived as outsiders, not performing as well in school (higher dropout rate) and unable to take advantage of affirmative action policies [16]. Therefore, it is unclear whether health inequalities have decreased in recent years. A shift in the disease burden in India is also evident [22], underscoring the need for new evidence on the health divide using clinical measures of morbidity.

The primary aim of this study is to investigate the magnitude of the health divide, as measured by morbidity rates, between tribe and non-tribe populations, as well as between different tribal groups in a rural community in the southern state of Kerala. We also examine the extent to which health inequalities are explained by material deprivation.

This study is part of a collaborative research program on inequalities in health and health care by the Centre for Development Studies and the University of Montreal in a single Gram Panchayat (the lowest territorial administrative unit) located in the northern district of Wayanad in Kerala [23]. Although Scheduled Tribe members account for only 1% of the population in Kerala State, there are clusters of tribal groups in some areas. The Panchayat in this study has a population of 16,110 of whom 28% have Scheduled Tribe affiliations. Three out of five Scheduled Tribe members in the Panchayat are Paniyas, who make up one-fifth of Kerala’s Scheduled Tribe population [24]. Historically, Paniyas were bonded labourers. Today they are predominantly landless agricultural labourers who tend to migrate for employment opportunities. Paniyas also face social marginalisation, high levels of impoverishment, and poor access to health care [25]. Small numbers of other tribal groups who have living standards similar to the general population are also found in the Panchayat. The Kuruchian constitutes the large majority of “other Scheduled Tribes” in this area (94% of non-Paniyas).

We draw on data from a health survey approved by the ethics committee of University of Montreal. Households were selected from a sampling frame with the complete list of households in the Gram Panchayat, developed by conducting a census. The list was stratified by location in 10 Panchayat wards and 17 percent of households were selected in each ward. A total of 543 households were selected through a circular systematic random sampling process; house numbers were arranged in a circular manner and every sixth household was selected. Over-sampling of the tribal population (n = 226) was done by surveying all households in a tribal colony where one household had been randomly sampled. Each individual belonging to the household was interviewed, with the exception of minors (<18 y). Members were asked to respond to a standard questionnaire and underwent a physical examination by trained health personnel. Four health conditions common in populations prior to an epidemiologic transition (underweight, anaemia, goitre and tuberculosis) and one health condition common in the post-transition period (hypertension) were assessed. These measures enabled us to explore various patterns of morbidity associated with nutritional deficiencies as well as the epidemiologic transition.

Weight and height were measured using standard methods [26]; underweight was defined as body mass index (BMI) < 18.5 kg/m²[27]. Pallor of the conjunctivae, tongue and nails was used to assess anaemia status [28]. Goitre was assessed by palpation of the thyroid gland [29]. Blood pressure was measured twice and the mean of the two readings was used for analyses. Hypertension was defined as the mean ≥140 and/or 90 mm Hg [30]. Suspected tuberculosis cases (cases eligible for sputum examination) were defined as either coughing for at least one week during the last four weeks and having had moderate fever at night in the last four weeks, or having had a positive tuberculosis test in the last 12 months [31].

To explore the health divide in adults, we compared individual age- and sex-standardised prevalence for morbidity measures. Standardised rates were computed using a direct method with the total study population as the standard population. Weights were assigned to generate representative estimates. Household caste or tribal affiliations were assigned to the three social groups conventionally used by the Government of India: Forward Castes (the most privileged), Other Backward Classes (a residual group of low castes), and the lowest ranked group, Scheduled Castes/Scheduled Tribes. The lowest group was further disaggregated by contrasting Paniyas with other Scheduled Tribe populations. To assess trends across age groups, analysis was done using three groups (18–30 vs. 31–59 vs. 60–96 y). The socioeconomic status of households was defined in three ways. The local administration classifies households as above (APL) or below (BPL) the poverty line using a three-step approach: households with land or asset holdings and a certain minimum income are considered APL; the poorest (destitute households, Maha Dalits, single women, households with disabled persons as breadwinner, households headed by minors) are considered BPL; and the remaining households are ranked based on a quality of life score (based on 13 parameters such as type of house, availability of clothes, sanitation, literacy, means of livelihood and indebtedness). In addition, we used household landholdings of more than 50 cents of land (100 cents is equal to 1 acre) and crowding (ratio of individuals to rooms ≥3) as indicators of socioeconomic status [32].

Multi-level logistic regression models were used to estimate the predicted prevalence of morbidity for each age and social group, controlling for other covariates. Estimates were based on the current distribution of poverty and education across caste-age groups and the local population sex ratio. Random effect models were used to adjust for clustering at the household level and provide robust standard error estimates.

To further explore the sources of the health gap between tribal and non-tribal groups, we used a Blinder-Oaxaca decomposition [33, 34]. This technique enables us to quantify the part of the health gap due to group differences in the distribution of health determinants and the part due to differences in the effects of these determinants. The first component reflects differences in observable characteristics (endowments) between groups. The latter measures the part of the gap that remains unexplained after these characteristics are taken into account and is often seen as an indication of a discriminatory effect. In reality, the unexplained gap might reflect discrimination [35] or unequal treatment [36] of the groups, and also differences in omitted determinants of health [37]. The larger this remaining gap, the more likely discrimination and other unobservable factors play a role in explaining the health gap. We applied the method to decompose the gap in the prevalence of underweight, anaemia and goitre between tribal and non-tribal groups first, and then between Paniyas and Other Scheduled Tribes. Observable characteristics included in the models were: age, sex, education, poverty status, land ownership, occupation (wage labourer), housing condition (crowding) and access to safe drinking water. Estimates were obtained with statistical routines designed for non-linear outcomes (fairlie.ado and ndecompose.ado) [38]. We used Stata version 11 software for all analyses.

Health assessments were completed for a total of 1660 adults aged 18 years and above. Of the initial 543 households, 20 could not be located (3.7%), and 21 out of the 2418 individuals (0.9%) refused to participate. Characteristics of the sample are presented in Table 1 by social group. Inequalities are evident between Scheduled Tribe and non-tribe members for indicators of education and socioeconomic status. Over one-third of tribe members have no formal education compared to less than 10% of non-tribe members. The proportion of tribe members living in BPL households is two times higher than for non-tribe members. Among those who are employed, 74.1% of tribal and 40.7% of non-tribal members reported working as wage labourers. Inequalities within the Scheduled Tribes group are also observed. Lack of education is much higher among the Paniya compared to the other Scheduled Tribes. On average, 63.9% of Scheduled Tribe households own <50 cents land, but this ranges from 89.3% of Paniya to 50.2% of other Scheduled Tribes. Crowding is markedly higher among Paniya households compared to all other groups.

Comparison of the age and sex standardised prevalence of morbidity between tribal and non-tribal groups (Figure 1) reveals a higher prevalence of underweight, anaemia and goitre among tribal members. When morbidity prevalence is further disaggregated by tribal and caste groups (Figure 2), a strong gradient is observed between social groups for underweight, anaemia and goitre, with the Paniya group having the highest prevalence, followed by the other Scheduled Tribes, and the lowest prevalence among Other Backward Class and Forward Caste members. Prevalence of suspected tuberculosis and hypertension is similar across the groups. (See Additional file 1 for non-standardised prevalence).

To examine trends among adults of different ages, morbidity was compared across social and age groups (Figure 3; goitre and tuberculosis are not shown owing to space constraints). For underweight and anaemia, a comparable social gradient is evident for all age groups, with the highest prevalence among the Paniya in each age group. Hypertension prevalence is positively associated with age group, as expected, for all social groups and is largely similar between social groups within the 31–59 y and 60–96 y age groups. However, within the youngest age group (18–30 y), a unique pattern is observed whereby Paniya members have a significantly higher prevalence of hypertension compared to young adults from Other Backward Class or Forward Caste groups.

The magnitude of excess morbidity was estimated by age group for each social group, with Forward Caste as the reference group (Table 2), adjusted for the local distribution of poverty and education across caste-age groups and the population sex ratio. Compared to a predicted prevalence of underweight of 27.1% (95% CI 18.8, 35.5) among Forward Caste members 18–30 y, the prevalence is 31 and 13 percentage points higher for Paniya and other Scheduled Tribe members, respectively. A similar pattern is seen in the two older age groups. The Paniya also have a significantly higher predicted prevalence of anaemia, between 11 and 14 percentage points, across all age groups. For hypertension, excess morbidity is only evident among Paniya adults 18–30 y, whose prevalence is 10 percentage points higher than among Forward Caste adults of the same age group (5.4%; 95% CI 1.6, 9.2). Hypertension in the two older age groups is similar across social groups, with the possible exception of older Paniya adults where a protective effect is suggested.

Figure 4 presents the predicted prevalence of morbidity for the poor (BPL) and non-poor (APL) within each social group, controlling for other covariates. These results highlight, again, the magnitude of the social gradient in health, but more importantly, they show that this social gradient holds for both the poor and the non-poor. There is no evidence in this context that poverty alone provides a satisfactory explanation for the existence of the health divide between tribes and non-tribes. This is further supported by the Blinder-Oaxaca decomposition of the health gaps; Table 3 presents the overall results showing the proportion of the health gap explained by endowments (detailed results for the relative contribution of each of the covariates are presented in Additional file 2). Even after controlling for a large set of social determinants of health, an important health gap (difference in predicted prevalence) remains between tribes and non-tribes as well as between Paniyas and other Scheduled Tribes. For underweight and goitre, differences in endowments between tribes and non-tribes explain 54% and 42% of the health gap respectively; roughly half of the health gap remains unexplained. The results are similar for these two outcomes in the model comparing the Paniyas to other Scheduled Tribes. The situation is different for anaemia. The health divide between tribes and non-tribes for anaemia is largely explained by the inequality in the distribution of endowments between the two groups (86% of the health gap). But when contrasting the Paniyas with other tribal groups, the gap remains largely unexplained after taking into consideration the distribution of the socioeconomic factors. Sickle cell anaemia, a hereditary disease that is prevalent among the Paniya [39], may explain this difference in predicted prevalence of anaemia from the other tribes. Nonetheless, as expected, the analysis of the respective contributions of the major socioeconomic factors shows that the concentration of poverty and lack of education among the Scheduled Tribes contributes significantly to the excess morbidity observed in this group compared to the general population. The results are less clear for the health gap between Paniyas and other tribes.

Health inequalities are evident in a rural district of Kerala, with Scheduled Tribe members bearing a higher burden of morbidity compared to non-tribe groups. However, not all indigenous groups are equally disadvantaged. The largest tribal group, the Paniya, have higher unmet health needs compared to other Scheduled Tribes in this area, specifically with respect to underweight, anaemia and goitre. Comparison of morbidity across age groups reveals that this social gradient of inequality is apparent across generations. In contrast to levels observed in older adults, higher hypertension levels among young Paniya adults compared to their peers in other social groups point to a greater double burden of disease in this subgroup. Although tribal groups have higher levels of poverty, stratifying morbidity by household economic status does not change the gradient observed.

In this paper, we used clinical measures of morbidity, supporting other studies that found a social gradient in mortality and self perceived measures of health [3, 53]. Our findings highlight that this gradient is not solely explained by differences in material deprivation, suggesting that other factors, such as social exclusion and caste discrimination, are also important [57]. There is a need for future research to systematically test the role of discrimination in the health of Scheduled Tribes and other marginalised groups in India.

Even in the egalitarian state of Kerala, the health of Scheduled Tribes continues to lag behind other social groups. Moreover, significant health inequalities also exist within these tribal populations, with one tribe having very distinct needs compared to others in our study context. This necessitates moving beyond the standard approach of categorising all tribes as one equally disadvantaged group. Policies and programmes designed to benefit the Scheduled Tribes need to be tailored to not only promote the well-being of tribes in general but also to target the specific needs of each tribe, needs that differ both in nature and severity. Furthermore, these policies “cannot be limited to enhancing endowments” [36] but must also enhance the capacity of the disadvantaged to equally take advantage of these endowments. Improving the health of Scheduled Tribes will likely require multi-pronged efforts that try to address the very roots of inequality, discrimination and social injustice that constrains their opportunities to live healthy lives.