A

Koppal is one of the poorer districts in Karnataka where drought periodically takes its toll on the agrarian economy. The lack of income and livelihood security forces people to migrate or undertake work at great risk to their health. Deprivation is widespread. Public services, including for health, exist but are inadequately developed and largely of poor quality. Even private services, because of the widespread poverty of consumers, tend to be thin on the ground, and leave much to be desired in terms of their quality. Belief systems are strongly gender-biased; traditional practices include many that are inimical to women’s health and well-being. Poor women’s lives are marked by low levels of literacy, inadequate diets, hard labour, below minimum wages, recurring fatigue and illness.

In the midst of these hardships, poor women are also more vulnerable to inferior health outcomes due to denial of their human rights, including their reproductive and sexual rights. Maternal morbidity and mortality are high even as son preference and high infant death rates contribute to repeated childbearing. Reproductive morbidity is common but is often unspoken or taken as a “natural” part of women’s existence. Anaemia is endemic but is only casually – and ineffectively – addressed by public health programmes. Many of these problems have clearly been present over a long period of time. More recently, the threat of HIV infection looms over the region, and infection rates including among women have been going up sharply due to a combination of poor awareness, weak prevention, cyclical out-migration for work, and the denial of women’s sexual and reproductive rights. Koppal shows up poorly in many of the health indicators that health administrators and policy-makers use.

Health and healthcare inequities in Koppal reflect systematic hierarchies based on gender, caste, economic class, and life stage; they also reveal systemic failures in healthcare services, both public and private. Although over the years, the government has implemented many public health programmes, they appear to have made little difference to girls’ and women’s health status or healthcare access on the ground. Three main reasons can be adduced. First, significant gender biases, low levels of health awareness and lack of acknowledgement of certain health problems by families and by health providers have a negative impact on health-seeking behaviour. Second, widespread poverty and, especially recently, rising health costs reduce access even to public health services. Third, government health services have tended to be so top-down in their approach, so gender-biased (or at best gender-blind), so poor in quality and so unresponsive that they have been unable to bridge the gap between service providers and their intended beneficiaries. On the other hand, NGO-implemented projects have tended to be difficult to replicate or scale up.

The unfortunate interplay between systematic hierarchies and systemic failures makes it possible for families and communities on the one side and health providers on the other to exonerate themselves and hold the other side responsible for poor health status and outcomes. Families and communities tend to blame health providers for insensitivity, negligence, and the sheer absence of services; health providers typically hold the view that women and their families are ignorant, superstitious, and careless. Our research reveals undoubted elements of truth on both sides. Nonetheless the “blame game” diverts attention away from the absence of accountability by both, and the low levels of acknowledgement overall of women’s health needs. At the same time, however, changes are also occurring. For instance, whether because of government awareness programmes or not, health seeking by women and their families from private and public providers is far higher than one might anticipate, especially for maternal health. Despite this, preventable maternal deaths continue to be high, and women die in unconscionable numbers from causes related to childbearing.

The paper will present empirical evidence and analysis of health inequities in 60 villages2 in two taluks (sub-districts) of Koppal. It draws on insights gained through research, community mobilisation and institution building efforts for the Gender and Health Equity project. It will argue that both the systemic and the systematic elements need to be tackled if any policy or programme changes are to really take hold. The research to date has had four main components:

I Koppal District – Human Development and Gender

Apart from son preference, high fertility in northern Karnataka [Sekher et al 2001] is also a counter to high neonatal and infant mortality with the trade-off being women’s reproductive well-being. It is not considered unusual for a woman to have repeated miscarriages or abortions, the reasoning being that if a pregnancy is lost, a woman can always get pregnant again [Umamani and Yogananda 2003]. Yet such attitudes hold significant risks for women’s health, especially considering existing levels of unrecognised but high reproductive morbidity. Maternal deaths, however, represent only the extreme end of a continuum of underlying maternal morbidity. Not only are the risks to women of maternal mortality and morbidity undervalued, but so are their direct linkages to neonatal well-being and survival.

Such risks to women’s and newborns’ health are heightened by the hazards of poverty as has been documented in various studies in different parts of south India. In Andhra Pradesh, all of the women dying from pregnancy and obstetric complications were reported to have been working as labourers for subsistence reasons [ANS 2001]. In Tamil Nadu, many women who had uterine prolapse ascribed their condition to heavy manual labour within a week or fortnight following delivery, possibly explaining why the mean age for developing symptoms was 26 years [Ravindran et al 1999]. In southern Karnataka, women continued to undertake strenuous work until late in their pregnancy [Mathews et al 2001]. When pregnant and postnatal women are forced to do heavy manual labour in order to survive, health education messages asking them to “take rest” while pregnant have little relevance to the hard reality of their lives.

II Methodology

Our research uses both quantitative and qualitative methods to study the patterns of systematic hierarchy and system failures in health-seeking behaviour and access to healthcare. The quantitative data are based on a cross-sectional survey designed to document intra- and inter-household inequities in healthcare-seeking during sickness and pregnancy. It also sought to elicit household level attitudes to education of girls, attitudes to gender power, and domestic violence. A household census conducted prior to the survey in 60 villages enumerated 15,358 households and 82,901 individuals. A unistage stratified sampling design was adopted with households as the sample units. The project villages under each PHC were grouped, and each group treated as a separate stratum. With eight PHCs in the project area, there were thus eight strata. A sample was drawn from each stratum to the extent of 12.5 per cent of all the households within it, leading to 1920 households.

III Systematic Hierarchies

Our broad starting hypothesis was that intersecting hierarchies of economic class, caste and gender (as well as the individual’s position in the life cycle) would affect attitudes to her health needs, and health-seeking behaviour. In this paper we do not go into the details of all these intersections, but only focus on the ways in which gender affects health-seeking behaviour. Nonetheless it is impossible to avoid commenting on the class and caste realities that permeate and define people’s lives, their perceptions, and their behaviour because of their possible interactions with gender hierarchies.

Table 2: Range of RCH Indicators in Karnataka

Indicator Minimum Maximum Koppal

1 Girls married below 18 (per cent) 2.2 Kodagu 59.4 Raichur 51.4 2 Illiterate eligible women (per cent) 19.9 Bangalore urban 74.9 Raichur 66.8 3 Total fertility rate 1.37 Mandya 2.98 Raichur 2.77 4 Birth order 3 and above (per cent) 11.9 B’lore urban 51.0 Gulbarga 48.2 5 Infant mortality rate (per1000 live births) 16.3 B’lore urban 88.7 Koppal 6 Knowledge of any modern family planning methods (per cent) 97.3 Raichur 99.8 Gulbarga 98.5 7 Current use of modern FP methods (per cent) 40.3 Gulbarga 73.5 Mandya 42.4 8 Full antenatal check up (per cent) 12.8 Gulbarga 49.8 Bangalore rural 24.4 9 Safe delivery (per cent) 41.2 Gulbarga 95.7 Bangalore urban 52.8 10 Full immunisation (per cent) 48.1 Gulbarga 95.2 Bangalore urban 50.2 11 Eligible women aware of HIV (per cent) 37.7 Raichur 89.2 Kodagu 41.4 12 Use of government health facilities for antenatal care (per cent) 7.2 Uttara Kannada 60.6 Kodagu 19.4

By contrast, self-employment without resort to casual wage labour characterised the larger unirrigated landholding (equal to or above five acres) households (70.1 per cent), as well as the irrigated landholding households whether small (72.8 per cent) or large (91.1 per cent). Nevertheless, it is clear that owning a plot of land larger than five acres or owning a small irrigated plot does not insure almost 30 per cent of such households against having to resort to casual wage labour.

Most families (78.3 per cent) owned the houses they inhabited. However, only 29.1 per cent of the owned houses were ‘pucca’3 structures. Around one-fourth (22.7 per cent) were semi-pucca structures but 48.1 per cent were ‘kuccha’ structures. While electricity was not uncommon, very few of even the pucca houses had piped water (15 per cent) or toilets (4.2 per cent). Government-allotted housing was only 14.3 per cent of the total (much less than the 26.7 per cent population share of SC/ST households for whom such housing is meant), not all of which was pucca, and only 57 per cent of which had electricity while none had toilets or piped water supply.

Caste variations tend to follow the economic class patterns4 as can be seen from Table 3. Upper castes have more large irrigated holdings than the average; while they also have more landless households than the middle castes. This may reflect the fact that almost 9 per cent have regular employment as the major source of income. Middle caste landholding is near the average for all households although they have less irrigated landholding above five acres than the average. SC/ST households have higher proportions of landless and casual wage labourers.

How do these variations in the economic and social status of households translate into health-seeking behaviour? Are there significant differences across households and within them by gender, age or life cycle status?

Healthcare Needs and Gendered Health-seeking

The survey classified illness by its duration – short term (lasting below three months) or long term (above three months), and by its severity.5 Self-reported morbidity was high overall, with 82 per cent of households reporting at least one sick person during the reference period. Considerable care was taken in designing probes to overcome the well known biases in self-reported morbidity, although this may not have been completely successful because our interviewers were local people who were trained by us but were relatively inexperienced. Treatment seeking for illness was also high – almost 90 per cent of illnesses reported for girls/women and over 90 per cent for boys/men were treated.6

Within this overall picture, health-seeking behaviour varied along a number of dimensions, such as gender within the household, and also by the economic class of the household, and the duration and severity of the illness. Economic class in this analysis is represented by per capita household consumption expenditure quintiles.7

The analysis below first presents some cross tabulated data for short- and long-term illnesses. For short-term illnesses the responses were classified into two categories based on whether the person ever received treatment. For long-term illnesses, the responses were classified into three categories based on whether the person ever received treatment, and whether the treatment was discontinued. Multinomial logit regressions (Appendix 1) were also run on the data to check for the nature and statistical significance of interactions among economic class and gender.8

Table 4 shows that, for short-term sickness, the proportions of girls/women who were never treated9 was significantly higher than the same proportions of boys/men within every expenditure quintile. However, there is not much variation across the expenditure quintiles per se; treatment levels generally appear to be high regardless of household economic class.

Table 5 suggests that for long-term sickness, there were both gender and economic class differences (in the expected directions) among those with continued treatment. There were also significant gender differences in all the quintiles for those who were

Notes: Totals are population estimates. Percentages are over the total number of sicknesses within each quintile and sex group.

(1) Chi-squared test, degrees of freedom = 1. Source: Gender and Health Equity household survey.

Table 3: Caste, Class and Landholding

Per Cent Landless Per Cent Owning Per Cent Owning Per Cent Owning Per Cent Owning Per Cent Casual Per Cent Less than Five Acres Five or More Less than Five Five or More Acres Wage Earning Distribution of of Unirrigated Acres of Unirrigated Acres of Irrigated of Irrigated Households All Households Land Land Land Land

Upper castes 13.3 28.6 29.1 8.5 20.2 24.7 28.3 Middle castes 11.4 35.2 28.0 9.8 15.6 36.9 37.8 SCs/STs 20.9 45.8 14.3 11.7 7.2 59.2 26.7 Muslims 29.6 31.2 17.1 8.5 13.6 39.9 4.1 All households1 15.9 36.0 24.0 9.8 14.3 39.8 100.0

Note: (1) 3 per cent belong to other castes. Source: Gender and Health Equity household survey.

Economic and Political Weekly February 24, 2007 never treated. Girls/women were more likely to never be treated for long-term illness in all households. However, when treatment was discontinued, it appeared to vary both by economic class and by gender. Furthermore, these gender differences are not uniform across the quintiles, indicating the presence of interactions between gender and economic class, which we tested further with the regression analysis.

treating or discontinuing treatment were only a few: either the illness was not considered serious by the patient or the family, or the treatment was too expensive, or the patient felt s/he was not getting cured. Short-term illness among men was also not treated because they said they did not have the time for treatment. However, the relative importance of the reasons given by women and men varied. Never treating either short- or long-term illness because they thought it was not “serious” was the most important reason for women. For men, expense was the dominant reason for never treating illness. Expense was an important barrier to treatment for women as well but lack of acknowledgement of illness (as reflected in the statement that it was not serious) was the more salient barrier. This barrier has its roots in powerfully ingrained gender norms that instil in women from an early age a lack of confidence or self-worth, a lower recognition of their needs, and a value to suffering in silence. Men have few such internalised norms of behaviour.

Both women and men gave “not getting cured” as a major reason for discontinuing treatment for long-term illness. More detailed analysis of the evidence shows that women tended to give up treatment more quickly.

Our discussion in this section highlights the fact that systematic hierarchies of economic class and gender play an important role in structuring the health-seeking behaviour of households. While it is noteworthy that the extent of health-seeking overall is high, there are also important ways in which it is distributed across different groups. Such rationing appears to happen both because of economic constraints, and also on the basis of deeply ingrained gender norms.

IV Systemic Failures

The Karnataka government has established an extensive network of health facilities structured according to a hierarchy of services based in theory on population norms.14 Facilities have proliferated due to the preference of elected representatives for sanctioning PHCs and hospitals in their own constituencies and due to the availability of budget lines for infrastructural development. Several PHCs had new labour rooms constructed with funding from the RCH programme and foreign funding supported infrastructural improvements of secondary level hospitals, with the aim of improving referral.

In Koppal, this investment in infrastructure has not translated into comprehensive emergency obstetric care as none of the higher-level government facilities have all the required specialists or critical supplies. Neither policy-makers nor implementers have addressed the lack of technical inputs for emergency obstetric care (including abortion) by ensuring the availability of specialists, upgrading the emergency skills of existing personnel and ensuring their access to critical supplies such as blood, and antiepileptic and anti-haemorrhagic drugs.

Even if the logistics of ensuring emergency obstetric care through appropriate inputs, supplies and staffing were addressed, there still remain large managerial barriers to improving the effectiveness of maternal healthcare services. A key contribution of the Karnataka Task Force on Health and Family Welfare was to highlight the need to address vacancies at the primary healthcare level [GoK 2001]. Medical officers, lab technicians, nurses and male junior health assistants were recruited within the district

Table 6: Per Cent Distribution of Main Reasons for Non-treatment of Sicknesses

Main Reason for No Treatment Short-Term Sicknesses Long-Term Sicknesses Never Treated Discontinued Treatment Never Treated Female Male Female Male Female Male N = 856 N = 240 N = 1944 N = 1083 N = 760 N = 237

I did not know what to do 6.4 0.0 1.5 0.7 9.6 3.4 I did not think it was serious 39.1 23.3 11.1 12.6 42.6 29.1 Family did not think it was serious 11.9 10.0 1.2 2.2 12.5 6.8 No one to accompany me 2.8 3.3 0.7 0.0 3.2 3.4 I didn’t have the time 2.8 10.0 1.6 0.0 0.9 3.4 Too expensive 23.1 46.7 20.0 21.6 17.6 40.5 Health provider unavailable 1.9 3.3 1.6 3.7 0.0 3.4 Health provider unhelpful 0.0 0.0 1.2 0.0 0.0 3.4 Medicines make me ill 0.9 0.0 0.0 0.0 1.1 0.0 Not getting cured 3.6 0.0 43.4 30.0 5.1 6.8 Other 7.4 3.3 4.7 5.8 7.4 0.0 No response 0.0 0.0 13.0 23.4 0.0 0.0 Total (per cent) 100.0 100.0 100.0 100.0 100.0 100.0

Note: Totals are population estimates. Source: Gender and Health Equity household survey.

Economic and Political Weekly February 24, 2007 on a contract basis, while junior female health assistants (ANMs) were selected for training and recruitment at the state level. Although staff postings are biased against equity considerations through corruption, vacancies in primary healthcare service delivery personnel in Koppal have substantially declined.

Health professionals also delegitimise women’s point of view. One reason why women are not able to get effective care despite physically accessing government facilities is due to the lack of agreement between women and health providers about what their health needs are. Although women seek help for labour pain, medical officers diagnose them with lower back pain or “false” labour pain. This disjuncture between women’s experiences of labour pain and its medical diagnosis indicates several problems in communication and care-seeking. Women might be misinterpreting their experiences or health providers may be misunderstanding the situation. Social bias may also be at play by inhibiting women from speaking freely about their intimate reproductive health concerns with health providers from a different gender, class, educational and caste background. At the same time, health providers may have social biases that invalidate women’s experiences. Finally, biases may exist in the technical understanding of what constitutes labour pain. Due to these factors, the process of seeking care and advice may be quite complex in practice.

Access to government facilities for institutional delivery once successfully negotiated does not mean an end to marginalising experiences. It is not just that cleanliness is not assured, but that the treatment received by women can be dehumanising. Women and their families are often left in labour rooms by themselves. They have to trust health workers who are strangers, often having to seek them out from other wards or their quarters. Women are expected to deliver in a position that is different from what they are used to at their homes and which helps the health worker more than it helps them. Even for normal deliveries, medical rituals involve shaving the pubic area, administering IV drips, repeated deep vaginal examinations and episiotomies. Health workers learn their skills in hospital wards giving orders to women in labour who are allowed little control over their situation. Yet if complications arise, health workers tell families, who are neither informed nor in control, to be prepared to face the consequences [Caleb Varkey 2004].

Women with poor entitlements within families and in health systems tolerate high levels of pain, discomfort and humiliation. Their rights to protest are weakened not only by their unequal access to resources, including finances, expertise, and authority, but also because of the shame that surrounds women’s bodies and the “normalisation” of many women’s reproductive morbidities. Explicit gender bias thus operates to disenfranchise women objectively through unequal status, and also normatively through disempowering normative local traditions and medical frames of knowledge.

V Conclusion

Appendix: Results of Logit Regressions – Estimates of Odds Ratios

Independent Variables2 Short-Term Sicknesses Long-Term Sicknesses Logit Regression Logit Regression Multinomial Logit Non-treatment=1 Continued Treatment=1 Continued Treatment=1

Otherwise=0 Otherwise=0 Discontinued Non-treatment=3 Treatment=2 Model 11 Model 22 Model 11 Model 22 Model 11 Model 22 Model 11 Model 22

Age 1.017 *** 1.017 *** Sex (Dummy: Female=1) 2.703 *** 0.667 *** 1.153 4.170 *** Income Earner (Dummy: Non-earner=1) 1.668 *** 1.658 *** 0.632 *** 0.636 *** 0.496 *** 0.498 *** Household Head (Dummy: Others=1) 0.690 ** 0.688 ** 1.764 *** 1.773 *** 0.714 0.714 Severity 0.811 *** 0.809 *** 1.119 ** 1.114 ** 0.936 0.939 0.767 *** 0.769 *** Economic Class (Dummy: Non-poor=1) 0.622 ** 1.453 *** 0.696 *** 0.658 * Male, Poor (Dummy: d1) 2.349 * 0.945 1.041 1.129 Female, Non-poor (Dummy: d2) 3.908 *** 0.924 0.817 3.180 *** Female, Poor (dummy: d3) 5.610 *** 0.528 *** 1.448 * 5.505 *** Sample Size Tests Coeff(d1)=Coeff(d3) *** *** * *** Coeff(d2)=Coeff(d3) *** *** ** Coeff(d3)-Coeff(d1) =Coeff(d2)3 ** *

Notes: Notations: * significance at 10 per cent level, ** significance at 5 per cent level, *** significance at 1 per cent level.

Definitions: Economic class: Poor: bottom 3 quintiles, Non-poor: top 2 quintiles.

1 Model 1 tested the independent effects of the explanatory variables on treatment seeking outcomes.

2 Model 2 tested interactions using non-poor men as the reference group, and dummies for poor men, non-poor women and poor women. Economic

3 The test was modified for continued treatment of long-term sicknesses to Coeff(d1)-Coeff(d3) =Coeff(d2) because a priori coeff(d2)>coeff(d1)>coeff(d3).

Economic and Political Weekly February 24, 2007 Karnataka. What should be obvious is that these systematic and systemic factors underwritten by gender bias and underpinning gender-biased outcomes must be addressed urgently if the state is to fulfil its development potential.

EPW

Email: gita@IIBM.ERNET.IN

Notes

[This paper is part of work done at the Indian Institute of Management, Bangalore under the Gender and Health Equity project. We would like to especially thank Chandan Mukherjee for his unstinting willingness to provide statistical and econometric advice and Shon John for competent statistical assistance. We also wish to acknowledge our anonymous reviewer for helpful comments on the first draft of this paper.]

1 See the report of the Nanjundappa Committee [GoK 2004] for a detailed analysis of the historical and contemporary reasons for these intrastate disparities.

2 Fifty-six villages were selected by MSK, four of which have ‘tandas’ large enough to be considered administratively by MSK as separate villages, which leads to the total of 60 villages. A tanda is a sub-group of houses populated, in the project site, by a ST group (lambanis).

3 Adopting the NSS definition, we defined pucca houses as those that had pucca roofs and pucca walls. Semi-pucca houses were those that had pucca roofs but kuccha walls, or kuccha roofs but pucca walls. Kuccha houses were those that had kuccha roofs and kuccha walls.

4 A detailed enumeration and checking of caste groups was undertaken; details are available in Annexure 3 of Iyer (2005).

5 Severity for short-term sickness was measured in terms of difficulty in eating normally, in doing regular work in – and outside – the house, and being able to go outside the house. For long-term sickness, severity was measured in terms of difficulty in going to school, doing housework or other work, and in income earning.

6 The high levels of treatment seeking in our survey may be affected by two factors: the confounding of illness with treatment, wherein people only acknowledge illness as such if it is treated; second, the fairly broad definition of treatment seeking used in the survey. Nonetheless, even our qualitative and indepth inquiry into the circumstances of maternal death reveals the same phenomenon of high levels of healthseeking.

7 The plausibility of these self-reported consumption expenditure data has been cross-checked by examining the distributions and also comparing the corresponding National Sample Survey data.

8 There are clearer hypotheses about how and why gender and class might influence health-seeking than there are for caste. Caste is likely to affect the choice of provider and quality of care obtained, but not the decision of whether or not to seek treatment. Our analysis of gender and caste vis-à-vis health-seeking for short- and long-term sickness shows that caste per se does not discriminate among households when it comes to discontinued, or non-treatment. Interactions based on caste are part of our ongoing work but not explored in this paper.

9 We refer to sick persons and sicknesses interchangeably while discussing Tables 4 and 5, because most persons (73.9 per cent) in fact reported only one sickness.

10 While the cross tabulations have been presented by quintile, we believe that there is variation even within the poorest and the better off quintiles. This variability is being explored in our ongoing work. For the purposes of this paper, the regressions were run using a fairly standard dichotomy between poor (bottom three quintiles) versus better off (top two quintiles).

11 This runs counter to received wisdom and further exploration is clearly warranted.

12 RMPs stands for rural medical practitioners. They are also sometimes called registered medical practitioners, although the Medical Council of India no longer registers them. They are usually men who primarily practise allopathy despite not having any formally recognised medical qualifications. We considered all those private doctors who did not hold current formally recognised medical degrees (MBBS, BAMS, BHMS,

BUMS, BYNS, BDS) as RMPs.

13 Detailed analysis is available in George et al 2005.

14 CHCs, PHCs and subcentres are supposed to cover populations of 100,000, 30,000 and 5,000 respectively. In 2001, PHCs and subcentres in Karnataka were catering, on average, to smaller populations than specified by national norms: 20,817 and 4,285 respectively. CHCs, on the other hand, were dealing with larger population loads than envisaged – 140,117 persons [GoI 2004].

15 This includes practitioners with degrees in dental sciences, ayurveda, unani, homeopathy and allopathy. Out of 43 private doctors, 13 (30 per cent) were allopaths.

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