Infant Mortality and Socio-Economic Inequality in Mumbai Slums and Its

Home , Andheri, Byculla, Chembur, Dadar, Dharavi, Eastern Suburbs (Mumbai)

Infant Mortality and Socio-economic Inequality in Mumbai Slums and its Wards Extended Abstract Introduction Improving the scenario of infant mortality in the country is an issue of national importance as these children, aptly known as the demographic dividend, hold the key to country’s development. The statistics show that although the IMR (infant mortality rate) in India has been steadily declining and the reduction has been enormous from 146 deaths per thousand live births in 1951 to 57 deaths per 1,000 live births in 2005 it is still unacceptable (IIPS & ORC Macro 20007). Even now more than one in 18 children dies within the age of one year in India (IIPS & ORC Macro 20007). As disappointing as these national figures may appear, the rate of infant mortality in Maharashtra is much lower than that in the country as a whole. It is currently estimated at 38 deaths per 1,000 live births (IIPS & ORC Macro 20007). In other words, it means that 1 in 27 children still dies within the first year of life. Infant mortality in rural areas of Maharashtra, at 50 deaths per 1,000 live births, is much more than that in the urban areas of the state at 22 per 1,000 live births (IIPS & ORC Macro 20007). Moreover in Mumbai, slums have a much lower infant mortality rate (25 deaths per 1,000 live births) than non-slums (40 deaths per 1,000 live births). Despite this relatively better looking statistic, this rate in urban areas is not in accordance with the MDGs (Millennium Development Goal) charted by United Nations Development Programme (UNDP) which aimed to bring down the IMR by two thirds from 1990 to 2015 (MDG, 2010); it becomes evident when the IMR is disaggregated at the level of individual wards in Mumbai. The wards of Chembur, Bandra, Byculla and Matunga have Infant mortality rates of over 50 per thousand live births (Mumbai Human Development Report, 2009). When the average health statistics of the urban areas are viewed, they appear better off as intra-urban health inequalities are masked by such figures (Shah et al., 2009). The poor situation described above is exaggerated by the fact that Mumbai city houses a considerable percentage of slum dwellers. Slums are a result of the rapid urbanization that Mumbai is facing. There is constant migration into Mumbai from the rural areas as it is the financial capital of the country, but there is not enough space to house everyone so more than half of its inhabitants (54.5 percent) have come to live in the slum areas (Office of the Registrar General and Census Commissioner, 2001; Byrne and Chakravarti, 2009). In other

1 words, a staggering 16.4 million people live on only 6 percent of Mumbai’s land (Office of the Registrar General and Census Commissioner, 2001). The notable slums in Mumbai are Byculla, Dharavi, Mahim, Parel, Dadar and Matunga. The slum dwellers are not the legal occupants of the areas they reside in, and cannot afford to live anywhere else. The living conditions in these slums are far from being healthful. They lack basic minimum standards of livelihood, such as sanitary facilities, hygienic conditions, and medical care (Vaid et al., 2007; Byrne and Chakravarti, 2009). An operational definition of slums as defined by a United Nations expert group is based on multiple domains- “inadequate access to safe water, sanitation and other infrastructure, poor structural quality of housing, overcrowding, and insecure residential status” (United Nations Human Settlements Programme, 2003) . Further, all slums are not alike in terms of development (Agarwal and Taneja, 2005; Taneja and Agarwal, 2004). Due to increased attention on the poor health services in some of the Mumbai slums, some slums are better performers than the rest (Mumbai Human Development Report, 2009). Even within a particular slum homogeneity does not exist with regard to the socioeconomic status of the population (Shah et al., 2009). A large amount of disparity occurs with respect to the socioeconomic status of the population living in the slums. Intraslum differences with respect to the health indicators are sometimes masked by the aggregate statistics for the same (Shah et al., 2009). There are two kinds of populations living in these slums, namely; urban poor and urban non poor. It would be a fallacy to assume similar kind of health related behaviours in these populations. Literature also supports a great amount of variation in the health seeking and treatment seeking behaviour amongst them (Shah et al., 2009). According to the World Bank report, 2001, the poor have much higher levels of mortality, malnutrition and fertility than the rich; the poor-rich risk ratio is 2.5 for infant mortality, 2.8 for under- five mortality, 1.7 for childhood underweight and 2 for total fertility rate (World Bank, 2001). The human development index (HDI) which is a composite index, takes the national average of life expectancy, GDP per capita (as an indicator of standard of living or income) and the education indicator using gross enrolment ratio accounting for one third of the education indicator, combined with the adult literacy rate that accounts for two thirds in the education parameter for India is 0.619. India has been ranked 128th out of 177 nations in terms of HDI and is classified in the group of Middle Human Development. The HDI for Mumbai is 0.566.

Thirteen wards in Mumbai lie above this mark and eleven below it. Those wards that are below it house the majority of Mumbai slums. Thus it is evident that the wards inhabited with slum population are the ones lagging in development as well. There have been studies that link the level of HDI with the various development indicators like literacy rate, maternal mortality, sex ratio and underweight children etc (Gupta, 2009). On the same lines, HDI has been found to be a powerful predictor of infant mortality in India as well as worldwide (Antony et al., 2001; Lee et al., 1997) and also evidence all over India testifies that living in slums is a predisposing factor for high infant mortality (Awasthi and Agarwal , 2003; Gupta and Baghel , 1999; Fernandez et al., 2003; Elizabeth and Bir, 2010) . Lack of sanitation, hygiene, less per capita space, poor housing conditions, sustenance on non-nutritious food and lack of potable water in the slums are all responsible for such a situation. Also, the antenatal and delivery care have also been found to be less than optimum among the urban poor as compared to either rural or urban non poor (Magadi et al., 2003; Sarode , 2009; Kapadia, and Kanitkar, 2002). Compared to urban poor, among the rural poor great emphasis has been placed on uplifting the health status of the population (Fernandez et al., 2003) programmes like National Rural Health Mission (NRHM) testify it. Additionally, unlike the rural areas, in urban areas planned hierarchy of health services like sub centres, primary health centres and tertiary health care centres are absent and to a great extent, duplication and mismanagement of health care services exists (Fernandez et al., 2003). Undoubtedly, the urban poor or the slum population is a deprived lot, facing the maximum brunt of the inequity in the cities. Mumbai has a double burden of ill health as two very different types of populations are living in it. On one hand, there is a population living in high-end areas of the city with modern health facilities and there are others who live in the slums, not being able to access even the basic health facilities. Evidence from all over the world suggests that inequity in the possession of resources and accesses to health care are strong predictors of infant deaths (David, 2008; Taneja and Agarwal, 2004; Shah et al., 2009; Caldwell and Caldwell, 2002; World Bank, 1993). Needless to mention that all of these factors related with standard of living, inequity, antenatal and delivery care mentioned above are the foremost in causing deaths due to sepsis, pneumonia, meningitis, major congenital malformations, birth asphyxia, or prematurity in new-borns in slums (Vaid et al., 2007; Gladstone et al., 2008; Bhandari et al., 2002). A study performed on the pathways of infant

3 mortality in urban slums of Delhi, concluded that infant deaths occur due to the aforementioned illnesses via the following pathways- poor health seeking behaviour, delay in seeking health care in neonates, low rates of referral to hospitalization by primary health care givers, inappropriate health care practices by primary health care givers and refusal of admission into hospitals (Bhandari et al., 2002). Though the IMR has declined on an average in Mumbai slums over the years yet it remains to be seen what progress in terms of Infant mortality has been made in the individual wards of the city. The development indicators in between slum and non slum and even amongst the various slums differ considerably. Not only that, the extent of disparity with regards to health indicators amongst the population of the same slum is also great. In this light, it becomes imperative to find out what effect is cast by the proportion of slum population, the inequity with respect to resources within the same slums and the Human Development Index on the Infant Mortality Rate in Mumbai.

Data and Methods

The data for the present study has been gleaned from published Human Development Report, Mumbai, 2009 and Census of India, 2001. Bivariate linear regression analysis has been carried out to understand the relationship between proportion of slum population, human development index and infant deaths. Infant mortality rate has been calculated based on the registration of births and deaths records (collected by the Public Health Department of the MCGM). The registration of births and deaths are highly subject to underreporting but Greater Mumbai is considered to have better births and deaths registration records because dead body disposal in Mumbai requires death certificate from a registered medical practitioner and all deaths have to be certified by registered doctors.

Results

Table 1 presents the percentage of slum population in the 24 administrative wards of Greater Mumbai in the census year 2001. Table clearly indicates the clustering of slum population in the Eastern suburbs as compared to City Island. The different wards (L-Kurla, M/E- Chembur East, M/W-Chembur West, N-Ghatkopar and S-Bhandup) of Eastern suburbs and Western

4 suburbs (Khar/ Santacruz and Malad) have more than 60 percent slum population. While most of the wards (B-Dongri, C-Marine Lines, D-Grant Road and E-Byculla) of City Island had very low concentration (below 15 percent) of slum population as compared to wards of Western and Eastern suburbs. All the wards of Western suburbs except R/C- Borivali have around 40 to 79 percent of population in the slum settlements.

Table 1 also shows the level of infant mortality rate (IMR) in Mumbai and its 24 wards during 1998 and 2006. It clearly indicates that the level of IMR has been declined in Mumbai and most of its administrative wards during eight years time period. The IMR in Mumbai has been turned down from the level of 40 to 35 during eight years time period. Of total 24 wards, there were around 10 wards in Mumbai where the infant death rate was found to be above 40 in the year 1998. Further, the number of wards having IMR more than 40 has been declined to six in the year 2006. It is also observed that there were only three wards in Mumbai (M/E- Chembur East, F/N-Matunga and C-Marine Lines) had IMR more than 50 in the year 1998. Surprisingly, the number of wards having IMR more than 50 has been increased to six in the year 2006. Further, the infant mortality rate was observed highest, that is, around 58 in the wards M/E-Chembur East and F/N-Matunga in the year 1998 and IMR remained highest in the wards of M/E-Chembur East in the year 2006.

Table 1: Proportion of Slum Population and Infant Mortality Rate in Mumbai and its Administrative Wards.

Ward Name Relative Slum Change in Population IMR IMR IMR Wards (%) 1998 2006 (in percent) City A Fort 28.88 38.53 33.88 -12.07 B Dongri 13.33 34.95 31.56 -9.70 Marine Lines C 0.00 52.20 35.88 -31.26 D Grant Road 9.95 32.57 29.42 -9.67 E Byculla 11.86 48.05 56.27 17.11 F/N Matunga 58.07 58.04 50.27 -13.39 F/S Parel 35.76 45.71 15.59 -65.89 G/N Dadar 55.82 43.93 35.70 -18.73 Elphinston G/S Road 33.08 38.34 31.80 -17.06 Western Suburbs H/E Khar/Santacruz 78.79 34.96 33.29 -4.78 H/W Ban dra 41.06 31.52 52.30 65.93 Andheri East K/E 58.30 32.55 25.99 -20.15 K/W Andheri West 45.11 37.01 27.89 -24.64 P/N Malad 63.65 45.32 28.91 -36.21 P/S Goregaon 48.10 28.47 16.45 -42.22 R/C Borivali 33.75 NA 16.35 NA R/N Dahisar 46.63 34.89 20.10 -42.39 R/S Kandivali 55.30 37.39 28.17 -24.66 Eastern Suburbs L Kurla 84.68 42.63 54.56 27.98 M/E Chembur East 77.55 58.35 66.47 13.92 Chembur West M/W 68.48 42.16 57.93 37.41 N Ghatkopar 70.21 40.08 32.83 -18.09 S Bhandup 85.83 37.10 18.76 -49.43 T Mu lund 35.21 27.52 19.53 -29.03 Mumbai 54.06 40.08 34.57 -13.75

The declined in the infant death rate was found to be highest in the ward F/S-Parel followed by wards S-Bhandup, R/N-Dahisar and P/S-Goregaon, respectively. The maximum increase in the infant deaths rate was observed in the H/W-Bandra ward during eight years time

6 period. The increase in death rate was also seen in the wards of M/W-Chembur West, L- Kurla, E-Byculla and M/E-Chembur East. The wards of M/E- Chembur East, M/W- Chembur West, E-Byculla, L-Kurla, H/W-Bandra and F/N-Matunga had higher infant deaths rate than rural Maharashtra in the year 2006 (NFHS-3). Most of the wards have predominantly slum population, but the percentage of the population living in slum areas varies from zero percent in the ward C-Marine Lines to around 85 percent in the ward L-Kurla. Moreover, the excess infant death rate during eight years time period was in the three wards of Mumbai namely M/W-Chembur West, L-Kurla and M/E-Chembur East, around 37, 28 and 14 percent, respectively. It is interesting to note that the percent of slum population was more than 68 in these three wards of Mumbai. The relationship between human development index (HDI) and percentage of slum population in the different administrative wards of Mumbai is depicted in the Figure 1.

Figure 1: Relationship between Human Development Index and Percentage of Slum Population in the different administrative Wards of Mumbai.

The high concentration of slum population is considered as a proxy for urban poor and also can be treated as a high concentration of deprived group in a society lacking basic needs of livelihood. Figure clearly revealed that as percentage of slum population decreases, the level of HDI increases in the different administrative wards of Mumbai. Further, it is also confirmed by presence of significant negative correlation (r= -0.76; p<=0.001) between these

7 two variables. Since these two variables were highly correlated, therefore, it is decided to assess how well HDI predicts infant mortality rate for Mumbai. Figure 2 provides the OLS regression result obtained by taking infant mortality rate in the year 2006 as a dependent variable and HDI as an independent variable. Figure 2: Linear relationship between Infant Mortality Rate and HDI in the different administrative wards of Mumbai.

Figure 2 clearly depicts the negative association between HDI of different wards and infant mortality rate for the corresponding wards. A significant negative slope (-0.468; p<0.01) signifies that the lower the level of HDI, higher is the infant death rate. Result clearly revealed that HDI is a powerful predictor of infant mortality rate and it accounts around 40 percent variation in the infant mortality rate. Bivariate regression analysis was carried out to identify the impact of HDI on IMR in Mumbai and other three regions of Mumbai namely, City Island, Western and Eastern Suburbs. The result is presented in the Table 2.

Table 2: Human Development Index: Dependent Variable-Infant Mortality-2006. Independent Variable Mumbai City Island Western Suburbs Eastern Suburbs 61.434*** 56.549*** 40.665* 73.351*** Constant (8.239) (3.835) (1.90) (8.348) -0.468*** -0.319 -0.208 -0.772** HDI (3.856) (1.467) (0.615) (4.313) R Square 40.3 23.5 5.1 82.3 Note: t statistics in parenthesis. ***Significant at 99% confidence; **Significant at 95%, *Significant at 90%.

It is observed that HDI is a significant predictor of IMR in Mumbai and Eastern Suburbs whereas the impact was not found to be statistically significant in case of City Island and Western Suburbs. However, the impact of HDI was found to be consistently negative in these three regions of Mumbai. The HDI accounted for around 40 percent and 82 percent of the variance in infant mortality rates in Mumbai and Eastern Suburbs, respectively. The percentage of infant deaths to the total deaths of 24 administrative wards of Greater Mumbai is presented in the Table 3. Table clearly shows that infant deaths contributed around seven percent of total deaths in Mumbai. Further, the proportion of infant deaths was found to be highest in the M/E-Chembur East (18.05 percent) followed by L-Kurla (11.61 percent) ward. It further confirms the poor health status of infants in the M/E-Chembur East. In addition to M/E-Chembur East (18.05 percent) and L-Kurla (11.61 percent) wards, the proportion of infant deaths was found to be more than the level of Mumbai (around 8.00 percent) in F/N-Matunga, R/S- Kandivali, H/E- Khar/Santacruz, M/W-Chembur West and R/N-Dahisar wards.

Table 3: Percentage of infant deaths to the total deaths in the different administrative wards of Mumbai, 2006. Ward Percentage Wards Ward Percentage Wards Ward Percentage Name of Infant Name of Infant Name of Infant Deaths Deaths Deaths Wards (2006) (2006) (2006) City Western Suburbs Eastern Suburbs Fort 6.70 H/E Khar/ 8.77 L Kurla 11.61 A Santacruz Dongri 4.24 H/W Bandra 5.73 M/E Chembur 18.05 B East Marine 2.85 K/E Andheri 5.81 M/W Chembur 8.76 C Lines East West Grant 3.56 K/W Andheri 6.39 N Ghatkopar 7.95 D Road West E Byculla 6.71 P/N Malad 7.53 S Bhandup 7.34 F/N Matunga 9.01 P/S Goregaon 7.90 T Mulund 4.24 F/S Parel 4.19 R/C Borivali 5.28 G/N Dadar 7.96 R/N Dahisar 8.43 Elphinston Kandivali G/S Road 5.12 R/S 8.87 Mumbai 7.44

Since the calculation of IMR depends both on the reporting of births and deaths while to compute the proportion of infant deaths to the total deaths only registration of deaths is required. Further, the relationship between proportion of infant deaths to the total deaths and IMR clearly confirmed that as the level of IMR increases, the proportion of infant deaths to the total deaths also increases (r=0.57; p<=0.01). Therefore, it could also be argued that reporting of births and deaths are quite consistent in the different administrative wards of Mumbai. Figure 3 depicts the linear relationship between proportion of slum population and contribution of infant deaths to the total deaths in the different administrative wards of

Greater Mumbai. The main idea behind portraying this graph is that the high level of concentration of slum population has relatively strong negative impact on the health of infants as compared to the health of other age group of population. Moreover, newly born infants in the slums are more likely to susceptible to the different types of communicable diseases resulting into less chance of survival. Figure 3 observes the best fit line for the given data of 24 administrative wards of Mumbai and reveals a positive association between proportion of slum population and share of infant deaths to the total deaths in the corresponding wards. Further, a positive significant slope signifies that the higher the percentage of slum population, higher will be the share of infant deaths to the total deaths. Figure 3: Linear relationship between Percentage of Slum Population and Percentage of Infant Deaths to Total Deaths in the different administrative wards of Mumbai

Figure 3 clearly brings out the fact that percentage of slum population of M/E- Chembur East ward has strongest impact on infant health. Further, around 50 percent variation in the proportion of infant deaths to the total deaths has been explained by the percentage of slum population in the different administrative wards of Mumbai.

Health Facilities in the Different Administrative Wards of Mumbai

Mumbai is a home to the highest number of migrants in comparison to any other cities of India. The city is under tremendous pressure in providing the basic health care facilities to ever growing population in spite of being the financial capital of the country. Table 4: Population per Health Facilities by Different Wards of Mumbai.

Population per Population per Population per Wards Ward Name Dispensaries Hospitals Aanganwadi City A Fort 7,912 - 2,848 B Dongri 2,166 - - C Marine Lines 2,710 44,986 - D Grant Road 1,980 4,173 16,873 E Byculla 1,393 9,109 6,698 F/N Matunga 1,787 12,772 4,084 F/S Parel 6,177 78,928 9,347 G/N Dadar 2,401 6,351 2,187 G/S Elphinston Road 1,652 22,528 3,413 Western Suburbs H/E Khar/Santacruz 8,071 18,266 1,766 H/W Bandra 4,400 5,123 3,281 K/E Andheri East 1,112 7,863 3,243 K/W Andheri West 2,365 7,294 4,056 P/N Malad 60,352 9,327 3,977 P/S Goregaon 6,421 8,668 2,343 R/C Borivali 3,477 3,211 9,146 R/N Dahisar 8,614 9,411 4,307 R/S Kandivali 2,755 1,38,188 2,859 Eastern Suburbs L Kurla 1,962 11,501 2,005 M/E Chembur East 66,881 27,438 2,175 M/W Chembur West 1,177 5,915 1,592 N Ghatkopar 52,665 6,006 2,664 S Bhandup 2,123 12,551 1,986 T Mulund 15,638 4,920 2,919 Mumbai 2,898 9,696 2,999

The disparities with regard to the availability of health facilities are clearly revealed when the wards are categorized according to different types of health facilities available to them. Since the reference period for the ward wise population is year 2001 and year 2008 is for health care facilities therefore, there is a chance of underestimation of population per available health facilities in the current scenario. Analogous to our anticipation, the population burden on health care facilities could be easily observed in the wards of Eastern suburbs as compared to the wards of City Island. It is also observed that M/E- Chembur East ward followed by P/N-Malad and N-Ghatkopar wards have large number of population dependent on per dispensary. It is clearly seen that dispensaries are more concentrated in the City Island followed by Western Suburbs (except P/N-Malad ward) in comparison to Eastern Suburbs. There are three wards each from City Island and Eastern Suburbs and two wards from Western Suburbs have more than ten thousand population dependent on per hospitals. The number of dependent population was highest in R/S-Kandivali (1, 38,188 population per hospital) followed by F/S-Parel, C-Marine Lines and M/E-Chembur East wards. However, the number of Aanganwadi workers was relatively higher in the Eastern suburbs as compared to Western suburbs and City Island. It seems that Aanganwadi workers were more concentrated in the slum areas of Mumbai. Moreover, the issue of affordability to seek treatment from private hospitals could be easily visualized that dispensaries are more likely to be located in the area of City Island. In the following Figure 5, an attempt has been made to explain the poor condition of infant health with the help of presence of hospitals in the different administrative wards of Mumbai. The correlation between share of infant deaths to total deaths and distribution of population per hospital clearly shows that as proportion of infant death increases, the population burden on per hospital also increases.

Figure 5: Relationship between Proportion of Infant Deaths to Total Deaths and Number of Persons per Hospital (in, 000) in the different administrative Wards of Mumbai.

Discussion and Conclusions In the present paper an attempt has been made to understand the infant mortality situation in the different administrative wards of Mumbai during 1998-2006. Another aim of this paper is to examine the relationship among proportion of slum population, human development index, availability of health facilities and infant health in Mumbai and its 24 administrative wards. Result shows that there is an improvement in the overall infant mortality rate during eight years time period. Further, result unfolds the high disparity in the incidence of infant mortality and availability of different health facilities across 24 administrative wards of Greater Mumbai. The ward wise analysis with respect to three different regions of Greater Mumbai clearly shows that most of the wards of City Island had infant death around below 35 whereas most of the wards of Eastern suburbs had infant deaths more than 55 per 1000 live births. Moreover, all the wards of City Island and Western suburbs has experienced the decline in IMR except E-Byculla and H/W-Bandra wards whereas the level of IMR has been increased in the three wards of Eastern suburbs during study period. Some wards of Eastern suburbs such as M/E-Chembur East, M/W- Chembur West and L-Kurla call for special attention because these wards are having high concentration of slum population, low level of human development index and high burden of population on available health facilities which

14 resulted into increase in the level of infant mortality rate during eight years time period. The wards of B-Dongri, C-Marine Lines, D-Grant Road and F/S-Parel of City Island; K/E- Andheri East and K/W-Andheri West from Western suburb region were having high level of human development index, low proportion of slum population and better access to health care facilities as a result of low level of IMR. Findings revealed that presence of slum population, low level of human development index and high population burden on health facilities emerged as significant predictors for infant deaths. Large numbers of migrants who are living in the slums areas are more vulnerable to poor health and environmental hazards. The poor environmental conditions of slums become more hazardous in the rainy season and these rains bring communicable diseases and unhygienic conditions to the poorly settled population. It is expected that areas where concentration of slum population and burden on health facilities is relatively high there is a higher chance of happening infant deaths during rainy season. It is a matter of argument whether living in the slum areas or being a poor matter in explaining the infant deaths of Mumbai and its wards. The relative contribution of these two factors on infant death is not possible to break separately because families, living in the slum areas, have been trapped in poverty since many years. Findings revealed that proportion of slum population and development index are negatively correlated which indicates that slum area can be considered as a place for poor people. Further, for poor people living in the slums, availability and affordability are the two most important factors for utilizing health care services as compared to any other factors as far as child’s health is concerned. The reason is that because of poverty and deprivation, they already suffer from ill health and malnutrition ( Arnold et al., 2009; Giashuddin et al., 2005; Kanjilal et al., 2010 ). This argument seems reasonable as poor population will remain in poverty whether they live in slum or non-slum areas. However, among the richer group, living in slums adds to their disadvantage. The living conditions and access to health care are poor in slums compared to non slums; therefore, richer section living in the slums faces worse with respect to child health as compared to their counterparts of those living in the non-slums.

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