Indirect Estimates of District wise IMR and Under 5 Mortality using Census 2011 data - Draft NHSRC. Dr Sandhya Ahuja

Transcription

1 Indirect Estimates of District wise IMR and Under 5 Mortality using 2011 data - Draft NHSRC Dr Sandhya Ahuja

2 CONTENTS PAGE PREFACE :...1 CHAPTER-1 CHAPTER-2 CHAPTER-3 CHAPTER-4 ANNEXURE : INTRODUCTION...4 : METHODOLOGY...6 : CHILD MORTALITY IN INDIA AND STATES...11 : CHILD MORTALITY IN DISTRICTS...16 : DETAILED TABLES ESTIMATES FOR DISTRICTS IN 35 STATES/UNION TERITORIES S01 : Andhra Pradesh...20 S02 : Assam...21 S03 : Andaman & Nicobar...22 S04 : Arunachal Pradesh...22 S05 : Bihar...23 S06 : Chandigarh...24 S07 : Chhattisgarh...24 S08 : Daman & Diu...25 S09 : Dadar & Nagar Haveli...25 S10 : Delhi...26 S11 : Goa...26 S12 : Gujarat...27 S13 : Haryana...28 S14 : Himachal Pradesh...29 Page 2 of 51

3 S15 : Jammu & Kashmir...29 S16 : Jharkhand...30 S17 : Karnataka...31 S18 : Kerala...32 S19 : Lakshadweep...33 S20 : Madhya Pradesh...33 S21 : Maharashtra...35 S22 : Manipur...36 S23 : Meghalaya...36 S24 : Mizoram...37 S25 : Nagaland...38 S26 : Oddisha...39 S27 : Pondicherry...40 S28 : Punjab...40 S29 : Rajasthan...41 S30 : Sikkim...42 S31 : Tamil Nadu...43 S32 : Tripura...44 S33 : West Bengal...44 S34 : Uttar Pradesh...45 S35 : Uttarakhand...48 References...49 Page 3 of 51

4 CHAPTER-1 INTRODUCTION Among the numerous challenges faced by India, children health is the most alarming and needs more attention of policy makers and planners. Infant and child mortality have traditionally been considered as the most significant indicator for assessing the social and economic wellbeing of the region in specific and country in general (Chandrasekhar, 1972; Saha and Roy, 2002). Despite great concerns and continued efforts of the government and policy makers towards high level of infant and child mortality in India, desired outcomes have not been seen. In recent past, the government has made milestone policies decisions to combat this issue by different strategies, and clear evidence has been observed in terms of steadily declining child mortality. Still some areas have unacceptably higher children mortality than others. Even a huge inter-state variation has been reported by many researchers in previous studies. This suggests need of monitoring the trends of child mortality on a periodic basis and at lowest possible geographical unit. In country like India where civil registration system is not unreliable and inconsistent, to find this data regularly one has to depend upon different sources. The Sample registration System (SRS) under the careful vigilance of office of the registrar general of India has been providing annual estimates of infant mortality along with other vital rates of states and national level. Some reports of Sample Registration System also give some natural geographic region of the states but for dealing with lower stage (districts) variation in vital rates this source is not helpful. National Family Health Survey (NFHS) also gives the estimates of child mortality by asking the question to women of reproductive ages about their child birth histories prior to date of survey, but this is also not useful for calculating at district level. District Level Household Survey (DLHS) also collects the data on fertility and child survival at district level but the sample size is quite less for estimating these rates with acceptable sampling errors. Therefore a need has been felt to explore the other options for obtaining estimates of child mortality at district level. The office of the registrar General has brought out volumes of indirect estimates for states, districts, and for certain socioeconomic classes after the 1981 and the 1991 censuses (India Registrar General, 1988; 1994; 1997; 2001). Besides, demographers have also independently obtained such estimates (Rajan and Mohanchandaran, 1998, 200; Ram and Chander Sekhar, 2006; Rajan et. al. 2008). Page 4 of 51

5 United Nation (U.N., 1983) has published the manual for different methods of indirect estimation of infant and child mortality by using different sets of census information. Among them the most common and widely used methods i.e. the Brass method which uses data of number of children ever born and number of children surviving, that can be easily obtained by asking the simple question to women about their children ever born and children surviving in census and surveys. Based on this method, infant and child mortality, viz. q (1) i.e. Infant Mortality Rate (IMR) and q (5) i.e. Under- Five Mortality Rate (U5MR), have been estimated. The 2011 census also asked the question on children surviving and number of children ever born as in the previous three censuses. These were asked to only ever married women. The question on number of children surviving at present also includes the number of sons and number of daughter who are not residing presently in the household, and both are recorded in separate columns. The question on number of children ever born alive also included the number of living daughter and sons and number of dead children, daughter and sons. Age of the mother at last birthday (i.e. age in completed years) is also asked. From this information, tabulation of numbers of children ever born by sex and numbers of children surviving by sex classified by age of the women has been provided by the census. The data is also available for India, states and union territories, districts. This enables us to estimate the infant and childhood mortality at national, states, districts and even for some socioeconomic classes. Page 5 of 51

6 I CHAPTER-2 METHODOLOGY ndirect estimates of child mortality presented in this report are obtained by applying the Brass method for estimating the child mortality by using the information on children surviving and children ever born. The advantage of using Brass method for estimation child mortality has been well known (see U.N., 1983) and hence it is widely used in the population where civil registration system is not good. It is quite robust, especially because in the modified version it can be used in variety of mortality regimes. The method however, requires the reliable data on children ever born and children surviving by age of women. Response error or selective omission of dead children in reporting will obviously bias the estimates downward. Though it is assumed that child mortality is not dependent upon the age of mother at birth, but using the data from very young women since it is known that infant mortality is higher than average for births to teenage mothers. Other notable cautions may arise due to default i.e. the information of children born and children surviving have been asked to women who survive at time of enumeration and no information is collected from women who died. If the children to women who have died have different survival level, the estimates would be biased; but the effect of this factor is not expected to be notable. Demographic notation, the probability of dying before exact age x is denoted as q(x). The Brass method provides estimates for q(1), q(2), q(3), q(5), q(10), q(15), q(20). For child mortality, the estimates up to age 5, i.e., q(1), q(2), q(3), q(5) are relevant. These are based on mean children ever born and mean children surviving to women ages 15-19, 20-24, 25-29, and respectively at time of enumeration. The two term q(1) and q(5) i.e. Infant Mortality and Under-Five Mortality have been most frequently used. It is customary to express these rates in terms of per thousand (1000) live births; thus 1000*q(1) and 1000*q(5) are given as IMR and U5MR respectively. It is well known that the proportion of children ever born who have died is indicators of child mortality and can yield robust estimates of childhood mortality. Brass (1964; 1975) was the first to develop a procedure for converting proportions dead of children ever born reported by women in different age groups of childbearing period into estimates of the probability of dying before attaining certain exact childhood ages. He observed that the relation between the proportions of children dead by age group of mothers (D(i), i = 1 for age group 15-19, i = 2 for age group 20-24, _, i = 7 for Page 6 of 51

7 age group 45-49) and the probability of dying before age x [q(x)], is primarily influenced by the age pattern of fertility. Brass established a set of correspondences between ages of mothers and ages of their children for whom cumulative mortality is best identified and these correspondences have been widely used by all subsequent analysts. These correspondences, however, are not exact and depend on the reproductive histories of the particular group of women reporting their births (Preston et al., 2003). Brass developed a set of multipliers (adjustment factors) to adjust for the particular reproductive histories of a group of women and to convert the observed values of the proportions of children dead into estimates of the probability of dying before age x. The estimates being obtained from information on mortality in the past refer to dates prior to the enumeration, and a method was developed by Feeney (1980) to identify he reference date. The computer Package MORTPAK enables for obtaining the estimates of child mortality from data of mean number of children ever born and mean number of children surviving given by age of the women (or marital duration) in conventional age groups. The package gives the alternate set of estimate based on the various model life tables, the four models of the Princeton Regional Life Tables (Coale et. al., 1983), and five models of the U.N. Model Life tables for Developing Countries (U.N. 1983), allowing the user to choose the appropriate one for the population studied. As the U.N. South Asian pattern is most commonly accepted for India, estimates based on this model has been used in this report. Estimates based on the other models often used for India, especially the Princeton Models West and South, are very close to those by South Asian pattern. MORTPAK also requires the value of mean age at childbearing and sex ratio at birth. The mean age at childbearing has been computed from the census 2011 data sets by MORTPAK option of FERTCB and sex ratio ate birth used from SRS for bigger states and national value as standard for others states. Though Brass method is not sensitive to these two values hence one can used the value mean age at childbearing and sex ratio at birth for India. In principle it is observed that the estimate of q(1) is generally quite high, well above the estimate of q(2). This occurs because the q(1) value is obtained from the women in the age group 15-19, and births to very young women, teenagers, have a high risk of mortality. Thus the estimate of q(1) is based on very select group of women and does not represent the overall infant mortality. It is therefore suggested to ignore this estimate. Instead, infant mortality can be estimated as the value of q(1) corresponding to the estimated value of q(2) or q(3) or q(5). This can be done via model life Page 7 of 51

8 tables. In this report the average of q(3) and q(5) has been used to estimate the infant and child mortality, because it has been observed that the estimates of q(2) also shows higher value than q(3), therefore it is suggested to ignore q(1) and q(2) and the final estimates can be obtained on basis of q (3) and q(5). The estimates of child mortality are based on the experience of child survival and chid born to women enumerated in the census 2011 census and therefore related to time periods prior to the census date. Estimates on experience of very young women year at census refers to time period just before the census and for estimates for based on the older women, the reference date goes backward because children to those women are born relatively earlier. The methods for calculating reference time given by Feeney have been already computed by MORTPAK as standard output. As mentioned earlier the estimate obtained by women of young ages are ignored. Hence the reference date for ultimate estimated infant and under-five mortality is corresponding to point yielded by these age groups via the Feeney method. In most cases these fall in the years Usefulness of estimates The indirect estimates of child mortality have been very useful for planning and policy perspectives for those areas, of which estimates are not directly available. In the Brass method, estimates of q(1), q(2), q(3), q(5) are independently obtained as these arise out of the information on proportion of children dead to women in the four age groups, 15-19, 20-24, 25-29, and respectively. A word of caution is in order before the estimates are interpreted. The general guiding principle is that indirect estimates are to be used only in case reliable direct estimates are not available. The techniques of indirect estimates are based on certain assumption about the demographic processes and the estimates would only be as good as the actual process is close to the assumed one. Therefore, whenever there are grounds to believe that estimates from the civil registration are of good quality, it is preferable to use these direct estimates rather than the indirect ones. The estimates are obtained for all the states and union territories and all districts. In each case, estimates are presented for males, females, and for both sexes together of each districts. Estimates for some states are coming higher than SRS, so its need cautions while using these estimates. 2.2 Consistency with SRS The Brass Techniques was based on certain assumption about the age pattern of fertility and mortality prevailing in the population and later the developments also assume that certain patterns Page 8 of 51

9 are followed. A deviation from these would naturally affect the estimates. The best way to see the how good these estimates are to check for consistency with other independent estimates. The Sample Registration System provides estimates of child mortality annually for large states. The SRS is dual recording system, continuous registration in selected villages or segment of villages and urban areas and half yearly surveys, followed by case to case matching, verification of unmatched events and correction. Over the year the SRS estimates are gained the wider acceptance of larger states, in which the sample sizes are large. The comparison is made for infant and under five mortality, whereas the under five mortality can be read from the SRS life table given for five year intervals. As noted above, indirect estimates refer to a period of Hence, these are compared with the SRS 2006 for infant mortality and for under-five the life table for based estimates of IMR for India is 58 and SRS estimate is slightly lower at 55. While for under-five mortality the census estimates is 75 and from SRS it is 75. Fig. 2.1 and fig. 2.2 shows the census estimates on the x-axis and the SRS estimates on the y-axis. Most states are either on or close to the line of equality. However the outliers are Kerala, Karnataka, Tamil Nadu, Maharashtra, and West Bengal in larger states. These states do not corroborate with the SRS estimates which are much lower than the census estimates. Kerala has the lowest low mortality from both the estimates. Some states also shows that the estimates from census is lower than SRS i.e. Andhra Pradesh, Oddisha, Rajasthan. Page 9 of 51

10 2011 Indirect Estimates Fig.2.1 Scatter Diagram showing estimates of the Infant Mortality Rate based on Indirect Estimates from 2011 data and the Sample Registration System for , Large States, India 2011 Indirect Estimates Fig.2.1 Scatter Diagram showing estimates of the Under-five Mortality Rate based on Indirect Estimates from 2011 data and the Sample Registration System (life tale) for , Large States, India Page 10 of 51

11 CHAPETR-3 INFANT AND CHILD MORTALITY IN INDIA AND STATES CENSUS 2011 ESTIMATES FOR INDIA The Sample registration System has been giving annual estimates of the IMR for India and larger states regularly and these can serve a good overview of level, trends and inter-state variations. Besides, the under-five mortality can be read from the SRS life table for five-year period. However the districts level estimates presented in this report are based on census. The census based estimates of IMR for India is 58 per thousand and U5MR (Under-five mortality rate) 80 per thousand. This can be interpreted as about 8 percent of new born do not survive upto five years. For the males, the U5MR is equal to national values i.e 80. For states of Uttar Pradesh the U5MR for males is 97 per thousand whereas for females it is 106. Thus, in spite of an overall higher female life expectancy in the last few decades in India, female disadvantage during childhood persists. The census based estimates of IMR for India was 68 per thousand and U5MR was 96 per thousand in 2001, this reduced to 58 and 80 respectively over the decade. The U5MR for girls was 106 in 2001 and it reduces to 79 in This shows a substantial reduction of U5MR for girls over the decade. INTER STATE VARIATIONS The level of survival has been varying substantially across the states of India. Infant mortality and U5 morality is high for the states of Uttar Pradesh, Madhya Pradesh, Rajasthan, Bihar, Chhattisgarh, Oddisha, Assam etc. and low in Kerala, Karnataka Maharashtra, and Delhi etc. Table 3.1 shows that the IMR was highest in Uttar Pradesh (71) followed by Madhya Pradesh (70), Oddisha (67), Chhattisgarh (66) per thousand and lowest is in Kerala (26), West Bengal (46), Tamil Nadu (46), Maharashtra (47) among the larger states based on the census 2011 indirect estimates. The decline in IMR over the census periods is seen almost in all the states with different quantum of change but the state of Gujarat and Maharashtra have been showing no change in child mortality over the census periods. Some states are also showing minor increase in IMR over the census periods i.e Karnataka (3 per thousand), Andhra Pradesh (1 per thousand), Delhi (3 per thousand) Puducherry (20 per thousand). While in case of U5MR, Gujarat and Maharashtra were same and show no change from census 2001 to census 2011 States showing an increase in the U5MR are Puducherry (32 per Page 11 of 51

12 thousand), Andhra Pradesh (1 per thousand), Karnataka (5 per thousand) and Delhi (6 per thousand). The highest decline in IMR and U5MR has been observed in Madhya Pradesh (23, 39 per thousand), followed by Arunachal Pradesh (22, 38 per thousand), West Bengal (22, 35 per thousand), Chandigarh (20, 31 per thousand), Chhattisgarh (18, 30 per thousand), and Rajasthan and Uttar Pradesh (13, 22 per thousand). North eastern states also show declining trends of IMR and U5MR over the census periods at varying pace viz. Manipur (9, 13 per thousand), Sikkim (15, 22 per thousand), Nagaland (11, 18 per thousand), lowest among them was Mizoram (4, 7 per thousand) followed by Meghalaya (5, 8 per thousand). The state of Jammu & Kashmir, Punjab, Haryana, Himachal Pradesh also showed declining child mortality viz. IMR and U5MR at different quantum. The decline in IMR and U5MR in Punjab was (9, 14 per thousand), Haryana (13, 21 per thousand), Jammu & Kashmir (16, 25 per thousand). The demographically advanced state of Kerala also recorded a decline (11, 13 per thousand) over the census period. SEX DIFFERENTIALS The sex differential present in states of India has been clearly observable. The decline in IMR and U5MR among the females was (16, 27 per thousand) at all India level whereas the corresponding decline for male was (5, 7 per thousand). This difference has come partly because of the already low level of child mortality prevalent among males as compared to female in base year, so the chances of improvements was higher among female mortality reduction. The inter-state variation among males and females is also quite substantial. In Southern states the decline in child mortality varied considerably. In Karnataka, the child mortality among male has increased whereas for females it decreased. Kerala showed a decrease in child mortality among males (4, 3 per thousand) while for females also a considerable reduce (18, 25 per thousand) was observed. In Himachal Pradesh child mortality for males has showed no change whereas for female it is (18, 29 per thousand). The decline in child mortality among Bigger states of Uttar Pradesh (7, 12), Madhya Pradesh (17, 28), Bihar (3, 5), Rajasthan (8, 12), Oddisha (12, 20), for male and for female it is (18, 34), (28, 51), (16, 27), (18, 33), (19, 33) respectively. Page 12 of 51

13 Table-3.1 INDIRECT ESTIMATES OF INDICATORS OF INFANT AND CHILD MORTALITY BY SEX, INDIA AND STATES/UNION TERRITORIES, BASED ON 2011 CENSUS INDIA/ States/UT s SRS (2006) SRS (2006) SRS (2006) India Andhra Pradesh Assam Bihar Chhattisgarh Delhi Gujarat Haryana Jammu Kashmir & Jharkhand Karnataka Kerala Madhya Pradesh Maharashtra Orissa Punjab Rajasthan Tamil Nadu Uttar Pradesh Page 13 of 51

14 INDIA/ States/UT s SRS (2006) SRS (2006) SRS (2006) West Bengal Arunachal Pradesh Goa Himachal Pradesh Manipur Meghalaya Mizoram Nagaland Sikkim Tripura Uttarakhand Andaman Nicobar Island & Chandigarh Dadar & Nagar Haveli Daman & Diu Lakshadweep Puducherry Page 14 of 51

15 Table-3.2 INDIRECT ESTIMATES OF INDICATORS OF INFANT AND CHILD MORTALITY BY SEX, INDIA AND STATES/UNION TERRITORIES, BASED ON 2011 CENSUS, AND CENSUS 2001 INDIA / States/UT s India Andhra Pradesh Assam Bihar Chhattisgarh Delhi Gujarat Haryana Jammu Kashmir & Jharkhand Karnataka Kerala Madhya Pradesh Maharashtra Orissa Punjab Rajasthan Page 15 of 51

16 INDIA / States/UT s Tamil Nadu Uttar Pradesh West Bengal Arunachal Pradesh Goa Himachal Pradesh Manipur Meghalaya Mizoram Nagaland Sikkim Tripura Uttarakhand Andaman Nicobar Island & Chandigarh Dadar & Nagar Haveli Daman & Diu Lakshadweep Puducherry Page 16 of 51

17 Page 17 of 51

18 CHAPTER-4 INFANT AND CHILD MORTALITY IN DISTRICTS OF INDIA Need For Districts Level Estimates Among the objectives of this report, the principal objective was to obtain and examine the estimation of infant and under-five mortality among the districts of India. While national and states level estimates are available from SRS and other sources such as NFHS, district level estimates permit an in-depth understanding of spatial variations in child mortality, and for that purpose, census is the only source, albeit indirect. Tables S01 to S35 give the estimates separately for male, female and both sexes combined in each districts of the 35 states and union territories of India. Inter Districts Variations The value of IMR ranges from a level of 22 per thousand in Kannur district of Kerala to 111 per thousand in East Kameng in Arunachal Pradesh. While for under-five mortality it ranges from the 28 per thousand in Kannur districts of Kerala to 171 per thousand in East Kameng districts of Arunachal Pradesh. Among the southern states of Kerala, Karnataka, Tamil Nadu and Andhra Pradesh the districts with highest IMR and U5MR was Kozhikode (34, 44), Koppal (71, 101), Ariyalur (93, 139), Anantpur (73, 104) per thousand respectively, whereas in the 2001 the districts with highest IMR and under-five mortality were Wayanad (42, 54), Bellary (76, 109), Theni (70, 99), Vizianagram (78, 113) per thousand respectively. In Uttar Pradesh the districts ranges from Sitapur (87, 127 per thousand) to Jalaun (56, 76 per thousand) while in 2001 these range from, Balrampur (105, 160 per thousand) to Ghaziabad (59, 81 per thousand). In Bihar these estimates range from, Kishanganj (72, 103 per thousand) to Samastipur (54, 73 per thousand), while in 2001 these were, Kishanganj (90, 134 per thousand) to Siwan (57, 78 per thousand). The inter-state and intra-state variations clearly exhibit the complete spatial picture of IMR and U5MR in India. Value of IMR and U5MR in North eastern states vary from 51, 69 per thousand in Chandel districts of Manipur to 40, 53 per thousand in Bishnupur district of Manipur. In Mizoran these estimates of IMR and U5MR varied from Lwangtalai (86, 127) to Aizwal (34, 44) per thousand. While in Himachal Pradesh, the values of IMR and U5MR varied from Una (59, 81) to Mandi (38, 50) per thousand. Overall high mortality is seen in the central-northern part of the country but also in parts of the north eastern, especially in Arunachal Pradesh and in some districts of Jammu & Kashmir. Broadly, districts in south-western Page 18 of 51

19 region, especially the coastal and adjoining areas, the southern region, Delhi and Punjab and some parts in north-eastern region show low IMR. Central India and some parts of Arunachal Pradesh seem to bear high Infants mortality. Table-4.1 DISTRICTS WITH THE HIGHEST INFANT AND CHILD MORTALITY RATE IN STATES/UNION TERRITORIES OF INDIA, CENSUS INDIRECT ESTIMATES State/ Union Territories District IMR U5 District IMR U5 Arunachal Pradesh 3. East Kameng East Kameng Bihar 8. Kishanganj Kishanganj Chhattisgarh 17.Dakshin Baster Dantewada Dantewada Daman & Diu 1. Diu Diu New delhi 5. New Delhi North East Haryana 17. Rewari Kaithal Himachal Pradesh 7. Una Kinnaur Jammu & Kashmir 4. Kargil Kargil Manipur 9. Chandel Tamenglong Meghalaya 1. West Garo Hills Soth Garo Hills Mizoram 7. Lwangtalai Lwangtalai Nagaland 1. Mon Mokokchung Punjab 8. Moga Mansa Sikkim 2. West West Tripura 3. Dhalai North Tripura Uttrakhand 13. Hardwar Uttarkashi Andhra Pradesh 21. Anantpur Vizianagram Aandman & Nicobar island 1. Nicobar Nicobar Assam 19. Hailakandi* Dhubri Goa 2. South Goa North Goa Gujrat 18. Dohad Patan Jharkhand 22. Simdega Gumla Karnataka 6. Koppal Bellary Kerala 4. Kozhikode Wayanad Madhya Pradesh 46. Singrauli Katni Page 19 of 51

20 State/ Union Territories District IMR U5 District IMR U5 Maharashtra 11. Gondiya Gadchiroli Oddisa 21. Kandhamal Kandhamal Pudducherry 1. Yanam Yanam Rajasthan 2. Hanumangarh Dungarpur Uttar Pradesh 23. Sitapur Balrampur West Bengal 6. Maldah Koch Bihar Tamil Nadu 15. Ariyalur Theni Chandigarh Dadar & Nagar Haveli Lakshadweep Note: *denotes that values are same for some other districts also but highest, Table-4.2 DISTRICTS WITH THE LOWEST INFANT AND CHILD MORTALITY RATE IN STATES/UNION TERRITORIES OF INDIA, CENSUS INDIRECT ESTIMATES State/ Union Territories District IMR U5 District IMR U5 Arunachal Pradesh 8. East Siang East Siang Bihar 19. Samastipur** Siwan Chhatisgarh 11. Raipur** Durg Daman & Diu 2. Daman Daman New delhi 2. North Delhi North Delhi Haryana 18. Gurgaon Ambala Himachal Pradesh 5. Mandi Hamirpur Jammu & Kashmir 22. Samba# Phulwana Manipur 4. Bishnupur Imphal West Meghalaya 6. East Khasi Hills East Khasi Hills Mizoram 3.Aizwal Aizwal Nagaland 10. Kohima Dimapur Punjab 18. Sahibzada Ajit Singh Nagar# Rupnagar Sikkim 3. South South Tripura 1. West Tripura West Tripura Uttrakhand 2. Chhamoli Bageshwar Andhra Pradesh 16. Guntur Karimnagar Aandman & Nicobar island 2. North & Middle Andaman Andamans Assam 23. Kamrup Metro# Dibrugarh Page 20 of 51

21 State/ Union Territories District IMR U5 District IMR U5 Goa 1. North Goa South Goa Gujrat 8. Surendranagar Porbandar Jharkhand 12. Purbi Singhbhumi Bokaro Karnataka 21. Dakshin Kannada Udupi Kerala 2. Kannur Kannur Madhya Pradesh 21. Dhar Indore Maharashtra 25. Pune Sangli Oddisa 1. Bargarh Jharsuguda Pudducherry 3. Mahe Mahe Rajasthan 12. Jaipur Bikaner Uttar Pradesh 34. Jalaun Ghaziabad West Bengal 13. Bankura Bankura Tamil Nadu 28. Kanniyakumari Kanniyakumari Chandigarh 1. Chandigarh Chandigarh Dadar & Nagar Haveli 1. D & N Haveli D & N Haveli Lakshadweep 1. Lakshadweep Lakshadweep Note: - (**denotes that values are same for some other districts also but highest, # new districts in census 2011) Page 21 of 51

22 ESTIMATES FOR DISTRICTS IN 35 STATES AND UNION TERRITORIES S01: INDIRECT ESTIMATES OF INDICATORS OF INFANT AND CHILD MORTALITY OF ANDHRA PRADESH BY SEX, BASED ON 2011 CENSUS States and Districts Andhra Pradesh Adilabad Nizamabad Karimnagar Medak Hyderabad Rangareddy Mehbubnagar Nalgonda Warangal Khammam Srikakulam Vizianagar Vishakhapatnam East Godavari West Godavari Krishna Guntur Prakasham Sri Potti Sriramulu Nellore Y.S.R Kurnool Anantpur Chittoor Page 22 of 51

23 S02: INDIRECT ESTIMATES OF INDICATORS OF INFANT AND CHILD MORTALITY OF ASSAM BY SEX, BASED ON 2011 CENSUS States and Districts Assam Kokrajhar Dhubri Goalpara Barpeta Marigaon Nagaon Sonitpur Lakhimpur Dhemaji Tinsukia Dibrugarh Sivasagar Jorhat Golaghat Karbi Anglong Dima Hasao Cachar Karimganj Hailakandi Bongaigaon Chirag Kamrup Kamrup Metro Nalbari Baksa Darang Udalguri Page 23 of 51

24 S03: INDIRECT ESTIMATES OF INDICATORS OF INFANT AND CHILD MORTALITY OF ANDMAN & NICOBAR ISLAND BY SEX, BASED ON 2011 CENSUS States and Districts Andaman & Nicobar island Nicobar North & Middle Andaman South Andaman S04: INDIRECT ESTIMATES OF INDICATORS OF INFANT AND CHILD MORTALITY OF ARUNACHAL PRADESH BY SEX, BASED ON 2011 CENSUS States and Districts Arunachal Pradesh Tawang West Kameng East Kameng Papum Pare Lower Subsansiri Upper Subansiri West Siang East Siang Changlang Tirap Upper Siang Kurug Kumey Dibang valley Lower Dibang Valley Lohit Anjaw Page 24 of 51

25 S05: INDIRECT ESTIMATES OF INDICATORS OF INFANT AND CHILD MORTALITY OF BIHAR BY SEX, BASED ON 2011 CENSUS States and Districts Bihar Paschim Champaran Purba Champaran Sheohar Sitamarahi Madhubani Supaul Araria Kishanganj Purnia Katihar Madepura Saharsa Darbhanga Muzaffarpur Gopalganj Siwan Saran Vaishali Samastipur Begusarai Khagaria Bhagalpur Banka Munger Lakhisarai Sheikhpura Nalanda Patna Bhojpur Buxar Kaimur(Bhabua) Rohtas Page 25 of 51

26 States and Districts 33. Aurangabad Gaya Nawada Jamui Jehanabad Arwal S06: INDIRECT ESTIMATES OF INDICATORS OF INFANT AND CHILD MORTALITY OF CHANDIGARH BY SEX, BASED ON 2011 CENSUS States and Districts Chandigarh Chandigarh S07: INDIRECT ESTIMATES OF INDICATORS OF INFANT AND CHILD MORTALITY OF CHHATISGARH BY SEX, BASED ON 2011 CENSUS States and Districts Chhattisgarh Koria Sarguja Jashpur Raigarh Page 26 of 51

27 States and Districts 5. Korba Janjgir-Champa Bilaspur Kabeerdham Rajnandangaon Durg Raipur Mahasamund Dhantari Uttar Baster Kanker Baster Narayanpur Dakshin Baster Dantewada Bijapur Page 27 of 51

28 S08: INDIRECT ESTIMATES OF INDICATORS OF INFANT AND CHILD MORTALITY OF DAMAN & DIU BY SEX, BASED ON 2011 CENSUS States and Districts Daman & Diu Diu Daman S09: INDIRECT ESTIMATES OF INDICATORS OF INFANT AND CHILD MORTALITY OF DADAR & NAGAR HAVELI BY SEX, BASED ON 2011 CENSUS States and Districts D & N Haveli D & N Haveli S10: INDIRECT ESTIMATES OF INDICATORS OF INFANT AND CHILD MORTALITY OF DELHI BY SEX, BASED ON 2011 CENSUS States and Districts Delhi North West Delhi North Delhi North east Delhi East Delhi New Delhi Central Delhi West Delhi South West Delhi South Delhi Page 28 of 51

29 S11: INDIRECT ESTIMATES OF INDICATORS OF INFANT AND CHILD MORTALITY OF GOA BY SEX, BASED ON 2011 CENSUS States and Districts Goa North Goa South Goa S12: INDIRECT ESTIMATES OF INDICATORS OF INFANT AND CHILD MORTALITY OF GUJRAT BY SEX, BASED ON 2011 CENSUS States and Districts Gujrat Kachchh Banas Kantha Patan Mahesana Sabar Kantha Gandhinagar Ahmadabad Surendranagar Rajkot Jamnagar Porbandar Junagarh Amreli Bhavnagar Anand Page 29 of 51

30 States and Districts 16. Kheda Panch Mahal Dohad Vadodara Narmada Bharuch The Dangs Navsari Valsad Surat Tapi S13: INDIRECT ESTIMATES OF INDICATORS OF INFANT AND CHILD MORTALITY OF HARYANA BY SEX, BASED ON 2011 CENSUS States and Districts Haryana Panchkula Ambala Yamuna Nagar Kurukshetra Kaithal Karnal Panipat Sonipat `05 9. Jind Fatehabad Sirsa Hisar Bhiwani Page 30 of 51

31 States and Districts 14. Rohtak Jhajjar Mahendgarh Rewari Gurgaon Mewat Faridabad Palwal S14: INDIRECT ESTIMATES OF INDICATORS OF INFANT AND CHILD MORTALITY OF HIMACHAL PRADESH BY SEX, BASED ON 2011 CENSUS States and Districts Himachal Pradesh Chamba Kangra Lahul & Spiti Kullu Mandi Hamirpur Una Bilaspur Solan Simaur Shimla Knnaur Page 31 of 51

32 S15: INDIRECT ESTIMATES OF INDICATORS OF INFANT AND CHILD MORTALITY OF JAMMU & KASHMIR BY SEX, BASED ON 2011 CENSUS States and Districts Jammu & Kashmir Kupwara Badgam Leh Kargil Punch Rajouri Kathua Baramulla Bandipore Srinagar Ganderbal Pulwama Shupiyan Anantnag Kulgam Doda Ramban Kishtawar Udhampur Reasi Jammu Samba Page 32 of 51

33 S16: INDIRECT ESTIMATES OF INDICATORS OF INFANT AND CHILD MORTALITY OF JHARKHAND BY SEX, BASED ON 2011 CENSUS States and Districts Jharkhand Garhwa Chhatra Kodarma Giridih Deoghar Godda Sahibganj Pakaur Dhanbad Bokaro Lohardaga Purbi Singhbhumi Palamau Latehar Hazaribagh Ramgarh Dumka Jamtara Ranchi Khunti Gumla Simdega Paschim Singhbhumi 24.Saraikela- Kharsawan Page 33 of 51

34 S17: INDIRECT ESTIMATES OF INDICATORS OF INFANT AND CHILD MORTALITY OF KARNATAKA BY SEX, BASED ON 2011 CENSUS States and Districts Karnataka Belgaum Bagalkot Bijapur Bidar Raichur Koppal Gadag Dharwad Utara Kannada Haveri Bellary Chhitradurga Devanagere Simoga Udupi Chikmagalur Tumkur Bangalore Mandya Hassan Dakshin Kannada Kodagu Mysore Chamarajanagar Gulbarga Yadgir Kolar Chikkaballapura Bangalore Rural Ramnagara Page 34 of 51

35 S18: INDIRECT ESTIMATES OF INDICATORS OF INFANT AND CHILD MORTALITY OF KERALA BY SEX, BASED ON 2011 CENSUS States and Districts Kerala Kasargod Kannur Wayanad Kozhikode Mallapuram Palakkad Thrissur Ernakulam Idukki Kottayam Alappuzha Pathanamthitta Kollam Thiruvananthapuram S19: INDIRECT ESTIMATES OF INDICATORS OF INFANT AND CHILD MORTALITY OF LAKSHADWEEP BY SEX, BASED ON 2011 CENSUS States and Districts Lakshadweep Lakshadweep Page 35 of 51

36 S20: INDIRECT ESTIMATES OF INDICATORS OF INFANT AND CHILD MORTALITY OF MADHYA PRADESH BY SEX, BASED ON 2011 CENSUS States and Districts Madhya Pradesh Sheopur Morena Bhind Gwalior Datia Shivpuri Tikamgarh Chhatrapur Panna Sagar Damoh Satna Rewa Umaria Neemuh Mandasasur Ratlam Ujjain Shajapur Dewas Dhar Indore Khargone (West Nimar) Bharwani Rajgarh Vidisha Bhopal Sehore Raisen Betul Harda Page 36 of 51