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The DHS Working Papers series is an unreviewed and unedited prepublication series of papers 

reporting on research in progress based on Demographic and Health Surveys (DHS) data. This 

research was supported by the East-West Center. Additional support was provided by the United 

States Agency for International Development (USAID) through the MEASURE DHS project 

(#GPO-C-00-03-00002-00). The views expressed are those of the authors and do not necessarily 

reflect the views of USAID, the United States Government, or the organizations with which the 

authors are affiliated. 

MEASURE DHS assists countries worldwide in the collection and use of data 

to monitor and evaluate population, health, and nutrition programs. Additional 

information about the MEASURE DHS project can be obtained by contacting 

ORC Macro, Demographic and Health Research Division, 11785 Beltsville 

Drive, Suite 300, Calverton, MD 20705 (telephone: 301-572-0200; fax: 301-

572-0999; e-mail: reports@orcmacro.com; internet: www.measuredhs.com).
The Effect of Antenatal Care on Professional Assistance at Delivery in Rural India 

Vinod Mishra1,2 and Robert D. Retherford2

March 2006 

Institutional affiliations: 1

ORC Macro, Calverton, MD; 2East-West Center, Honolulu, HI 

Corresponding author: Vinod Mishra, Demographic and Health Research Division, ORC 

Macro,11785 Beltsville Drive, Suite 300, Calverton, MD 20705. Phone: 301-572-0220, 

Fax: 301-572-0999, Email: vinod.mishra@orcmacro.com

ABSTRACT 

Delivering births in a medical institution or at home with professional medical assistance 

has been shown to promote safe motherhood and child survival. Yet three-quarters of 

births in rural India continue to take place at home, most of them without the assistance 

of any trained health worker. This study examines the role of antenatal care (ANC) in 

promoting professional assistance at delivery, using data from India’s 1992-93 and 1998-

99 National Family Health Surveys (NFHS-1 and NFHS-2). We estimate the effect of 

number of antenatal care visits (0, 1-2, 3+) on professional assistance at delivery (no 

assistance, professional assistance at home, delivery in a medical institution), using 

multinomial logistic regression, controlling for demographic, geographic, and 

socioeconomic factors, pregnancy complications, and two measures of access to health 

facilities. The results indicate that, after controlling for other variables (including ANC), 

pregnancy complications and access to health facilities do not have much effect on 

assistance at delivery. By contrast, ANC has a large effect on assistance at delivery, even 

after all other variables are controlled. The effect of ANC on professional assistance at 

delivery is larger in South India than in North India, and predicted percentages receiving 

professional assistance are higher in South India than in North India. A policy implication 

is that increased antenatal care coverage can be an effective means of increasing 

professional assistance at delivery, especially delivery in a medical institution. 

KEY WORDS: safe motherhood, antenatal care, skilled assistance at delivery, 

reproductive health, women 

iii
ACKNOWLEDGEMENTS 

The authors thank Alfredo Fort and Fred Arnold for their comments. They also thank 

Gayle Yamashita, Sally Dai, and Shane Khan for programming and research assistance. 

An earlier version of this paper was presented at the 2004 annual meeting of the 

American Public Health Association in Washington, DC. 

iv
INTRODUCTION

It is well established that giving birth under the care and supervision of trained healthcare providers promotes child survival and reduces the risk of maternal mortality (Tsui et 

al. 1997; WHO 2004a; WHO 2005). Maternal and child health programs in India have 

been promoting availability and access to trained midwives and upgrading rural health 

services to include facilities for institutional delivery (MOHFW 2005). Yet three-quarters 

of births in rural India continue to take place at home, most of them without the 

assistance of any trained health worker (IIPS and ORC Macro 2000). At the same time, 

both child mortality (especially neonatal mortality) and maternal mortality remain high. 

Seven out of every 100 children born in India die before reaching age one (Dyson et al. 

2004), and approximately five out of every 1,000 women who become pregnant die of 

causes related to pregnancy and childbirth (MOHFW 2005). India accounts for more than 

one-fourth of all maternal deaths from causes related to pregnancy and childbirth 

worldwide (WHO 2004b).

Historically, improving access to services has been a primary strategy for increasing 

health-service utilization in developing countries. Several studies have stressed the 

importance of access to health services as a factor affecting the utilization of services 

(Rao and Richard 1984; Sarita and Tuominen 1993; Kumar et al. 1997; Rohde and 

Viswanathan 1995). In recent years, field experience and data from both qualitative and 

quantitative studies have indicated that improvements in the quality of services can 

further increase service utilization. Programs that maximize quality as well as access to 

services enhance client satisfaction, leading to greater utilization (Koenig and Khan 1999; 

Shelton and Davis 1996; Levine et al. 1992). It is argued that access helps determine 

whether an individual makes contact with the provider, while quality of care influences a 

client’s decision whether to accept and use the service or to continue using the service 

(Bertrand et al. 1995). In addition to expanding health-care facilities and infrastructure, 

India’s family welfare program has been emphasizing outreach programs, including 

home visits, mobile clinics, and community-based delivery systems, as mechanisms to 

increase both the quantity and quality of services (MOHFW 2005). 

5
Utilization of health services is affected not only by access but also by demand for 

services, which is determined largely by socioeconomic factors, personal health beliefs, 

and perceptions of illness. A number of studies have assessed the role of socioeconomic 

and demographic factors in influencing demand for and utilization of maternal and child 

health services (Kanitkar and Sinha 1989; Elo 1992; Swenson et al. 1993; Abdalla 1993; 

Govindasamy 1994; Khan et al. 1994; Barlow and Diop 1995; Ahmed and Mosley 1997; 

Regmi and Manandhar 1997; Govindasamy and Ramesh 1997; Das et al. 2001). Of 

course, availability and quality of services can also influence demand for services. 

In this paper, we examine factors associated with the likelihood of receiving professional 

assistance at delivery for rural Indian women. More specifically, we examine the role of 

antenatal care (ANC) in promoting professional assistance at delivery. An earlier study in 

four Indian states observed that women who received any antenatal care were 

significantly more likely to deliver in a medical institution (Sugathan et al. 2001). The 

present study builds on this previous research to include professional assistance for noninstitutional deliveries, and expands its coverage to India as a whole and the North and 

South regions separately, using data from the 1992-93 and 1998-99 National Family 

Health Surveys (NFHS-1 and NFHS-2). 

Antenatal care is hypothesized to have a positive effect on the likelihood of receiving 

professional assistance at delivery, inasmuch as women receiving antenatal care come in 

contact with health-care providers who are likely to encourage them to seek professional 

assistance at delivery or to give birth in a medical facility. A complicating factor is that 

women with pregnancy complications are more likely than other pregnant women both to 

receive antenatal check-ups and to receive professional medical assistance at delivery 

because of the pregnancy complication. The study examines the association between ANC 

and professional assistance at delivery after adjusting for the potential confounding effect of 

pregnancy complications. 

6
DATA AND METHODS 

Data are from India’s first and second National Family Health Surveys— NFHS-1 

(conducted in 1992–93) and NFHS-2 (conducted in 1998–99). Both surveys were part of 

the Demographic and Health Surveys (DHS) that have been conducted worldwide in 

many developing countries since the 1980s. NFHS-1 comprises 88,562 households and 

89,777 ever-married women, and NFHS-2 comprises 91,196 households and 89,199 evermarried women. Details of sample design are contained in the basic survey reports for 

NFHS-1 and NFHS-2 (IIPS 1995; IIPS and ORC Macro 2000). 

The units of analysis in our study are births to rural women that took place during the 

three-year period immediately preceding survey interview. The analysis is confined to 

rural women partly because the vast majority (about 70 percent) of India’s population 

lives in rural areas, and partly because our health-care-access variables are meaningful 

only in a rural context. The analysis for rural India (all states) is based on 29,069 births 

that occurred during the three-year period before NFHS-1 and 25,499 births that occurred 

during the three-year period before NFHS-2. In order to capture some of India’s 

demographic diversity, we also examine births to rural women in two subsets of states, 

one set from North India (Bihar, Uttar Pradesh, Rajasthan, and Madhya Pradesh) and one 

set from South India (Andhra Pradesh, Goa, Karnataka, Kerala, and Tamil Nadu). 

The method of analysis is multinomial logistic regression (m-logit for short). The 

response variable is a three-category variable representing professional assistance at 

delivery (no assistance, professional assistance at home, institutional delivery). 

Professional assistance for home delivery includes assistance by a doctor, nurse/midwife, 

auxiliary nurse midwife (ANM), lady health visitor (LHV), or other health professional. 

The principal predictor variable is number of antenatal care (ANC) visits (none, 1-2, 3+). 

Other predictor variables of particular interest (because they are important variables from 

the point of view of the health system) are pregnancy complications (none, 

mild/moderate, severe) and two health-care-access variables—availability of a hospital 

within 5 km (no, yes) and whether the mother’s village is accessible by means of an allweather road (no, yes). Additional control variables are mother’s age at childbirth in 

7
years (<20, 20-24, 25-29, 30-49), birth order (1, 2, 3, 4+), religion of household head 

(Hindu, Muslim, other), caste/tribe of household head (scheduled caste/scheduled tribe, 

other), geographic region (north, central and west, east and northeast, south—not 

included in the models when the North and South groups of states are examined 

separately), mother’s education (illiterate, literate but not completed middle school, 

middle complete or higher), mother’s current work status (not working, working), 

mother’s media exposure (low, high), and household standard of living (low, medium, 

high).

Some of the predictor variables require further explanation. In the categorization of 

mother’s education, “middle complete” is defined as eight or more completed years of 

education, and “less than middle complete” does not include illiterate. Scheduled castes 

and scheduled tribes are castes and tribes identified by the Government of India as 

socially and economically backward and in need of special protection from social 

injustice and exploitation.1

 Regarding media exposure, NFHS-1 asked questions about 

exposure to radio (at least once a week), television (at least once a week), and cinema (at 

least once a month), and NFHS-2 asked not only about these three media but also about 

reading newspapers and magazines (at least once a week). In our analysis, low media 

exposure is defined as positive responses on none or one of these four types of exposure, 

and high media exposure is defined as positive responses on two, three, or four types. 

Information on pregnancy complications was collected only in NFHS-2. Mild or 

moderate complications include: night blindness, blurred vision, swelling of the legs, 

body, or face, excessive fatigue, or anemia; and severe complications include any vaginal 

bleeding or convulsions not from fever. Standard of living is defined in terms of an index 

of ownership of various household goods, with index values grouped into high, medium, 

1

In NFHS-1, respondents were asked to report their caste/tribe, and their answers were compared with the 

official government list of scheduled castes/tribes before coding the response as scheduled caste, scheduled 

tribe, or “other.” In NFHS-2, there was no checking against the official government list. Instead, 

respondents were first asked the name of their caste/tribe, if any. Then, if they belonged to a caste/tribe, 

they were asked whether the caste/tribe was a scheduled caste, a scheduled tribe, an “other backward 

class,” or none of these. Because of these differences in the way the questions were asked, the caste/tribe 

variable is not precisely comparable between the two surveys.

8
and low standard of living.2

 The categories of the remaining predictor variables are selfexplanatory.

Human subjects informed consent 

The analysis presented in this study is based on further analysis of existing survey data 

with all identifying information removed. The survey obtained informed consent from all 

respondents before asking questions.

2

In NFHS-1, the standard of living index (SLI) was calculated by adding the following scores: house type: 

4 for pucca (high-quality construction materials throughout), 2 for semi-pucca, 0 for kachha (low-quality 

construction materials throughout); toilet facility: 4 for own flush toilet, 2 for public or shared flush toilet 

or own pit toilet, 1 for shared or public pit toilet, 0 for no facility or other facility; source of lighting: 2 for 

electricity, 1 for kerosene, gas, or oil, 0 for other source of lighting; main fuel for cooking: 2 for electricity, 

liquid petroleum gas, or biogas, 1 for coal, charcoal, or kerosene, 0 for other fuel; source of drinking water: 

2 for pipe, hand pump, or well in residence/yard/plot, 1 for public tap, hand pump, or well, 0 for other 

water source; separate room for cooking: 1 for yes, 0 for no; ownership of agricultural land: 4 for 5 acres or 

more, 3 for 2.0–4.9 acres, 2 for less than 2 acres or acreage not known, 0 for no agricultural land; 

ownership of irrigated land: 2 if household owns at least some irrigated land, 0 if no irrigated land; 

ownership of livestock: 2 if own livestock, 0 if not own livestock; durable goods ownership: 4 each for a 

car or tractor, 3 each for a scooter/motorcycle or refrigerator, 2.5 for a television, 2 each for a bicycle, 

electric fan, radio/transistor, sewing machine, water pump, bullock cart, or thresher, 1 for a clock/watch. 

Index scores range from 0–10 for low SLI to 10.5–20 for medium SLI and 20.5–55 for high SLI. 

In NFHS-2, the standard of living index was calculated by adding the following scores: house type: 4 for 

pucca, 2 for semi-pucca, 0 for kachha; toilet facility: 4 for own flush toilet, 2 for public or shared flush 

toilet or own pit toilet, 1 for shared or public pit toilet, 0 for no facility; source of lighting: 2 for electricity, 

1 for kerosene, gas, or oil, 0 for other source of lighting; main fuel for cooking: 2 for electricity, liquid 

petroleum gas, or biogas, 1 for coal, charcoal, or kerosene, 0 for other fuel; source of drinking water: 2 for 

pipe, hand pump, or well in residence/yard/plot, 1 for public tap, hand pump, or well, 0 for other water 

source; separate room for cooking: 1 for yes, 0 for no; ownership of house: 2 for yes, 0 for no; ownership of 

agricultural land: 4 for 5 acres or more, 3 for 2.0–4.9 acres, 2 for less than 2 acres or acreage not known, 0 

for no agricultural land; ownership of irrigated land: 2 if household owns at least some irrigated land, 0 for 

no irrigated land; ownership of livestock: 2 if own livestock, 0 if not own livestock; durable goods 

ownership: 4 each for a car or tractor, 3 each for a moped/scooter/motorcycle, telephone, refrigerator, or 

color television, 2 each for a bicycle, electric fan, radio/transistor, sewing machine, black and white 

television, water pump, bullock cart, or thresher, 1 each for a mattress, pressure cooker, chair, cot/bed, 

table, or clock/watch. Index scores range from 0–14 for low SLI to 15–24 for medium SLI to 25–67 for 

high SLI. 

Because the standard of living index is calculated slightly differently in NFHS-1 and NFHS-2, comparisons 

on this index between NFHS-1 and NFHS-2 are not exact. 

9
RESULTS

Table 1 shows the sample distribution of births on the main predictor variables. In the 

case of rural India as a whole, only one-fozurth of the births were delivered in a medical 

institution, another 8 percent received professional assistance for home delivery, and the 

remaining two-thirds received no professional assistance at delivery. The proportion 

receiving no assistance declined from 74 percent in NHFS-1 to 67 percent in NFHS-2. 

Mothers in the southern states were much more likely to have received professional 

assistance at delivery than those in the northern states in both surveys. Mothers of twofifths of the births in each of the two surveys received no antenatal care (ANC) visits, and 

mothers of only 37 percent of the births received the recommended three or more ANC 

visits. Mothers of 35 percent of births in NFHS-2 reported at least one severe pregnancy 

complication, and mothers of another 28 percent of the births reported a mild or moderate 

pregnancy complication. About three-fifths of the births in each of the two surveys 

occurred in villages within five km of a hospital. About one-half (in NFHS-1) to threefifths (in NFHS-2) of the births occurred in villages accessible by an all-weather road. 

The distributions for North and South separately indicate that the southern states are 

considerably more advanced than the northern states on the health and development 

indicators shown in the table. 

10
Table 1 Sample distribution of births in the three years before NFHS-1 and NFHS-2 by type of assistance at 

delivery and selected predictor variables for rural areas of All India, North India, and South India (percent) 

NFHS-1 NFHS-2 

Characteristic All India North South All India North South 

Assistance at delivery 

 No assistance 74 85 47 67 81 35

 Assistance at home 10 8 14 8 7 9

 Institutional delivery 16 7 39 25 12 56 

Antenatal care visits 

 None 41 60 10 40 63 8

 1–2 22 22 12 23 22 12

 3+ 37 19 78 37 15 80

Pregnancy complications 

 None NA NA NA 37 33 46

 Mild/moderate NA NA NA 28 27 28

 Severe NA NA NA 35 40 26

Availability of a hospital within 5 km 

 No 61 69 50 58 61 43

 Yes 39 31 50 42 39 57

Village has all-weather road 

 No 51 59 40 58 66 46

 Yes 49 41 60 42 34 54

Number of births 29,069 14,549 4,979 25,499 13,015 4,485 

NA: Not available 

Note: North India includes Bihar, Uttar Pradesh, Rajasthan, and Madhya Pradesh. South India includes 

Andhra Pradesh, Goa, Karnataka, Kerala, and Tamil Nadu. 

11
Table 2 shows unadjusted and adjusted effects of antenatal care on assistance at delivery 

for rural India as a whole. Effects are measured by relative risk ratios (RRRs) calculated 

for each survey separately from the fitted m-logit regression underlying each of the five 

models. “Unadjusted” means that effects are estimated without controlling for other 

variables, and “adjusted” means that effects are estimated with controls for other 

variables. Control variables are introduced simply by adding them to the set of predictor 

variables. In the unadjusted case, ANC is the only predictor variable. 

In the context of Table 2, relative risks may be denoted as P1/P3 and P2/P3, where P1 

denotes the predicted probability of delivery assistance at home, P2 denotes the predicted 

probability of institutional delivery, and P3 denotes the predicted probability of no 

assistance. “Predicted” means predicted from a fitted m-logit model. “No assistance” is 

designated (arbitrarily) as the reference category of the delivery assistance variable. In 

each of the five models shown in Table 2, the column labeled “assistance at home” shows 

RRRs based on values of P1/P3, and the column labeled “institutional delivery” shows 

RRRs based on values of P2/P3. In Model 1 for “assistance at home” for NFHS-2, the 

RRR of 2.24 is the ratio of the estimated value of P1/P3 for 1-2 ANC visits to the 

estimated value of P1/P3 for no ANC visits, and the RRR of 4.38 is the ratio of the 

estimated value of P1/P3 for 3+ ANC visits to the estimated value of P1/P3 for no ANC 

visits. The RRRs shown in the other columns have a similar interpretation, except that 

P2/P3 replaces P1/P3 in the case of columns pertaining to institutional delivery. 

The relative risks P1/P3 and P2/P3 are sometimes referred to as “improper odds” 

(Retherford and Choe 1993). The adjective “improper” applies when the response 

variable has three or more categories, as in Table 2. In this case, the numerator and 

denominator of the relative risk do not add to one, as they do when the response variable 

has only two categories (in which case the m-logit model reduces to the ordinary logit 

model). This “improper” aspect of relative risks makes RRRs somewhat difficult to 

interpret. Consider again the RRR of 2.24 in the first column of Table 2. Because this 

RRR is greater than one, it suggests that P1, the probability of professional delivery 

assistance at home, is higher in the case of 1-2 ANC visits than in the case of no ANC

12
Table 2 Unadjusted and adjusted effects (relative risk ratios) of antenatal care on type of assistance at delivery, rural India, NFHS-1 and NFHS-2 

Model 1 Model 2 Model 3 Model 4 Model 5 

Assistance Institutional Assistance Institutional Assistance Institutional Assistance Institutional Assistance Institutional

Characteristic at home delivery at home delivery at home delivery at home delivery at home delivery NFHS -1 Antenatal care visits  None† 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 NA NA 1–2 2.53 *** 4.59*** 2.35*** 3.92*** 2.07*** 2.97*** 2.03*** 2.91*** NA NA 3+ 4.79 *** 17.92*** 3.52*** 10.14*** 2.88*** 5.79*** 2.79*** 5.37*** NA NA Number of births 26,747 26,747 26,344 24,820NFHS -2 Antenatal care visits  None† 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1–2 2.24 *** 3.85*** 2.16*** 3.29*** 1.91*** 2.70*** 1.96*** 2.69*** 1.93*** 2.65*** 3+ 4.38 *** 17.42*** 3.49*** 9.18*** 2.71*** 6.12*** 2.66*** 5.93*** 2.61*** 5.82*** Number of births 24,333 23,056 22,762 21,655 21,640* p<.05; ** p<.01; *** p<.001† Reference category Notes: The table is based on four multinomial logistic regression models in the case of NFHS-1, and five models in the case of NFHS-2. The reference category for the response variable (type of delivery care) is "no assistance at delivery." In Model 1 (the unadjusted case), number of antenatal care visits is the sole predictor variable. The predictors in Model 2 additionally include mother's age at childbirth (in years), child's birth order, religion of household head, caste/tribe of household head, and geographic region. The predictors in Model 3 

include all those in Model 2 plus mother's education, mother's current work status, mother's media exposure, and household standard of living. The predictors in Model 4 include all those in Model 3 plus availability of a hospital within 5 km and whether the village can be accessed by an all-weather road. The predictors in Model 5 include all those in Model 4 plus 

whether the mother experience pregnancy complications. 

13
visits. But this is not necessarily the case, because P1 can decrease even when P1/P3 

increases. This can occur if both P1 and P3 decrease as the number of ANC visits 

increases, and the proportionate decrease in P3 exceeds the proportionate decrease in P1. 

This movement in opposite directions is not possible in ordinary logit regression, where 

the relative risk simplifies to the (proper) odds P/(1-P), because in this case it is not 

possible for both numerator and denominator to decrease simultaneously; i.e., if P/(1-P) 

increases, P must increase and (1-P) must decrease. 

Because of this ambiguity in the interpretation of RRRs, we also present values of P1, P2, 

and P3 as an alternative way of presenting the m-logit results. These predicted 

probabilities are presented in the form of predicted percentages born with no assistance, 

assistance at home, or institutional delivery, as shown in Figure 1 for two of the models 

in Table 2. When calculating the predicted percentages, it is necessary to assign values to 

any predictor variables other than ANC that are included in the m-logit regression, 

because the predicted percentages are affected by the values of the other predictors. 

Typically the other predictors are held constant at their mean values in the subsample of 

births for which the m-logit regression is run, and that is what is done here. Both modes 

of presentation of results—RRRs and predicted percentages—are useful. In this regard, it 

should be noted that RRRs have the advantage of not depending on the values of the 

other predictor variables. 

14
Figure 1 Adjusted effects of antenatal care on type of assistance at delivery in rural India, NFHS-1 and 

NFHS-2

7

11

13

7

11 11

8

16

23

12

23

35

50

40353025201510

None

1-2

3+

None

1-2

3+

None

1-2

3+

None

1-2

3+

Number of ANC visits

Institutional delivery

NFHS-1 NFHS-2

Assistance at home 

NFHS-1 NFHS-2

Predicted percentage 

Source: Derived from Model 4 for NFHS-1 and Model 5 for NFHS-2 in Table 2 using the PREDICT command in STATA. 

15
The RRRs shown in Table 2 are rather similar for NFHS-1 and NFHS-2, so the 

discussion here is limited to NFHS-2. Model 1 shows unadjusted effects of ANC on 

delivery assistance. RRRs rise substantially for both home assistance and institutional 

delivery as the number of ANC visits increases, but more so for institutional delivery 

than for home assistance. The RRR is especially high in the case of institutional delivery 

for 3+ ANC visits. 

Models 2-5 progressively add controls, as explained in detail in the footnote to Table 2. 

The addition of controls reduces the RRRs for assistance at home, but not greatly so. The 

reduction is greater in the case of institutional delivery. In Models 4 and 5, it is 

noteworthy that the addition of the two health-care-access variables (availability of a 

hospital within 5 km and accessibility of the village by an all-weather road) and the 

pregnancy-complication variable makes little difference in the RRRs once controls have 

been introduced for the demographic, geographic, and socioeconomic factors. The 

unimportance of the controls for health-care accessibility suggests that India’s network of 

health services is extensive enough that access to ANC and delivery services is not a 

major problem. The unimportance of pregnancy complications indicates that this variable 

is not confounding the relationship between ANC and professional assistance at delivery, 

at least not to any significant extent. (Confounding is possible because pregnancy 

complications are a common cause of both ANC and professional assistance at delivery.) 

Figure 1 shows the alternative way of presenting the m-logit results, in terms of predicted 

percentages receiving no assistance, assistance at home, or institutional delivery by 

number of ANC visits. This is done only for the full models, namely Model 4 for NFHS1 and Model 5 for NFHS-2. The predicted percentage receiving assistance at home 

increases much less dramatically than does the RRR as the number of ANC visits 

increases. This is especially true in NFHS-2, where the RRR increases from 1.93 to 2.61 

between 1-2 and 3+ ANC visits, whereas P2, the predicted percentage receiving 

assistance at home, remains constant at 11 percent for both 1-2 and 3+ ANC visits. In the 

case of institutional delivery, both the RRR and the predicted percentage receiving 

institutional delivery increase dramatically as the number of ANC visits increases. 

16
Table 3 Adjusted effects (relative risk ratios) of antenatal care on type of assistance at delivery and institutional 

delivery in rural areas of North and South India, NFHS-1 and NFHS-2 

North South 

Assistance Institutional Assistance Institutional 

Characteristic at home delivery at home delivery

NFHS-1 

 Antenatal care visits 

 None† 1.00 1.00 1.00 1.00

 1–2 2.31*** 3.16*** 1.56 2.84*** 

 3+ 3.44*** 4.87*** 2.33*** 5.43*** 

 Number of births 10,094 4,148 

NFHS-2 

 Antenatal care visits 

 None† 1.00 1.00 1.00 1.00

 1–2 2.35*** 2.84*** 1.94 2.88*** 

 3+ 3.41*** 6.11*** 3.61*** 8.54*** 

 Number of births 9,873 2,792 

* p<.05; ** p<.01; *** p<.001 

† Reference category 

Note: In the case of NFHS-1 in the left half of the table, the multinomial logistic regression models include all the 

predictors in Model 4 in Table 2. In the case of NFHS-2 in the right half of the table, the models include all the 

predictors in Model 5 in Table 2. 

Table 3 and Figures 2 and 3 show results for North and South separately. In NFHS-2, 

North and South do not differ much regarding the effect of ANC on assistance at home. 

By contrast, the effect of ANC on institutional delivery is larger in the South than in the 

North, and the predicted percentages receiving institutional delivery are higher in the 

South than in the North. It is also noteworthy that in the South, the increase in RRR, but 

not in the predicted percentage, is especially large between 1-2 and 3+ ANC visits—

again illustrating the methodological point, made earlier, that the pattern of variation in 

the RRR does not necessarily parallel the pattern of variation in the predicted percentage. 

17
Figure 2 Adjusted effects of antenatal care on type of assistance at delivery in rural areas of North and 

South India, NFHS-1

6

11

15

12 13 14

4

9

12

25

39

48

50

504540353025201510

None

1-2

3+

None

1-2

3+

None

1-2

3+

None

1-2

3+

Number of ANC visits

Institutional delivery

North South

Assistance at home 

North South

Predicted percentage 

Source: Derived from the multinomial logistic regression models underlying Table 3 using the PREDICT command in STATA. 

18
Figure 3 Adjusted effects of antenatal care on type of assistance at delivery in rural areas of North and 

South India, NFHS-2

6

10 12

7

9 10

7

15

25

29

45

62

0

70605040302010

None

1-2

3+

None

1-2

3+

None

1-2

3+

None

1-2

3+

Number of ANC visits

Institutional delivery

North South

Assistance at home 

North South

Predicted percentage 

Source: Derived from the multinomial logistic regression models underlying Table 3 using the PREDICT command in STATA. 

19
Table 4 Adjusted effects (relative risk ratios) of selected predictor variables on type of assistance at delivery in 

rural India, NFHS-2 

Assistance Institutional 

Characteristic at home delivery

Pregnancy complications 

 None† 1.00 1.00

 Mild/moderate 1.25** 1.16*

 Severe 1.36*** 1.31*** 

Availability of a hospital within 5 km 

 No† 1.00 1.00

 Yes 1.06 1.31*** 

Village has all-weather road 

 No† 1.00 1.00

 Yes 1.43*** 1.32*** 

Number of births 21,640 

* p<.05; ** p<.01; *** p<.001 

† Reference category 

Note: The underlying multinomial logistic regression model is Model 5 for NFHS-2 in Table 2. 

Table 4 and Figures 4 and 5 show adjusted effects of pregnancy complications and the 

two health-care-access variables on assistance at delivery, based on Model 5 for NFHS-2 

in Table 2, which controls for all of the other predictor variables (including number of 

ANC visits) considered in our study. The table and figures show that the effects of 

pregnancy complications and the two health-care-access variables are in the expected 

direction but small. By contrast, the effects of ANC on assistance at delivery (derived 

from the same m-logit regression) are much larger, as shown earlier. 

20
Figure 4 Adjusted effects of pregnancy complications on type of assistance at delivery, NFHS-2

8

9 10

23

25 26

50

3025201510

None Mild or

moderate

Severe None Mild or

moderate

Severe

Pregnancy complications

Institutional delivery Assistance at home 

Predicted percentage 

Source: Multinomial logistic regression model underlying Table 4 using the PREDICT command in STATA. 

21
Figure 5 Adjusted effects of health-care-access variables on type of assistance at delivery, NFHS-2

9 9

8

10

23

27

24

26

50

3025201510

No Yes No Yes No Yes No Yes

Institutional delivery

Hospital <5 km All-weather road

Assistance at home 

Hospital <5 km All-weather road

Predicted percentage 

Source: Multinomial logistic regression model underlying Table 4 using the PREDICT command in STATA. 

22
CONCLUSION 

The analysis indicates that antenatal care has a large effect on type of professional 

assistance at delivery. The effect on assistance at home is comparatively small, and the 

effect on institutional delivery is comparatively large, even after controlling for a large 

number of demographic, geographic, socioeconomic, and health-care-access variables. 

The effect of ANC on institutional delivery is larger in the South than in the North, and 

the predicted percentages receiving institutional delivery are higher in the South than in 

the North. With other variables controlled, the effects of pregnancy complications and 

health-care-access variables on assistance at delivery are in the expected direction but 

small. By contrast, the effects of ANC on assistance at delivery are large. 

Overall, the analysis indicates that improvements in antenatal care coverage are an 

effective means for increasing professional assistance at delivery, and especially for 

increasing institutional delivery. Since three out of four deliveries in rural India still occur 

at home, most without any professional assistance, efforts to train birth attendants, 

increase the availability of trained midwives, and promote home visits by paramedics for 

antenatal care need further strengthening.

23
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