closing the gap on institutional delivery in northern ......religion hindu vs. other demographics...

1

Closing the gap on institutional delivery in northern India: A case study

of how integrated machine learning approaches can enable precision

public health

Supplementary Material

This document is a supplement to the paper titled, “Closing the gap on institutional delivery in northern India: A case study of how integrated machine learning approaches can enable

precision public health.” It contains the following sections:

1. Analytical sample for the household survey

2. Overall sample descriptive statistics for the household survey.

3. Predictive model results for the household survey.

4. Analytic details, including variable list and results, for the causal model built on the

Community Behavior Tracking Survey (CBTS).

5. Population attributable fraction for the causal model using the household dataset

Analytical sample for the household survey

The figure below illustrates the complete analytical sample flow to report the number of

participants considered in each stage of the household survey and in the analysis using this

data.

BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any relianceSupplemental material placed on this supplemental material which has been supplied by the author(s) BMJ Global Health

doi: 10.1136/bmjgh-2020-002340:e002340. 5 2020;BMJ Global Health, et al. Huang VS

3

Table 1. Household survey variables and corresponding survey question text

Variable Response

Options

Question Text

Demographics

Education 0-4 years, 5-9

years, 10-12

years, 13+ years

Demographics

Parity 1, 2, 3, 4+ Demographics

Religion Hindu vs. Other Demographics

Caste ST, SC, OBC,

none of these

Demographics

Income Little (<=40000)

vs. Lot (> 40000)

In the last 12 months, how many rupees

total did you earn from working?

Financial

Insecurity

2-item

composite; 1-5

Likert scale

How often do you worry about not having enough money to pay for food and

fuel?

How often do you worry about not having enough money to pay for medical

expenses?

Electricity in

home

Yes vs. No Do you have light (use Hindi word for light = electricity) in the house?

Household type Nuclear vs.

Joint/Other

Demographics

Internal Beliefs

Opinion of

Hospital Facilities

7-item

composite of 1-

5 Likert scale;

median split

into Low vs.

High

We can never know how much money we will need for hospital delivery.

There is no food to eat at the hospital.

There is no safe place for woman to sleep overnight at the hospital.

There is no decent place for family to sleep overnight at the hospital.

It is difficult to come home from hospital after birth.

There is no one to cook and do housework if we go to the hospital.

There is no one to take of young and old at home if we go to the hospital.

Opinion of

Hospital Services

6-item

composite of 1-

5 Likert scale;

median split

into Low vs.

High

Hospital staff listens to patients’ needs. Hospitals do surgery even when not needed.

Hospitals only take care of people who give money.

Hospital is full of diseases that we can catch.

Hospital staff are fierce and short-tempered.

Hospital staff are incompetent and always make errors.

Rank Importance

of Hospital

Delivery

Important vs.

Unimportant

If pregnant woman can only do one thing, which one should she do? Planning for

hospital delivery

Risk Perception of

Childbirth

(1-10) median

split into Low vs.

High

Again, out of every 10 women who give birth, how many women had problems

during childbirth? Problems like prolonged labor for more than 12 hours, or too

much bleeding, or baby in wrong position, and so on.

Worry about

Delivery Problems

Little vs. Lot Before this baby was born, did you worry that you may have problems

during delivery?

Perception of

Hospital Safety

Hospital safer

vs. Home safer

Do you think childbirth at home is safer or childbirth at hospital is safer?

Nurse Gives

Injection to Make

Delivery Easier

Agree vs.

Disagree

Hospital nurse can use injection to make delivery faster and easier.



4

Hospital is Not

Necessary if Birth

Attendant is Good

Agree vs.

Disagree

There is no need to go to the hospital for delivery if there is an highly skilled Dai

in the village.

Hospital is Not

Necessary if Past

Home Delivery

Agree vs.

Disagree

There is no need to go to the hospital for delivery if woman has delivered strong

baby at home in the past.

Pregnant Women

Attract Evil Spirits

Agree vs.

disagree

Pregnant woman going out will attract evil spirits.

False Beliefs

about ANC

checkups

3-item

composite of 1-

5 Likert scale;

Few (<=4.33) vs.

Many (>4.33)

Vaccinations for woman during pregnancy can hurt baby.

Pregnant woman going out will attract evil spirits.

There is no need for checkups during pregnancy if woman is strong.

Barriers to ANC

checkups

5-item

composite; Few

vs. Many

It is too far to travel to the place for checkup.

It is too expensive to travel to the place for checkup.

There is no privacy at the place where women go for checkup.

There is no one to cook and do housework if women go for checkup.

There is no one to take care of young and old at home if women go for checkup.

Knowledge of IFA Percent correct

recall (0-100)

Do you know why pregnant women are asked to take iron "ki goli"?

Agency 10-item

composite; 1-5

Likert scale

People's misfortunes are caused by their own mistakes.

When bad things happen, it is God's will.

Other people decide what I can and cannot do.

I feel helpless in dealing with the problems of life.

When I want to do something, I will find the strength to do it.

I want others to make decisions for me instead of me having to make decisions.

I do what everyone else in my community would do.

I do whatever my family wants of me.

Problems will go away if we just ignore them.

I have bad luck.

Insecurity 2-item

composite; 1-5

Likert scale

I am afraid of things I don't know.

I avoid situations with uncertain outcomes.

Conscientiousness 3-item

composite; 1-5

Likert scale

I need to know the reason for doing something before I do it.

I act after thinking.

I lose things or forget where I put things.

Empathy 1-5 Likert scale I can feel other people's emotions.

Openness 2-item

composite; 1-5

Likert scale

I like meeting new people.

I like to try new things and new ideas.

Optimism 1-5 Likert scale There is some good in everybody.

Neuroticism 1-5 Likert scale I am sensitive and anxious.

Structural

Social Norms Low (1-7) vs.

High (8-10)

Think about the families you know who had newborns in the past few years. Out

of every 10 women who gave birth, how many had given birth at a hospital?

Hospital Distance 0-20 min vs. 21-

40 min vs. 40+

min

How many minutes or hours does it take you to get from your home to the

nearest hospital?

Labor Start Time Middle of the

night vs. Day vs.

Evening

Around what time that day did you first start having labor pain?

Money Borrowed None vs. Some Did you have to take a loan to pay for this birth?



5

Incentive

Awareness

Yes vs. No BEFORE this birth, did you know that the government will give families money if

woman delivers baby in hospital instead of home?

Influencers

Discussed

Delivery Location

with ASHA

True vs. False Did you discuss with ASHA about where you should give birth?

Primary Decision

Maker

Self vs. Husband

vs. Mother-in-

law vs. Other

Who in your family decided where you should give birth?

People for Social

Support

Few vs. Many How many people can you talk openly about your worries?

Number of ASHA

Home Visits

None vs. 1-2 vs.

3-4 vs. 5+

About how many times did ASHA come here during your pregnancy?

Behavior

Pregnancy

Registration

Not registered

vs. 1st trimester

vs. 2nd

trimester vs. 3rd

trimester

Pregnancy registration date

Delivery Plan Planned ahead

of time vs. Last

minute decision

Did you plan to deliver baby at the location the baby was born or was it a last-

minute decision?

Number of ANC

Checkups

0 - 9 How many checkups did you get before childbirth?

Take IFA during

pregnancy

None vs. Less

than

recommended

amount vs.

Recommended

amount or more

How many iron “ki goli" did you take during your pregnancy?

Household survey descriptive statistics

Descriptive statistics for the overall sample of mothers from the household survey are

presented in Table 2. These estimates are weighted to account for the TSU oversample.

Table 2. Overall Sample Descriptive Statistics Variable % SE

Institutional Delivery

Home Delivery 17.8% 0.5%

Hospital Delivery 82.2% 0.5%

Education 0-4 years 39.1% 0.7%

5-9 years 28.9% 0.6%

10-12 years 19.8% 0.6%

13+ years 12.2% 0.5%

Parity 1 34.9% 0.7%

2 27.2% 0.6%

3 17.9% 0.5%

4+ 19.9% 0.6%

Religion Hindu 84.1% 0.5%

Other 15.9% 0.5%



6

Caste Scheduled tribe 3.4% 0.2%

Scheduled caste 28.0% 0.6%

Other backward class 56.9% 0.7%

None of the above 11.7% 0.4%

Income Little 73.7% 0.6%

Lot 26.3% 0.6%

Electricity in Home No 20.2% 0.5%

Yes 79.8% 0.5%

Household Type Joint or Other 65.9% 0.7%

Nuclear 34.1% 0.7%

Opinion of Hospital Facilities High 44.4% 0.7%

Low 55.6% 0.7%

Opinion of Hospital Services High 52.8% 0.7%

Low 47.2% 0.7%

Rank Importance of Hospital Delivery Important 39.5% 0.7%

Unimportant 60.5% 0.7%

Risk Perception Childbirth High 67.5% 0.7%

Low 32.5% 0.7%

Worry About Delivery Problems Little 37.7% 0.7%

Lot 62.3% 0.7%

Perceptions of Hospital Safety Home safer 17.2% 0.5%

Hospital safer 82.8% 0.5%

Nurse Gives Injection to Make Delivery Easier

Agree 72.8% 0.6%

Disagree 27.2% 0.6%

Hospital Not Necessary if Skilled Dai Agree 42.5% 0.7%

Disagree 57.5% 0.7%

Hospital Not Necessary if Past Home Delivery Agree 44.1% 0.7%

Disagree 55.9% 0.7%

Pregnant Women Attract Evil Spirits Agree 60.8% 0.7%

Disagree 39.2% 0.7%

False Beliefs about ANC Checkups Few 45.5% 0.7%

Many 54.5% 0.7%

Barriers to ANC Checkups Few 51.4% 0.7%

Many 48.6% 0.7%

ID is the Social Norm Low 33.6% 0.7%

High 66.4% 0.7%

Minutes to Hospital 20 minutes or less 49.5% 0.7%

20-40 minutes 39.4% 0.7%



7

More than 40 minutes 11.1% 0.4%

Labor Start Time Day 46.4% 0.7%

Evening 27.6% 0.6%

Middle of the night 26.0% 0.6%

Money Borrowed None 82.0% 0.5%

Some 18.0% 0.5%

ID Incentive Awareness Yes 85.4% 0.5%

No 14.6% 0.5%

Discuss Location with ASHA No 63.5% 0.7%

Yes 36.5% 0.7%

Primary Decision Maker Self 26.4% 0.6%

Husband 37.1% 0.7%

Mother-in-law 14.7% 0.5%

Other 21.8% 0.6%

Social Support Few 29.6% 0.6%

Many 70.4% 0.6%

Number ASHA Visits 0 times 16.6% 0.5%

1-2 time 17.4% 0.5%

3-4 time 29.0% 0.6%

5+ times 37.0% 0.7%

Amount IFA Taken None 19.1% 0.6%

Less than recommended amount 66.6% 0.7%

Recommended amount or more 14.3% 0.5%

Trimester of Pregnancy Registration Not registered 7.6% 0.4%

1st trimester 56.4% 0.7%

2nd trimester 30.9% 0.6%

3rd trimester 5.1% 0.3%

Delivered in Planned Location No 33.0% 0.7%

Yes 67.0% 0.7%

Mean Estimate SE

Knowledge of IFA 51.41 0.409

Agency 3.0095 0.00680

Insecurity 3.8089 0.01272

Conscientiousness 3.6837 0.00884

Empathy 3.4038 0.01611

Openness 3.9893 0.01200

Optimism 3.8529 0.01386

Neuroticism 3.5432 0.01578

Number of ANC Checkups 2.4643 0.02439

Predictive Model Results

A logistic regression model was conducted using the outcome variable of location of delivery

(home vs. facility). The final set of 41 predictor variables (variable selection process is described

in the manuscript) was included in the model. Given the high number of predictor variables, a p

value of .01 (i.e., 99% confidence interval) was used as the threshold for statistical significance.



8

Mothers who indicated having a C-section that was planned ahead of time were excluded from

the analysis (n = 131). Missing data was removed using listwise deletion, leaving a final analytic

sample of 5,613 mothers. The model was weighted to account for the TSU oversample.

The logistic regression model correctly classified 85.8% of cases; 41.3% of home deliveries were

correctly classified and 95.5% of ID were correctly classified. The model explained 41% of the

variance in delivery location (Nagelkerke R2 = .410). Sixteen predictors were statistically

significant. These findings are displayed in Figure 2 and table 3.

The predictive model converges with past research to show that several demographic and

structural factors are associated with delivery at a healthcare facility (institutional delivery or

ID). Mothers with at least 10 of education are more likely to deliver in a hospital than those

with fewer than four years of education (< 4 years vs. 10-12 years, OR = 2.096, CI = 1.411,

3.113; < 4 years vs. 13+ years, OR = 1.992, CI = 1.160, 3.420). First-time mothers are more likely

to deliver in a hospital than those who have had two (OR = 0.360, CI = 0.256, 0.505), three (OR =

0.372, CI = 0.258, 0.536), or four or more previous deliveries (OR = 0.292, CI = 0.202, 0.421).

ASHA visits (Accredited Social Health Activists, i.e., community health workers) also have an

impact on ID; mothers who receive zero (OR = 0.578, CI = 0.373, 0.896) or one to two (OR =

0.598, CI = 0.426, 0.839) ASHA home visits are less likely to deliver in a hospital than those who

receive 5 or more visits. Additionally, living far from the hospital (OR = 0.605, CI = 0.420, 0.872),

not having electricity in the home (OR = 0.757, CI = 0.576, 0.996), and attending few ANC

checkups (OR = 1.162, CI = 1.068, 1.263) all decrease the odds of a mother having a facility

delivery.

One of the strongest predictors of ID was having a delivery plan. Mothers who said they

delivered in their planned location were far more likely to have delivered in a hospital than

those who said it was a last-minute decision (OR = 4.912, CI = 3.840, 6.284). Several new factors

associated with ID were also identified. Mothers who believe home is safer than the hospital

are much less likely to deliver in a facility (OR = 0.232, CI = 0.176, 0.306). Additionally, mothers

who are unaware of ID incentives are also less likely to deliver in a hospital (OR = 0.458, CI =

0.335, 0.627). Perceptions that ID is the social norm in a woman’s community increases the odds that she will deliver there (OR = 1.763, CI = 1.383, 2.248).

In the main variance decomposition analysis, we attempted to identified variance of delivery

location attributable to geographical factors (i.e., district and block). We also looked at ASHAs

as an additional variable. With this added variable, we found that 31% of the variance in

delivery location can be attributed to geographical levels (8% to district, 9% to block and 14% to

ASHA), with residual variance accounting for 69% of the variance in delivery location. This is not

surprising, as many of the individual-level factors are related to ASHAs.



9

Figure 2. Predictive model results. Values represent the odds of facility delivery; errors bars

represent 99% confidence intervals.

Table 3: Predictive model results OR LL UL

Education (<4 vs. 5-9) 1.325 0.995 1.766

Education (<4 vs. 10-12) 2.096 1.411 3.113

Education (<4 vs. 13+) 1.992 1.160 3.420

Parity (1 vs. 2) 0.360 0.256 0.505

Parity (1 vs. 3) 0.372 0.258 0.536

Parity (1 vs. 4+) 0.292 0.202 0.421

Religion (Other vs. Hindu) 1.271 0.924 1.747

Education (<4 vs. 5-9)

Education (<4 vs. 10-12)

Education (<4 vs. 13+)

Parity (1 vs. 2)

Parity (1 vs. 3)

Parity (1 vs. 4+)

Religion (Other vs. Hindu)

Caste (ST vs. SC)

Caste (ST vs. OB)

Caste (ST vs. none)

Income (Little vs. Lot)

Financial Insecurity

Electricity (Yes vs. No)

Household Type (Nuclear vs. Joint)

Opinion of Hosp. Facilities (Low vs. High)

Opinion of Hosp. Services (Low vs. High)

Rank Delivery (Unimportant vs. Important)

Risk Perception (Low vs. High)

Worry (Lot vs. Little)

Safety (Hospital Safer vs. Home Safer)Nurse Gives Injection to Make Delivery Easier

(Disagree vs. Agree)Unnecessary if Good Dai (Disagree vs. Agree)

Unnecessary if Past Home (Disagree vs. Agree)

Evil Spirits (Disagree vs. Agree)False Beliefs about ANC Checkups (Few vs.

Many)Barriers to ANC Checkups (Few vs. Many)

Knowledge of IFA

Agency

Insecurity

Conscientousness

Empathy

Openness

Optimism

Neuroticism

Social Norms (Low vs. High)

Hosp. Distance (<20 min vs. 20-40 min)

Hosp. Distance (<20 min vs. 40+ min)

Labor Time (Middle of Night vs. Day)

Labor Time (Middle of Night vs. Evening)

Money Borrowed (None vs. Some)

Incentive Awareness (Yes vs. No)

Discuss with ASHA (No vs. Yes)

Decision Maker (Self vs. Husband)

Decision Maker (Self vs. MIL)

Decision Maker (Self vs. Other)

Social Support (High vs. Low)

ASHA Visits (5+ vs. None)

ASHA Visits (5+ vs. 1-2)

ASHA Visits (5+ vs. 3-4)

Pregnancy Registration (none vs. 1st tri)

Pregnancy Registration (none vs. 2nd tri)

Pregnancy Registration (none vs. 3rd tri)

Delivery plan (last minute vs. planned)

Num. ANC checkups

Took IFA (0 vs. some)

Took IFA (0 vs. 100+)

0.010 0.100 1.000 10.000

OR (log scale)



10

Caste (None vs. ST) 0.649 0.325 1.295

Caste (None vs. SC) 0.809 0.511 1.281

Caste (None vs. OBC) 0.87 0.571 1.326

Income (Little vs. Lot) 0.849 0.637 1.132

Financial Insecurity 0.916 0.813 1.032

Electricity (No vs. Yes) 1.321 1.004 1.738

Household Type (Nuclear vs. Joint) 1.183 0.909 1.539

Opinion of Hosp. Facilities (Low vs. High) 0.913 0.695 1.198

Opinion of Hosp. Services (Low vs. High) 1.567 1.196 2.054

Rank Delivery (Unimportant vs. Important) 1.437 1.121 1.841

Risk Perception (Low vs. High) 0.944 0.722 1.233

Worry (Lot vs. Little) 1.033 0.803 1.329

Safety (Home Safer vs. Hospital Safer) 4.304 3.267 5.67

Nurse Gives Injection to Make Delivery Easier (Disagree vs. Agree) 1.152 0.875 1.516

Unnecessary if Good Dai (Disagree vs. Agree) 0.843 0.626 1.135

Unnecessary if Past Home (Disagree vs. Agree) 0.987 0.734 1.325

Evil Spirits (Disagree vs. Agree) 1.036 0.804 1.335

False Beliefs about ANC Checkups (Few vs. Many) 1.023 0.780 1.342

Barriers to ANC Checkups (Few vs. Many) 1.105 0.839 1.454

Knowledge of IFA 0.998 0.994 1.003

Agency 0.884 0.683 1.145

Insecurity 0.908 0.778 1.060

Conscientousness 0.932 0.755 1.152

Empathy 0.970 0.868 1.084

Openness 0.991 0.843 1.164

Optimism 0.957 0.840 1.091

Neuroticism 1.083 0.970 1.209

Social Norms (Low vs. High) 1.763 1.383 2.248

Hosp. Distance (<20 min vs. 20-40 min) 0.800 0.620 1.033

Hosp. Distance (<20 min vs. 40+ min) 0.605 0.420 0.872

Labor Time (Day vs. Evening) 0.715 0.54 0.948

Labor Time (Day vs. Middle of Night) 0.682 0.512 0.908

Money Borrowed (None vs. Some) 1.794 1.293 2.488

Incentive Awareness (No vs. Yes) 2.182 1.594 2.987

Discuss with ASHA (No vs. Yes) 1.100 0.841 1.437

Decision Maker (Husband vs. Self) 0.502 0.371 0.678

Decision Maker (Husband vs. MIL) 0.707 0.469 1.066

Decision Maker (Husband vs. Other) 0.677 0.487 0.939

Social Support (High vs. Low) 1.193 0.912 1.560

ASHA Visits (None vs. 1-2) 1.034 0.668 1.601

ASHA Visits (None vs. 3-4) 1.529 0.993 2.354

ASHA Visits (None vs. 5+) 1.729 1.116 2.681



11

Pregnancy Registration (none vs. 1st tri) 0.941 0.528 1.677

Pregnancy Registration (none vs. 2nd tri) 1.087 0.624 1.896

Pregnancy Registration (none vs. 3rd tri) 1.097 0.551 2.184

Delivery plan (last minute vs. planned) 4.912 3.840 6.284

Num. ANC checkups 1.162 1.068 1.263

Took IFA (0 vs. some) 1.326 0.983 1.788

Took IFA (0 vs. 100+) 1.623 1.041 2.533

Causal Model: CBTS Data

About Data Source:

The Community Behavior Tracking Survey (CBTS) is a periodic rolling short sample survey

designed and conducted by the Uttar Pradesh Technical Support Unit in India to track maternal

care behaviors and practice in 100 blocks of 25 districts in Uttar Pradesh. The first round of

CBTS was completed in February 2015. From this data set, after removing duplicate entries, we

selected women who reported that they had live births (not abortion or stillbirth) in the past 2

months at the time of survey (“Group 1” in the Survey), whose babies survived at least one day

and whose babies at the time of survey were less than 1 day old (n=49,840). See Figure 3 for

the full analytical sample flowchart. For the purpose of modeling institutional delivery, we

analyzed the answers in demographics, antenatal care and birth preparedness and excluded

most questions in post-natal and newborn care, and reproductive health and family planning.

The variables included for modeling and their response options in the processed data set are

presented in Table 4.



13

Table 4. CBTS variables using in causal modeling. Variable Response Options

Demographics

Age 0-25 years old, 25+ years old

Place of Stay Resident, Visitor

Religion Hindu, Non-Hindu

Caste Schedule Caste / Schedule Tribe, Do Not Know, Other Backward Caste, Upper Caste

Education 0-4 years, 5-9 years, 10+ years

Below Poverty Line (BPL) Non-BPL, BPL

Self-Help Group Non-Member, Member

Antenatal Care

Trimester of Pregnancy Registration Not Registered, Trimester 1, Trimester 2, Trimester 3

Number of Tetanus Toxoid injections 0, 1, 2+

Received IFA during Pregnancy No, Yes

Take IFA during Pregnancy No, Yes

Number of Checkups in First and Second Trimester 0,1, 2+

Number of Checkups in Third Trimester 0,1, 2+

Number of ASHA Home Visits in First and Second Trimester 0,1, 2+

Number of ASHA Home Visits in Third Trimester 0,1, 2+

Blood Pressure Check in Third Trimester No, Yes, Do Not Know

Hb Check in Third Trimester No, Yes, Do Not Know

Family Planning Advice during Pregnancy No, Yes

Delivery Plan Not Planned, Facility, Home

Vehicle Identified No, Yes

Who Conducted Delivery Doctor, Trained Facility Staff, Other

Treated for Complications No Complication, Complication Not Treated, Complication Treated

Normal Delivery Normal, Cesarean/Assisted

Child Registered No, Yes

Delivery Location Home, Private Facility, Public Facility

Several variables are designated as “input only” (i.e., cannot be caused by any other variables); these are age, religion, and caste. In addition, if a variable A (e.g., Hb check in third trimester)

cannot feasibly occur prior to a variable B (e.g., number of checkups in first and second

trimester), a restriction is imposed such that A is not allowed to be a causal factor of B.

The causal model we obtained showed that having a delivery plan, pregnancy checkups and

education level are all directly causal to public hospital delivery (see Figure 4). In addition,

hemoglobin checkup (presumably as a proxy for the quality of the checkup) and having

identified a transportation vehicle are also causal. Of these, having a delivery plan is by far the

most important. A mother is more than 6 times more likely to deliver at a public hospital than

at home if she has a delivery plan. The more education the woman has, the more likely it is

(OR=2.9) that she will deliver at a public hospital. Vehicle identification also increases the odds

ratio to 1.9. Various other antenatal events such as checkups, frontline health worker (ASHA)

home visits are also significant, albeit to a lesser degree.



14

Figure 4. Structural model for CBTS data



15

Population attributable fraction for the causal model using the household dataset

Table 5. PAF from causal model. Risk factor (reference) is bolded in second column. Prevalence

refers to the prevalence of the risk factor in the sample. PAF is estimated with simulated

frequency using the causal model. PAF of a variable is interpreted as the proportion of home

deliveries that is preventable with an intervention to the risk factor in that variable. Risk ratio is

unadjusted.

Variable

Risk factor

(reference) Intervention Risk Ratio

95% lower

CI

95% upper

CI Prevalence PAF*

Education 0-4 vs. 10+ 2.49 2.38 2.60

42.3% 46.8%

vs. 5-9 1.59 1.53 1.65

Delivery plan FALSE vs. TRUE 3.52 3.46 3.58 33.7%% 45.9%

Parity Not first

time vs. First time 1.69 1.63 1.76 65.6% 31.2%

Safety Home safer vs. Hospital safer 3.00 2.92 3.11 17.9% 26.4%

Incentive Awareness No vs. Yes 2.26 2.20 2.33 14.1% 15.1%

Num. ANC checkups 0 vs. 1-2 1.26 1.21 1.30

17.3% 9.5%

vs. 3+ 1.35 1.30 1.41

Social norms Low vs. High 1.22 1.18 1.29 36.1% 7.5%

Num. ASHA visits 0

vs. 1-2 1.11 1.04 1.15

16.7% 5.2%

vs. 3+ 1.22 1.08 1.28

Electricity No vs. Yes 1.21 1.00 1.31 23.2% 4.6%

Caste Scheduled vs. Scheduled 1.06 1.02 1.10 32.2% 2.0%

Took IFA None

vs. <

recommended 1.04 1.00 1.13 18.6% 1.3%

vs.

recommended+ 1.03 0.99 1.22

Hosp. distance <20 min vs. 20-40 min 1.00 0.96 1.04

49.2% 0.1%

vs. 40+ min 1.00 0.97 1.04

Labor time 6am-6pm vs. 6pm-midnight 1.00 0.96 1.04

47.7% 0.0%

vs. midnight-6am 1.00 0.96 1.04

Opinion of hosp.

services Low

vs. High 1.00 0.97 1.04 47.2% 0.0%

Decision maker Husband

vs. MIL 1.00 0.97 1.03

36.6% 0.0% vs. Other 1.00 0.97 1.03

vs. Self 1.00 0.97 1.03

Rank delivery Unimportant vs. Important 1.00 0.97 1.04 61.9% 0.0%

Money borrowed None vs. Some 1.00 0.97 1.03 81.8% 0.0%



16



closing the gap on institutional delivery in northern ......religion hindu vs. other demographics...

Documents