Closing the gap on institutional delivery in northern ......Religion Hindu vs. Other Demographics...
Transcript of Closing the gap on institutional delivery in northern ......Religion Hindu vs. Other Demographics...
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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.
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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.
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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?
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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%
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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%
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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.
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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.
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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)
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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
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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.
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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.
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doi: 10.1136/bmjgh-2020-002340:e002340. 5 2020;BMJ Global Health, et al. Huang VS
14
Figure 4. Structural model for CBTS 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
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%
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
16
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