Sharma_Sicure

advertisement
Evaluation of a health promotion
intervention to improve maternal
health in rural Nepal
PhD: Mixed–methods evaluation of a
maternity care intervention in rural
Nepal
S Sharma, E Sicuri, J Belizan, E van Teijlingen,
Padam Simkhada, Jane Stephens
Spanish Stata Users Group meeting, Barcelona September 2012
Leading research at:
Leading research at:
Overview of talk
•
Nepal and intervention background
•
Maternal and Rural Health Care Issues
•
Evaluation of the intervention project in Nepal:
Methodology
Some early findings
•
Next steps
Leading research at:
Objective of the PhD
To evaluate using mixed-methods:
1. Improved knowledge and increased uptake of
reproductive health, antenatal and postnatal care
services
2. Improved capacity of community to identify, negotiate
and solve health related problems relating to maternal
and child health
3. What are the range of barriers to accessing care
4. To determine if the intervention is cost-effective – we
first measure the efficacy of the intervention
Leading research at:
Nepal
• Large rural population, majority Hindu (80.6%)
• Land-locked between India & China; 240 peaks over
6,096 m
• GDP about $1,200 per person per year
• Nepal's MMR better than India, Pakistan and Bangladesh
with 415 deaths in 2000 to 170 in 2010 per 100,000 live
births.
Leading research at:
Leading research at:
Green Tara Nepal (GTN) Health
promotion intervention:
Improving maternity care in rural
Nepal
Leading research at:
2007 Green Tara Nepal Intervention 2012
Evaluation (quantitative and
qualitative)
Leading research at:
Health Promotion Cycle
Needs
assessment
Evaluation
Health
Promotion
Cycle
Implementation
Programme
Planning
Leading research at:
Results of Needs assessment
• High burden of preventable disease, most is avoidable
through health promotion
• Over 70% deliveries take place at home
• Lack of knowledge/information
• ANC uptake is low, only 28% have 4 visits
• Uptake of ANC strongly influenced by socio-cultural factors
• The family is very influential: the mother-in-law and daughterin-law relationship influences ANC uptake
Leading research at:
Health Promotion Groups
• Total Participants n=1100 (mostly
women aged 15-49; with children less
than 2 years old but also Male=56;
Mother-in-law=138;Dalit=7)
• Groups n=40, covering all villages
• 22 mothers-in-law groups
• Visited 134 households to support
women most in need
• Mobile phone given to several groups:
- emergency call ambulance
- communicate with GTN staff
Leading research at:
Health Promotion (HP) Groups
• The project supported in 64
government’s clinics
• 145 warm-baby blankets
• Monitoring of Pregnant women
and under 2 years Children
Leading research at:
Before & after study with controls
2006
2007
2008
2010
2012
Preparatory work
Control community
Intervention community
Baseline information
Mid-term evaluation
Final evaluation
I
n
t
e
r
v
e
n
t
i
o
n
Baseline information
Mid-term evaluation
Final evaluation
Leading research at:
Study design
- controlled before and after
- repeated cross-sections
- non-randomised study
- 833 women of childbearing age (either participating to health
promotion activities or not) were interviewed in 4 village
development communities included in two surveys in 2008
(baseline) and 2010 (mid-term evaluation)
N
2008
2010
INT
208
217
CON
204
204
Leading research at:
Control
Intervention area
Leading research at:
Control and Intervention areas selection
Visited 6 different communities, two were selected:
•Access
•Health problems/needs
•Political commitment of local leaders
•Advice District Public Health office
•Distance to Kathmandu + 20 km
•No statistically significant differences between individuals in
treatment and control groups
Leading research at:
Demographics of respondents
women 15-49, last child<2years of age
N=833
(intervention +
control)
Age at marriage
Age at first
pregnancy
up to 14 years
15-19 years
20-24 years
25-29 years
30 and above years
3%
53.12%
37.75%
5.53%
0.60%
0.84%
40.26%
48.67%
9.03%
0.84%
Leading research at:
The efficacy of the GTN intervention?
In order to ascertain the impact of the
intervention:
a. we used Difference in Difference
approach
b. we control for factors such as socioeconomic factors, age, number of
children in the household and
education
Leading research at:
Efficacy is determined by the change in
ANC uptake due to the intervention
• Difference in Difference analysis
whereby intervention has had an
impact on health uptake
behaviour (i.e. ANC visits)
i.e. E = (TA-TB) – (CA-CB)
Treatment
Before (TB)
Control
Before
(CB)
Treatment
After
(TA)
Control After
(CA)
Leading research at:
Leading research at:
Data base structure for diff-in-diff
analysis
date
treat
after
treata~r
Jan
Jan
Feb
Jan
Jan
08
08
08
08
08
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
Jan
Jan
Apr
Apr
Apr
08
08
10
10
10
1
0
1
1
0
0
0
1
1
1
0
0
1
1
0
07 Apr 10
16 Apr 10
08 Apr 10
1
0
1
1
1
1
1
0
1
anc2_5
identify
survey
idno
66.
88.
196.
314.
371.
yes
yes
no
yes
yes
intervent
control
intervent
control
control
1
1
1
1
1
459
66
507
19
14
20
10
04
10
11
391.
395.
418.
421.
533.
yes
yes
yes
yes
yes
intervent
control
intervent
intervent
control
1
1
2
2
2
317
137
137
14
278
27
12
11
07
17
574.
623.
810.
yes
no
yes
intervent
control
intervent
2
2
2
19
317
66
Leading research at:
PCA: to construct SES variable
• We construct a SES based on assets by using
Principal Component Analysis PCA,
• PCA assets for SES variable were: ownership of
household assets (goods such as bicycle,
motorcycle, goat, car), type of access to hygienic
facilities (sources of drinking water, types of toilet),
number of rooms, and construction materials used
in the dwelling
• Stata: pca dwelling_roof goat1 landgreater3 ownpiped commpiped
nonpipedopensource pipedshared flushtoilet pitlatrine othertoiletor_no_t
ratioroom_person source_biogas source_lpggas cookersource_elec bicy1_28
mob1_23 friz1_23 com1_23 motorised_veh
Leading research at:
Definition of efficacy in Difference in difference analysis
•
To analyse the efficacy of the intervention on
different outcomes of health promotion activities
•
We expect: Measured aspect of health seeking
behaviour should improve in the intervention
area relative to the control.
Non-clinical outcomes chosen ANC uptake:
1. If they attended ANC at least once
2. When? (if during first trimester)
3. How many ANC visits?
Results of Diff in Diff
ANC attendance at least once
Logistic
regression
N=830
P<0.05
Leading research at:
ANC
attendance
Odds Ratio
P
95%Conf Interval
Treatment
1.40
0.27
0.76
2.59
After
1.36
0.34
0.73
2.54
after*treatm
ent
6.05
0.00
1.98
18.48
SES
2.94
0.00
1.95
4.43
Age
0.90
0.00
0.86
0.94
Education
3.39
0.00
2.09
5.49
N. Of
Children
0.77
0.03
0.60
0.98
STATA: logit anc2_5 treat after treatafter SES age1_2 schle1_9 u10_1_13
Results of Diff in Diff –
ANC at least once during 1st trimester
Logistic
regression
N=830
P<0.05
Leading research at:
ANC in the
1st trimester
Odds Ratio
P
Treat
0.76
0.21
0.49
1.17
After
1.60
0.03
1.04
2.47
after*treatm
ent
1.53
0.17
0.83
2.83
SES
2.11
0.00
1.69
2.64
Age
0.98
0.29
0.95
1.01
Education
1.62
0.00
1.34
1.95
N. Of
Children
0.76
0.00
0.64
0.89
95%Conf Interval
STATA: logit anc2_8btrimester treat after treatafter SES age1_2 schle1_9 u10_1_13
Leading research at:
Results of Diff in Diff –
How many ANC visits?
Poisson
regression
N= 830
P<0.05
ANC visits
Coef.
P
95%Conf Interval
Treat
0.095
0.05
0.001
0.19
After
0.012
0.81
-0.083
0.11
after*treatme
nt
0.12
0.05
-0.007
0.25
SES
0.26
0.00
0.21
0.31
Age
-0.02
0.00
-0.024
-0.01
Education
3.39
0.00
2.09
5.50
N. Of
Children
-0.073
0.00
-0.11
-0.036
Cons
1.29
0.00
1.09
1.50
STATA: poisson anc2_9 treat after treatafter SES age1_2 schle1_9 u10_1_13
Leading research at:
Summary of diff in diff analysis and further steps
• HP improves the probability of ANC attendance at least once and
has a positive impact on the number of visits
OR ANC attendance is 6.05 → 6 times more likely to attend ANC
Coef. N ANC visits 0.12 (ALTHOUGH BORDERLINE) → women
receiving the intervention attended 1.13 times as many ANC visits
as women in the control group
• But not on going during the 1st trimester :
OR ANC in the 1st trimester 1.53
• Can ANC or a combination of maternal health factors be converted
in DALYs, i.e. maternal deaths averted by ANC attendance? How
can we translate ANC attendance into health outcome?
Leading research at:
Further analysis of intervention
• Overall effect → (Direct + Indirect) All women
a) Direct Effect → Women who attend GTN groups
b) Indirect (herd) Effect → Women who did not
•Efficacy → Health Effectiveness
1) The Cost Efficacy Ratio where intervention costs
are divided by increased probability of ANC
attendance. Can efficacy be translated into DALYs
averted due to intervention?
2) Cost-efficacy → Costs effectiveness
GRÀCIES!
Leading research at:
Download
Related flashcards

Neonatology

55 cards

Neonatology

54 cards

Pediatric cancers

12 cards

Breastfeeding

11 cards

French pediatricians

13 cards

Create Flashcards