The Effects of Distance and Quality on Uptake of
Sexual, Reproductive, and Other Health Services in
Rural Mozambique
Julia Driessen, Zan Dodson, and Victor Agadjanian
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Overview
Research question: How do distance and quality affect health care
utilization in rural Mozambique?
Results suggest both factors predict utilization, and that improvements in
allocation of services could meaningfully boost utilization.
Contributions/strengths:
I
Access defined two ways- consideration of quality and distance
I
Precise geographic data
I
Addresses potential endogeneity of clinic/service placement
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Introduction
Three common types of barriers to health care utilization:
1. Knowledge
2. Physical/spatial
3. Quality
Barriers 2 and 3 are functions of how resources are allocated.
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Introduction
The ability to translate improved allocations into improved utilization is a
function of barrier 1.
Hypothesis: Better access (distance, quality) to health facilities improves
utilization of services.
Caveat: Allocation of services is often endogenous.
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Introduction
Literature summary
Assorted studies have documented the roles of service distance and quality
in determining utilization.
I
Evidence that both quality and distance are factors
I
Some support for idea that individuals will ‘bypass’ nearby facilities to
access higher quality services
I
Function of broader infrastructure (e.g., transportation)
I
Distance is difficult to measure precisely, and travel times are a
somewhat noisy representation
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Introduction
Mozambique
I
HIV prevalence is approximately 12%, with significant geographic
variation.
I
ART was rolled out nationwide in 2004 but only through a very
limited number of health clinics. (Audet et al. 2010)
I
Significant communicable disease burden, including malaria,
tuberculosis, and diarrhea. (Mabunda et al. 2009)
I
Area is prone to flooding and post-flood infections increase
dramatically as drinking conditions deteriorate and disease vectors
appear. (Kondo et al. 2002)
I
National family planning program initiated in 1980, currently still
significant geographic variation in uptake (Yao et al. 2012)
I
Unscheduled deliveries and poor antenatal care are significant
contributors to maternal deaths in Mozambique. (Granja et al. 1998)
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Introduction
Mozambique
Mozambique uses primary health clinics as a platform for expanding access
to a variety of services.
Stated emphasis on improving access of low-SES households as a poverty
alleviation strategy, prioritizing service such as family planning and HIV
testing.
Services reflect integrated model of antenatal care, using the clinics to
address prenatal and other health needs. Services include:
I
Family planning
I
In-facility deliveries
I
Vaccinations
I
HIV testing
I
Malaria testing
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Introduction
Clinics and services in Mozambique
Not all services are available in all clinics.
Consequence of harsh resource constraints.
I
During our time period of interest, health spending per capita was
about $25.
I
In 2003, coverage of health services was estimated at less than 40%.
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Introduction
Thus, this is a multi-layered allocation problem, involving assignment of
clinics and then the services associated with each.
These decisions are not random but are also not easily observable →
potentially confound studies of the access-utilization relationship.
For example, allocation decisions may prioritize higher-uptake areas, either
because they are higher-need or more engaged, which would overestimate
the effect of distance on utilization.
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Data
Data
Data collection as part of project examining health care utilization and
childbearing behavior against the backdrop of HIV/AIDS and the rollout of
ART in rural Mozambique.
I
Survey from 2009 conducted in rural areas of four districts of Gaza
province in southern Mozambique
I
Survey enrolled 1,834 women between the ages of 18-40 living in 55
villages
I
14 villages per district with sampled with probability proportional to
their population size based on census data
I
Highly agriculturally-reliant area; largely subsistence
I
Survey included standard demographic and economic questions, as
well as extensive questions about health status, health care
utilization, and health knowledge.
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Data
Data
I
All health clinics (53) in the study region were also surveyed and
included questions about clinic services, infrastructure, and staffing to
assess quality.
I
Village characteristics were documented through structured interviews
with village leadership
I
All households, villages and health clinics include latitude/longitude
location
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Data
Study area
I
Four districts: Chibuto, Chokwe, Guija and Mandlakaze located in the
Gaza province
I
Combined area of the four districts is approximately 5,900 sq. miles.
I
Total population at time of study was approximately 625,000.
I
Province has highest HIV prevalence in Mozambique (25.1%).
I
Fertility in the area is quite high though family planning use is
relatively low. (Yao et al. 2012).
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Data
Map of study area
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Methods
Overview
Goal: Assess how distance and quality affect health care utilization
Basics:
I
Outcomes: Utilization of three IANC services
I
Clinic distance: Euclidean based on spatial data
I
Clinic quality: Composite measure of clinic infrastructure and services
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Methods
Overview
Other aspects:
I
IV to deal with endogeneity of clinic placement
I
Instrument: Composite measure of access to other non-health services
I
Then can examine how improvement on existing resource allocations
might affect uptake
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Methods
Distance and quality
Distance
I
For each village, Euclidean distance from village to nearest clinic
I
Evidence that Euclidean distance approximates actual road distance
(Boscoe et al. 2012; Cudnik et al. 2012)
Quality
I
PCA applied to clinic characteristics
I
Infrastructure: electricity, piped water, NGO aid, number of rooms
I
Services: prenatal consults, HIV counseling and testing, PMTCT,
ART for pregnant women, in-facility deliveries, postpartum consults,
child consults, child vaccinations, family planning services
I
Measure was quality/km, or the PCA score divided by the Euclidean
distance for the nearest clinic
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Methods
Instrument
We instrument for distance and quality using a broader, village-level
measure of access.
Applied PCA to self-reported distance to: asphalt road, administrative
post, school, general store, market, police station, sports field
Assumptions:
1. Relevance: This measure predicts distance to/quality of nearest clinic.
2. Excludability: This measure does not affect utilization other than
through clinic distance/quality.
Background: Distance is common approach in development literature for
education and health outcomes (Lavy 1996; Attanasio and Vera-Hernandez
2004; Mukhopadhyay and Sahoo 2012; Kumar et al. 2012).
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Methods
Regression model
Utilizationij = β1 + β2 Accessi + β3 Xij + αj + i
I
Accessi = distance or quality/km
I
Xij : HH size, womans schooling, husbands schooling, womans age,
HH asset score, HH religion, HH language
I
αj : district indicators
Estimated using probit model. Standard errors clustered at village level.
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Results
Descriptive statistics: Households
Table: Summary statistics
Variable
Uses family planning
Last child received measles vaccination
Had an HIV test
Had a malaria test after symptomatic
Last pregnancy was an institutional delivery
HH size
Woman’s schooling (years)
Husband’s schooling (years)
Woman’s age (years)
HH asset score
Mean
0.156
0.765
0.563
0.616
0.719
7.205
2.937
3.079
30.453
-0.017
Std. Dev.
0.363
0.424
0.496
0.487
0.449
3.569
2.408
2.858
6.286
1.647
Min
0
0
0
0
0
1
0
0
19
-2.22
Max
1
1
1
1
1
34
12
12
54
6.712
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N=1,834
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Distance, Quality, and SRH Uptake
Results
Descriptive statistics: Villages
Table: Summary statistics
Variable
Distance to nearest clinic (km)
HSQ/km
School within 1 hour
General store within 1 hour
Market within 1 hour
Police station within 1 hour
Sports field within 1 hour
Number of radio stations received
Number of TV channels received
Mean
5.363
0.390
0.982
0.436
0.273
0.200
0.691
2.764
0.400
Std. Dev.
4.449
3.535
0.135
0.501
0.449
0.404
0.466
1.347
0.627
Min
0.062
-8.001
0
0
0
0
0
0
0
Max
19.67
18.362
1
1
1
1
1
6
3
N=55
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Results
Descriptive statistics: Clinics
Table: Summary statistics
Variable
Services
Prenatal consultations
HIV counseling and testing
Prenatal HIV testing
PMTCT
ART for pregnant women
Institutional deliveries
Postpartum consults
Child consults
Family planning
Child vaccinations
Mean
Std. Dev.
Min
Max
0.981
0.547
0.509
0.491
0.189
0.679
0.981
0.962
0.981
0.925
0.137
0.503
0.505
0.505
0.395
0.471
0.137
0.192
0.137
0.267
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
1
N=53
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Results
Descriptive statistics: Clinics
Table: Summary statistics
Variable
Infrastructure
Electricity
Piped water
Number of rooms
Receives NGO aid
Mean
Std. Dev.
Min
Max
0.566
0.264
2.170
0.472
0.500
0.445
1.297
0.504
0
0
1
0
1
1
6
1
N=53
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Results
Distance: Probit results
Distance to nearest clinic (km)
HH size
Woman’s schooling (years)
Husband’s schooling (years)
Woman’s age
HH religion: Roman Catholic
HH religion: Zionist
HH religion: Assemblies of God
HH religion: Other
HH speaks Changana
HH asset score
N
r2 p
ll
Family planning
Measles vaccination
In-facility delivery
-0.006∗∗
(0.003)
0.003
(0.002)
0.006
(0.004)
0.010∗∗∗
(0.004)
0.000
(0.001)
0.046
(0.050)
0.072
(0.045)
0.135∗∗
(0.057)
0.088∗
(0.051)
0.006
(0.035)
0.012∗∗
(0.006)
-0.005∗∗
(0.003)
-0.010∗∗∗
(0.003)
0.008∗
(0.005)
0.001
(0.005)
0.010∗∗∗
(0.002)
-0.057
(0.045)
-0.049
(0.039)
0.001
(0.047)
-0.059
(0.047)
0.020
(0.042)
0.005
(0.007)
-0.018∗∗
(0.007)
0.003
(0.004)
0.002
(0.005)
0.008
(0.005)
-0.000
(0.002)
0.084∗
(0.043)
-0.029
(0.046)
0.086∗∗
(0.043)
0.027
(0.043)
-0.061
(0.054)
0.024∗∗∗
(0.009)
1781
0.041
-746.426
1624
0.035
-851.862
1724
0.061
-961.482
Marginal effects. Robust standard errors clustered at village in parentheses.
∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
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Results
Distance: IV results
Distance to nearest clinic (km)
HH size
Woman’s schooling (years)
Husband’s schooling (years)
Woman’s age
HH religion: Roman Catholic
HH religion: Zionist
HH religion: Assemblies of God
HH religion: Other
HH speaks Changana
HH asset score
N
r2 p
ll
chi2 exog
p exog
Family planning
Measles vaccination
In-facility delivery
-0.008∗
(0.004)
0.002
(0.002)
0.006
(0.004)
0.010∗∗
(0.004)
-0.000
(0.001)
0.037
(0.049)
0.061
(0.045)
0.127∗∗
(0.056)
0.079
(0.050)
0.003
(0.034)
0.011∗
(0.006)
-0.010∗∗
(0.004)
-0.009∗∗∗
(0.003)
0.007
(0.005)
0.001
(0.005)
0.010∗∗∗
(0.002)
-0.060
(0.048)
-0.057
(0.042)
0.004
(0.049)
-0.064
(0.050)
0.012
(0.041)
0.005
(0.007)
-0.019∗
(0.010)
0.002
(0.004)
0.002
(0.005)
0.008
(0.005)
-0.000
(0.002)
0.083∗
(0.048)
-0.034
(0.050)
0.087∗∗
(0.043)
0.028
(0.047)
-0.063
(0.056)
0.023∗∗
(0.009)
1748
1595
1691
-5092.041
0.115
0.734
-4798.295
2.419
0.120
-5142.533
0.002
0.968
Marginal effects. Robust standard errors clustered at village in parentheses.
∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
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Results
Quality: Probit results
Quality/km
HH size
Woman’s schooling (years)
Husband’s schooling (years)
Woman’s age
HH religion: Roman Catholic (d)
HH religion: Zionist (d)
HH religion: Assemblies of God (d)
HH religion: Other (d)
HH speaks Changana (d)
HH asset score
N
r2 p
Family planning
Measles vaccination
In-facility delivery
-0.001
(0.002)
0.002
(0.002)
0.007∗
(0.004)
0.010∗∗∗
(0.004)
0.000
(0.001)
0.053
(0.051)
0.077∗
(0.044)
0.131∗∗
(0.061)
0.095∗
(0.049)
0.015
(0.031)
0.012∗∗
(0.006)
0.008∗∗
(0.003)
-0.010∗∗∗
(0.003)
0.009∗
(0.005)
0.002
(0.004)
0.010∗∗∗
(0.002)
-0.051
(0.053)
-0.045
(0.046)
-0.005
(0.053)
-0.054
(0.049)
0.029
(0.041)
0.006
(0.007)
0.016∗∗∗
(0.004)
0.001
(0.003)
0.007
(0.005)
0.011∗∗
(0.005)
0.000
(0.002)
0.097∗∗
(0.042)
-0.017
(0.043)
0.064
(0.046)
0.038
(0.044)
-0.029
(0.043)
0.025∗∗∗
(0.008)
1781
0.036
1624
0.036
1724
0.050
Marginal effects. Robust standard errors clustered at village in parentheses.
∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
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Results
Quality: IV results
Quality/km
HH size
Woman’s schooling (years)
Husband’s schooling (years)
Woman’s age
HH religion: Roman Catholic (d)
HH religion: Zionist (d)
HH religion: Assemblies of God (d)
HH religion: Other (d)
HH speaks Changana (d)
HH asset score
N
r2 p
ll
chi2 exog
p exog
Family planning
Measles vaccination
In-facility delivery
0.054∗∗
(0.024)
-0.001
(0.003)
0.006
(0.005)
0.014∗∗∗
(0.004)
0.000
(0.002)
0.036
(0.051)
0.058
(0.046)
0.071
(0.061)
0.066
(0.050)
0.029
(0.034)
0.012∗
(0.006)
1748
0.063∗∗∗
(0.023)
-0.012∗∗∗
(0.003)
0.008
(0.005)
0.006
(0.005)
0.010∗∗∗
(0.002)
-0.048
(0.054)
-0.053
(0.048)
-0.045
(0.059)
-0.065
(0.051)
0.041
(0.042)
0.004
(0.007)
1595
0.098∗∗∗
(0.013)
-0.003
(0.003)
0.003
(0.005)
0.015∗∗∗
(0.005)
0.001
(0.002)
0.066
(0.048)
-0.026
(0.044)
-0.009
(0.054)
0.001
(0.047)
0.002
(0.042)
0.017∗∗
(0.008)
1691
-5327.682
5.495
0.019
-5030.107
4.527
0.033
-5386.034
15.113
0.000
Marginal effects. Robust standard errors clustered at village in parentheses.
∗
∗∗
∗∗∗
p < 0.10,
p < 0.05, Seminar)
p < 0.01 Distance, Quality, and SRH Uptake
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Results
Location-allocation
We can apply this approach to understand how a change in the allocation
of existing resources would affect utilization.
Location-allocation analysis is a geospatial method that allocates supply to
minimize impedance for a given population.
Here, we can look at how IFD capacity is allocated. Specifically, we
compare the current allocation of IFD services with the optimal allocation,
using population weights for villages.
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Results
Location-allocation
36 clinics offered IFD in 2009.
Average distance of villages from IFD clinics was 7.5 km.
Applying the regression approach to examine distance from IFD clinics
indicated that each additional km decreased the likelihood of having an
IFD by 1%.
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Results
Location-allocation comparison
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Results
Location-allocation
The suggested reallocation would reduce the average distance from 7.5 km
to 6.0km, a reduction of 20%.
In this sample of about 1800 women, this would have resulted in
approximately 27 additional IFDs.
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Discussion and Conclusion
Summary
Evidence that distance and quality affect uptake of health care services in
Mozambique.
Suggests that demand is responsive to existing policy levers, and that
optimizing rollout of services will demonstrably affect uptake.
Existing allocation of IFD services could be reorganized to have greater
impact.
Practical approach because works within existing resource constraints to
improve efficiency.
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Discussion and Conclusion
Strenghts and Limitations
Adds to evidence base around barriers to care, with a few distinct
strengths:
I
Precise spatial data
I
Consideration of allocation endogeneity
I
Use of econometric and geospatial methods
Limitations
I
Doesn’t look at intervening knowledge barrier
I
Assumed excludability of instrument (also have tried fixed effects with
panel data)
I
Location-allocation sensitive to choice of weights
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Discussion and Conclusion
Next steps
Adds to evidence base around barriers to care, with a few distinct
strengths:
I
More comprehensive measure of quality that considers all nearby
clinics
I
Joint consideration of distance and quality to capture tradeoffs and
relative importance
I
Longitudinal examination of change in access through time
I
Extending location-allocation to think about how to best expand
services given existing allocations
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