Exploring the use of routinely-available, retrospective data to study the association between malaria control scale-up and micro-economic outcomes in Zambia View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-01-04

AUTHORS

Alison Comfort, Anthony Leegwater, Sharon Nakhimovsky, Henry Kansembe, Busiku Hamainza, Benson Bwalya, Martin Alilio, Ben Johns, Lauren Olsho

ABSTRACT

BACKGROUND: Country-level evidence on the impact of malaria control on micro-economic outcomes is vital for mobilizing domestic and donor resources for malaria control. Using routinely available survey data could facilitate this investigation in a cost-efficient way. METHODS: The authors used Malaria Indicator Surveys (MIS) and Living Conditions Monitoring Survey (LCMS) data from 2006 to 2010 for all 72 districts in Zambia to relate malaria control scale-up with household food spending (proxy for household well-being), educational attainment and agricultural production. The authors used two quasi-experimental designs: (1) a generalized propensity score for a continuous treatment variable (defined as coverage from owning insecticide-treated bed nets and/or receipt of indoor residual spraying); and, (2) a district fixed effects model to assess changes in the outcome relative to changes in treatment pre-post scale-up. The unit of analysis was at district level. The authors also conducted simulations post-analysis to assess statistical power. RESULTS: Micro-economic outcomes increased (33% increase in food spending) concurrently with malaria control coverage (62% increase) from 2006 to 2010. Despite using data from all 72 districts, both analytic methods yielded wide confidence intervals that do not conclusively link outcomes and malaria control coverage increases. The authors cannot rule out positive, null or negative effects. The upper bound estimates of the results show that if malaria control coverage increases from 60 to 70%, food spending could increase up to 14%, maize production could increase up to 57%, and years of schooling could increase up to 0.5 years. Simulations indicated that the generalized propensity score model did not have good statistical power. CONCLUSION: While it is technically possible to use routinely available survey data to relate malaria control scale-up and micro-economic outcomes, it is not clear from this analysis that meaningful results can be obtained when survey data are highly aggregated. Researchers in similar settings should assess the feasibility of disaggregating existing survey data. Additionally, large surveys, such as LCMS and MIS, could incorporate data on both malaria coverage and household expenditures, respectively. More... »

PAGES

15

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s12936-016-1665-z

DOI

http://dx.doi.org/10.1186/s12936-016-1665-z

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1040568615

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/28052759


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58 control coverage
59 control coverage increases
60 cost-efficient way
61 country-level evidence
62 coverage
63 coverage increases
64 data
65 design
66 district
67 district level
68 donor resources
69 effect
70 effects model
71 estimates
72 evidence
73 expenditure
74 feasibility
75 food spending
76 generalized propensity score
77 generalized propensity score model
78 good statistical power
79 household expenditure
80 household food spending
81 impact
82 increase
83 intervals
84 investigation
85 large survey
86 levels
87 maize production
88 malaria control
89 malaria control coverage
90 malaria control coverage increases
91 malaria coverage
92 meaningful results
93 method
94 micro-economic outcomes
95 model
96 negative effects
97 outcomes
98 power
99 production
100 propensity score
101 propensity score model
102 quasi-experimental design
103 researchers
104 resources
105 results
106 retrospective data
107 schooling
108 score model
109 scores
110 setting
111 similar settings
112 simulations
113 spending
114 statistical power
115 survey
116 survey data
117 treatment
118 treatment variables
119 unit of analysis
120 units
121 use
122 variables
123 way
124 wide confidence intervals
125 years
126 years of schooling
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