Modeling the Cost Effectiveness of Malaria Control Interventions in the Highlands of Western Kenya
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{"title"=>"Modeling the cost effectiveness of malaria control interventions in the highlands of western Kenya", "type"=>"journal", "authors"=>[{"first_name"=>"Erin M.", "last_name"=>"Stuckey", "scopus_author_id"=>"55570041300"}, {"first_name"=>"Jennifer", "last_name"=>"Stevenson", "scopus_author_id"=>"55572812900"}, {"first_name"=>"Katya", "last_name"=>"Galactionova", "scopus_author_id"=>"23034205300"}, {"first_name"=>"Amrish Y.", "last_name"=>"Baidjoe", "scopus_author_id"=>"42160990700"}, {"first_name"=>"Teun", "last_name"=>"Bousema", "scopus_author_id"=>"6506709831"}, {"first_name"=>"Wycliffe", "last_name"=>"Odongo", "scopus_author_id"=>"55880775400"}, {"first_name"=>"Simon", "last_name"=>"Kariuki", "scopus_author_id"=>"7005601304"}, {"first_name"=>"Chris", "last_name"=>"Drakeley", "scopus_author_id"=>"7003491251"}, {"first_name"=>"Thomas A.", "last_name"=>"Smith", "scopus_author_id"=>"55547140840"}, {"first_name"=>"Jonathan", "last_name"=>"Cox", "scopus_author_id"=>"7404022414"}, {"first_name"=>"Nakul", "last_name"=>"Chitnis", "scopus_author_id"=>"36607585700"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"84907830990", "doi"=>"10.1371/journal.pone.0107700", "issn"=>"19326203", "pui"=>"600116631", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "pmid"=>"25290939", "scopus"=>"2-s2.0-84907830990"}, "id"=>"fefcb2e2-f444-3f19-8830-1f45155c4bd7", "abstract"=>"INTRODUCTION: Tools that allow for in silico optimization of available malaria control strategies can assist the decision-making process for prioritizing interventions. The OpenMalaria stochastic simulation modeling platform can be applied to simulate the impact of interventions singly and in combination as implemented in Rachuonyo South District, western Kenya, to support this goal.\\n\\nMETHODS: Combinations of malaria interventions were simulated using a previously-published, validated model of malaria epidemiology and control in the study area. An economic model of the costs of case management and malaria control interventions in Kenya was applied to simulation results and cost-effectiveness of each intervention combination compared to the corresponding simulated outputs of a scenario without interventions. Uncertainty was evaluated by varying health system and intervention delivery parameters.\\n\\nRESULTS: The intervention strategy with the greatest simulated health impact employed long lasting insecticide treated net (LLIN) use by 80% of the population, 90% of households covered by indoor residual spraying (IRS) with deployment starting in April, and intermittent screen and treat (IST) of school children using Artemether lumefantrine (AL) with 80% coverage twice per term. However, the current malaria control strategy in the study area including LLIN use of 56% and IRS coverage of 70% was the most cost effective at reducing disability-adjusted life years (DALYs) over a five year period.\\n\\nCONCLUSIONS: All the simulated intervention combinations can be considered cost effective in the context of available resources for health in Kenya. Increasing coverage of vector control interventions has a larger simulated impact compared to adding IST to the current implementation strategy, suggesting that transmission in the study area is not at a level to warrant replacing vector control to a school-based screen and treat program. These results have the potential to assist malaria control program managers in the study area in adding new or changing implementation of current interventions.", "link"=>"http://www.mendeley.com/research/modeling-cost-effectiveness-malaria-control-interventions-highlands-western-kenya", "reader_count"=>58, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Professor > Associate Professor"=>1, "Researcher"=>17, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>14, "Student > Postgraduate"=>4, "Student > Master"=>12, "Other"=>1, "Student > Bachelor"=>3, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>3, "Professor > Associate Professor"=>1, "Researcher"=>17, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>14, "Student > Postgraduate"=>4, "Student > Master"=>12, "Other"=>1, "Student > Bachelor"=>3, "Professor"=>2}, "reader_count_by_subject_area"=>{"Unspecified"=>4, "Agricultural and Biological Sciences"=>10, "Veterinary Science and Veterinary Medicine"=>1, "Business, Management and Accounting"=>1, "Computer Science"=>2, "Decision Sciences"=>1, "Economics, Econometrics and Finance"=>4, "Environmental Science"=>1, "Nursing and Health Professions"=>1, "Biochemistry, Genetics and Molecular Biology"=>1, "Mathematics"=>4, "Medicine and Dentistry"=>20, "Neuroscience"=>1, "Physics and Astronomy"=>1, "Social Sciences"=>5, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>20}, "Social Sciences"=>{"Social Sciences"=>5}, "Decision Sciences"=>{"Decision Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Mathematics"=>{"Mathematics"=>4}, "Unspecified"=>{"Unspecified"=>4}, "Environmental Science"=>{"Environmental Science"=>1}, "Neuroscience"=>{"Neuroscience"=>1}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>4}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>10}, "Computer Science"=>{"Computer Science"=>2}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}, "Veterinary Science and Veterinary Medicine"=>{"Veterinary Science and Veterinary Medicine"=>1}}, "reader_count_by_country"=>{"Netherlands"=>1, "United States"=>1, "United Kingdom"=>1}, "group_count"=>12}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1707950"], "description"=>"<p>Tornado diagram of the change in the ACER of an intervention with 80% LLIN use, 90% IRS coverage, and 80% IST coverage twice per term in relation to variation in component costs.</p>", "links"=>[], "tags"=>["llin", "intervention delivery parameters.ResultsThe intervention strategy", "study area", "malaria control strategies", "malaria control program managers", "Rachuonyo South District", "Western Kenya IntroductionTools", "ist", "malaria control interventions", "vector control interventions", "daly", "irs", "al", "simulation modeling platform", "malaria control strategy"], "article_id"=>1196333, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Erin M. Stuckey", "Jennifer Stevenson", "Katya Galactionova", "Amrish Y. Baidjoe", "Teun Bousema", "Wycliffe Odongo", "Simon Kariuki", "Chris Drakeley", "Thomas A. Smith", "Jonathan Cox", "Nakul Chitnis"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0107700.g003", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Sensitivity_analysis_/1196333", "title"=>"Sensitivity analysis.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-10-07 03:02:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1707949"], "description"=>"<p>Simulated cumulative DALYs averted after five years compared to the no intervention scenario by net program costs for different implementation strategies of <b>a</b>) vector control interventions, <b>b</b>) intermittent screen and treat in school children, and <b>c</b>) combinations of interventions. Symbols represent the mean simulation results across 14 model variants and five random seeds. Horizontal capped bars represent range of simulated DALYs averted. Vertical capped bars represent range of simulated net program costs. Negative DALYs averted indicate simulated interventions that have a worse health outcome than the no intervention scenario. Negative net program costs indicate simulated interventions where the savings to the health system are greater than the delivery costs.</p>", "links"=>[], "tags"=>["llin", "intervention delivery parameters.ResultsThe intervention strategy", "study area", "malaria control strategies", "malaria control program managers", "Rachuonyo South District", "Western Kenya IntroductionTools", "ist", "malaria control interventions", "vector control interventions", "daly", "irs", "al", "simulation modeling platform", "malaria control strategy"], "article_id"=>1196332, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Erin M. Stuckey", "Jennifer Stevenson", "Katya Galactionova", "Amrish Y. Baidjoe", "Teun Bousema", "Wycliffe Odongo", "Simon Kariuki", "Chris Drakeley", "Thomas A. Smith", "Jonathan Cox", "Nakul Chitnis"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0107700.g002", "stats"=>{"downloads"=>1, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Relationship_between_cost_and_simulated_health_impact_/1196332", "title"=>"Relationship between cost and simulated health impact.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-10-07 03:02:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1707958", "https://ndownloader.figshare.com/files/1707959"], "description"=>"<div><p>Introduction</p><p>Tools that allow for in silico optimization of available malaria control strategies can assist the decision-making process for prioritizing interventions. The OpenMalaria stochastic simulation modeling platform can be applied to simulate the impact of interventions singly and in combination as implemented in Rachuonyo South District, western Kenya, to support this goal.</p><p>Methods</p><p>Combinations of malaria interventions were simulated using a previously-published, validated model of malaria epidemiology and control in the study area. An economic model of the costs of case management and malaria control interventions in Kenya was applied to simulation results and cost-effectiveness of each intervention combination compared to the corresponding simulated outputs of a scenario without interventions. Uncertainty was evaluated by varying health system and intervention delivery parameters.</p><p>Results</p><p>The intervention strategy with the greatest simulated health impact employed long lasting insecticide treated net (LLIN) use by 80% of the population, 90% of households covered by indoor residual spraying (IRS) with deployment starting in April, and intermittent screen and treat (IST) of school children using Artemether lumefantrine (AL) with 80% coverage twice per term. However, the current malaria control strategy in the study area including LLIN use of 56% and IRS coverage of 70% was the most cost effective at reducing disability-adjusted life years (DALYs) over a five year period.</p><p>Conclusions</p><p>All the simulated intervention combinations can be considered cost effective in the context of available resources for health in Kenya. Increasing coverage of vector control interventions has a larger simulated impact compared to adding IST to the current implementation strategy, suggesting that transmission in the study area is not at a level to warrant replacing vector control to a school-based screen and treat program. These results have the potential to assist malaria control program managers in the study area in adding new or changing implementation of current interventions.</p></div>", "links"=>[], "tags"=>["llin", "intervention delivery parameters.ResultsThe intervention strategy", "study area", "malaria control strategies", "malaria control program managers", "Rachuonyo South District", "Western Kenya IntroductionTools", "ist", "malaria control interventions", "vector control interventions", "daly", "irs", "al", "simulation modeling platform", "malaria control strategy"], "article_id"=>1196341, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Erin M. Stuckey", "Jennifer Stevenson", "Katya Galactionova", "Amrish Y. Baidjoe", "Teun Bousema", "Wycliffe Odongo", "Simon Kariuki", "Chris Drakeley", "Thomas A. Smith", "Jonathan Cox", "Nakul Chitnis"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0107700.s001", "https://dx.doi.org/10.1371/journal.pone.0107700.s002"], "stats"=>{"downloads"=>6, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Modeling_the_Cost_Effectiveness_of_Malaria_Control_Interventions_in_the_Highlands_of_Western_Kenya_/1196341", "title"=>"Modeling the Cost Effectiveness of Malaria Control Interventions in the Highlands of Western Kenya", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-10-07 03:02:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1707957"], "description"=>"<p>The mean and inter-quartile range of the average cost effectiveness ratios (ACER) compared to a scenario with no interventions outside the existing case management system, and incremental cost effectiveness ratios (ICER) compared to the currently implemented strategy for different intervention combinations with more simulated DALYs averted than the currently implemented strategy. ACERs and ICERs are calculated using costs reported in <b><a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0107700#pone.0107700.s001\" target=\"_blank\">Table S1</a></b> and effectiveness reported in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0107700#pone-0107700-t001\" target=\"_blank\"><b>Table 1</b></a>. Interventions are displayed in ascending order of simulated DALYs averted (<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0107700#pone-0107700-t001\" target=\"_blank\"><b>Table 1</b></a>). IQR represents mean costs values applied to the inter-quartile range of simulated health effects.</p><p>Cost effectiveness of different intervention combinations for a population of 100,000 over five years of implementation (2012 US$).</p>", "links"=>[], "tags"=>["llin", "intervention delivery parameters.ResultsThe intervention strategy", "study area", "malaria control strategies", "malaria control program managers", "Rachuonyo South District", "Western Kenya IntroductionTools", "ist", "malaria control interventions", "vector control interventions", "daly", "irs", "al", "simulation modeling platform", "malaria control strategy"], "article_id"=>1196340, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Erin M. Stuckey", "Jennifer Stevenson", "Katya Galactionova", "Amrish Y. Baidjoe", "Teun Bousema", "Wycliffe Odongo", "Simon Kariuki", "Chris Drakeley", "Thomas A. Smith", "Jonathan Cox", "Nakul Chitnis"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0107700.t005", "stats"=>{"downloads"=>0, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Cost_effectiveness_of_different_intervention_combinations_for_a_population_of_100_000_over_five_years_of_implementation_2012_US_/1196340", "title"=>"Cost effectiveness of different intervention combinations for a population of 100,000 over five years of implementation (2012 US$).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-10-07 03:02:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1707954"], "description"=>"<p>All costs are in 2012 USD.</p><p>Costing and sensitivity analysis of the Kenya public sector case management system.</p>", "links"=>[], "tags"=>["llin", "intervention delivery parameters.ResultsThe intervention strategy", "study area", "malaria control strategies", "malaria control program managers", "Rachuonyo South District", "Western Kenya IntroductionTools", "ist", "malaria control interventions", "vector control interventions", "daly", "irs", "al", "simulation modeling platform", "malaria control strategy"], "article_id"=>1196337, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Erin M. Stuckey", "Jennifer Stevenson", "Katya Galactionova", "Amrish Y. Baidjoe", "Teun Bousema", "Wycliffe Odongo", "Simon Kariuki", "Chris Drakeley", "Thomas A. Smith", "Jonathan Cox", "Nakul Chitnis"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0107700.t002", "stats"=>{"downloads"=>0, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Costing_and_sensitivity_analysis_of_the_Kenya_public_sector_case_management_system_/1196337", "title"=>"Costing and sensitivity analysis of the Kenya public sector case management system.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-10-07 03:02:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1707952"], "description"=>"<p>Simulated cumulative DALYs averted in a population of 100,000 individuals after five years compared to the no intervention scenario by net program costs for the intervention combinations with a better simulated health outcome than the currently implemented malaria control strategy, ranked in descending order of ACER. Black dots represent the mean simulation results across 14 model variants and five seeds. Circles represent the of simulated DALYs averted by net program costs with different assumptions of input costs of the case management system and malaria control interventions in the study area represented in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0107700#pone-0107700-t002\" target=\"_blank\">Table 2</a> and <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0107700#pone-0107700-t003\" target=\"_blank\">Table 3</a>. Dark blue circles are within the inter-quartile range of simulated DALYs averted and light blue circles are outside the range. Negative DALYs averted indicate simulated interventions that have a worse health outcome than the no intervention scenario. Negative net program costs indicate simulated interventions where the savings to the health system are greater than the delivery costs. Diagonal lines correspond to the ratios of mean (4.29 USD per DALY averted) ACER of the currently implemented intervention combination in the study area (LLIN use 56%, IRS coverage 70%).</p>", "links"=>[], "tags"=>["llin", "intervention delivery parameters.ResultsThe intervention strategy", "study area", "malaria control strategies", "malaria control program managers", "Rachuonyo South District", "Western Kenya IntroductionTools", "ist", "malaria control interventions", "vector control interventions", "daly", "irs", "al", "simulation modeling platform", "malaria control strategy"], "article_id"=>1196335, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Erin M. Stuckey", "Jennifer Stevenson", "Katya Galactionova", "Amrish Y. Baidjoe", "Teun Bousema", "Wycliffe Odongo", "Simon Kariuki", "Chris Drakeley", "Thomas A. Smith", "Jonathan Cox", "Nakul Chitnis"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0107700.g004", "stats"=>{"downloads"=>1, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Cost_effectiveness_planes_/1196335", "title"=>"Cost effectiveness planes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-10-07 03:02:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1707953"], "description"=>"<p>*Represents the base case scenario as parameterized in Stuckey et al. 2012 <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0107700#pone.0107700-Stuckey1\" target=\"_blank\">[4]</a>.</p><p>Experiment design of the combinations and coverage levels of interventions simulated for the study.</p>", "links"=>[], "tags"=>["llin", "intervention delivery parameters.ResultsThe intervention strategy", "study area", "malaria control strategies", "malaria control program managers", "Rachuonyo South District", "Western Kenya IntroductionTools", "ist", "malaria control interventions", "vector control interventions", "daly", "irs", "al", "simulation modeling platform", "malaria control strategy"], "article_id"=>1196336, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Erin M. Stuckey", "Jennifer Stevenson", "Katya Galactionova", "Amrish Y. Baidjoe", "Teun Bousema", "Wycliffe Odongo", "Simon Kariuki", "Chris Drakeley", "Thomas A. Smith", "Jonathan Cox", "Nakul Chitnis"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0107700.t001", "stats"=>{"downloads"=>0, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Experiment_design_of_the_combinations_and_coverage_levels_of_interventions_simulated_for_the_study_/1196336", "title"=>"Experiment design of the combinations and coverage levels of interventions simulated for the study.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-10-07 03:02:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1707948"], "description"=>"<p>White lines represent the simulated median value, blue boxes represent the inter-quartile range, and capped bars represent the upper and lower adjacent values for simulated results for each intervention combination using an ensemble of 14 model variants and five random seeds. Choice of intervention combinations is based on the criteria of simulated reduction in parasite prevalence greater than the strategy currently implemented in the study area.</p>", "links"=>[], "tags"=>["llin", "intervention delivery parameters.ResultsThe intervention strategy", "study area", "malaria control strategies", "malaria control program managers", "Rachuonyo South District", "Western Kenya IntroductionTools", "ist", "malaria control interventions", "vector control interventions", "daly", "irs", "al", "simulation modeling platform", "malaria control strategy"], "article_id"=>1196331, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Erin M. Stuckey", "Jennifer Stevenson", "Katya Galactionova", "Amrish Y. Baidjoe", "Teun Bousema", "Wycliffe Odongo", "Simon Kariuki", "Chris Drakeley", "Thomas A. Smith", "Jonathan Cox", "Nakul Chitnis"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0107700.g001", "stats"=>{"downloads"=>0, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Simulated_reduction_in_all_age_annual_average_parasite_prevalence_by_intervention_combination_compared_to_a_scenario_with_no_intervention_/1196331", "title"=>"Simulated reduction in all-age annual average parasite prevalence by intervention combination compared to a scenario with no intervention.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-10-07 03:02:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1707956"], "description"=>"<p>Compared to a scenario with no interventions outside the existing case management system, the mean and inter-quartile range of the impact of different intervention combinations (<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0107700#pone-0107700-t001\" target=\"_blank\"><b>Table 1</b></a>) on epidemiological outcomes in a population of 100,000 individuals over a time period of five years*. <b>Bold</b> figures indicate mean results improved from the current strategy.</p><p><i>*Unless otherwise indicated.</i></p><p>Simulated effect of intervention combinations.</p>", "links"=>[], "tags"=>["llin", "intervention delivery parameters.ResultsThe intervention strategy", "study area", "malaria control strategies", "malaria control program managers", "Rachuonyo South District", "Western Kenya IntroductionTools", "ist", "malaria control interventions", "vector control interventions", "daly", "irs", "al", "simulation modeling platform", "malaria control strategy"], "article_id"=>1196339, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Erin M. Stuckey", "Jennifer Stevenson", "Katya Galactionova", "Amrish Y. Baidjoe", "Teun Bousema", "Wycliffe Odongo", "Simon Kariuki", "Chris Drakeley", "Thomas A. Smith", "Jonathan Cox", "Nakul Chitnis"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0107700.t004", "stats"=>{"downloads"=>3, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Simulated_effect_of_intervention_combinations_/1196339", "title"=>"Simulated effect of intervention combinations.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-10-07 03:02:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1707955"], "description"=>"<p>All costs are in 2012 USD.</p><p>Costing and sensitivity analysis of malaria control interventions in Kenya.</p>", "links"=>[], "tags"=>["llin", "intervention delivery parameters.ResultsThe intervention strategy", "study area", "malaria control strategies", "malaria control program managers", "Rachuonyo South District", "Western Kenya IntroductionTools", "ist", "malaria control interventions", "vector control interventions", "daly", "irs", "al", "simulation modeling platform", "malaria control strategy"], "article_id"=>1196338, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Erin M. Stuckey", "Jennifer Stevenson", "Katya Galactionova", "Amrish Y. Baidjoe", "Teun Bousema", "Wycliffe Odongo", "Simon Kariuki", "Chris Drakeley", "Thomas A. Smith", "Jonathan Cox", "Nakul Chitnis"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0107700.t003", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Costing_and_sensitivity_analysis_of_malaria_control_interventions_in_Kenya_/1196338", "title"=>"Costing and sensitivity analysis of malaria control interventions in Kenya.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-10-07 03:02:44"}

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Relative Metric

{"start_date"=>"2014-01-01T00:00:00Z", "end_date"=>"2014-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Medicine and health sciences/Infectious diseases", "average_usage"=>[319]}, {"subject_area"=>"/People and places/Population groupings", "average_usage"=>[300]}, {"subject_area"=>"/Social sciences", "average_usage"=>[318]}, {"subject_area"=>"/Social sciences/Economics", "average_usage"=>[340, 521]}]}
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