Sensitivity analysis.

<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

Costing and sensitivity analysis of malaria control interventions in Kenya.

<p>All costs are in 2012 USD.</p><p>Costing and sensitivity analysis of malaria control interventions in Kenya.</p

Simulated effect of intervention combinations.

<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

Simulated reduction in all-age annual average parasite prevalence by intervention combination compared to a scenario with no intervention.

<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

Cost effectiveness of different intervention combinations for a population of 100,000 over five years of implementation (2012 US$).

<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

Cost effectiveness planes.

<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

Costing and sensitivity analysis of the Kenya public sector case management system.

<p>All costs are in 2012 USD.</p><p>Costing and sensitivity analysis of the Kenya public sector case management system.</p

Dataset 1. Information on the 276 SNPs genotyped in 177 genes in P. falciparum parasite populations from The Gambia, Kilifi and Rachuonyo South

<p><b>Dataset 1:</b> <b>Information on the 276 SNPs genotyped in 177 genes in <i>P. falciparum </i>parasite populations from The Gambia, Kilifi and Rachuonyo South. </b>The columns contain the following information: study_location, site of sample collection; sample_id, unique sample identifier; gene_symbol, gene name (if available); chr_valid, chromosome; coord_valid= base position of SNP on chromosome; sequence_code, SNP name; assay_code, name of assay; rsnumber, unique SNP identifier in dbSNP; reference_allele, 3D7 reference allele, alternative_allele, alternative allele; single letter code, IUPAC code for SNPs; result, genotype call after processing; allele1, IUPAC code for allele 1; allele2, IUPAC code for allele 2; allele_ratio1, proportion of allele 1; allele_ratio2, proportion of allele 2; pass_fail, coding of SNP based on availability of valid genotype (pass) or lack of a valid genotype (fail). Geospatial data for homestead location is considered sensitive data and therefore cannot be made open access. However, it can be accessed through a request to our data governance committee, using the email address mmunene@uat/newsite. </p

Dataset 1: Information on the 276 SNPs genotyped in 177 genes in P. falciparum parasite populations from The Gambia, Kilifi and Rachuonyo South

<p><b>Dataset 1:</b> <b>Information on the 276 SNPs genotyped in 177 genes in <i>P. falciparum </i>parasite populations from The Gambia, Kilifi and Rachuonyo South. </b>The columns contain the following information: study_location, site of sample collection; sample_id, unique sample identifier; gene_symbol, gene name (if available); chr_valid, chromosome; coord_valid= base position of SNP on chromosome; sequence_code, SNP name; assay_code, name of assay; rsnumber, unique SNP identifier in dbSNP; reference_allele, 3D7 reference allele, alternative_allele, alternative allele; single letter code, IUPAC code for SNPs; result, genotype call after processing; allele1, IUPAC code for allele 1; allele2, IUPAC code for allele 2; allele_ratio1, proportion of allele 1; allele_ratio2, proportion of allele 2; pass_fail, coding of SNP based on availability of valid genotype (pass) or lack of a valid genotype (fail). Geospatial data for homestead location is considered sensitive data and therefore cannot be made open access. However, it can be accessed through a request to our data governance committee, using the email address mmunene@uat/newsite. </p

Malaria parasite prevalence by nPCR inside and outside serologically defined hotspots in the study area in Rachuonyo South District during a community survey conducted in June–July 2011.

<p>nPCR-based parasite prevalence is plotted for individuals residing inside 27 serologically defined hotspots (black bars), 1–249 meters from the hotspot boundary (grey hatched bars), 250–500 m from the hotspot boundary (open hatched bars), and >500 meters from the hotspot boundary (open bars). Parasite prevalence by nPCR is shown per altitude band. Error bars indicate the upper limit of the 95% confidence interval; the <i>p</i>-value for the trend test is given, adjusting for correlations between observations from individuals living in the same compound. The number of individuals for whom samples were available for nPCR inside hotspot boundaries was 2,222 individuals (1,350–1,449 m), 2,494 (1,450–1,499 m), 1,348 (1,500–1,549 m), and 118 (1,550–1,650 m). The number of individuals for 1–249 m from hotspot boundaries was 698 (1,350–1,449 m), 1,248 (1,450–1,499 m), 1,113 (1,500–1,549 m), and 246 (1,550–1,650 m). The number of individuals for 250–500 m from hotspot boundaries was 544 (1,350–1,449 m), 681 (1,450–1,499 m), 661 (1,500–1,549 m), and 164 (1,550–1,650 m). The number of individuals for >500 m from hotspot boundaries was 544 (1,350–1,449 m), 176 (1,450–1,499 m), 405 (1,500–1,549 m), and 135 (1,550–1,650 m).</p