Fig 1.
Map of Rachuonyo and Nyando Districts in western Kenya.
Rachuonyo district received one round of IRS in July-September 2008 and a second round in May-April 2009.
Fig 2.
Total monthly rainfall at Kisumu airport from January 2008 through December 2009.
The timing of the IRS campaigns are indicated by the shaded areas while arrows indicate the timing of the three cross-sectional surveys.
Table 1.
Characteristics of the survey population during the baseline survey (May 2008).
Values are presented with 95% CI and sample size in parentheses. P-values represent univariate comparisons between IRS and non-IRS districts. Comparisons that were statistically significant at p<0.05 are indicated in bold. All comparisons controlled for clustering within enumeration areas.
Table 2.
Characteristics of the survey population during the 1st follow up survey (November 2008).
Values are presented with 95% CI and sample size in parentheses. P-values represent univariate comparisons between IRS and non-IRS districts. Comparisons that were statistically significant at p<0.05 are indicated in bold. All comparisons controlled for clustering within enumeration areas.
Table 3.
Characteristics of the survey population during the 2nd post-IRS survey (August 2009).
Values are presented with 95% CI and sample size in parentheses. P-values represent univariate comparisons between IRS and non-IRS districts. Comparisons that were statistically significant at p<0.05 are indicated in bold. All comparisons controlled for clustering within enumeration areas.
Fig 3.
Prevalence of malaria parasitemia by age survey and district.
Error bars represent 95% confidence limits.
Fig 4.
Prevalence of clinical malaria by age, survey and district.
Error bars represent 95% confidence limits.
Fig 5.
Prevalence of anemia by age, survey and district.
Rachuonyo district received one round of IRS before the 2nd survey and another round of IRS before the 3rd survey. Error bars represent 95% confidence limits.
Table 4.
Prevalence of malaria related outcomes by survey and district.
Values in bold are significantly different between IRS and non-IRS districts at a<0.05 in univariate analyses. All comparisons controlled for clustering within compounds and within enumeration areas.
Fig 6.
Prevalence of malaria parasitemia, clinical malaria, anemia and the prevalence of anemia in children <5 years of age by survey.
Survey respondents were categorized as those who received IRS in the previous 12 months and used an ITN the previous night, those who received IRS alone, those who used an ITN the previous night but did not receive IRS and among those who neither used an ITN the previous night nor received IRS in the 12 months prior to the survey. Error bars represent 95% confidence limits.
Table 5.
Odds ratios from a multivariate regression model of the impact of different factors on infection with P. falciparum malaria parasitemia, clinical malaria and anemia.
Vector control (ITNs, IRS, both or neither) was included as a single categorical variable in all models. Comparisons that were statistically significant a p<0.05 are indicated in bold.
Fig 7.
Prevalence of malaria parasitemia, clinical malaria, anemia and the prevalence of anemia in children <5 years of age by survey, district and ITN use the previous night.
Table 6.
Multivariate regression model of the impact of different factors on infection with P. falciparum malaria parasitemia.
The model included an interaction between net use and district (a proxy for IRS). Comparisons that were statistically significant a p<0.05 are indicated in bold.
Table 7.
Effect of using a net conditional on district and effect of district conditional on net use on the risk of infection with P. falciparum malaria parasitemia.
Comparisons that were statistically significant a p<0.05 are indicated in bold.
Table 8.
Multivariate regression model of the impact of different factors on clinical malaria.
The model included an interaction between net use and district (a proxy for IRS). Comparisons that were statistically significant a p<0.05 are indicated in bold.
Table 9.
Effect of using a net conditional on district and effect of district conditional on net use on the risk of clinical malaria.
Comparisons that were statistically significant a p<0.05 are indicated in bold.
Table 10.
Multivariate regression model of the impact of different factors on anemia (Hb≤8).
The model included an interaction between net use and district (a proxy for IRS). Comparisons that were statistically significant a p<0.05 are indicated in bold.
Table 11.
Effect of using a net conditional on district and effect of district conditional on net use on the risk of anemia (Hb<8).
Comparisons that were statistically significant a p<0.05 are indicated in bold.