Table 1.

Statistical analysis of differences between mature irrigated and nonirrigated areas

Variable	High risk	Low risk	t value	df	P value	W	W P value
Literates/illiterates	0.7577	0.8935	−0.8827	16.1796	0.3903	71	0.7675
Improved drinking water	0.9227	0.9903	−2.2142	13.9951	0.0439	36.5	0.0282
Agricultural credit societies	0.6106	0.8183	−2.7805	18.2579	0.0122	18	7.00E-04
Banks access	0.2565	0.2464	0.1966	19.1836	0.8462	83	0.7675
Education access	0.9946	0.9975	−1.0629	22.9806	0.2989	51	0.1492
Medical access	0.7705	0.6615	1.5383	22.9944	0.1376	102	0.1832
% irrigated land	0.1615	0.7172	−9.1045	21.25	<0.0001	2	<0.0001
IRS control	0.4558	0.069	4.6783	16.176	<0.0001	146	2.00E-04

Socioeconomic variables consist of the ratio between literate and illiterate populations, the proportion of the population in each taluka with access to potable water, agricultural credit societies, banks, and education, public health, and medical facilities. Education facilities encompass all primary and secondary schools. Medical access corresponds to the community health centers, primary health centers, subcenters, and hospitals available in each taluka. A two-sample location unpaired Welch’s t test, with the level of malaria risk obtained from the cluster algorithm as a categorical variable, was used to test if the socioeconomic indicators from the talukas were sampled from two different normal distributions based on the level of the malaria risk. We also performed a nonparametric Wilcoxon test that does not assume normality. We applied these tests for indoor residual spray (IRS) control and for the percentage of the talukas under irrigation in 2001. W, the Wilcoxon statistic.