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Socio-economic status was the main factor in determining the malaria infection in this study. The following shows the factor analysis was used to determine the SES of the individuals. Factor analysis is a useful tool for investigating variables relationships for complex concepts such as socioeconomic status ADDIN EN.CITE Gwatkin200094[1]949417Gwatkin, Davidson RRutstein, SheaJohnson, KierstenPande, RohiniWagstaff, AdamSocio-economic differences in health, nutrition, and populationWashington, DC: World BankWashington, DC: World Bank2000[1]. It allows researchers to investigate concepts that are not easily measured directly by collapsing a large number of factors into a few interpretable underlying factors. The following is the list of household assets;
1- Source of water
2- Type of toilet facility
3- Has a radio
4- Has a bicycle
5- Has a motorcycle
6- Has a car
7- Has a telephone
8- Share toilet
9- Has a mobile phone
10- Has cattle/s
11- Has goats
12- Has sheep
13- Has chickens
14- Has bulls
15- Has pigs
16- Has rabbits
17- Has bank account
18- Main floor material
19- Main roof material
20- Main wall material
21- Type of cooking oil use for cooking
22- Ownership of land
23- Has watch
Figure 5 shows the statistical process (mathematical model) to determine socio-economic of the individuals.
Household assets are the basic data used as input of the model. The household assets have many factors which were used to determine the overall SES of the individuals. The Figure 5 explains the statistical process step by step.
Step 1- Input household assets, that all coefficients are positive so that the index does indeed give an overall measure of SES. Any variable with a negative coefficient as well as variables with a large number of missing values should be omitted.
Step 2- This step investigates the property of input variable could be continuous or categorical.
Step 3- Data management, recoding of variables and checked for missing value. Variables were excluded with more than 10% missing values. In additional, variable is also recoding in a binary format.
Step 4- Selections of final variables and factorization were done. The highest Eigenvalue was used in the computation of the score. The first factor had an Eigenvalue of "2.48" and explained "64%" of the variability. Predication of factor score was done in step 4.
Step 5- Categories of SES were formed by using the factor score obtained in step 5. From the histogram as shown (Appendix A1.1), cutoff points were decided and formed three categories of SES. The first category was "Most Poor" with cutoff point " (min/-0.333878=1)". The second category was "Poor" with cutoff value "(-0.333879/0.3148308=2)". The third category was "Least Poor" and the cutoff point (0.3148308/max=3).
These categories are further used in the study to determine the influence of SES on malaria infection.
(Direct pathway
Direct pathway defines as an effect of exposure which is not affected by a given set of potential mediators. In Figure S-3, SES has shown direct pathway on malaria infection. Direct pathway gives direct effect.
(Indirect pathway
Indirect pathway defines as exposure effect which is affected by a given set of potential mediators. In Figure S-4, level of education has shown indirect effect on malaria infection through SES. Indirect pathway gives indirect effect. Indirect pathway has explained as shown below;
Level of education ( SES ( Malaria infection
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