Principal component analysis of socioeconomic factors and their association with malaria in children from the Ashanti Region, Ghana

Abstract

BackgroundThe socioeconomic and sociodemographic situation are important components for the design and assessment of malaria control measures. In malaria endemic areas, however, valid classification of socioeconomic factors is difficult due to the lack of standardized tax and income data. The objective of this study was to quantify household socioeconomic levels using principal component analyses (PCA) to a set of indicator variables and to use a classification scheme for the multivariate analysis of children < 15 years of age presented with and without malaria to an outpatient department of a rural hospital.MethodsIn total, 1,496 children presenting to the hospital were examined for malaria parasites and interviewed with a standardized questionnaire. The information of eleven indicators of the family's housing situation was reduced by PCA to a socioeconomic score, which was then classified into three socioeconomic status (poor, average and rich). Their influence on the malaria occurrence was analysed together with malaria risk co-factors, such as sex, parent's educational and ethnic background, number of children living in a household, applied malaria protection measures, place of residence and age of the child and the mother.ResultsThe multivariate regression analysis demonstrated that the proportion of children with malaria decreased with increasing socioeconomic status as classified by PCA (p < 0.05). Other independent factors for malaria risk were the use of malaria protection measures (p < 0.05), the place of residence (p < 0.05), and the age of the child (p < 0.05).ConclusionsThe socioeconomic situation is significantly associated with malaria even in holoendemic rural areas where economic differences are not much pronounced. Valid classification of the socioeconomic level is crucial to be considered as confounder in intervention trials and in the planning of malaria control measures.

Factors Associated with Malaria Prevalence among Children under Five Years in the Hohoe Municipality of Ghana

“…This study found that children whose parents/ guardians were into farming and trading were 1.73 and 1.83 times more likely to have a malaria infection. This is in agreement with other reports where those with high socioeconomic status (SES) (OR=0.4, 95% CI: 0.3, 0.5, p<0.001) compared to low SES were associated with lower odds of malaria infection [11]. A parent/ guardian who is a farmer or trader may have lower education and be very busy with activities that bring lower income.…”

“…Okebe et al [14] also who found fever in 3% of those infected with malaria and there was increased odds of fever. This is in agreement with studies by Temu et al, in 2012 in Mozambique [11], who found that, children presenting with current fever were infected with malaria (56.7%; 95%CI: 43.7%, 68.9%) compared with those without fever (46.7%; 95%CI: 37.6, 55.9, p=0.037).…”

“…It was found that older children 24 months and above were more likely to have malaria when compared to children aged 6-11 months. This is similar to other studies, which indicated that an older child was associated with a higher risk of malaria [7,10,11]. Age of child could also contribute to the individual's way of life, where older children could move about freely and may go outside more often and therefore may be more susceptible to mosquito bites, and thus malaria.…”

“…These results are in agreement with those of other authors, who also found no evidence of a lower odds of malaria infection among children sleeping under LLIN in relative to those not [9,11,14]. This observed lack of association in our study could possibly be attributed to an inconsistent or inappropriate use of the nets or perhaps a child was exposed to mosquito bites during other times of the day or evening when the net was not in use.…”

School Enrollment among Children with Disability in Rural Eastern Ethiopia: A Community-Based Survey

“…The questionnaire included a series of questions divided in the following sections: (a) Gender, (b) Age during disability onset in years, (c) Status of multiple disabilities, (d) Status of assistive device availability, (e) Educational status of mother, (f) Reasons for not being enrolled, (g) Household socioeconomic status and (h) School enrollment status of the CwD. We developed household wealth index from nine asset variables using principal component analysis [21,23] as a proxy for household socioeconomic status and classified the score into three categories using terciles.…”