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

Malaria is one of the major public health challenges subverting development in the poorest countries in the world. The direct and indirect costs of malaria are very high and the disease has played a significant role in the poor economic performance of sub-Saharan Africa. Sachs (2002) estimated, that the gross domestic product in these countries would be up to 32% greater today if malaria had been eliminated 35 years ago [1]. In contrast to a retrogressive trend of malaria morbidity and mortality in many areas malaria burden has been increasing in other areas [2]. Factors such as deteriorating health systems, growing drug and insecticide resistance, failure of water management but also socioeconomic, land-use factors, and climate are hypothesized to influence the emergence of malaria [3, 4].

In Ghana, where the study was conducted, malaria is prevalent during the entire year and accounts for about 32-42% of all outpatient admissions and for major in-patient causes of death [5]. Sociodemographic factors such as ethnic group, parent's education and occupation, use of protective measures, and living standard of the family are suggested to be important risk factors for malaria and malaria epidemics [6–8]. The impact of socioeconomic factors, namely the family's financial situation, is difficult to assess due to the lack of standardized economic data of income and tax. The use of single indicators for the household's economical situation reduces the available information and may imperfectly set the focus point on the selected parameters. Additional socioeconomic factors assessed in the Demographic and Health Survey 2008 and not considered here are marital status and religion, which we did not found appropriate in the context of the study.

The aim of the presented study was to investigate the association between the socioeconomic status of families classified with a number of indicators as a PCA-based score and their association with childhood malaria.

In total, 1,496 children were examined for malaria parasites and participated in the questionnaire survey; 1,478 without missing values were included in the multivariate model. Most participants came from the region Greater Agogo (n = 871), fewer came from Greater Konongo (n = 333), Near Street (n = 229), and West of Agogo (n = 63). Most malaria cases were reported from Greater Agogo (n = 364), followed by Greater Konongo (n = 66), Near Street (n = 61), and West of Agogo (n = 21) (See additional file 1 describing characteristics of the study group).

Of those variables with a possible influence on malaria use of individual control measures had a protective effect on malaria (crude OR = 0.69, p = 0.02) (See additional file 1 describing characteristics of the study group). Malaria odds were increased if a child was between > 1-< 5 or above 5 years of age (OR = 3.41, and OR = 2.07, both p < 0.001). Notably, the area of residence was strongly associated with the frequency of malaria (OR Greater Agogo compared to Greater Konongo = 3.1, p < 0.001). The variables "ethnic groups", "sex", "mother's age" and "number of children in the family" did not show any significant association with malaria.

Of those factors indicating the family's socioeconomic status the proportion of literate fathers was very high (> 75%) and evenly distributed under children with and without malaria. Likewise, the variables "house type", "income manage", "membership in a health insurance", "existence of an indoor kitchen" and "mother's and father's occupation" did not show any distinct association with malaria (See additional file 1 describing characteristics of the study group). In the univariate analysis, the variables "existence of electricity", "indoor toilet", "use of freezing as food conservation", "mother's ability to read and write" and a "closed water supply" were negatively associated with malaria odds (OR = 0.72, OR = 0.67, OR = 0.63, OR = 0.68, OR = 0.70, respectively, all p-values < 0.01). Additionally, the variable "existence of a relative abroad" known to be indicative for a substantive contribution to the household income in Africa had a protective effect on malaria (p = 0.05). All variables with relevant contributions (> 10%) to the combined socioeconomic score were used to generate a combined socioeconomic indicator by PCA; hence "mother's occupation" and "father's occupation" were excluded from the final PCA (weight mother's occupation and father's occupation: 5% and 4%, respectively).

Additionally, clumping and truncation (if the distribution of scores tend not to follow a normal curve or if they were skewed) was checked by using a histogram to show the distribution of socioeconomic scores. Internal coherence for our study region could be shown, suggesting appropriate and sufficient choice of asset variables.

The analysis showed that, in an area of high endemicity, the proportion of malaria in children presented to a hospital is markedly influenced by the socioeconomic status of the family: children from households classified as poor had a significantly higher chance to get malaria. This is in agreement with previous reports on distinct socioeconomic risk factors for malaria [6–8, 15, 16]. One possible explanation of this observation is that the proportion of children using protective bed nets increases with the socioeconomic status as reported before [17, 18]. However, after adjustment for the use of bed nets in the multivariate analysis an association of the socioeconomic status with malaria still remained. Other possible explanations for the association between malaria and socioeconomic status are (i) differences in the coverage of health insurance [19] which was, however, not of significant influence in the univariate analysis, (ii) differing access to health facilities, whereas such a selection bias might be low due to the hospital-based study design, (iii) various environmental or housing conditions in the vicinity of households e.g. preferred habitats or breeding sites for vectors what is difficult to exclude [20, 21].

Socioeconomic levels might also be associated with diseases beyond malaria and may influence the proportion of malaria cases among all children seen in the hospital. This would have an indirect influence on the calculated odds ratios. However, the two symptom complexes predominant in children without malaria, namely respiratory distress and gastrointestinal symptoms, were not or only weakly associated with socioeconomic levels. The study design, which bases on a single hospital, might limit the generalization of the results to other regions. On the other hand, the focus on one hospital allowed the thorough collection of data on the clinical condition, infectious disease agents, and exact diagnosis.

One problem of the determination of individual socioeconomic levels in Africa is the fact that unambiguous quantitative measures often do not exist and various proxy measures must be used as an approximation. The use of single variables as risk indicators often leads to false conclusions because they only reflect parts of the general view. In contrast, in PCAs socioeconomic indicator variables were combined to enable a quantification and classification of individual socioeconomic levels and to use the resulting score for risk analyses. The PCA showed that usage of a freezer as conservation method, which was interpreted as ownership of a freezer in a household, had the highest weight for the socioeconomic score. On the other hand, having a health insurance was the smallest compared to the other ten variables and, hence, was of a minor importance for the socioeconomic score.

An advantage of the PCA is that it reduces measurement problems, such as recall bias, and that it reduces the complexity of correlated data, which can be easily collected as single indicator variables in household surveys [12, 22]. On the other hand, the process of generalization leads to a loss of information, the criteria for the selection of variables for PCA are not well defined, and the number of selected components is arbitrary. Whether a single principal component can sufficiently determine the socioeconomic status is entirely dependent on the data and the correlation matrix of the variables, their validity and reliability [12].

Apart from the socioeconomic status, sociodemographic factors were associated with malaria. As expected the malaria risk was highest in the age group of children between 1 and 5 years, compared to children below the age of 1 year [23] and lower in children from families, which reported the use of mosquito protection measures [16, 24]. There was a decrease of odds for malaria with increasing distance from the study hospital. A simple selection bias is not a sufficient explanation for that observation since all children included had access to the hospital. However, it is conceivable that the willingness to bring a child with malaria symptoms decreases with increasing distance from the study hospital and, in contrast, the readiness to bring a child with other symptoms is more independent from distance. If so, the relative contribution of malaria cases decreases with distance and this would falsely suggest a protective effect of distance against malaria. Nevertheless, data from a recently performed community survey in the same area showed that the health seeking behaviour among differing symptoms did not change with distance. It was not possible to include the village population size as influence factor due to the village-cluster based analysis. Geographical risk factors seem to exist independently from other influences, maybe through environmental and habitat factors favouring the occurrence of vectors. To assess environmental influence factors remotely sensed data with high resolution should be analysed with Geographical Information System (GIS) to detect microspatial patterns in relation to malaria risk.

Age of mothers did not influence the occurrence of malaria of their children. However, in the study group only 3% of the mothers were younger than 20 years of age, which does not represent the mother's age distribution in Ghana or other African countries. Although ethnicity was found to influence malaria risk in a study conducted in an adjacent region in Ghana [6] this could not be confirmed in the presented study possibly due to the predominance of one ethnic group.

In conclusion, the herein presented results show that children from poorer households are of greater risk for malaria. It is under discussion how far poverty influences the occurrence of malaria or malaria influences the occurrence of poverty. In either case, the fight against malaria has to be escorted by the fight against poverty and improvement of living standard. Moreover, the spatial variability of malaria risk might be of importance for the planning of control measures and the conduction of intervention trials.