Fig 1.
Analytical framework of the relationship between SEP and NTD infection.
Note that we use NTD infection to signify the NTD and/or the underlying infection. Pathway (1) shows the effect of SEP on NTD infection via more proximate determinants of such infection, e.g., (lack of) sanitation facilities. The effect of SEP on NTD infection may be modified (2), e.g., by age. Associations between SEP and NTD infection may be partly explained by confounders (3), e.g., sex. Finally, NTD infection can also affect SEP (4). Dashed line: not the focus of our review (part of other reviews in this series). The framework is based on [11–16].
Fig 2.
PRISMA flow diagram.
Fig 3.
Number of papers reporting greater odds of infection among lower socioeconomic strata than among higher socioeconomic strata, number of papers reporting mixed results, and number of papers reporting no inequality or a reverse association, 2004–2013.
Inequality: Statistically significant (p < 0.05) inequality in NTD distribution, with greater odds of infection among lower socioeconomic strata. This also includes papers reporting statistically significant inequality for one SEP indicator and nonsignificant inequality of at least 50% greater odds of infection among lower strata for another SEP indicator. The same criterion was used for socioeconomic inequality in NTD prevalence in one age group and not in another age group and for one NTD outcome measure and not for another NTD outcome measure. Mixed: Studies reporting a combination of statistically significant inequality—with greater odds of infection among lower socioeconomic strata—for one SEP indicator, age group, or NTD outcome measure and no such inequality (or reversed pattern) for another SEP indicator, age group, or NTD outcome measure. No inequality: Studies reporting no substantial and statistically significantly greater odds of infection among lower socioeconomic strata or reporting a reverse pattern, with greater odds of infection among higher strata. Of the 93 publications included in the review, two studies (Balen et al. 2011 and Steinmann et al. Acta Tropica 2007) reported findings for both schistosomiasis and STH separately. In the figure, we included these studies under both schistosomisasis and STH. Two other studies that reported on combined schistosomiasis and STH infection in the same individuals were not included in this figure (one reported inequality, the other no inequality).
Fig 4.
Example of socioeconomic inequalities in LF: association between microfilaria prevalence (%) and household monthly income (US$) in Karimnagar district, India (2004–2007) [20].
The study was performed in Karimnagar district (Andhra Pradesh, India), which is a filariasis-endemic region where a mass drug administration (MDA) program has been active since 2004. Blood samples were taken from 5,394 inhabitants of all ages from 30 villages and tested for Wuchereria bancrofti microfilaria. Socioeconomic information was collected through an interview with the household head or other family members. Household monthly income was divided into three categories (<US$22; US$22–US$66; >US$66; currency rate used: 1 Indian Rupee = US$0.022, January 2006). In total, 3.7% of the blood samples tested positive for microfilaria. Microfilaria prevalence was statistically significantly (p = 0.02) associated with household income, with the poorer households being more affected than the richer households.
Fig 5.
Example of socioeconomic inequalities in LF: association between filarial antigen prevalence (%) and family wealth in the city of Tanga, eastern Tanzania (2012) [22].
The study was conducted in the city of Tanga in eastern Tanzania, along the Indian Ocean, where a mass drug administration program (MDA) has been active since 2004. Two urban and one peri-urban ward were selected as being representative for the city, and 960 children aged five to 16 years from public primary schools were examined for circulating filarial antigens (CFA) in their blood. Of these children, 895 also filled in a questionnaire. Ownership of a fridge and TV was used as indicator of family wealth; this information was aggregated to constitute a measure of wealth at the ward level. To create the figure, we divided the wards into three groups according to the family wealth measures: “most poor,” “intermediate poor,” and “least poor.” The overall CFA prevalence was 5.5%. CFA prevalence was statistically significantly lower in the least poor as compared to the intermediate and most poor (p = 0.04).
Fig 6.
Example of socioeconomic inequalities in schistosomiasis: association between S. japonicum and income per capita at village level in Hunan province, China (2005) [28].
The studied villages represent four types of areas: lake-embankment, lake-beach, inside embankment, and hill areas. A total of 10,245 residents aged six years and older from 16 villages were included in the study. The presence of antibodies to S. japonicum was screened using the indirect hemagglutination (IHA) test, and stool samples were examined for IHA-positive cases. Per capita income was measured at the village level (currency rate used: 1 Yuan = US$0.121, July 2005). The overall infection prevalence was 4.1%. Infection prevalence was higher in poorer villages, (p < 0.001).
Fig 7.
Example of socioeconomic inequalities in schistosomiasis: association between S. mansoni prevalence (%) and educational attainment of household head in the town of Man, western Côte d’Ivoire (2004–2005) [30].
The study was conducted in urban farming communities in the town of Man, western Côte d’Ivoire. A total of 113 farming households (586 individuals from all ages) and 21 nonfarming households (130 individuals from all ages) from six agricultural zones were interviewed, and stool samples were examined for S. mansoni. Infection prevalence was 51.4% in farming households and 44.6% in nonfarming households. Lower educational attainment was associated with higher infection prevalence in farming households (p = 0.008) but not in nonfarming households. Infection prevalence was higher in poorer households but not statistically significantly so.
Fig 8.
Example of socioeconomic inequalities in schistosomiasis: association between S. haematobium prevalence (%) and educational attainment in South Kordofan State, Sudan (2009) [6].
South Kordofan State is located in the south of Sudan, bordering South Sudan. A total of 1,826 adults (aged >18 years) were recruited from all nine localities (36 villages or towns) of South Kordofan State. Urine samples were examined for the presence of S. haematobium, and a questionnaire on demographics and economic status of the family head was administered. Overall prevalence of S. haematobium eggs was 6.9%. Infection prevalence was higher among people with a lower educational attainment.
Fig 9.
Example of socioeconomic inequalities in STH: parental education and ascariasis infection in a rural community in Osun State, Nigeria (2005–2006) [71].
A total of 440 children <16 years of age from randomly selected households were included. Information on parental education was collected through a questionnaire, and faecal samples were examined for the presence of Ascaris eggs. The prevalence of ascariasis was statistically significantly higher among children of parents without a primary education (p < 0.001).
Fig 10.
Example of socioeconomic inequalities in trachoma: association between educational attainment of household head and follicular trachoma in young children in Tanzania (2008) [79].
The study was conducted among children aged 0–5 years in 36 communities (3,122 children) in 2008. The eyes of the children were examined for active trachoma, and information on the educational attainment of the household head was collected through a questionnaire. The prevalence of follicular trachoma was 30.9%. Lower educational attainment of the household head was associated with a higher risk of follicular trachoma among children in both countries (Tanzania p = 0.001).