Socioeconomic Condition and Prevalence of Malaria Fever in Pakistani Children: Findings from a Community Health Survey

Socioeconomic Condition and Prevalence of Malaria Fever in Pakistani Children: Findings from a... Abstract Objective We assessed the prevalence of malarial fever and its association with demographic and socioeconomic factors in children <5 years of age. Methods Using the data of Pakistan Demographic and Health Survey (PDHS), the socioeconomic condition (SEC) was assessed by using a household wealth index as a proxy indicator, generated through principal component analysis. Two-stage sampling was used for selection of households, and multilevel logistic regression analysis was performed. Results The PDHS contains 10 935 children <5 years of age with valid information about malaria fever. In total, 36% (3930) children have malaria 2 weeks before the survey. A decreasing trend in prevalence of malaria fever was found with increasing SEC. Compared with SEC Quintile V, children of SEC Quintile I were more likely to get fever [adjusted odds ratio (AOR)=1.40 (1.15–1.69)] and of SEC Quintile II [AOR = 1.23 (1.03–1.45)]. Conclusion SEC has a significant impact on the prevalence of malaria fever in the context of different regions in Pakistan. malaria fever, socioeconomic condition, multilevel logistic regression INTRODUCTION Malaria fever is a common presentation in Pakistani children <5 years of age that accounts for >60% of the cases [1]. Malaria fever is also associated with high level of malnutrition and mortality. The estimated number of malaria cases in Pakistan is 1.6 million per year including 300 000 confirmed cases in public health-care facilities [2]. The Millennium Development Goal-6 (MDG) advocates control and elimination of preventable infectious diseases including malaria. According to World Health Organization, Pakistan is one of the seven countries of Eastern Mediterranean Region that have areas of high malaria transmission and confirmed cases. Unfortunately, only 40% of the population receives effective malaria prevention and treatment plan, while MDG stipulates these statistics to be around 75% [3]. The major part of the population in Pakistan lives in rural areas, and over two-thirds live on <US $2 a day. Previous work indicates that socioeconomic condition (SEC) may increase the risk of malaria fever in Pakistan; yet, there is limited evidence available about its association with fever in children <5 years of age. The purpose of this study was to examine the relationship between SEC and the prevalence of malaria fever among children of age <5 years at two levels, i.e. individual and regional (provinces). METHODS Data The data on children of age <5 years were extracted from Pakistan Demographic and Health Survey (PDHS) for the year 2012–13. The primary objective of the looking in to PDHS was to provide information about the population and health indicators for national and regional levels including rural and urban areas. This is a cross-sectional study. The main purpose of considering PDHS was the availability of comprehensive information on predictors in resource-poor setting in Pakistan. Only one episode was counted as a case of malarial fever. Two-stage sampling technique was used for the selection of household to be interviewed. Five hundred primary sampling units (communities) were selected randomly at first stage using probability proportional to size scheme (252 from rural and 248 from urban areas). At second stage, systematic sampling technique was used to select a fixed number of households (28) from each primary sampling units selected at first stage, and in this way, 14 000 households were selected for an interview (7056 in rural areas and 6944 in urban). Information of 11 763 children was presented in the survey; however, the analysis remained limited to 10 935 children of age <5 years with valid information about fever. The children were considered infected who had malaria fever 2 weeks before the survey and received any malarial drugs. In the PDHS database, 65% had received advice or treatment from a health facility or provider. Moreover, Demographic and Health Surveys use field-friendly equipment and rapid diagnostic testing technologies, whenever possible, to get quick and reliable results. The information about the PDHS can be consulted from elsewhere [2]. Variables The main outcome variable was presence of fever in the children of age <5 years during 2 weeks preceding the survey. The main independent variable was SEC based on wealth index, which was derived from principal component analysis using household possessions (radio, television, refrigerator, car, truck, boat, etc.) and change belonging to facilities (source of drinking water, type of toilet facility, electricity, material of roof, material of floor, etc.). The SEC of the household was measured by dividing this wealth index into five quintiles from lowest 20% to highest 20% [2], i.e. SEC-I (lowest) to SEC-V (highest), to intricate the trend of malaria fever. Other covariates considered in this study were mother’s education [none, primary (5 years) and secondary+ (≥8 years)], possession of bed nets (yes/no), sex of child (male/female), type of residence (urban/rural), source of drinking water (water through pipe, tube well, filtration plant or bottled was considered as ‘safe’, while water through any other source such as river, pond, uncovered water was considered as ‘unsafe’), type of toilet facility (the toilet or latrine connected to sewage system was considered as ‘hygienic’, while all others were considered as ‘non-hygienic’) and access to health center (easy/not easy). All variables were selected after literature review [4, 5]. Statistical analysis The data were analyzed in two steps. First, we assessed the prevalence of fever in selected sample and a bivariate association of covariates in the presence of malaria fever. In the second step, the significant factors in bivariate analysis were further analyzed by multilevel logistic regression analysis. The multilevel model that assessed the relation of individual and regional levels was estimated using Stata 11.0 software. Multilevel logistic regression analysis was used to account for the hierarchical nature of PDHS data; two-level model was applied, i.e. children were at Level 1 and regions were at Level 2. Two-level model for binary response uses a binomial sampling and a logit link [6]. RESULTS Descriptive analysis The sample used in this study was extracted from the national representative PDHS data available for 11 763 children. For analysis, 10 935 children of age <5 years were considered who have valid information about malaria fever. Equal number of male (5575) and female (5360) children were observed in the sample (male–female ratio = 1.04). About 3930 children have malaria fever 2 weeks before the survey that is 36% of the study sample. Table 1 shows frequency and percentage distribution of the selected covariates and their bivariate association with the outcome variable. The results demonstrate that prevalence of malaria has significant association with mother’s education. The mother with no education has high ratio (53.4%: 2099 of 3930) of getting malaria fever, and the ratio goes down with an improvement in mother education. The resident of a household with or without bed nets did not show the significant association with the outcome variable. Sex of children has significant association with malaria fever, and its prevalence was found high in male children (53.3%: 2093 of 3930). Fever prevalence stood high in children who were residents of rural areas (58.6%: 2304 of 3930). The residents of households with hygienic toilets have more malaria prevalence (69.4%: 2726 of 3930). The prevalence of malaria stood comparatively high for the children of age group 1–2 years (44.1%: 1733 of 3930). One of the key determinants of this study was the SEC of the household. The Table 1 also shows that there is no significant association between SEC fever presence in the children of age <5 years. Table 2 shows that lower two levels of SEC are at high risk of having the disease than that of the highest. Table 1 Percentage distribution of baseline characteristics and their bivariate association with malaria fever Characteristics Presence of malaria fever Yes (%) No (%) Total Chi-square value p-value Mother education  No education 2099 (53.4) 4058 (57.9) 6157 22.78 0.0001  Primary 621 (15.8) 944 (13.5) 1565  Secondary+ 1210 (30.8) 2003 (28.5) 3213 Possession of bed net  No 3300 (84.0) 5884 (84.0) 9187 0.0001 0.99  Yes 630 (16.0) 1121 (16.0) 1748 Sex of child  Male 2093 (53.3) 3482 (49.7) 5575 12.69 0.0001  Female 1837 (46.7) 3523 (50.3) 5360 Type of residence  Urban 1626 (41.4) 3054 (43.6) 4680 5.08 0.024  Rural 2304 (58.6) 3951 (56.4) 6235 Source of drinking water  Safe 3329 (84.7) 5925 (84.6) 9254 0.03 0.862  Not safe 601 (15.3) 1080 (15.4) 1681 Toilet facility  Hygienic 2726 (69.4) 4649 (66.4) 7375 10.29 0.001  Not hygienic 1204 (30.6) 2356 (33.6) 3560 Access to health centre  Easy 2199 (56.3) 3889 (55.5) 6088 0.19 0.659  Not easy 1731 (44.0) 3116 (44.5) 4847 Child age (years)  <1 844 (21.5) 1287 (18.4) 2131 121.16 0.0001  1–2 1733 (44.1) 2551 (36.4) 4284  3+ 1353 (34.4) 3167 (45.2) 4520 Region  Punjab 1180 (30.0) 1833 (26.2) 3013 198.65 <0.0001  Sindh 845 (21.5) 1483 (21.1) 2328  Khyber Pakhtunkhwa 895 (22.8) 1259 (18.0) 2154  Balochistan 382 (9.7) 1356 (19.3) 1738  Gilgit-Baltistan 348 (8.9) 668 (9.5) 1016  Islamabad 280 (7.1) 406 (5.8) 686 SEC  SEC-V 754 (19.2) 1339 (19.1) 2093 2.2 0.699  SEC-IV 758 (19.3) 1313 (18.7) 2071  SEC-III 763 (19.4) 1353 (19.4) 2116  SEC-II 790 (20.1) 1376 (19.6) 2166  SEC-I 865 (22.0) 1624 (23.2) 2489 Total 3930 (100) 7005 (100) 10 935 Characteristics Presence of malaria fever Yes (%) No (%) Total Chi-square value p-value Mother education  No education 2099 (53.4) 4058 (57.9) 6157 22.78 0.0001  Primary 621 (15.8) 944 (13.5) 1565  Secondary+ 1210 (30.8) 2003 (28.5) 3213 Possession of bed net  No 3300 (84.0) 5884 (84.0) 9187 0.0001 0.99  Yes 630 (16.0) 1121 (16.0) 1748 Sex of child  Male 2093 (53.3) 3482 (49.7) 5575 12.69 0.0001  Female 1837 (46.7) 3523 (50.3) 5360 Type of residence  Urban 1626 (41.4) 3054 (43.6) 4680 5.08 0.024  Rural 2304 (58.6) 3951 (56.4) 6235 Source of drinking water  Safe 3329 (84.7) 5925 (84.6) 9254 0.03 0.862  Not safe 601 (15.3) 1080 (15.4) 1681 Toilet facility  Hygienic 2726 (69.4) 4649 (66.4) 7375 10.29 0.001  Not hygienic 1204 (30.6) 2356 (33.6) 3560 Access to health centre  Easy 2199 (56.3) 3889 (55.5) 6088 0.19 0.659  Not easy 1731 (44.0) 3116 (44.5) 4847 Child age (years)  <1 844 (21.5) 1287 (18.4) 2131 121.16 0.0001  1–2 1733 (44.1) 2551 (36.4) 4284  3+ 1353 (34.4) 3167 (45.2) 4520 Region  Punjab 1180 (30.0) 1833 (26.2) 3013 198.65 <0.0001  Sindh 845 (21.5) 1483 (21.1) 2328  Khyber Pakhtunkhwa 895 (22.8) 1259 (18.0) 2154  Balochistan 382 (9.7) 1356 (19.3) 1738  Gilgit-Baltistan 348 (8.9) 668 (9.5) 1016  Islamabad 280 (7.1) 406 (5.8) 686 SEC  SEC-V 754 (19.2) 1339 (19.1) 2093 2.2 0.699  SEC-IV 758 (19.3) 1313 (18.7) 2071  SEC-III 763 (19.4) 1353 (19.4) 2116  SEC-II 790 (20.1) 1376 (19.6) 2166  SEC-I 865 (22.0) 1624 (23.2) 2489 Total 3930 (100) 7005 (100) 10 935 Table 1 Percentage distribution of baseline characteristics and their bivariate association with malaria fever Characteristics Presence of malaria fever Yes (%) No (%) Total Chi-square value p-value Mother education  No education 2099 (53.4) 4058 (57.9) 6157 22.78 0.0001  Primary 621 (15.8) 944 (13.5) 1565  Secondary+ 1210 (30.8) 2003 (28.5) 3213 Possession of bed net  No 3300 (84.0) 5884 (84.0) 9187 0.0001 0.99  Yes 630 (16.0) 1121 (16.0) 1748 Sex of child  Male 2093 (53.3) 3482 (49.7) 5575 12.69 0.0001  Female 1837 (46.7) 3523 (50.3) 5360 Type of residence  Urban 1626 (41.4) 3054 (43.6) 4680 5.08 0.024  Rural 2304 (58.6) 3951 (56.4) 6235 Source of drinking water  Safe 3329 (84.7) 5925 (84.6) 9254 0.03 0.862  Not safe 601 (15.3) 1080 (15.4) 1681 Toilet facility  Hygienic 2726 (69.4) 4649 (66.4) 7375 10.29 0.001  Not hygienic 1204 (30.6) 2356 (33.6) 3560 Access to health centre  Easy 2199 (56.3) 3889 (55.5) 6088 0.19 0.659  Not easy 1731 (44.0) 3116 (44.5) 4847 Child age (years)  <1 844 (21.5) 1287 (18.4) 2131 121.16 0.0001  1–2 1733 (44.1) 2551 (36.4) 4284  3+ 1353 (34.4) 3167 (45.2) 4520 Region  Punjab 1180 (30.0) 1833 (26.2) 3013 198.65 <0.0001  Sindh 845 (21.5) 1483 (21.1) 2328  Khyber Pakhtunkhwa 895 (22.8) 1259 (18.0) 2154  Balochistan 382 (9.7) 1356 (19.3) 1738  Gilgit-Baltistan 348 (8.9) 668 (9.5) 1016  Islamabad 280 (7.1) 406 (5.8) 686 SEC  SEC-V 754 (19.2) 1339 (19.1) 2093 2.2 0.699  SEC-IV 758 (19.3) 1313 (18.7) 2071  SEC-III 763 (19.4) 1353 (19.4) 2116  SEC-II 790 (20.1) 1376 (19.6) 2166  SEC-I 865 (22.0) 1624 (23.2) 2489 Total 3930 (100) 7005 (100) 10 935 Characteristics Presence of malaria fever Yes (%) No (%) Total Chi-square value p-value Mother education  No education 2099 (53.4) 4058 (57.9) 6157 22.78 0.0001  Primary 621 (15.8) 944 (13.5) 1565  Secondary+ 1210 (30.8) 2003 (28.5) 3213 Possession of bed net  No 3300 (84.0) 5884 (84.0) 9187 0.0001 0.99  Yes 630 (16.0) 1121 (16.0) 1748 Sex of child  Male 2093 (53.3) 3482 (49.7) 5575 12.69 0.0001  Female 1837 (46.7) 3523 (50.3) 5360 Type of residence  Urban 1626 (41.4) 3054 (43.6) 4680 5.08 0.024  Rural 2304 (58.6) 3951 (56.4) 6235 Source of drinking water  Safe 3329 (84.7) 5925 (84.6) 9254 0.03 0.862  Not safe 601 (15.3) 1080 (15.4) 1681 Toilet facility  Hygienic 2726 (69.4) 4649 (66.4) 7375 10.29 0.001  Not hygienic 1204 (30.6) 2356 (33.6) 3560 Access to health centre  Easy 2199 (56.3) 3889 (55.5) 6088 0.19 0.659  Not easy 1731 (44.0) 3116 (44.5) 4847 Child age (years)  <1 844 (21.5) 1287 (18.4) 2131 121.16 0.0001  1–2 1733 (44.1) 2551 (36.4) 4284  3+ 1353 (34.4) 3167 (45.2) 4520 Region  Punjab 1180 (30.0) 1833 (26.2) 3013 198.65 <0.0001  Sindh 845 (21.5) 1483 (21.1) 2328  Khyber Pakhtunkhwa 895 (22.8) 1259 (18.0) 2154  Balochistan 382 (9.7) 1356 (19.3) 1738  Gilgit-Baltistan 348 (8.9) 668 (9.5) 1016  Islamabad 280 (7.1) 406 (5.8) 686 SEC  SEC-V 754 (19.2) 1339 (19.1) 2093 2.2 0.699  SEC-IV 758 (19.3) 1313 (18.7) 2071  SEC-III 763 (19.4) 1353 (19.4) 2116  SEC-II 790 (20.1) 1376 (19.6) 2166  SEC-I 865 (22.0) 1624 (23.2) 2489 Total 3930 (100) 7005 (100) 10 935 Table 2 Multilevel logistic regression analysis taking region as random effect Characteristics ORs CI p-value Mother’s education  No education Reference  Primary 1.21 1.06–1.36 0.002  Secondary+ 1.17 1.03–1.30 0.009 Sex of child  Female Reference  Male 1.15 1.06–1.25 0.001 Type of residence  Urban Reference  Rural 1.02 0.92–1.12 0.679 Toilet facility  Hygienic Reference  Not hygienic 0.89 0.79–0.99 0.049 Child age (years)  3+ Reference  1–2 1.57 1.44–1.72 <0.0001  <1 1.51 1.35–1.68 <0.0001 SEC  SEC-V (highest) Reference  SEC-I (lowest) 1.40 1.15–1.69 0.001  SEC-II 1.23 1.03–1.45 0.016  SEC-III 1.15 0.99–1.34 0.066  SEC-IV 1.07 0.94–1.23 0.303 Random effects   Variance (SE) 0.093 (0.056)    ICC 0.03 Model fit criteria Full model Null model    AIC 13 979 14 104 Characteristics ORs CI p-value Mother’s education  No education Reference  Primary 1.21 1.06–1.36 0.002  Secondary+ 1.17 1.03–1.30 0.009 Sex of child  Female Reference  Male 1.15 1.06–1.25 0.001 Type of residence  Urban Reference  Rural 1.02 0.92–1.12 0.679 Toilet facility  Hygienic Reference  Not hygienic 0.89 0.79–0.99 0.049 Child age (years)  3+ Reference  1–2 1.57 1.44–1.72 <0.0001  <1 1.51 1.35–1.68 <0.0001 SEC  SEC-V (highest) Reference  SEC-I (lowest) 1.40 1.15–1.69 0.001  SEC-II 1.23 1.03–1.45 0.016  SEC-III 1.15 0.99–1.34 0.066  SEC-IV 1.07 0.94–1.23 0.303 Random effects   Variance (SE) 0.093 (0.056)    ICC 0.03 Model fit criteria Full model Null model    AIC 13 979 14 104 Table 2 Multilevel logistic regression analysis taking region as random effect Characteristics ORs CI p-value Mother’s education  No education Reference  Primary 1.21 1.06–1.36 0.002  Secondary+ 1.17 1.03–1.30 0.009 Sex of child  Female Reference  Male 1.15 1.06–1.25 0.001 Type of residence  Urban Reference  Rural 1.02 0.92–1.12 0.679 Toilet facility  Hygienic Reference  Not hygienic 0.89 0.79–0.99 0.049 Child age (years)  3+ Reference  1–2 1.57 1.44–1.72 <0.0001  <1 1.51 1.35–1.68 <0.0001 SEC  SEC-V (highest) Reference  SEC-I (lowest) 1.40 1.15–1.69 0.001  SEC-II 1.23 1.03–1.45 0.016  SEC-III 1.15 0.99–1.34 0.066  SEC-IV 1.07 0.94–1.23 0.303 Random effects   Variance (SE) 0.093 (0.056)    ICC 0.03 Model fit criteria Full model Null model    AIC 13 979 14 104 Characteristics ORs CI p-value Mother’s education  No education Reference  Primary 1.21 1.06–1.36 0.002  Secondary+ 1.17 1.03–1.30 0.009 Sex of child  Female Reference  Male 1.15 1.06–1.25 0.001 Type of residence  Urban Reference  Rural 1.02 0.92–1.12 0.679 Toilet facility  Hygienic Reference  Not hygienic 0.89 0.79–0.99 0.049 Child age (years)  3+ Reference  1–2 1.57 1.44–1.72 <0.0001  <1 1.51 1.35–1.68 <0.0001 SEC  SEC-V (highest) Reference  SEC-I (lowest) 1.40 1.15–1.69 0.001  SEC-II 1.23 1.03–1.45 0.016  SEC-III 1.15 0.99–1.34 0.066  SEC-IV 1.07 0.94–1.23 0.303 Random effects   Variance (SE) 0.093 (0.056)    ICC 0.03 Model fit criteria Full model Null model    AIC 13 979 14 104 Multilevel analysis We have considered the significant factors, found in bivariate analysis, for multilevel analysis and presented the results in Table 2. In univariate analysis, the results showed that the children in rural areas have more chances of malaria fever as compared with children in urban areas; however, this result is no more significant in multivariate analysis. The likelihood of having disease is higher in children with educated mothers than those of uneducated mothers [odds ratio (OR) = 1.17, p < 0.009; confidence interval (CI) = 1.03–1.30). Male children had more chances of getting sick than female children (OR = 1.15, p < 0.001; CI = 1.06–1.25). The children resident of rural areas were more likely to have the fever when compared with those of urban areas; however, the significance appeared in bivariate analysis disappeared in multilevel analysis. The child of a household with non-hygienic toilet has lower chances of getting malaria (OR = 0.89, p < 0.049; CI = 0.79–0.99). Children of age <3 years have more chances of getting sick than children of age ≥3 years (OR = 1.37, 95% CI = 1.26–1.48). Compared with children ≥3 years of age, children of age 1–2 years of age were more likely of having the fever (OR = 1.57, p < 0.0001; CI = 1.44–1.72) and those of <1 year of age were also more likely of having the fever (OR = 1.51, p < 0.0001; CI = 1.44–1.72). The key independent variable, SEC that was turned out to be insignificant in a bivariate association, was also included for multilevel analysis because the focus of the study was on the changing behavior of malaria present in children for different households with a different SEC. The association of SEC with the prevalence of fever turned out to be significant for the SEC Quintiles I and II. Compared with the SEC-V households, children of SEC-I households were more likely to have fever (OR = 1.40, p < 0.001; CI = 1.15–1.69) and those with SEC were also more likely to have fever (OR = 1.23, p < 0.016; CI = 1.03–1.45). The estimated variance between regions was 0.093 with a SD of 0.05, while the value of intra class correlation (ICC) stood 0.03. The values of akaike information criterion (AIC) are reduced from 13 979 to 13 952, and for the full model, the values reduced from 14 104 to 14 100, which indicate that the full model is better than an empty model. DISCUSSIONS Our study showed that one-third of children of age <5 years had fever before 2 weeks of the survey. The results adjusted for socioeconomic variables indicate a significant association of the malaria fever with mother education, sex of child, type of toilet, age of child and SEC of the household. The results showed that the children of the SEC Quintile V were less likely to have the fever as compared with the children of SEC-I category, and the odds of fever presence increase gradually as the SEC goes down. That is, the households with higher SEC are better able to purchase and correctly use malaria prevention methods resulting in lower risk of malarial fever. The results are consistent with some of the previous work [7, 8], may be because of the same SEC, as both reference studies are conducted in developing countries (Tanzania and Gambia). The results also showed that the prevalence of malaria fever is higher in rural areas than urban, but these results were no more significant for multilevel analysis. It may be linked to the high level of poverty, type of construction and limited access to the health services in rural areas as shown in [9–11]. The results of this study show the absence of association of possession of sleeping bed nets and malaria fever prevalence. However, in the descriptive analysis, it has been observed that the possession of bed nets reduces the chances of fever. These results are in line with other findings [11, 12], may be because of same population characteristics. Finding of their studies also demonstrates that use of bed net reduces the malaria morbidity and awareness of fever [13]. Female children have lower likelihoods of fever than male children. Type of toilet is one of the significant predictors of the prevalence of malaria fever. The results of this study show that the households with non-hygienic toilets have lower likelihoods of malaria fever. It might be because of the reason that hygienic toilets have greater chances of stagnant water, which could be the major cause of mosquito growth, while a non-hygienic toilet may be outside the house or in open fields where there was lower chance of mosquito growth. As the child got older, the likelihood of fever reduced, which was similar to the results of other studies [5]. This study has several limitations. First, the use of indirect measure of household wealth position may be criticized. However, it is hard to collect reliable information about income and expenditure data [14]. This study is based on cross-sectional data, which did not allow us to determine the direction of any causal association between household wealth position and nutritional status among women. Finally, we assumed that most cases of fever were because of malaria based on the literature. Conclusion Our study found that the likelihood of malaria fever was high in households with lower SEC. We also found that bed nets in a household were associated with lower likelihood of having malaria fever. Therefore, the possession of bed nets to poor families could be a crucial step to control the malaria prevalence. Finally, this study advocates policies for the reduction in poverty to prevent the malaria fever and other diseases associated with low SEC. REFERENCES 1 World Health Organization malaria report 2013 . 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For Permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Tropical Pediatrics Oxford University Press

Socioeconomic Condition and Prevalence of Malaria Fever in Pakistani Children: Findings from a Community Health Survey

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Abstract

Abstract Objective We assessed the prevalence of malarial fever and its association with demographic and socioeconomic factors in children <5 years of age. Methods Using the data of Pakistan Demographic and Health Survey (PDHS), the socioeconomic condition (SEC) was assessed by using a household wealth index as a proxy indicator, generated through principal component analysis. Two-stage sampling was used for selection of households, and multilevel logistic regression analysis was performed. Results The PDHS contains 10 935 children <5 years of age with valid information about malaria fever. In total, 36% (3930) children have malaria 2 weeks before the survey. A decreasing trend in prevalence of malaria fever was found with increasing SEC. Compared with SEC Quintile V, children of SEC Quintile I were more likely to get fever [adjusted odds ratio (AOR)=1.40 (1.15–1.69)] and of SEC Quintile II [AOR = 1.23 (1.03–1.45)]. Conclusion SEC has a significant impact on the prevalence of malaria fever in the context of different regions in Pakistan. malaria fever, socioeconomic condition, multilevel logistic regression INTRODUCTION Malaria fever is a common presentation in Pakistani children <5 years of age that accounts for >60% of the cases [1]. Malaria fever is also associated with high level of malnutrition and mortality. The estimated number of malaria cases in Pakistan is 1.6 million per year including 300 000 confirmed cases in public health-care facilities [2]. The Millennium Development Goal-6 (MDG) advocates control and elimination of preventable infectious diseases including malaria. According to World Health Organization, Pakistan is one of the seven countries of Eastern Mediterranean Region that have areas of high malaria transmission and confirmed cases. Unfortunately, only 40% of the population receives effective malaria prevention and treatment plan, while MDG stipulates these statistics to be around 75% [3]. The major part of the population in Pakistan lives in rural areas, and over two-thirds live on <US $2 a day. Previous work indicates that socioeconomic condition (SEC) may increase the risk of malaria fever in Pakistan; yet, there is limited evidence available about its association with fever in children <5 years of age. The purpose of this study was to examine the relationship between SEC and the prevalence of malaria fever among children of age <5 years at two levels, i.e. individual and regional (provinces). METHODS Data The data on children of age <5 years were extracted from Pakistan Demographic and Health Survey (PDHS) for the year 2012–13. The primary objective of the looking in to PDHS was to provide information about the population and health indicators for national and regional levels including rural and urban areas. This is a cross-sectional study. The main purpose of considering PDHS was the availability of comprehensive information on predictors in resource-poor setting in Pakistan. Only one episode was counted as a case of malarial fever. Two-stage sampling technique was used for the selection of household to be interviewed. Five hundred primary sampling units (communities) were selected randomly at first stage using probability proportional to size scheme (252 from rural and 248 from urban areas). At second stage, systematic sampling technique was used to select a fixed number of households (28) from each primary sampling units selected at first stage, and in this way, 14 000 households were selected for an interview (7056 in rural areas and 6944 in urban). Information of 11 763 children was presented in the survey; however, the analysis remained limited to 10 935 children of age <5 years with valid information about fever. The children were considered infected who had malaria fever 2 weeks before the survey and received any malarial drugs. In the PDHS database, 65% had received advice or treatment from a health facility or provider. Moreover, Demographic and Health Surveys use field-friendly equipment and rapid diagnostic testing technologies, whenever possible, to get quick and reliable results. The information about the PDHS can be consulted from elsewhere [2]. Variables The main outcome variable was presence of fever in the children of age <5 years during 2 weeks preceding the survey. The main independent variable was SEC based on wealth index, which was derived from principal component analysis using household possessions (radio, television, refrigerator, car, truck, boat, etc.) and change belonging to facilities (source of drinking water, type of toilet facility, electricity, material of roof, material of floor, etc.). The SEC of the household was measured by dividing this wealth index into five quintiles from lowest 20% to highest 20% [2], i.e. SEC-I (lowest) to SEC-V (highest), to intricate the trend of malaria fever. Other covariates considered in this study were mother’s education [none, primary (5 years) and secondary+ (≥8 years)], possession of bed nets (yes/no), sex of child (male/female), type of residence (urban/rural), source of drinking water (water through pipe, tube well, filtration plant or bottled was considered as ‘safe’, while water through any other source such as river, pond, uncovered water was considered as ‘unsafe’), type of toilet facility (the toilet or latrine connected to sewage system was considered as ‘hygienic’, while all others were considered as ‘non-hygienic’) and access to health center (easy/not easy). All variables were selected after literature review [4, 5]. Statistical analysis The data were analyzed in two steps. First, we assessed the prevalence of fever in selected sample and a bivariate association of covariates in the presence of malaria fever. In the second step, the significant factors in bivariate analysis were further analyzed by multilevel logistic regression analysis. The multilevel model that assessed the relation of individual and regional levels was estimated using Stata 11.0 software. Multilevel logistic regression analysis was used to account for the hierarchical nature of PDHS data; two-level model was applied, i.e. children were at Level 1 and regions were at Level 2. Two-level model for binary response uses a binomial sampling and a logit link [6]. RESULTS Descriptive analysis The sample used in this study was extracted from the national representative PDHS data available for 11 763 children. For analysis, 10 935 children of age <5 years were considered who have valid information about malaria fever. Equal number of male (5575) and female (5360) children were observed in the sample (male–female ratio = 1.04). About 3930 children have malaria fever 2 weeks before the survey that is 36% of the study sample. Table 1 shows frequency and percentage distribution of the selected covariates and their bivariate association with the outcome variable. The results demonstrate that prevalence of malaria has significant association with mother’s education. The mother with no education has high ratio (53.4%: 2099 of 3930) of getting malaria fever, and the ratio goes down with an improvement in mother education. The resident of a household with or without bed nets did not show the significant association with the outcome variable. Sex of children has significant association with malaria fever, and its prevalence was found high in male children (53.3%: 2093 of 3930). Fever prevalence stood high in children who were residents of rural areas (58.6%: 2304 of 3930). The residents of households with hygienic toilets have more malaria prevalence (69.4%: 2726 of 3930). The prevalence of malaria stood comparatively high for the children of age group 1–2 years (44.1%: 1733 of 3930). One of the key determinants of this study was the SEC of the household. The Table 1 also shows that there is no significant association between SEC fever presence in the children of age <5 years. Table 2 shows that lower two levels of SEC are at high risk of having the disease than that of the highest. Table 1 Percentage distribution of baseline characteristics and their bivariate association with malaria fever Characteristics Presence of malaria fever Yes (%) No (%) Total Chi-square value p-value Mother education  No education 2099 (53.4) 4058 (57.9) 6157 22.78 0.0001  Primary 621 (15.8) 944 (13.5) 1565  Secondary+ 1210 (30.8) 2003 (28.5) 3213 Possession of bed net  No 3300 (84.0) 5884 (84.0) 9187 0.0001 0.99  Yes 630 (16.0) 1121 (16.0) 1748 Sex of child  Male 2093 (53.3) 3482 (49.7) 5575 12.69 0.0001  Female 1837 (46.7) 3523 (50.3) 5360 Type of residence  Urban 1626 (41.4) 3054 (43.6) 4680 5.08 0.024  Rural 2304 (58.6) 3951 (56.4) 6235 Source of drinking water  Safe 3329 (84.7) 5925 (84.6) 9254 0.03 0.862  Not safe 601 (15.3) 1080 (15.4) 1681 Toilet facility  Hygienic 2726 (69.4) 4649 (66.4) 7375 10.29 0.001  Not hygienic 1204 (30.6) 2356 (33.6) 3560 Access to health centre  Easy 2199 (56.3) 3889 (55.5) 6088 0.19 0.659  Not easy 1731 (44.0) 3116 (44.5) 4847 Child age (years)  <1 844 (21.5) 1287 (18.4) 2131 121.16 0.0001  1–2 1733 (44.1) 2551 (36.4) 4284  3+ 1353 (34.4) 3167 (45.2) 4520 Region  Punjab 1180 (30.0) 1833 (26.2) 3013 198.65 <0.0001  Sindh 845 (21.5) 1483 (21.1) 2328  Khyber Pakhtunkhwa 895 (22.8) 1259 (18.0) 2154  Balochistan 382 (9.7) 1356 (19.3) 1738  Gilgit-Baltistan 348 (8.9) 668 (9.5) 1016  Islamabad 280 (7.1) 406 (5.8) 686 SEC  SEC-V 754 (19.2) 1339 (19.1) 2093 2.2 0.699  SEC-IV 758 (19.3) 1313 (18.7) 2071  SEC-III 763 (19.4) 1353 (19.4) 2116  SEC-II 790 (20.1) 1376 (19.6) 2166  SEC-I 865 (22.0) 1624 (23.2) 2489 Total 3930 (100) 7005 (100) 10 935 Characteristics Presence of malaria fever Yes (%) No (%) Total Chi-square value p-value Mother education  No education 2099 (53.4) 4058 (57.9) 6157 22.78 0.0001  Primary 621 (15.8) 944 (13.5) 1565  Secondary+ 1210 (30.8) 2003 (28.5) 3213 Possession of bed net  No 3300 (84.0) 5884 (84.0) 9187 0.0001 0.99  Yes 630 (16.0) 1121 (16.0) 1748 Sex of child  Male 2093 (53.3) 3482 (49.7) 5575 12.69 0.0001  Female 1837 (46.7) 3523 (50.3) 5360 Type of residence  Urban 1626 (41.4) 3054 (43.6) 4680 5.08 0.024  Rural 2304 (58.6) 3951 (56.4) 6235 Source of drinking water  Safe 3329 (84.7) 5925 (84.6) 9254 0.03 0.862  Not safe 601 (15.3) 1080 (15.4) 1681 Toilet facility  Hygienic 2726 (69.4) 4649 (66.4) 7375 10.29 0.001  Not hygienic 1204 (30.6) 2356 (33.6) 3560 Access to health centre  Easy 2199 (56.3) 3889 (55.5) 6088 0.19 0.659  Not easy 1731 (44.0) 3116 (44.5) 4847 Child age (years)  <1 844 (21.5) 1287 (18.4) 2131 121.16 0.0001  1–2 1733 (44.1) 2551 (36.4) 4284  3+ 1353 (34.4) 3167 (45.2) 4520 Region  Punjab 1180 (30.0) 1833 (26.2) 3013 198.65 <0.0001  Sindh 845 (21.5) 1483 (21.1) 2328  Khyber Pakhtunkhwa 895 (22.8) 1259 (18.0) 2154  Balochistan 382 (9.7) 1356 (19.3) 1738  Gilgit-Baltistan 348 (8.9) 668 (9.5) 1016  Islamabad 280 (7.1) 406 (5.8) 686 SEC  SEC-V 754 (19.2) 1339 (19.1) 2093 2.2 0.699  SEC-IV 758 (19.3) 1313 (18.7) 2071  SEC-III 763 (19.4) 1353 (19.4) 2116  SEC-II 790 (20.1) 1376 (19.6) 2166  SEC-I 865 (22.0) 1624 (23.2) 2489 Total 3930 (100) 7005 (100) 10 935 Table 1 Percentage distribution of baseline characteristics and their bivariate association with malaria fever Characteristics Presence of malaria fever Yes (%) No (%) Total Chi-square value p-value Mother education  No education 2099 (53.4) 4058 (57.9) 6157 22.78 0.0001  Primary 621 (15.8) 944 (13.5) 1565  Secondary+ 1210 (30.8) 2003 (28.5) 3213 Possession of bed net  No 3300 (84.0) 5884 (84.0) 9187 0.0001 0.99  Yes 630 (16.0) 1121 (16.0) 1748 Sex of child  Male 2093 (53.3) 3482 (49.7) 5575 12.69 0.0001  Female 1837 (46.7) 3523 (50.3) 5360 Type of residence  Urban 1626 (41.4) 3054 (43.6) 4680 5.08 0.024  Rural 2304 (58.6) 3951 (56.4) 6235 Source of drinking water  Safe 3329 (84.7) 5925 (84.6) 9254 0.03 0.862  Not safe 601 (15.3) 1080 (15.4) 1681 Toilet facility  Hygienic 2726 (69.4) 4649 (66.4) 7375 10.29 0.001  Not hygienic 1204 (30.6) 2356 (33.6) 3560 Access to health centre  Easy 2199 (56.3) 3889 (55.5) 6088 0.19 0.659  Not easy 1731 (44.0) 3116 (44.5) 4847 Child age (years)  <1 844 (21.5) 1287 (18.4) 2131 121.16 0.0001  1–2 1733 (44.1) 2551 (36.4) 4284  3+ 1353 (34.4) 3167 (45.2) 4520 Region  Punjab 1180 (30.0) 1833 (26.2) 3013 198.65 <0.0001  Sindh 845 (21.5) 1483 (21.1) 2328  Khyber Pakhtunkhwa 895 (22.8) 1259 (18.0) 2154  Balochistan 382 (9.7) 1356 (19.3) 1738  Gilgit-Baltistan 348 (8.9) 668 (9.5) 1016  Islamabad 280 (7.1) 406 (5.8) 686 SEC  SEC-V 754 (19.2) 1339 (19.1) 2093 2.2 0.699  SEC-IV 758 (19.3) 1313 (18.7) 2071  SEC-III 763 (19.4) 1353 (19.4) 2116  SEC-II 790 (20.1) 1376 (19.6) 2166  SEC-I 865 (22.0) 1624 (23.2) 2489 Total 3930 (100) 7005 (100) 10 935 Characteristics Presence of malaria fever Yes (%) No (%) Total Chi-square value p-value Mother education  No education 2099 (53.4) 4058 (57.9) 6157 22.78 0.0001  Primary 621 (15.8) 944 (13.5) 1565  Secondary+ 1210 (30.8) 2003 (28.5) 3213 Possession of bed net  No 3300 (84.0) 5884 (84.0) 9187 0.0001 0.99  Yes 630 (16.0) 1121 (16.0) 1748 Sex of child  Male 2093 (53.3) 3482 (49.7) 5575 12.69 0.0001  Female 1837 (46.7) 3523 (50.3) 5360 Type of residence  Urban 1626 (41.4) 3054 (43.6) 4680 5.08 0.024  Rural 2304 (58.6) 3951 (56.4) 6235 Source of drinking water  Safe 3329 (84.7) 5925 (84.6) 9254 0.03 0.862  Not safe 601 (15.3) 1080 (15.4) 1681 Toilet facility  Hygienic 2726 (69.4) 4649 (66.4) 7375 10.29 0.001  Not hygienic 1204 (30.6) 2356 (33.6) 3560 Access to health centre  Easy 2199 (56.3) 3889 (55.5) 6088 0.19 0.659  Not easy 1731 (44.0) 3116 (44.5) 4847 Child age (years)  <1 844 (21.5) 1287 (18.4) 2131 121.16 0.0001  1–2 1733 (44.1) 2551 (36.4) 4284  3+ 1353 (34.4) 3167 (45.2) 4520 Region  Punjab 1180 (30.0) 1833 (26.2) 3013 198.65 <0.0001  Sindh 845 (21.5) 1483 (21.1) 2328  Khyber Pakhtunkhwa 895 (22.8) 1259 (18.0) 2154  Balochistan 382 (9.7) 1356 (19.3) 1738  Gilgit-Baltistan 348 (8.9) 668 (9.5) 1016  Islamabad 280 (7.1) 406 (5.8) 686 SEC  SEC-V 754 (19.2) 1339 (19.1) 2093 2.2 0.699  SEC-IV 758 (19.3) 1313 (18.7) 2071  SEC-III 763 (19.4) 1353 (19.4) 2116  SEC-II 790 (20.1) 1376 (19.6) 2166  SEC-I 865 (22.0) 1624 (23.2) 2489 Total 3930 (100) 7005 (100) 10 935 Table 2 Multilevel logistic regression analysis taking region as random effect Characteristics ORs CI p-value Mother’s education  No education Reference  Primary 1.21 1.06–1.36 0.002  Secondary+ 1.17 1.03–1.30 0.009 Sex of child  Female Reference  Male 1.15 1.06–1.25 0.001 Type of residence  Urban Reference  Rural 1.02 0.92–1.12 0.679 Toilet facility  Hygienic Reference  Not hygienic 0.89 0.79–0.99 0.049 Child age (years)  3+ Reference  1–2 1.57 1.44–1.72 <0.0001  <1 1.51 1.35–1.68 <0.0001 SEC  SEC-V (highest) Reference  SEC-I (lowest) 1.40 1.15–1.69 0.001  SEC-II 1.23 1.03–1.45 0.016  SEC-III 1.15 0.99–1.34 0.066  SEC-IV 1.07 0.94–1.23 0.303 Random effects   Variance (SE) 0.093 (0.056)    ICC 0.03 Model fit criteria Full model Null model    AIC 13 979 14 104 Characteristics ORs CI p-value Mother’s education  No education Reference  Primary 1.21 1.06–1.36 0.002  Secondary+ 1.17 1.03–1.30 0.009 Sex of child  Female Reference  Male 1.15 1.06–1.25 0.001 Type of residence  Urban Reference  Rural 1.02 0.92–1.12 0.679 Toilet facility  Hygienic Reference  Not hygienic 0.89 0.79–0.99 0.049 Child age (years)  3+ Reference  1–2 1.57 1.44–1.72 <0.0001  <1 1.51 1.35–1.68 <0.0001 SEC  SEC-V (highest) Reference  SEC-I (lowest) 1.40 1.15–1.69 0.001  SEC-II 1.23 1.03–1.45 0.016  SEC-III 1.15 0.99–1.34 0.066  SEC-IV 1.07 0.94–1.23 0.303 Random effects   Variance (SE) 0.093 (0.056)    ICC 0.03 Model fit criteria Full model Null model    AIC 13 979 14 104 Table 2 Multilevel logistic regression analysis taking region as random effect Characteristics ORs CI p-value Mother’s education  No education Reference  Primary 1.21 1.06–1.36 0.002  Secondary+ 1.17 1.03–1.30 0.009 Sex of child  Female Reference  Male 1.15 1.06–1.25 0.001 Type of residence  Urban Reference  Rural 1.02 0.92–1.12 0.679 Toilet facility  Hygienic Reference  Not hygienic 0.89 0.79–0.99 0.049 Child age (years)  3+ Reference  1–2 1.57 1.44–1.72 <0.0001  <1 1.51 1.35–1.68 <0.0001 SEC  SEC-V (highest) Reference  SEC-I (lowest) 1.40 1.15–1.69 0.001  SEC-II 1.23 1.03–1.45 0.016  SEC-III 1.15 0.99–1.34 0.066  SEC-IV 1.07 0.94–1.23 0.303 Random effects   Variance (SE) 0.093 (0.056)    ICC 0.03 Model fit criteria Full model Null model    AIC 13 979 14 104 Characteristics ORs CI p-value Mother’s education  No education Reference  Primary 1.21 1.06–1.36 0.002  Secondary+ 1.17 1.03–1.30 0.009 Sex of child  Female Reference  Male 1.15 1.06–1.25 0.001 Type of residence  Urban Reference  Rural 1.02 0.92–1.12 0.679 Toilet facility  Hygienic Reference  Not hygienic 0.89 0.79–0.99 0.049 Child age (years)  3+ Reference  1–2 1.57 1.44–1.72 <0.0001  <1 1.51 1.35–1.68 <0.0001 SEC  SEC-V (highest) Reference  SEC-I (lowest) 1.40 1.15–1.69 0.001  SEC-II 1.23 1.03–1.45 0.016  SEC-III 1.15 0.99–1.34 0.066  SEC-IV 1.07 0.94–1.23 0.303 Random effects   Variance (SE) 0.093 (0.056)    ICC 0.03 Model fit criteria Full model Null model    AIC 13 979 14 104 Multilevel analysis We have considered the significant factors, found in bivariate analysis, for multilevel analysis and presented the results in Table 2. In univariate analysis, the results showed that the children in rural areas have more chances of malaria fever as compared with children in urban areas; however, this result is no more significant in multivariate analysis. The likelihood of having disease is higher in children with educated mothers than those of uneducated mothers [odds ratio (OR) = 1.17, p < 0.009; confidence interval (CI) = 1.03–1.30). Male children had more chances of getting sick than female children (OR = 1.15, p < 0.001; CI = 1.06–1.25). The children resident of rural areas were more likely to have the fever when compared with those of urban areas; however, the significance appeared in bivariate analysis disappeared in multilevel analysis. The child of a household with non-hygienic toilet has lower chances of getting malaria (OR = 0.89, p < 0.049; CI = 0.79–0.99). Children of age <3 years have more chances of getting sick than children of age ≥3 years (OR = 1.37, 95% CI = 1.26–1.48). Compared with children ≥3 years of age, children of age 1–2 years of age were more likely of having the fever (OR = 1.57, p < 0.0001; CI = 1.44–1.72) and those of <1 year of age were also more likely of having the fever (OR = 1.51, p < 0.0001; CI = 1.44–1.72). The key independent variable, SEC that was turned out to be insignificant in a bivariate association, was also included for multilevel analysis because the focus of the study was on the changing behavior of malaria present in children for different households with a different SEC. The association of SEC with the prevalence of fever turned out to be significant for the SEC Quintiles I and II. Compared with the SEC-V households, children of SEC-I households were more likely to have fever (OR = 1.40, p < 0.001; CI = 1.15–1.69) and those with SEC were also more likely to have fever (OR = 1.23, p < 0.016; CI = 1.03–1.45). The estimated variance between regions was 0.093 with a SD of 0.05, while the value of intra class correlation (ICC) stood 0.03. The values of akaike information criterion (AIC) are reduced from 13 979 to 13 952, and for the full model, the values reduced from 14 104 to 14 100, which indicate that the full model is better than an empty model. DISCUSSIONS Our study showed that one-third of children of age <5 years had fever before 2 weeks of the survey. The results adjusted for socioeconomic variables indicate a significant association of the malaria fever with mother education, sex of child, type of toilet, age of child and SEC of the household. The results showed that the children of the SEC Quintile V were less likely to have the fever as compared with the children of SEC-I category, and the odds of fever presence increase gradually as the SEC goes down. That is, the households with higher SEC are better able to purchase and correctly use malaria prevention methods resulting in lower risk of malarial fever. The results are consistent with some of the previous work [7, 8], may be because of the same SEC, as both reference studies are conducted in developing countries (Tanzania and Gambia). The results also showed that the prevalence of malaria fever is higher in rural areas than urban, but these results were no more significant for multilevel analysis. It may be linked to the high level of poverty, type of construction and limited access to the health services in rural areas as shown in [9–11]. The results of this study show the absence of association of possession of sleeping bed nets and malaria fever prevalence. However, in the descriptive analysis, it has been observed that the possession of bed nets reduces the chances of fever. These results are in line with other findings [11, 12], may be because of same population characteristics. Finding of their studies also demonstrates that use of bed net reduces the malaria morbidity and awareness of fever [13]. Female children have lower likelihoods of fever than male children. Type of toilet is one of the significant predictors of the prevalence of malaria fever. The results of this study show that the households with non-hygienic toilets have lower likelihoods of malaria fever. It might be because of the reason that hygienic toilets have greater chances of stagnant water, which could be the major cause of mosquito growth, while a non-hygienic toilet may be outside the house or in open fields where there was lower chance of mosquito growth. As the child got older, the likelihood of fever reduced, which was similar to the results of other studies [5]. This study has several limitations. First, the use of indirect measure of household wealth position may be criticized. However, it is hard to collect reliable information about income and expenditure data [14]. This study is based on cross-sectional data, which did not allow us to determine the direction of any causal association between household wealth position and nutritional status among women. Finally, we assumed that most cases of fever were because of malaria based on the literature. Conclusion Our study found that the likelihood of malaria fever was high in households with lower SEC. We also found that bed nets in a household were associated with lower likelihood of having malaria fever. Therefore, the possession of bed nets to poor families could be a crucial step to control the malaria prevalence. Finally, this study advocates policies for the reduction in poverty to prevent the malaria fever and other diseases associated with low SEC. REFERENCES 1 World Health Organization malaria report 2013 . Geneva. http://www.who.int/malaria/publications/world_malaria_report_2013/en/ 2 Pakistan demographic and health survey 2013 . http://www.nips.org.pk/abstract_files/PDHS%20Final%20Report%20as%20of%20Jan%2022-2014.pdf 3 Pakistan millennium development goals report 2013 . http://www.pk.undp.org/content/pakistan/en/home/library/mdg/pakistan-mdgs-report-2013.html 4 Alemu A , Tsegaye W , Abebe G. Urban malaria and associated risk factors in Jimma Town South-West Ethiopia . Malar J 2001 ; 10 : 173 . Google Scholar CrossRef Search ADS 5 Yusuf OB , Adeoye BW , Oladepo O , et al. Poverty and fever vulnerability . Malar J 2010 ; 9 : 235 . Google Scholar CrossRef Search ADS PubMed 6 Ononokpono DN , Odimegwu CO. Determinants of maternal health care utilization in Nigeria: a multilevel approach . Pan Afr Med J 2014 ; 176 : 2 . 7 Dickinsona KL , Randellb HF , Kramerc RA , et al. Socio-economic status and malaria-related outcomes in Mvomero District, Tanzania . Glob Public Health 2012 ; 7 : 384 – 99 . Google Scholar CrossRef Search ADS PubMed 8 Sonko ST , Jaiteh M , Jafali J , et al. Does socio-economic status explain the differentials in malaria parasite prevalence? Evidence from the Gambia . Malar J 2014 ; 13 : 449 . Google Scholar CrossRef Search ADS PubMed 9 Teklehaimanot A , Mejia P. Malaria and poverty . Ann N Y Acad Sci 2008 ; 1136 : 32 – 7 . Google Scholar CrossRef Search ADS PubMed 10 Onwujekwe O , Uzochukwu B , Dike N , et al. Are there geographic and socio-economic differences in incidence, burden and prevention of malaria? A study in Southeast Nigeria . Int J Equity Health 2009 ; 8 : 45 . Google Scholar CrossRef Search ADS PubMed 11 Yadav K , Dhiman S , Rabha B , et al. Socio-economic determinants for malaria transmission risk in an endemic primary health centre in Assam, India . Infect Dis Poverty 2014 ; 3 : 19 . Google Scholar CrossRef Search ADS PubMed 12 Nonvignon J , Nonvignon J. Socioeconomic status and the prevalence of fever in children under age five: evidence from four Sub-Saharan African countries . BMC Res Notes 2012 ; 5 : 380 . Google Scholar CrossRef Search ADS PubMed 13 Nuwaha F. Factors influencing the use of bed nets in Mbarara municipality of Uganda . Am J Trop Med Hyg 2001 ; 65 : 877 – 82 . Google Scholar CrossRef Search ADS PubMed 14 Filmer D , Pritchett L. Estimating wealth effects without expenditure data—or tears: an application to educational enrollments in states of India . Demography 2001 ; 38 : 115 – 32 . Google Scholar PubMed © The Author [2017]. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)

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Journal of Tropical PediatricsOxford University Press

Published: Jul 18, 2017

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