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Association of Socioeconomic Status With Childhood Anemia Among Infant, Toddler, and Preschool Children in Bangladesh
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Association of Socioeconomic Status With Childhood Anemia Among Infant, Toddler, and Preschool Children in Bangladesh G. M. Rabiul Islam, PhD* Department of Food Engineering and Tea Technology, Shahjalal University of Science and Technology, Bangladesh.
A B S T R A C T Objectives: This study aims to delineate the association between economic inequality, along with other confounders, and anemia among infants, toddlers, and preschool children. Methods: From the 2011 Bangladesh Demographic Health Survey, a cross-sectional population sample comprising 2068 children between ages 6 and 59 months were selected for this study. Analyses were performed with a proportional odds model and finally stratified with the child age groups. Results: Infants belonging to a low and medium socioeconomic status (SES) have approximately a 3-fold higher chance of being affected by mild, moderate, or severe anemia compared with infants of high SES (odds ratio [OR] 2.94; 95% CI 1.097.91; P=.03 and OR 2.76; 95% CI .87-8.82; P=.08, respectively). Preschool children from low and medium SES households are 2.733 (95% CI 1.20-6.18; P=.02) and 2.473 (95% CI .99-6.14; P=.04) more likely to be anemic compared with their counterparts from higher SES households. The place of residence and childhood stunting are associated with childhood anemia (urban vs rural: OR 1.27; 95% CI .21-.35; P = .04; and stunted vs normal: OR 1.34; 95% CI 1.11-1.63; P = .003). Besides, vitamin A supplementations appear to serve as protective agents against the occurrence of the childhood anemia (OR 1.18; 95% CI .99-1.41; P = .06). Conclusion: Urgent preventive measures are needed to control the impending childhood anemia among infants and preschool children, especially in the low and medium SES households (ClinicalTrials.gov Identifier: NCT03126253). Keywords: childhood anemia, infants, toddlers, preschool children, socioeconomic status. VALUE IN HEALTH REGIONAL ISSUES. 2020; 21(C):141–148
Introduction Childhood anemia is a global public health problem that is associated with life-threatening consequences such as growth retardation, impaired motor and cognitive development, and increased morbidity and mortality.1 Anemia can be caused by a variety of factors such as nutritional deficiencies (eg, iron, folic acid, vitamin B12, and vitamin A), infections (eg, helminth), and hereditary anemia (eg, hemoglobinopathies).2 The World Health Organization (WHO) estimates that approximately 50% of anemia cases can be attributed to iron deficiency.3 This is an estimated global average that varies widely depending upon the location in question.3 The World Bank estimates for 2011 claim that approximately 55.60% of all Bangladeshi children younger than 5 years are suffering from anemia.4 The relationship between socioeconomic inequality and anemia among the children has never been conclusive, and it is unclear whether children aged 6 to 59 months
have uniformly high levels of anemia during all the stages of development, for example, during the infant, toddler, and preschool stages. In addition, there is a dearth of evidence from Bangladesh, where the meaning of sociodemographic characteristics may be different from that in other countries. This study attempts to fill the aforementioned lacuna by investigating and evaluating the association of socioeconomic status (SES) inequality, among other explanatory variables, with the development of childhood anemia among infant, toddler, and preschool children. The results reported herein represent a confident and trustworthy approach for elucidating the potential effects of SES and the stages of child development that are usually neglected in the conventional scientific literature. Moreover, because anemia is one of the current key health issues in Bangladesh, it is also expected that the findings of this study will contribute significantly toward shaping the health policy strategy of the country.
G.M.R.I. performed all the statistical analysis and drafted the manuscript. Conflict of interest: The authors declare no conflicts of interest. * Address correspondence to: G. M. Rabiul Islam, PhD, Department of Food Engineering and Tea Technology, Shahjalal University of Science and Technology, Sylhet3114, Bangladesh. Email: [email protected] 2212-1099/$36.00 - see front matter ª 2019 ISPOR–The professional society for health economics and outcomes research. Published by Elsevier Inc. https://doi.org/10.1016/j.vhri.2019.09.008
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Materials and Methods Study Design and Participants The 2011 Bangladesh Health and Demographic Health Survey (BDHS) data collected under the authority of National Institute for Population Research and Training (Ministry of Health and Family Welfare, Bangladesh) from May 11, 2011, to July 8, 2012, were used for the purpose of this study. A stratified, multistage cluster sampling strategy was adopted to construct a national representative household-based sample. To generate this, 600 primary sampling units (207 from urban areas and 393 from rural areas) were selected from the sampling frame created for the 2011 Bangladeshi census.5 There were 2320 children aged 6 to 59 months for the examination of anemia in BDHS 2011 raised from every third household of the BDHS sample. After excluding participants with missing data, a total of 2068 individuals were selected for this analysis (see Appendix Table 1 in Supplemental Materials found at https://doi.org/10.1016/j. vhri.2019.09.0081).
Outcomes In this study, hemoglobin level was used as the parameter for diagnosing anemia. The HemoCue system was used to estimate the concentration of hemoglobin in the capillary blood.5 The WHO guidelines for the diagnosis of anemia that were adopted by the Demographic Health Surveys were used in this study.5,6 Briefly, a hemoglobin concentration of less than 70 g/L was used to define severe anemia, 70 to 99 g/L defined moderate anemia, and 100 to 109 g/L was presumed to correspond to mild anemia. The aforementioned classification of anemia as severe, moderate, and mild categories is based on blood hemoglobin cutoffs (adjusted for altitude and smoking) recommended by the Centers for Disease Prevention and Prevention of United States7; this classification has also been adopted by the WHO.6 Because only 0.73% (n = 15) of the studied children were classified as being severely anemic, for the purpose of the regression model used in this study, the severe and moderate groups were combined to create a severe/moderate category (a hemoglobin concentration of less than 99 g/L) to avoid problems associated with 0 cell counts in estimating the models.
Explanatory Variables Socioeconomic status, defined per the wealth status of the individual, was used as a key predictor in this study. The wealth index used in the BDHS survey is a measure that has been adopted by many DHS and other country-level surveys for measuring inequalities in household characteristics, in the use of health and other services, and in health outcomes.8 It serves as an indicator of household wealth that is consistent with factors such as expenditure and income measures.9 The index is constructed using household asset data via principal components analysis. In this study, the wealth index score of the BDHS 2011 survey was used as a proxy for SES, and inequality was measured by dividing the wealth index into tertiles and categorizing them as low, medium, and high. In addition to SES, the age group of the child was also used as a key predictor. The child concerned was regarded as an infant if the age was less than or equal to 12 months, as a toddler if the age was between 13 and 36 months, and as a preschooler if age was between 37 and 59 months. Apart from the factors enumerated above, this study also included related explanatory variables, which are listed in Table 1.
Ethical Considerations The data collection procedure for the BDHS was carried out with the approval of the Office of Research Compliance macroinstitutional review board, and National Research Ethics Committee, Bangladesh Medical Research Council. Per the guidelines of the BDHS, before the interview, informed consent was obtained from individual respondents, and this was followed by an oral explanation given by the interviewers. The respondents were informed of the voluntary nature of the survey, the potential risks involved in participation, the purpose of the gathered data (assessment of health needs and planning health services), and the confidentiality of the results of the individual interview along with the free-of-charge nature of the examination. In the case of biomarkers and other information regarding children, the consent of the parent or caretaker was taken accordingly.5
Statistical Analysis Pearson’s chi-square test was used to determine statistically significant differences observed within the various categories of the Centers for Disease Control and Prevention/WHO hemoglobin grouping variable in relation to the explanatory variables. Order logit model using maximum likelihood estimation was used to identify socioeconomic and other determinants of anemia status as fitted in the “ologit” command. Explanatory variables that had a significant association with anemia as determined by chi-square analysis (viz SES, age of the child, mother’s highest education level, place of residence, and stunting) were included in the model (see Appendix Table 2 in Supplemental Materials found at https://doi.org/10.1016/j.vhri.2019. 09.008). The interaction between child age group and SES was also tested, and no interaction was found (results not shown). The data were then stratified by the age group of the child. Subsequent to the aforementioned analysis, the post-estimation of the predictive probability of being mildly, moderately, and severely anemic was also generated using the “margins” command to make the explanations more clear.10 To specify the association of SES with childhood anemia in infant, toddler, and preschool children, the interaction notation in the “margins” command is used as even though there were no interactions, all possible combinations of SES and child age were captured as stratification.10 To account for the complex survey design, this study included strata (divisions of Bangladesh) and clustering variables (primary sampling unit) by using the survey (svy) option of Stata. Considering both stratifications and clustering of data enables the generation of a more robust estimate of the survey design characteristics.11 Models were fitted using the Stata software (statistics software) for Windows version 13.12
Results Descriptive Analysis The population characteristics, the percentage of children belonging to each anemia class as categorized by the explanatory variables, and the results of the chi-square statistical analysis are summarized in Table 1. These variables are reflective of the main hypotheses being tested in the study, that is, the differences in the prevalence of anemia are related to SES and other explanatory variables. The results of the chisquare test proved that the factors such as SES, mother’s education level, place of residence, supplementation with vitamin A in the last 6 months, age of the child, and stunting produced statically significant differences (P , .05).
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Table 1. Percentages of child with anemia by standard of explanatory variables (N = 2068). Anemia status by hemoglobin level %* Variable
n (%)
No anemic
Mild
Moderate/severe
Chi-square result
P value
21.33
,.001
12.47
.01
3.00
.55
6.07
.04
0.36
.83
3.95
.13
0.10
.95
7.22
.02
0.52
.76
92.83
,.001
2.72
.60
Socioeconomic status Low
1710 (82.69)
46.94
31.56
21.50
Medium
216 (10.44)
47.44
29.65
22.92
High
142 (6.87)
48.18
31.39
13.53
Sociodemographic factors Mother’s highest education level No education
557 (26.94)
48.75
32.82
18.43
Completed primary
634 (30.66)
44.86
29.85
25.30
Completed secondary or higher
877 (42.40)
51.59
28.66
19.76
367 (17.73)
46.94
31.49
21.57
Husband’s highest education level No education Completed primary
666 (32.25)
47.44
29.65
22.92
Completed secondary or higher
1035 (50.03)
50.21
30.06
19.73
Place of residence Urban
659 (31.83)
52.60
26.95
20.45
Rural
1409 (68.17)
46.93
31.69
21.38
1901 (91.94)
48.68
30.07
21.25
167 (8.06)
49.36
31.41
19.23
Sex of household head Male Female Living in large family† No
1411 (68.22)
47.42
30.64
21.94
Yes
657 (31.78)
52.00
29.17
18.83
1071 (51.78)
50.39
30.29
19.32
997 (48.22)
51.16
29.19
19.65
No
786 (38.01)
45.18
30.99
23.83
Yes
1282 (61.99)
50.76
29.81
19.43
1754 (84.83)
48.62
29.93
21.45
314 (15.17)
49.48
30.93
19.59
325 (15.86)
32.13
32.46
35.41
Sex of the child Male Female Child got vitamin A in last 6 month
Time of introducing complementary food Before 6 months After 6 months Age of the child Infant (6-12 months) Toddler (13-36 months)
926 (44.72)
45.12
32.12
22.67
Preschool (37-59 months)
815 (39.42)
59.37
27.18
13.46
Size of the child at birth‡ Small average large
305 (14.74)
44.91
33.33
21.75
1401 (67.74)
49.08
30.00
20.92
362 (17.53)
50.74
28.02
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Table 1. Continued Anemia status by hemoglobin level %* Variable Nutritional status of the child
n (%)
No anemic
Mild
Moderate/severe
Chi-square result
P value
9.15
.01
1.72
.42
0.07
.96
§
Stunting No
1233 (59.64)
51.83
28.50
19.68
Yes
835 (40.36)
44.74
32.37
22.89
No
1735 (82.95)
48.85
30.60
20.55
Yes
353 (17.05)
49.53
27.41
23.05
No
766 (37.02)
48.82
30.27
20.91
Yes
1302 (62.98)
49.21
29.70
21.09
Wasted
Underweight
BDHS indicates Bangladesh Health and Demographic Health Survey; WHO, World Health Organization. *Prevalence is adjusted for individual sampling weight5 altitude using the formula of the Centers for Disease Control and Prevention of United States8 that is adopted by the BDHS5 and WHO6 as hemoglobin requirements differ substantially depending on altitude. † Household . 7 people. ‡ A child who born less than 2.5 kg was considered small, 2.5 kg to 3.1 kg was average, and greater than 3.1 kg was large.31 § The height-for-age Z-score (HAZ), that is, stunting; weight-for-age Z-score (WAZ), that is, wasting; and weight-for-height Z-score, that is, underweight, were used as screening-out tools of malnutrition of child. The cutoff point of wasting, stunting, and underweight was considered less than 22 SD from the median of the reference population, WHO 2006 by using WHO Anthro calculator (version 3).30
Childhood Anemia and Bivariate Analysis of the Influence of SES The association between SES and childhood anemia appears to follow the expected trend (Table 2). Compared with the children of the higher SES group, those belonging to the medium and low SES groups have 23 higher odds of being mildly, moderately, or severely anemic (OR 2.18; 95% CI 1.43-3.31; P,.001 and OR 1.99; 95% CI 1.24-3.31, P=.004, respectively). The post-stratification results indicated that the association between anemia and SES is worryingly high for the children belonging to the infant and preschooler group. Stratified bivariate analysis demonstrated that the likelihood of being anemic in infants was 3.43 (95% CI 1.597.43; P , .002) and 3.71 (95% CI 1.09-6.70 P = .07) in the low and medium SES, respectively. Likewise, the odds of having mild, moderate, or severe anemia in the case of preschool children belonging to the low and medium SES were 2.22 (95% CI .98-5.04; P = .04) and 2.56 (CI 0.91-7.25; P = .03), respectively. Toddlers from low SES households were 78% more likely to be anemic compared with the same from high SES groups (OR 1.78, CI .99-3.12, P = .06). Nevertheless, the association was insignificant for the children of medium SES (OR 1.54; 95% CI 0.79-3.07; P = .21). In the association of anemia with age group, the post-estimation analysis showed that the predictive probability for being anemic is higher among infants (68%; 95% CI 63-73, P , .001) compared to toddlers or preschool children (see Appendix Fig. 1 in Supplemental Materials found at https://doi.org/10.1016/j.vhri.2019.09.008). Similar analysis for SES revealed that the predictive probability of developing childhood anemia rises from 33% (95% CI 24-41; P , .001) to 53% (95% CI 51-56; P , .001) among the children belonging to high and low SES households, respectively (P , .001; see Appendix Fig. 1 in Supplemental Materials found at https://doi.org/10.1016/j.vhri.201 9.09.008). There is a clear age trend (ie, intercepts of the groups), but owing to the proportional odds assumption, the trend in each age group is the same between the SES groups (Fig. 1, Table 2). The predictive margin of being anemic among the infants belonging to the low SES and high SES varies from 49% (95% CI 65-75, P , .001)
to 69% (95% CI 65-75, P , .001). In contrast, the same for the toddlers and preschool children of low SES households was seen to decrease from 57% (95% CI 53-60, P , .001) to 42% (95% CI 3856, P , .001; Fig. 1).
Multivariate Analysis of Childhood Anemia and SES: Are Some Age Groups More Vulnerable? Among all the respondents (Table 2), it was noted that children belonging to low and medium SES are approximately 23 more likely to contract mild, moderate, or severe anemia compared with the children belonging to high SES (OR 1.98; 95% CI 1.23-3.19; P,.004 and OR 2; 95% CI 1.23-3.27; P=.004). After stratifying by child age, the results show a distinctive relationship between SES and the probability of being anemic. There is a strong association between SES and the chance of developing childhood anemia in the case of infants and preschool children. The infants from low and medium SES households have a 3-fold higher risk of suffering from anemia (OR 2.94; 95% CI 1.09-7.91; P = .03 and OR 2.76, 95% CI .87-8.82; P = .08, respectively); the preschool children from low and medium SES households have a 2- to 3-fold higher chance of being affected by anemia (OR 2.73; 95% CI 1.20-6.18; P = .02 and OR 2.47; 95% CI .99-6.14; P = .04) compared with children of the same age group belonging to high SES households. Although the results showed that in the toddlers the chance of being affected by anemia for the children belonging to low and medium SES is more than 1-fold; however, the associations were not observed to be statically significant (OR 1.42; 95% CI .76-2.66; P = .27 and OR 1.60, 95% CI .80-3.22; P = .80, respectively). The post-estimation analysis showed a clear pattern of predictive probability for age group (for infant 59%; 95% CI 56-63; P , .001 and for preschooler 31%; 95% CI 25-36; P , .001) and SES (in case of low SES 64%; 95% CI 54-57, P , .001 and for high SES 48%; 95% CI 45-50, P , .001) of being mildly, moderately, and severely anemic. The post-estimation also shows that the predictive probability for being anemic decreases from 79% (95% CI 67-92, P , .001) to 57% (95% CI 54-61, P , .001) among infants with low and high SES, respectively (see Appendix Fig 2 in
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Table 2. Bivariate and multivariate analyses of the effects of household SES status on childhood anemia, stratified by age obtained from ordinal logistic regression models.* Bivariate analysis Odd ratio (95% CI)
Multivariate analysis P value
Odd ratio (95% CI)
P value
Overall sample (N = 2068) Low
2.18 (1.43-3.31)
,.001
Medium
1.99 (1.24-3.31)
.004
High
1
1.98 (1.23-3.19)
.004
2.00 (1.23-3.27)
.004
1
Infant (N = 440) Low
3.43 (1.59-7.43)
.002
2.94 (1.09-7.91)
.03
Medium
3.71 (1.09-6.70)
.07
2.76 (.87-8.82)
.08
High
1
Toddler (N = 860) Low
1.78 (0.99-3.12)
.06
1.42 (.76-2.66)
.27
Medium
1.54 (0.79-3.07)
.21
1.60 (.80-3.22)
.18
High
1
1
Preschool (758) Low
2.22 (0.98-5.04)
.04
2.73 (1.20-6.18)
.02
Medium
2.56 (0.91-7.25)
.03
2.47 (.99-6.14)
.04
High
1
1
*The probability of being mildly, moderately, or severely anemic vs non-anemic in the relevant category of the socioeconomic status from the reference category of the independent variables.
Supplemental Materials found at https://doi.org/10.1016/j.vhri.201 9.09.008). Similar analysis for toddlers revealed that the predictive probability of developing childhood anemia rises from 45% (95% CI 41-49) to 56% (95% CI 43-71) among the children belonging to high and low SES households, respectively (P , .001; Fig. 2, Table 3). Likewise, the predictive margin for preschoolers goes from 29% (95% CI 24-36, P , .001) to 46% (95% CI 24-69, P , .001).
Other Determinants of Childhood Anemia The complete results of the multivariate order logit analysis are presented in Table 3. The results demonstrate that the odds of being affected by anemia are more than 3-fold higher (OR 3.50; 95% CI 2.68-4.57; P , .001) in infants and around 2-fold higher (OR 1.78, CI 1.46-2.14; P , .001) in the case of children belonging to the toddler age group compared with the preschool age group. Further, the children whose mothers were uneducated are significantly more at risk than those whose mothers had completed secondary or higher education; the former are 20% more likely to be anemic (OR 1.21; 95% CI .97-1.50; P = .09). The post-estimation for predicted probability for mothers with no educational attainment is 50% (95% CI 46-4; P , .001), for the mothers who attained the primary level is 46% (95% CI 42-50; P , .001), and for the mothers who attained secondary or higher education is 50% (95% CI 46-53; P , .001) (Table 4). As expected, children who had received oral vitamin A in last 6 months were 18% less likely to be affected by childhood anemia compared with those who did not receive any supplementation (OR 1.18; 95% CI 99-1.41; P = .06). For the same, the predictive probability was 46% (95% CI 43-50) and 50% (95% CI 47-53), respectively (P , .001). The place of residence was found to be a confounder of childhood anemia, where the children residing in rural areas appeared more vulnerable to mild, moderate, or severe anemia
compared to those living in urban areas (OR 1.27; 95% CI 1.21-1.35; P = .04). The post-estimation of the margin of being anemic was 49% (95% CI 45-52) for rural inhabitants and 48% (95% CI 45-52) for urban inhabitants (P , .001). Finally, stunting was observed as an associated factor for childhood anemia. The results showed that stunted children (height-for-age Z-score (HAZ) , –2 SD per WHO reference population 2006) are likely to be 1.343 more anemic (95% CI
Figure 1. The predictive margins of being anemic among infants, toddlers, and preschool children in different SES (unadjusted).
SES, socioeconomic status.
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1.11-1.63; P = .003) than children within the normal HAZ range. The predictive probability increases from 44% (95% CI 40-50) to 52% (95% CI 49-55) for the stunted and normal HAZ, respectively (P , .001).
Figure 2. The predictive margins of being anemic among infants, toddler, and preschool children in different SES (adjusted).
Discussion How Does SES Affect Childhood Anemia at Different Ages? For the entire sample population, both univariate and multivariate analyses indicate that SES plays an important role in the prevalence of childhood anemia. This relationship exposes the higher prevalence of anemia in the children belonging to low and medium SES households compared to rich households; this can be considered indicative of declining household nutritional status.13 This finding is consistent with those reported by previous studies.14-17 Both stratified bivariate and multivariate analyses showed that in the case of infants, there is a very strong association between SES and mild, moderate, or severe anemia. The odds of low SES infants being mildly, moderately, or severely anemic are around 3-fold higher compared with their counterparts from high SES households. Although both bivariate and multivariate analyses showed that the association between children from medium SES groups and anemia is weaker by comparison, it is still almost 33 higher than that observed for children belonging to high SES households. One possible explanation for this phenomenon could be that in comparison to rich households, poor and middle-class households usually wean children using rice-based food items that are known to have low iron content.18 This may also potentially be explained by the factors such as maternal iron deficiency and low concentration of iron in breast milk13 owing to heavy postpartum blood loss.19,20 Moreover, the choice of other low-quality nutrient-rich complementary foods in poor and middle-class households may also account for this phenomenon, as complementary foods at this age can markedly influence the risk of iron deficiency and anemia.21 In the case of preschool children, the stratified bivariate and multivariate analyses of the results revealed the presence of strong and statistically significant associations between children from low and medium SES households and mild, moderate, or severe anemia. The odds for children from such households carrying anemia were computed to vary between 23 and 43. It is likely that this is due to the high growth rates of children in this age group, which necessitate a rapidly expanding blood volume with a correspondingly large requirement for iron.21 This is compounded by the fact that food in poor and middle-class households in Bangladesh is mostly rice-based, which is intrinsically low in iron content (0.039-0.04 mg iron/g rice).22
Other Determinants of Childhood Anemia As deduced from descriptive and multivariate models, the 2 most important determinants of childhood anemia are age and SES. The results obtained in this investigation are in agreement with that reported by other studies.15,23,24 The relationship between maternal educational status and childhood anemia is not well established.3,25 The result shows that the children whose mothers have no education are significantly more likely to be affected by anemia compared with children whose mothers have secondary or higher educational attainment (P = .09). In general, lower or no maternal educational attainment is related to the lower socioeconomic status and also reflects in the relatively poor understanding of optimum childcare and nutritional practices that may result in the burden of the childhood anemia of a child whose
SES, socioeconomic status.
Table 3. The determinants of anemia status for children in Bangladesh from the BDHS 2011 (obtained from ordinal logistic regression models)* (N = 2068). Variables
Odd ratio
P value
95% CI
Socioeconomic status (as mentioned in Table 3) Low
1.98
.004
1.23-3.19
Medium
2.00
.004
1.22-2.26
High
1
Age of the child Infant (6-12 months)
3.50
,.001
2.68-4.57
Toddler (13-36 months)
1.78
,.001
1.46-2.14
Preschool (37-59 months)
1
Mother’s highest educational level No education
1.21
.09
0.97-1.50
Completed primary
0.97
.80
0.77-1.22
Completed secondary or higher
1
.04
1.21-1.35
.06
0.99-1.41
.003
1.11-1.63
Place of residence Rural
1.27
Urban
1
Vitamin A to the child in last 6 month No
1.18
Yes
1
Stunted Yes
1.34
No
1
BDHS indicates Bangladesh Health and Demographic Health Survey. *The probability of being mildly, moderately, or severely anemic vs non-anemic in the relevant category of the explanatory variables from that in the reference category of the independent variables.
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Table 4. The preventive probability of the determinants of being anemic* among the children in Bangladesh from the BDHS 2011 (obtained from post-estimation ordinal logistic regression models, N = 2068). Variables
Predictive margin (95% CI) P value
Socioeconomic status (as mentioned in Table 3) 59 (56-63)
,.001
Medium
46 (43-49)
,.001
High
31 (25-36)
,.001
Infant (6-12 months)
64 (54-57)
,.001
Toddler (13-36 months)
49 (42-57)
,.001
Preschool (37-59 months)
48 (0.45-50)
,.001
No education
50 (46-54)
,.001
Completed primary
46 (42-50)
,.001
Completed secondary or higher
50 (46-54)
,.001
Rural
49 (45-52)
,.001
Urban
48 (45-52)
,.001
No
46 (43-50)
,.001
Yes
50 (47-53)
,.001
Yes
52 (49-55)
,.001
No
44 (40-48)
,.001
Low
147
affect hemoglobin concentration. Additionally, the information regarding acute and chronic inflammation, parasitic infections, and inherited or acquired disorders that may potentially affect hemoglobin synthesis was overlooked.
Conclusion The predictive margin of being anemic is high among the infants, toddlers, and preschool children belonging to all SES groups. Therefore, it should be a matter of concern in Bangladesh. The children belonging to low SES households are at maximal risk for childhood anemia, which suggests that greater attention should be diverted toward this group for interventional purposes. The results indicate that the association between SES and childhood anemia is very complex in nature and influenced by a multitude of factors, such as maternal education, place of residence, vitamin A supplementation, and chronic childhood malnutrition. The results also suggest that improving the overall nutrition status of the children and increasing their access to iron-rich complementary food during weaning may potentially reduce the burden of childhood anemia in Bangladesh. In the short term, food and iron supplementation for the mother during the lactating phase, especially during the postpartum period, can be helpful for combating childhood anemia. This can be inferred from results that infants are most vulnerable for anemia because blood loss during the delivery and postpartum reduces the iron content in breast milk.
Age of the child
Mother’s highest educational level
Place of residence
Vitamin A to the child in last 6 month
Acknowledgements
Stunted
BDHS indicates Bangladesh Health and Demographic Health Survey. *The probability of being mildly, moderately, or severely anemic vs non-anemic in the relevant category of the explanatory variables from that in the reference category of the independent variables.
26
mother has no education or completed only primary education. The findings of our study regarding the place of residence as a determinant of childhood anemia are in parallel with similar findings reported by other investigations of a similar nature.27,28 The increased vulnerability of the rural children to childhood anemia can be linked to malnutrition caused by the limited availability of nutritious food, which can be seen as an outcome of belonging to a lower socioeconomic class. The lack of access to proper hygiene and sanitation29 in combination with other chronic diseases are also associated with an increased risk of anemia. This study also identified childhood stunting as a significant and strong predictor of anemia. Statistics revealed that stunted children are more likely to be affected by anemia. Stunting, a manifestation of several macroutrient and micronutrient deficiencies and poor health, is regarded as an indicator of chronic malnutrition in developing countries; this condition is also known to be positively associated with both childhood immunity and anemia.21,28 In Bangladesh, approximately 36% of children can be classified as suffering from stunted growth; this is a matter of great concern for the public health system of the country. This study did not include biomarkers of nutritional deficiencies such as folate, vitamin B12, and vitamin A, which can
The author is grateful to ICF International for providing permission for this secondary analysis. ICF International provided financial and technical assistance for the survey through the U.S. Agency for International Development/ Bangladesh. The funding bodies had no role in data extraction and analysis, writing of the manuscript, or the decision to submit the paper for publication.
Supplemental Material Supplementary data associated with this article can be found in the online version at https://doi.org/10.1016/j.vhri.2019.09.008.
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Rutstein S. Wealth Versus Expenditure: Comparison Between the DHS Wealth Index and Household Expenditures in Four Departments of Guatemala. Calverton, MD: ORC Macro; 1999. Rising B. Using predictive margins to make clearer explanations. https://www. stata.com/meeting/india13/materials/in13_rising.pdf. Accessed December 2, 2019. Stata Corporation. Stata Statistical Software: release 5.0. College Station, TX: Stata Corporation; 1997. StataCorp. Stata Statistical Software: release 13. College Station, TX: StataCorp LP; 2013. Singh MB, Fotedar R, Lakshminarayana J, Anand PK. Studies on the nutritional status of children aged 0–5 years in a drought-affected desert area of western Rajasthan, India. Public Health Nutr. 2006;9(8):961–967. Horton S, Ross J. The economics of iron deficiency. Food Policy. 2003;28(1):51–75. Goswmai S, Das KK. Socio-economic and demographic determinants of childhood anemia. J Pediatr (Rio J). 2015;91(5):471–477. Muniz PT, de Castro TG, de Araujo TS, et al. Child health and nutrition in the Western Brazilian Amazon: population-based surveys in two counties in Acre State. Cadernos Saude Publica. 2007;23(6):1283–1293. Singh RK, Patra S. Extent of anaemia among preschool children in EAG States, India: a challenge to policy makers. Anemia. 2014;2014(1): 1–9. Brown LV, Zeitlin MF, Peterson KE, et al. Evaluation of the impact of weaning food messages on infant feeding practices and child growth in rural Bangladesh. Am J Clin Nutr. 1992;56(6):994–1003. Milman N. Postpartum anemia I: definition, prevalence, causes, and consequences. Ann Hematol. 2011;90(11):1247.
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Allen LH. Anemia and iron deficiency: effects on pregnancy outcome. Am J Clin Nutr. 2000;71(5):1280S–1284S. Konstantyner T, Roma Oliveira TC, de Aguiar Carrazedo Taddei JA. Risk factors for anemia among Brazilian infants from the 2006 National Demographic Health Survey. Anemia. 2012;2012:850681. Tamanna S, Parvin S, Kumar S, et al. Content of some minerals and their bioavailability in selected popular rice varieties from Bangladesh. Int J Curr Microbiol Appl Sci. 2013;2(7):35–43. Baker RD, Greer FR. Diagnosis and prevention of iron deficiency and irondeficiency anemia in infants and young children (0-3 years of age). Pediatrics. 2010;126(5):1040–1050. Dey S, Raheem E, Lu Z. Multilevel multinomial logistic regression model for identifying factors associated with anemia in children aged 6–59 months in northeastern states of India. Cogent Math. 2016;3. World Health Organization. Worldwide Prevalence of Anaemia 1993–2005: WHO Global Database on Anaemia. World Health Organization; 2008. Choi HJ, Lee HJ, Jang HB, et al. Effects of maternal education on diet, anemia, and iron deficiency in Korean school-aged children. BMC Public Health. 2011;11:870. Agho K, Dibley M, D’Este C, Gibberd R. Factors associated with haemoglobin concentration among Timor-Leste children aged 6-59 months. J Health Popul Nutr. 2008;26(2):200–209. Ngnie-Teta I, Receveur O, Kuate-Defo B. Risk factors for moderate to severe anemia among children in Benin and Mali: insights from a multilevel analysis. Food Nutr Bull. 2007;28(1):76–89. Beresford CH, Neale RJ, Brooks OG. Iron absorption and pyrexia. Lancet. 1971;1(7699):568–572. World Health Organization. WHO Anthro (version 3.2.2, January 2011) and macros. World Health Organization. 2011.
Contents lists available at sciencedirect.com Journal homepage: www.elsevier.com/locate/vhri
Association of Socioeconomic Status With Childhood Anemia Among Infant, Toddler, and Preschool Children in Bangladesh G. M. Rabiul Islam, PhD* Department of Food Engineering and Tea Technology, Shahjalal University of Science and Technology, Bangladesh.
A B S T R A C T Objectives: This study aims to delineate the association between economic inequality, along with other confounders, and anemia among infants, toddlers, and preschool children. Methods: From the 2011 Bangladesh Demographic Health Survey, a cross-sectional population sample comprising 2068 children between ages 6 and 59 months were selected for this study. Analyses were performed with a proportional odds model and finally stratified with the child age groups. Results: Infants belonging to a low and medium socioeconomic status (SES) have approximately a 3-fold higher chance of being affected by mild, moderate, or severe anemia compared with infants of high SES (odds ratio [OR] 2.94; 95% CI 1.097.91; P=.03 and OR 2.76; 95% CI .87-8.82; P=.08, respectively). Preschool children from low and medium SES households are 2.733 (95% CI 1.20-6.18; P=.02) and 2.473 (95% CI .99-6.14; P=.04) more likely to be anemic compared with their counterparts from higher SES households. The place of residence and childhood stunting are associated with childhood anemia (urban vs rural: OR 1.27; 95% CI .21-.35; P = .04; and stunted vs normal: OR 1.34; 95% CI 1.11-1.63; P = .003). Besides, vitamin A supplementations appear to serve as protective agents against the occurrence of the childhood anemia (OR 1.18; 95% CI .99-1.41; P = .06). Conclusion: Urgent preventive measures are needed to control the impending childhood anemia among infants and preschool children, especially in the low and medium SES households (ClinicalTrials.gov Identifier: NCT03126253). Keywords: childhood anemia, infants, toddlers, preschool children, socioeconomic status. VALUE IN HEALTH REGIONAL ISSUES. 2020; 21(C):141–148
Introduction Childhood anemia is a global public health problem that is associated with life-threatening consequences such as growth retardation, impaired motor and cognitive development, and increased morbidity and mortality.1 Anemia can be caused by a variety of factors such as nutritional deficiencies (eg, iron, folic acid, vitamin B12, and vitamin A), infections (eg, helminth), and hereditary anemia (eg, hemoglobinopathies).2 The World Health Organization (WHO) estimates that approximately 50% of anemia cases can be attributed to iron deficiency.3 This is an estimated global average that varies widely depending upon the location in question.3 The World Bank estimates for 2011 claim that approximately 55.60% of all Bangladeshi children younger than 5 years are suffering from anemia.4 The relationship between socioeconomic inequality and anemia among the children has never been conclusive, and it is unclear whether children aged 6 to 59 months
have uniformly high levels of anemia during all the stages of development, for example, during the infant, toddler, and preschool stages. In addition, there is a dearth of evidence from Bangladesh, where the meaning of sociodemographic characteristics may be different from that in other countries. This study attempts to fill the aforementioned lacuna by investigating and evaluating the association of socioeconomic status (SES) inequality, among other explanatory variables, with the development of childhood anemia among infant, toddler, and preschool children. The results reported herein represent a confident and trustworthy approach for elucidating the potential effects of SES and the stages of child development that are usually neglected in the conventional scientific literature. Moreover, because anemia is one of the current key health issues in Bangladesh, it is also expected that the findings of this study will contribute significantly toward shaping the health policy strategy of the country.
G.M.R.I. performed all the statistical analysis and drafted the manuscript. Conflict of interest: The authors declare no conflicts of interest. * Address correspondence to: G. M. Rabiul Islam, PhD, Department of Food Engineering and Tea Technology, Shahjalal University of Science and Technology, Sylhet3114, Bangladesh. Email: [email protected] 2212-1099/$36.00 - see front matter ª 2019 ISPOR–The professional society for health economics and outcomes research. Published by Elsevier Inc. https://doi.org/10.1016/j.vhri.2019.09.008
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Materials and Methods Study Design and Participants The 2011 Bangladesh Health and Demographic Health Survey (BDHS) data collected under the authority of National Institute for Population Research and Training (Ministry of Health and Family Welfare, Bangladesh) from May 11, 2011, to July 8, 2012, were used for the purpose of this study. A stratified, multistage cluster sampling strategy was adopted to construct a national representative household-based sample. To generate this, 600 primary sampling units (207 from urban areas and 393 from rural areas) were selected from the sampling frame created for the 2011 Bangladeshi census.5 There were 2320 children aged 6 to 59 months for the examination of anemia in BDHS 2011 raised from every third household of the BDHS sample. After excluding participants with missing data, a total of 2068 individuals were selected for this analysis (see Appendix Table 1 in Supplemental Materials found at https://doi.org/10.1016/j. vhri.2019.09.0081).
Outcomes In this study, hemoglobin level was used as the parameter for diagnosing anemia. The HemoCue system was used to estimate the concentration of hemoglobin in the capillary blood.5 The WHO guidelines for the diagnosis of anemia that were adopted by the Demographic Health Surveys were used in this study.5,6 Briefly, a hemoglobin concentration of less than 70 g/L was used to define severe anemia, 70 to 99 g/L defined moderate anemia, and 100 to 109 g/L was presumed to correspond to mild anemia. The aforementioned classification of anemia as severe, moderate, and mild categories is based on blood hemoglobin cutoffs (adjusted for altitude and smoking) recommended by the Centers for Disease Prevention and Prevention of United States7; this classification has also been adopted by the WHO.6 Because only 0.73% (n = 15) of the studied children were classified as being severely anemic, for the purpose of the regression model used in this study, the severe and moderate groups were combined to create a severe/moderate category (a hemoglobin concentration of less than 99 g/L) to avoid problems associated with 0 cell counts in estimating the models.
Explanatory Variables Socioeconomic status, defined per the wealth status of the individual, was used as a key predictor in this study. The wealth index used in the BDHS survey is a measure that has been adopted by many DHS and other country-level surveys for measuring inequalities in household characteristics, in the use of health and other services, and in health outcomes.8 It serves as an indicator of household wealth that is consistent with factors such as expenditure and income measures.9 The index is constructed using household asset data via principal components analysis. In this study, the wealth index score of the BDHS 2011 survey was used as a proxy for SES, and inequality was measured by dividing the wealth index into tertiles and categorizing them as low, medium, and high. In addition to SES, the age group of the child was also used as a key predictor. The child concerned was regarded as an infant if the age was less than or equal to 12 months, as a toddler if the age was between 13 and 36 months, and as a preschooler if age was between 37 and 59 months. Apart from the factors enumerated above, this study also included related explanatory variables, which are listed in Table 1.
Ethical Considerations The data collection procedure for the BDHS was carried out with the approval of the Office of Research Compliance macroinstitutional review board, and National Research Ethics Committee, Bangladesh Medical Research Council. Per the guidelines of the BDHS, before the interview, informed consent was obtained from individual respondents, and this was followed by an oral explanation given by the interviewers. The respondents were informed of the voluntary nature of the survey, the potential risks involved in participation, the purpose of the gathered data (assessment of health needs and planning health services), and the confidentiality of the results of the individual interview along with the free-of-charge nature of the examination. In the case of biomarkers and other information regarding children, the consent of the parent or caretaker was taken accordingly.5
Statistical Analysis Pearson’s chi-square test was used to determine statistically significant differences observed within the various categories of the Centers for Disease Control and Prevention/WHO hemoglobin grouping variable in relation to the explanatory variables. Order logit model using maximum likelihood estimation was used to identify socioeconomic and other determinants of anemia status as fitted in the “ologit” command. Explanatory variables that had a significant association with anemia as determined by chi-square analysis (viz SES, age of the child, mother’s highest education level, place of residence, and stunting) were included in the model (see Appendix Table 2 in Supplemental Materials found at https://doi.org/10.1016/j.vhri.2019. 09.008). The interaction between child age group and SES was also tested, and no interaction was found (results not shown). The data were then stratified by the age group of the child. Subsequent to the aforementioned analysis, the post-estimation of the predictive probability of being mildly, moderately, and severely anemic was also generated using the “margins” command to make the explanations more clear.10 To specify the association of SES with childhood anemia in infant, toddler, and preschool children, the interaction notation in the “margins” command is used as even though there were no interactions, all possible combinations of SES and child age were captured as stratification.10 To account for the complex survey design, this study included strata (divisions of Bangladesh) and clustering variables (primary sampling unit) by using the survey (svy) option of Stata. Considering both stratifications and clustering of data enables the generation of a more robust estimate of the survey design characteristics.11 Models were fitted using the Stata software (statistics software) for Windows version 13.12
Results Descriptive Analysis The population characteristics, the percentage of children belonging to each anemia class as categorized by the explanatory variables, and the results of the chi-square statistical analysis are summarized in Table 1. These variables are reflective of the main hypotheses being tested in the study, that is, the differences in the prevalence of anemia are related to SES and other explanatory variables. The results of the chisquare test proved that the factors such as SES, mother’s education level, place of residence, supplementation with vitamin A in the last 6 months, age of the child, and stunting produced statically significant differences (P , .05).
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Table 1. Percentages of child with anemia by standard of explanatory variables (N = 2068). Anemia status by hemoglobin level %* Variable
n (%)
No anemic
Mild
Moderate/severe
Chi-square result
P value
21.33
,.001
12.47
.01
3.00
.55
6.07
.04
0.36
.83
3.95
.13
0.10
.95
7.22
.02
0.52
.76
92.83
,.001
2.72
.60
Socioeconomic status Low
1710 (82.69)
46.94
31.56
21.50
Medium
216 (10.44)
47.44
29.65
22.92
High
142 (6.87)
48.18
31.39
13.53
Sociodemographic factors Mother’s highest education level No education
557 (26.94)
48.75
32.82
18.43
Completed primary
634 (30.66)
44.86
29.85
25.30
Completed secondary or higher
877 (42.40)
51.59
28.66
19.76
367 (17.73)
46.94
31.49
21.57
Husband’s highest education level No education Completed primary
666 (32.25)
47.44
29.65
22.92
Completed secondary or higher
1035 (50.03)
50.21
30.06
19.73
Place of residence Urban
659 (31.83)
52.60
26.95
20.45
Rural
1409 (68.17)
46.93
31.69
21.38
1901 (91.94)
48.68
30.07
21.25
167 (8.06)
49.36
31.41
19.23
Sex of household head Male Female Living in large family† No
1411 (68.22)
47.42
30.64
21.94
Yes
657 (31.78)
52.00
29.17
18.83
1071 (51.78)
50.39
30.29
19.32
997 (48.22)
51.16
29.19
19.65
No
786 (38.01)
45.18
30.99
23.83
Yes
1282 (61.99)
50.76
29.81
19.43
1754 (84.83)
48.62
29.93
21.45
314 (15.17)
49.48
30.93
19.59
325 (15.86)
32.13
32.46
35.41
Sex of the child Male Female Child got vitamin A in last 6 month
Time of introducing complementary food Before 6 months After 6 months Age of the child Infant (6-12 months) Toddler (13-36 months)
926 (44.72)
45.12
32.12
22.67
Preschool (37-59 months)
815 (39.42)
59.37
27.18
13.46
Size of the child at birth‡ Small average large
305 (14.74)
44.91
33.33
21.75
1401 (67.74)
49.08
30.00
20.92
362 (17.53)
50.74
28.02
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Table 1. Continued Anemia status by hemoglobin level %* Variable Nutritional status of the child
n (%)
No anemic
Mild
Moderate/severe
Chi-square result
P value
9.15
.01
1.72
.42
0.07
.96
§
Stunting No
1233 (59.64)
51.83
28.50
19.68
Yes
835 (40.36)
44.74
32.37
22.89
No
1735 (82.95)
48.85
30.60
20.55
Yes
353 (17.05)
49.53
27.41
23.05
No
766 (37.02)
48.82
30.27
20.91
Yes
1302 (62.98)
49.21
29.70
21.09
Wasted
Underweight
BDHS indicates Bangladesh Health and Demographic Health Survey; WHO, World Health Organization. *Prevalence is adjusted for individual sampling weight5 altitude using the formula of the Centers for Disease Control and Prevention of United States8 that is adopted by the BDHS5 and WHO6 as hemoglobin requirements differ substantially depending on altitude. † Household . 7 people. ‡ A child who born less than 2.5 kg was considered small, 2.5 kg to 3.1 kg was average, and greater than 3.1 kg was large.31 § The height-for-age Z-score (HAZ), that is, stunting; weight-for-age Z-score (WAZ), that is, wasting; and weight-for-height Z-score, that is, underweight, were used as screening-out tools of malnutrition of child. The cutoff point of wasting, stunting, and underweight was considered less than 22 SD from the median of the reference population, WHO 2006 by using WHO Anthro calculator (version 3).30
Childhood Anemia and Bivariate Analysis of the Influence of SES The association between SES and childhood anemia appears to follow the expected trend (Table 2). Compared with the children of the higher SES group, those belonging to the medium and low SES groups have 23 higher odds of being mildly, moderately, or severely anemic (OR 2.18; 95% CI 1.43-3.31; P,.001 and OR 1.99; 95% CI 1.24-3.31, P=.004, respectively). The post-stratification results indicated that the association between anemia and SES is worryingly high for the children belonging to the infant and preschooler group. Stratified bivariate analysis demonstrated that the likelihood of being anemic in infants was 3.43 (95% CI 1.597.43; P , .002) and 3.71 (95% CI 1.09-6.70 P = .07) in the low and medium SES, respectively. Likewise, the odds of having mild, moderate, or severe anemia in the case of preschool children belonging to the low and medium SES were 2.22 (95% CI .98-5.04; P = .04) and 2.56 (CI 0.91-7.25; P = .03), respectively. Toddlers from low SES households were 78% more likely to be anemic compared with the same from high SES groups (OR 1.78, CI .99-3.12, P = .06). Nevertheless, the association was insignificant for the children of medium SES (OR 1.54; 95% CI 0.79-3.07; P = .21). In the association of anemia with age group, the post-estimation analysis showed that the predictive probability for being anemic is higher among infants (68%; 95% CI 63-73, P , .001) compared to toddlers or preschool children (see Appendix Fig. 1 in Supplemental Materials found at https://doi.org/10.1016/j.vhri.2019.09.008). Similar analysis for SES revealed that the predictive probability of developing childhood anemia rises from 33% (95% CI 24-41; P , .001) to 53% (95% CI 51-56; P , .001) among the children belonging to high and low SES households, respectively (P , .001; see Appendix Fig. 1 in Supplemental Materials found at https://doi.org/10.1016/j.vhri.201 9.09.008). There is a clear age trend (ie, intercepts of the groups), but owing to the proportional odds assumption, the trend in each age group is the same between the SES groups (Fig. 1, Table 2). The predictive margin of being anemic among the infants belonging to the low SES and high SES varies from 49% (95% CI 65-75, P , .001)
to 69% (95% CI 65-75, P , .001). In contrast, the same for the toddlers and preschool children of low SES households was seen to decrease from 57% (95% CI 53-60, P , .001) to 42% (95% CI 3856, P , .001; Fig. 1).
Multivariate Analysis of Childhood Anemia and SES: Are Some Age Groups More Vulnerable? Among all the respondents (Table 2), it was noted that children belonging to low and medium SES are approximately 23 more likely to contract mild, moderate, or severe anemia compared with the children belonging to high SES (OR 1.98; 95% CI 1.23-3.19; P,.004 and OR 2; 95% CI 1.23-3.27; P=.004). After stratifying by child age, the results show a distinctive relationship between SES and the probability of being anemic. There is a strong association between SES and the chance of developing childhood anemia in the case of infants and preschool children. The infants from low and medium SES households have a 3-fold higher risk of suffering from anemia (OR 2.94; 95% CI 1.09-7.91; P = .03 and OR 2.76, 95% CI .87-8.82; P = .08, respectively); the preschool children from low and medium SES households have a 2- to 3-fold higher chance of being affected by anemia (OR 2.73; 95% CI 1.20-6.18; P = .02 and OR 2.47; 95% CI .99-6.14; P = .04) compared with children of the same age group belonging to high SES households. Although the results showed that in the toddlers the chance of being affected by anemia for the children belonging to low and medium SES is more than 1-fold; however, the associations were not observed to be statically significant (OR 1.42; 95% CI .76-2.66; P = .27 and OR 1.60, 95% CI .80-3.22; P = .80, respectively). The post-estimation analysis showed a clear pattern of predictive probability for age group (for infant 59%; 95% CI 56-63; P , .001 and for preschooler 31%; 95% CI 25-36; P , .001) and SES (in case of low SES 64%; 95% CI 54-57, P , .001 and for high SES 48%; 95% CI 45-50, P , .001) of being mildly, moderately, and severely anemic. The post-estimation also shows that the predictive probability for being anemic decreases from 79% (95% CI 67-92, P , .001) to 57% (95% CI 54-61, P , .001) among infants with low and high SES, respectively (see Appendix Fig 2 in
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145
Table 2. Bivariate and multivariate analyses of the effects of household SES status on childhood anemia, stratified by age obtained from ordinal logistic regression models.* Bivariate analysis Odd ratio (95% CI)
Multivariate analysis P value
Odd ratio (95% CI)
P value
Overall sample (N = 2068) Low
2.18 (1.43-3.31)
,.001
Medium
1.99 (1.24-3.31)
.004
High
1
1.98 (1.23-3.19)
.004
2.00 (1.23-3.27)
.004
1
Infant (N = 440) Low
3.43 (1.59-7.43)
.002
2.94 (1.09-7.91)
.03
Medium
3.71 (1.09-6.70)
.07
2.76 (.87-8.82)
.08
High
1
Toddler (N = 860) Low
1.78 (0.99-3.12)
.06
1.42 (.76-2.66)
.27
Medium
1.54 (0.79-3.07)
.21
1.60 (.80-3.22)
.18
High
1
1
Preschool (758) Low
2.22 (0.98-5.04)
.04
2.73 (1.20-6.18)
.02
Medium
2.56 (0.91-7.25)
.03
2.47 (.99-6.14)
.04
High
1
1
*The probability of being mildly, moderately, or severely anemic vs non-anemic in the relevant category of the socioeconomic status from the reference category of the independent variables.
Supplemental Materials found at https://doi.org/10.1016/j.vhri.201 9.09.008). Similar analysis for toddlers revealed that the predictive probability of developing childhood anemia rises from 45% (95% CI 41-49) to 56% (95% CI 43-71) among the children belonging to high and low SES households, respectively (P , .001; Fig. 2, Table 3). Likewise, the predictive margin for preschoolers goes from 29% (95% CI 24-36, P , .001) to 46% (95% CI 24-69, P , .001).
Other Determinants of Childhood Anemia The complete results of the multivariate order logit analysis are presented in Table 3. The results demonstrate that the odds of being affected by anemia are more than 3-fold higher (OR 3.50; 95% CI 2.68-4.57; P , .001) in infants and around 2-fold higher (OR 1.78, CI 1.46-2.14; P , .001) in the case of children belonging to the toddler age group compared with the preschool age group. Further, the children whose mothers were uneducated are significantly more at risk than those whose mothers had completed secondary or higher education; the former are 20% more likely to be anemic (OR 1.21; 95% CI .97-1.50; P = .09). The post-estimation for predicted probability for mothers with no educational attainment is 50% (95% CI 46-4; P , .001), for the mothers who attained the primary level is 46% (95% CI 42-50; P , .001), and for the mothers who attained secondary or higher education is 50% (95% CI 46-53; P , .001) (Table 4). As expected, children who had received oral vitamin A in last 6 months were 18% less likely to be affected by childhood anemia compared with those who did not receive any supplementation (OR 1.18; 95% CI 99-1.41; P = .06). For the same, the predictive probability was 46% (95% CI 43-50) and 50% (95% CI 47-53), respectively (P , .001). The place of residence was found to be a confounder of childhood anemia, where the children residing in rural areas appeared more vulnerable to mild, moderate, or severe anemia
compared to those living in urban areas (OR 1.27; 95% CI 1.21-1.35; P = .04). The post-estimation of the margin of being anemic was 49% (95% CI 45-52) for rural inhabitants and 48% (95% CI 45-52) for urban inhabitants (P , .001). Finally, stunting was observed as an associated factor for childhood anemia. The results showed that stunted children (height-for-age Z-score (HAZ) , –2 SD per WHO reference population 2006) are likely to be 1.343 more anemic (95% CI
Figure 1. The predictive margins of being anemic among infants, toddlers, and preschool children in different SES (unadjusted).
SES, socioeconomic status.
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1.11-1.63; P = .003) than children within the normal HAZ range. The predictive probability increases from 44% (95% CI 40-50) to 52% (95% CI 49-55) for the stunted and normal HAZ, respectively (P , .001).
Figure 2. The predictive margins of being anemic among infants, toddler, and preschool children in different SES (adjusted).
Discussion How Does SES Affect Childhood Anemia at Different Ages? For the entire sample population, both univariate and multivariate analyses indicate that SES plays an important role in the prevalence of childhood anemia. This relationship exposes the higher prevalence of anemia in the children belonging to low and medium SES households compared to rich households; this can be considered indicative of declining household nutritional status.13 This finding is consistent with those reported by previous studies.14-17 Both stratified bivariate and multivariate analyses showed that in the case of infants, there is a very strong association between SES and mild, moderate, or severe anemia. The odds of low SES infants being mildly, moderately, or severely anemic are around 3-fold higher compared with their counterparts from high SES households. Although both bivariate and multivariate analyses showed that the association between children from medium SES groups and anemia is weaker by comparison, it is still almost 33 higher than that observed for children belonging to high SES households. One possible explanation for this phenomenon could be that in comparison to rich households, poor and middle-class households usually wean children using rice-based food items that are known to have low iron content.18 This may also potentially be explained by the factors such as maternal iron deficiency and low concentration of iron in breast milk13 owing to heavy postpartum blood loss.19,20 Moreover, the choice of other low-quality nutrient-rich complementary foods in poor and middle-class households may also account for this phenomenon, as complementary foods at this age can markedly influence the risk of iron deficiency and anemia.21 In the case of preschool children, the stratified bivariate and multivariate analyses of the results revealed the presence of strong and statistically significant associations between children from low and medium SES households and mild, moderate, or severe anemia. The odds for children from such households carrying anemia were computed to vary between 23 and 43. It is likely that this is due to the high growth rates of children in this age group, which necessitate a rapidly expanding blood volume with a correspondingly large requirement for iron.21 This is compounded by the fact that food in poor and middle-class households in Bangladesh is mostly rice-based, which is intrinsically low in iron content (0.039-0.04 mg iron/g rice).22
Other Determinants of Childhood Anemia As deduced from descriptive and multivariate models, the 2 most important determinants of childhood anemia are age and SES. The results obtained in this investigation are in agreement with that reported by other studies.15,23,24 The relationship between maternal educational status and childhood anemia is not well established.3,25 The result shows that the children whose mothers have no education are significantly more likely to be affected by anemia compared with children whose mothers have secondary or higher educational attainment (P = .09). In general, lower or no maternal educational attainment is related to the lower socioeconomic status and also reflects in the relatively poor understanding of optimum childcare and nutritional practices that may result in the burden of the childhood anemia of a child whose
SES, socioeconomic status.
Table 3. The determinants of anemia status for children in Bangladesh from the BDHS 2011 (obtained from ordinal logistic regression models)* (N = 2068). Variables
Odd ratio
P value
95% CI
Socioeconomic status (as mentioned in Table 3) Low
1.98
.004
1.23-3.19
Medium
2.00
.004
1.22-2.26
High
1
Age of the child Infant (6-12 months)
3.50
,.001
2.68-4.57
Toddler (13-36 months)
1.78
,.001
1.46-2.14
Preschool (37-59 months)
1
Mother’s highest educational level No education
1.21
.09
0.97-1.50
Completed primary
0.97
.80
0.77-1.22
Completed secondary or higher
1
.04
1.21-1.35
.06
0.99-1.41
.003
1.11-1.63
Place of residence Rural
1.27
Urban
1
Vitamin A to the child in last 6 month No
1.18
Yes
1
Stunted Yes
1.34
No
1
BDHS indicates Bangladesh Health and Demographic Health Survey. *The probability of being mildly, moderately, or severely anemic vs non-anemic in the relevant category of the explanatory variables from that in the reference category of the independent variables.
--
Table 4. The preventive probability of the determinants of being anemic* among the children in Bangladesh from the BDHS 2011 (obtained from post-estimation ordinal logistic regression models, N = 2068). Variables
Predictive margin (95% CI) P value
Socioeconomic status (as mentioned in Table 3) 59 (56-63)
,.001
Medium
46 (43-49)
,.001
High
31 (25-36)
,.001
Infant (6-12 months)
64 (54-57)
,.001
Toddler (13-36 months)
49 (42-57)
,.001
Preschool (37-59 months)
48 (0.45-50)
,.001
No education
50 (46-54)
,.001
Completed primary
46 (42-50)
,.001
Completed secondary or higher
50 (46-54)
,.001
Rural
49 (45-52)
,.001
Urban
48 (45-52)
,.001
No
46 (43-50)
,.001
Yes
50 (47-53)
,.001
Yes
52 (49-55)
,.001
No
44 (40-48)
,.001
Low
147
affect hemoglobin concentration. Additionally, the information regarding acute and chronic inflammation, parasitic infections, and inherited or acquired disorders that may potentially affect hemoglobin synthesis was overlooked.
Conclusion The predictive margin of being anemic is high among the infants, toddlers, and preschool children belonging to all SES groups. Therefore, it should be a matter of concern in Bangladesh. The children belonging to low SES households are at maximal risk for childhood anemia, which suggests that greater attention should be diverted toward this group for interventional purposes. The results indicate that the association between SES and childhood anemia is very complex in nature and influenced by a multitude of factors, such as maternal education, place of residence, vitamin A supplementation, and chronic childhood malnutrition. The results also suggest that improving the overall nutrition status of the children and increasing their access to iron-rich complementary food during weaning may potentially reduce the burden of childhood anemia in Bangladesh. In the short term, food and iron supplementation for the mother during the lactating phase, especially during the postpartum period, can be helpful for combating childhood anemia. This can be inferred from results that infants are most vulnerable for anemia because blood loss during the delivery and postpartum reduces the iron content in breast milk.
Age of the child
Mother’s highest educational level
Place of residence
Vitamin A to the child in last 6 month
Acknowledgements
Stunted
BDHS indicates Bangladesh Health and Demographic Health Survey. *The probability of being mildly, moderately, or severely anemic vs non-anemic in the relevant category of the explanatory variables from that in the reference category of the independent variables.
26
mother has no education or completed only primary education. The findings of our study regarding the place of residence as a determinant of childhood anemia are in parallel with similar findings reported by other investigations of a similar nature.27,28 The increased vulnerability of the rural children to childhood anemia can be linked to malnutrition caused by the limited availability of nutritious food, which can be seen as an outcome of belonging to a lower socioeconomic class. The lack of access to proper hygiene and sanitation29 in combination with other chronic diseases are also associated with an increased risk of anemia. This study also identified childhood stunting as a significant and strong predictor of anemia. Statistics revealed that stunted children are more likely to be affected by anemia. Stunting, a manifestation of several macroutrient and micronutrient deficiencies and poor health, is regarded as an indicator of chronic malnutrition in developing countries; this condition is also known to be positively associated with both childhood immunity and anemia.21,28 In Bangladesh, approximately 36% of children can be classified as suffering from stunted growth; this is a matter of great concern for the public health system of the country. This study did not include biomarkers of nutritional deficiencies such as folate, vitamin B12, and vitamin A, which can
The author is grateful to ICF International for providing permission for this secondary analysis. ICF International provided financial and technical assistance for the survey through the U.S. Agency for International Development/ Bangladesh. The funding bodies had no role in data extraction and analysis, writing of the manuscript, or the decision to submit the paper for publication.
Supplemental Material Supplementary data associated with this article can be found in the online version at https://doi.org/10.1016/j.vhri.2019.09.008.
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