The effects of social determinants on children’s health outcomes in Bangladesh slums through an intersectionality lens: An application of multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA)

Proloy Barua; Eliud Kibuchi; Bachera Aktar; Sabrina Fatema Chowdhury; Imran Hossain Mithu; Zahidul Quayyum; Noemia Teixeira de Siqueira Filha; Alastair H Leyland; Sabina Faiz Rashid; Linsay Gray

doi:10.1371/journal.pgph.0001588

. 2023 Mar 8;3(3):e0001588. doi: 10.1371/journal.pgph.0001588

The effects of social determinants on children’s health outcomes in Bangladesh slums through an intersectionality lens: An application of multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA)

Proloy Barua ^1,^*, Eliud Kibuchi ², Bachera Aktar ^1,³, Sabrina Fatema Chowdhury ¹, Imran Hossain Mithu ¹, Zahidul Quayyum ¹, Noemia Teixeira de Siqueira Filha ⁴, Alastair H Leyland ², Sabina Faiz Rashid ¹, Linsay Gray ²

Editor: Ashti Doobay-Persaud⁵

PMCID: PMC10022045 PMID: 36963045

Abstract

Empirical evidence suggests that the health outcomes of children living in slums are poorer than those living in non-slums and other urban areas. Improving health especially among children under five years old (U5y) living in slums, requires a better understanding of the social determinants of health (SDoH) that drive their health outcomes. Therefore, we aim to investigate how SDoH collectively affects health outcomes of U5y living in Bangladesh slums through an intersectionality lens. We used data from the most recent national Urban Health Survey (UHS) 2013 covering urban populations in Dhaka, Chittagong, Khulna, Rajshahi, Barisal, Sylhet, and Rangpur divisions. We applied multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) to estimate the Discriminatory Accuracy (DA) of the intersectional effects estimates using Variance Partition Coefficient (VPC) and the Area Under the Receiver Operating Characteristic Curve (AUC-ROC). We also assessed the Proportional Change in Variance (PCV) to calculate intersectional effects. We considered three health outcomes: cough, fever, and acute respiratory infections (ARI) in U5y.We found a low DA for cough (VPC = 0.77%, AUC-ROC = 61.90%), fever (VPC = 0.87%, AUC-ROC = 61.89%) and ARI (VPC = 1.32%, AUC-ROC = 66.36%) of intersectional strata suggesting that SDoH considered do not collectively differentiate U5y with a health outcome from those with and without a health outcome. The PCV for cough (85.90%), fever (78.42%) and ARI (69.77%) indicates the existence of moderate intersectional effects. We also found that SDoH factors such as slum location, mother’s employment, age of household head, and household’s garbage disposal system are associated with U5y health outcomes. The variables used in this analysis have low ability to distinguish between those with and without health outcomes. However, the existence of moderate intersectional effect estimates indicates that U5y in some social groups have worse health outcomes compared to others. Therefore, policymakers need to consider different social groups when designing intervention policies aimed to improve U5y health outcomes in Bangladesh slums.

Introduction

Background

In the Bangladeshi context, the most widely accepted definition of slums (also known as informal urban settlements) is “settlements with a minimum of 10 households or a small and untidy housing unit with a minimum of 25 members and predominantly very poor quality; very high population density and room crowding; very poor environmental services, especially water and sanitation; very low socio-economic status; and lack of security of tenure” [1, 2]. According to the Census of Slum Areas and Floating Population 2014, 22,27,754 people live in different slums in Bangladesh with almost 48% living in Dhaka city (the capital of Bangladesh) [3] and of which 10% are children under five years old (U5y) [4].

The common causes of child mortality in Bangladesh slums are fever and acute respiratory infections (ARI) [5–7]. In this study, ARI is defined as cough accompanied by short, rapid, or difficult breathing which is chest related [4]. The prevalence of ARI (33%) and fever (36%) are higher among children living in urban slums compared to their rural counterparts in Bangladesh [8, 9]. There are also regional variations in the prevalence of childhood illnesses such as cough, fever, and ARI. For example, fever (41.6%) and cough (21.5%) are also common among all slum dwellers in Dhaka [5, 10] while ARI among children is most prevalent in Rajshahi (10.2%), Khulna (8.3%) and Barisal (8%) which are administrative divisions of Bangladesh [6]. The ARI hospitalisation rate among children in Khulna is 46.2% and is much higher among males (65.8%) compared to females at 36.2% [7]. Children living in Chittagong (6.1%), Sylhet (6.2%), and Rangpur (6.6%) regions are at higher risk of developing ARI [6].

U5y mortality varies in different divisions of Bangladesh. For instance, the highest U5y mortality is found in Chittagong division (79 per 1000) followed by Barisal (71 per 1000), Rajshahi (71 per 1000), Dhaka (69 per 1000) and Khulna division (58 per 1000) [11]. In addition, U5y mortality rates are higher in the slums (80.7%) than in non-slums (31%) in Bangladesh [9]. The higher susceptibility of these health outcomes for U5y living in Bangladesh slums impede both their physical and cognitive development which leads to poor health both currently and in the future [12].

The poor health outcomes experienced by U5y living in Bangladesh slums are often shaped by social determinants of health (SDoH) [13–15]. SDoH are described as the conditions in which people live, grow, learn, work, play, and age [13, 16]. For instance, unhygienic conditions, overcrowding, lack of formal health facilities, the transmission of vaccine-preventable diseases, low immunization coverage coupled with low socioeconomic status of parents make U5y more vulnerable in slums [17, 18]. SDoH also form social hierarchies that in turn determine the distribution of power, prestige, and resources among social groups [19]. People living in slums are usually excluded from social structures of power, denying their rights to access public resources, which leads to health inequities [20, 21]. Therefore, to promote health equity among U5y living in slums requires an understanding of how collectively SDoH shape their health outcomes through an intersectionality lens.

The concept of intersectionality describes the phenomenon in which social determinants such as gender, race, ethnicity, sexual orientation, religion, disability, class and other forms of identity “intersect” to create unique dynamics and effects [22]. When these identities do not work independently but interact, they lead to aggravated social inequities. We have used the intersectionality concept to identify group level effects (termed as “intersectional effects” in this paper) and individual levels effects (termed as “main effects” in this paper) in under-five health outcomes [23].

Previous studies have investigated the associations between SDoH and health outcomes (i.e. diarrhoea, fever, cough and ARI) among U5y in Bangladesh [7, 8, 14, 15, 24–29]. They have found that child’s age [7, 8, 14, 28, 29], child’s gender [7, 14, 15], mother’s employment [28], father’s occupation [24], housing conditions [28], and low birth weight [29] are associated with ARI among U5y in the country. Similarly, low socio-economic status measured by the household wealth index [8, 14, 15, 24, 25, 27, 28], parental education [8, 14, 24, 27], rural residency and regional differences [8] are associated with fever, ARI, diarrhoea, and cough in U5y. In addition, the mother’s nutritional status [24], mother’s age [8, 14, 29], and child malnutrition [14, 24] are associated with diarrhoea and ARI in U5y. Finally, maternal depression [15], the household’s access to improved water and sanitation [15, 27, 28], and the household’s garbage disposal system [10, 15] are associated with ARI and cough in U5y. We aim to extend these analyses by evaluating the association of collective SDoH and health outcomes (in this case with or without fever, cough or ARI) for U5y via intersectional approach [23, 30].

Objective

The primary analytic objective of this study is to explore how collectively SDoH affect health outcomes (i.e., fever, cough, and ARI) for U5y in Bangladesh slums.

Methods

Ethics statement

We used a secondary deidentified data set. Ethical approval was not required for the study. The dataset named “Bangladesh Urban Health Survey 2013 (UHS 2013)” which was used in this study is publicly available at the Website (https://dataverse.unc.edu/dataset.xhtml?persistentId=doi:10.15139/S3/12274) upon responsible request. For UHS 2013, ethical approval was obtained from the Bangladesh Medical Research Council and the Institutional Review Board at the School of Public Health, University of North Carolina at Chapel Hill [4].

Data sources, participants, and settings

This study used UHS 2013 data, a representative cross-sectional household survey covering 53,700 households [4]. This is the most recent national survey covering urban population in Bangladesh and was implemented through a collaborative effort of four organisations [4]. The UHS 2013 data were collected between July 2013 and January 2014 using face-to-face interviews. The eligible respondents were all ever-married women aged 15–49 and ever-married men aged 15–54. Questions on child health were contained in the “General Child Health and Nutrition” section of the women’s long questionnaire and administered to the women respondents [4]. We retrieved 3,173 records for U5y for our analysis after dropping 10 (0.31%) records due to missing values in cough. Similarly, we retrieved 3,183 records for fever and ARI in U5y without any missing data. Further details can be found in Kibuchi et al. [31].

The study area comprised of seven divisions: Barisal, Chittagong, Dhaka, Khulna, Rajshahi, Rangpur and Sylhet as shown in Fig 1 below. We combined Barisal, Chittagong, Rangpur or Sylhet divisions into one and named “Other divisions” because these are relatively small and new divisions except Chittagong. In addition, we needed to reduce the number of categories for simplifying analysis.

Measures

The analytical approach used in this research requires all outcomes and explanatory variables to be categorical; hence continuous variables were grouped into categories [23]. The definitions and categorisations of outcome and predictor variables are provided in the S1 Table

The three health outcomes we considered for this study were: cough (whether a child had a cough or not), fever (whether a child had fever or not), and ARI. In UHS 2013, ARI is defined as cough accompanied by short, rapid, or difficult breathing, which is chest related [4]. The reference or recall period of these three health outcomes among U5y was the last two weeks from the day of the interview. Separate models were fitted for each of the three outcomes.

The explanatory variables used in the study were selected based on existing literature on factors that influence health outcomes for U5y as well as informed by the framework developed by the Commission for Social Determinants of Health (CSDH) [13, 32]. The explanatory variables were categorised into four groups: U5y demographic characteristics (e.g., age, sex), mothers’ sociodemographic characteristics (e.g., age, ever attended school, employment, and marital status), head of households’ sociodemographic characteristics (e.g., age, sex, marital status), and households’ social structural characteristics (e.g., wealth index, housing condition, having separate kitchen, type of cooking fuel used, migration status, garbage disposal method, ownership of dwelling, and ownership of land) (S1 Table). We have used the wealth index variable already developed and available in the data set [33]. Further details on the choice of variables can be found in Kibuchi et al. [31]. The wealth index quintiles were constructed using Principal Components Analysis (PCA) [34]. The analysis was based on complete case analyses. We have selected social determinants of health (SDoH) based on their statistical significance with respective outcomes in the univariate analyses.

Statistical methods

We applied multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) [23, 35–38] to explore the effects of SDoH on three health outcomes via intersectionality. Unlike common measures of average association, MAIHDA allows for a better understanding of the heterogeneity of individual effects and inter-individual heterogeneity around group averages [23]. This method involves defining a number of intersectional strata based on combinations of social attributes such as a set of SDoH associated with their intersectional social identities and individual characteristics [31, 39].

MAIHDA approach has advantages over conventional fixed-effect approaches such as precision‐weighted estimates, model parsimony, ease of interpretation, and reliable estimates for strata with small sample sizes [23, 35]. Additionally, MAIHDA allows for the inductive analysis of numerous stratum-specific interactions and it avoids pre-specifying specific interactions or intersections to explore [23, 35, 40, 41]. Most importantly, MAIHDA estimates the measures of Discriminatory Accuracy (DA) together with traditional comparison of population averages, enabling the most comprehensively informed decisions about suitable public health action to address an issue under study. MAIHDA provides a feasible way of measuring multiple intersections and analysing groups of small size from an intersectionality perspective. MAIDHA also provides a parsimonious way of analysing high dimensional interactions which is not possible with single level models [23, 38].

MAIHDA estimates DA and intersectional effect in two steps through two successive multilevel logistic regression models (Model 1 and Model 2). In the first step, Model 1 includes only random intercept without any predictor variables to estimate the mean of a health outcome and intersectional strata differences (i.e., variance). The Model 1 allows us to estimate the extent to which the variance in the health outcomes is explained by differences between intersectional strata versus differences within stratum via the Variance Partition Coefficient (VPC). The VPC presents as the percentage share of individual variance which lies within intersectional strata [42] and values higher than 5% indicating an acceptable DA [43]. A low VPC suggests that intersections have similar mean levels of a particular outcome and that individuals differ substantially within intersectional strata, and vice-versa for a high VPC [23, 39]. The VPC of Model 1 represents the ceiling of the explanatory power of the intersectional strata and encompasses both additive and potential interactive effects of the variables that define the strata [43].

In the second step, in Model 2, the main effects (predictor variables used to construct intersectional strata) are added to the fixed part of the Model 1. The VPC now represents the extent to which intersectional clustering is multiplicative (e.g., aggravating effects). For instance, if the VPC reduces to around 0%, intersectional differences across strata are fully explained by the main effects suggesting additive effects rather than multiplicative effects. However, if the VPC in Model 2 is not equal to zero, it suggests the presence of interactional effects [39]. The main effects are presented as odds ratio (OR) to describe the association between the SDoH factors and health outcomes in U5y.

We also calculated DA using the Area Under the Receiver Operating Characteristic Curve (AUC-ROC) using the predicted probabilities obtained from each model [44, 45]. The AUC represents an overall accuracy of the model to classify children with or without a health outcome. The AUC-ROC values extend from 0.5 to 1, where 0.5 represents absence DA, and 1 represents perfect DA. The higher the AUC-ROC values above 0.5, the higher the influence of the intersectional strata to differentiate U5y with and without a health outcome (in this case, cough, fever and ARI) [46].

We also calculated the Proportional Change in Variance (PCV) to measure the overall proportion of intersectional strata variance of Model 1 that was explained after adding the main effects in Model 2 [43–45]. In the absence of any intersectional effects, the main effects would explain all strata variances (PCV = 100%) [36, 45, 47].

We also plotted intersectional effect estimates (stratum-level residual), and their corresponding 95% credible intervals (CIs) ranked from lowest to highest for both Model 1 and Model 2. A positive estimate suggests that children in an intersectional stratum have a higher risk than expected (e.g., disadvantaged group), while a negative intersectional estimate means a lower risk than expected (e.g., privileged or advantaged group) based on the addition of the risks factors associated with the variables that define the intersectional stratum [35, 48]. A residual of zero indicates that a social stratum represents exactly the mean outcome predicted by the additive main effects [48]. Further details on the application of MAIHDA for these analyses are described in the protocol document [31]. The statistical details can be found in the S1 File.

We used R statistical software (version 4.1.1) to fit MAIHDA models using the “brms” package (version 2.14.4) which is fully Bayesian inference through Markov Chain Monte Carlo (MCMC) methods [49]. We used weakly informative priors and ran all analyses using 50,000 iterations with a burning period of 5,000 [43]. MCMC chains were checked graphically for convergence [50].

Results

Sociodemographic characteristics of the population

We retrieved 3,183 children under five years old (U5y) from the UHS 2013 data set. Around 49% of U5y were male, and 51% were female. Around 37% of U5y were 0–1 years old, and 63% were 2–5 years old. Around 19%, 28%, and 5% of U5y were found with cough, fever, and ARI, respectively. The detailed socio demographic profile of the population can be found in the S2–S4 Tables. The following sections describe the results of three health outcomes in U5y such as cough, fever and ARI.

Intersectional strata

We have created 147 intersectional strata for cough based on the following social positions of U5y (e.g., child age, division, age of household head, garbage disposal, and mother’s employment), which were statistically significant in the univariate analyses (S5 Table). For fever, we have created 441 strata using child age, division, age of household head, mother’s age, garbage disposal, mother ever attended school, mother’s employment, and separate kitchen variables which were statistically significant in the univariate analyses (S6 Table). ARI had 247 strata based on child age, division, wealth index, age of household head, types of cooking fuel used, and mother’s employment variables (S7 Table). The calculations of intersectional strata for each of the three outcomes can be found in S8 Table.

Table 1 presents models 1 and 2 based on the number of intersectional strata described above for each health outcome.

Table 1. Main effects coefficient, strata variance, area under the curve, variance partition coefficient (VPC) and proportional change in variance (PCV) for cough (Model 1 and 2), fever (Model 1 and 2), and ARI (Model 1 and 2).

		Cough				Fever				ARI
		Model 1		Model 2		Model 1		Model 2		Model 1		Model 2
Variable	Category (Ref)	Odds Ratio	95% CI	Odds Ratio	95% CI	Odds Ratio	95% CI	Odds Ratio	95% CI	Odds Ratio	95% CI	Odds Ratio	95% CI
Intercept		0.24^*	(0.21,0.28)	0.47^*	(0.31, 0.71)	0.39^*	(0.36, 0.43)	0.75	(0.42,1.31)	0.05^*	(0.04,0.06)	0.10^*	(0.03, 0.27)
Child age	1 year and less (Ref)			1.00				1.00				1.00
Child age	2 to 5			0.76^*	(0.64, 0.96)			0.89	(0.74,1.07)			0.78	(0.54, 1.12)
Division	Dhaka (Ref)			1.00								1.00
	Khulna			1.41	(0.91, 2.15)			1.10	(0.74,1.60)			2.46^*	(1.14, 5.17)
	Rajshahi			1.94^*	(1.00, 3.48)			1.52	(0.84,2.69)			1.66	(0.45, 4.98)
	Other divisions			1.76^*	(1.34, 2.16)			1.50^*	(1.22,1.84)			2.06^*	(1.39, 3.07)
Age of the household head	13 to 29 years (Ref)			1.00				1.00				1.00
	30 to 44 years old			0.62^*	(0.54, 0.87)			0.66^*	(0.54,0.80)			0.48^*	(0.31, 0.73)
	45 years and above			0.81	(0.66, 1.18)			0.81	(0.63,1.05)			0.88	(0.52, 1.43)
Garbage Disposal	Disposed within premises (Ref)			1.00				1.00
	Collected from home			0.55^*	(0.39, 0.90)			0.65^*	(0.45,0.95)
	Disposed in bin outside			0.54^*	(0.36, 0.89)			0.63^*	(0.42,0.95)
	Disposed in open spaces			0.51^*	(0.38,0.84)			0.74	(0.52,1.06)
Mothers’ employment	No (Ref)			1.00				1.00				1.00
Mothers’ employment	Yes			0.75^*	(0.60, 0.98)			1.15	(0.94,1.42)			0.67	(0.42, 1.06)
Mothers’ age	Below 18 years (Ref)							1.00
Mothers’ age	18 years and above							0.72	(0.46,1.14)
Mother ever attended school	No (Ref)							1.00
Mother ever attended school	Yes							0.84	(0.68,1.03)
Separate kitchen	No (Ref)							1.00
Separate kitchen	Yes							1.15	(0.96,1.37)
Cooking fuel used	Charcoal, dung cakes (Ref)											1.00
	Kerosene or liquid gas											0.82	(0.19, 3.06)
	Natural gas											0.67	(0.30, 1.63)
	Wood fuel											0.48	(0.21, 1.19)
Wealth index	Rich (Ref)											1.00
	Middle											0.74	(0.39, 1.40)
	Poor											0.93	(0.54, 1.66)
Variance		0.18		0.05		0.13		0.02		0.14		0.04
Strata		147		147		441		441		247		247
Observations		3,173		3,173		3,183		3,183		3,183		3,183
AUC-ROC		64.8%		61.90%		68%		61.89%		72.93%		66.36%
VPC		5.25%		0.77%		3.91%		0.87%		4.24%		1.32%
PCV				85.90%				78.42%				69.77%

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Ref: Reference category; 95% CI: 95% Credible Interval; AUC-ROC: Area Under the Receiver Operating Characteristic Curve; VPC: Variance Partition Coefficient.; PCV: Proportional Change in Variance

* Statistically Significant at 5% level of significance.

Cough

In Model 1, the VPC is 5.3% indicating an acceptable DA. Only 5.3% of the total individual variance among U5y with and without cough are located at the intersectional strata level (Table 1). The AUC-ROC is 64.8%, suggesting a poor DA (Table 1). It indicates that intersectional strata have weak explanatory power to discriminate U5y with a cough from U5y and without a cough.

The VPC and the AUC-ROC have dropped to 0.8% and 61.9% respectively, after adjusting for main effects variables (e.g., child age, division, age of household head, garbage disposal, and mothers’ employment) in Model 2. This indicates the existence of moderate intersectional effects estimates (Table 1). The PCV is 85.9% indicating that 14.1% of the total variance was not explained by adding main effects, which can be attributable to intersectional effects. This suggests that, the moderate intersectional inequalities in U5y of either having a cough or not are due to their social positions based on variables used to create intersectional strata.

The variables, child age, division, age of the household head, household garbage disposal method, and mother’s employment had significant association with cough in U5y (Table 1). Older children aged between 2 to 5 years had 24% lower odds of cough than their younger counterparts aged 1 year or less. Compared to U5y living in Dhaka slums, the odds of having a cough were 1.9 times and 1.7 times higher among U5y living in slums in Rajshahi and other divisions respectively. U5y living with relatively older household heads (aged 30 to 44) had 38% lower odds of having cough. Furthermore, U5y from households having a better garbage disposal system (e.g., garbage collected from home, garbage disposed in bins outside of the house, and garbage disposed in open spaces) had 45%, 46% and 49%about 50% lower odds of having cough, respectively. Finally, mothers’ employment had 25% lower odds of having cough in U5y (Table 1).

Fig 2 presents estimates of intersectional effects (stratum-level residuals) for cough with their corresponding 95% credible intervals (CIs) ranked from lowest to highest. The Panel A shows that the estimates for Model 1 ranging from -1.12 to 1.47, with only 4 (2.7%) of 147 intersectional strata’s 95% CI not crossing zero (on the far left and far right), indicating significant intersectional effects estimates. Similarly, the Panel B represents intersectional effects estimates for Model 2 ranging from -0.51 to 0.63, and all intersectional strata’s 95% CI having crossed zero. This indicates that although intersectional effects were present for cough among U5y, even after controlling for main effects variables, we did not find any significant intersectional effects for cough (Fig 2).

Fever

In Model 1, the VPC is 3.9% indicating a low DA. This indicates, only 3.9% of the total individual variance among U5y with and without a fever are attributed to the intersectional strata level (Table 1). The AUC-ROC is 68.0%, suggesting a poor DA or intersectional strata have weak explanatory power to discriminate U5y with a fever from U5y without a fever.

The VPC and the AUC-ROC have dropped to 0.9% and 61.9%, respectively, after adjusting for main effects variables (e.g., child age, division, age of household head, mother’s age, garbage disposal, mother ever attended school, mother’s employment, and separate kitchen) in Model 2. The non-zero VPC indicates the existence of little multiplicative effects of intersectional strata in relation to the main effect variables included in the model. The AUC-ROC of 61.9% also indicates the low discriminatory power of intersectional strata to discriminate U5y with a fever from those without a fever. The PCV is 78.42% indicating that 21.6% of the total variance is not explained by adding main effects, which can be attributable to intersectional effects estimates (Table 1). The low DA suggests that most of the observed differences in fever across intersectional strata are due to the main effects, and only a small share of the differences is attributable to intersectional strata.

The main effects variables which were significantly associated with fever were division, age of household head, and household garbage disposal method are significantly associated with fever in U5y (Table 1). Children living in slums in other divisions were at a higher risk of getting a fever than those living in Dhaka slums. Children from households with relatively older household heads (aged 30 to 44) had 38% lower risk of having a fever. Finally, children from households with a better garbage disposal system (e.g., garbage collected from home, garbage disposed in bins outside of the house) had 35% and 37% lower risk of having a fever, respectively, compared to children from those households having a poor garbage disposal system (e.g., dispose garbage within the premises of the house) (Table 1).

Fig 3 presents estimates of intersectional effects (strum-level residuals) for fever with their corresponding 95% CI ranked from lowest to highest. The Panel A shows that the estimates for Model 1 ranging from -1.12 to 1.09 with only 2 (0.45%) of 441 intersectional strata’s 95% CI not crossing zero (on the far left and far right), indicating significant intersectional effects estimates. Similarly, the Panel B represents intersectional effects for Model 2 ranging from -0.58 to 0.65, with all their 95% CI having crossed zero. This indicates that although intersectional effects are present for fever in U5y, reduced after controlling for main effects.

ARI

For Model 1, the VPC was 4.2% indicating a low DA. That is only 4.2% of the total individual variance among U5y with and without ARI was attributable to the intersectional strata level (Table 1). The AUC-ROC was 72.9%, which is an acceptable DA implying that intersectional strata have good explanatory power to discriminate U5y with and without ARI.

The VPC and the AUC-ROC dropped to 1.3% and 66.4%, respectively, after adjusting for main effects variables (e.g., child age, division, wealth index, age of household head, types of cooking fuel used, and mother’s employment) in Model 2. The non-zero VPC indicates the existence of moderate multiplicative effects of intersectional strata based on the main effects variables included in the model. The reduced AUC-ROC (66.4%) suggests a low DA of intersectional strata to discriminate U5y with an ARI from those without an ARI. The PCV was 69.8%, indicating that 30.2% of the total individual variance was not explained by the main effects, which can be attributable to intersectional effects in Model 2.

Division and age of the household head were significantly associated with ARI in U5y (Table 1). Children living in slums in Khulna and other divisions were at higher risk of getting ARI compared to children living in Dhaka slums. Children from households with older household heads (aged 30 to 44) had 34% lower risk of having ARI compared to children from households with younger household heads (aged 13 to 29) (Table 1).

Fig 4 presents estimates of intersectional effects (stratum-level residuals) for ARI with their corresponding 95% CI ranked from lowest to highest. The Panel A shows intersectional effects estimates for Model 1 ranging from -1.36 to 1.48, with all their 95% CI crossing zero (on far left and far right), indicating non-significant intersectional effects estimates. Similarly, the Panel B represents intersectional effects for Model 2 ranging from -0.76 to 0.84, with all their 95% CI having crossed zero, suggesting the intersectional estimates are not significantly different across intersectional strata (Fig 4).

Discussion

While previous studies mostly investigated the association between social determinants and health outcomes, this study has extended those analyses by evaluating the association of collective SDoH and health outcomes for children under five years old (U5y) via an intersectional approach. Using MAIHDA approach we find evidence of moderate intersectional effects estimates on children’s health condition based on social attributes. The PCV values (e.g., 85.90%, 78.42% and 69.77% for cough, fever and ARI, respectively) suggest that some differences in U5y health outcomes can be attributed to intersectional effects [51]. Based on the analyses, we have found that some children in certain intersectional strata are more vulnerable compared to others and therefore need a greater level of interventions compared to other groups despite all coming from an already marginalized population of urban slum dwellers.

For example, older children (e.g., 2 to 5 years) with younger household heads (e.g., 13 to 29 years), households having better waste disposal system (bin outside) and unemployed mothers and U5y living in Dhaka division are more vulnerable to having a cough. Similarly, older children (e.g., 2 to 5 years) with older household heads (e.g., 30 to 44 years) and from households having better waste management system (collected from home) and separate kitchens, employed mothers aged 18 years and above living in Dhaka Division are more vulnerable to having a fever. Older children (e.g., 2 to 5 years) from households of middle wealth quintile with older household heads (30 to 44 years) and unemployed mothers, households using natural gas and U5y living in Dhaka Division are more vulnerable to having an ARI.

The combined divisions of Barisal, Chittagong, Rangpur and Sylhet had higher risk of cough, fever and ARI than those living in Dhaka Division. Similarly, U5y living in Rajshahi and Khulna divisions were more likely to have cough and ARI, respectively, compared to those living in slums in in Dhaka Division (Fig 5). These findings are consistent with previous studies [8, 27].

Fig 5 — R software was used to create the map. The source of the basemap shapefile is available on GADM website at: https://geodata.ucdavis.edu/gadm/gadm4.1/shp/gadm41_BGD_shp.zip. The data are freely available for academic use and other non-commercial use (CC-BY 2.0). The GADM license is available at https://gadm.org/license.html.

This regional variation in risks might be due to temperature, indoor air pollution, access to healthcare facilities, and fuels used for cooking [25]. According to Bangladesh Child Well-Being Survey 2016, the usage of solid fuels (wood and charcoal) for cooking was found to be the lowest in Dhaka Division (29.1%) compared to Barisal (82.9%), Chittagong (45.2%), Khulna (79.1%), Rajshahi (78.9%), Rangpur (88.9%) and Sylhet (47.7%) [6]. Combustion of solid fuels for cooking releases health damaging pollutants and children are the most affected as exposure to biomass smoke is associated with ARI [52]. Unlike other divisions, non-government organisations and informal private-for-profit providers have been dominating in Dhaka slums [53]. Additionally, slum dwellers in Dhaka have relatively stable sources of income (e.g., readymade garments and service sectors) and higher income compared to those in Rajshahi (e.g., day laborers—both agricultural and non-agricultural laborers, domestic maids etc.). Additionally, children in financially well-off families are more likely to have greater access to healthcare services both preventive and promotive compared to those from impoverished families [54]. These factors might partly explain the higher risk of childhood illnesses among U5y living in Rajshahi, Khulna and other divisions compared to those living in Dhaka Division.

We have also found that being an older child (2 to 5 years) appeared to be a protective factor against cough which is similar to previous studies in the country [8, 14]. Rahman and Hossain reported that older children have a lower risk of developing diarrhoea, fever and ARI [8], while Kundu et al. found that younger children were more likely to develop both diarrhoea and ARI compared to their older counterparts [14]. The possible reason is that as children get older, they develop immunity and know how to interact better with their caregivers and eat healthy foods, leading to reducing chances of incidence of childhood [55–57].

Our findings show that maternal employment is associated with better child health outcomes such as children are less likely to suffer if their mothers are employed which is consistent with previous studies [58, 59]. We have also found those households which are headed by older persons to have lower prevalence of cough, fever, and ARI in children, which is consistent with a previous study [60]. Finally, our study also shows that children from households with better garbage disposal systems present improved child health outcomes (e.g., cough and fever) which is consistent with a previous study that reported children living in households with better garbage disposal systems had lower prevalence of respiratory infections such as cough or nasal discharge in the past 6 months [15]. Households having better garbage disposal systems may indicate relatively well-off households with better knowledge of parenting and child development and environmental awareness who might take better care of their children leading to better child health outcomes [61].

Strengths and limitations

Our study has several strengths as follows. First, to the best of our knowledge, this is the first study to apply quantitative intersectionality to understand SDoH that drivers of health outcomes for U5y living in slums in Bangladesh. The use of quantitative intersectionality informed evidence will bridge the knowledge gap and inform policy. Second, while measures of discriminatory accuracy such as VPC, PCV, and AUC-ROC are extensively used in clinical epidemiology to identify new biomarkers for diseases are rarely used in public health and social epidemiology [23, 62–64]. The application of these measures in our study to investigate how social positions drive health outcomes for U5y in Bangladesh slums will lead better interventions among these marginalised population. Third, the most salient advantage of the MAIHDA method is its improved and inductive analysis with numerous stratum-specific interactions or stratum-level residuals in a model that includes the main effects of the variables that define the stratum [23, 48]. For instance, we created 441 strata for fever in combination of eight main effect variables and the number of observations within an intersectional stratum have no impact on the MAIHDA model as stratum with few observations can pool information from others via multilevel modelling to obtain better inferences [38].

However, our study has some limitations. First, the UHS 2013 is nine years old. Therefore, our results may need to be interpreted with caution as the socioeconomic position of the slum population may have changed over time due to the increasing size and density of slums [65]. Second, our analyses are based on only those cases in the data set without any missing values on any of the three health outcomes. We assumed that those missing values were completely at random and, therefore, not susceptible to selection bias [66]. Third, MAIDHA approach assumes no omitted variable bias after adjusting for main effects variables in the full model to measure intersectional effects [35]. However, we have selected variables based on statistical significance to keep the analysis tractable [35]. In addition, we have used secondary data that were collected for different purposes, and we had limited options to select variables. Finally, we cannot generalise our findings beyond the slums in Bangladesh.

Conclusion and recommendations

Applying MAIHDA approach we have found that SDoH collectively affects the health outcomes of U5y moderately. In addition, we found that the slum location, mother’s employment, age of household head, and the household’s garbage disposal system are associated with U5y health outcomes.

MAIHDA approach helps make more comprehensively informed decisions about suitable public health action to address an issue under study. We find the low discriminatory accuracy of the intersectional strata suggesting universal interventions rather than interventions exclusively focused on strata with a higher average risk of ARI, cough or fever. From the policymaking perspective, our findings have important policymaking implications. First, our findings suggest that with moderate intersectional effects, policy makers are supposed to consider those social groups which are more disadvantaged when making policies aimed at improving U5y health outcomes. Second, policymakers should design interventions and promote protective factors in relatively high-risk areas such as slums in Rajshahi, and Khulna where children are at higher risk of having childhood illness such as cough and ARI compared to those living in Dhaka slums. As a protective factor, our findings show that maternal employment, economically productive age of household heads and better garbage management systems are associated with improved child health. Therefore, the local government institutions of the cities and towns in Bangladesh (e.g., City Corporation Mayor, Municipality or Pourasabha Chairman) who have responsibility and legal authority should consider these protective factors when making decisions and designing interventions for improving U5y health. Finally, while U5y living in urban slums with precarious and crowded environments frequently suffer from childhood illness, our study shows that better waste management protects children from having cough and/or fever. This finding underlines the significance of the improvement of housing quality in slum areas affected by poor waste management. Therefore, integrated efforts from government and non-government are needed for designing public health intervention to minimise the risk of diseases associated with garbage disposal systems as well as to create public awareness about hygiene practice among slum dwellers in the country.

Supporting information

S1 File. Statistical details.

(DOCX)

Click here for additional data file.^{(40.8KB, docx)}

S1 Table. Description of health outcomes and predictor variables for the analysis for Bangladesh Urban Health Survey 2013 (UHS 2013).

(DOCX)

Click here for additional data file.^{(36.7KB, docx)}

S2 Table. Distribution of socio determinants characteristics for cough.

(DOCX)

Click here for additional data file.^{(38.7KB, docx)}

S3 Table. Distribution of socio determinants characteristics for fever.

(DOCX)

Click here for additional data file.^{(38.8KB, docx)}

S4 Table. Distribution of socio determinants characteristics for acute respiratory infections (ARI).

(DOCX)

Click here for additional data file.^{(39KB, docx)}

S5 Table. Univariate analyses for cough.

(DOCX)

Click here for additional data file.^{(37.4KB, docx)}

S6 Table. Univariate analyses for fever.

(DOCX)

Click here for additional data file.^{(37.4KB, docx)}

S7 Table. Univariate analyses for acute respiratory infections (ARI).

(DOCX)

Click here for additional data file.^{(37.5KB, docx)}

S8 Table. Calculations of intersectional strata for each of three outcomes.

(DOCX)

Click here for additional data file.^{(32.3KB, docx)}

Data Availability

We used secondary deidentified dataset. The dataset named “Bangladesh Urban Health Survey (UHS) 2013” which was used in this study is publicly available at the Website (https://dataverse.unc.edu/dataset.xhtml?persistentId=doi:10.15139/S3/12274) upon responsible request.

Funding Statement

The GCRF Accountability for Informal Urban Equity Hub (‘ARISE’) is a UKRI Collective Fund award with award reference ES/S00811X/1. EK, AHL and LG are also funded by the Medical Research Council (MC_UU_00022/2) and the Scottish Government Chief Scientist Office (SPHSU17). The funders had no role in the design and conduct of the study, nor the decision to prepare and submit the manuscript for publication.

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PLOS Glob Public Health. doi: 10.1371/journal.pgph.0001588.r001

Decision Letter 0

Ashti Doobay-Persaud

22 Nov 2022

PGPH-D-22-01685

The effects of social determinants on children’s health outcomes in Bangladesh slums through an intersectionality lens: an application of multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA)

PLOS Global Public Health

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Reviewer #1: Yes

Reviewer #2: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I don't know

Reviewer #2: No

**********

3. Have the authors made all data underlying the findings in their manuscript fully available (please refer to the Data Availability Statement at the start of the manuscript PDF file)?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception. The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS Global Public Health does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Overall, the authors have worked diligently to conduct a statistical analysis using MAIHDA to evaluate the effects of SDoH on child health outcomes in Bangladeshi slums. I was not familiar with this statistical methodology prior to reviewing this work, which may contribute to my relative lack of understanding of its application here. While I believe this work could be impactful, this manuscript needs revision in a few aspects before it is ready for publication.

1. It is unclear why cough is separate from ARI as a cause of mortality. Can the authors say more about how they chose these three health outcomes to evaluate? Why was diarrhea not included? The paragraph in the introduction where the causes of child mortality is introduced is a bit hard to follow, as it begins by stating that fever, cough, and ARI are common causes of child mortality but then goes on to discuss prevalence of ARI and then return to mortality in the middle of the paragraph, then back to ARI hospitalization rate. Would rework the introduction to be more direct and clearly state why these three health outcomes were selected.

2. It is not clear to me how the intersectional strata are defined. It is entirely possible that this is due to my personal unfamiliarity with this methodology, but the methods section does not clearly explain this to the average researcher.

3. The statistical analysis reported varied factors for each of the three health outcomes. It is unclear why certain social determinant factors were reported for cough vs fever vs ARI – it makes it difficult to understand the impact of SDoH on health outcomes in aggregate if it not clearly stated which determinant factors are included in each analysis.

4. The conclusions drawn in the discussion are not necessarily supported by this evidence as there is moderate and disparate effects of SDoH that can be drawn for each of the three health outcomes. Also, it is unclear how these analyses provide additional meaningful information beyond what has been published about the association of SDoH and health outcomes in pediatric populations, as the authors reference extensively in the introduction.

5. The authors reference risks of each health outcome relative to each division of slums. Perhaps it would be more illustrative to use a map to highlight the significant risks present in each division, to allow the reader to create a relational understanding of the risks in Dhaka slums vs others around the country (which is referenced in the conclusions and policy implications).

6. Try to avoid language that could be viewed as insulting or pejorative. For example, would not use the phrase “slum people”.

7. At times, the word choice and grammar are confusing and unclear, which contributed to the difficulty I had in understanding the text. I would recommend reviewing for grammar, typos, and clarity.

Reviewer #2: The study aims to examine the effects of social determinants of children’s health outcomes in slums in Bangladesh. Association between individual social determinants and health outcomes have previously been analyzed and established; arguably, the authors method adds value by looking at the same question from an intersectional approach. I enjoyed reading the piece, and would like to highlight a few other areas where the manuscript can be strengthened with regard to its scientific merit.

1) Avoid causal language throughout the manuscript. There shouldn’t really be any reference to “effects” since this is an associational study.

2) There needs to be a much clearer discussion—in the introduction—of what the authors mean by intersectional. The authors never fully explain it anywhere.

3) Related to (2), please explain the advantage of using MAIHDA compared to using a general approach (regression with interaction terms for various combinations of the independent variables). The distinction between the two—and the justification for using MAIHDA—is important because the authors have categorized several variables that could have been used as continuous variables—thus retaining and utilizing the variation in the variable—if they had not used MAIHDA. Also, my understanding is that it is relatively straightforward to obtain marginal effect of interaction terms in regression models (essentially getting at the intersectional aspect, from what I understand), so I am not clear what MAIHDA buys us here.

4) Also related, instead of picking variables for strata based on statistical significance in univariate analysis, why not base it on existing literature and on authors’ understanding of the context? Or, based on variables over which policymakers have some control (e.g., employment, education, housing conditions)?

5) On page 8, the authors need to explain the method more clearly. S1 File is useful. I wish the part in the main text was written in more plain language.

6) In the methods (and in the method appendix), there is reference to “assuming no relevant variables are omitted suggesting presence of interaction effect”. This seems like a big assumption. How do we know that it holds?

7) Stylistically, please consider reorganizing the results section. Results in 3 outcomes are explained separately. Tables 1, 2 and 3 can be combined into one table, and the results can be explained more coherently—focusing on the key points.

8) Minor: based on the findings of the study, what do we now know that we did not from so many previous studies that have been conducted on this topic (at least from the policy perspective in Bangladesh)? Some recommendations are made in the Conclusion, but they seem too general to be useful.

Other minor points:

• Line 132 – the authors likely mean the quintiles of wealth index (of from S1 – categories of wealth index), not wealth index, as the method requires categorical variables

• Line 158-159 – incomplete sentence

• Line 164 – “1” missing

• Line 165 – Is it 0.5 to 1 or 0.05 to 1? The previous sentence says 0.5.

• Line 186 – I’d put the link to the 2013 Urban Health Survey page, not the general link to UNC’s dataverse.

• Line 197 – Explain how you created 147 strata for cough. There seem to be 9 coefficients that are statistically significant. How do we go from those 9 coefficients to 147 strata? Similar explanation can work for how 441 and 247 strata were created for fever and ARI.

• Table 1 – Significant at what level? Also, the authors likely mean *statistically* significant.

• Table 1 – Strata variance in what? Is this for outcome?

• In S3/S4 Table, mothers with no education seem to be in both ‘Mother Ever Attended School’ and ‘Mother’s Highest Education’. The first one seems redundant.

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6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #1: No

Reviewer #2: No

**********

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PLOS Glob Public Health. 2023 Mar 8;3(3):e0001588. doi: 10.1371/journal.pgph.0001588.r002

Author response to Decision Letter 0

4 Dec 2022

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PLOS Glob Public Health. doi: 10.1371/journal.pgph.0001588.r003

Decision Letter 1

Ashti Doobay-Persaud

13 Jan 2023

PGPH-D-22-01685R1

PLOS Global Public Health

Dear Dr. Barua,

Thank you for submitting your manuscript to PLOS Global Public Health. After careful consideration, we feel that you have made many changes that are acceptable however some minor revisions still need to be made to meet publication criteria for PLOS global health. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Feb 12 2023 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at globalpubhealth@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pgph/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

We look forward to receiving your revised manuscript.

Kind regards,

Ashti Doobay-Persaud

Academic Editor

PLOS Global Public Health

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

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2. Does this manuscript meet PLOS Global Public Health’s publication criteria? Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe methodologically and ethically rigorous research with conclusions that are appropriately drawn based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available (please refer to the Data Availability Statement at the start of the manuscript PDF file)?

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: No

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: 1. Thank you for the edits and thoughtful responses to my initial comments. I believe you have addressed the majority of my questions; however, the text still requires copyediting for grammar. A few instances from the introduction are listed below, though this is not comprehensive as copyediting is not the specific role of a reviewer. Some common errors noted are missing articles (for example, "the"), plural vs singular conjugation, and verb-tense agreement. Would recommend a *detailed* review specifically for grammar.

Line 59: "In Bangladesh context, most widely accepted definition..." should read "In the Bangladeshi context, the most widely accepted definition..."

Line 63: should read, "according to the Census of..."

Line 69: should read, "there is also regional variation in [prevalence? incidence?] of childhood illness..."

Line 74: should read, "among males compared to females"

Line 81: "in future" should be "in the future"

Line 96: should read "they lead to aggravated social inequities"

2. I appreciate the addition of the map. There is a typo in the "reference division" label that should be corrected.

Reviewer #2: Thank you for addressing the substantive comments. I do have reservations about picking variables based on statistical significance but I understand if that is consistent with practice in this line of the literature. "It was not possible to include all variables because our analyses included main effects that only showed statistical significance (a p-value less than 0.05) on univariate analysis" (page 9 of the manuscript) is circular because that was a decision you made in the first place. Perhaps a better justification would be that you wanted to keep the analysis tractable.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

Do you want your identity to be public for this peer review? If you choose “no”, your identity will remain anonymous but your review may still be made public.

For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

**********

PLOS Glob Public Health. 2023 Mar 8;3(3):e0001588. doi: 10.1371/journal.pgph.0001588.r004

Author response to Decision Letter 1

19 Jan 2023

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PLOS Glob Public Health. doi: 10.1371/journal.pgph.0001588.r005

Decision Letter 2

Ashti Doobay-Persaud

3 Feb 2023

PGPH-D-22-01685R2

Dear Dr. Barua,

We are pleased to inform you that your manuscript 'The effects of social determinants on children’s health outcomes in Bangladesh slums through an intersectionality lens: an application of multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA)' has been provisionally accepted for publication in PLOS Global Public Health.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

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Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Global Public Health and for your patience.

Best regards,

Ashti Doobay-Persaud

Academic Editor

PLOS Global Public Health

***********************************************************

Reviewer Comments (if any, and for reference):

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 File. Statistical details.

(DOCX)

Click here for additional data file.^{(40.8KB, docx)}

S1 Table. Description of health outcomes and predictor variables for the analysis for Bangladesh Urban Health Survey 2013 (UHS 2013).

(DOCX)

Click here for additional data file.^{(36.7KB, docx)}

S2 Table. Distribution of socio determinants characteristics for cough.

(DOCX)

Click here for additional data file.^{(38.7KB, docx)}

S3 Table. Distribution of socio determinants characteristics for fever.

(DOCX)

Click here for additional data file.^{(38.8KB, docx)}

S4 Table. Distribution of socio determinants characteristics for acute respiratory infections (ARI).

(DOCX)

Click here for additional data file.^{(39KB, docx)}

S5 Table. Univariate analyses for cough.

(DOCX)

Click here for additional data file.^{(37.4KB, docx)}

S6 Table. Univariate analyses for fever.

(DOCX)

Click here for additional data file.^{(37.4KB, docx)}

S7 Table. Univariate analyses for acute respiratory infections (ARI).

(DOCX)

Click here for additional data file.^{(37.5KB, docx)}

S8 Table. Calculations of intersectional strata for each of three outcomes.

(DOCX)

Click here for additional data file.^{(32.3KB, docx)}

Attachment

Submitted filename: Response to Reviewers.docx

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Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(693.8KB, docx)}

Data Availability Statement

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PERMALINK

The effects of social determinants on children’s health outcomes in Bangladesh slums through an intersectionality lens: An application of multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA)

Proloy Barua

Eliud Kibuchi

Bachera Aktar

Sabrina Fatema Chowdhury

Imran Hossain Mithu

Zahidul Quayyum

Noemia Teixeira de Siqueira Filha

Alastair H Leyland

Sabina Faiz Rashid

Linsay Gray

Roles

Abstract

Introduction

Background

Objective

Methods

Ethics statement

Data sources, participants, and settings

Fig 1. Study area of Bangladesh Urban Health Survey 2013 (UHS 2013).

Measures

Statistical methods

Results

Sociodemographic characteristics of the population

Intersectional strata

Table 1. Main effects coefficient, strata variance, area under the curve, variance partition coefficient (VPC) and proportional change in variance (PCV) for cough (Model 1 and 2), fever (Model 1 and 2), and ARI (Model 1 and 2).

Cough

Fig 2. Estimated intersectional effects (stratum-level residuals) and their corresponding 95% credible intervals (CIs) for each stratum ranked from lowest to highest for cough.

Fever

Fig 3. Estimated intersectional effects (stratum-level residuals) and their corresponding 95% credible intervals (CIs) for each stratum ranked from lowest to highest for fever.

ARI

Fig 4. Estimated intersectional effects (stratum-level residuals) and their corresponding 95% credible intervals (CIs) for each stratum ranked from lowest to highest for acute respiratory infections (ARI).

Discussion

Fig 5. Health outcomes of children under five years old (U5y) living in slums in different divisions of Bangladesh.

Strengths and limitations

Conclusion and recommendations

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Ashti Doobay-Persaud

Roles

Author response to Decision Letter 0

Decision Letter 1

Ashti Doobay-Persaud

Roles

Author response to Decision Letter 1

Decision Letter 2

Ashti Doobay-Persaud

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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