Introduction

Reducing child mortality is one of the main aims in achieving Sustainable Development Goal 3: To ensure healthy lives and promote well-being for all at all ages [1]. This is an important task for India in particular, a rapidly growing country, which in 2015, saw the highest rate of child mortality with 1.2 million deaths occurring in under-5 (U5) children (47.8 per 1000 live births). Infectious diseases, particularly Respiratory Tract Infections (RTIs) and Gastro-Intestinal (GI) infections are the leading cause of child mortality in India, with disparities in child mortality across both states and regions [2]. More than nine percent of all U5 mortality in 2015 was due to diarrhoea, and 15.9% was due to pneumonia. These figures indicate that understanding the current infection pathways are important to design interventions for decreasing morbidity and mortality from these infections. This includes tackling a complex network of multiple factors, including the built environment, nutrition, Water and Sanitation Hygiene (WASH), animal husbandry practices, vector control open defecation, and air and water pollution due to open defecation and drainage. As children’s health is intertwined in a complex network between people, animals and their environment (Fig 1), it is important to consider the One Health paradigm. This is particularly vital in the current climate, whereby the burdens of emerging infections as well as traditional infection co-exist.

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Fig 1. A conceptual map of one health factors associated with infections and AMR in U5s living in urban slums [3].

https://doi.org/10.1371/journal.pone.0257797.g001

As per 2011 census data, 30.8% of India’s total population fall between 0 and 15 years of age, while 26.5% of its population live in urban areas. With the rapid, unchecked increase in rural-urban migration, it is now estimated that between 11.2 and 21.1% of India’s urban population are currently living in slums, with a significant portion of this population being children as reflected in India’s total youth population [4]. The UN-Habitat currently defines slums or informal settlements as following [5, 6]:

“household[s] where the inhabitants suffer one or more of the following ‘household deprivations’: 1) Lack of access to improved water source, 2) Lack of access to improved sanitation facilities, 3) Lack of sufficient living area, 4) Lack of housing durability and, 5) Lack of security of tenure, 6) Lack of separate space for animals, 7)Lack of separate space for cooking, 8)Lack of open space for recreation/playing).”

Children living in slums are not only exposed to the environmental hazards of disease-causing bacteria, viruses and parasites, but the health effects of these infections are exacerbated by the social and environmental determinants driving health inequalities [7, 8]. For example, the living environments of U5 children often involve high levels of pollution and antimicrobial resistance compared to non-slums; slum environments suffer from severe urban poverty and lack of basic infrastructure [9]. As life course epidemiology posits that childhood exposures are detrimental to later life health increased morbidity, decreased socio-economic positions and higher mortality [10], understanding these variables in slums has significant public health importance.

Despite the strength of evidence for the link between the social and environmental determinants of children’s health and vulnerability to RTIs and GI infections, this association has not been explored intensively in peri-urban slum settings in India [11–14].

This study aims to take an in-depth look at social determinants of health in slums, its effect on the risk of RTIs and GIs in children, which are the leading causes of child mortality in India. This research builds on ongoing work by the Childhood Infections and Pollution Study (CHIP), which aims to reduce infections and antimicrobial resistance in U5 urban slum populations through low-cost behaviour change and slum-upgrading interventions using One health and technology enabled citizen science approaches.

Moreover, this study will aim to explore the social and environmental determinants of children’s health in peri-urban slums in India, using a multilevel logistic regression analysis of child-, household-, and slum-level determinants associated with phenotypic symptoms of RTIs and GI infections in U5 children. Identified protective or risk factors will also be used to inform the explanatory variables of CHIP formative research, from theoretical deduction. This study is innovative in its holistic view of environmental, socio-economic and biological factors of children’s health to examine how different factors simultaneously influence each other and determine outcomes of fever and cough and diarrhoea.

Methods

Model and variable specification

Outcome variables are binary in nature and were defined as 0 for No and 1 for Yes, for this analysis fever and cough were measures of RTIs and diarrhoea was used to measure GI infections.

1. A child had both fever and cough in the last two weeks (recoded using: had a fever in the last two weeks and had a cough in the last two weeks). Hereby onwards referred to as FeCo.
2. A child had diarrhoea in the last two weeks. Hereby onwards referred to as diarrhoea.

Multilevel models were used in the analyses the consider the association between exposures and outcomes variables; a prior of Cauchy distribution with a scale of 2.5 was selected when building the multilevel logistic models. Exposures were categorised into three levels: individual, household, and slum level [18, 19] as per the World Health Organisation (WHO) Social Determinants of Health Framework (Fig 2), that both structural and intermediary determinants of health, with structural determinants influencing the health inequalities in the wider society [20].

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Fig 2. A conceptual framework for action on the social determinants of health [20].

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Explanatory variables informed by existing literature (selection rationale for individual and household level variables are explained in further detail in Tables 1 and 2 in the S1 File):

1. Individual level: Sex of child; Child’s age in months, Birthweight (mother’s perception of child’s size at birth); The month of the interview; Never breastfed.
   The sex of the child was included as a variable due to sex-differences in virulence of infections being observed in epidemiological studies. The child’s age was also included in the analysis as it has been observed that older children have more mature immune systems to fight off pathogens [21, 22]. Next, Birthweight (mother’s perception) was also used as epidemiological studies point towards children with low-birthweight having an increased risk of contracting RTIs and GI infections. Since there is a significant incidence of home delivery (M15), a mother’s opinion of the birthweight was used as a variable instead of the actual birth weight [23, 24]. The month of the interview was also used to account for how India faces heavy rainfall during the Monsoon season, which has associations with RTIs and GI infections if there are inadequate sanitation or water sources (related to slum environment) [25–27]. Finally, at the individual level, never breastfed was used as a variable in the analysis to account for the difference in immunity of breastfed children and non-breastfed children as observed in epidemiological studies [28].
2. Household level: Highest year of maternal education attained; Source of drinking water; Type of toilet facility; Wealth index (a composite score with various ownership overlapping with other household variables listed here as calculated by the NFHS dataset [15]; household has mosquito bed net for sleeping in the household; covered by health insurance; main material of floor; wall; roof; Religion; Caste or tribe; type of cooking fuel; maternal smoking and current marital status of the mother.
   Highest year of education of a mother was included in the analysis as epidemiological studies point towards positive effects of maternal education on childhood health outcomes [29, 30]. Source of drinking water was also added in the analysis as different levels of contaminants (linked to infections) are found in different water sources in LMIC urban slums [31]. Type of toilet facility was also included in this study as previous studies has shown how it has been linked to health and social benefits in LMIC [32]. Furthermore, the Wealth Index was used to present a composite measure of a household’s cumulative living standard; this includes the following variables: household has electricity; radio; television; refrigerator; telephone (landline); bicycle; motorcycle/scooter; car/truck; mosquito bed net for sleeping in the household; covered by health insurance. Next, the main material of floor wall and roof was used to account for the findings of associations between housing materials on children’s health [33, 34]. Type of cooking fuel was also included in the analysis to account for its associations with children’s respiratory infections [35]. Additionally, Religion; Caste or Tribe was included so as to account for possible associations of health outcomes with Scheduled Caste status (that play an important role in social determinants in India) [36, 37]. Finally, the marital status of the mother was chosen for the analysis to observe any associations between single motherhood and vulnerability in children as observed in previous studies [38].
3. Slum (cluster) level: State; The aggregated mean of years of maternal education attained; wealth index; perceived distance to health facilities. These aggregated means were calculated by slum cluster.

Results

The descriptive analysis of all variables is presented in Table 1. Chi-squared was used for categorical variables and Kruskal-Wallis test was used for numeric variables. The total sample size of the number of children involved in the analysis was 1,424; from a total of 259,627 total children in the NFHS-4 and 4,168 U5 population in eight cities (slum and non-slum). The distribution of both our imputed and original sample are shown in Table 1. In comparison with the distribution of individuals with FeCo and Diarrhoea in the original sample, our imputed sample show a similar distribution.

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Table 1. Descriptive analysis of under-5 children in total slum population of eight Indian cities, NFHS-4, 2015–16.

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Table 1 also shows that both diarrhoea and FeCo were found to be present in younger children more than older children by a few months (mean age 25 months; 29 months respectively and 22 months; 30 months, respectively).

Table 2 represents the AORs (95% confidence interval) of final models 1, 2, 3 and 4. In fixed effects (model 1 and 3), the odds of developing FeCo were higher if the mother perceives the child was born smaller than average (AOR 4.41, 1.13–17.17). On the other hand, the odds of the diarrhoea outcome were lower if the child was older (AOR 0.97, 0.96–0.98) and household’s water source is public tap or standpipe (AOR 0.54, 0.31–0.96).

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Table 2. Adjusted odd ratios (95% CI of all models).

https://doi.org/10.1371/journal.pone.0257797.t002

Although we report on statistically significant finding here, it is noteworthy that many of our variables at individual, slum and household level were not statistically significant in our models.

Discussion

The descriptive analysis reflects the challenging socio-economic settings of Indian slums. For example, the mean number of total household members was 6.65 (SD: 3.21), which may indicate a problem of overcrowding as most slum households are unlikely to have sufficient space even if the mean number is similar to non-slum households. Additionally, the mean of the highest year of education completed by mother was 4.1 years out of a possible 15 years.

This study finds that children who are younger in months of age and/or male were discovered to be at higher risk of developing both morbidities; reaffirming results from other epidemiological studies [27–31]. Having very small- or smaller-than-average maternally reported birth weight also increased the odds of both morbidities. This echoes existing findings that highlight associations between low birth weight and increased risk of infections and other aspects of ill health [42]. Low birth weight is also a well-established determinant of child development as well as decreased survival; and plays a major role on the onset of health consequences in an individuals’ life course [43].

Interestingly, the month in which the survey interviews were conducted showed variation in reporting both the morbidities; FeCo was most prevalent in May, whereas diarrhoea was least prevalent in June. This suggests a seasonality in childhood infections, this has previously been observed in past studies whereby infection transmission patterns are impacted by the weather [44]. In addition to seasonal variations, climate conditions and environmental pollutions may also make a contribution to childhood infections [45]. While it would have been beneficial to explore this using multiple year data, it was not feasible to link NFHS-4 individual code to NFHS-3, as many individuals were missing. This was mainly due to the fact that children were not born at the time when NFHS-3 was conducted. Lastly, the odds of being breastfed were protective towards any symptoms of infection, this has been affirmed by past epidemiological studies that show breastfeeding to have several health benefits for children, due to the protective factors in the breast milk against infections [46]. However, it is important to note the lack of precision in the survey instrument; as children who were breastfed at least once were simply recorded as ever breastfed, thus there is a possibility in underestimation of association–meaning that the effect of association may have been bigger than presented in our results.

At the household-level, the type of housing material showed distinctive differences, finished floor and wall materials showed highly protected trends for FeCo. However, the results also showed, that for diarrhoea; finished materials for wall and floors showed adverse associations. This finding does not sit in line with other studies conducted, whereby finished flooring showed protective effects against the occurrence of diarrhoea [47]. The difference in results may be due to the possibilities in that these results having uncontrolled confounding. For instance, variables such as hand washing behaviour and proximity to sanitation facilities were not controlled for [48]. This was due to the lack of data availability on these mentioned determinants.

Interestingly, having a dry toilet in the household was protective for both morbidities; previous studies surrounding the use of dry toilets have observed a reduced risk of infection transmission due to the waterless mechanism by sealing the faeces through creating a barrier between faeces and the human [49]. This should be further explored in future epidemiological research. The benefits of dry toilets also include: the usage of less water and environmental benefits associated with less contamination of water sources reduction [50]. Thus, dry toilets could be a good policy intervention that is both sustainable towards global water shortage, whilst also affordable to be implemented for slum regeneration. However, we must note that despite the environmental benefits dry toilets deliver, issues concerning social and cultural factors must be considered. Perceptions and prejudices regarding handling excreta that may not provide modest coverage have an important and significant impact on individuals. Additionally, gender issues are frequently discussed in literature surrounding the use of dry toilets. Women use toilets for reasons other than excretion in many geographic locations: for instance, during their menstrual cycles and for child care [51]. Thus, it is important to note that when making suggestions about the use of dry toilets; the issues of inconvenience for women must also be taken into serious consideration.

The results also showed that the use of tanker trucks for supplying water to the slums presented an increased odd in some morbidities. However, it must be stated that these results may have been due to the tanker trucks sold in slum environments containing water that was contaminated, as well as further issues with water storage; this includes lack of hygiene upkeep [52, 53]. For instance, in order for the water source to be safe from tanker trucks, the water tank pumps must be rigorously cleaned before use, after major maintenance and at least every 3 months. If this is not completed under a correct cleaning procedure, the chances of clean and safe water from tanker trucks are significantly lowered [52]. Additionally, we are able to assume that this upkeep might not have been regularly maintained; this is due to the prevalence of individuals in our dataset that mention the use of tanker trucks being small (2.5% of those residing in slums). Thus, there is a possibility that the safe delivery and management of tanker trucks were limited and not operationalised to optimise sufficiency as explored by previous studies surrounding water safety and hygiene [52].

In terms of cooking fuels, animal dung was associated with higher reporting of FeCo, whereas no food cooked in house were adversely associated with diarrhoea. However, this may also be associated with affluence, rather than only the type of fuel used; thus, further research is required. Moreover, children with mothers who do not smoke were protective towards both morbidities, this sits in line with many epidemiological studies whereby maternal smoking is associated with increased risk of infant and childhood disease and mortality (through environmental exposure and the diversion of household income) [54, 55]. Hence encouraging mothers not to smoke could be another area to increase awareness as part of Indian public health campaigns. For both morbidities, it was evident that owning health insurance was protective for children, however, higher wealth index was only protective towards diarrhoea. This may indicate that U5 children from households that have higher socio-economic status are more likely to get less diarrhoea, whereas FeCo could not be a main concern for many households, even if their socio-economic status is higher. This implies that public health campaigns should increase the awareness of the danger of RTIs and potential mortality or morbidity that U5 children can face from infecting them. It is important to note how these morbidities are cared for in each household; whether they sought clinical treatment or home remedies.

At the slum-level, there were clear differences between states in each morbidity, children in Telangana were most protective from FeCo whereas children in Delhi and Uttar Pradesh were most likely to develop diarrhoea. This can be explained by state differences in income inequality and level of air pollution [56]. It is recommended to further elaborate this outcome using spatial epidemiology and neighbourhood effects in future. Lastly, diarrhoea was associated with a slum-level increase of the highest maternal education year, whereas FeCo was more prevalent in the slums where the community felt the distance between their home to the healthcare centre was the greatest. This is also true in past epidemiological studies; specifically, a qualitative study surrounding under-five child health services conducted in urban slums in Malawi found that health-seeking behaviours were negatively impacted by long distances to health facilities [57].

Most explanatory variables in our models were insignificant; with a P-value >0.05 (Delivery, birthweight, toilet type, floor type, wall type, distance to health centre and cooking fuel). Wide confidence intervals suggest that insignificant results may have been due to the small sample sizes in the analysed models. Although insignificant, it is important to address these results with regard to its wider public and global health impact, and in order to evade issues relating to underreporting bias. Our analyses therefore highlight how the explanatory variables listed above had no significant effect on the odds of developing FeCo and diarrhoea.

There were some limitations identified in this study, which were primarily linked to the dataset being a self-reported interview. Firstly, the dataset has a low prevalence of the outcomes, causing potential data separation (whereby observations of a particular predictor have the same outcome). This was controlled for using a Bayesian perspective generalised linear regression model, both with fixed- and random-effects, to conduct multilevel logistic modelling. Mothers’ responses regarding children’s symptoms in the last two weeks may suffer from recall bias or misinterpretation, which could have led to random misclassification and bias toward the null value. We must also note, that the variable used as a proxy for birthweight of a baby (mother’s perception) is open to issues such as measurement error; this is likely as it is subject to personal perceptions and possible systematic errors [58]. However, such perceptions from the NFHS-4 used in this study has been validated in various studies as aforementioned in Table 1 in S1 File.

Lastly, some important variables that were chosen to be included in the initial model had to be omitted at the final model, due to data limitations. For example, the Hot-deck imputation method was used to handle missing data for variables that had 50% or less missingness. This method is widely used for missing data in statistics as it reflects the real values and avoids strong parametric assumptions [39]. Multiple Imputation by Chained Equations (MICE) was also considered, however, due to MICE’s limitation on potential incompatible conditional distributions between categorical and continuous variables, it was concluded that it was not suitable for this study’s dataset [59]. It also incorporates the original dataset’s covariate information and can provide strong inferences in regression models, if imputation uncertainty is carefully considered beforehand. Variables with over 50% of values missing were excluded from the analyses.

Current literature show that this method requires a good match of donors to be able to create meaningful values. As this disadvantage is controlled better when there is a large dataset; a sample size of 1424 can be limited in providing high quality covariate variables of Missing-Not-at-Random (MNAR) NAs. This is the suggested reason on why some values showed infinite upper confidence intervals (values that were very low in proportion compared to other values in the categorical variable) and needed to be omitted from the final modelling analysis. It is recommended that a future study should use the MICE instead of hot-deck imputation for qualified missing variables to investigate the changes raw bias and Root Mean Squared Error (RMSE) in the dataset. This can improve the results in odds ratio and provide tighter models with higher statistical significance.

Conclusions

This study has examined how the different social determinants of children’s health in peri-urban slums in eight cities in India impact the phenotype of RTIs (proxy: FeCo) and GI infections (proxy: diarrheoa). In sum, the study found that at an individual level, the odds of developing FeCo were higher if the mother thinks the child was born smaller than average (AOR 4.41, 1.13–17.17). It was also found that the odds of the diarrhoea outcome were lower if the child was older (AOR 0.97, 0.96–0.98), and at a household level; the diarrhoea outcome was found to also be lower if the household’s water source is public tap or standpipe (AOR 0.54, 0.31–0.96).

This study provides valuable information for future policy interventions to lower the odds of childhood morbidities in marginalised slum populations. It provides empirical evidence of the need to approach each slum cluster with different ideas, firmly rooted in the local context. Crucially, any intervention aiming to improve child health should tackle all levels, and all areas, of the social determinants of health outcomes. Moreover, the protective and risk factors identified in this study should provide a guide to state and national level policymakers to prioritise addressing to improve child health outcomes across these eight cities and serve as a template for other Indian cities. The policies should utilise a cross-sector, yet multilevel approach.

Supporting information

Acknowledgments

The authors are members of the Childhood Infections and Pollution (CHIP) Consortium aimed at reducing the infection & antimicrobial resistance burden amongst children under five in slums using One Health (i.e. human, animal, environment) and technology-enabled citizen science approaches. The Consortium has undertaken work in urban slums in Jaipur, Jakarta, Antofagasta & Istanbul. Dr Logan Manikam and Prof Monica Lakhanpaul are co-directors of the CHIP Consortium.

In addition to the authors, members of the CHIP Consortium consists of Hemant Chaturvedi, Emma Maynard, Prof Muki Haklay, Dr Hector Altamirano-Medina, Dr Neha Batura, Dr Alexander Albert, Dr Clare Llewellyn, Julia Vila, Dr Dewi Aisyah, Chynthia Mayadewi, Natasha Mayandra, Anila Atin, Dr Rajensh Khanna, Sanjay Sharma, Satya Pattaniak, Sandeep Soni, Dr Prejit, Dr Rajib Dasgupta, Dr Sanweer Khatoon, Dr Krishna Mohan, Dr Kaushik Sarkar, Rajendra Prasad, Jacob Paulose, Dr Pradeep Srivastava, Radhika Sharma, Premraj Sharma, Prof Erika Ota, Obaidur Rahman, Prof Joseph Malik Peiris, Prof Keiji Fukuda, Dr Hein Tun, Dr Mahendra Perara, Dr Nancy Leung, Dr Rintaro Mori, Dr Wiku Adisasmito, Mariana Wieske, Berkay Hacimustafa & Gizem Kulekcioglu.

Organisations include; global universities (UCL, Columbia University, University of Hong Kong, St Luke’s University Tokyo, Public Health Foundation India, University of Antofagasta, Jawaharlal Nehru University, Rajasthan University of Veterinary &Animal Sciences), NGOs (i.e. Save the Children India, Jeevan Ashram Sanstha, Tarlabasi Toplum Merkezi), private sector (Aceso Global Health Consultants PTE Ltd) & networks (India Center for One Health Education, Advocacy, Research and Training & Indonesia One Health University Network). Consortium members had an opportunity to critically review results and contribute to the process of finalisation of this paper. The co-authors vouch for the accuracy and integrity of the work and accept full responsibility for the content of the paper.