The magnitude of multimorbidity in childhood: a global systematic review

Abstract

Background

Childhood multimorbidity, defined as the co-occurrence of two or more chronic conditions, is an emerging yet underexplored global health concern. Unlike adult multimorbidity, which is predominantly linked to ageing, childhood multimorbidity affects critical developmental phases, impacting physical, cognitive, and emotional well-being. Despite increasing recognition, its prevalence, correlates, and long-term implications remain inadequately understood.

Objectives

This systematic review aims to synthesize existing evidence on the prevalence, correlates, and outcomes of childhood multimorbidity in the general paediatric population.

Methods

A comprehensive search was conducted across PubMed, EMBASE, Web of Science, and CINAHL (EBSCO) to identify observational studies published up to May 31, 2024. Studies were included if they reported multimorbidity prevalence among children aged 0–18 years. Owing to high heterogeneity across studies, a meta-analysis was not performed, and the results were synthesized narratively.

Results

Nine studies, covering diverse paediatric populations, met the inclusion criteria. The individual study prevalence varied widely, ranging from 1.26% to 17.04%, reflecting differences in chronic conditions, multimorbidity definitions, study designs, and data sources. The key correlates included socioeconomic disadvantage, early-life factors (e.g., preterm birth), and poly-victimization. The co-occurrence of physical and mental health conditions commonly affects health-related quality of life (HRQOL), healthcare utilization, and educational performance.

Conclusions

Childhood multimorbidity poses significant health and social challenges, necessitating integrated care approaches and standardized definitions for cross-study comparability. Preventive strategies targeting social determinants and early-life interventions are crucial to mitigate its long-term burden. Future research should focus on longitudinal studies to explore progression and inform targeted interventions.

Systematic review registration

PROSPERO CRD42024601137.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13643-026-03087-z.

Introduction

Childhood multimorbidity, defined as the co-occurrence of two or more chronic conditions in a child, represents a pressing yet often overlooked challenge globally. Unlike adult multimorbidity, which is often related to ageing and chronic disease progression, childhood multimorbidity can disrupt critical periods of physical, cognitive, and emotional development [1]. This disruption can have significant implications for a child’s quality of life, education, social interactions, and future health trajectories [2]. While multimorbidity is frequently studied in adults, its impact on younger populations remains underexplored. Addressing childhood multimorbidity is essential not only for improving immediate health status but also for mitigating long-term health risks associated with life-course outcomes and psychosocial consequences that can extend into adulthood [3].

Childhood multimorbidity encompasses a range of chronic illnesses, congenital disorders, mental health issues, and developmentally linked conditions [4]. These variations complicate the identification and diagnosis of multimorbidity in children, often leading to underdiagnosis or overdiagnosis and corresponding disparities in treatment approaches [5]. Recent reviews have highlighted key risk factors for childhood multimorbidity, demonstrating that physical conditions such as epilepsy, asthma, and allergies often coincide with mental health issues such as anxiety and mood disorders, worsening quality of life and increasing healthcare use [6–9]. Additionally, the prevalence and characteristics of childhood multimorbidity vary across regions and are influenced by genetic and environmental factors [10]. A systematic review in a lower-middle-income country (LMIC) highlighted the co-occurrence of anaemia, malaria, and malnutrition in young children, underscoring the impact of socio-economic and environmental factors on the incidence of childhood multimorbidity in low-resource settings [11].

Despite recent advancements in childhood multimorbidity research, significant challenges remain in fully understanding this issue. Additionally, the lack of a standardized definition and measurement approaches presents significant difficulties in advancing research and developing effective strategies to address childhood multimorbidity [12]. As evident from the literature, assessing childhood multimorbidity remains significantly challenging because of variations in the number and types of chronic conditions studied, differing age ranges, and inconsistencies in definitions and methodologies across studies [13–17]. Many recent studies have focused on individual conditions or disease-specific groups, which limits the understanding of how multimorbidity manifests in the general paediatric population [18–22]. Furthermore, much of the existing research on multimorbidity has focused primarily on childhood risk factors that contribute to the development of multimorbidity in adulthood, with a limited focus on the prevalence and impact of multimorbidity within the paediatric population itself [23–25]. Estimating the burden of childhood multimorbidity can serve as a crucial step toward addressing these knowledge gaps by providing standardized data that facilitate comparisons across studies and inform targeted research as well as policy interventions [3, 26] This systematic review aims to examine and synthesize existing evidence on the prevalence, correlates, and outcomes of childhood multimorbidity in the general paediatric population based on observational studies.

Materials and methods

Search strategy

We conducted a comprehensive search across the PubMed, EMBASE, Web of Science, and CINAHL (EBSCO) databases to identify all relevant peer-reviewed articles. We focused on observational studies to examine the global prevalence and correlates of multimorbidity within paediatric populations. The search covered publications in any language from the earliest records in each database through 31st May 2024. The screening was primarily conducted in English, although articles in other languages were assessed with the assistance of translation tools to ensure comprehensive coverage. The search terms and combinations, which are detailed in the supplementary document (Additional file 1), were tailored to capture a wide range of studies on childhood multimorbidity. We further reviewed the reference lists of the selected studies and related systematic reviews to identify additional relevant articles. The study protocol was registered with PROSPERO (Registration ID: CRD42024601137).

Our systematic review included studies that met the following inclusion criteria to ensure that the data represented multimorbidity prevalence among the general paediatric population: (1) assessed multimorbidity as an outcome in the general paediatric population (aged 0–18 years); (2) were observational in nature (either cross-sectional or cohort studies); and (3) provided prevalence data on multimorbidity in the target age group. Only the most recent study was included if multiple studies assessed the same population. We excluded the following studies if they (1) estimated prevalence exclusively in populations with specific disease conditions; (2) reported multimorbidity prevalence limited to specific sub-categories or types of multimorbidity; (3) used other study designs, including randomized controlled trials (RCTs), case–control studies, and qualitative research; or (4) were opinion articles, conference presentations, books, letters, editorials, reviews, dissertations/theses, or abstracts.

Data extraction

We used Rayaan software to streamline the screening, selection, and data extraction processes [27]. Two independent reviewers (JM and AS) conducted initial title and abstract screenings for relevance, followed by full-text assessments of potentially eligible articles. Any disagreements in selection or extraction were resolved through consensus or, if necessary, with the input of a third reviewer (SKP). We extracted the data via a standardized extraction form in Microsoft Excel (version 2021) to ensure consistency and comprehensiveness. The extracted information included study characteristics (e.g., author, publication year, study location, study design), population demographics (age range, sample size), and multimorbidity prevalence data. Additionally, the extracted data captured methods of ascertaining multimorbidity, the number and types of chronic conditions included, and any given correlates of childhood multimorbidity. In cases where the multimorbidity prevalence was not directly provided, we calculated the prevalence manually via reported data on the number of children with multiple chronic conditions and the total sample size, if provided in the publication.

Quality assessment

We assessed the quality of the included studies via the JBI Critical Appraisal Checklist for Studies Reporting Prevalence Data, which evaluates key methodological aspects such as sample representativeness, data collection methods, and outcome measurement reliability [28]. This checklist assigns a score from 0 to 9, where we classify studies as follows: scores of 7 to 9 indicate a low risk of bias, scores of 4 to 6 indicate some concern, and scores of 0 to 3 indicate a high risk of bias. Two reviewers (JM and AS) independently performed the quality assessments and cross-verified each other’s evaluations to ensure accuracy. They resolved any discrepancies through discussion or by consulting a third reviewer (SKP) when necessary. We adhered to PRISMA guidelines throughout the review, and details of our methodological approach are documented in the PRISMA checklist (Supplementary document: Additional file 2, Appendix A) to ensure transparency and rigor in reporting [29].

Data synthesis

Given the expected methodological and clinical diversity across studies, we anticipated substantial heterogeneity in prevalence estimates. To quantify between-study variability, we applied the Inverse Variance Heterogeneity (IVhet) model as an exploratory step [30]. In view of the extreme heterogeneity, we did not report a formal pooled prevalence and instead synthesized findings descriptively. We, therefore, used the Synthesis Without Meta-analysis (SWiM) guidelines to synthesize findings narratively and summarize prevalence estimates as well as key correlates reported across studies (Supplementary document: Additional file 2, Appendix B) [31]. We also analysed trends in multimorbidity prevalence by considering differences in study populations, methodological approaches, and healthcare settings. Additionally, we examined how the definitions and measurement of multimorbidity varied across the included studies and discussed their implications for comparability and research gaps. We evaluated the consistency of reported risk factors across studies to identify common predictors and potential disparities in childhood multimorbidity prevalence.

Results

Identification and selection of studies

The study selection process is detailed in the PRISMA flow diagram (Fig. 1). Our comprehensive search across four databases initially identified 18,465 records. Following the removal of duplicates via an automated tool, we screened 9885 unique records by title and abstract, which resulted in the exclusion of 9802 records that did not meet the inclusion criteria. Additionally, 74 studies were excluded (not meeting eligibility criteria) following the full-text review of 83 studies. Ultimately, nine studies met the eligibility criteria, encompassing a total sample size of 1,810,542 participants.

Fig. 1.

PRISMA flow diagram of the study selection process

Characteristics of the studies

The included studies spanned several countries, including the USA [32, 35], Australia [33], Hong Kong [34], Finland [40], Norway [40], Spain [38], New Zealand [37], and the Netherlands [36], reflecting a geographically broad yet socio-economically limited range. Among the nine studies, seven employed cross-sectional designs [32–36, 38, 39], whereas two used cohort data [37, 40] for multimorbidity prevalence estimation. The sample sizes in the studies ranged from 2691 to 1,743,019 participants, covering diverse age groups within the paediatric population (from infancy to 18 years). Additionally, the publication years of the included studies ranged from 1994 [32] to 2023 [40], indicating a wide temporal span in the study period. The studies also varied in the number of conditions included for measuring multimorbidity, with the number of conditions ranging from less than 10 [37, 39] to as many as 68 [40]. Most studies used parent-reported data, health surveys, or administrative health records to assess chronic conditions. For instance, Newacheck et al. [32] used an extensive checklist for chronic conditions administered through a household survey, whereas Zhong et al. [35] utilized medical insurance claims data from hospital employees. In Hong Kong, Lee et al. [34] combined parent and child self-reports to capture chronic health issues through a standard questionnaire (Table 1).

Table 1.

Overview of the study characteristics included in the systematic review (chronological order by publication year)

Study, year and country of publication	Study period	World Bank income country	Study design	Study settinga	Source of data/morbidity ascertainment	Sample size	Age group/mean age in years (SD)	Method of assessment of morbidity/number of categories of chronic conditions included	Prevalence of multimorbidity (%)b	Correlates/risk factors assessed	Outcome/impact assessed
Newacheck et al.,1994, USA [32]	1988	HIC	Cross-sectional	Population-based	National Health Interview Survey (NHIS) 1988 from Netherland/Parent-reported	17,110	0–17 years/NR	Conditions having duration of more than 3 months and occurring during the previous 12 months. / 17	4.40	Age, gender, race, and poverty status	Occurrence of developmental, learning, and behavioural problems, annual number of school absences because of illness or injury, annual number of days spent ill in bed, limitation of usual activity, perceived health status of the child, annual number of physician contacts for children and the likelihood of hospitalization during the year
Waters et al.,2007, Australia [33]	1997–2005	HIC	Cross-sectional	Community-based (School based)	The Health of Young Victorians Study of Australia/Parent-reported	5414	5–18 years/11.1 ± 3.5	NR/ 19	13.85	NR	Health Related Quality of Life (HRQOL)
Lee et al.,2012, Hong Kong [34]	2005–2006	HIC	Cross-sectional	Population-based	The Child Health Survey (CHS) 2005–06/Parent-and child reported	5880	5–14 years/5–10 years (mean = 7.8 ± 1.7 years) and 11–14 years (mean = 12.5 ± 1.2 years)	NR/ 29	1.26	NR	Health Related Quality of Life (HRQOL)
Zhong et al.,2015, USA [35]	2004–2007	HIC	Cross-sectional	Hospital-based	Hospital employee’s insurance claims/objective	14,727	0—17 years/NR	Conditions that had lasted or were expected to last 12 or more months and resulted in functional limitations and/or the need for ongoing medical care. / 56	17.04	NR	Health expenditure
Bai et al.,2017, Netherlands [36]	2010–2013	HIC	Cross-sectional	Population-based	Dutch Health Interview Survey (DHIS) 2010–13/Parent-reported	6499	4–11 years/NR	Presence of any of the listed conditions during the previous 12 months. / 18	6.06	NR	NR
Russell et al.,2019, Newzealand [37]	2009–2012	HIC	Cohort study	Community-based	Growing Up in New Zealand (GUiNZ) child cohort study/Parent-reported	3838	2 years/2.07 years	A “chronic condition subscale” (CCS) was used to assess multimorbidity. CCS is a count of the child’s chronic conditions from available data including common early childhood chronic conditions reported by mothers and obesity at age 2 years derived from anthropometric measurements. / 7	9.74	Maternal socio-economic position	NR
Cortes et al.,2020, Spain [38]	2015	HIC	Cross-sectional	Hospital-based	Primary care utilization database/objective	2691	0–17 years/NR	The Adjusted Morbidity Group (AMG) grouping system based on the diagnostic codes recorded in the PC-Madrid database for each patient by the healthcare provider in charge of the patient stratifies the population into mutually exclusive groups based on multimorbidity and complexity. / 56	1.78	NR	Level of disease complexity
Seppala et al.,2021, Finland [39]	2013	HIC	Cross-sectional	Community -based	Web-based survey/Self-reported	11,364	12–17 years/NR	Web-based survey which was led by the teacher during the lesson in schools and participants were asked regarding presence or absence of any of the listed conditions. /8	2.90	Poly-victimization (physical and mental abuse)	NR
Heikkilä et al.,2023, Finland Norway [40]	1988–2017	HIC	Cohort study	Hospital-based	Medical Birth Register, Hospital care register, Causes of death register, and National statistics office’s population data/objective	1,743,019	10–18 years/NR	Researchers used longitudinal data from multiple, linked nationwide registers from Finland and Norway for this observational cohort study. Health outcomes during adolescence were ascertained from inpatient and outpatient care records in the Norwegian Patient Register and Finnish Care Register for Healthcare/ 68	3.01	Preterm birth and gestational age-related early life factors	NR

NR not reported, HIC high-income country

^aPopulation-based = census/registry sampling; Community-based = community/school recruitment; Hospital-based = clinical or health facility-based data

^bAll prevalence estimates are crude; no age- or sex-standardized estimates were reported in source studies

Correlates and outcomes of childhood multimorbidity

The included studies assessed a range of factors and correlates related to childhood multimorbidity, covering sociodemographic, health status, and socioeconomic variables. Age, sex, race, and socioeconomic status are common demographic correlates, with several studies linking socioeconomic disadvantage to a higher prevalence of multimorbidity [32, 37]. In addition to sociodemographic factors, early-life influences were also identified, with one study highlighting preterm birth as a significant correlate of increased multimorbidity risk in adolescence, reinforcing the long-term health impact of early developmental factors [37]. Furthermore, Seppala et al. uniquely examined the association between multimorbidity and poly-victimization and reported that children with both somatic and psychiatric conditions faced significantly greater odds of experiencing both mental and physical violence [39].

The outcomes associated with childhood multimorbidity are multifaceted and impact health-related quality of life (HRQOL), healthcare utilization, and educational performance. HRQOL is frequently evaluated via tools such as the Child Health Questionnaire (CHQ), which highlights significant declines in physical and emotional well-being among children with multimorbidity [33, 34]. One study assessed healthcare utilization and costs and reported that healthcare expenditures and hospital visits increased notably with the number of chronic conditions [35]. Additional factors included school performance indicators, such as school absences and days spent ill in bed, as well as mental and behavioural issues, with studies observing a greater likelihood of these concerns among children with multimorbidity [32]. Another study by Cortes et al. explored the complexity of care in children with multimorbidity, assessing factors such as the level of disease complexity and the frequency of healthcare contact [38]. However, it should be noted that one study by Bai et al. within this review specifically excluded children with multimorbidity at the outset, as its primary focus was on the impact of individual chronic conditions rather than multimorbidity [36] (Table 1).

Prevalence of childhood multimorbidity in the general paediatric population

The individual study prevalence in this systematic review ranged from 1.26% [34] to 17.04% [35]. The exploratory heterogeneity assessment using the IVhet model demonstrated extreme between-study heterogeneity (Q = 9466.92, p < 0.001; I² = 100%), indicating that the observed variability was far greater than expected by chance. Although the IVhet model produced a pooled prevalence estimate, the level of heterogeneity rendered any summary estimate unreliable and inappropriate for interpretation (Fig. 2).

Fig. 2.

Forest plot: Visual summary of childhood multimorbidity prevalence estimates in reviewed studies. (*All prevalence estimates are crude (unadjusted); none of the included studies reported standardized prevalence estimates)

Quality and risk of bias assessment

Detailed results of the methodological quality and risk of bias assessment of individual studies according to the JBI tool are presented in Fig. 3. In summary, four (44%) studies had a low risk of bias, whereas five (56%) studies showed some concerns. No studies were classified as having a high overall risk of bias. The most frequent methodological limitations were related to participant sampling, sample size adequacy, and response rate management (Fig. 3).

Fig. 3.

Summary of methodological quality assessment using the JBI Checklist for prevalence studies

Discussion

Considering the developmental implications and healthcare demands associated with managing multiple chronic conditions in children, childhood multimorbidity currently poses a significant challenge to global health [41–45]. This study represents the first systematic review to examine the prevalence of childhood multimorbidity across the general paediatric population. The reported prevalence of childhood multimorbidity varies widely, ranging from 1.26% to 17.04%, reflecting differences in study methodologies, chronic condition definitions, and population characteristics. This prevalence, although lower than that often reported in adult populations, reflects a substantial burden in paediatric settings. The variability observed among studies emphasizes the context-dependent nature of childhood multimorbidity and suggests that this condition is influenced by a multitude of factors, including regional, socioeconomic, and healthcare-related disparities [46, 47]. All included studies were conducted in high-income countries, reflecting a notable absence of population-based evidence from LMICs and low-income countries (LICs). Limited evidence from LMICs has explored aspects of childhood multimorbidity; however, such work did not meet our inclusion criteria as it focused on restricted sets of conditions rather than the broader spectrum of chronic disorders [11].

The heterogeneity across studies in terms of multimorbidity definitions, age groups, and methodologies complicates efforts to establish a single, unified prevalence estimate. For example, Newacheck et al. defined chronic conditions as those with a duration of more than three months, whereas Zhong et al. included conditions that had lasted or were expected to last at least 12 months [32, 35]. This inconsistency in definitions and criteria limits comparability across studies and echoes similar issues observed in adult multimorbidity research, underscoring the urgent need for standardized definitions and tools in paediatric multimorbidity research [48]. Establishing a standard framework would enhance comparability and help inform public health policies better equipped to address paediatric multimorbidity effectively.

The current review reveals several factors associated with childhood multimorbidity, underscoring the significant impact of sociodemographic, psychological, and early-life influences on health outcomes. Age, gender, and socioeconomic status were prominent correlates, aligning with some of the included studies that reported higher multimorbidity prevalence among adolescents, boys, and children from socioeconomically advantaged backgrounds [32, 37]. The role of socioeconomic status as a correlate of multimorbidity is well supported by various other studies that consistently demonstrate that children from socioeconomically disadvantaged backgrounds face increased risks of multimorbidity due to limited access to preventive healthcare, exposure to adverse living conditions, and nutritional deficiencies [49–51]. These conditions exacerbate the development and severity of multimorbidity, contributing to health disparities that begin in childhood and can persist throughout life [52]. These social disparities also increase the likelihood of poly-victimization, as economic instability and social marginalization can expose children with multimorbidity to greater risks of abuse, neglect, and violence. This underscores the heightened vulnerability of children with multimorbidity to adverse social experiences, which may exacerbate their health challenges and complicate their management [53–55]. These findings suggest that addressing childhood multimorbidity requires more than medical intervention; rather, policies that address social determinants of health are needed to mitigate the broader impacts of socioeconomic inequities on children’s health.

Additionally, certain factors, particularly preterm birth, have been strongly linked with an increased risk of multimorbidity, corroborating our review findings [40]. Notably, a longitudinal cohort study in Norway and Finland revealed that children born preterm face increased risks of respiratory, cardiovascular, and developmental disorders, contributing to a greater likelihood of multimorbidity in adolescence. This study revealed a dose–response relationship between earlier gestational age and the risk of developing complex multimorbidity during childhood and adolescence [56]. These findings align with the developmental origins of health and disease hypothesis, which posits that early-life adversity contributes to chronic conditions later in life, reinforcing the importance of early intervention and continuous support for high-risk children to mitigate long-term health impacts [57, 58].

In this systematic review, we also highlighted the frequent co-occurrence of physical and mental health conditions in children with multimorbidity [33, 36]. This overlap aligns with previous research indicating that chronic physical conditions, such as asthma, eczema, and epilepsy, are often accompanied by mental health issues such as anxiety and depression [59, 60]. This intersection of physical and mental health conditions not only exacerbates the health burden but also presents challenges for healthcare systems structured around single-disease treatment models. This interdependence also underscores the importance of integrative care models that address both physical and psychological health, as isolated treatment approaches risk overlooking the compounded effects of these conditions on children’s health. The observed co-occurrence of physical and mental health conditions further emphasizes the importance of HRQOL as a critical measure of overall well-being in children with multimorbidity. For instance, Ferro et al. reported that children with physical and mental comorbidities had significantly lower HRQOL scores, reinforcing the need for healthcare systems to adopt family-centred, multidisciplinary approaches to support these children comprehensively [61]. Moreover, HRQOL, which has been frequently measured across studies, has significantly decreased in children with multimorbidity, indicating the broad-reaching effects of multiple chronic conditions on physical, emotional, and social functioning [62, 63]. This decline points to the necessity of support systems in healthcare, education, and social settings that can address the multifaceted needs of children with multimorbidity, improving their quality of life and social integration during their formative years.

This review also demonstrates that children with multimorbidity are more likely to experience higher healthcare utilization and associated costs, reflecting that chronic illness in children imposes substantial demands on healthcare systems [35]. Prior studies have reported that children with multimorbidity typically require more frequent medical visits, specialist consultations, and potentially long-term use of medications, which cumulatively increase healthcare costs [42, 64–66]. This burden highlights the need for comprehensive paediatric care models that go beyond addressing individual illnesses, instead offering a holistic approach tailored to the unique developmental and psychosocial needs of children with multiple conditions.

The review findings have important clinical and research implications. Clinically, the evidence supports a move toward integrated paediatric care models that address the complexity of multimorbidity. Research on the wide heterogeneity in prevalence estimates highlights the urgent need for standardized definitions and methods to enable reliable cross-study comparisons. Longitudinal studies should be prioritized to understand the progression and early risk factors for childhood multimorbidity, as most existing evidence is limited to cross-sectional designs, restricting insights into causation and long-term impact. There is also a critical need for population-based research from LMICs and LICs to strengthen the global evidence base on childhood multimorbidity.

While this systematic review offers a critical synthesis of childhood multimorbidity, several limitations must be noted. First, owing to substantial heterogeneity in sample size, age range, definitions, and data sources, a meta-analysis was not feasible, as pooled estimates would lack interpretability. Second, the reliance on parent-reported or administrative data may lead to an underestimation of prevalence due to recall bias or diagnostic limitations. Finally, the cross-sectional nature of most of the included studies precludes causal inference, limiting our ability to determine which factors contribute to the onset or progression of multimorbidity over time. Given the evolving nature of this field, we intend to update this review as new studies emerge to ensure its continued relevance.

Conclusion

This systematic review provides a comprehensive overview of childhood multimorbidity, synthesizing existing evidence on its prevalence, correlates, and associated health outcomes within the global paediatric population. Our findings indicate that childhood multimorbidity encompasses a wide array of chronic physical, mental, and developmental conditions, each contributing to complex health trajectories that affect not only immediate well-being but also long-term health and socioeconomic outcomes. This review emphasizes that effective management of childhood multimorbidity requires a shift toward integrated healthcare approaches that address the diverse needs associated with multiple chronic conditions. Ultimately, improved surveillance and policy attention to childhood multimorbidity are critical for reducing future health inequities and optimizing healthcare delivery for children.

Abbreviations

HRQOL

Health-related quality of life

HIC

High-income country

LMIC

Low- and Middle-Income Country

JBI

Joanna Briggs Institute

PRISMA

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

PROSPERO

International Prospective Register of Systematic Reviews

CINAHL

Cumulative Index to Nursing and Allied Health Literature

EMBASE

Excerpta Medica Database

EBSCO

Elton B. Stephens Company (platform hosting CINAHL)

ICMR

Indian Council of Medical Research

CHQ

Child Health Questionnaire

SWiM

Synthesis Without Meta-analysis

Authors’ contributions

SP, SKP, AS, KCS, and DB contributed to the overall concept and design of the study. MVA, MAH, JSK, DMS, and SP supervised the drafting process and provided critical revisions for intellectual content. JM, UN, SKP, and AS were responsible for conceptualization, methodological assessment, and preparation of the original draft. All the authors reviewed and approved the final version of the manuscript.

Funding

This study was supported by funding from the Indian Council of Medical Research (ICMR).

Data availability

Data supporting the findings of this study are available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

No individual-level data are included in this systematic review. All the data are derived from previously published observational studies and are presented in aggregated form.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.