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. 2021 Apr 17;2021(4):CD014847. doi: 10.1002/14651858.CD014847

Vitamin A for preventing acute lower respiratory tract infections in children up to seven years of age

Renjun Gu 1,✉, Hao Chen 1, Arjab Adhikari 2, Yihuang Gu 1, Joey SW Kwong 3, Guochun Li 4, Ziyun Li 1, Yujing Pan 1
Editor: Cochrane Acute Respiratory Infections Group
PMCID: PMC8078157

Notes

Editorial note

This protocol will not be progressed to the review stage as it no longer meets Cochrane's methodological standards.

Objectives

This is a protocol for a Cochrane Review (intervention). The objectives are as follows:

To assess the effectiveness and safety of vitamin A for preventing acute LRTIs in children up to seven years of age.

Background

Description of the condition

Acute lower respiratory tract infections (LRTIs) refer to the infection of the major passages and structures below the level of the larynx. These include acute bronchitis, acute bronchiolitis, unspecified acute LRTIs, and infection of the lung parenchyma, including pneumonia and influenza (WHO 2019a). Globally, these infections are frequently seen in primary care settings. Although acute LRTIs can be hospital acquired, they are usually community acquired, and mainly affect the paediatric population. Clinical features of LRTIs in children include age‐related fast‐breathing, chest indrawing, grunting, nasal flaring, cough, and fever (Rambaud‐Althaus 2015).

In 2015, the Global Burden of Diseases, Injuries, and Risk Factors (GBD) estimated that LRTIs were the fifth‐leading cause of death. Similarly, LRTIs were the third‐leading cause of deaths in children under the age of five, following preterm birth and neonatal brain injury due to the lack of oxygen or blood flow to the brain (GBD 2015a). A study involving 195 countries, which estimated the morbidity, mortality of LRTIs at the global, regional, and national level, showed that LRTIs largely affect children up to the age of five years (GBD 2015b). Low‐income countries are most affected in terms of the number of infections and mortality. Mortality due to LRTIs is highest in sub‐Saharan Africa and South Asia, with about 200 and 130 deaths per 100,000 children under the age of five, respectively (Beck 2015; GBD 2016).

The pathogenesis of LRTIs involves transmission of disease‐causing viruses and bacteria into the upper respiratory tract, where they multiply and subsequently spread into the lower respiratory tract (Man 2019). Streptococcus pneumoniae is the most common organism responsible for LRTIs followed by influenza virus, respiratory syncytial virus, and Haemophilus influenzae type b. Amongst these organisms, pneumococcal pneumonia is the leading cause of LRTIs morbidity and mortality (GBD 2016). Antibiotics are indicated to treat bacterial LRTIs (Kraus 2017). Viral LRTIs do not require treatment with antibiotics, and are managed conservatively with adequate rehydration and oxygen supplementation (Mazur 2015).

Description of the intervention

Vitamin A is a group of fat‐soluble compounds that includes retinol and provitamin A carotenoids, which are found in animal and plant products, respectively. Vitamin A is one of the essential micronutrients, and is necessary for normal metabolism in humans (Zhong 2020). Retinoic acid produced from vitamin A is required for lower respiratory tract development (Alizadeh 2014; Ng‐Blichfeldt 2018; Ross 2006). It regulates the intestinal epithelium and mucosal immune system (Cantorna 2019). Its supplementation can decrease susceptibility to disease‐causing micro‐organisms in the intestine (McDaniel 2015). Supplementation of even one dose of vitamin A in preschool children can increase the ingestion of micro‐organisms by white blood cells (Jimenez 2010). Furthermore, vitamin A promotes resistance to skin infection. It increases the expression of an antimicrobial protein known as resistin‐like molecule alpha on the surface of skin cells, whose function is to kill bacteria in the skin (Harris 2019). Supplementation of vitamin A alone has been shown to reduce the risk of anaemia (da Cunha 2019).

Vitamin A deficiency is defined as serum concentration of retinol below 0.70 µmol/L. It is common in preschool children in Africa and South‐East Asia, and weakens their resistance to infection, resulting in increased susceptibility to acute infectious diseases (Stevens 2015; WHO 2019b; Wiseman 2017). Studies show that vitamin A supplementation can reduce the risk and severity of infections in preschool children (Semba 2010). Vitamin A deficiency is more common in low‐income countries. Animal products such as milk and eggs, and plant products such as green vegetables, are good sources of vitamin A. However, food prices influence vitamin A deficiency risk in preschool children from poor families, because a large part of their household food budget is spent on cereals, which contain less vitamin A (Thornton 2014).

How the intervention might work

Poor micronutrient status is a risk factor for infection‐related diseases in young children. Micronutrient deficiency decreases the function of the immune system, and thus increases children's predisposition to infection. It also delays full recovery in those infected, and increases the risk of severe illness (Maggini 2018). Vitamin A plays a significant role in regulating the immune system (Wiseman 2017). It influences replication of genetic material and cell division in the intestinal epithelium, leading to increased resistance to some disease causing micro‐organisms (Ibrahim 2017). The relationship between vitamin A and inflammation is well established. Inflammation increases vitamin A metabolism, leading to its deficiency (Rubin 2017). Vitamin A deficiency in mice causes fluid hyposecretion from the submucosal glands in the airway, suggesting that deficiency of vitamin A may be associated with respiratory disease (Kim 2012).

Why it is important to do this review

The incidence of LRTIs is highest in areas with low socio‐economic status, people who use solid fuels as a source of energy, and in undernourished and immune‐compromised populations (Bhutta 2013). Severe acute LRTIs impose a significant burden on health care globally. They lead to increased inpatient admission of children (Nair 2013), and impose an illness burden comparable to influenza in primary care adult patients (Vos 2020).

The main drugs currently used to treat acute LRTIs are antibiotics. Most antibiotics used for treating LRTIs are prescribed (Doan 2014; Laopaiboon 2015). However, antibiotic abuse is a serious problem. Studies have shown inappropriate use of antibiotics in multiple settings. Antibiotics have been prescribed contrary to the guidelines, and in situations where there is strong evidence that they do not reduce symptoms, duration, and severity. For example, between 2009 and 2011 about 65% to 75% of patients were inappropriately prescribed antibiotics (Fleming‐Dutra 2016; Hay 2017). Antimicrobial resistance is one of the greatest challenges to modern public health (Hay 2017). In terms of treating LRTIs, there is the risk of adverse events with both broad‐ and narrow‐spectrum antibiotics (Gerber 2017). Consequently, there is a need to find interventions to prevent acute LRTIs.

Acute LRTIs are one of the main causes of death in children (Taylor 2020). They seriously affect the growth of children and carry a huge economic burden (Bhutta 2013; GBD 2016; Nair 2013; Vos 2020). Early intervention could help decrease mortality and disease severity, and reduce the economic burden, especially in low‐ and middle‐income countries (Beck 2015; Xing 2020).

Previous Cochrane Reviews seem to indicate that vitamin A does not have a positive effect on existing respiratory diseases. Even though vitamin A is proven to reduce morbidity and mortality in children up to five years of age, it seems to have no significant effect on the specific mortality of respiratory disease (Imdad 2016; Imdad 2017; Mayo‐Wilson 2011). Also, adjunctive vitamin A has shown no significant reduction in mortality, morbidity, and clinical course of non‐measles pneumonia for children (Wu 2005). Similarly, a meta‐analysis revealed that vitamin A supplementation somewhat increases the incidence of respiratory tract infections, and that high‐dose vitamin A should not be recommended for preschool children except in the case of vitamin A deficiency (Grotto 2003). Furthermore, vitamin A supplements can even increase the incidence of lung cancer and mortality in smokers and people exposed to asbestos (Cortés‐Jofré 2020).

However, the World Health Organization (WHO) recommends using vitamin A to support the rapid growth of children and to help fight infections (WHO 2019b). WHO guidelines even encourage vitamin A supplements for infants and children from 6 to 59 months of age (WHO 2011). One study has shown that vitamin A supplementation could decrease the incidence of infections of the respiratory tract and their symptoms of runny nose, cough, and fever amongst preschool children (Chen 2013). Moreover, vitamin A deficiency leads to a decrease in resistance to infection (Wiseman 2017). This suggests vitamin A may prevent LRTIs by maintaining immune function for preschool children.

We will therefore conduct a systematic review to evaluate the effectiveness and safety of vitamin A for preventing acute LRTIs in children up to seven years of age.

Objectives

To assess the effectiveness and safety of vitamin A for preventing acute LRTIs in children up to seven years of age.

Methods

Criteria for considering studies for this review

Types of studies

We will include randomised controlled trials (RCTs) and cross‐over studies. We will also consider cluster‐RCTs where participants are randomised at the cluster level and outcomes are measured at the individual level. We will include studies reported as full text, those published as abstract only, and unpublished data.

Types of participants

We will include all children up to seven years old without a diagnosis of acute lower respiratory tract infections (LRTIs). We will assess whether vitamin A is helpful in preventing LRTIs for both healthy preschool‐aged children and those with conditions other than LRTIs. Early childhood usually ranges from infancy to the age of six years, and different countries have different definitions of preschool children (some countries until six years old). We will therefore consider children up to seven years of age.

Types of interventions

We will include trials comparing vitamin A with placebo or no supplementation. We will include the following co‐interventions, provided they are not part of the randomised treatment: combining with micronutrients such as folic acid, zinc, or other vitamins in both the intervention and control group. There will be no restrictions on the formulations, dose, and duration of interventions.

Types of outcome measures

Primary outcomes

  1. Incidence of acute LRTIs (expressed as a proportion of children who experience acute LRTIs or the number of acute LRTIs over a period of time, or both).

  2. Acute LRTI‐specific mortality (expressed as a proportion of deaths caused by acute LRTIs).

Secondary outcomes

  1. Severity of acute LRTIs.

    1. Incidence of symptoms of acute LRTIs (e.g. fever, breathlessness).

    2. Numbers of days infected.

    3. Rate of hospitalisation.

    4. Number of days hospitalised.

  2. Antibiotic use for LRTIs (expressed as a proportion of those using antibiotics).

  3. Health‐related quality of life, measured using a validated scale.

  4. Change in serum vitamin A level from baseline to after the intervention is complete.

  5. Change in anthropometry (such as weight, height, body mass index (BMI)).

  6. Adverse effects as reported by trial investigators.

    1. Vitamin A toxicity, such as nausea or vomiting following supplementation.

    2. Other non‐infectious diseases likely to be related to the use of vitamin A.

Reporting one or more of the outcomes listed here in the trial is not an inclusion criterion of the review.

Time of outcome assessment

If multiple time points are reported for a given outcome, we will include the longest‐reported follow‐up period in the main analyses. We will group outcomes according to follow‐up period: 0 to 12 months; 13 to 60 months; and over 60 months.

Search methods for identification of studies

Electronic searches

We will search the following databases from inception to present.

  1. CENTRAL (Cochrane Central Register of Controlled Trials).

  2. MEDLINE (PubMed).

  3. Embase.

We will also search the following databases, if relevant.

  1. CINAHL (Cumulative Index to Nursing and Allied Health Literature).

  2. PsycINFO.

  3. Web of Science.

  4. LILACS (Latin American and Caribbean Health Science Information database).

We will use the search strategy described in Appendix 1 to search MEDLINE. We will combine the MEDLINE search with the Cochrane Highly Sensitive Search Strategy for randomised trials: sensitivity and precision‐maximising version (2008 revision) (Lefebvre 2021).

We will also search the US National Institutes of Health Ongoing Trials Register ClinicalTrials.gov (www.clinicaltrials.gov) and the World Health Organization International Clinical Trials Registry Platform (apps.who.int/trialsearch/). We will impose no language or publication restrictions.

Searching other resources

We will check the reference lists of all primary studies and review articles for additional references. We will contact experts in the field to identify additional unpublished materials.

Data collection and analysis

Selection of studies

Three review authors (RG, HC, AA) will independently screen the titles and abstracts of all studies identified as a result of the search for potential eligibility. We will retrieve the full‐text study reports/publication of those studies deemed potentially eligible, and three review authors (RG, HC, AA) will independently screen the full‐texts and identify studies for inclusion, and identify and record reasons for exclusion of the ineligible studies. Any disagreements will be resolved through discussion or by consulting a third review author (JSK or GL) if necessary. We will identify and exclude duplicates and collate multiple reports of the same study so that each study, rather than each report, is the unit of interest. We will record the selection process in sufficient detail to complete a PRISMA flow diagram and 'Characteristics of excluded studies' table (Moher 2009).

Data extraction and management

We will use a data collection form for study characteristics and outcome data which has been piloted on at least one study in the review. Two review authors (RG, AA) will extract study characteristics from the included studies. We will extract the following study characteristics.

  1. Methods: study design, total duration of study, details of any 'run in' period, number of study centres and location, study setting, withdrawals, and date of study.

  2. Participants: N, mean age, age range, gender, severity of condition, diagnostic criteria, baseline lung function, smoking history, inclusion criteria, and exclusion criteria.

  3. Interventions: intervention, comparison, concomitant medications, and excluded medications.

  4. Outcomes: primary and secondary outcomes specified and collected, and time points reported.

  5. Notes: funding for trial, and notable conflicts of interest of trial authors.

Two review authors (RG, AA) will independently extract outcome data from the included studies. We will note in the 'Characteristics of included studies' table (Table 1) if outcome data are not reported in a useable way. Any disagreements will be resolved by consensus or by involving a third review author (HC). Three review authors (RG, HC, AA) will input data into the RevMan Web (RevMan Web 2021). We will double‐check that data are entered correctly by comparing the data presented in the systematic review with the study reports. A second review author (ZL or YG) will spot‐check the study characteristics for accuracy against the trial report.

1. 'Characteristics of included studies' table template.
Methods Study design (e.g. parallel RCT, cross‐over RCT, cluster RCT)
Study duration: date of first recruitment to last follow‐up
Participants Inclusion criteria
Setting: e.g. outpatient, inpatient, multicentre, national/international
Country: list all countries
Relevant health status:
Number: treatment (N = x); control (N = x)
Age (mean, SD/median, range)
Treatment group:
Control group:
Sex (M/F): treatment (N/N M/F); control (N/N M/F)
Any other relevant info, such as comorbidities
Exclusion criteria
<list>
Interventions Treatment group
Intervention
Dose, duration, frequency, administration
Other relevant info
Control group
Intervention(e.g. placebo, no treatment)
Dose, duration, frequency, administration
Other relevant info
Outcomes Primary outcomes
<list>
Secondary outcomes
<list>
Note: describe the methods used to measure the outcomes
Notes Declaration of Interest:
Funding source:
Contact with study authors for additional information:
Other:
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Assessment of risk of bias in included studies

Two review authors (RG, AA) will independently assess risk of bias for each study using the criteria outlined in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2021). Any disagreements will be resolved by discussion or by involving another review author (HC). We will assess the risk of bias according to the following domains.

  1. Random sequence generation.

  2. Allocation concealment.

  3. Blinding of participants and personnel.

  4. Blinding of outcome assessment.

  5. Incomplete outcome data.

  6. Selective outcome reporting.

  7. Other bias.

We will grade each potential source of bias as low, high, or unclear and provide a quote from the study report together with a justification for our judgement in the 'Risk of bias' table. We will summarise the 'Risk of bias' judgements across different studies for each of the domains listed. We will consider blinding separately for different key outcomes, where necessary. Where information on risk of bias relates to unpublished data or correspondence with a trialist, we will note this in the 'Risk of bias' table.

When considering treatment effects, we will take into account the risk of bias for the studies that contribute to that outcome.

Assessment of bias in conducting the systematic review

We will conduct the review according to this published protocol and report any deviations from it in the 'Differences between protocol and review' section of the systematic review.

Measures of treatment effect

We will enter the outcome data for each study into the data tables in RevMan Web to calculate the treatment effects. We will calculate risk ratios (RR) with 95% confidence interval (CI) for dichotomous outcomes to estimate the strength of the association between interventions and outcomes (RevMan Web 2021). We will also calculate mean difference (MD) with 95% CI for continuous outcomes.

We will undertake meta‐analyses only where this is meaningful, that is if the treatments, participants, and the underlying clinical question are similar enough for pooling to make sense.

Unit of analysis issues

The unit of analysis is based on the level when randomisation occurred, and observation numbers should match the units of individuals randomised. Following the methods and recommendations described in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2021), we will only include data from the first period for cross‐over trials in order to reduce the effects of an intervention given in one period to a subsequent period. Regarding cluster‐RCTs, we will analyse the data as if each cluster is a single individual, using a summary measurement from each cluster. We will use the generic inverse‐variance approach with RevMan Web for effect estimates and their standard errors at the same allocation level (RevMan Web 2021).

Dealing with missing data

We will contact investigators or study sponsors in order to verify key study characteristics and to obtain missing numerical outcome data where possible (e.g. when a study is identified as an abstract only). Where this is not possible, and the missing data are thought to introduce serious bias, we will explore the impact of including such studies in the overall assessment of results by a sensitivity analysis.

If numerical outcome data such as standard deviations or correlation coefficients are missing, and cannot be obtained from the trial authors, we will calculate them from other available statistics such as P values according to the methods described in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2021).

Assessment of heterogeneity

We will use the I² statistic to measure heterogeneity amongst the trials in each analysis. If we identify substantial heterogeneity, indicated as I² above 50% (Higgins 2021), we will report it and explore possible causes by prespecified subgroup analysis.

Assessment of reporting biases

If more than 10 trials can be pooled, we will create and examine a funnel plot to explore possible small‐study and publication biases.

Data synthesis

We will pool data from studies we judge to be clinically homogeneous using RevMan Web (RevMan Web 2021). We will perform a meta‐analysis if more than one study provides useable data in any single comparison. We will use the random‐effects model for meta‐analysis in case there is clinical or methodological heterogeneity between the studies.

Subgroup analysis and investigation of heterogeneity

We plan to carry out the following subgroup analyses.

  1. Possible differences in the vitamin A intervention.

    1. Vitamin A dosage (< 100,000 IU, 100,000 IU to 200,000 IU, > 200,000 IU) (WHO 2021).

    2. Duration of use of vitamin A.

    3. Vitamin A formulation (e.g. liquid, chewable tablet).

    4. Type of co‐interventions (e.g. folic acid, other vitamins).

  2. Possible differences in the participants.

    1. Gender.

    2. Country.

    3. Underlying disease condition.

    4. Baseline vitamin A deficiency (subclinical vitamin A deficiency: serum/plasma concentration of retinol < 0.70 μmol/L; severe vitamin A deficiency: serum/plasma concentration of retinol < 0.35 μmol/L) (WHO 2009).

We will use the Chi² test to test for subgroup interactions in RevMan Web (RevMan Web 2021).

Sensitivity analysis

We plan to carry out the following sensitivity analyses.

  1. Repeating the analysis excluding each study if allocation concealment indicates high risk of bias.

  2. Repeating the analysis excluding each study if sequence generation indicates high risk of bias.

  3. Repeating the analysis excluding published studies with abstract only or unpublished studies, if available.

  4. Repeating the analysis excluding studies with missing data.

  5. We will consider both fixed‐effect or random‐effects models as sensitivity analyses.

Summary of findings and assessment of the certainty of the evidence

We will create a 'Summary of findings' table using the following outcomes: incidence of acute LRTIs, acute LRTIs‐specific mortality, severity of acute LRTIs, antibiotics use for LRTIs, adverse effects, change in serum vitamin A level, and change in anthropometry. We will use the five GRADE considerations (study limitations, consistency of effect, imprecision, indirectness, and publication bias) to assess the quality of a body of evidence as it relates to the studies which contribute data to the meta‐analyses for the prespecified outcomes (Atkins 2004). We will use the methods and recommendations described in Section 8.5 and Chapter 12 of the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2021), employing GRADEpro GDT software (GRADEpro GDT). We will justify all decisions to down‐ or upgrade the quality of the evidence using footnotes, and make comments to aid the reader's understanding of the review where necessary.

What's new

Date Event Description
23 April 2024 Amended This protocol will not be progressed to the review stage as it no longer meets Cochrane's methodological standards.
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History

Protocol first published: Issue 4, 2021

Notes

Acknowledgements

The methods section of this protocol is based on a standard template developed by the Cochrane Airways Group and adapted by the Cochrane Acute Respiratory Infections (ARI) Group. We are particularly grateful for the assistance given by ARI Group Managing Editor Liz Dooley. We thank ARI Group Information Specialist Justin Clark and Nanjing University of Chinese Medicine librarians for helping to develop the search strategies. We thank the original team for their research ideas and efforts (Chen 2011). We thank Dr Rashmi Ranjan Das (AIIMS Bhubaneswar, India), Dr Olabisi Oduwole (Achievers University, Owo, Nigeria), Ann Fonfa (founder/president Annie Appleseed Project, USA), Theresa Wrangham, Dr Fiona Russell, Dr Mark Jones, and Prof Roderick Venekamp for commenting on this protocol.

Appendices

Appendix 1. MEDLINE search strategy

MEDLINE (OVID)

  1. exp respiratory tract infections/

  2. lower respiratory tract infection.mp.

  3. lower respiratory infection.mp.

  4. LRTI.mp.

  5. ALRI.mp.

  6. exp pneumonia/

  7. pneumon*.mp.

  8. pleuropneumon*.mp.

  9. ((lung or pulmonary or respiratory) adj3 (inflam* or infect*)).mp

  10. exp bronchitis/

  11. exp bronchiolitis/

  12. bronchit*.mp.

  13. bronchiolit*.mp.

  14. bronchopneumon*.mp.

  15. tracheobronchit*.mp.

  16. bronchopneumon*.mp.

  17. or/1‐16

  18. exp vitamin A/

  19. vitamin A.mp.

  20. retinal.mp.

  21. retinol.mp.

  22. or/18‐21

  23. 17 and 22

  24. randomized controlled trial.pt.

  25. controlled clinical trial.pt.

  26. randomized.ab.

  27. randomised.ab.

  28. placebo.tw.

  29. clinical trials as topic.sh.

  30. randomly.ab.

  31. trial.ti.

  32. (crossover or cross over).tw.

  33. or/24‐32

  34. 23 and 33

  35. exp infant/

  36. infant.mp.

  37. exp child/

  38. child.mp.

  39. (baby or babies or infant* or toddler* or child* or girl* or boy* or pre school* or preschool*).tw.

  40. or/35‐39

  41. 34 and 40

Contributions of authors

Renjun Gu: contributed to all sections.
Hao Chen: revised all sections.
Arjab Adhikari: revised all sections.
Yihuang Gu: contributed to all sections.
Joey SW Kwong: revised all sections.
Guochun Li: revised the Method section.
Ziyun Li: contributed to the Background section.
Yujing Pan: contributed to the Background section.

Sources of support

Internal sources

  • No sources of support provided

External sources

  • No sources of support provided

Declarations of interest

Renjun Gu: declared that they have no conflict of interest.
Hao Chen: declared that they have no conflict of interest.
Arjab Adhikari: declared that they have no conflict of interest.
Yihuang Gu: declared that they have no conflict of interest.
Joey SW Kwong: declared that they have no conflict of interest.
Guochun Li: declared that they have no conflict of interest.
Ziyun Li: declared that they have no conflict of interest.
Yujing Pan: declared that they have no conflict of interest.

Edited (no change to conclusions)

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