Intervention Protocol

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Fortification of staple foods with vitamin A for preventing vitamin A deficiency

  1. Ingvil Saeterdal1,
  2. Jose O Mora2,
  3. Luz Maria De-Regil3,*

Editorial Group: Cochrane Public Health Group

Published Online: 12 SEP 2012

DOI: 10.1002/14651858.CD010068


How to Cite

Saeterdal I, Mora JO, De-Regil LM. Fortification of staple foods with vitamin A for preventing vitamin A deficiency (Protocol). Cochrane Database of Systematic Reviews 2012, Issue 9. Art. No.: CD010068. DOI: 10.1002/14651858.CD010068.

Author Information

  1. 1

    Norwegian Knowledge Centre for the Health Services, Oslo, Norway

  2. 2

    Independent consultant, Rockville, Maryland, USA

  3. 3

    World Health Organization, Evidence and Programme Guidance, Department of Nutrition for Health and Development, Geneva, Switzerland

*Luz Maria De-Regil, Evidence and Programme Guidance, Department of Nutrition for Health and Development, World Health Organization, 20 Avenue Appia, Geneva, 1211, Switzerland. deregillu@who.int.

Publication History

  1. Publication Status: New
  2. Published Online: 12 SEP 2012

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Background

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Acknowledgements
  6. Appendices
  7. History
  8. Contributions of authors
  9. Declarations of interest
  10. Sources of support
 

Description of the condition

Vitamin A is the generic term for a large number of related compounds including retinol, retinal, retinoic acid and other carotenoids with provitamin A activity such as beta-carotene, alpha-carotene, gamma-carotene, and beta-cryptoxanthin which have provitamin A activity (meaning they can be converted to retinal). Vitamin A is an essential nutrient, soluble in fats, that is important for cell division, organ and skeletal growth and maturation, strengthening of the immune system, and development and maintenance of eye health and night vision (NIH 2001).

Dietary sources of provitamin A include vegetables such as carrot, pumpkin, papaya, and red palm oil; human milk, and animal foods rich in preformed vitamin A including dairy products (whole milk, yogurt, cheese), liver, and fish oils. After ingestion, the provitamin A is converted to retinol in the small intestine and stored in this form in the liver and then mobilised, as needed. Vitamin A deficiency occurs mostly after prolonged deprivation of this vitamin (WHO/FAO 2004) and is a significant public health problem in many developing countries, most seriously affecting young children, women of reproductive age, and pregnant women. According to recent estimates from the World Health Organization (WHO), 190 million preschool-age children (under five years of age) and 19.1 million pregnant women have inadequate concentrations of retinol. Roughly 45% of all preschool-age children and pregnant women with vitamin A deficiency live in the WHO regions of Africa and South-East Asia, while Sub-Saharan Africa accounts for another 25% to 35% of cases (WHO 2009a). Vitamin A deficiency alone is responsible for almost 6% of child deaths under the age of 5 years in Africa and 8% in South-East Asia (WHO 2009b).

Vitamin A deficiency has several consequences throughout the life cycle that can affect health and physical performance. Infants and young children have increased vitamin A requirements to support rapid growth and to help combat infections. Generally, infants are born with low vitamin A stores and receive this nutrient through breast milk. The amount of vitamin A that neonates receive from colostrum and milk depends significantly on the mother’s vitamin A nutritional status. Overall, breast milk vitamin A levels reflect the mother’s recent diet or supplementation status more than it does her long-term stores as indicated by liver vitamin A concentrations (Ross 2012). During childhood, mild vitamin A deficiency leads to increased risk and severity of infectious disease morbidity, possibly by altering the structure and function of the immune system (Nalubola 1999; Sommer 1996). Severe vitamin A deficiency can cause visual impairment (night blindness), anaemia, weakened resistance to infections, and increase the risk of illness and death from common childhood infections such as measles and those causing diarrhoea.

During pregnancy, vitamin A is essential for foetal organ and skeletal growth and maturation, maintenance of maternal immune system, development of vision in the foetus as well as maintenance of maternal eye health and night vision (NIH 2001). Although pregnant women are susceptible to vitamin A deficiency throughout gestation, deficiency is most common in the third trimester. It is unclear whether this is due to increased demands during pregnancy given the accelerated foetal development and the physiological increase in blood volume during this period (Mills 2007) or to lowered serum retinol concentration due to an increase in plasma volume. In a pregnant woman with moderate vitamin A deficiency, the foetus can still obtain sufficient vitamin A to develop appropriately, but at the expense of the maternal vitamin A stores (Quadro 2005).

Subclinical vitamin A deficiency is diagnosed when retinol concentrations in plasma or serum are lower than 70 mmol per litre (WHO 2011a) while long-term or severe Vitamin A deficiency may lead to ocular lesions such as xerophthalmia and keratomalacia, eventually resulting in visual impairment and blindness (West 1991).  

 

Description of the intervention

There are several strategies to prevent and treat vitamin A deficiency in populations including mass and point-of-use fortification of foods, dietary diversification to increase vitamin A intake, and supplementation with vitamin A capsules or tablets. Supplementation is probably the most widespread intervention practised clinically and in public health. It has been reported to be effective in improving vitamin A status, and significantly reducing infant and child mortality and morbidity (particularly diarrhoea) in infants and children 6 to 59 months of age, living in developing countries (Beaton 1993; Fawzi 1993; Imdad 2010), and is currently recommended by the WHO for countries where vitamin A deficiency is a public health problem (WHO 2011b). However, nationwide supplementation programmes may pose logistical difficulties to implement, and in some countries it may be challenging to achieve an effective population coverage.

Food-based approaches to increase the intake of vitamin A in populations include dietary diversification, point-of-use fortification with micronutrient powders or mass fortification of staple foods with vitamin A. The first involves improvements in dietary practices through community education and other means to improve intake of vitamin A-rich foods and enhance its absorption and utilisation in the body. Mantaining such diet may be difficult all year-round, particularly for the most vulnerable segments of the population in resource-poor settings.

Point-of-use fortification refers to the addition of vitamins and minerals in powder form to energy-containing foods at home or in any other place where meals are to be consumed, such as schools and refugee camps. Micronutrient powders containing vitamin A can be added to foods either during or after cooking, or immediately before consumption, without the explicit purpose of improving the flavour or colour (De-Regil 2012). Point-of-use fortification with micronutrient powders can be targeted to specific populations so that the number and amount of micronutrients can be tailored to meet the target groups' needs without increasing the risk of excessive intake among other population groups. Similar to vitamin A supplementation, programmes providing micronutrient powders containing vitamin A for point-of-use fortification require active participation from the target population in order to achieve high coverage and regular and appropriate use (Zlotkin 2005).

Food fortification is the practice of deliberately increasing the content of essential micronutrients (including trace elements) in a food with the aim to improve the nutritional quality of the food supply and provide a public health benefit with minimal risk to health (WHO/FAO 2006). Three types of fortification approaches are available: 1) mass (usually compulsory), fortification of one or more staple foods that are regularly consumed by the population in sufficient amounts; 2) voluntary, market driven, fortification of one or more industry products; and 3) targeted fortification using especially designed fortified foods that are targeted to specific groups, such as preschool and school children. Fortification of staple foods with vitamin A may be a cost-effective intervention in settings where food variety for improved dietary quality is not possible, provided that the nutrient remains at sufficient levels in the fortified food at the time of consumption, and the food is consumed regularly in adequate amounts (Dary 2002). Thus, the selection of an appropriate food or vehicle is an important element of the intervention and may vary among countries.

Potentially suitable staple food vehicles for vitamin A fortification in public health programmes include refined sugar, edible vegetable oils and fats, cereal grains (rice); wheat, and maize flours; condiments and seasonings; and powdered or liquid milk (WHO/FAO 2006). These foods should be regularly consumed in significant amounts by the majority of the target population for purposes of public health programmes. The compatibility of the vitamin A fortificants with the food, the stability of vitamin A in the fortified food throughout the marketing process, and the relative cost of the specific fortification for each particular food are important considerations that may vary by country (WHO/FAO 2006).

Experimental or quasi-experimental studies have been conducted to assess the effects of vitamin A fortification of monosodium glutamate (MSG) in Indonesia (Muhilal 1988), of margarine in the Philippines (Solon 1996) and of instant noodles in Thailand (Chavasit 1998), whereas programme evaluations of sugar fortification have been reported from Guatemala (Arroyave 1979; Arroyave 1981), Nicaragua (Mora 2005), and Zambia (Bessa 2001).

Some countries have implemented mandatory programmes at national level to fortify staple foods with vitamin A, including sugar (Zambia and several Central American countries), edible oil (Morocco), wheat flour (Philippines), instant noodles (Thailand), and pre-cooked refined maize flour (Venezuela) (Dary 2002). Voluntary fortification of margarine and milk is practised in several countries worldwide.

 

How the intervention might work

Food fortification with vitamin A is proposed to work by increasing the daily intake and absorption of preformed vitamin A (retinol) to levels sufficiently high to close the existing intake gap and to significantly increase liver stores to correct vitamin A deficiency and its health and survival implications. The mechanisms by which vitamin A reduces mortality and infections are not fully understood, and it is not clear whether its action is mediated through the correction of underlying deficiencies or through adjuvant therapeutic effects.

There are no known deaths attributed solely to vitamin A toxicity due to over-consumption of vitamin A (Bauernfeind 1980). Vitamin A toxicity is uncommon and generally results from excessive ingestion of vitamin A supplements rather than from foods, although regular intake of large amounts of liver may also result in toxicity due to its high content of vitamin A. The symptoms of acute vitamin A toxicity following supplementation include dizziness, nausea, vomiting, headaches, blurred vision, vertigo, reduced muscle co-ordination, skin exfoliation, weight loss, and fatigue. However, these symptoms are transient, with the large majority starting and disappearing within 24 hours of dosing.

 

Why it is important to do this review

Vitamin A deficiency is a significant public health problem in many developing countries and food fortification of staple foods with vitamin A may increase vitamin A consumption by vulnerable populations. To date, there is a lack of systematically reviewed evidence on the effectiveness and safety of this intervention to inform policy-making.

This review will complement the findings of other systematic reviews that explore the effects of interventions that may contribute to reduce vitamin A deficiency and its associated morbidities among different age groups. The effects of vitamin A supplementation as a public health intervention or with the explicit purpose of preventing or treating diseases during childhood (Bello 2011; Darlow 2011; Gogia 2011; Haider 2011; Wu 2005), pregnancy (van den Broek 2010; Wiysonge 2011) and the postpartum period (Horvath 2009; Oliveira-Menegozzo 2010) have been extensively reviewed.

A review on the harms and benefits of vitamin A as part of multiple micronutrient powder formulations intended for point-of-use fortification (at home) among children under two years of age has recently been published (De-Regil 2011), and a review for preschool- and school-age children (De-Regil 2012) is underway. A review on the use of vitamin A to fortify rice is currently also in progress (Ashong 2012).

 

Objectives

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Acknowledgements
  6. Appendices
  7. History
  8. Contributions of authors
  9. Declarations of interest
  10. Sources of support

To assess the benefits and harms of fortifying staple foods with vitamin A on vitamin A status and health-related outcomes in the general population.

 

Methods

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Acknowledgements
  6. Appendices
  7. History
  8. Contributions of authors
  9. Declarations of interest
  10. Sources of support
 

Criteria for considering studies for this review

 

Types of studies

Fortification of staple foods is an intervention that aims at reaching the entire population of a country, or large sections of the population, and is frequently delivered through the market system. We anticipate, therefore, that we will not be able to assess the benefits and risks of this intervention if we only include randomised trials; thus in addition, we plan to examine data from other study designs.

In summary, we will include:

  • randomised controlled trials (RCTs), with randomisation at either individual or cluster level;
  • quasi-RCTs (where allocation of treatment has been made, for example, by alternate allocation, date of birth, alphabetical order, etc);
  • non-RCTs; and
  • observational studies that are prospective and have a control group, including:

    • cohort studies,
    • controlled before-and-after studies, and
    • interrupted time series (ITS) with a clearly defined point-in-time when the intervention occurred and at least three measure points both before and after intervention.

Although we plan to include both randomised and non-randomised studies, we will not pool results from randomised trials together with those from non-randomised studies in meta-analysis; we will present these in separate meta-analysis estimates based on the different study designs. We will consider quasi-RCTs together with the non-RCTs.

In addition to the above mentioned study designs, we will consider before-and-after studies without control groups for inclusion in this review. We will present the results for the primary outcomes from these studies in a table but we will not include them in any meta-analysis nor will they contribute to the conclusions of the review. Such studies may provide information on the implementation and feasibility of the intervention, along with other contextual factors related to the intervention under review.

 

Types of participants

General population of all age groups (including pregnant women), from any country. We will exclude studies of interventions targeted toward participants with a critical illness or severe comorbidities.

 

Types of interventions

We will include interventions in the review in which staple foods have been fortified with any combination of vitamin A and other vitamins and minerals, irrespective of the fortification technology used.

We will include the following staple foods in this review.

We will make the following comparisons.

  • Staple foods fortified with vitamin A versus same unfortified staple foods.
  • Staple foods fortified with vitamin A versus no intervention.
  • Staple foods fortified with vitamin A plus other micronutrients versus same unfortified staple foods.
  • Staple foods fortified with vitamin A plus other micronutrients versus no intervention.

We will include studies with co-interventions only if the comparison group also receives the co-intervention. For example, fortified food plus education versus unfortified foods plus education.

We will exclude studies examining rice fortification with vitamin A as this comparison is assessed by another Cochrane review (Ashong 2012). We will also exclude other types of interventions such as biofortification, point-of-use fortification with multiple micronutrient powders, or supplementation. We will not compare the effects of vitamin A fortification with other forms of micronutrient interventions either.

 

Types of outcome measures

We will include studies if they assess any of the following primary and secondary outcomes. (Timepoint for measurements of the outcomes will be at the end of the intervention or the closest time period to the end of the intervention).

 

Primary outcomes

  1. Serum/plasma retinol (mmol/L)
  2. Subclinical vitamin A deficiency (serum/plasma retinol 70mmol/L or less)
  3. Clinical vitamin A deficiency (night blindness, xerophtalamia (as defined by the trialists))
  4. All-cause mortality
  5. All-cause morbidity

 

Secondary outcomes

  • Any adverse effects (e.g. hypervitaminosis, as defined by the trialists)
  • Liver vitamin A stores (determined with a relative dose-response test)
  • Food intake (g/day)
  • Congenital anomalies (for pregnant women)
  • Breast milk vitamin A content of lactating women (milk retinol in mmol/L)

 

Search methods for identification of studies

 

Electronic searches

We will search the following international and regional sources. We will use the search strategy shown in Appendix 1 and adapt it to the included databases.

 

International databases

  1. CENTRAL (The Cochrane Library)
  2. Ovid MEDLINE
  3. Ovid MEDLINE In-Process and Other Non-Index Citations
  4. EMBASE
  5. CINAHL
  6. Web of Science (both the social science citation index and the science citation index)
  7. POPLINE
  8. AGRICOLA (http://agricola.nal.usda.gov/)
  9. BIOSIS
  10. Food Science and Technology Abstracts (FSTA)
  11. EPPI centre databases BibiolMap and TRoPHI (public health databases)
  12. ASSIA (social science database)

 

Regional databases

  1. IBECS (http://plip.eifl.net/negotiations/free-e-resources/spanish/ibecs-indice-bibliogra)
  2. Scielo (http://www.scielo.br/)
  3. Global Index Medicus - AFRO (includes African Index Medicus); EMRO (includes Index Medicus for the Eastern Mediterranean Region)
  4. LILACS
  5. PAHO (Pan American Health library)
  6. WHOLIS (WHO Library)
  7. WPRO (includes Western Pacific Region Index Medicus)
  8. IMSEAR; Index Medicus for the South-East Asian Region
  9. IndMED, Indian medical journals (http://indmed.nic.in/)
  10. Native Health Research Database (http://hsc.unm.edu/library/nhd)

For thesis we will search WorldCat, Networked Digital Library of Theses and Dissertations, DART-Europe E-theses Portal, Australasian Digital Theses Program, Theses Canada Portal and ProQuest-Desertations and Theses. We will search OpenGrey for additional grey literature (http://www.opengrey.eu/).

We will also contact the Trials Search Co-ordinator of the Cochrane Public Health Group to search the Cochrane Public Health Group's Specialised Register. The search will use keyword and controlled vocabulary (when available), using the search terms set out in the Appendices and adapted as appropriate for each database. We will not apply any language restrictions.

We will search the International Clinical Trials Registry Platform (ICTRP) for any ongoing or planned trials, and contact authors of such studies to obtain further information or eligible data if available.

We will not apply any language or date restrictions.

If we identify articles written in a language other than English, Spanish or French we will commission their translations into English. If this is not possible, we will seek advice from the Cochrane Public Health Group. We will store such articles in the 'Awaiting classification' section of the review until a translation is available.

 

Searching other resources

For assistance in identifying ongoing or unpublished studies, we will contact the Department of Nutrition for Health and Development and the regional offices from the World Health Organization (WHO), the International Micronutrient Malnutrition Prevention and Control Programme (IMMPACT) of the US Centers for Disease Control and Prevention (CDC), the nutrition sections of the United Nations International Children's Emergency Fund (UNICEF), the World Food Programme (WFP), the Micronutrient Initiative (MI), Global Alliance for Improved Nutrition (GAIN), Hellen Keller International, the US Agency for International Development (USAID, and the Flour Fortification Initiative (FFI)).

We will search the reference list of all included papers and search the ISI Web of Science (both the social science citation index and the science citation index) for papers that cited the studies included in the review.

 

Data collection and analysis

 

Selection of studies

 

Managing references identified by the search strategy

We will store all the references identified by the search in Reference Manager software to prepare for importing them into Review Manager software (RevMan 2011).

Two review authors will independently screen the titles and abstracts of articles retrieved by each search to assess eligibility, as determined by the inclusion and exclusion criteria listed above; Luz Maria De-Regil (LMD) will screen all titles and abstracts and the rest of the authors one-half each. For those studies that are selected as potentially eligible for inclusion, we will retrieve full copies, and all review authors will be involved in assessing whether studies meet the review's inclusion criteria; each full-text report will be assessed independently and in duplicate. We will keep records of all eligibility decisions and will store the eligibility assessment form (with brief details of study design, participants and interventions, along with the final eligibility decision) with each study report.

We will resolve disagreements at any stage of the eligibility assessment process through discussion.

 

Data extraction and management

We will use the Reference Manager software version 12 to manage the records retrieved from the search.

Two review authors will extract data independently using data extraction forms, based on those from the Cochrane Public Health Group (Cochrane PHG 2010) and the Cochrane Effective Practice and Organisation of Care (EPOC) Group (EPOC 2010). LMD will extract the information from all papers and JM and IS from half of the papers each. We will use a data extraction form which will include questions to capture data on identification details of the study, study characteristics, details of participants, types and length of intervention and the primary and secondary outcomes with results. One of the authors (LMD) will enter the data into the Review Manager software (RevMan 2011) and the data will be checked by the author who extracted the information in duplicate. We will resolve any discrepancies through discussion. For studies published only as abstracts, or study reports containing little information about methods, we will attempt to contact the authors to obtain further details on results and study design. If there is insufficient information for us to be able to extract appropriate data, we will mark studies as 'awaiting classification' until further information is published, or made available to us.

All review authors will be involved in piloting the form using a subset of articles to enhance consistency amongst reviewers, and based on this, we will modify the form if necessary. We will collect information on study design, study setting, participants (number and characteristics), and provide a full description of the interventions examined. We will extract details of outcomes measured (including a description of how and when outcomes were measured) and results.

We will design the form so that we are able to record results for our prespecified outcomes and for other (non-prespecified) outcomes (although such outcomes will not underpin any of our conclusions). We will extract additional items relating to study recruitment and the implementation of the intervention; these will include number of sites for an intervention, whether recruitment was similar in different places, resource use or costs of intervention, whether there were protocol deviations, levels of compliance/use of foods in different sites within studies and whether a process evaluation of the intervention was conducted.

We will use the PROGRESS (place, race, occupation, gender, religion, education, socioeconomic status, social capital) checklist to record whether or not outcome data have been reported by sociodemographic characteristics known to be important from an equity perspective (Evans 2003). We will also record whether or not studies included specific strategies to address diversity or disadvantage.

 

Assessment of risk of bias in included studies

We will use the EPOC 'Risk of bias' tool for studies with a separate control group to assess the risk of bias of all studies. This includes five domains of bias: selection, performance, attrition, detection, and reporting, as well as an 'other' bias category to capture other potential threats to validity.

Two review authors will independently assess risk of bias for each study and we will resolve any disagreement by discussion or by involving an additional review team member. If there is insufficient information to be able to address risk of bias, we will assess it as “unclear”. By insufficient we mean that more than half of the domains were judged as unclear and that the authors did not respond when we contacted them. 

 

Assessing risk of bias in randomised trials

 
(1) Sequence generation (checking for possible selection bias)

We will assess studies as:

  • low risk of bias if there is a random component in the sequence generation process (any truly random process, e.g. random number table; computer random number generator);
  • high risk of bias if a non-random approach has been used (any non-random process, e.g. odd or even date of birth; hospital or clinic record number); or
  • unclear.   

 
(2) Allocation concealment (checking for possible selection bias)

We will assess studies as:

  • low risk of bias if participants and investigators enrolling participants could not foresee assignment because an appropriate method was used to conceal allocation (e.g. telephone or central randomisation; consecutively numbered sealed opaque envelopes). This rating will be given to studies where the unit of allocation was by institution and allocation was performed on all units at the start of the study;
  • high risk of bias if participants of investigators enrolling participants could possibly foresee assignments and potentially introduce selection bias (e.g. open random allocation; unsealed or non-opaque envelopes); or
  • unclear.   

 
(3) Similarity of baseline outcome measurements (checking for confounding, a potential consequence of selection bias)

We will assess studies as:

  • low risk of bias if outcomes were measured prior to the intervention, and no important differences were present across intervention groups;
  • high risk of bias if important differences in outcomes between groups were present prior to intervention and were not adjusted for in analysis; or
  • unclear risk of bias if there was no baseline measure of outcome (note: if 'high' or 'unclear' but there is sufficient information to do an adjusted analysis, the assessment should be 'low').

 
(4) Similarity of baseline characteristics (checking for confounding, a potential consequence of selection bias)

We will assess studies as:

  • low risk of bias if baseline characteristics are reported and similar across intervention groups;
  • high risk of bias if baseline characteristics are not reported or if there are differences across groups; or
  • unclear risk of bias if it is not clear (e.g. characteristics mentioned in text but no data presented).

 
5) Incomplete outcome data (checking for possible attrition bias through withdrawals, dropouts and protocol deviations)

We will assess outcomes in each included study as:

  • low risk of bias due to incomplete outcome data (this could be either that there were no missing outcome data, or that the missing outcome data were unlikely to bias the results based on the following considerations: study authors provided transparent documentation of participant flow throughout the study; the proportion of missing data was similar in the intervention and control groups; the reasons for missing data were provided and balanced across intervention and control groups; the reasons for missing data were not likely to bias the results (e.g. moving house));
  • high risk of bias if missing outcome data were likely to bias the results. Studies will also receive this rating if an 'as-treated (per protocol)' analysis is performed with substantial differences between the intervention received and that assigned at randomisation, or if potentially inappropriate methods for imputation have been used; or
  • unclear risk of bias.

 
(6) Blinding (checking for possible performance and detection bias)

We will assess the risk of performance bias associated with blinding as:

  • low, high or unclear risk of bias for participants; and
  • low, high or unclear risk of bias for personnel.

We will assess the risk of detection bias associated with blinding as:

  • low, high or unclear risk of bias for outcome assessors.

Whilst assessed separately, we will combine the results into a single evaluation of risk of bias associated with blinding as:

  • low risk of bias if there was blinding of participants and key study personnel and it was unlikely to have been broken, or the outcomes are objective. We will also give this rating to studies where either participants and key study personnel were not blinded, but outcome assessment was blinded and the non-blinding of others was unlikely to introduce bias;
  • high risk of bias if there was no blinding or incomplete blinding, or if there was blinding that was likely to have been broken, and the outcome or outcome assessment was likely to be influenced by a lack of blinding; or
  • unclear risk of bias.

 
(7) Contamination (checking for possible performance bias)

We will assess studies as:

  • low risk of bias if allocation was by community, institution or practice and it is unlikely that the control group received the intervention;
  • high risk of bias if it is likely that the control group received the intervention; or
  • unclear risk of bias if it is possible that contamination occurred but the risk of this happening is not clear.

 
(8) Selective reporting bias

We will describe for each included study how we investigated the possibility of selective outcome reporting bias and what we found. We will assess studies for this domain as:

  • low risk of bias (where it is clear that all of the study’s prespecified outcomes and all expected outcomes of interest to the review have been reported);
  • high risk of bias (where not all the study’s prespecified outcomes have been reported; one or more reported primary outcome(s) were not prespecified; outcomes of interest were reported incompletely and so cannot be used; or study fails to include results of a key outcome that would have been expected to have been reported); or
  • unclear risk of bias.

 
(9) Other sources of bias

We will describe other possible sources of bias for each included study and give a rating of low, high or unclear risk of bias for this item.

We will assess the risk of bias for interrupted time series (ITS) studies using the EPOC 'Risk of bias' tool for ITS study designs which includes items (5), (6), (8) and (9) from the EPOC 'Risk of bias' tool above, as well as the following additional items.

  1. Was the intervention independent of other changes?
    1. low risk of bias if there are compelling arguments that the intervention occurred independently of other changes over time, and the outcome was not influenced by other confounding variables/historic events during the study period;
    2. high risk of bias if it is reported or if there are grounds to suspect that the intervention was not independent of other changes over the time period of the study; or
    3. unclear risk of bias.
  2. Was the shape of the intervention effect prespecified?
    1. low risk of bias if the point of analysis is the point of intervention or a rational explanation for the shape of the intervention effect was provided;
    2. high risk of bias if it clear that these conditions were not met; or
    3. unclear risk of bias.
  3. Was the intervention unlikely to affect data collection?
    1. low risk of bias if it is reported that the intervention itself was unlikely to affect data collection (e.g. sources and methods of data collection were the same before and after the intervention);
    2. high risk of bias if the intervention itself was likely to affect data collection; or
    3. unclear risk of bias.

 

Overall risk of bias

We will summarise the risk of bias at two levels: within studies (across domains) and across studies (for each primary outcome).

Judgement on the overall risk of bias will take into account the likely magnitude and direction of the bias in each of the above mentioned domains and whether we consider they will likely impact on the findings. Studies at high risk of bias will be those with high or unclear risk of bias in all of the following domains: allocation concealment, similarity of baseline outcome measurements, and completeness of outcome data. Judgements will also take into account the likely magnitude and direction of bias and whether it is likely to impact on the findings of the study.

For the assessment across studies, the main findings of the review will be set out in 'Summary of findings' (SoF) tables prepared using GRADE profiler software (GRADEpro 2008). We will list the primary review outcomes for each comparison with estimates of relative effects along with the number of participants and studies contributing data for those outcomes. For each individual outcome, we will assess the quality of the evidence using the GRADE approach (Balshem 2010), which involves consideration of within-study risk of bias (limitations in design, inconsistency, indirectness, imprecision, publication bias, magnitude of the effect, dose-response effect and other plausible confounders. We will express the results as one of four levels of quality (high, moderate, low or very low).

 

Measures of treatment effect

 

Dichotomous data

For dichotomous data, we will present proportions and, for two-group comparisons, results as average risk ratio (RR) or odds ratio (OR) with 95% confidence intervals (CIs).

 

Continuous data

We will report results for continuous outcomes as the mean difference (MD) with 95% CIs if outcomes are measured in the same way between trials. Where some studies have reported endpoint data and others have reported change from baseline data (with errors) we will combine these in the meta-analysis if the outcomes have been reported using the same scale.

We will use standardised mean difference (SMD) with 95% CIs to combine trials that measure the same outcome (e.g. haemoglobin) but use different methods.

If we do not find enough studies, or the studies cannot be pooled, we will summarise the results in a narrative form.

 

Unit of analysis issues

 

Cluster-randomised trials

We will combine results from both cluster and individually randomised studies if there is little heterogeneity between the studies. If the authors of cluster-randomised trials have conducted their analyses at a different level to that of allocation and they have not appropriately accounted for the cluster design in their analyses, we will calculate trials' effective sample size to account for the effect of clustering in data. We will utilise the intra-cluster correlation coefficient (ICC) derived from the trial (if available), or from another source (e.g. using the ICCs derived from other, similar trials) and then calculate the design effect with the formula provided in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011). If this approach is used, we will report this and undertake sensitivity analysis to investigate the effect of variations in ICC.

 

Studies with more than two treatment groups

If we identify studies with more than two intervention groups (multi-arm studies) where possible, we will combine groups to create a single pair-wise comparison or use the methods set out in the Cochrane Handbook to avoid double-counting study participants (Higgins 2011). For the subgroup analyses, when the control group is shared by two or more study arms, we will divide the control group (events and total population) over the number of relevant subgroups to avoid double-counting the participants.

 

Cross-over trials

From cross-over trials, we will consider the first period of measurement only and will analyse the results together with the parallel group studies.

 

Interrupted time series

We will analyse interrupted time series (ITS) studies separately using the method described in Ramsay 2003.

 

Dealing with missing data

We will try to contact the authors if missing outcome data are unclear or have not been fully reported. We will capture the missing data in the data extraction form and report it in the 'Risk of bias' tables.

For all outcomes, we will carry out analysis, as far as possible, on an intention-to-treat basis, i.e. for randomised trials, we will attempt to include all participants randomised to each group in the analyses. The denominator for each outcome in each trial will be the number randomised, minus any participants whose outcomes are known to be missing. For non-randomised studies, where possible, we will analyse data according to initial group allocation, irrespective of whether or not participants received, or complied with the planned intervention.

When assessing adverse events, adhering to the principle of "Intention-to-treat" may be misleading, thus we will relate the results to the treatment received ('per protocol' or 'as observed'). This means that for side effects, we will base the analyses on the participants who actually received treatment and the number of adverse events that are reported in the studies.

 

Assessment of heterogeneity

We will examine the forest plots from meta-analysis to visually assess the level of heterogeneity (in terms of the size or direction of treatment effect) among studies. We will use the I² statistic, Tau2, and the Chi2 statistic to quantify the level of heterogeneity among the trials in each analysis. If we identify substantial heterogeneity, we will explore it by prespecified subgroup effects analysis.

Heterogeneity may be a particular concern in non-randomised studies, and where there is evidence of unexplained heterogeneity, we will summarise findings using a forest plot but will not present the pooled estimate.

We will exercise caution in the interpretation of those results with high levels of unexplained heterogeneity.

 

Assessment of reporting biases

Where we suspect reporting bias (see 'Selective reporting bias' above), we will attempt to contact study authors asking them to provide missing outcome data. 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.

We do not anticipate that there will be sufficient studies contributing data for any particular outcome for us to examine possible publication bias; if more than 10 studies reporting the same outcome of interest are available, we will generate funnel plots in RevMan 2011 and visually examine them for asymmetry. Where we pool studies in meta-analysis, we will order studies in terms of weight, so that a visual examination of forest plots may allow us to assess whether the results from smaller and larger studies are similar, or if there are any apparent differences (i.e. we will check that the effect size is similar in smaller and larger studies).

 

Data synthesis

We will carry out meta-analysis to provide an overall estimate of treatment effect when more that one study examines the same intervention, provided that studies use similar methods, and measure the same outcome in similar ways in similar populations. We will not combine results from randomised and non-randomised trials together in meta-analysis, nor will we present pooled estimates for non-randomised studies with different types of study designs. Evidence on different outcomes may be available from different types of studies (for example, it is likely that data on less common adverse events will be reported in larger non-randomised studies). Where there is evidence on a particular outcome from both randomised trials and non-randomised studies, we will use the evidence from trials which are at lower risk of bias to estimate treatment effect.

Where there is evidence from several randomised trials, or non-randomised studies at low risk of bias, we will carry out statistical analysis using the Review Manager 5 software (RevMan 2011). We will use a random-effects meta-analysis for combining data, as we anticipate that there may be natural heterogeneity between studies attributable to the different doses, durations, populations and implementation/delivery strategies. For continuous variables we will use the inverse variance method, while for dichotomous variables we will use the method proposed by Mantel-Haenzen.

For non-randomised studies, where results have been adjusted to take account of possible confounding factors, we will use the generic inverse variance method in RevMan 2011 to carry out any meta-analysis (if both adjusted and non-adjusted figures are provided we will carry out a sensitivity analysis using the unadjusted figures to examine any possible impact on the estimate of treatment effect).

We will also use narrative synthesis, guided by the data extraction form in terms of the ways in which studies may be grouped and summarised in this review to describe the outcomes, explore intervention processes, and describe the impact of interventions by sociodemographic characteristics, known to be important from an equity perspective based on the PROGRESS framework, where this information is available (Evans 2003).

 

Subgroup analysis and investigation of heterogeneity

Where data are available we will carry out the following subgroup analyses:

  • by age and physiological condition population: children (2 to 11.9 years), adolescents (12 to 19 years), adults (20 years and older), pregnant women, lactating women (6 months postpartum), and mixed populations;
  • by food intake: high versus low consumers of vitamin A-fortified edible oils, fats, sugar, and wheat or maize flours (defined as people consuming more or less than the median of their population);
  • by public health significance: countries where vitamin A deficiency is a public health problem versus countries without vitamin A deficiency as a public health problem (according to WHO 2009a);
  • by sex: males, females, and mixed/unknown; and
  • by length of the intervention: less than six months, six months to one year, and more than one year;

We will only use the primary outcomes in subgroup analysis. We will limit this analysis to those outcomes for which three or more trials contributed data.

We will examine differences between subgroups by visual inspection of the subgroups’ CIs; non-overlapping CIs suggesting a statistically significant difference in treatment effect between the subgroups. We will test for subgroup differences as described by Borenstein 2008 and implemented in RevMan 2011.

 

Sensitivity analysis

We will carry out sensitivity analysis to examine the effects of removing studies at high risk of bias (those with high or unclear risk of bias for allocation concealment, lack of similarity of baseline outcome measurements, or incomplete outcome data) from the meta-analysis. If cluster trials are included, we will carry out sensitivity analysis using a range of ICC values.

 

Acknowledgements

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Acknowledgements
  6. Appendices
  7. History
  8. Contributions of authors
  9. Declarations of interest
  10. Sources of support

We would like to thank Silke Walleser for her contributions to the protocol. We are also grateful to Ingrid Harboe, at the Norwegian Knowledge Centre, for her help in devising the search strategy.

We are also grateful to the editorial office of the Public Health Group for their support in the preparation of this review. As part of the pre-publication editorial process the protocol and the review were commented on by three peers (an editor and two referees who are external to the editorial team).

 

Appendices

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Acknowledgements
  6. Appendices
  7. History
  8. Contributions of authors
  9. Declarations of interest
  10. Sources of support
 

Appendix 1. Ovid MEDLINE search strategy

Ovid MEDLINE(R) In-Process and other Non-Indexed Citations and Ovid MEDLINE(R) 1946 to Present (2012-03-16)


#Searches

1Food, Fortified/

2((fortif* or enrich* or boost* or enhanc* or increas* or improv* or add*) adj3 food*).tw.

3((fortif* or enrich* or boost* or enhanc* or increas* or improv* or add*) adj3 diet).tw.

4or/1-3

5exp Vitamin A/

6vitamin A.tw.

7(retinol or vitamin a1 or retinoid or beta-carotene or carotenoid* or retinyl palmitate).tw.

8or/5-7

94 and 8



 

History

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Acknowledgements
  6. Appendices
  7. History
  8. Contributions of authors
  9. Declarations of interest
  10. Sources of support

Protocol first published: Issue 9, 2012

 

Contributions of authors

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Acknowledgements
  6. Appendices
  7. History
  8. Contributions of authors
  9. Declarations of interest
  10. Sources of support

All three review authors contributed to drafting the background. Luz Maria De-Regil (LMD) and Ingvil Saeterdal (IS) wrote the methods of the protocol. All review authors approved the final manuscript.

Disclaimer: Luz Maria De-Regil is a full-time staff member of the World Health Organization. The authors alone are responsible for the views expressed in this publication and they do not necessarily represent the official position, decisions, policy or views of this organisation.

 

Declarations of interest

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Acknowledgements
  6. Appendices
  7. History
  8. Contributions of authors
  9. Declarations of interest
  10. Sources of support

None known

 

Sources of support

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Acknowledgements
  6. Appendices
  7. History
  8. Contributions of authors
  9. Declarations of interest
  10. Sources of support
 

Internal sources

  • Evidence and Programme Guidance Unit, Department of Nutrition for Health and Development, World Health Organization, Switzerland.

 

External sources

  • The Micronutrient Initiative, Canada.
    WHO acknowledges The Micronutrient Initiative for their financial support to the Evidence and Programme Guidance Unit for conducting systematic reviews on micronutrient interventions
  • Victorian Health Promotion Foundation (VicHealth), Australia.
    VicHealth provides funding to support the editorial process for all Cochrane Public Health Group reviews

References

Additional references

  1. Top of page
  2. Abstract
  3. Background
  4. Objectives
  5. Methods
  6. Acknowledgements
  7. Appendices
  8. History
  9. Contributions of authors
  10. Declarations of interest
  11. Sources of support
  12. Additional references
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