research paper on anemia in india

• Study protocol
• Open access
• Published: 07 February 2020

The reduction in anemia through normative innovations (RANI) project: study protocol for a cluster randomized controlled trial in Odisha, India

• Hagere Yilma   ORCID: orcid.org/0000-0002-4042-3484 1 ,
• Erica Sedlander 1 ,
• Rajiv N. Rimal 2 ,
• Ichhya Pant 1 ,
• Ashita Munjral 3 &
• Satyanarayan Mohanty 4  

BMC Public Health volume  20 , Article number:  203 ( 2020 ) Cite this article

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More than half of women in India are anemic. Anemia can result in fatigue, poor work productivity, higher risk of pre-term delivery, and maternal mortality. The Indian government has promoted the use of iron-folic acid supplements (IFA) for the prevention and treatment of anemia for the past five decades, but uptake remains low and anemia prevalence high. Current programs target individual-level barriers among pregnant women and adolescents, but a more comprehensive approach that targets multiple levels among all women of reproductive age is needed to increase uptake of IFA and iron-rich foods.

The Reduction in Anemia through Normative Innovations (RANI) project is a norms-based intervention to reduce anemia among women of reproductive age. We will evaluate the intervention through a clustered randomized controlled trial in Odisha, India. We will collect data at three time points (baseline, midline, and end line). For the study, we selected 89 clusters of villages, which we randomized into treatment and control on a 1:1 basis. The treatment arm will receive the RANI project components while the control arm will receive usual care. Fifteen clusters (40–41 villages) were selected and 4000 women (2000 in each arm) living in the selected clusters will be randomly selected to take part in data collection. Women in both study arms will have their hemoglobin concentrations measured. They will also complete in-person surveys about their knowledge, attitudes, perceptions of iron folic acid supplements, and nutritional intake. We will also select a smaller cohort of 300 non-pregnant women (150 in each arm) from this cohort for additional physical activity and cognitive testing. We will conduct both within- and between-group comparisons (treatment and control) at baseline, midline and end line using t-tests. We will also conduct structural equation modeling to examine how much each factor accounts for IFA use and hemoglobin levels.

This RCT will enable us to examine whether a social norms-based intervention can increase uptake of iron folic acid supplements and iron rich foods to reduce anemia.

Trial registration

This trial was registered with Clinical Trial Registry- India (CTRI) ( CTRI/2018/10/016186 ) on 29 October 2018.

Peer Review reports

Anemia is a serious health concern in India, where more than half of women of reproductive age (WRA) are anemic [ 1 ]. It is mostly associated with fatigue and thus poor work productivity [ 2 ], but if left untreated, anemia can lead to poor birth outcomes, including higher risk for preterm delivery and maternal mortality [ 3 ]. Anemia during pregnancy can also inhibit physical and cognitive development in children [ 4 , 5 , 6 ].

In Odisha, India (the site of this study) the majority of anemia cases are a result of iron-deficiency, due to poor dietary iron intake, low iron absorption, and iron-loss during intestinal worm infection, pregnancy and menstruation. As one of six Global Nutrition Targets for 2025, the World Health Organization (WHO) has set forth a series of recommendations to prevent and reduce anemia [ 7 ]. Among these recommendations is regular iron-folic acid (IFA) supplementation for all women of reproductive age between 15 and 40 years old (including pregnant and non-pregnant women) in regions where more than 40% of women are anemic [ 7 ].

India has implemented several national-level programs to increase IFA supply over the last 50 years. However, anemia levels remain high, partly because of the scarcity of interventions to improve the demand for and uptake of IFA and iron-rich foods. Of late, efforts to promote IFA consumption in India have adopted a life cycle approach by including women of reproductive age (non-pregnant and non-lactating) for IFA supplementation rather than exclusively targeting pregnant and lactating women, adolescents and/or children [ 8 , 9 ]. Unlike pregnant women, non-pregnant and out-of-school women are poorly served as the government is currently in the process of rolling out its IFA supplementation strategy to these important sub-populations. Not surprisingly, adherence rates in this group is also poorly understood. For example, the Indian National Family Health Survey (NFHS) collects data on IFA adherence only for pregnant women [ 1 ]. Nevertheless, available data indicate that adherence is poor as only 30.3% of mothers in India reported consuming IFA for 100 days or more when they were pregnant, although 91% percent received IFA [ 1 ]. To effectively reduce anemia in India, both pregnant and non-pregnant WRA should not only receive IFA, but they should also take it regularly.

Innovative approaches that increase IFA demand can propel changes at multiple levels (individual, interpersonal, community and policy). Shet et al. [ 10 ] demonstrated that educational counseling delivered to mothers and caregivers can improve IFA consumption and reduce anemia in children. Behavior change interventions that target the individual directly are also effective in improving IFA consumption. Adolescent girls in Delhi showed improvement in their IFA consumption, along with their knowledge and attitudes around IFA and anemia, after receiving an educational intervention delivered in schools [ 11 ]. Many IFA-focused interventions in India that target adolescent school girls have also been successful in reducing anemia prevalence through supplementing IFA provision with educational information [ 12 ]. While programs of this sort for adult women are limited, a similar communication intervention delivered through women’s Self Help Groups to pregnant women in rural India was effective in improving IFA consumption among other pregnancy-related behaviors [ 13 ]. While the vast majority of behavior change interventions that promote IFA consumption target in-school girls or pregnant women, they should be extended and adapted for all WRA, regardless of pregnancy or school status.

The Reduction in Anemia through Normative Innovations (RANI) Project aims to reduce the burden of anemia among all WRA in India through a social norms-based approach. Social norms are based on the idea that people conform to the behaviors they perceive others around them are engaging in. Thus, the extent to which WRA believe others are taking IFA can influence their own IFA consumption. The theoretical underpinnings of the intervention are described in greater detail in later sections. In this paper, we describe the randomized control trial protocol, which we are using to test the efficacy of the RANI Project in increasing IFA and iron rich food consumption to reduce anemia among WRA.

The objective of this study is to investigate the ability of a norms-based behavior change intervention to reduce anemia among women of reproductive age in Odisha, India. We will test the following hypotheses:

H1. Changes in women from baseline to end line in the intervention arm will be significantly greater than corresponding changes in the control arm in the following outcomes: (a) anemia status, (b) IFA use, (c) mental health/depression, (d) physical activity (e) work capacity, (f) consumption of iron rich foods and (g) cognitive functioning;

H2. Social norms serve as a mediator in the relationship between intervention exposure and study outcomes; and.

H3. Changes in women baseline to end line in the intervention arm will be significantly greater than corresponding changes in the control arm in knowledge, attitudes, perceptions, consumption of iron-rich foods, and use of IFA.

Study setting

We will conduct the study in Odisha, which is on the eastern coast of India, where 83% of residents live in rural areas. Across Odisha, 94% of households are Hindu and 23% belong to a tribal culture. Around three fourths (73%) of the total population and nearly two-thirds (64%) of women in Odisha are literate [ 14 ]. Additionally, the total fertility rate (TFR) is approximately 2.1 children per woman in Odisha. Around half of WRA in Odisha are anemic (51.0%). The prevalence of anemia among women is high across subgroups: those who are breastfeeding (54.8%), pregnant (47.6%), and WRA who are neither breastfeeding nor pregnant (50.3%). Women with less education and who belong to Scheduled Tribes are more likely to be anemic [ 1 ].

Within Odisha, we chose Angul district for our study site because its anemia prevalence is similar to that of the state and the rest of India [ 1 ]. Anemia rates and IFA adherence in Angul also follow a similar pattern to Odisha State: 44.0% of the Angul population is anemic and only 38% of women consume IFA when they were pregnant. We selected two blocks within the Angul district, Athmallik and Kishorenagar, as study sites (a block is the administrative unit larger than a village but smaller than a district and each block encompasses several villages). The two blocks were not randomly selected, rather they were selected because they are adjacent to each other, spread over an area of 1278 sq. kilometers (499 sq. miles) and are representative of the district. According to the 2011 census, the two blocks have a total of 588 villages, accounting for a total of 218,373 people in 50,463 households [ 14 ]. In the two blocks, nearly one fourth of people are tribal, a third are literate, and about half of women work outside of the home.

We will use a cluster randomized controlled trial (RCT) design. In this design, villages will be randomized on a 1:1 ratio to receive the treatment or continue with usual care (defined as the currently existing and ongoing efforts to reduce anemia in Odisha). Treatment is defined as exposure to one or more components of the RANI project. As this is a community-level intervention, we used a cluster design to prevent contamination across communities.

We grouped together villages into clusters of 1–4 villages, resulting in eighty-nine total clusters. A geographical buffer of at least one village or natural structure (e.g., a mountain) was maintained between clusters to limit contamination. We first used a random number generator to randomly assign clusters into treatment ( k  = 50 clusters) and control arms ( k  = 38 clusters). Clusters were randomly given a value of ‘1’ or ‘2’; those that were given a ‘1’ were assigned to the treatment and those that were given a ‘2’ were assigned to the control. Thus, the number of clusters in each arm are not exactly equal.

We then segmented clusters by proportion of minority populations (in India, they are called scheduled castes and scheduled tribes) and then selected three clusters from each stratum for data collection so that 15 clusters (which comprised 41 villages) from the treatment arm and 15 (comprising 40 villages) from the control arm were selected for data collection. The decision to select a smaller subset of 15 clusters from each arm for data collection was made in order to maximize the sample size per cluster. Data collectors and program implementers will be blinded with regard to treatment and control status of villages. Data collection will occur at three time points: baseline, midline, and end line. The overall schema of the study design is depicted in Fig.  1 .

Custer Randomized Control Trial Schema

Participants

All members of the treatment clusters meeting the inclusion criteria will be eligible to participate in the trial.

Inclusion criteria

All women selected for data collection must be between 15 and 49 years old, a resident of the village, and speak Odiya. Additionally, as this is a longitudinal study, participants must indicate that they are not planning to move out of the village for the next two years.

Exclusion criteria

We will exclude women with an active fever at data collection and refer them to the closest health center, as the interview may take up to an hour or longer and may exacerbate any illness they may already have. Once excluded from baseline, the woman will no longer be eligible for data collection and midline and endline. However, women who are excluded from data collection may still be reached in the intervention if they live in a treatment village. We will also refer those with severe anemia to the local health center, but they will not be excluded from data collection. Though pregnancy status is not an inclusion or exclusion criterion, we will exclude currently pregnant women from certain components of data collection that could put them at risk —they will only take part in the survey and provide hemoglobin measurements.

Rational and overview of the intervention

We developed the intervention based on the literature as well as findings from our formative research to understand barriers to and facilitators of IFA use. The formative research [ 15 ] comprised the following components:

16 focus group discussions with women of reproductive age, their husbands, and mothers-in-law

25 key informant interviews with self-help group leaders, medical doctors, teachers, natural healers, and frontline health workers

A perceptual mapping exercise to understand how women of reproductive age, their mothers-in-law, and their husbands conceptualize IFA and other anemia-related items (e.g., fatigue, fruits and vegetables, medical care, etc.)

Formative research results

The formative research provided insights at multiple levels. At the individual level , we learned that the majority of people had basic knowledge about anemia and knew that IFA can prevent and treat anemia. However, women did not have a clear understanding about their own anemia risk; rather, they had normalized the existence of milder forms of anemia. We also identified both real and perceived side effects of IFA use, including some misperceptions.

At the interpersonal level , we found that perceptions of approval from referent groups (i.e., husbands and mothers-in-law) played a major role in women’s decisions to take IFA. These referent groups, largely mothers-in-law, were also found to perpetuate misconceptions around IFA use, including the belief that taking IFA during pregnancy would result in an abnormally large baby during and thus complicating delivery.

At the community level , we found that women’s health was not a priority and women were expected to take care of their families before thinking about their own well-being. They were also expected to work for the household all day, leaving little time for themselves, thus reducing their ability to visit a health center to get tested for anemia or to obtain IFA.

At the policy level , we found that out-of-school non-pregnant women were not directly served by existing government practices of delivering IFA. Health workers distribute IFA to pregnant women in their homes and in village health and nutrition days, and adolescents obtain them in schools; non-pregnant women do not know that they should be taking IFA weekly (per Indian government guidelines) and therefore do not seek it out [ 8 , 9 , 16 ].

The role of social norms

The proposed intervention will use a social norms approach to incorporate factors at play at multiple levels of the socio-ecological model. Social norms are based on the idea that people change behaviors because they perceive that others around them are changing and they do not want to be left behind. Descriptive norms refer to people’s perceptions about the prevalence of a behavior – what they believe others are doing – and injunctive norms refer to pressures people feel to conform [ 17 ]. Additionally, collective norms refers to the actual prevalence of behavior among one’s peers (e.g., the actual number of women taking IFA in a woman’s village) [ 18 ].

Theoretical underpinning

The RANI project intervention is based on the theory of normative social behavior (TNSB), which posits that social norms drive behavior and that this influence is further heightened when moderators are in favor of the behavior [ 19 ]. According to the TNSB, the relationship between social norms and behavior is moderated by a number of factors, including behavioral (e.g., access and outcome expectations), individual-level (e.g., self-efficacy, knowledge, and risk perception), and contextual-level (e.g., interpersonal discussion, gender norms, and nutrition) factors. Following the theoretical guidelines, this project will focus on descriptive norms (perceived prevalence), injunctive norms (pressures people feel to conform), and collective norms (actual prevalence) surrounding IFA consumption.

The TNSB also posits that norms, by themselves, may not be enough to propel change [ 20 ]; normative information often must be coupled with information about benefits of performing the behavior [ 21 ], the behavior itself must be easy to enact [ 22 ], and people must be convinced that others in their social network are also engaging in the behavior [ 23 ]. Thus, if people learn that others in their social network are taking IFA, that they themselves can also take them, and that these supplements have benefits (e.g., improving their health or providing them with more energy), they may be persuaded to do the same. The overall theory of change for the intervention can be found in Fig.  2 . The consideration of the potential moderators that can propel norms into action can help combat attributable barriers of IFA consumption, such as unpleasant side effects. For example, we know from our formative research that women prioritize their ability to help their family. Guided by TNSB, we suspect that when positive descriptive norms around IFA (i.e., the belief that other WRA are taking IFA) are coupled with positive injunctive norms (i.e., perceptions of support from their mother-in-laws and husbands) and strong risk perceptions and other psychosocial factors related to anemia and IFA, norms may translate into IFA consumption despite the barriers related to side effects.

Theory of Change

Our approach will focus on generating demand at multiple levels. At the individual level, we will raise awareness and knowledge around anemia, correct misperceptions about the role of iron (in making deliveries more difficult), increase risk perception (susceptibility and severity), and enhance self-efficacy. At the interpersonal level, we will promote positive social norms around taking IFA and eating iron rich foods, along with other foods that promote iron absorption. We will focus on improving descriptive norms through demonstration events in communities in which women take IFA in a public setting and where community-level hemoglobin counts are graphically displayed. To improve injunctive norms, we will focus on persuading women’s husbands and their mothers-in-law to support them taking IFA. At the policy level, we will engage with health officials at multiple levels and policymakers at the state level to ensure that they are promoting IFA guidelines, that there is a continuous supply of IFA, and that they are promoting demand-generation efforts. A description of all RANI activites can be found in Table  1 , along with the timeline for the intervention (Table  2 ).

To catalyze individual-level change, the intervention will use a T4 approach: Train, Tell, Test, and Tweak . We will train WRA and other community members through self-help group (SHG) meetings about anemia, IFA, and iron-rich foods so they can bring this knowledge to their community. SHGs are the primary platform of women’s empowerment across India. Within each village, several SHGs convene regularly to empower women with financial literacy and other forms of support. The involvement of SHGs in the intervention comes through our partnership with the Odisha Livelihood Mission (OLM), the government organization responsible for the formation and management of women SHGs in the state. We will develop ten modules that will include a mix of didactic learning and games focused on specific behavior changes and then use the SHG platform to conduct follow-up sessions in small groups.

The intervention will also tell the stories of overcoming barriers to IFA use through six short videos that feature members of the target audiences (WRA, husbands, mothers-in-law, and frontline workers) overcoming the barriers that we identified in the formative research. We will show the videos during SHG meetings, village health and nutrition days, and community festivals. The goal of the videos is to promote collective interest around anemia prevention by increasing knowledge, improving risk perception, enhancing perceptions of efficacy, and promoting positive social norms. We will also send regular voice-based messages to mobile phones to remind women to take the IFA, and we will also reinforce social norms around taking them.

We plan to test WRA both in the SHGs and throughout the community for anemia via a point-of-care hemoglobin test. We will then display the individual- and SHG- level results in the community, using graphic methods appropriate for low-literacy audiences.. The goal of this activity is to promote three types of feedback – ipsative (comparisons between community hemoglobin levels in the past and the present), social (how two neighboring communities are faring, compared to participants’ own community), and aspirational (how the community is faring, compared to goals set by the community early on).

Based on continuous monitoring and evaluation, we will tweak the curriculum, messages, and/or overall approach. We will gather real-time quantitative and periodic qualitative data about each intervention component to ensure fidelity and to gather feedback about which areas are working and which areas need improvement. The qualitative data will also capture unintended consequences (both positive and negative) as a result of the intervention.

The primary evaluation outcome is anemia among women of reproductive age, defined as having hemoglobin count less than 12 g/dcl among non-pregnant women and less than 11 g/dcl among pregnant women. We will measure this via a HemoCue point-of-care blood prick. We will also measure self-reported IFA use via a tablet-based survey.

Several secondary outcomes will also be measured in all participants to understand the mechanism of change, including: (a) knowledge and perceptions about anemia and IFA, (b) social norms, (c) diet, (d) mental health, and (e) quality of life. In a smaller sample of non-pregnant women, we will assess other secondary outcomes, including (a) physical activity (through ActivPal readings), (b) work capacity (through the modified Queens College step test), and (c) socio-cognitive functioning (through paper and computer-based response time tasks).

Recruitment

Within the selected clusters for data collection (described above), women between the ages of 15 and 49 residing in treatment ( n  = 2000) and control ( n  = 2000) clusters will be randomly selected and recruited to participate in the impact evaluation. Sampling will be stratified by treatment/control, village size and household.

To do so, we will create a household listing of eligible women within the selected clusters. The sampling size from each cluster will be proportional to population so that 60% of women in each arm come from high-population areas, 30% come from medium population areas, and 10% come from low population areas. Once we determine the number of eligible women, we will sample every n th household to get our total sample.

As mentioned and shown in Fig. 2 , the sampling design also includes a greater-intensity subset of participants from which certain secondary outcomes will be measured (i.e., physical activity, work capacity, and socio-cognitive functioning). Procedures for this group are described below. Only non-pregnant women will participate in the greater-intensity activities for reasons related to participant burden. We will select the non-pregnant subset of women for these outcomes through the household listing based on proportion-to-size principles (and by considering costs to minimize travel by limiting the smallest sample size per village to at least 10 participants). Pregnant women will only be excluded from the three tests included as greater-intensity activities and anthropometric measurements, they are still eligible for hemoglobin measurements and the interview.

Everyone involved in the study (data collectors, the principal investigator, program implementers, project managers, etc.) except two staff members, will be blinded to who is in the treatment and control clusters.

Data Collection & Measurement

All participants ( n  = 2000 in treatment and n  = 2000 in control clusters) will first undergo a point-of-care hemoglobin test to assess anemia status, followed by biometric assessments (height and weight), and a one-on-one survey interview to assess demographic information, psychosocial factors, and anemia-related behaviors. In order to minimize interview time, a planned missingness design was used to create four shortened versions of the survey. All versions of the survey contain the main study outcomes, certain secondary outcomes, and basic demographics. Participants will be randomly assigned to receive one of the six versions. This process will significantly reduce participant burden (in comparison to having all participants answer all questions).

The following procedures will be occur for all participants inside their home.

Hemoglobin measurements

We will obtain hemoglobin levels from all participants, through point-of-care hemoglobin tests, using a HemoCue photometer (in line with India’s National Family Health Survey methodology). This instrument provides hemoglobin levels immediately and accurately [ 24 ].

All participants will respond to a structured interview administered by a member of the local data collection team. This survey will measure self-reported IFA consumption and anemia status, as well as other secondary outcomes: knowledge, attitudes, and perceptions among participants; social norms; gender norms; mental health (via the CES-D scale); quality of life (via the SF-12); and diet (via the MDD-W questionnaire).

As mentioned, a smaller subset of non-pregnant women ( n  = 150 in each arm) will be randomly selected to provide further data on physical activity, work capacity, and socio-cognitive functioning. These measurements will be collected in a nearby community facility.

Work capacity

The Queens College Step Test assesses aerobic fitness [ 25 ]. The participant steps up and down on the 16.25-in. (41.3 cm) high platform at a rate of 22 steps per minute (88 beats per minute), assisted by the use of a metronome to maintain the right speed. Participants use a four-step cadence, ‘up-up-down-down’ for 3 min, and heartbeat is assessed at five points: t 0 to t 4 , corresponding to the beginning (before starting the step test), at the 1-, 2-, and 3-min marks, and then at the end (1 min after completing the step test). During our pilot study, we learned that the 16.25 in. height was too high for our sari-wearing participants and that a 12-in. height was found to be ideal. We will use this (12-in.) height in our study. Although using this lower height will not make our study readings directly comparable with other published studies, it will help us address our study objective (to compare longitudinally and across treatment-control arms).

Socio-cognitive functioning

We will also measure cognitive functioning within this sub-cohort through attention and working memory tasks. We will use the Simon Task and a Simple Reaction Time test to measure attention. We will also use a Corsi Blocks task and a Word Span test to assess working memory. To account for low computer literacy, these tests include both computer and non-computerized tests. Manual and computer based cognitive testing has successfully been carried out in several low and middle income countries in both rural and peri-urban settings including India [ 26 , 27 , 28 ]. All four tests will be administered by data collection staff who have been trained by the PI using a framework designed by an expert in cognitive testing.

Physical activity

Participants in the sub-cohort will be asked to wear an ActivPAL (PAL Technologies, LTD; Glasgow, UK) for three consecutive days to establish baseline measures of daily reclining, sitting, standing, and walking. The ActivPAL is small (53 × 35 × 7 mm), light-weight (15 g) and is attached to the mid-thigh.

Statistical power and sample size calculations

We assume a 7% reduction in anemia (from 47 to 40%), which is a lower effect size than typically found, alpha level of .05 with 80% power, the required sample size is 787 per arm [ 29 , 30 ]. Further assuming a design effect of 2.0 to account for clustering effects within villages, the required sample size with 20% loss to follow up is N  = 1968 per arm, which will be rounded up to 2000 per arm for a total of N  = 4000 across the treatment and control arms at baseline.

The more intense assessments consisted of three components – cognitive functioning, physical activity, and step test. We calculated the required sample size for the cognitive functioning component, assuming that cognitive functioning would improve by 16% and we assumed that ICC would not be an issue. This required a sample size of 288, which we rounded up to 300. Sample size calculations for the other two components, ActivPal and Step Test, were not done separately because these tests are administered to the same sample as the cognitive test. The sample size matrix can be found in Table  3 .

Statistical analysis

At baseline, we will conduct a series of bivariate tests across treatment and control arms. While randomization should ensure baseline matching, this is an effective technique to find any baseline difference. If any are found, they will be controlled for in any midline analysis. If we are unable to find baseline differences at midline, we will conduct a series of bivariate tests, including chi-square tests in which the binary outcome of anemia status at midterm is compared across treatment and control arms. This will be repeated at end line. Additionally, we will test the hypothesis that the treatment group will display a greater increase in hemoglobin count in comparison to the control group, supplemented with a difference-in-difference analysis in which change in hemoglobin levels between midline and baseline in the treatment group will be compared with corresponding changes in the control group. Thus, if there are differences across arms at baseline, we can still compare any observed change across arms. We will conduct another similar analysis at end line, using multivariate analysis of variance (MANOVA) techniques to uncover non-linear trends in the data. We will also evaluate the ability of the intervention to reduce anemia among pregnant versus non-pregnant women, as well as younger versus older women.

The primary analysis for hypothesis 1 will follow an intent-to-treat assumption. We will analyze data at the individual level, adjusting for clustering (at the village and cluster level), using generalized estimating equations (GEE). We will conduct a similar analysis through hierarchical linear modeling to account for village-level clustering effects, but this time with hemoglobin levels (continuous variable) as the dependent variable. In our MANOVA, time, treatment, and time x treatment will serve as the independent variables.

To test the hypothesis that social norms are the primary mediator between the intervention and change in hemoglobin levels, we will use structural equation models (SEM). The mediation analysis will include the secondary outcomes in the pathway between exposure to the intervention and hemoglobin level change.

Subsequent analyses will include other variables as outcomes, including those pertaining to physical activity; work capacity; quality of life; cognitive functioning; mental health; and psychosocial factors, including knowledge, attitude, normative beliefs, IFA intentions, and IFA use.

Ethics and dissemination

Research ethics approval.

This study was approved by the George Washington University Institutional Review Board (IRB) and Sigma Science and Research, an IRB located in New Delhi, India. The study was also reviewed and approved by Indian Council for Medical Research’s (ICMR’s) Health Ministry’s Screening Committee (HMSC). Any changes to study protocol will be communicated with these regulatory entities for approval immediately.

Participant consent and confidentiality

Informed consent will be obtained in Oriya by local data collectors from DCOR Consulting. Data collectors will read the consent document to participants, who will then sign to indicate their consent. Participants under the age of 18 are required to obtain the written permission of one parent or legal guardian. All data from participants will be de-identified by the local principle investigator and stored in secure, password-protected computers that only the study team and its affiliates have access to.

Dissemination

In addition to disseminating our work at conferences and in peer-reviewed academic journals, we will disseminate findings through multi-media channels as peer-reviewed findings get published. We will also update policy makers and stakeholders continuously with progress reports and newsletters. Finally, we will report findings back to the community where the research takes place after each data collection phase.

Risk mitigation plan

This is a low risk study so we do not anticipate serious adverse events. However, we will take precautions to ensure the safety of participants. Data collectors will be trained on HemoCue testing, how to communicate hemoglobin levels and anemia status to women following each test, and how to properly dispose of all HemoCue testing materials. While there will not be an independent assessment team to evaluate overall impact, we have put together an independent data safety and monitoring board (DSMB) to assess the outcomes on an ongoing process in order to ensure that the intervention does not inadvertently harm any participants.. The independent DSMB will review any serious adverse events and make recommendations for informing the IRB or stopping the study altogether. The DSMB includes researchers from India and the United States who will meet quarterly to review and discuss initial results from the study and any potential risks for participants.

While anemia has been a public health concern in India for decades, to our knowledge, no intervention has used a social-norms based model to encourage IFA use and iron rich food consumption. Our formative research findings showed the importance of shifting social norms among WRA and their primary referent groups (e.g., their husbands and mothers-in-law). We developed the T4 approach based on the formative research findings and the theory of normative social behavior. The longitudinal evaluation of the RANI T4 approach will both evaluate the efficacy of a norms-based intervention to increase uptake of IFA and iron-rich food as well as investigate the role of social norms as a mediator in anemia-related behavior change.

We will encourage input from stakeholders throughout the implementation and evaluation of the RANI project. The promotion of IFA uptake is in line with the agenda of the Government of Odisha [ 16 ], the National Iron Plus Initiative of India [ 8 ], the WHO Sustainable Development Goals [ 7 ], and the Anemia Mukt Bharat [ 31 ]. The findings from this study can provide evidence-based methods to reduce anemia within the state of Odisha through an innovative social-norms lens. Findings may be applicable to other rural areas of India and South Asia.

The intervention design and implementation is based on tested theory and extensive formative research with the target community. A cluster-randomized controlled design improves internal validity. We minimized the risk of contamination between the intervention and control conditions by including a geographical buffer of one or two villages in between treatment and control clusters. As village clusters were randomly selected for inclusion into the study, and then randomly assigned into treatment and control arms, both between- and within- cluster variation should be similar across both treatment and control groups. The inclusion of a control group further improves the internal validity of this study as it allows for the consideration of secular trends, which is particularly important in Odisha, India where ongoing efforts to reduce anemia by other groups can introduce history bias.

Additionally, a longitudinal evaluation design provides a better understanding of hemoglobin level changes with IFA use. The use of three time points also allows for the investigation of non-linear trends in hemoglobin levels after exposure to the intervention.

Blinding at two levels may strengthen the quality of our data. At the first level, the intervention implementers have been blinded to which villages are selected for data collection. This will minimize bias in implementation, allowing for our selected sample for data collection to be representative of all the villages that received the RANI treatment. At the second level, the data collectors will be unaware of which villages are selected for treatment and control. This will also minimize any potential recording bias as the data collectors will not know if they are currently collecting data from an individual in the treatment or control arm.

Limitations

To our knowledge, this is the first study that tests the effects of a norms-based intervention to improve IFA demand among WRA in India through a randomized controlled trial. As a result, we use a “kitchen sink” approach, in which we assess the overall impact of all intervention components, without distinguishing which component may have been most effective. However, we will measure exposure to components of the intervention as well as collect monitoring and processes evaluation data to understand the effects of specific intervention activities to the extent possible.

Additionally, this is primarily a demand-side intervention. Our formative research shows that access to IFA is not a major barrier to use, but if our intervention is successful, an increase in demand may impact supply. If supply chain problem occurs, changes in social norms may have limited impact. Therefore, we will be monitoring stock-outs throughout the intervention and we will use monitoring and evaluation data at the block level to advocate for additional supply if/when demand increases. The study length supports an evaluation of the short-term effectiveness of the intervention but does not evaluate longer-term sustainability of the social norms or behavior changes.

The current Indian guidelines suggest IFA supplementation once a week for non-pregnant women and once a day for pregnant women in their second and third trimester (both comprised of 60 mg elemental Iron + 500 mcg Folic Acid) [ 16 ]. Abdominal pain is commonly reported among women who take IFA daily, but less frequently in women who take IFA weekly [ 32 ]. As pregnant women experience the side effects associated with daily dosage, the reputation of IFA may dampen and reduce demand. Previous studies show that women who experience side effects, such as abdominal pain, or believe that they are caused by IFA are less likely to adhere to IFA than those who do not [ 33 , 34 ]. Dosage regimen also influences absorption of iron; clinical studies have shown that IFA with ≥60 mg iron administered daily increases hepcidin, subsequently reducing absorption on the next day [ 35 ]. However, IFA administered on alternating days yielded approximately twice the amount of iron absorption than daily administration [ 36 ].

It is important to note that we only include women in this study’s impact evaluation – understanding normative change among men/husbands would add valuable information to this study. Additionally, the intervention does not target the behaviors of frontline workers; therefore frontline workers may continue practices of distributing IFA only to pregnant women or failing to follow up on IFA adherence.

Additionally, we anticipate that attrition may occur through the course of this study. To minimize any effects to internal validity, the power size calculations were conducted with 20% anticipated attrition. If attrition does occur, we will investigate if systematic differences are observed in baseline among those who drop out of the study.

Availability of data and materials

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study. The dataset(s) that will come out of this study will be available in the Gates Open Access repository.

Abbreviations

Iron-folic acid

Randomized controlled trial

World health organization

Women of reproductive health

National iron plus initiative

Reduction in anemia through normative innovations

Self-help groups

Theory of normative social behavior

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Acknowledgments

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This work was supported by a grant from The Bill and Melinda Gates Foundation (OPP1182519) to the George Washington University, Rajiv N. Rimal, principal investigator. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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HY made contributions to the sampling and recruitment procedures, as well as the data collection procedures. HY also played a large role in drafting and revising the protocol. ES made contributions to the conception and all parts of the design, as well as played a large role in drafting the protocol. RR was responsible for the conception of the study; the development of the research questions, objectives, and rational; and the design of the study. He also played a large role in revising the protocol. IP, AM, and SM all played a role in drafting components of the protocol and revising it. All authors have read and approved the manuscript.

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Yilma, H., Sedlander, E., Rimal, R.N. et al. The reduction in anemia through normative innovations (RANI) project: study protocol for a cluster randomized controlled trial in Odisha, India. BMC Public Health 20 , 203 (2020). https://doi.org/10.1186/s12889-020-8271-2

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Anaemia in Indians aged 10-19 years: Prevalence, burden and associated factors at national and regional levels

Samuel Scott, et.al

Anaemia control programmes in India are hampered by a lack of representative evidence on anaemia prevalence, burden and associated factors for adolescents. The aim of this study was to: (1) describe the national and subnational prevalence, severity and burden of anaemia among Indian adolescents; (2) examine factors associated with anaemia at national and regional levels. Data (n = 14,673 individuals aged 10–19 years) were from India's Comprehensive National Nutrition Survey (CNNS, 2016–2018). CNNS used a multistage, stratified, probability proportion to size cluster sampling design. Prevalence was estimated using globally comparable age‐ and sex‐specific cut offs, using survey weights for biomarker sample collection. Burden analysis used prevalence estimates and projected population from 2011 Census data. Multivariable logistic regression models were used to analyse factors (diet, micronutrient deficiencies, haemoglobinopathies, sociodemographic factors, environment) associated with anaemia. Anaemia was present in 40% of girls and 18% of boys, equivalent to 72 million adolescents in 2018, and varied by region (girls 29%–46%; boys 11%–28%) and state (girls 7%–62%; boys 4%–32%). Iron deficiency (ferritin < 15 μg/L) was the strongest predictor of anaemia (odds ratio [OR]: 4.68, 95% confidence interval [CI]: [3.21,6.83]), followed by haemoglobinopathies (HbA2 > 3.5% or any HbS) (OR: 2.81, 95% CI: [1.66,4.74]), vitamin A deficiency (serum retinol <20 ng/ml) (OR: 1.86, 95% CI: [1.23,2.80]) and zinc deficiency (serum zinc < 70 μg/L) (OR: 1.32, 95% CI: [1.02,1.72]). Regional models show heterogeneity in the strength of association between factors and anaemia by region. Adolescent anaemia control programmes in India should continue to address iron deficiency, strengthen strategies to identify haemoglobinopathies and other micronutrient deficiencies, and further explore geographic variation in associated factors.

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• Published: 20 December 2023

Exploring factors influencing the severity of pregnancy anemia in India: a study using proportional odds model

• Iffat Ara Talin 1 ,
• Mahmudul Hasan Abid 1 ,
• Md Abdus Samad 2 ,
• Irma Domínguez Azpíroz 3 , 4 , 5 ,
• Isabel de la Torre Diez 6 ,
• Imran Ashraf 2 &
• Abdullah-Al Nahid 1  

Scientific Reports volume  13 , Article number:  22816 ( 2023 ) Cite this article

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• Computational biology and bioinformatics
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Pregnancy-associated anemia is a significant health issue that poses negative consequences for both the mother and the developing fetus. This study explores the triggering factors of anemia among pregnant females in India, utilizing data from the Demographic and Health Survey 2019–21. Chi-squared and gamma tests were conducted to find out the relationship between anemia and various socioeconomic and sociodemographic elements. Furthermore, ordinal logistic regression and multinomial logistic regression were used to gain deeper insight into the factors that affect anemia among pregnant women in India. According to these findings, anemia affects about 50% of pregnant women in India. Anemia is significantly associated with various factors such as geographical location, level of education, and wealth index. The results of our study indicate that enhancing education and socioeconomic status may serve as viable approaches for mitigating the prevalence of anemia disease developed in pregnant females in India. Employing both Ordinal and Multinominal logistic regression provides a more comprehensive understanding of the risk factors associated with anemia, enabling the development of targeted interventions to prevent and manage this health condition. This paper aims to enhance the efficacy of anemia prevention and management strategies for pregnant women in India by offering an in-depth understanding of the causative factors of anemia.

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Nkechi G. Onyeneho, Benjamin C. Ozumba & S. V. Subramanian

Introduction

Anemia is a global health issue affecting pregnant women and is a leading cause of maternal morbidity and mortality 1 , 2 . Anemia is characterized by a hemoglobin (Hb) level lower than 11 g/dL 3 . In India, pregnancy-associated anemia is a significant public health concern 4 , 5 . Pregnancy-associated anemia can be caused by inadequate dietary intake, insufficient iron consumption, or pre-existing conditions 6 , 7 , 8 . In India, an alarming proportion of pregnant women suffer from anemia, which makes it a major public health concern 4 , 5 . The Indian government has taken various measures to prevent anemia in pregnant women. Examples include distributing iron and folic acid supplements and conducting nutrition education programs 9 , 10 . Despite the preventive measures mentioned earlier, anemia remains a common problem among pregnant females in India. It is therefore necessary to investigate the factors that contribute to anemia in India.

The Demographic and Health Surveys (DHS) program 11 collects data on population, health, and nutrition in more than 90 nations, including India. DHS also provides data on a range of health indicators, such as anemia, tuberculosis (TB), and high blood pressure (BP). Processing the DHS data can help determine the factors correlated with anemia within this particular demographic. This study aims to examine two points regarding anemia through processing the DHS dataset—firstly, to analyze the prevalence of anemia among pregnant women in India and secondly, to discover the factors that are associated with it. In order to assess the correlation between anemia and various factors such as geographic region, place of residence, education level, the origin of drinking water, socioeconomic status, religion, iodine intake, toilet facility, body mass index (BMI), and age, we employ the Chi-square test and gamma test. Furthermore, we used multinominal logistic regression to develop a comprehensive understanding of the influencing factors of anemia in India 12 , 13 , 14 . To develop a deeper understanding of the relationship between socioeconomic and sociodemographic factors and anemia, both ordinal logistic regression and multi-nominal logistic regression analysis have been used in this study. Our research aims to enhance the comprehension of the primary determinants of anemia during pregnancy in India for policymakers, healthcare providers, and researchers. The aim is to identify the principal causal factors of anemia in order to facilitate the creation of focused interventions that can alleviate the impact of this significant public health concern.

Literature review

Anemia is a prevalent public health issue among pregnant women globally, with a higher incidence in developing nations like India. There has been much research conducted on the factors contributing to anemia during pregnancy in India, focusing on demographics, socioeconomics, nutrition, and health issues. The findings of research conducted in Jhalawar, Rajasthan, India 15 reveal a substantial prevalence of anemia (81.1%) among pregnant females in a rural locality. The study also identifies certain sociodemographic variables that correlate significantly with anemia. The findings underscore the need for interventions to improve access to nutritional resources and antenatal care services to address this issue. Similarly, another study conducted in Karnataka, India, found that anemia was significantly associated with lower education levels and a lower household income 16 . Besides demographic and socioeconomic variables, numerous research studies have explored the correlation between prenatal anemia and dietary adequacy 17 discusses the prevalence of anemia among pregnant women and adolescent females in India. The results show that almost (85%) of pregnant women and over (58%) of adolescent females are anemic. The lack of anemia prevention and control programs for adolescent girls is a significant problem. The study recommends implementing effective programs, especially for adolescent girls who do not receive iron and folic acid supplementation and have low serum ferritin levels. Insufficient intake of folic acid and vitamin B12 are additional nutritional factors recognized as potential risk factors for anemia during pregnancy in India 18 . Several studies have also investigated the association between anemia during pregnancy and health-related factors such as infections and chronic diseases. In a study conducted in a rural region of Haryana, India, parasitic infestations were found to be substantially linked to anemia during pregnancy 19 . Another study carried out in a tertiary care hospital in Mumbai, India, found that women with sickle cell anemia were at higher risk of anemia during pregnancy 20 .

While previous studies have identified various factors associated with anemia during pregnancy in India, there is limited research on the relative importance of these factors and their combined effects on anemia prevalence. For example 21 , found that several risk factors were significantly associated with LBW neonates, including anemia, pregnancy-induced hypertension, pre-pregnancy maternal weight less than 45 kgs, maternal height of less than 145 cm, and inadequate antenatal care. In addition, a large number of rural mothers did not receive antenatal care services or did not use them adequately, which indicated the need for health education, socioeconomic development, maternal nutrition, and increased access to health services during pregnancy. Another study 22 reported that maternal education, socioeconomic status, and maternal body mass index directly correlate to birth weight. Underprivileged mothers have a higher prevalence of anemia and give birth to a greater number of very premature babies, they found. The study 23 investigates the pregnant women’s risk factors linked to anemia during pregnancy through a prospective-retrospective cohort study of antenatal and postnatal women. According to the study, the prevalence of anemia was high, particularly among women with low socioeconomic status. The study also identified several other factors that contributed to anemia, including education, age, spacing, parity, history of bleeding, worm infestation, knowledge regarding anemia during pregnancy, gestation period, ability to choose food, and compliance with iron supplementation.

A study conducted in South India examined the relationship, between mental health and anemia during pregnancy. The findings revealed that women with levels of stress and anxiety were more susceptible to anemia. This suggests that interventions addressing pregnancy-related anemia should consider health aspects well 24 . Another research conducted in the slums of Kolkata, India highlighted the role of factors like poor sanitation and water quality in contributing to the prevalence of anemia among pregnant women 25 . To understand the impact of cultural practices on anemia prevalence, a study was conducted in Odisha, India. The study discovered that traditional beliefs and taboos related to foods during pregnancy led to deficiencies. This emphasizes the importance of interventions that are culturally sensitive 26 . Additionally, a study in Mumbai, India explored how maternal employment influenced anemia rates. It found that working mothers faced challenges in accessing care indicating the need for policies supporting maternal health within the workforce 27 .

Although the previous studies provided significant insights into the occurrence and underlying causes of anemia among pregnant women in India, further research is necessary to identify the key determinants contributing to anemia in this population. Moreover, most studies conducted in India have utilized simple logistic regression, which may not account for the ordinal nature of the severity of pregnancy anemia. In this study, ordinal logistic regression is used to determine the primary determinants of anemia among pregnant women in India which provides us with a better understanding of the risk factors that are associated with it. Furthermore, our analysis utilizes the latest DHS dataset, and to the best of our knowledge, the IDHS 2019–21 dataset has not been previously analyzed to assess the prevalence of pregnancy anemia in India.

Assumptions

In conducting this study on the factors that affect the severity of pregnancy anemia in India using an odds model, we have some assumptions. Firstly, we trust that the DHS 2019–21 dataset accurately and reliably captures all the variables related to pregnancy anemia, including socioeconomic, nutritional, and health-related information. Moreover, we assume that the sample selected from this dataset represents the population of women in India and reflects their diverse geographic, cultural, and economic backgrounds. Our study assumes that utilizing techniques like Chi-squared tests, gamma tests as well as ordinal and multinomial regression is both appropriate and valid for analyzing the connections between identified factors and the severity of pregnancy anemia, in an Indian context. Finally, we want to acknowledge that our study is observational in nature and we should not assume that the relationships we found between factors and the severity of pregnancy anemia in the odds model imply causation. This highlights the fact that this research is exploratory and focused on correlations, then establishing cause and effect relationships.

Organization of the paper

Firstly, we present an overview of anemia and its effects on maternal and fetal health. The present study investigates the existing literature regarding the prevalence and causative factors of anemia that develop in females during pregnancy in India. In order to explicate our research methodology, we expound upon the data and methods employed in our analysis in the Materials and Methods section. The Chi-squared and gamma tests were employed to evaluate the correlation between anemia and sociodemographic and economic variables. In contrast, ordinal and multinominal logistic regression was utilized to identify the most significant determinants of anemia. In the later section, the limitations of our study and its future aspects have been discussed. In the Results section, we present the results of our analyses, which are then discussed in the Discussion section. Lastly, we draw our paper to a close in the Conclusion section, summarizing our main findings.

Materials and methods

Data sources.

The primary data for this study is obtained from the Demographic and Health Survey (DHS) dataset, which was conducted in the years 2019 to 2021. The DHS is a nationally representative survey conducted in numerous countries in the world including India. The aim of this survey is to collect key information about public health, demographics, and socio-economic factors countrywide. The data set is available on DSH’s website 11 upon request and does not contain any identifiable information about the study participants. The selection of the DHS 2019–2021 dataset is intended to offer a current perspective on the factors that contribute to the severity of pregnancy anemia, in India. The DHS follows a nationally representative sampling procedure, ensuring the inclusion of diverse demographics. Additionally, DHS measures anthropometric indicators objectively and collects a variety of monitoring and impact assessment statistics. The survey also boasts a high response rate, further enhancing the reliability and validity of the gathered data. A total of 27494 respondents were included in this study after limiting the data selection to pregnant women and eliminating any missing valued samples.

In this research, we examined the anemia status of pregnant women, which was initially categorized as severe, moderate, mild, and not anemic. To enhance the statistical analysis, we merged the ‘severe’ and ‘moderate’ as ‘severe to moderate’, and recategorized the target variable into three groups: severe to moderate, mild, and not anemic. We used explanatory variables at both the individual and household levels to conduct our study, such as type of residence, source of drinking water, type of toilet facility, sex of household head, wealth index, the religious affiliation of household head, educational level, geographic region, iodine level in salt, and high blood pressure, BMI, and age 12 , 13 , 14 . The variables “Wealth index” and “Educational level” have ordinal categories, while the others have nominal categories. The categories for each variable and the corresponding anemic status are provided in Table  1 . Latrines with flushing to the piped sewer system, flushing to the septic tank, flushing to pit latrine, Ventilated improved pit latrine, Pit latrine with slab and Composting toilets are considered as safe toilet systems and others are unsafe 28 , 29 . Similarly, Piped into dwelling, Piped to the yard, Public tap or standpipe, Tube well or borehole, Protected dug well, Protected spring, Rainwater, and Bottled water are considered safe water sources. All other types of water sources are considered unsafe water sources 29 , 30 .

Ordinal logistic regression

Ordinal logistic regression is a statistical method used to model the relationship between an ordinal outcome variable and one or more predictor variables 31 , 32 . Unlike binary logistic regression, which is used for binary outcomes, ordinal logistic regression is used for outcomes that have three or more ordered categories. In this model, the probability of an outcome falling into a particular category is transformed into a logit value that is linearly related to the predictor variables. The cumulative logits function can be written as 33 , 34 :

where \(P(Y \le j)\) indicates the probability of the outcome variable being less than or equal to category j , \(\beta _{j0}\) represents the intercept of category j and \(\beta _{j1}\) to \(\beta _{j_p}\) correspond to the coefficients associated with the predictor variables \(x_1\) to \(x_p\) , respectively.

It can be seen from equation 1 that there is a correlation between the predictor variables and the likelihood of the outcome variable falling into each category. For each category j, the coefficients represent the difference in log odds between category j and category j-1, and the model takes these differences as constant across all predictor variables. Ordinal logistic regression is used in many fields of science, such as psychology, medicine, and social sciences. Specifically, it is used where the researchers are interested in predicting outcomes that have multiple ordered categories. It is possible to use both categorical and continuous predictor variables in the model, and maximum likelihood estimation is typically used in its implementation.

Multinomial logistic regression

Multinomial Logistic Regression is a technique that helps us model and analyze the relationship, between categorical outcomes and one or more predictor variables 35 , 36 . It is an expansion of binary logistic regression designed to handle scenarios where the dependent variable has more than two categories. In this approach, we create logistic regression models for each category of the dependent variable comparing them to a reference category. We calculate probabilities for each category. Predict the one with the highest probability. Assuming Y represents the categorical dependent variable with K categories, and X denotes the predictor variables, the formula for regression can be expressed as follows 37 , 38 :

where the variable \(P(Y = k|X)\) represents the likelihood of the variable Y belonging to category k . The coefficients, for each predictor variable, for category \(k\) are represented by \(\beta _{0k}, \beta _{1k}, \beta _{2k}, \ldots , \beta _{pk}\) . \(X_1, X_2, \ldots , X_p\) . \(X_1, X_2, \ldots , X_p\) are the predictor variables. And the total number of categories in the dependent variable is represented by \(K\) .

Chi-squared test

The chi-squared analysis is a statistical procedure used for evaluating if there is any significant relationship between two unordered categorical variables or not 39 , 40 . It is based on the Chi-squared test statistic, which measures the difference between the observed frequencies of the two variables and the expected frequencies under the assumption that there is no association. The Chi-squared test statistic can be mathematically formulated as 41 , 42 :

In this case, \(O_{ij}\) is the observed frequency in a contingency table located in i th row and j th column, \(E_{ij}\) is the expected frequency under the assumption of no association, and the contingency table comprises r rows and c columns.

If the computed Chi-squared test statistic exceeds the critical value derived from the Chi-squared distribution with \((r-1)\times (c-1)\) degrees of freedom, it leads to the rejection of the null hypothesis. Accordingly, the alternative hypothesis, which indicates a substantial relationship between the variables under study, is accepted. The Chi-squared test is frequently used to determine whether there is a significant relationship between two unordered categorical variables or not. This tool is used for hypothesis testing and can be used in making decisions and policies.

The gamma test is a statistical technique used to determine if there is any correlation between two ordinal variables or not. Also, the degree of correlation between ordinal variables can be determined by the value of the gamma coefficient 43 , 44 . The gamma coefficient can be calculated as 45 , 46 :

Among the two variables presented on the right-hand side of the equation, \(n_{\hbox {discordant}}\) is the number of discordant observational pairs (the pairs that have different relative orders on the two variables), and \(n_{\hbox {concordant}}\) is the number of concordant observational pairs (the pairs that have the same relative order on both variables). Here the denominator represents the total number of observation pairs.

The gamma coefficient ranges from the minimum value of -1 to the maximum value of 1. The value -1 indicates complete disagreement between the two variables, 1 indicates complete agreement and 0 indicates no association. The gamma test is used in a wide variety of fields such as education, psychology, and sociology to measure the relationship between ordinal variables, such as education and income, or job satisfaction and employment length. It is a useful tool for evaluating the strength of association between variables that cannot be measured on a continuous scale.

The chi-squared test is useful in comparing variables if both are equally spaced and the relationship between them is linear. However, this assumption is not satisfied if the data are ordinal in nature. The gamma test does not rely on these assumptions and it can be used to measure the strength of association between ordinal variables that are not linearly related to each other. Therefore, the gamma test may be more appropriate than the chi-squared test for analyzing the relationship between two ordinal variables.

Bivariate analyses

Bivariate analysis is one of the simplest statistical approaches for finding an association between two variables. Chi-square and gamma tests were utilized to analyze the relationship between variables and can provide valuable insights into their association. Table  2 indicates that except “Result of salt test”, “High BP” and “Source of drinking water”, all other variables are statistically significant. Among them, the “Highest educational level”, “Wealth index” and “Geographic region” are highly associated. Furthermore, Indian women with improved toilet facilities have a lower risk of maternal anemia.

Multivariate analysis

multivariate analysis allows for the analysis of the relationship between a categorical dependent variable and multiple independent variables. As discussed, both ordinal and multinominal logistic regression is used for multivariate analysis. Features such as the source of drinking water, High BP, and Salt test results were suggested as insignificant by both bivariate and multivariate analyses. Hence, we did not discuss these features in these sections. Several noteworthy observations were made during these analyses, which are briefly discussed in the following sections.

Ordinal logistic regression studies the ordered nature of the target variable, that is the severity of anemia. Table  3 shows the findings of ordinal logistic regression. This analysis found relationships between several critical factors and anemia in detail. The observational outcome is highlighted below:

Pregnancy-associated anemia is more common in urban India than rural areas. Urban pregnant Indians are 9.4% less susceptible to severe to moderate anemia than their rural counterparts(OR = 1.094, p  = 0.007).

Socioeconomic status significantly influences the prevalence of pregnancy-associated anemia in India. Where the poorest individuals have 34.2% more likelihood of being affected by severe to moderate anemia than the richest ones (OR = 0.658, p < 0.001), the prevalence of it reduces as the wealth index improves to poorer(OR = 0.768, p < 0.001), middle(OR = 0.833, p < 0.001) and richer(OR = 0.846, p < 0.001).

Educated women are observed to have more resistance to the prevalence of anemia. When considering higher education as the reference level, pregnant women with minimum or no education were 37%(OR = 0.621, p < 0.001), with primary education were 32% (OR = 0.675, p < 0.001) and those with secondary education were 22.2% (OR = 0.778, p < 0.001) more vulnerable to being affected by anemia. It is observed that the higher the education level of the individual, the lower the odds of being anemic.

Type of toilet facility is another concerning factor of anemia in Indian pregnants. Women using improved toilet facilities have a 7.5% less probability of being severe to moderately anemic (OR = 1.075, p = 0.032).

For analyzing anemia prevalence across the geographic regions, Southern regions of India have been considered as the reference category. The Northeastern and Northern regions have a comparatively lower likelihood of severe to moderate anemia (OR = 1.359, p < 0.001; OR = 1.130, p = 0.003), whereas Eastern parts of India have 17.4% more prevalence of anemia than the southern parts (OR = 0.826 p < 0.001).

The spread of anemia is different among religious groups. Christian pregnants have the lowest odds of being anemic, that is 31.2% (OR = 1.688. p < 0.001), and Muslim pregnant women have a 74.5% (OR = 1.255, p < 0.001) probability of being anemic when compared with the baseline group.

Underweight pregnant mothers are found to have the highest likelihood of anemia (OR = 0.993, p = 0.914). Moreover, the odds of being anemic decrease as the BMI increases when the obese group is considered as the reference category. Pregnant women of healthy weight have 3.1% (OR = 1.031, p = 0.618), and overweight pregnant women have 6.6% (OR = 1.066, p = 0.328) fewer odds of being anemic respectively.

Age of the pregnant women is another concerning factor of pregnancy-associated anemia. While teenage pregnants have the highest probability of being anemic (OR = 0.951, p = 0.742), the rate decreases as the age of pregnant women increases.

Multinominal logistic regression

Multinominal logistic regression considers the target variable as having a set of unordered categories defining different degrees of anemia. Table  4 summarizes the outcome of multivariate logistic regression, which illustrates the relationship between the estimated parameters of the covariates used. The key findings are pointed out below:

Table  4 reveals a statistically significant difference in anemia prevalence between rural and urban pregnant women, with urban women exhibiting 9.5% lower odds of being severe to moderately anemic (OR = 0.905, p = 0.021) and 9.1% lower odds of being mild anemic (OR = 0.909, p = 0.029) compared to rural Indian pregnants.

The type of family structure (patriarchal or matriarchal) did not have any significant impact on the prevalence of severe to moderate anemia, but it has an influence on the prevalence of mild anemia. Indian women of patriarchal families are 12.5% less susceptible to maternal anemia than ones belonging to a matriarchal family.

Socioeconomic status highly influences the prevalence of anemia. Severe to moderate anemia is more likely to occur in pregnant females from lower socioeconomic backgrounds. Compared to pregnant women of the richest families, severe to moderate anemia is more common in the poorest pregnant women by 69.8% (OR = 1.698, p < 0.001). Poorer, middle and richer pregnant women had 39.5%, 27.2%, and 24.7% more likelihood of severe to moderate anemia respectively (OR = 1.395, p < 0.001; OR = 1.272, p < 0.001; OR = 1.247, p < 0.001). Mild anemia is also 33.3% and 17.9% more prevalent in the poorest and poorer pregnant women compared to the richest group.

Educational status also has a significant impact on anemia prevalence. Compared to educated pregnant, it is found that lower levels of educational qualification are associated with higher odds of being anemic for both severe to moderate(OR = 1.646, p < 0.001; OR = 1.376, p  < 0.001 ) and mild anemic (OR = 1.272, p < 0.001; OR = 1.182, p < 0.001) cases while considering “higher” as the baseline category. The absence of formal education (“no education”, OR = 1.836, p < 0.001; OR = 1.316, p < 0.001) is correlated with an increased probability of both severe to moderate and mild anemia. It is observed that increased levels of education decrease the likelihood of anemia.

The type of toilet system used also plays an important role in anemia prevalence. The pregnants using safer sanitation systems are found to be 9.2% less vulnerable to severe to moderate anemia than the users of no proper toilet facilities (OR = 0.908, p = 0.024).

The prevalence of anemia varies across geographical locations. Northeastern regions have 30.4% (OR = 0.696, p < 0.001) and 21.2% (OR = 0.788, p = 0.001) less likelihood of severe to moderate and mild maternal anemia when considering the Southern region as the reference category. In Northern states of India, the probability of severe to moderate anemia is 11.2% (OR = 0.888, p = 0.027) and mild anemia is 16.4% (0.836, p = 0.001) lower than in Southern India. On the other hand, pregnant women residing in eastern India are 26.8% more prone to severe to moderate anemia (OR = 1.268, p < 0.001) and 29.6% more prone to mild anemia (OR = 1.296, p < 0.001) compared to the baseline category.

Investigating the influence of religious affiliation on anemia severity, the findings exposed intriguing patterns. Muslim pregnant women exhibited a reduced likelihood of 26.5% severe to moderate anemia(OR = 0.735, p < 0.001). Conversely, Christian women displayed a 48.1% lower probability for severe to moderate anemia (OR = 0.519, p < 0.001), and a 27.3% lower probability of mild anemia (OR = 0.727, p = 0.001) with respect to “other/no religion” group as reference.

Maternal anemia is associated with the age of the individuals as well. Especially in the case of mild anemia, teen Indian mothers are 67.6% more vulnerable to mild anemia (OR = 1.676, p = 0.027) than pregnants in/above their forties. Also, pregnants in their twenties have 56.7% more likelihood of experiencing mild anemia (OR = 1.567, p = 0.51)than pregnant individuals aged forty and above.

The probability of being both severe to moderate and mild anemic is more pronounced in pregnants with lower BMI. As the BMI increases, their likelihood of being anemic decreases. The ‘obese’ group has been considered as the reference category.

The study identifies several important factors that increase the probability of pregnancy anemia in India. The study indicates that the prevalence of anemia during pregnancy is possible to mitigate by designing a targeted scheme based on the analysis of data about various variables such as place of residency location, the origin of potable water, type of toilet structure, sex of the head of the family, wealth index, the religious affiliation of the head of household, educational level, geographic region, iodine level in salt, and high blood pressure.

The findings of this study indicate that pregnant women who reside in rural areas exhibit a higher likelihood of developing anemia than their urban counterparts. The results of the study are similar to those of several other studies 47 , 48 . The pace of modernization and industrialization in rural areas is slower than in urban areas. Health services are often unavailable to rural communities, and pregnant women are mostly unaware of proper nutrition, which could contribute to higher rates of anemia. In order to reduce the prevalence of anemia in India, effective interventions and government programs should be designed based on the disparities in socioeconomic, health, and nutrition factors between rural and urban areas.

This study clearly indicates that education plays a vital role in preventing pregnancy-associated anemia. One who receives education possesses better nutrition knowledge and a better understanding of pregnancy factors compared to someone who is not privileged to have access to education. By being aware and knowledgeable, educated people can better take care of themselves or other women during pregnancy and reduce their risk of developing anemia 49 , 27 . The literacy rate among women is one of the most crucial underlying factors of the prevalence of anemia. An educated woman typically has more access to healthcare services and facilities, which helps reduce the risk of anemia. With the increasing level of education, women become more equipped with knowledge of the health risks associated with anemia. Consequently, the more the women are educated the better they are able to prevent or manage the condition, reducing its chances of occurring.

This study revealed that, in India, pregnancy-associated anemia and affiliation to religious groups have a significant association, which resonates with several other studies 50 , 51 . According to the findings of our research, pregnant belonging to Hindu communities suffer more from anemia compared to other religious groups. Pregnancy-associated anemia was less prominent in the Christian community. This variation in the prevalence of anemia between different religious groups may be because of the differences in their dietary habits and lifestyles.

A significant association was found between the socioeconomic status of pregnant women and their risk of anemia. Pregnant women from low-income households in India are more susceptible to anemia than their high-income counterparts. Earlier research studies also testify the same findings 27 , 52 . This could be due to a lack of access to quality healthcare services. Poorer families may also have limited access to a wide range of foods, which can result in poor nutrition and anemia. Therefore, there is a crucial need for targeted efforts to address the issue of anemia in pregnant women from low-income families.

This research found an inverse relationship between the availability of toilet facilities and the occurrence of pregnancy-associated anemia. Not only this research, but some other studies also support the same findings 53 , 54 . If empirically analyzed, possible explanations for this relationship can be found. Improved toilet facilities help to reduce many parasitic infections, which are common causes of anemia, for example, hookworm. Additionally, improved sanitation facilities can promote better hygiene practices, such as handwashing, which can prevent other infectious diseases that may later cause anemia. The findings indicate how ensuring basic needs such as access to safe and hygienic sanitation facilities can have a greater influence on improving maternal health. It also suggests that investing in sanitation infrastructure could have a significant impact on reducing the burden of anemia among pregnant women.

As observed from the findings, the prevalence of anemia is not the same across all the regions in India, demonstrating the importance of dedicated interventions to tackle the problem. In this case, also, various other studies also validate the same finding 55 , 56 . eastern regions of India showed high rates of pregnancy-associated anemia, which is quite alarming and requires immediate attention to control and prevent it. Such preventive measures may include increasing access to iron and folic acid supplements, nutrition education, and improving healthcare services for pregnant women in these geographic regions. It is encouraging to note that northern and western India have a significantly lower prevalence of pregnancy-associated anemia compared to other regions. There are plenty of reasons behind this, including better access to healthcare services and nutrition, higher levels of education, etc. in these regions. It can set a valuable example for other Indian geographic regions in implementing effective interventions aimed at reducing maternal anemia.

Prevalence of maternal anemia in India is not even for all age groups, rather specific age groups are more affected. Particularly, mild anemia is more prevalent in teenage and young adult Indian mothers. This finding is evident in several other studies as well 57 , 58 . It may be due to underlying factors such as inadequate nutrition, limited prenatal care, socioeconomic disparities, and early pregnancies. On the other hand, pregnant women aged over 40 are more susceptible to severe anemia, probably due to reduced ability of nutrient absorption, menopausal changes, chronic illnesses, dietary patterns, and so on.

Resonating with several other studies 59 , 47 , the probability of pregnancy-associated anemia is higher in underweight women. Lower BMI is associated with malnutrition, indicating inadequate nutritional reserves in the body, such as iron, vitamin B12, and folic acid which are essential for the production of red blood cells. Conversely, individuals with a healthy BMI are more likely to have a balanced and nutritious diet, reducing the risk of nutritional deficiencies leading to anemia.

This research leads to an unexpected finding, that is the absence of a significant impact of iodized salt on pregnancy-associated anemia. Iodine deficiency is a well-known cause of goiter, intellectual impairment, and cretinism. Pregnant women require higher amounts of iodine to ensure proper development of the fetal brain, and therefore iodine supplementation is crucial during pregnancy. Our study suggests that, although iodized salt may help to prevent iodine deficiency, it may not be significantly impactful to the prevention of anemia in pregnant women. However, this result contradicts some of the existing studies 56 , 60 .

Limitation and future work

There are some limitations to our study of pregnancy-related anemia in India. Firstly, our reliance on the DHS 2019–2021 dataset restricts our findings to a timeframe, which means we might miss out on some recent changes in anemia prevalence. Moreover, the cross-sectional nature of the data limits our ability to establish causal relationships highlighting the need for longitudinal studies to understand how these dynamics evolve over time. Additionally, the variables available in the dataset limit the scope of our analysis causing some other influential factors of anemia to be left out. It is crucial to include variables, than what the DHS dataset provides in order to encompass all the factors involved. Additionally investigating the efficacy of interventions aimed at addressing these factors could lead to some promising strategies to minimize the occurrence of pregnancy-related anemia. India, with its range of geography, culture, and economy, is a country of diversity. Therefore it would be more beneficial to concentrate on research that explores estimations at the micro level such as districts and PSUs, in order to gain insights for formulating more targeted policy recommendations.

This investigation aimed to identify the underlying determinants of anemia among pregnant females in India by utilizing information from the Demographic and Health Survey from 2019–21 (India). More than half of the pregnant females in our research sample had anemia, indicating a significant prevalence of anemia in India. Our analysis identified several essential parameters related to suffering from anemia by pregnant females, including the highest educational level, wealth index, residing geographical region, type of toilet structure, the religious affiliation of the household head, urbanicity of the residence, age, BMI, and family structure. From the observations of bivariate and multivariate analyses, the highest educational level, wealth index, and geographical region were identified as the most crucial elements that influence anemia among pregnant women in India. These findings have important implications for policymakers and healthcare providers in India. Interventions targeted toward enhancing antenatal care services can potentially alleviate the prevalence of anemia among pregnant females. Additionally, efforts to improve education and economic opportunities for women may also have a positive impact on the prevention and control of anemia. Overall, this study emphasizes the need for targeted interventions to mitigate the alarming prevalence of anemia among pregnant females in India.

Data availibility

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

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This research was funded by the European University of Atlantic.

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Iffat Ara Talin, Mahmudul Hasan Abid & Abdullah-Al Nahid

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I.A.T.: Conceptualization, Formal Analysis, Writing—Original Draft. M.H.A.: Conceptualization, Data Curation, Writing—Original Draft. M.A.S.: Methodology, Data Curation, Formal Analysis. I.D.A.: Methodology, Software, Visualization. I.de la T.D.: Funding Acquisition, Project Administration, Investigation. I.A.: Validation, Writing—Review and Edit, Supervision. A.-A.N.: Software, Visualization, Investigation. All authors reviewed the manuscript and approved it.

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Talin, I.A., Abid, M.H., Samad, M.A. et al. Exploring factors influencing the severity of pregnancy anemia in India: a study using proportional odds model. Sci Rep 13 , 22816 (2023). https://doi.org/10.1038/s41598-023-49872-x

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DOI : https://doi.org/10.1038/s41598-023-49872-x

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Effectiveness of Dietary Interventions to Treat Iron-Deficiency Anemia in Women: A Systematic Review of Randomized Controlled Trials

Dominika skolmowska.

1 Department of Dietetics, Institute of Human Nutrition Sciences, Warsaw University of Life Sciences (SGGW-WULS), 159C Nowoursynowska Street, 02-776 Warsaw, Poland; lp.ude.wggs@akswomloks_akinimod (D.S.); lp.ude.wggs@atolok_ardnaskela (A.K.)

Dominika Głąbska

Aleksandra kołota, dominika guzek.

2 Department of Food Market and Consumer Research, Institute of Human Nutrition Sciences, Warsaw University of Life Sciences (SGGW-WULS), 159C Nowoursynowska Street, 02-776 Warsaw, Poland; lp.ude.wggs@kezug_akinimod

Associated Data

Iron-deficiency anemia is the most frequent nutritional deficiency, with women of reproductive age being particularly at risk of its development. The aim of the systematic review was to assess the effectiveness of dietary interventions to treat iron-deficiency anemia in women based on the randomized controlled trials. The systematic review was conducted according to the PRISMA guidelines and registered in the PROSPERO database (CRD42021261235). The searching procedure was based on PubMed and Web of Science databases, while it covered records published until June 2021. It included all randomized controlled trials assessing effectiveness of various dietary interventions on treatment of iron-deficiency anemia in women of childbearing age. The total number of 7825 records were screened, while 14 of them were finally included in the systematic review. The studies were screened, included, and reported, and the risk of bias was assessed using the revised Cochrane risk-of-bias tool for randomized trials by two independent researchers. The included studies compared the effectiveness of various dietary interventions with supplementation, placebo, control, or any other dietary intervention, while the assessed dietary interventions were based either on increasing iron supply and/or on increasing its absorption (by increasing vitamin C or vitamin D or decreasing phytate intake). The duration of applied intervention was diversified from 3 months or less, through 4 or 5 months, to half of a year or more. Among the assessed biochemical measures, the following were analyzed in majority of studies: hemoglobin, ferritin, transferrin receptor, hematocrit, and transferrin. The majority of included studies supported the influence of dietary interventions on the treatment of iron-deficiency anemia, as the applied dietary intervention was not effective in only three studies. The majority of included studies were assessed as characterized by medium risk of bias, while the overall risk was high for only four studies, which resulted from the randomization process, deviations from the intended interventions, and selection of the reported result. The majority of included studies were conducted for increasing iron supply and/or increasing vitamin C supply; however, only for the interventions including increasing iron supply and simultaneously increasing its absorption by vitamin C supply were all results confirmed effective. Vitamin D also seems to be an effective dietary treatment, but further studies are necessary to confirm the observations. Considering this fact, dietary interventions recommended for anemic female patients should include increased intake of iron and vitamin C.

1. Introduction

Iron-deficiency anemia is the most frequent nutritional deficiency [ 1 ], which has been highlighted by the World Health Organization (WHO) to be a serious health problem not only in developing but also developed countries [ 2 ]. It is estimated that anemia affects a third of the world’s population [ 3 ], while women of reproductive age are particularly at risk of its development [ 4 ]. There is a number of serious health consequences of anemia which concern this population group, including a lack of concentration and focus, reduced exercise tolerance, poor work performance, and adverse maternal outcomes in pregnant women [ 5 ]. Taking this into consideration, one of the Global Nutrition Targets set by the WHO which should be achieved by 2025 is a 50% reduction in anemia frequency among women of childbearing age [ 6 ].

Dietary iron occurs in two forms, as heme iron and non-heme iron, which vary in their chemical form and bioavailability [ 7 ]. Heme iron is found only in hemoglobin and myoglobin derived from meat, poultry, and fish [ 8 ], while non-heme iron is present both in animal and plant products [ 9 ]. The bioavailability of these two forms of iron significantly differs, as heme iron may be absorbed up to 30% in the human body, while absorption of non-heme form is affected by other nutrients and ranges from 1% to 10% [ 10 ]. However, a majority of iron in an omnivorous diet is non-heme iron, which makes up 85–90% of total iron intake [ 11 ].

There are two main dietary strategies to treat iron-deficiency anemia—increasing the intake of foods which are naturally rich in iron and ensuring a high bioavailability of iron (by providing enhancers of iron absorption within a meal and decreasing the intake of iron inhibitors) [ 12 ]. According to the National Institutes of Health, the richest sources of heme iron in the diet are lean meat and seafood, while nuts, beans, vegetables, and fortified grain products provide non-heme iron [ 13 ]. As indicated by the WHO, since iron from plant products is less well absorbed, it is advisable to include the enhancers of non-heme iron absorption, such as ascorbic, citric, or malic acid, to a meal or to apply food processing that may improve non-heme iron bioavailability, such as fermentation, soaking, and germination [ 12 ]. Already, a well-implemented strategy may be to fortify staple food products with iron, such as cereals and flour [ 14 ].

The other strategy is to apply oral supplements which provide various nutrients missing in the diet at higher doses to promptly combat nutritional deficiencies and related anemia [ 15 ]. However, applying iron supplementation may result in adverse gastrointestinal effects, such as abdominal pain, constipation, or nausea [ 16 ]. Moreover, non-physiological amounts can increase the associated health risks, such as infections [ 17 ]. Taking this into account, such an approach may be less recommended than dietary intervention, especially for some populations, as lower quantities of iron provided within a food matrix are indicated to be in most cases a safer option, representing a more logical strategy providing the best balance of risk and benefits [ 18 ]. Moreover, it is pointed out that iron supplementation may be considered rather as a short-term strategy for the management of iron-deficiency anemia, while dietary interventions may be treated as a long-term strategies [ 19 ]. A reliable and objective evaluation of different models of iron-deficiency therapy is crucial. Taking this into account, the aim of this systematic review was to assess the effectiveness of dietary interventions to treat iron-deficiency anemia in women based on the randomized controlled trials.

2. Materials and Methods

2.1. design.

The literature search, screening, including, and reporting was carried out in accordance with the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [ 20 ]. It covered peer-reviewed randomized controlled trials published and included in the databases of PubMed and Web of Science until June 2021. The review was registered in the International Prospective Register of Systematic Reviews (PROSPERO) database (CRD42021261235).

2.2. Inclusion and Exclusion Criteria

The studies included in the presented systematic review were planned to be randomized controlled trials presenting the assessment of effectiveness of various dietary interventions (while compared with supplementation, or placebo, or control, or the other dietary approach) on treatment of iron-deficiency anemia in women of childbearing age.

The inclusion criteria were formulated as follows:

• (1) Research study;
• (2) Randomized controlled trial;
• (3) Study carried out in a group of female menstruating subjects;
• (4) Study carried out in a group of subjects with diagnosed anemia or low iron stores;
• (5) Dietary intervention applied within the study, while using either regular food products, or fortified food products;
• (6) The effectiveness of dietary intervention assessed within the study, while using any biochemical measure of anemia/iron stores;
• (7) The effectiveness of dietary intervention, assessed within the study, compared with the effectiveness of supplementation, placebo, control, or another dietary approach;
• (8) Full text of the study published in a peer-reviewed journal;
• (9) Full text of the study published in English.

The exclusion criteria were formulated as follows:

• (1) Study carried out in animal model;
• (2) Study carried out in a mixed population (e.g., female and male, menstruating and not menstruating), if not presenting results separately for sub-groups;
• (3) Study carried out in a group of pregnant women;
• (4) Study carried out in a group of subjects with any condition which may influence iron status (e.g., celiac disease, bariatric surgery);
• (5) Study carried out in a group of subjects with any eating disorder which may influence the reliability of results;
• (6) Study carried out in a group of subjects with any intellectual disability which may influence the reliability of results;
• (7) Applied dietary intervention not described within the study;
• (8) The effectiveness of dietary intervention influenced by interfering variables applied within the study (e.g., pharmacological intervention, physical activity intervention).

No other additional criteria associated with diseases and conditions, other than those which may influence iron status, or which influence the reliability of results were included.

The applied criteria for a population, intervention/exposure, comparator, outcome, and study design (PICOS) [ 21 ] are presented in Table 1 .

The applied criteria for a population, intervention/exposure, comparator, outcome, and study design (PICOS).

2.3. Searching Strategy

The detailed electronic searching strategy for the databases of PubMed and Web of Science is presented in Table 2 .

The detailed electronic searching strategy for the databases of PubMed and Web of Science.

The procedure of identification of studies via PubMed and Web of Science databases is presented in Figure 1 . Within the whole procedure, identification, screening, and inclusion were conducted by two independent researchers, and these were conducted separately based on the title and abstract and based on the full text of the study. Any disagreement between proceeding researchers was consulted with the other researcher. If the full text of the study was not available within electronic databases or the university library, the corresponding author of the article was contacted to obtain the full text.

The procedure of identification of studies via PubMed and Web of Science (WoS) databases.

2.4. Data Extraction Procedure

The data extraction was conducted by two independent researchers. Any disagreement between proceeding researchers was consulted with the other researcher. If any information was not available within the full text of the article, the corresponding author of the article was contacted to obtain the necessary information (14 emails sent; data referred as provided on request).

The data were extracted based on the common approach to describe the following characteristics of the included studies:

• (1) General characteristics of the study, including: authors and year of the study, studied intervention, studied group, country/location, studied period;
• (2) Study participants, including: number of participants, age, inclusion criteria, exclusion criteria;
• (3) Applied dietary intervention, including: studied treatment/treatments, iron intake in groups, vitamin C intake in groups, intervention duration, biochemical measures;
• (4) Findings of the study, including: observations described by authors of the study; conclusions formulated by the authors of the study.

The risk of bias was assessed in order to define the quality of the included studies [ 22 ], while the revised Cochrane risk-of-bias tool for randomized trials was chosen and the dedicated RoB 2 tool (7.0) was used [ 23 ]. The studies were assessed within 5 domains, as follows: risk of bias arising from the randomization process, risk of bias due to deviations from the intended interventions, risk of bias due to missing outcome data, risk of bias in measurement of the outcome, risk of bias in selection of the reported result, as well as for the overall risk of bias, as it is commonly applied [ 24 ].

The general characteristics of the randomized controlled trials included in the systematic review [ 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ] is presented in Table 3 . The included randomized controlled trials presented the effectiveness of the dietary intervention assessed within the study and were compared with the effectiveness of supplementation [ 25 , 26 , 35 ], placebo [ 25 , 27 , 32 , 33 ], control [ 25 , 26 , 28 , 34 , 35 , 37 , 38 ], or other dietary approach [ 29 , 30 , 31 , 36 ]. The studies were conducted mainly in samples of young women [ 30 , 32 , 33 , 34 , 36 , 37 , 38 ] or young to middle-aged women [ 25 , 26 , 27 , 28 , 29 , 31 ], while one study was conducted in a group of adolescent girls [ 35 ]. The studied individuals were described as those with iron deficiency/anemia [ 25 , 26 , 27 , 35 , 38 ] or low iron stores [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 36 , 37 ]. The studies were conducted mainly in developed countries, such as Spain [ 33 , 34 , 35 ], Denmark [ 28 , 29 ], the United States of America [ 36 ], Australia [ 26 ], or New Zealand [ 25 , 31 ], but also in India [ 35 , 38 ], Mexico [ 27 ], and Rwanda [ 37 ].

The general characteristics of the randomized controlled trials included in the systematic review.

* data provided on request.

The characteristics of the study participants of the randomized controlled trials included in the systematic review is presented in Table 4 . The included randomized controlled trials were conducted mainly in small groups of less than 50 participants [ 27 , 28 , 30 , 33 , 35 , 36 ] or medium-size groups of 51–100 participants [ 25 , 26 , 29 , 31 ], and some studies conducted in large samples of over 100 participants were included [ 32 , 34 , 37 , 38 ]. Among the inclusion criteria, mainly iron deficiency/anemia [ 25 , 26 , 27 , 35 , 38 ] or low iron stores [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 36 , 37 ] were indicated. Among the exclusion criteria, mainly health problems which may influence iron status [ 25 , 30 , 31 , 32 , 33 , 34 , 36 , 37 ] and applied supplementation were indicated [ 25 , 29 , 30 , 31 , 32 , 33 , 34 , 37 , 38 ].

The characteristics of the study participants of the randomized controlled trials included in the systematic review.

The characteristics of the applied dietary intervention within the randomized controlled trials included in the systematic review are presented in Table 5 . The assessed dietary interventions were based either on increasing iron supply [ 25 , 26 , 28 , 29 , 30 , 32 , 33 , 35 , 37 , 38 ] and/or on increasing its absorption, which was obtained by increasing vitamin C [ 25 , 26 , 27 , 31 , 35 ] or vitamin D [ 34 ] or decreasing phytate intake [ 36 ]. In the vast majority of studies, the supply of iron [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 36 , 37 ] and of vitamin C was assessed [ 28 , 29 , 30 , 31 , 32 , 33 , 36 ]. The durations of the applied interventions were diversified range from 3 months or less [ 26 , 35 , 36 , 38 ], through 4 or 5 months [ 25 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 37 ], to half of a year or more [ 27 ]. Among the assessed biochemical measures, the following were analyzed in the majority of studies: hemoglobin [ 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 37 , 38 ], ferritin [ 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 36 , 37 ], transferrin receptor [ 25 , 27 , 30 , 34 , 36 , 37 ], hematocrit [ 30 , 32 , 34 , 38 ], and transferrin [ 30 , 32 , 33 , 34 ].

The characteristics of the applied dietary intervention within the randomized controlled trials included in the systematic review.

The findings formulated within the randomized controlled trials included in the systematic review are presented in Table S1 . The summary of conclusions from the randomized controlled trials included in the systematic review is presented in Table 6 . It should be indicated that the majority of included studies supported the influence of dietary interventions on the treatment of iron-deficiency anemia, as the applied dietary intervention was not effective in only three studies [ 27 , 30 , 36 ]. The majority of the included studies were conducted for increasing iron supply and/or increasing vitamin C supply; however, only for the interventions including increasing iron supply and simultaneously increasing its absorption by vitamin C supply [ 25 , 26 , 35 ] were all the results confirmed to be effective.

The summary of conclusions from the randomized controlled trials included in the systematic review.

* the conclusion of the study assessed as supporting applied dietary intervention (if confirmed by the assessed biochemical measures) or not supporting applied dietary intervention (if not confirmed by the assessed biochemical measures).

The assessment of the risk of bias for the randomized controlled trials included in the systematic review, conducted while using the revised Cochrane risk-of-bias tool for randomized trials, is presented in Table 7 . The majority of included studies were assessed as characterized by a medium risk of bias, while the overall risk was high for only four studies, which resulted from the risk of bias arising from the randomization process [ 26 , 28 ], the risk of bias due to deviations from the intended interventions [ 38 ], and the risk of bias in selection of the reported result [ 33 ]. The studies associated with the highest risk of bias were indicated within various groups of studies—for interventions increasing iron supply [ 28 , 33 , 38 ], as well as increasing iron supply and increasing its absorption by vitamin C supply [ 26 ]. Taking this into account, more studies should be conducted to confirm the observations, especially for increasing iron supply and/or increasing vitamin C supply. This results from the fact that the majority of included studies were conducted for increasing iron supply and/or increasing vitamin C supply, while only the interventions including increasing iron supply and simultaneously increasing its absorption by vitamin C supply [ 25 , 26 , 35 ] had all their results confirmed to be effective.

The assessment of the risk of bias for the randomized controlled trials included in the systematic review, conducted while using the revised Cochrane risk-of-bias tool for randomized trials.

Assessed domains: D1—risk of bias arising from the randomization process; D2—risk of bias due to deviations from the intended interventions; D3—risk of bias due to missing outcome data; D4—risk of bias in measurement of the outcome; D5—risk of bias in selection of the reported result.

4. Discussion

The results of the studies described within this systematic review confirm that various dietary interventions may be effective in the treatment of diagnosed anemia or low iron stores in women of reproductive age [ 25 , 26 , 28 , 29 , 31 , 32 , 33 , 34 , 35 , 37 , 38 ]. Only some dietary approaches were proven to be insufficient to enhance the iron status of the studied groups within the studies included to presented systematic review, among them, one of those increasing iron supply [ 30 ], one of those increasing vitamin C supply [ 27 ], or one decreasing phytate supply [ 36 ]. Meanwhile, according to the revised Cochrane risk-of-bias tool for randomized trials, the overall risk was high for only four studies, which resulted from the risk of bias arising from randomization process [ 26 , 28 ], deviations from the intended interventions [ 38 ], and selection of the reported result [ 33 ].

One of the possible strategies to manage iron-deficient anemia is dietary modification promoting an increase in iron-containing food products [ 39 ], where efforts should be focused on promotion to increase the intake of meat, poultry, fish, and some non-animal products, such as green leafy vegetables and legumes [ 40 ]. However, specific recommendations should be adjusted to regional variations in diets [ 39 ]. Such an approach to promoting the intake of iron-rich products is commonly applied in the analyzed studies; in the present systematic review, the majority of included studies used such a dietary intervention [ 28 , 29 , 30 , 32 , 33 , 37 , 38 ]. Although meat is a good source of the well-absorbed heme form of iron [ 8 ], there are some specific gender-dependent food preferences which may influence the overall intake of iron [ 41 ]. Women are generally more concerned about a healthy diet than men [ 42 ], and they tend to have a lower preference towards meat [ 43 ]. Therefore, women are also more likely to include in their diet non-heme iron sources, such as legumes and vegetables [ 44 ]. At the same time, population groups which consume mainly plant-based diets with limited amount of meat may be vulnerable to iron deficiency anemia as a result of co-consumption with dietary iron inhibitors [ 45 ]. In such cases, there is a strong need to provide not only a considerable amount of dietary iron but to also enhance its bioavailability from a meal, which seems to be the most effective dietary strategy, as it was proven in the presented systematic review for a number of studies [ 25 , 26 , 35 ].

Increasing iron absorption is another way to improve and maintain iron status [ 39 ]. There are several nutrients which improve iron bioavailability, such as vitamin C, organic acids, fish and meat protein, and peptides from partially digested muscle tissue [ 46 ]. Vitamin C is reported to be the most powerful enhancer of iron absorption [ 47 ], which can increase the absorption of ferrous ions (Fe 3+ ) and ferric ions (Fe 2+ ) [ 48 ]. Such an effect results from the reducing properties of ascorbic acid, which allows the iron to be soluble in a wide range of pHs, as well as to be absorbed through iron transporters, namely, divalent metal transporter 1 (DMT1) in the small intestine [ 49 ]. However, a proper ascorbic acid-to-iron molar ratio of about 2:1 is necessary to increase iron bioavailability [ 50 ]. Some studies suggest that vitamin D may also increase iron absorption by downregulating pro-inflammatory cytokines and hepcidin [ 51 ]. Another possible mechanism may involve the expression of 1,25-hydroxyvitamin D receptors by erythrocyte precursor cells which induces both proliferation and maturation of erythroid progenitor cells [ 52 ]. Such results are consistent with the study included in the presented systematic review which highlights the potential of vitamin D in increasing iron bioavailability [ 34 ]. On the other hand, there are some inhibitors of iron absorption, such as phytates, calcium, and polyphenols [ 53 ]. There are useful strategies to lower the amount of iron inhibitors in food products, such as removal or degradation of phytic acid [ 50 ]. However, as it was shown in presented systematic review, decreasing phytate supply did not result in improving iron status in women with suboptimal iron stores [ 36 ].

It should be also borne in mind that in the group of women of reproductive age, blood loss during menstruation is the most prevalent cause of iron deficiency and iron-deficiency anemia [ 54 ]. It is estimated that 40 mL of menstrual blood loss results in an average loss of 1.6 mg of iron [ 55 ]. However, women with heavy menstrual bleeding (more than 80 mL per one cycle) lose on average up to six times more iron per menstrual cycle compared to women with a normal blood loss, which may lead to a total depletion of their iron stores [ 56 ]. Therefore, it may be particularly challenging for them to provide an adequate iron intake in order to compensate for iron losses during menstruation [ 57 ]. Therefore, effective dietary interventions combining increasing iron supply and increasing vitamin C supply which will promote long-term adherence are especially needed.

Although the present review provided some interesting observations, its limitations must be also highlighted. First of all, randomized controlled trials included in the review differed in their durations, applied dietary interventions, as well as hematological parameters assessed. Therefore, the obtained results may have been difficult to compare, and conducting a meta-analysis was not possible due to the fact that various dietary interventions and hematological parameters were presented. Moreover, in some studies, the samples which were assessed were relatively small [ 27 , 28 , 30 , 33 , 35 , 36 ]. Taking this into account, more randomized controlled trials assessing the effectiveness of various dietary interventions on treatment of iron-deficiency anemia in women of childbearing age are needed, as randomized controlled trials are indicated to provide much more reliable information than other sources of evidence [ 58 ].

5. Conclusions

It should be concluded that the majority of dietary interventions are effective in the treatment of iron-deficiency anemia. While the majority of randomized controlled trials assessed the effect of increasing iron supply and/or increasing vitamin C supply, the most effective seems to be combining both options and planning within applied diet increased intake of iron and vitamin C at the same time. Vitamin D also seems to potentially be an effective therapeutic option, but more studies should be conducted to confirm observations. Considering this fact, dietary interventions recommended for anemic female patients should include increasing their intake of iron and vitamin C.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/nu14132724/s1 , Table S1: The findings formulated within the randomized controlled trials included to the systematic review.

Funding Statement

This research was funded by the Polish Ministry of Science and Higher Education within funds of Institute of Human Nutrition Sciences, Warsaw University of Life Sciences (WULS), for scientific research.

Author Contributions

D.S., D.G. (Dominika Głąbska), A.K. and D.G. (Dominika Guzek) made study conception and design; D.S., D.G. (Dominika Głąbska), A.K. and D.G. (Dominika Guzek) performed the research; D.S., D.G. (Dominika Głąbska), A.K. and D.G. (Dominika Guzek) analyzed the data; D.S., D.G. (Dominika Głąbska), A.K. and D.G. (Dominika Guzek) interpreted the data; D.S., D.G. (Dominika Głąbska), A.K. and D.G. (Dominika Guzek) wrote the paper. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

The literature search was conducted according to the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and the review was registered in the International Prospective Register of Systematic Reviews (PROSPERO) database (CRD42021261235).

Conflicts of Interest

The authors declare no conflict of interest.

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