Frontiers | PRAYAS: individual patient data meta-analysis database for Pooled Research and Analysis for Yielding Anemia-free Sol

Frontiers | PRAYAS: individual patient data meta-analysis database for Pooled Research and Analysis for Yielding Anemia-free Solutions in India
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Front. Public Health
, 23 December 2025
Sec. Public Health and Nutrition
Volume 13 - 2025 |
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ORIGINAL RESEARCH article
Front. Public Health
, 23 December 2025
Sec. Public Health and Nutrition
Volume 13 - 2025 |
PRAYAS: individual patient data meta-analysis database for Pooled Research and Analysis for Yielding Anemia-free Solutions in India
Anuj Kumar Pandey
Anju Pradhan Sinha
† *
Ramu Rawat
Ranadip Chowdhury
Shivaprasad S. Goudar
Jitender Nagpal
Shrey Desai
Avula Laxmaiah
Kalpana Basany
Sadhana Joshi
10
Chittaranjan Yajnik
11
Aparna Mukherjee
Pratibha Dwarkanath
12
Priyanka Gupta Bansal
Molly Jacob
13
Shinjini Bhatnagar
14
Komal Shah
15
Debarati Mukherjee
16
Amlin Shukla
Raghu Pullakhandam
Varsha Dhurde
17
Aditi Apte
18
Rajeev Singh
19
Aakriti Gupta
20
Yamini Priyanka
Usha Dhingra
Ravi Prakash Upadhyay
Sutapa Bandyopadhyay Neogi
Manjunath S. Somannavar
Anirban Mandal
Gayatri Desai
Shantanu Sengupta
21
Shailendra Dandge
Girija Wagh
22
Urmila Deshmukh
11
Gunjan Kumar
Anura V. Kurpad
12
G. S. Toteja
Nikhitha Mariya John
13
Shailaja Sopory
14
Somen Saha
15
Giridhar R. Babu
23
Anandika Suryavanshi
Ravindranadh Palika
Archana Patel
17
‡ #
Radhika Nimkar
18
Gaurav Raj Dwivedi
19
Umesh Kapil
20
Dilip Raja
Arup Dutta
Sunita Taneja
Diksha Gautam
Avinash Kavi
Swapnil Rawat
Kapilkumar Dave
Rajiva Raman
24
Catherine L. Haggerty
25
Sanjay Lalwani
22
Prachi Phadke
11
Alka Turuk
Tinku Thomas
12
Neena Bhatia
26
Manisha Madai Beck
13
Lovejeet Kaur
14
Aakansha Shukla
15
R. Deepa
16
Lindsey M. Locks
27
Dhiraj Motilal Agarwal
18
Raja Sriswan Mamidi
Harshpal Singh Sachdev
Rounik Talukdar
28
Sayan Das
Nita Bhandari
Ranjana Singh
29
S. Yogeshkumar
Ramasheesh Yadav
P. S. Reddy
25
Sanjay Gupte
30
S. Rasika Ladkat
11
Zaozianlungliu Gonmei
Swati Rathore
13
Dharmendra Sharma
14
Apurvakumar Pandya
15
Yamuna Ana
16
Patricia Hibberd
31
Himangi Lubree
18
Anwar Basha Dudekula
Priti Rishi Lal
20
Pearlin Amaan Khan
Aruna Verma
32
Umesh S. Charantimath
Indrapal I. Meshram
Karuna Randhir
10
Onkar Deshmukh
11
Ashok Kumar Roy
Obed John
13
Nolita Dolcy Saldanha
33
Ashish Bavdekar
18
Raj Kumar
34
Shyam Prakash
20
Wafaie W. Fawzi
34
Sunil Sazawal
1.
Department of Health Systems and Implementation Research, International Institute of Health Management Research, New Delhi, India
2.
Indian Council for Medical Research, New Delhi, India
3.
Centre for Public Health Kinetics, New Delhi, India
4.
Society for Applied Studies, New Delhi, India
5.
J N Medical College, KLE Academy of Higher Education and Research, Belagavi, Karnataka, India
6.
Sitaram Bhartia Institute of Science and Research New Delhi (SBISR), New Delhi, India
7.
Society for Education, Welfare and Action–Rural, Bharuch, India
8.
ICMR-National Institute of Nutrition, Hyderabad, India
9.
SHARE India, MediCiti Institute of Medical Sciences, Hyderabad, India
10.
Interactive Research School for Health Affairs, Bharati Vidyapeeth Deemed to be University, Pune, India
11.
KEM Hospital Research Centre, Pune, India
12.
St. John’s Research Institute, Bangalore, India
13.
Christian Medical College, Vellore, India
14.
Translational Health Science and Technology Institute, Faridabad, Haryana, India
15.
Indian Institute of Public Health Gandhinagar, Gandhinagar, India
16.
Public Health Foundation of India, Bangalore, India
17.
Lata Medical Research Foundation, Nagpur, India
18.
Vadu Rural Health Program, KEM Hospital Research Centre, Pune, India
19.
ICMR-Regional Medical Research Centre, Gorakhpur, India
20.
All India Institute of Medical Sciences, New Delhi, India
21.
Council of Scientific and Industrial Research-Institute of Genomics and Integrative Biology (CSIR-IGIB), New Delhi, India
22.
Bharati Hospital and Research Centre, Pune, India
23.
Department of Population Medicine, College of Medicine, QU Health, Qatar University, Doha, Qatar
24.
Banaras Hindu University (BHU), Varanasi, India
25.
School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States
26.
Lady Irwin College, New Delhi, India
27.
Department of Health Sciences, Boston University, Boston, MA, United States
28.
Ahmedabad University, Ahmedabad, Gujarat, India
29.
Indian Institute of Public Health, Delhi, India
30.
Gupte Hospital and Research Centre, Pune, India
31.
Boston University School of Medicine, Corsstown, Boston, MA, United States
32.
LLRM Medical College, Meerut, Uttar Pradesh, India
33.
Avon and Wiltshire Mental Health Partnership NHS Trust, Bath, United Kingdom
34.
BRD Medical College, Gorakhpur, Uttar Pradesh, India
35.
Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA, United States
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Abstract
Purpose:
The PRAYAS Individual Patient Data Meta-analysis (IPD-MA) database aims to estimate the prevalence of anemia among children under 18 years, non-pregnant and non-lactating (NPNL) women, and pregnant women (by trimester), with further stratification by age group, year, and region of India. Beyond prevalence, it seeks to address the etiological contribution of iron and other erythropoietic micronutrient deficiencies and to evaluate the effectiveness of anemia prevention and treatment interventions, including factors associated with non-response. This will directly support India’s “test–treat–track” approach under the Anemia Mukt Bharat program.
Participants:
Children (0–18 years), pregnant women, and NPNL women in India.
Findings to date:
The database currently includes 88 datasets (1994–2023), with 319,721 participants for prevalence analysis—children (19,762), NPNL women (17,883), and pregnant women (282,076). Intervention studies comprise 59,292 participants—children (13,435), NPNL women (11,594), and pregnant women (34,263). Over half the datasets (55.7%, 49/88) are randomized controlled trials, while 35.2% (31/88) are observational. Geographically, 43.2% (38/88) are from northern India, 22.7% (20/88) from the west, and 18.2% (16/88) from the south. Most studies (67%, 59/88) are community-based. Median ages were 26 years (IQR 23–32) for NPNL and 23 years (IQR 21–25) for pregnant women, while children’s data covered 6 months to 18 years. Mean gestational age at enrollment in pregnancy was 10.24 weeks (SD 17.65). Of the total sample, 10.8% had complete blood count data, 9% ferritin, and 4.5% vitamin B12.
Among interventions, pregnant women received intravenous iron sucrose, ferric carboxymaltose, iron isomaltoside, combined IV iron with vitamin B12/folic acid/niacinamide, integrated packages, and low-dose calcium supplementation. NPNL women were often part of trials comparing 60 mg daily ferrous sulfate with 120 mg on alternate days. Children’s interventions mainly included ferrous sulfate, food supplementation, and select Ayush-based approaches.
Future plans:
PRAYAS will generate robust, policy-relevant evidence to refine anemia prevention and treatment strategies. Findings will directly inform the Anemia Mukt Bharat program, supporting targeted, evidence-driven interventions to reduce anemia and associated health burdens across children, women, and pregnant populations in India.
Clinical Trial Registration:
OSF—
Highlights
The harmonized PRAYAS pooled Indian dataset is one of the largest, reliable, and most comprehensive datasets on pregnant/non-pregnant and non-lactating women and children.
One of its kind of dataset with information on hemoglobin levels, relevant biochemical and key micronutrients parameters, and varied interventions from across India.
Heterogeneity of interventions, dosage, duration, and data collection approaches.
Studies lack critical parameters needed to assess changes in hemoglobin concentration like non-availability of key erythropoietic micronutrients in most of the studies, limiting the scope of certain analyses.
Introduction
Nutritional anemia remains a significant global public health challenge (
), with profound implications for the health and productivity of women and children. In 2019, anemia affected approximately one-third of women of reproductive age (WRA) globally, with approximately 269 million children aged 6–59 months also impacted (
). The burden of anemia is disproportionately high in low- and middle-income countries (LMICs) (
), with the African and South-East Asian regions contributing the most to global prevalence (
). In LMIC, anemia affects 43% of the population compared to just 9% in developed nations, with WRA and children being the most vulnerable groups (
). While anemia has a multifactorial etiology, iron deficiency is the most prevalent cause, particularly in LMICs, where nutritional deficiencies are widespread (
). The World Health Organization (WHO) Global Nutrition Targets aim for a 50% reduction in anemia among WRA by 2030, reflecting its prioritization in global health initiatives (
10
).
In India, the burden of anemia has shown alarming trends. The National Family Health Survey (NFHS-5) highlights an increase in anemia prevalence among WRA (from 53.1% in 2015–16 to 59.1% in 2019–21), pregnant women (from 50.3 to 52.6%), and children aged 6–59 months (from 58.6 to 67.1%) over the same period (
11
). However, the methods used for assessments and the interpretation of the results have highlighted several challenges, like the use of capillary blood, unlike gold standard methods of assessments through venous blood (
12–14
). Additionally, a national survey reported that ~41% of preschoolers, school-age children, and adolescents (aged 1–19 years) were anemic, with female adolescents experiencing higher prevalence rates (40%) than males (18%) (
15
). Anemia’s consequences are far-reaching, including fatigue, impaired cognitive and immune function, reduced productivity, and increased morbidity and mortality (
16
17
). Addressing anemia remains a public health priority in India, as evidenced by initiatives like the Anemia Mukt Bharat (AMB) program, launched in 2018 to reduce anemia prevalence by three percentage points annually among children, adolescents, and WRA (
17
). The reduction of anemia is one of the important objectives of the POSHAN Abhiyaan launched in March 2018.
Despite these efforts, the prevalence of anemia remains unacceptably high (
18
19
). Improving the understanding of anemia’s burden across demographic groups and evaluating the effectiveness of interventions are critical for guiding policy and program decisions. Complying with the targets of POSHAN Abhiyaan and National Nutrition Strategy set by the NITI Aayog, AMB strategy has been designed to reduce the prevalence of anemia.
There is a need to synthesize the evidence on anemia and analyze the progress made under AMB or reasons for its inadequate progress. In this context, it was decided to collate all existing data on anemia from studies conducted over more than four decades across diverse regions of the country by contacting the investigators. The primary goal of this initiative is to answer the questions that remain unanswered by individual studies. The results would feed into the AMB program recommendations for WRA and children. The results would also guide targeted strategies to reduce anemia in India. It is expected that by pooling the observational and interventional studies, we may investigate the etiological fractions of various causes of this recalcitrant public health problem and synthesize evidence on the effectiveness of several interventions used for prevention and treatment of anemia. By integrating data from a wide range of geographical, community, and healthcare settings, this extensive pooling of studies aims to provide a comprehensive and nuanced understanding of the underlying trends and patterns. Our approach not only enhances the depth and breadth of available evidence but also ensures a more representative and holistic perspective on the factors influencing health outcomes over time.
Database description
Selection of studies
Given the persistent and high burden of anemia in India despite ongoing initiatives, there was a recognized need for an in-depth understanding of the issues concerned with anemia across the country. In response, the Director General of the Indian Council for Medical Research (ICMR) commissioned an initiative in early 2023 to conduct a comprehensive assessment of the anemia burden in India. To facilitate this effort, ICMR constituted a committee of eminent experts in the field of anemia in India. The initial phase involved key preparatory activities, including the development of database search keywords, identification of principal investigators (PIs), and relevant studies across India, as well as contacting the study PIs. These activities were undertaken by the Secretariat at the ICMR.
A designated committee conducted the selection of studies under the chairperson’s guidance. To identify relevant studies, the committee adopted two well-established approaches. First, a systematic search of trial registries, and second, collaborative discussions with investigators of ongoing studies involving children and women (pregnant and NPNL) (
20
21
). The process began with a database search of the Clinical Trials Registry of India (CTRI) (
22
). The initial search was conducted using designated keywords such as “anemia,” “prevalence,” “children,” “randomized controlled trial,” “intervention studies,” “anemia etiology,” “pregnant women,” “women of reproductive age,” and “non-pregnant women” (
22
).
The committee also expanded the search by examining cross-references from related studies, following up with leads provided by principal investigators (PIs) of studies included, and reaching out to additional researchers in the field. After identifying related studies, committee members contacted the PIs with requests for collaboration in early 2024. Once the PIs agreed, a data sharing agreement was prepared and signed by them, and several online meetings were held to discuss the details of the proposed database and the datasets involved. After the online meetings with study PIs, the harmonization meeting was held at ICMR headquarters in New Delhi in early August 2024 with the objective of discussing methodological issues, explaining to the participants what the database would be, and discussing the difficulties in filling the data extraction sheets. Such meetings were scheduled at the ICMR headquarters at routine intervals of 3–4 months of time. The meeting also aimed at handholding the PIs on how to fill in the data cells. A second round of data harmonization meeting was held at the end of October 2024. After identifying and removing duplicates, a total of 88 datasets from studies, conducted by 23 organizations at different time points in India, were included in the database synthesis following certain inclusion criteria -
This database included studies on anemia conducted on children under 18 years, NPNL women, and pregnant women, where data on hemoglobin levels were available.
Eligible studies included cross-sectional and interventional designs (both randomized and non-randomized), longitudinal studies, unpublished studies, including the COVID registries with due approval from the relevant authorities.
Studies conducted in India, with data on hemoglobin and other relevant biochemical parameters (e.g., serum ferritin levels, complete blood counts, vitamin B12 levels, and inflammatory markers) at baseline and post-intervention were also included within the database.
Data sets were excluded if data on hemoglobin and other relevant biochemical parameters were not available.
Data sources in the pooled data set
After completing the formalities and harmonization, the PIs shared their anonymized datasets in a predefined format. The data included information such as study ID, woman/subject ID, demographic details, interventional strategies, hemoglobin and ferritin levels, relevant biochemical parameters at baseline and post-intervention, comorbidities, adverse effects, etc.
Figure 1
provides details of the studies identified through databases and the number of studies included in the final database. The details of each of the studies included are available elsewhere (
Supplementary Table 1
).
Figure 1
The ICMR team ensured that the included studies complied with relevant ethical guidelines and regulations. All included primary studies had received approval from ethics committees recognized by the Department of Health Research in India, and informed consent was obtained from all study participants.
Table 1
provides details of studies included within the database of PRAYAS.
Table 1
Name of institute (no of dataset)
Sample size
Period (year of blood collection during the study)
Study setting (Community-based or hospital-based)
Region (North, West, South, Central, North-East)
Study design (observational or RCT)
Prevalence-WRA
IIHMR—New Delhi (2)
2,457
2014–14
Hospital
North, South, East
Observational
838
2018–19
Hospital
South, East
Observational
KEM Hospital, Pune (2)
691
2001–03
Community
West
Observational
656
2006–08
Community
West
Observational
SAS—New Delhi (3)
408
2018–2019
Community
North
RCT
907
2017–2021
Community
North
RCT
6,672
2017–2021
Community
North
RCT
ICMR-NIN, Hyderabad (1)
470
2019
Hospital
South
Longitudinal
ICMR-RMRC, Gorakhpur (1)
536
2022–23
Community
North
Observational
ICMR—Headquarter (1)
4,128
2020–22
Hospital
Across India
Observational
CMC Vellore (1)
120
2021
Hospital
South
Longitudinal
Total sample
17,883
Intervention-WRA
SAS—New Delhi (4)
816
2018–2019
Community
North
RCT
4,069
2017–2021
Community
North
RCT
4,069
2017–2021
Community
North
RCT
2,050
2017–2021
Community
North
RCT
CMC, Vellore (1)
120
2020–21
Hospital
South
RCT
ICMR-NIN, Hyderabad (1)
470
2019
Community
South
Longitudinal
Total sample
11,594
Prevalence—pregnant
CMC—Vellore (3)
107
2019–22
Hospital
South
Longitudinal
107
2019–22
Hospital
South
Longitudinal
107
2019–23
Hospital
South
Longitudinal
KLE (JN Medical College) Belagavi (2)
11,220
2002–23
Community
South, North
RCT
125,180
2010–19
Community
South
Observational
IIPH—Bengaluru (2)
1,634
2016–19
Hospital
South
Observational
1,317
2016–19
Hospital
South
Observational
KEM Hospital—Pune (2)
737
1994–95
Community
West
Observational
670
1994–95
Community
West
Observational
SAS—New Delhi (2)
2,269
2017–2021
Community
North
RCT
910
2017–2021
Community
North
RCT
SEWA Rural—Gujarat (1)
458
2023
Hospital
West
Observational
ICMR—Headquarter (1)
16,539
2021
Hospital
Across India
Observational
ICMR—Headquarter (1)
874
2020–22
Hospital
Across India
Observational
THSTI—Faridabad (1)
6,000
2015–23
Hospital
North
Observational
8,665
6,481
IIPH—Gandhinagar (1)
207
2020
Community
West
Observational
MIMS—Telangana (1)
1,257
2010–18
Hospital
South
Longitudinal
Bharati Vidyapeeth, Pune (1)
1,062
2017–21
Hospital
West
Observational
Lata Foundation Nagpur (1)
85,277
2010–2021
Community
Central
Observational
SJRI Bangalore (1)
10,998
2018–22
Hospital
South
RCT
Total sample
282,076
Intervention-pregnant
SAS—New Delhi (3)
4,081
2017–21
Community
North
RCT
4,081
2017–21
Community
North
RCT
2,059
2017–21
Community
North
RCT
KLE (JN Medical College) Belagavi (2)
2,912
2022–23
Community
South, North
RCT
2,906
2022–23
Community
South, North
RCT
IIHMR—New Delhi (1)
1,999
2017
Hospital
North, East
RCT
SJRI—Bengaluru (1)
10,998
2018–22
Hospital
South
RCT
SEWA Rural—Gujarat (1)
100
2017–18
Hospital
West
Pre-post
MIMS Hyderabad, Telangana
5,127
2009–18
Hospital
South
Observational Cohort
Total sample
34,263
Prevalence children
SAS—New Delhi (5)
517
2018–2019
Community
North
RCT
408
2018–2019
Community
North
RCT
652
2021–2022
Community
North
RCT
1,300
2021–2022
Community
North
RCT
319
2020–2021
Community
North
RCT
CPHK—New Delhi (4)
1,257
2002–2004
Community
North
RCT
3,002
2014–15
Community
North
RCT
300
2009–2011
Community
North
RCT
2,250
2017–19
Hospital
North
RCT
KEM Hospital, Pune (3)
704
2001–03
Community
West
Observational
685
2005–08
Community
West
Observational
685
2012
Community
West
Observational
KEM Vadu—Pune (2)
972
2004
Community
West
RCT
551
2007
Community
West
RCT
SBISR—New Delhi (1)
100
1999
Hospital
North
RCT
ICMR—RMRC-Gorakhpur (1)
1,017
2022–23
Community
North
Observational
IIPH—Bengaluru (1)
256
2023–24
Hospital
South
Observational
IIPH, Gandhi Nagar (1)
450
2021
Community
West
RCT
Lata Foundation—Nagpur (1)
225
2020
Community
Central
Observational
AIIMS, New Delhi (1)
1,054
2017
Community
North
Observational
ICMR—Headquarter (1)
658
2020–22
Hospital
Across India
Observational
ICMR—Headquarter (1)
446
2015
Community
North
Observational
ICMR—Headquarter (1)
446
2020–22
Hospital
Across India
Observational
NIN—Telangana (1)
1,508
2017
Community
Central, Northeast, South, West, East
Observational
Total sample
19,762
Intervention children
SAS—New Delhi (8)
816
2018–2019
Community
North
RCT
1,036
2018–2019
Community
North
RCT
1,029
2018–2019
Community
North
RCT
1,300
2021–2022
Community
North
RCT
1,300
2021–2022
Community
North
RCT
1,678
2020–2021
Community
North
RCT
1,678
2020–2021
Community
North
RCT
837
2020–2021
Community
North
RCT
KEM Vadu—Pune (5)
184
2004–05
Community
West
RCT
167
2004–05
Community
West
RCT
165
2004–05
Community
West
RCT
165
2004–05
Community
West
RCT
414
2007
Community
West
RCT
IIPH—Gandhi Nagar (1)
245
2022
Community
West
RCT
MIMS—Telangana (1)
1,286
2010–18
Hospital
South
Observational
ICMR—Gorakhpur (1)
461
2023
Community
North
RCT
SBISR—New Delhi (1)
100
1999–2000
Hospital
North
RCT
AIIMS, New Delhi (1)
1,054
2017
Community
North
RCT
Total sample
13,435
Details of studies—database profile PRAYAS.
After finalizing the datasets, the entire database was separated into groups for children under 18 years, NPNL, and pregnant women. The studies were then categorized into two groups:
Prevalence studies and
Intervention studies
This categorization was important since it dictated the type of analysis and statistical methods to be applied. The included studies (
Table 1
) span both hospital and community settings and include observational, longitudinal, randomized controlled trials (RCTs), and pre–post-intervention designs. States and UTs were classified as regions for analysis following the classification system set by the Registrar General & Census Commissioner of India for sample registration system (SRS) (
23
).
Variable availability and definition
Data harmonization is a critical step while developing a database profile, ensuring that data from diverse studies can be integrated and analyzed collectively, thus enhancing the reliability and generalizability of the findings (
24–26
). A significant aspect of harmonization was to ensure uniform units for all biochemical variables. For example, hemoglobin levels (reported in grams per deciliter or grams per liter by different studies) were standardized to a single unit (grams per deciliter). This step, along with the standardization of other blood parameters such as red blood cell count and serum ferritin, was also undertaken. A separate sheet with standardized parameters was developed for reference (
Table 2
).
Table 2
Hemoglobin cutoff
Unit
Children 6–23 months of age
Children 6–59 months of age
NPNL
Pregnant women (first and third trimester)
Pregnant women (second trimester)
Non-anemia
gm/dl
10.5 or higher
11.0 or higher
12.0 or higher
11.0 or higher
10.5 or higher
Mild anemia
9.5–10.4
10.0–10.9
11.0–11.9
10.0–10.9
9.5–10.4
Moderate anemia
7–9.4
7.0–9.9
8.0–10.9
7.0–9.9
7–9.4
Severe anemia
<7.0
<7.0
<8.0
<7.0
<7.0
Cutoff values for hemoglobin along with the unit for data collection.
Hemoglobin (Hb), the primary outcome indicator of anemia, was measured in grams per deciliter (g/dL), and categorized as mild, moderate, and severe based on the hemoglobin threshold as mentioned in the updated guideline on hemoglobin cutoffs to define anemia, released in 2024 (
Table 2
) (
27
28
).
Table 3
also presents the acceptable upper and lower values for each hematological and biochemical biomarkers for children, pregnant, and non-pregnant women. These values served as quality control measures to exclude implausible values. Additionally, the table also presents the acceptable unit for each parameter. Definition for the micronutrient-related thresholds, inflammatory, and metabolic markers was also defined to check for the quality of collected data.
Table 3
Parameter
Children
Pregnant women
Non-pregnant women
Lower acceptable value
Upper acceptable value
Lower acceptable value
Upper acceptable value
Lower acceptable value
Upper acceptable value
Hematocrit (
36
<30%
>44.1%
<36%
>48%
<36%
>48%
MCV (
37
>86 femtoliter (fL)
<80 femtoliter (fL)
>100 femtoliter (fL)
<80 femtoliter (fL)
>100 femtoliter (fL)
MCH (
38
39
(6 m–1 yr)23 pg
(6 m–1 yr) 31 pg
>33 picograms (pg) per cell
>33 picograms (pg) per cell
MCHC (
40
(6 m–1 yr) < 32 g/dL
(6 m–1 yr) > 36 g/dL
<32 g/dL
>36 g/dL
<32 g/dL
>36 g/dL
Ferritin (
41
42
140 μg/L
13 μg/L
150 μg/L
13 μg/L
150 μg/L
Transferrin saturation (
43–45
).
(0 to <1 year) 4.1%
30% (0 to <1 year) 59%
15%
50%
15%
50%
sTfR (
46
).
4.4 mg/L
4.4 mg/L
μg/dL (
46
47
).
(Abnormal values) 3–6 years >70 μmol/mol heme
100 μg/㎗
100 μg/㎗
Vitamin A (
48
49
<0.70 μmol/L
0.07 μmol/g
3,000 retinol activity equivalents (RAE)/Day
0.07 μmol/L
3,000 retinol activity equivalents (RAE)/Day
Vitamin B12 (
50
51
<150 pmol/L (203 pg./mL)
100 pmol/L
350 pmol/L
100 pmol/L
350 pmol/L
Folate (Serum) (
52
53
<4 ng/mL (<10 nmol/L)
2.0 ng/mL
7.0 ng/mL
2.0 ng/mL
7.0 ng/mL
Folate (RBC) (
53
54
<151 ng/mL (<340 nmol/L)
>400 ng/mL
>400 ng/mL
Zinc (
55
<10 years:65 mg/dL
<10 years:65 mg/dL
70 mcg/dL
<56 (μg/dL)
70 mcg/dL
Vitamin D (
56–58
<12 ng/mL
<30 nmol/L
10 ng/mL
50 ng/mL
CRP (
59
> 5 mg/L
0.1 mg/L
>5.0 mg/L
0.1 mg/L
>5.0 mg/L
AGP (
60
>1 g/L
0.4 mg/mL
3 mg/mL
0.4 mg/mL
3 mg/mL
IL-6 (
55
5 pg./mL
25 pg./mL
5 pg./mL
25 pg./mL
D-Dimer (
61
500 ng/mL
10,000 ng/mL
500 ng/mL
10,000 ng/mL
Acceptable values to eliminate abnormal values from the database.
RDW, red cell distribution width; MCV, mean corpuscular volume; MCH, mean corpuscular hemoglobin; MCHC, mean corpuscular hemoglobin concentration; sTfR, soluble transferrin receptor; ZnPP, zinc protoporphyrin; CRP, C-reactive protein; AGP, alpha-1-acid glycoprotein; IL-6, interleukin-6.
Principles and plans for statistical analysis
A detailed statistical analysis and reporting plan was formulated in collaboration with the Technical Advisory Group of the PRAYAS consortium. This plan delineated the statistical techniques, underlying assumptions, and procedural steps, ensuring systematic, and transparent analyses (details will be reported in subsequent papers). One-stage meta-analysis and two-stage meta-analysis approaches would be used for analyzing all the available data to calculate the prevalence of anemia and its severity across age groups. Using a weighted sample, the prevalence of anemia and its severity will be calculated as the number of anemics divided by the total number of participants in different age groups. To account for differences in sample sizes across datasets, each dataset will be weighted, with
weights computed as the inverse of the ratio of the individual dataset sample size to the overall pooled database sample size
The analysis will use a one-stage individual participant data meta-analysis approach, pooling harmonized data from all included studies to estimate the adjusted etiological fractions of anemia due to specific micronutrient deficiencies (such as iron, folate, vitamin B12, vitamin A, vitamin D, and zinc) in children, non-pregnant/non-lactating women, and pregnant women (by trimester where possible). Multilevel regression models will be employed, with study as a random effect and relevant covariates included to account for confounding and between-study heterogeneity. Adjusted risk ratios for each deficiency will be used to calculate PAFs, with subgroup analyses by age, region, and other modifiers. Sensitivity analyses will assess the robustness of findings to different deficiency cut-offs and model specifications. To evaluate intervention effects, logistic regression will estimate relative risks (RR) for anemia prevalence, while linear regression will compute mean differences (MD) in hemoglobin levels, adjusted for confounders. Additionally, the database would also be utilized to develop risk prediction models using machine learning approaches.
Patient and public involvement
No patients or members of the public were directly involved in the design or conduct.
Findings to date
The PRAYAS database, spanning over 379,013 individuals, offers a rich, regionally diverse, and methodologically varied resource to derive meaningful insights into nutritional anemia across India.
Study profile
Pooled database comprises 88 datasets, encompassing a total of 319,721 participants for prevalence analysis—children (19,762), NPNL (17,883), and pregnant women (282,076). Additionally, 59,292 participants were included in intervention studies—children (13,435), NPNL (11,594), and pregnant women (34,263). RCTs comprised 55.7% (49/88) of the datasets, whereas observational studies comprised 35.2% (31/88) of the datasets. Others were longitudinal studies (8%–7/88) and pre–post-study (1.1%–1/88). The included studies were conducted across various regions of India: the major contributions were from the northern region of India with 38 studies (43.2%), followed by the western part of India with 20 studies (22.7%). The southern part contributed 16 studies (18.2%). A smaller share from the central part of India (2.3%–2/88) followed by 12 studies (13.6%) from across India or have spanned multiple regions. These studies span from 1994 to 2023. A majority [59/88 (67%)] of the datasets originates from community-based studies, while 29/88 (33%) were derived from hospital-based research (
Table 1
).
Baseline characteristics—prevalence datasets
Of the included studies, more than 85% of the sample had information on hemoglobin concentration with highest among the pregnant women datasets with information from 96.1% (270,939) sample followed by NPNL and children with 94.4% (16,878) and 87.8% (17,351), respectively. The sample included NPNL and pregnant women with a median age of 26 years (IQR 23–32) and 23 years (IQR 21–25), respectively. Within the children datasets, information from 6 months up to 18 years was pooled within the database. Ultrasonography was used in 76.8% (198,819) of the sample for gestational age assessment (23.2%–60,053 used the LMP method). The mean gestational age at enrollment was 10.24 weeks (SD
17.65). Specifically, more than one-third (41.32%–105,103) of the participants were enrolled in the first trimester of pregnancy, whereas 37% (94,249) in the second trimester.
Further assessment of information on each hematological and biochemical biomarker reported that overall, 10.8% (34,442/319,721) of the sample had information on complete blood count (CBC). Whereas of the total sample, 9% (28,672) and 4.5% (14,240) had information on ferritin and vitamin B12, respectively. Less than 5% of the sample had information on other essential parameters (
Figure 2
). We are yet to analyze other essential parameters within the database.
Figure 2
illustrates the distribution of available data on hemoglobin, vitamins, and complete blood count (CBC) among children, NPNL, and pregnant women dataset.
Figure 2
Baseline characteristics—intervention datasets
Within the PRAYAS database, a total of 33 datasets (sample
59,292) were from intervention studies. Of these, 87.9% (29/33) datasets are randomized controlled trials with maximum within the children database (17 datasets). This dataset focuses on addressing anemia through nutritional and therapeutic approaches.
For the pregnancy database, 23.8% (8,150/34,263) of the interventions were specified as therapeutic. It is pertinent to note that 54.6% (18,719) of the samples were not specified within the one single category of therapeutic or preventive. Among pregnant women, a broader range of interventions was implemented, including intravenous iron sucrose (
18
), ferric carboxymaltose (IV FCM) (
29
), iron isomaltoside (IV IIM) (
29
), IV iron combined with vitamin B12, folic acid, and niacinamide, integrated interventions (a combination of health, nutrition, psychosocial care, and WASH) (
30
31
), as well as low-dose calcium supplementation (
32
). These were administered either during pregnancy alone, during both preconception and pregnancy, or in the preconception period only (
30
31
). The control groups primarily received either high-dose calcium in one study (
32
) or oral iron in the rest others.
For the WRA group, 53.9% (6,246/11,594) of interventions were categorized as therapeutic. A total of 4 study namely WINGS (
30
31
), IMPRINT (
33
), ICMR NIN study (
34
), CMC-RCT contributed to the database. WINGS provided integrated interventions (a combination of health, nutrition, psychosocial care, and WASH). These interventions were delivered at different stages, namely during preconception, during preconception + pregnancy, and during pregnancy with a control of oral iron. A study by CMC compared ferrous sulfate tablets of 60 mg elemental iron daily with a control of 120 mg on alternate days. Whereas NIN study administered prophylactic IFA and assessed for iron deficiency anemia in pre–post-method. Lastly, IMPRINT study provided food supplements and compared them with the oral iron group.
Within the children’s datasets, 7 studies contributed to a total of 18 datasets (sample—13,435). First study IMPRINT (
33
) contributed to a total of eight dataset delivered interventions as supplement or food vehicle, whereas others have delivered interventions as supplement or through fortification. Studies have administered ferrous sulfate as interventions along with food supplements, and some were Ayush trials.
Discussion
The PRAYAS database is a compilation of datasets from India on Anemia among women and children. This compilation is in response to prolonged deliberations regarding stagnancy in the prevalence of anemia in India despite focused interventions like AMB. Studies have explained an increase in compliance with such programmatic interventions that can accelerate reductions in anemia prevalence (
35
). Despite such decisive interventions and framework, findings from nationally representative sample surveys highlight an increase in anemia prevalence among WRA (from 53.1% in 2015–16 to 59.1% in 2019–21), pregnant women (from 50.3 to 52.6%), and children aged 6–59 months (from 58.6 to 67.1%) over the same period in India (
11
). Another study noted that there is an obvious shift in the distribution of Hb to the right among pregnant women over the past several years (
28
). This shift could be attributed to the implementation of the programmatic interventions with a focus on pregnant women or to factors stemming from overall development. The dearth of robust evidence around the diverse clinical etiologies of anemia, effective interventions, etc., demands a study that can be used for further policy decision-making.
Strengths and limitations
Data synthesized from the pooled data database would be used for calculating anemia indicators for the given population as these data have been collected from high-quality and closely observed observational and randomized controlled studies mostly using venous blood samples. This is an important resource considering several challenges associated with the existing health surveys (
12–14
). Additionally, the analysis would provide etiological fractions for anemia prevalence importantly fraction due to iron deficiency in all age groups of children under 18 years, NPNL, and pregnant women. These details can help the program to decide on the necessity of continuing prophylactic supplementation for these age groups and also finetune the doses for the same. Furthermore, the individual patient data meta-analysis of intervention studies can inform robust evidence regarding the type of iron intervention and dose of iron in both therapeutic and prophylactic studies. Additional social parameters could further enrich the analysis; these were not included due to the nature of the secondary data used.
The analysis from this database is expected to generate robust, high-quality evidence from large high-quality studies to inform public health policies and guide strategies for reducing the anemia’s burden in India. The systematic harmonization approach employed in this study ensures the validity and reliability of the datasets by addressing variations in data collection and standardizing outcome measures. This methodological rigor will enable more precise estimates and facilitate meaningful comparisons across populations and interventions (
24–26
).
However, several limitations should be noted. First, pooling data from studies with varying intervention types may result in high heterogeneity, which will be addressed through subgroup and sensitivity analyses. Second, some studies may lack critical parameters needed to assess changes in hemoglobin concentration, limiting the scope of certain analyses. Additionally, challenges in obtaining participant-level data due to restrictions from principal investigators or unpublished results could lead to data gaps. The inclusion of heterogeneous intervention and control conditions may also introduce a risk of bias, complicating the generalizability of findings. To mitigate these issues, we will evaluate heterogeneity using advanced statistical models, such as random-effects meta-analysis, and conduct subgroup analyses to explore the impact of differences across geographic, demographic, and intervention-specific factors.
The ability to analyze participant-level data allows for greater flexibility in adjusting for confounders, exploring effect modifiers, and conducting tailored subgroup analyses. By addressing sources of heterogeneity and potential biases, this meta-analysis aims to provide nuanced and reliable insights into the epidemiology of anemia and the effectiveness of various interventions.
Analyses from this database, to be presented in subsequent manuscripts, will provide findings that enhance understanding of the factors driving the high prevalence of anemia in India and the effectiveness of interventions to address this public health challenge. The findings will support evidence-based policymaking, i.e., will feed into the Anemia Mukt Bharat program recommendations for WRA and children and guide targeted strategies to reduce anemia and its associated health burdens across vulnerable populations.
Statements
Author’s note
Study findings will be published in peer-reviewed journals and will also be communicated to the policy makers for effective decision-making to curb the increasing trend of anemia in India. Commissioned by the Indian Council of Medical Research—India.
Data availability statement
The data analyzed in this study are subject to the following licenses/restrictions: All the collaborating PIs have acknowledged that the pooled data can only be used for this IPD analysis, with no transfer of ownership. Requests to access these datasets should be directed to
apradhandr@gmail.com
Ethics statement
Ethical approval for this study was not required since all included primary studies had received approval from ethics committees recognized by the Department of Health Research in India, and informed consent was obtained from all study participants. The ICMR team ensured that the included studies complied with relevant ethical guidelines and regulations.
Author contributions
AnP: Data curation, Formal analysis, Investigation, Methodology, Project administration, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. AnjS: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. RRaw: Data curation, Formal analysis, Investigation, Methodology, Project administration, Supervision, Validation, Visualization, Writing – review & editing. RC: Data curation, Formal analysis, Investigation, Methodology, Project administration, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. ShG: Investigation, Validation, Writing – review & editing. JN: Investigation, Validation, Writing – review & editing. SDes: Investigation, Validation, Writing – review & editing. AL: Investigation, Validation, Writing – review & editing. KB: Investigation, Validation, Writing – review & editing. SJ: Investigation, Validation, Writing – review & editing. CY: Investigation, Validation, Writing – review & editing. ApM: Investigation, Validation, Writing – review & editing. PD: Data curation, Formal analysis, Investigation, Methodology, Project administration, Supervision, Validation, Visualization, Writing – review & editing. PB: Investigation, Validation, Writing – review & editing. MJ: Data curation, Formal analysis, Investigation, Methodology, Project administration, Supervision, Validation, Visualization, Writing – review & editing. SB: Investigation, Validation, Writing – review & editing. KS: Investigation, Validation, Writing – review & editing. DM: Investigation, Validation, Writing – review & editing. AmS: Investigation, Validation, Writing – review & editing. RPu: Investigation, Validation, Writing – review & editing. VD: Investigation, Validation, Writing – review & editing. AA: Data curation, Formal analysis, Investigation, Methodology, Project administration, Supervision, Validation, Visualization, Writing – review & editing. RajS: Investigation, Validation, Writing – review & editing. AG: Investigation, Validation, Writing – review & editing. YP: Investigation, Validation, Writing – review & editing. UsD: Data curation, Formal analysis, Investigation, Methodology, Project administration, Supervision, Validation, Visualization, Writing – review & editing. RU: Investigation, Validation, Writing – review & editing. SN: Data curation, Formal analysis, Investigation, Methodology, Project administration, Supervision, Validation, Visualization, Writing – review & editing. MS: Investigation, Validation, Writing – review & editing. AnM: Investigation, Validation, Writing – review & editing. GDe: Investigation, Validation, Writing – review & editing. SSen: Investigation, Validation, Writing – review & editing. SDan: Investigation, Validation, Writing – review & editing. GW: Investigation, Validation, Writing – review & editing. UrD: Investigation, Validation, Writing – review & editing. GK: Investigation, Validation, Writing – review & editing. AKu: Investigation, Validation, Writing – review & editing. GT: Investigation, Validation, Writing – review & editing. NJ: Investigation, Validation, Writing – review & editing. SSop: Data curation, Formal analysis, Investigation, Methodology, Project administration, Supervision, Validation, Visualization, Writing – review & editing. SSah: Investigation, Validation, Writing – review & editing. GB: Investigation, Validation, Writing – review & editing. AnaS: Investigation, Validation, Writing – review & editing. RPa: Investigation, Validation, Writing – review & editing. ArP: Investigation, Validation, Writing – review & editing. RN: Investigation, Validation, Writing – review & editing. GDw: Investigation, Validation, Writing – review & editing. UK: Investigation, Validation, Writing – review & editing. DR: Investigation, Validation, Writing – review & editing. ArD: Investigation, Validation, Writing – review & editing. ST: Investigation, Validation, Writing – review & editing. DG: Investigation, Validation, Writing – review & editing. AKa: Investigation, Validation, Writing – review & editing. SRaw: Investigation, Validation, Writing – review & editing. KD: Investigation, Validation, Writing – review & editing. RRam: Investigation, Validation, Writing – review & editing. CH: Investigation, Validation, Writing – review & editing. SLal: Investigation, Validation, Writing – review & editing. PP: Investigation, Validation, Writing – review & editing. AT: Investigation, Validation, Writing – review & editing. TT: Investigation, Validation, Writing – review & editing. NeB: Investigation, Validation, Writing – review & editing. MB: Investigation, Validation, Writing – review & editing. LK: Investigation, Validation, Writing – review & editing. AaS: Investigation, Validation, Writing – review & editing. RD: Investigation, Validation, Writing – review & editing. LL: Investigation, Validation, Writing – review & editing. DA: Investigation, Validation, Writing – review & editing. RM: Investigation, Validation, Writing – review & editing. HS: Investigation, Validation, Writing – review & editing. RT: Investigation, Validation, Writing – review & editing. SDas: Investigation, Validation, Writing – review & editing. NiB: Investigation, Validation, Writing – review & editing. RanS: Investigation, Validation, Writing – review & editing. SY: Investigation, Validation, Writing – review & editing. RY: Investigation, Validation, Writing – review & editing. PR: Investigation, Validation, Writing – review & editing. SaG: Investigation, Validation, Writing – review & editing. SLad: Investigation, Validation, Writing – review & editing. ZG: Investigation, Validation, Writing – review & editing. SRat: Investigation, Validation, Writing – review & editing. DS: Investigation, Validation, Writing – review & editing. AP: Investigation, Validation, Writing – review & editing. YA: Investigation, Validation, Writing – review & editing. PH: Investigation, Validation, Writing – review & editing. HL: Investigation, Validation, Writing – review & editing. AnD: Investigation, Validation, Writing – review & editing. PL: Investigation, Validation, Writing – review & editing. PK: Investigation, Validation, Writing – review & editing. AV: Investigation, Validation, Writing – review & editing. UC: Investigation, Validation, Writing – review & editing. IM: Investigation, Validation, Writing – review & editing. KR: Investigation, Validation, Writing – review & editing. OD: Investigation, Validation, Writing – review & editing. AR: Investigation, Validation, Writing – review & editing. OJ: Investigation, Validation, Writing – review & editing. NS: Investigation, Validation, Writing – review & editing. AB: Investigation, Validation, Writing – review & editing. RK: Investigation, Validation, Writing – review & editing. SP: Investigation, Validation, Writing – review & editing. WF: Investigation, Validation, Writing – review & editing. SSaz: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing.
Funding
The author(s) declare that financial support was received for the research and/or publication of this article. This work was supported by Indian Council of Medical Research (ICMR) [vide letter no. 5/7/Pooled Analysis/2023-RCN].
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The authors declare that no Gen AI was used in the creation of this manuscript.
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Supplementary material
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Summary
Keywords
anemia
iron-deficiency
intervention
public policy and governance
sustainable development goals
Citation
Pandey AK, Sinha AP, Rawat R, Chowdhury R, Goudar SS, Nagpal J, Desai S, Laxmaiah A, Basany K, Joshi S, Yajnik C, Mukherjee A, Dwarkanath P, Bansal PG, Jacob M, Bhatnagar S, Shah K, Mukherjee D, Shukla A, Pullakhandam R, Dhurde V, Apte A, Singh R, Gupta A, Priyanka Y, Dhingra U, Upadhyay RP, Neogi SB, Somannavar MS, Mandal A, Desai G, Sengupta S, Dandge S, Wagh G, Deshmukh U, Kumar G, Kurpad AV, Toteja GS, John NM, Sopory S, Saha S, Babu GR, Suryavanshi A, Palika R, Patel A, Nimkar R, Dwivedi GR, Kapil U, Raja D, Dutta A, Taneja S, Gautam D, Kavi A, Rawat S, Dave K, Raman R, Haggerty CL, Lalwani S, Phadke P, Turuk A, Thomas T, Bhatia N, Beck MM, Kaur L, Shukla A, Deepa R, Locks LM, Agarwal DM, Mamidi RS, Sachdev HS, Talukdar R, Das S, Bhandari N, Singh R, Yogeshkumar S, Yadav R, Reddy PS, Gupte S, Ladkat SR, Gonmei Z, Rathore S, Sharma D, Pandya A, Ana Y, Hibberd P, Lubree H, Dudekula AB, Lal PR, Khan PA, Verma A, Charantimath US, Meshram II, Randhir K, Deshmukh O, Roy AK, John O, Saldanha ND, Bavdekar A, Kumar R, Prakash S, Fawzi WW and Sazawal S (2025)
PRAYAS: individual patient data meta-analysis database for Pooled Research and Analysis for Yielding Anemia-free Solutions in India
Front. Public Health
13:1696787. doi:
10.3389/fpubh.2025.1696787
Received
01 September 2025
Revised
17 November 2025
Accepted
20 November 2025
Published
23 December 2025
Volume
13 - 2025
Edited by
Sujosh Nandi
, Indian Institute of Technology Kharagpur, India
Reviewed by
Zeinab Gholamnia Shirvani
, Babol University of Medical Sciences, Iran
Sagar Acharya
, Vidyasagar University, India
Anit Kujur
, Rajendra Institute of Medical Sciences, India
Updates
© 2025 Pandey, Sinha, Rawat, Chowdhury, Goudar, Nagpal, Desai, Laxmaiah, Basany, Joshi, Yajnik, Mukherjee, Dwarkanath, Bansal, Jacob, Bhatnagar, Shah, Mukherjee, Shukla, Pullakhandam, Dhurde, Apte, Singh, Gupta, Priyanka, Dhingra, Upadhyay, Neogi, Somannavar, Mandal, Desai, Sengupta, Dandge, Wagh, Deshmukh, Kumar, Kurpad, Toteja, John, Sopory, Saha, Babu, Suryavanshi, Palika, Patel, Nimkar, Dwivedi, Kapil, Raja, Dutta, Taneja, Gautam, Kavi, Rawat, Dave, Raman, Haggerty, Lalwani, Phadke, Turuk, Thomas, Bhatia, Beck, Kaur, Shukla, Deepa, Locks, Agarwal, Mamidi, Sachdev, Talukdar, Das, Bhandari, Singh, Yogeshkumar, Yadav, Reddy, Gupte, Ladkat, Gonmei, Rathore, Sharma, Pandya, Ana, Hibberd, Lubree, Dudekula, Lal, Khan, Verma, Charantimath, Meshram, Randhir, Deshmukh, Roy, John, Saldanha, Bavdekar, Kumar, Prakash, Fawzi and Sazawal.
This is an open-access article distributed under the terms of the
Creative Commons Attribution License (CC BY)
. The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Correspondence: Anju Pradhan Sinha,
apradhandr@gmail.com
†1st set of equal contributors
‡2nd set of equal contributors
§3rd set of equal contributors
‖4th set of equal contributors
¶5th set of equal contributors
ORCID: Archana Patel,
orcid.org/0000-0002-2558-7421
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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