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Annals of African Medicine logoLink to Annals of African Medicine
. 2026 Apr 6;25(6):1625–1641. doi: 10.4103/aam.aam_464_25

Systematic Review and Meta-analysis on the Prevalence of Common Hemoglobinopathies in India

Soumyajit Das 1,*,#, Glory Ghai 1,*,#, Shruthy Sathianath 1,*,#, S Savitha 1, S Sumathi 2, Felista Karen Joseph 2, Prakasini Satapathy 3,4, Aravind P Gandhi 1,✉
PMCID: PMC13645255  PMID: 41943545

Abstract

Context:

In India, hemoglobinopathies such as beta-thalassemia, sickle cell disease, and glucose-6-phosphate dehydrogenase (G6PD) deficiency are major public health concerns.

Aims:

The present meta-analysis aimed to aggregate data on the prevalence and distribution of these nationally significant hemoglobinopathies across various demographic segments of India.

Materials and Methods:

The systematic review adhered to PRISMA 2020 guidelines. Four databases – Web of Science, PubMed, ProQuest, and Embase – were searched till 12.05.2024. Risk of bias assessment studies were undertaken using the JBI Critical Appraisal Checklist.

Statistical Analysis Used:

Pooled estimate of prevalence of each hemoglobinopathies and their traits was calculated with 95% confidence interval (CI). I2 statistics and the prediction interval were calculated to depict the heterogeneity. All statistical analyses were done in R studio.

Results:

Database search yielded 1418 articles, among which 99 studies were finally included. Pooled prevalence of sickle cell anemia from 656,005 participants was 2% (95% CI: 1%–3%, I2 = 99.6%). Pooled prevalence of sickle cell trait was 5% (95% CI: 2%–8%, I2 = 99.9%). Pooled prevalence of beta thalassemia was 6% (95% CI: 2%–12%, I2 = 99.9%). Pooled prevalence of beta thalassemia trait was 7% (95% CI: 4%–9%, I2 = 99.6%). Pooled prevalence of G6PD deficiency was 5% (95% CI: 3%–8%, I2 = 98.8%). Subgroup analysis revealed varying prevalence of hemoglobinopathies across the regions and states.

Conclusions:

The study findings highlight the burden and subsequent need for region-specific (decentralized) strategies for managing hemoglobinopathies. Addressing the burden through early diagnosis, genetic counseling, and public health interventions is crucial in reducing morbidity and improving health outcomes.

Keywords: Beta-thalassemia, glucose-6-phosphate dehydrogenase deficiency, hemoglobinopathy, India, prevalence, sickle cell anemia

INTRODUCTION

Hemoglobinopathies are inherited disorders that affect the structure and function of hemoglobin, leading to a significant burden and mortality worldwide. In India, hemoglobinopathies such as beta-thalassemia, sickle cell disease (SCD), and glucose-6-phosphate dehydrogenase (G6PD) deficiency are major public health concerns, particularly among certain ethnic and tribal populations. These chronic diseases require lifelong therapy and are linked to serious complications, placing significant strain on healthcare resources.

Beta-thalassemia is an autosomal recessive disorder causing defective hemoglobin synthesis, leading to anemia, growth retardation, and multiorgan complications. Estimate from the 2011 Census of India, β thalassemia carriers comprise 3%–4% of population, or 35–45 million carriers, out of 1.21 billion people, who are multiethnic, culturally, and linguistically diverse with higher prevalence in certain ethnic groups (4%–17%).[1]

Sickle cell anemia results from a point mutation in the beta-globin gene, leading to abnormal hemoglobin S, which causes red blood cells to stiffen and assume a sickle shape, inducing a vaso-occlusive crises, hemolysis, and organ damage. Tribal communities in Western, Central, and Southern states have a high burden of hemoglobin S variant, with some groups having a rate as high as 48%.[2]

G6PD deficiency, an X-linked enzymatic disorder that affects red blood cell metabolism, predisposing individuals to hemolysis upon exposure to oxidative stress. It is one of the most common enzymopathies worldwide, with prevalence in India ranging from 2% to 27% in different communities.[3]

The National Sickle Cell Anemia Elimination Mission is dedicated to tackling hemoglobinopathies, with a primary goal of eradicating sickle cell anemia by 2047. The program emphasizes widespread screening, awareness campaigns, and enhanced healthcare services, especially for tribal communities. Despite efforts, hemoglobinopathies remain a major health burden in India. Understanding their prevalence, distribution, and contributing factors is crucial for developing appropriate management plans, early diagnosis, genetic counseling, and policy. The review aimed to aggregate data on the prevalence and distribution of hemoglobinopathies across various demographic segments.

MATERIALS AND METHODS

Eligibility criteria

This study was undertaken with the primary research question: “What is the prevalence of thalassemia, SCD, G6PD deficiency in India?”. The study followed “Preferred Reporting standard for systematic reviews and meta-analyses (PRISMA)” checklist [Supplementary Table 1]. A thorough search approach using the criteria listed in Supplementary Table 2 was used to find the pertinent studies. The meta-analysis protocol with reference ID was registered in “International Prospective Register of Systematic Reviews (PROSPERO)” (CRD42024542284).

Table S1.

PRISMA Checklist (2020)

Section and Topic Item # Checklist item Location where item is reported
TITLE
Title 1 Identify the report as a systematic review. Pg 1
ABSTRACT
Abstract 2 Made as per the Journal guidelines Pg 1
INTRODUCTION
Rationale 3 Describe the rationale for the review in the context of existing knowledge. Pg 2
Objectives 4 Provide an explicit statement of the objective(s) or question(s) the review addresses. Pg 3
METHODS
Eligibility criteria 5 Specify the inclusion and exclusion criteria for the review and how studies were grouped for the syntheses. Supplementary Table S2
Information sources 6 Specify all databases, registers, websites, organisations, reference lists and other sources searched or consulted to identify studies. Specify the date when each source was last searched or consulted. Pg 4
Search strategy 7 Present the full search strategies for all databases, registers and websites, including any filters and limits used. Supplementary Table
Selection 8 Specify the methods used to decide whether a study met the Pg 4
process inclusion criteria of the review, including how many reviewers screened each record and each report retrieved, whether they worked independently, and if applicable, details of automation tools used in the process.
Data collection process 9 Specify the methods used to collect data from reports, including how many reviewers collected data from each report, whether they worked independently, any processes for obtaining or confirming data from study investigators, and if applicable, details of automation tools used in the process. Pg 4
Data items 10a List and define all outcomes for which data were sought. Specify whether all results that were compatible with each outcome domain in each study were sought (e.g. for all measures, time points, analyses), and if not, the methods used to decide which results to collect. Pg 4
10b List and define all other variables for which data were sought (e.g. participant and intervention characteristics, funding sources). Describe any assumptions made about any missing or unclear information. Pg 4
Study risk of 11 Specify the methods used to assess risk of bias in the included Pg 5
bias assessment studies, including details of the tool(s) used, how many reviewers assessed each study and whether they worked independently, and if applicable, details of automation tools used in the process.
Effect measures 12 Specify for each outcome the effect measure(s) (e.g. risk ratio, mean difference) used in the synthesis or presentation of results. Pg 4
Synthesis methods 13a Describe the processes used to decide which studies were eligible for each synthesis (e.g. tabulating the study intervention characteristics and comparing against the planned groups for each synthesis (item #5)). Pg 4
13b Describe any methods required to prepare the data for presentation or synthesis, such as handling of missing summary statistics, or data conversions.
13c Describe any methods used to tabulate or visually display results of individual studies and syntheses. Pg 5
13d Describe any methods used to synthesize results and provide a rationale for the choice(s). If meta-analysis was performed, describe the model(s), method(s) to identify the presence and extent of statistical heterogeneity, and software package(s) used. Pg 5
13e Describe any methods used to explore possible causes of heterogeneity among study results (e.g. subgroup analysis, meta- regression). Pg 5
13f Describe any sensitivity analyses conducted to assess robustness of the synthesized results. Pg 5
Reporting bias assessment 14 Describe any methods used to assess risk of bias due to missing results in a synthesis (arising from reporting biases). Pg 5
Certainty assessment 15 Describe any methods used to assess certainty (or confidence) in the body of evidence for an outcome. Pg 5
RESULTS
Study selection 16a Describe the results of the search and selection process, from the number of records identified in the search to the number of studies included in the review, ideally using a flow diagram. Figure 1
16b Cite studies that might appear to meet the inclusion criteria, but which were excluded, and explain why they were excluded. Pg 5
Study characteristics 17 Cite each included study and present its characteristics. Pg 5
Risk of bias in studies 18 Present assessments of risk of bias for each included study. Fig S4
Results of individual studies 19 For all outcomes, present, for each study: (a) summary statistics for each group (where appropriate) and (b) an effect estimate and its precision (e.g. confidence/credible interval), ideally using structured tables or plots. Pg 6,7,8
Results of syntheses 20a For each synthesis, briefly summarise the characteristics and risk of bias among contributing studies. Pg 6,7,8
20b Present results of all statistical syntheses conducted. If meta- analysis was done, present for each the summary estimate and its precision (e.g. confidence/credible interval) and measures of statistical heterogeneity. If comparing groups, describe the direction of the effect. Pg 6,7,8
20c Present results of all investigations of possible causes of heterogeneity among study results.
20d Present results of all sensitivity analyses conducted to assess the robustness of the synthesized results. Pg 6,7,8
Reporting biases 21 Present assessments of risk of bias due to missing results (arising from reporting biases) for each synthesis assessed. Pg 6,7,8
Certainty of evidence 22 Present assessments of certainty (or confidence) in the body of evidence for each outcome assessed. Pg 6,7,8
DISCUSSION
Discussion 23a Provide a general interpretation of the results in the context of other evidence. Pg 9
23b Discuss any limitations of the evidence included in the review. Pg 13
23c Discuss any limitations of the review processes used. Pg 13
23d Discuss implications of the results for practice, policy, and future research. Pg 13
OTHER INFORMATION
Registration and protocol 24a Provide registration information for the review, including register name and registration number, or state that the review was not registered. Pg 3
24b Indicate where the review protocol can be accessed, or state that a protocol was not prepared. Pg 3
24c Describe and explain any amendments to information provided at registration or in the protocol. NA
Support 25 Describe sources of financial or non-financial support for the review, and the role of the funders or sponsors in the review. Title letter
Competing interests 26 Declare any competing interests of review authors. Title letter
Availability of data, code and other materials 27 Report which of the following are publicly available and where they can be found: template data collection forms; data extracted from included studies; data used for all analyses; analytic code; any other materials used in the review. Title letter
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Table S2.

Inclusion and exclusion criteria for systematic review and meta-analysis What is the prevalence of thalassemia, Sickle Cell disease, and G6PD deficiency in India?

Inclusion Exclusion
Participants • Participants of all age groups and sex irrespective of control status
Primary outcome is the prevalence of thalassemia, Sickle Cell disease, and G6PD deficiency.
Prevalence of Hemoglobinopathies: - The proportion of individuals diagnosed with thalassemia, sickle cell disease, or G6PD deficiency within various Indian populations.
Measurement: Derived from diagnostic results using standard blood tests like complete blood count (CBC), hemoglobin electrophoresis, and enzyme assays reported in the studies.
Outcomes Secondary outcomes include identifying associated determinants like genetic, environmental, and socioeconomic factors.
Determinants of Hemoglobinopathies: - Factors influencing the incidence and distribution of thalassemia, sickle cell disease, and G6PD deficiency, such as genetic traits, environmental conditions, and socioeconomic factors.
Measurement: Determinants are assessed through the collection of demographic data, genetic information, environmental exposures, and socioeconomic details as provided in the studies.
Timing of Measurement: Evaluated either cross-sectionally or longitudinally based on the design of each included study.
Study Designs Observational Studies: Includes cross- sectional, cohort, and case-control studies. Randomized Controlled Trials (RCTs), Case Reports and Case Series, Policy Briefs, Protocols, Short Communications, Editorials, Commentaries, and Opinion Pieces.
Only studies conducted within India are included. Studies Outside India.
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Systematic search and duplicates removal

Four databases – Web of Science, PubMed, ProQuest, and Embase – were searched for pertinent studies till 12.05.2024. Keywords such as “thalassemia,” “sickle cell anemia,” “glucosephosphate dehydrogenase deficiency,” “deficiency of glucose 6 phosphate dehydrogenase,” and “sickle cell disorder” were utilized in the search strategy. To find more studies that fit the inclusion criteria, a manual examination of the references in qualified original research publications and reviews was conducted [Supplementary Table 2]. The detailed search strategy is given in Supplementary Table 3. Identified articles were managed using Nested Knowledge software.

Table S3.

The adjusted search terms as per searched electronic databases [Date of search: 12.05.2024]

Datab No ase Search Query Results
PubMed
#1 "thalassemia*"[Title/Abstract] OR "thalassemia"[MeSH Terms] OR "anemia, sickle cell"[MeSH Terms] OR "sickle cell disease*"[Title/Abstract] OR "sickle cell anemia*"[Title/Abstract] OR "sickle cell anaemia*"[Title/Abstract] OR "hemoglobin s disease"[Title/Abstract] OR "hbs disease"[Title/Abstract] OR "sickle cell disorder*"[Title/Abstract] OR "sickling disorder"[Title/Abstract] OR "glucosephosphate dehydrogenase deficiency"[MeSH Terms] OR "g6pd deficien*"[Title/Abstract] OR "glucosephosphate dehydrogenase deficienc*"[Title/Abstract] OR "gpd deficienc*"[Title/Abstract] OR "deficiency of glucose 6 phosphate dehydrogenase"[Title/Abstract] 65,24 6
#2 "india*"[Title/Abstract] OR "India"[MeSH Terms] OR "India"[Affiliation] 853,5 60
#3 "prevalence"[MeSH Terms] OR "prevalence"[Title/Abstract] OR "epidemiology"[Title/Abstract] OR "epidemiology"[MeSH Terms] OR "determinant*"[Title/Abstract] OR "risk factor*"[Title/Abstract] OR "risk factors"[MeSH Terms] 2,582 ,450
#4 ("thalassemia*"[Title/Abstract] OR "thalassemia"[MeSH Terms] OR ("anemia, sickle cell"[MeSH Terms] OR "sickle cell disease*"[Title/Abstract] OR "sickle cell anemia*"[Title/Abstract] OR "sickle cell anaemia*"[Title/Abstract] OR "hemoglobin s disease"[Title/Abstract] OR "hbs disease"[Title/Abstract] OR "sickle cell disorder*"[Title/Abstract] OR "sickling disorder"[Title/Abstract]) OR ("glucosephosphate dehydrogenase deficiency"[MeSH Terms] OR "g6pd deficien*"[Title/Abstract] OR "glucosephosphate dehydrogenase deficienc*"[Title/Abstract] OR "gpd deficienc*"[Title/Abstract] OR "deficiency of glucose 6 phosphate dehydrogenase"[Title/Abstract])) AND ("india*"[Title/Abstract] OR "India"[MeSH Terms] OR "India"[Affiliation]) AND ("prevalence"[MeSH Terms] OR "prevalence"[Title/Abstract] OR ("epidemiology"[Title/Abstract] OR "epidemiology"[MeSH Terms]) OR ("determinant*"[Title/Abstract] OR "risk factor*"[Title/Abstract] OR "risk factors"[MeSH Terms])) 533
#5 (("thalassemia*"[Title/Abstract] OR "thalassemia"[MeSH Terms] OR ("anemia, sickle cell"[MeSH Terms] OR "sickle cell disease*"[Title/Abstract] OR "sickle cell anemia*"[Title/Abstract] OR "sickle cell anaemia*"[Title/Abstract] OR "hemoglobin s disease"[Title/Abstract] OR "hbs disease"[Title/Abstract] OR "sickle cell disorder*"[Title/Abstract] OR "sickling disorder"[Title/Abstract]) OR ("glucosephosphate dehydrogenase deficiency"[MeSH Terms] OR "g6pd deficien*"[Title/Abstract] OR "glucosephosphate dehydrogenase deficienc*"[Title/Abstract] OR "gpd deficienc*"[Title/Abstract] OR "deficiency of glucose 6 phosphate dehydrogenase"[Title/Abstract])) AND ("india*"[Title/Abstract] OR "India"[MeSH Terms] OR "India"[Affiliation]) AND ("prevalence"[MeSH Terms] OR "prevalence"[Title/Abstract] OR ("epidemiology"[Title/Abstract] OR "epidemiology"[MeSH Terms]) OR ("determinant*"[Title/Abstract] OR "risk factor*"[Title/Abstract] OR "risk factors"[MeSH Terms]))) AND (humans[Filter]) 394
#6 (("thalassemia*"[Title/Abstract] OR "thalassemia"[MeSH Terms] OR ("anemia, sickle cell"[MeSH Terms] OR "sickle cell disease*"[Title/Abstract] OR "sickle cell anemia*"[Title/Abstract] OR "sickle cell anaemia*"[Title/Abstract] OR "hemoglobin s disease"[Title/Abstract] OR "hbs disease"[Title/Abstract] OR "sickle cell disorder*"[Title/Abstract] OR "sickling disorder"[Title/Abstract]) OR ("glucosephosphate dehydrogenase deficiency"[MeSH Terms] OR "g6pd deficien*"[Title/Abstract] OR "glucosephosphate dehydrogenase deficienc*"[Title/Abstract] OR "gpd deficienc*"[Title/Abstract] OR "deficiency of glucose 6 phosphate dehydrogenase"[Title/Abstract])) AND ("india*"[Title/Abstract] OR "India"[MeSH Terms] OR "India"[Affiliation]) AND ("prevalence"[MeSH Terms] OR "prevalence"[Title/Abstract] OR ("epidemiology"[Title/Abstract] OR "epidemiology"[MeSH Terms]) OR ("determinant*"[Title/Abstract] OR "risk factor*"[Title/Abstract] OR "risk factors"[MeSH Terms]))) AND ((humans[Filter]) AND (english[Filter])) 386
Embase
#1 ’thalassemia’/de OR ’thalassemia’:ab,kw,ti OR ’sickle cell anemia’/de OR ’sickle cell anemia’:ab,kw,ti OR ’sickle cell disease’:ab,kw,ti OR ’hemoglobin s disease’:ab,kw,ti OR ’hbs disease’:ab,kw,ti OR ’sickle cell disorder’:ab,kw,ti OR ’sickling disorder’:ab,kw,ti OR ’glucose 6 phosphate dehydrogenase deficiency’/de OR ’g6pd deficiency’:ab,kw,ti OR ’glucose 6 phosphate dehydrogenase deficiency’:ab,kw,ti OR ’gpd deficiency’:ab,kw,ti 8948 9
#2 ’india’/de OR ’india’:ab,kw,ti OR ’india’:ff 3622 27
#3 ’prevalence’/de OR ’prevalence’:ab,kw,ti OR ’epidemiology’/de OR ’epidemiology’:ab,kw,ti OR ’determinant’/de OR ’determinant’:ab,kw,ti OR ’risk factor’/de OR ’risk factor’:ab,kw,ti 3290 193
#4 #1 AND #2 AND #3 745
#5 #1 AND #2 AND #3 AND [humans]/lim AND [english]/lim 705
Web of Science
1 ((((((((((TS=("thalassemia*")) OR TS=("sickle cell anemia*")) OR TS=("Sickle Cell disease*")) OR TS=("Hemoglobin S Disease")) OR TS=("HbS Disease")) OR TS=("Sickle Cell Disorder*")) OR TS=("Sickling Disorder")) OR TS=("G6PD deficiency")) OR TS=("Glucosephosphate Dehydrogenase Deficiency")) OR TS=("GPD Deficiency")) OR TS=("Deficiency of Glucose-6- Phosphate Dehydrogenase") 5572 2
2 (OG=(India)) OR TS=(India) 2274 13
3 (((TS=(prevalence)) OR TS=(epidemiology)) OR TS=("determinant*")) OR TS=("risk factor*") 2428 176
4 #1 AND #2 AND #3 244
5 #1 AND #2 AND #3 and English (Languages) 241
ProQuest
#1 summary(thalassemia* OR "sickle cell anemia*" OR "sickle cell disease" OR "sickle cell disorder" OR "Hemoglobin S Disease" OR "Sickling Disorder" OR "HbS Disease" OR "G6PD deficiency" OR "Glucosephosphate Dehydrogenase Deficiency" OR "GPD Deficiency" OR "Deficiency of Glucose-6-Phosphate Dehydrogenase") OR title(thalassemia* OR "sickle cell anemia*" OR "sickle cell disease" OR "sickle cell disorder" OR "Hemoglobin S Disease" OR "Sickling Disorder" OR "HbS Disease" OR "G6PD deficiency" OR "Glucosephosphate Dehydrogenase Deficiency" OR "GPD Deficiency" OR "Deficiency of Glucose-6-Phosphate Dehydrogenase") 5851
#2 summary(India) OR title(India) OR ulo(India) OR location(India) 1927 89
#3 summary(prevalence OR epidemiology OR "determinant*" OR "risk factor*") OR title(prevalence OR epidemiology OR "determinant*" OR "risk factor*") 5494 82
#4 [S1] AND [S2] AND [S3] 87
#5 ([S1] AND [S2] AND [S3]) AND la.exact("ENG") 86
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Selection of eligible studies

We included all observational studies (cross-sectional, cohort, and case–control studies) which are vital for assessing prevalence and risk factors. The analysis included people belonging to all age groups and genders. Studies published in the English language were only considered. Based on the predetermined eligibility criteria, three investigators independently screened the titles and abstracts of the articles obtained from the systematic search; disagreements regarding the selection of articles were resolved through mutual discussion.

Full-text screening and data extraction

The suitability of the relevant full-text papers was assessed separately by three investigators. Data were extracted using Microsoft Excel, followed by consensus meeting to address discrepancies. The fourth researcher resolved any remaining contradictions. Information was taken from the final eligible articles, including the mean or median age of the population, the state or area, the prevalence of sickle cell anemia, sickle cell trait (SCT), thalassemia, and G6PD deficiency. A PRISMA flow-chart was used to record the entire procedure in order to guarantee scientific accuracy [Figure 1].

Figure 1.

Figure 1

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PRISMA flowchart describing study selection procedure

Quality assessment

Two researchers independently evaluated study quality using the “JBI Critical Appraisal Checklist for studies reporting prevalence data.”

Data analysis

The pooled outcome estimates, prevalence of beta thalassemia, sickle cell anemia, and G6PD deficiency along with 95% confidence interval (CI), were computed. Heterogeneity was determined using the I2 test. Heterogeneity was considered significantly high when the I2 was >50%.[4] Subgroup analysis by states and regions was performed. Doi plots and the LFK index were utilized to assess publication bias when an outcome included a minimum of five studies. P < 0.05 was considered statistically significant. All statistical analyses were done in R Studio using standard codes.[5]

RESULTS

The systematic search across four databases (Embase, PubMed, ProQuest, and Web of Science) identified 1418 articles. After removing 394 duplicate items, 1024 unique articles were screened, leading to 301 articles for full-text review. 202 studies were excluded: Article that did not report the outcomes of interest (78), had different study designs (32), study population did not align with the research objectives (29), or only the conference abstracts (29) were available. 99 studies were included, out of which 48 on SCD, 55 on anemia SCT, 34 on beta thalassemia disease, 59 on beta thalassemia trait (BTT), and 21 on G6PD deficiency [Figure 1]. The risk of bias assessment indicated that 99 studies were of either fair or good quality [Supplementary Table 4].

Table S4.

Quality assessment of included cross sectional study with the use of Joanna Briggs Institute Critical Appraisal Checklist for cross sectional studies

Author (YOP) Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Overall Quality
Singh, E et al (2024) Y N Y N Y Y Y N Y Fair
Putchen, DD et al (2023) Y Y Y Y Y N Y Y Y Good
Thakkar M et al (2023) Y Y Y Y Y Y Y Y Y Good
Kavitha A et al (2023) Y N Y N Y Y Y N Y Fair
Narang, V et al (2023) Y N Y N Y Y Y N Y Fair
Dawar A et al (2023) Y N Y N Y Y Y N Y Fair
Mahajan, S et al (2023) UC Y N Y Y Y Y N Y Fair
Minal T et al (2023) Y N Y Y Y Y Y N Y Fair
Karapurkar S et al (2023) Y N Y N Y Y Y N Y Fair
Parmar JKS et al (2023) Y Y Y Y Y Y Y N Y Good
Baruah D et al (2023) Y N Y Y Y Y Y Y Y Good
Sampagar A et al (2023) Y Y Y Y Y Y Y N Y Good
Gupta T et al (2023) Y Y Y Y Y Y Y Y Y Good
Rao M H S et al (2023) Y Y Y N Y Y Y N Y Fair
Setia S et al (2023) Y Y Y N Y Y Y N Y Fair
Arora D et al (2023) Y N Y Y Y Y Y N Y Fair
Shaw J et al (2023) Y Y Y Y Y N Y Y Y Good
Dixit S et al (2022) Y Y Y Y Y Y Y N Y Good
Cherian S et al (2022) Y Y Y N Y Y Y Y Y Good
Patel R.K et al (2022) Y N Y Y Y Y Y Y Y Good
Mukherjee S et al (2022) Y Y Y Y Y Y Y Y Y Good
Rani S. et al (2022) Y Y Y Y Y N Y Y Y Good
Mohanty S.S. et al (2022) Y Y Y Y Y Y Y N Y Good
Mohanty S.S et al (2022) Y N Y N Y Y Y Y Y Fair
Sinha K et al (2022) Y Y Y Y Y Y Y Y Y Good
Mukhopadhyay D et al (2022) Y N Y Y Y Y Y N Y Fair
Vachhani. N et al (2022) Y Y Y Y Y Y Y N Y Good
Singh. K et al (2022) Y N Y Y Y N Y N Y Fair
Asati A et al (2022) Y Y Y Y Y Y Y Y Y Good
Ghosh S et al (2021) Y Y Y Y Y Y Y N Y Good
Dharmarajan S et al (2021) Y N Y Y Y Y Y Y Y Good
Patel S et al (2021) Y Y Y Y Y Y Y Y Y Good
Mishra M.N et al (2021) Y Y Y Y Y Y Y N Y Good
Saha J. et al (2021) Y N Y N Y Y Y Y Y Fair
Basumatary N et al (2021) Y Y Y Y Y N Y Y Y Good
Saha S et al (2020) Y N Y Y Y Y Y N Y Fair
Devendra R. et al (2020) Y N Y Y Y Y Y Y Y Good
Devendra R. et al (2020) Y Y Y N Y N Y Y Y Fair
Achoubi N et al (2020) Y N Y N Y Y Y N Y Fair
Maji S K et al (2020) Y N Y Y Y Y Y Y Y Good
Chourasia S et al (2020) Y N Y Y Y Y Y Y Y Good
Verma J et al (2020) Y Y Y Y Y Y Y Y UC Good
Ghosh M et al (2019) Y N Y Y Y Y Y Y UC Fair
Tripathi P et al (2019) Y UC Y Y Y Y Y Y UC Fair
Gorakshakar AC et al (2019) Y Y UC Y Y Y Y Y UC Fair
Gupta S et al (2017) N N Y Y Y Y Y Y UC Fair
Babu STV et al (2017) Y Y Y Y Y Y Y Y UC Good
Teli AB et al (2016) Y Y Y Y Y Y Y Y Y Good
Singh MPSS et al (2016) Y Y Y Y Y Y Y Y NA Good
Buch A et al (2016) N N Y Y Y Y Y Y NA Fair
Chakrobarti S et al (2016) N N Y Y Y Y Y Y NA Fair
Kasi K et al (2016) N Y Y Y Y Y Y Y NA Fair
Balgir RS et al (2015) N Y Y Y Y Y Y Y NA Fair
Mohanty D et al (2015) Y Y Y Y Y Y Y Y Y Good
Maji SK et al (2014) Y Y Y Y Y Y Y Y Y Good
Samal J et al (2014) N N Y Y Y Y Y Y N Fair
Purohit P et al (2014) Y Y Y Y Y Y Y Y Y Good
Mohanty D et al (2014) Y Y Y Y Y Y Y Y Y Good
Shrikhande AV et al (2014) Y Y Y Y Y Y Y Y Y Good
Ratan S et al (2014) N Y Y Y Y Y Y Y Y Good
Verma P et al (2013) Y Y Y Y Y Y Y Y Y Good
Meena LP et al (2013) Y Y Y Y Y Y Y Y NA Good
Kulkarni P et al (2013) N Y N Y Y Y Y Y N Fair
Baxi A et al (2013) Y Y Y Y Y Y Y N Y Good
Mohanty D et al (2013) UC UC Y Y Y Y Y Y UC Fair
Patel J et al (2013) Y Y Y Y Y Y Y N Y Good
Achoubi N et al (2012) Y Y Y Y Y Y Y Y Y Good
Jain BB et al (2012) N Y Y Y Y Y Y Y NA Fair
Mondal B et al (2012) N Y Y Y Y Y Y Y NA Fair
Patel AG et al (2012) Y Y Y Y Y Y Y Y Y Good
Gunjal SS et al (2012) Y N Y Y Y Y Y Y Y Good
Gorakshakar A et al (2012) Y Y UC Y Y Y Y Y Y Good
Bisoi S et al (2012) Y N UC Y Y Y Y Y UC Fair
Vasaikar M et al (2012) Y UC UC Y Y Y Y Y Y Fair
Dolai TK et al (2011) Y Y Y Y Y Y Y Y Y Good
Tripathi S et al (2021) Y Y Y Y Y Y Y Y Y Good
Kaur G et al (2010) Y Y Y Y Y Y Y Y Y Good
Patra PK et al (2010) Y Y Y Y Y Y Y Y UC Good
Madan N et al (2010) Y Y Y Y Y Y Y Y UC Good
Patel J et al (2009) N N Y Y Y Y Y Y UC Fair
Mandot S et al (2009) N N Y Y Y Y Y Y UC Fair
Choubisa SL(2009) N N Y Y Y Y Y Y UC Fair
Sukumar S et al (2004) Y Y Y Y Y Y Y Y Y Good
Joshi SR et al (2004) UC Y Y Y Y Y Y N N Fair
Balgir RS et al (1999) Y Y Y Y Y Y Y Y UC Good
Colah R et al (2010) Y Y Y Y Y Y Y Y UC Good
Mulchandani DV et al (2008) Y Y Y Y Y Y Y Y UC Good
Jawahirani A et ak (2007) Y Y Y Y Y Y N Y UC Fair
Balgir RS et al (2005) Y Y Y Y Y Y Y Y N Good
Shrikhande AV et al (2014) Y Y Y Y Y Y Y Y NA Good
Panigrahi S et al (2015) Y Y Y Y Y Y Y Y Y Good
Singh MPSS et al (2016) Y Y N Y Y Y Y Y N Fair
Panda A et al (2009) N N Y Y Y Y Y Y UC Fair
Sinha S et al (2004) Y Y Y Y Y Y Y Y UC Good
Singh N et al (2023) Y Y Y Y Y Y Y Y Y Good
Jain P et al (2019) Y Y Y Y Y Y Y Y Y Good
Warghade S et al (2017) Y Y Y Y Y Y Y Y Y Good
Iqbal MS et al (2016) Y Y Y Y Y Y Y Y Y Good
Mondal SK et al (2016) Y Y Y Y Y Y Y Y Y Good
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Baseline features of eligible studies

The 99 studies varied in terms of geographic location, study design, sample size, and the conditions investigated. Most studies were cross-sectional (80%), followed by retrospective (11%) and prospective (8%). Sample size ranged from 56 in the study by Karapurkar et al. to 287,258 participants in the study by Maji et al.[6,7] The majority of studies were from Maharashtra (14), followed by West Bengal (13) and Madhya Pradesh (9). Region-wise, Western (26), Eastern (22), and Central Indian region (18) reported 68% of all included studies. The study population in 10 studies included only pregnant females, while other studies included both males and females. The mean age of participants across studies varied, with most studies focusing on adults (18–60 years), though some studies included children and adolescents [Table 1].

Table 1.

Characteristics of included studies (n=99)

Study State Study design Sample Prevalence Age Participants
SCD Sickle cell trait Beta thalassemia disease Beta thalassemia trait G6PD deficiency
Balgir RS et al (1999)[8] Odisha Cross sectional study 465 - 0.65% - 3.01% 9.25% - Male Female
Joshi SR et al (2001)[9] Gujarat Cross sectional study 471 - - - - 24.20% - Male Female
Sukumar S et al (2004)[10] Multiple state Cross sectional study 3166 - - - - 10.49% - Male Female
Sinha S et al (2004)[11] Eastern UP and adjoining regions of Bihar, Madhya Pradesh (MP), Chhattisgarh and Jharkhand Cross sectional study 120 11.67% - 45.83% - - - Male Female
Balgir RS et al (2005)[12] Odisha Cross sectional study 1603 0.26% 3.81% - - - - Male Female
Jawahirani A et al. (2007)[13] Maharashtra two-phase, community-based survey 1563 - - - 16.38% - - Male Female
Mulchandani DV et al (2008)[14] Maharashtra Cross sectional study 446 - - - 16.82% - - Male Female
Patel J et al (2009)[15] Gujarat Cross sectional study 428 3.97% 0.70% 6.78% 5.14% - - Male Female
Mandot S et al (2009)[16] Rajasthan Cross sectional study 1676 0.84% 8.41% - - - - Male Female
Choubisa SL (2009)[17] Rajasthan Cross sectional study 368 0.27% 5.98% - 7.61% - - Male Female
Panda A et al (2009)[18] Odisha Hospital based cross sectional study 62 - 6.45% - 1.61% - - Female
Kaur G et al (2010)[19] Chandigarh Cross sectional study 6813 - - - - 0.90% - Male Female
Patra PK et al (2010)[20] Chhattisgarh Cross sectional study 38472 3.43% - - - - - Male Female
Madan N et al (2010)[21] Delhi, Maharashtra Cross sectional study 11090 - 0.08% - 4.05% - - Male Female
Colah R et al (2010)[22] Maharashtra & Gujarat Cross sectional study 18651 - - - - - - Male Female
Dolai TK et al (2012)[23] West Bengal Cross-sectional 35413 - 1.13% - 10.38% - - Male Female
Achoubi N et al (2012)[24] Manipur cross sectional 602 - - - 0.17% - - Male Female
Jain BB et al (2012)[25] West Bengal cross sectional study 3823 0.16% 0.63% 3.23% 19.07% - - Male Female
Mondal B et al (2012)[26] West Bengal cross sectional 958 - 1.15% 2.92% 17.64% - - Male Female
Patel AG et al (2012)[27] Gujarat cross sectional 32857 0.03% 1.32% - 4.38% - - Male Female
Gunjal SS et al (2012)[28] Maharashtra cross sectional 908 0.44% 6.06% - - - - Male Female
Gorakshakar A et al (2012)[29] Maharashtra Cross-sectional 846 - - 3.66% - - - Male Female
Bisoi S et al (2012)[30] West Bengal Longitudinal 109 - - - - 14.68% - Male Female
Vasaikar M et al (2012)[31] Maharashtra Prospective 24723 1.09% 9.11% 0.76% - - - Male Female
Verma P et al (2013)[32] Uttar Pradesh cross sectional 933 - 0.86% 0.43% 5.04% - 18-55 years Male Female
Meena LP et al (2013)[33] Uttar Pradesh cross sectional 1000 - - - 2.80% - 18-40 years Male Female
Kulkarni P et al (2013)[34] Karnataka exploratory study 210 - - - 8.57% - - Female
Baxi A et al (2013)[35] Madhya Pradesh cross sectional 1006 - 0.10% - 2.78% - - Female
Mohanty D et al (2013)[36] Maharashtra, Gujarat, West Bengal, Assam, Karnataka and Punjab cross sectional 56780 0.04% 0.70% - 2.78% - 17-26 years Male Female
Patel J et al (2013)[37] Gujarat cross sectional 7307 1.53% 23.81% - - - - Male Female
Maji SK et al (2014)[38] West Bengal field survey 13609 - - - 10.50% - - Male Female
Samal J et al (2014)[39] Maharashtra cross section 560 12.32% - - - - - Male Female
Purohit P et al (2014)[40] Odisha cross sectional 594 3.03% 10.10% - 3.37% - 21.5 years (mean) Male Female
Mohanty D et al (2014)[41] Maharashtra, Gujarat, Assam, West Bengal, Punjab, Karnataka, Bangalore, New Delhi cross sectional 56772 0.04% 0.70% - 2.78% - - Male Female
Shrikhande AV et al (2014)[42] Maharashtra cross sectional 35636 2.83% 15.34% - - - - Male Female
Ratan S et al (2014)[43] Delhi cross sectional 1660 - - - - 0.66% 37.41 years (mean) Male Female
Putchen, DD et al (2023)[44] Karnataka Retrospective analysis 17006 0.64% 1.60% 16.58% 15.09% - - Male Female
Balgir RS et al (2015)[45] Madhya Pradesh prospective cross-sectional study 416 1.68% 7.45% - 2.88% - - Female
Mohanty D et al (2015)[46] Odisha, Maharashtra, Gujarat, Tamil nadu multicentric study 15200 0.78% 13.74% - 1.88% - 2-70 years Male Female
Panigrahi S et al (2015)[47] Chhattisgarh Community based cross sectional study 15701 0.62% 10.65% - - - - Male Female
Teli AB et al (2016)[48] Assam cross sectional study 1204 - - 1.25% 8.64% - - Male Female
Singh MPSS et al (2016)[49] Madhya Pradesh cross sectional 267 0.37% 7.13% - 3.22% 5.98% - Male Female
Buch A et al (2016)[50] Maharashtra cross section 3465 0.20% 0.23% 0.03% 3.72% - 27 days to 80 years Male Female
Chakrobarti S et al (2016)[51] West Bengal Cross-Sectional study 3500 - 0.31% - 4.51% - - Female
Chandrashekhar C et al (2016)[52] Karnataka prospective study 500 - - - - 0.00% - Male Female
Singh MPSS et al (2016)[49] Madhya Pradesh Community based cross sectional study 435 0.37% 7.13% - - 5.98% - Male Female
Iqbal MS et al (2016)[53] Multiple States Cross sectional study 518 2.32% 10.81% 0.39% 7.72% - - Male Female
Mondal SK et al (2016)[54] West Bengal Hospital based cross sectional study 119336 0.32% 0.38% 1.66% 4.60% - - Male Female
Gupta S et al (2017)[55] Jammu and Kashmir cross sectional study 303 - - 0.33% 1.32% 0.99% - Male Female
Babu STV et al (2017)[56] Karnataka cross sectional study 800 - - - 3.88% - - Male Female
Warghade S et al (2018)[57] Maharashtra Hospital based cross sectional study 65779 1.59% 2.01% 0.80% 11.21% - - Male Female
Ghosh M et al (2019)[58] West Bengal prospective, cross-sectional study 7340 - - - 3.96% - Normal 24.75 , Thalassemia carrier 24.55 Female
Tripathi P et al (2019)[59] Uttar Pradesh cross sectional study 1069 - - - - 8.89% Newborn-50 years Male Female
Gorakshakar AC et al (2019)[60] Maharashtra cross sectional study 257 - - - 3.89% - - Male Female
Jain P et al (2019)[61] Haryana Hospital based cross sectional study 4275 0.05% 0.05% 1.01% 7.44% - - Male Female
Devendra R. et al (2020)[62] 15 states across India Cross-Sectional Study 20896 - - - - 1.94% - Male Female
Saha S et al (2020)[63] West Bengal Retrospective Study 21695 0.46% - 4.34% - - 22 years (mean) Male Female
Devendra R. et al (2020)[64] Karnataka Cross-Sectional Study 775 - - - - 9.16% - Male Female
Achoubi N et al (2020)[65] Manipur Cross-Sectional Study 263 - - - - 15.59% - Male Female
Maji S K et al (2020)[7] West Bengal Retrospective Study 287258 - - 7.22% - - - Male Female
Chourasia S et al (2020)[66] Madhya Pradesh community-based cross-sectional study 3992 0.73% 14.43% 0.00% 1.40% - 15 years (median) 13-16 (IQR) Male Female
Verma J et al (2020)[67] Delhi cross sectional study 13376 - - - - 0.81% - Male Female
Dharmarajan S et al (2021)[68] Maharashtra Cross-sectional 360 - 1.67% - 3.33% - 22.9 years (mean) Female
Patel S et al (2021)[69] Chhattisgarh Cross-sectional 1282 - - - - 0.62% - Male Female
Mishra M.N et al (2021)[70] Assam Cross-Sectional Study 9936 0.08% 0.12% 0.04% - - - Male Female
Saha J. et al (2021)[71] West Bengal Cross-Sectional Study 110 - - 38.18% 61.82% - 3 years (mean) Male Female
Basumatary N et al (2021)[72] Assam Cross-Sectional Study 1436 - - - - 6.62% - Male Female
Tripathi S et al (2021)[73] Chhattisgarh Cross sectional study 6088 1.10% 2.99% - - - - Male Female
Ghosh S et al (2021)[74] West Bengal Retrospective analysis 657 - - 25.27% 24.81% - 29.3 years (mean) Male Female
Cherian S et al (2022)[75] Kerala Observational Study 10025 0.04% 0.93% - - - 29.92 years (mean) Male Female
Dixit S et al (2022)[76] Odisha Cross-sectional 1461 0.14% 3.42% - 0.34% - 32.59 years (mean) Male Female
Patel J et al (2022)[77] Odisha Cross-Sectional Study 1243 1.37% - - - - - Male Female
Mukherjee S et al (2022)[78] Eastern India Prospective Observational Study 1037 - - - - 0.48% - Male Female
Rani S. et al (2022)[79] Telangana Cross-Sectional Study 200 7.00% 5.00% - - - 1-60 month Male Female
Mohanty S.S. et al (2022)[80] Rajasthan Cross-Sectional Study 36752 5.78% - - - - - Male Female
Mohanty S.S et al (2022)[81] Rajasthan Cross-Sectional Study 7167 8.51% - 7.25% - 2.80% - Male Female
Sinha K et al (2022)[82] Bihar Retrospective Study 80 - - 100.00% - - 6.2 years (mean) Male Female
Vachhani. N et al (2022)[83] Gujarat Cross-sectional 16780 - 0.36% - 4.52% - Above 18 years Male Female
Mukhopadhyay D et al (2022)[84] West Bengal Cross-sectional 1458 - 3.57% - 7.89% - 24.72 years (mean) Female
Asati A et al (2022)[85] Madhya Pradesh Cross-sectional 100 10.00% - 7.00% 10.00% - 4.63 years (mean) Male Female
Singh. K et al (2022)[86] Madhya Pradesh Retrospective 600 9.00% 88.67% 0.33% 0.33% - 24.9 years (mean) Male Female
Thakkar M et al (2023)[87] Gujarat Cross-sectional 2195 0.09% 0.46% - - - - Male Female
Kavitha A et al (2023)[88] Tamil Nadu, Andhra Pradesh Retrospective Cohort Study 17678 - 0.31% 0.02% 4.66% - - Male Female
Narang, V et al (2023)[89] Punjab Retrospective Observational Study 2509 - - 6.74% - - 28.07 years (mean) - Female
Mahajan, S et al (2023)[90] Maharashtra Prospective Analysis 186 - - 18.82% 81.18% - - Male Female
Dawar A et al (2023)[91] Madhya Pradesh Retrospective & Prospective 600 9.00% 88.67% 0.33% 0.33% - - Male Female
Minal T et al (2023)[92] Gujarat Cross-Sectional Study 395 0.51% 2.78% - 1.77% - - Male Female
Karapurkar S et al (2023)[6] Maharashtra Cross-Sectional Study 56 17.86% - - - - - Male Female
Parmar JKS et al (2023)[93] Dadra & Nagar Haveli Retrospective Study 2944 0.37% 2.24% - 2.24% - 1-53 years Male Female
Baruah D et al (2023)[94] Assam Cross-sectional 600 - 0.67% - - - - Male Female
Sampagar A et al (2023)[95] Karnataka Cross-Sectional Study 1000 - - 0.80% 0.20% - - Female
Gupta T et al (2023)[96] Uttar Pradesh Retrospective Study 396 - - - - 2.27% - Male Female
Rao M H S et al (2023)[97] Andhra Pradesh Prospective Observational Study 64 34.38% 4.69% 37.50% 12.50% - 0.5-12 years Male Female
Setia S et al (2023)[98] Punjab Prospective 107 - 0.93% 4.67% 27.10% - 23.13 years (mean) Male Female
Arora D et al (2023)[99] Madhya Pradesh Prospective 150 - 2.67% 2.67% 16.67% - - Male Female
Shaw J et al (2023)[100] West Bengal Cross-sectional 52280 - 2.35% - 5.41% - - Male Female
Singh N et al (2023)[101] Uttarakhand Hospital based cross sectional study 460 - - - 2.61% - 26.9 years (mean) Female
Singh, E et al (2024)[102] Chhattisgarh Cross-sectional 810 9.75% 8.77% - - 17.16% - Male Female
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Pooled estimates of prevalence

Sickle cell anemia

The pooled prevalence from 656,005 participants was estimated at 2% (95% CI: 1%–3%), with a prediction interval of 0%–14% and high heterogeneity (I2 = 99.6%) [Figure 2]. Sensitivity analysis displayed a similar pooled prevalence of 1.9% (95% CI: 1%–3%) [Supplementary Figure 1 (1.2MB, tif) ]. The pooled prevalence of sickle cell anaemia varies by region, from 1% to 6%, with the highest in the Southern region (6%, 95% CI: 0%–39%) and the lowest in Eastern regions (1%, 95% CI: 0%–1%), with significant heterogeneity (I² =99.6%) [Figure 3]. Across different states, prevalence was highest in Chhattisgarh, Madhya Pradesh, and Rajasthan with 3% and the lowest in West Bengal (0%, 95% CI: 0%–1%) [Figure 4]. Meta regression revealed no significant trend over time [Supplementary Figure 2 (921.1KB, tif) ]. The Doi plot was asymmetrical, with an LFK index of 3.5, suggesting publication bias [Supplementary Figure 3 (765.3KB, tif) ].

Figure 2.

Figure 2

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Forest plot showing the pooled prevalence of sickle cell anaemia

Figure 3.

Figure 3

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Forest plot showing state wise subgroup analysis of prevalence of sickle cell anaemia

Figure 4.

Figure 4

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Forest plot showing region wise subgroup analysis of prevalence of sickle cell anaemia

Sickle cell anaemia trait (SCT)

The pooled prevalence of SCT was estimated at 5% (95% CI: 2%–8%), with 0%–40% prediction interval and high heterogeneity (I2 = 99.9%) [Supplementary Figure 4 (952.3KB, tif) ]. Sensitivity analysis showed a similar pooled prevalence of 4.6% (95% CI: 2.3%–7.6%) [Supplementary Figure 5 (1MB, tif) ]. For SCT prevalence varied by region, from 0% to 15%, with the highest in the Central region (15%, 95% CI: 2%–37%) and the lowest in the Northeast (0%, 95% CI: 0%–14%), showing significant heterogeneity (I2 = 99.9%). The highest prevalence was seen in Madhya Pradesh with 22%, and the lowest in Assam, 95% CI: 0%–14% [Supplementary Figures 6 (846.5KB, tif) and 7 (1.1MB, tif) ] Meta-regression showed no significant trend in prevalence of SCT over the year of publication [Supplementary Figure 8 (930.3KB, tif) ]. The Doi plot with an LFK index of 2.1 indicated publication bias [Supplementary Figure 9 (819.3KB, tif) ].

Beta thalassemia

The meta-analysis of 600,876 participants estimated the pooled prevalence of beta thalassemia at 6% (95% CI: 2%–12%), with wide prediction interval (0%–56%) and high heterogeneity (I2 = 99.9%) [Supplementary Figure 10 (822.1KB, tif) ]. Sensitivity analysis demonstrated a similar prevalence of 5.9% (95% CI: 2%–11.7%) [Supplementary Figure 11 (883.7KB, tif) ]. On subgroup analysis, the highest prevalence was in West Bengal (9%, 95% CI: 1%–22%) and the lowest in Assam (0%, 95% CI: 0%–37%) with significant heterogeneity (I2 = 99.9%). Region-wise analysis showed prevalence ranging from 1% to 19%, with the highest in the Eastern region (19%, 95% CI: 0%–56%) and the lowest in the central region (0%, 95% CI: 0%–4%) [Supplementary Figures 12 (590.6KB, tif) and 13 (1.2MB, tif) ]. Meta-regression showed no significant trend in the prevalence of beta thalassemia with publication year [Supplementary Figure 14 (920.2KB, tif) ]. Doi plot shows an LFK index of 0.13, indicating a minimal publication bias [Supplementary Figure 15 (765.5KB, tif) ].

Beta thalassemia trait

The meta-analysis of 590,369 participants estimated a pooled prevalence of BTT at 7% (95% CI: 4%–9%), with high heterogeneity (I2 = 99.6%) [Supplementary Figure 16 (1.2MB, tif) ]. Sensitivity analysis showed a similar prevalence of 6.6% (95% CI: 4.4%–9.1%) [Supplementary Figure 17 (1.4MB, tif) ]. The prevalence of BTT fluctuates by region, ranging from 3% to 9%, with 9% in Western, Northern, and Eastern regions and 3% in Central and Northeastern regions. Across states, Maharashtra had the highest prevalence at 14% (95% CI: 1%–36%), and 3% in Madhya Pradesh and Odisha [Supplementary Figures 18 (860.7KB, tif) and 19 (1.4MB, tif) ]. Meta-regression showed no significant effect of year of publication on the prevalence of BTT [Supplementary Figure 20 (906KB, tif) ]. The Doi plot was asymmetrical, with an LFK index of 0.78 indicating a moderate publication bias [Supplementary Figure 21 (797.9KB, tif) ].

G6PD deficiency

The meta-analysis of 56,162 participants estimated the pooled prevalence of G6PD deficiency at 5% (95% CI: 3%–8%), with 0%–25% prediction interval and high heterogeneity (I2 = 98.8%) [Supplementary Figure 22 (569.1KB, tif) ]. Sensitivity analysis showed a similar prevalence of 5% (95% CI: 2.5%–8.1%) [Supplementary Figure 23 (648.7KB, tif) ]. On region-wise analysis, prevalence ranged from 1% to 11%, with the highest in the Northeastern region (11%, 95% CI: 0%–91%) and the least in Northern regions (1%, 95% CI: 0%–2%). Across Indian states, prevalence of 3% was seen in Karnataka, and 6% in Madhya Pradesh and Chhattisgarh [Supplementary Figures 24 (846.7KB, tif) and 25 (892.9KB, tif) ] Meta-regression showed no significant trend of G6PD deficiency over the year of publication [Supplementary Figure 26 (758.4KB, tif) ]. The Doi plot was asymmetrical, with an LFK index of 4.4 indicating a potential publication bias [Supplementary Figure 27 (801.7KB, tif) ].

DISCUSSION

The current study estimates the pooled prevalence of 2% for sickle cell anemia, 5% for SCT, 6% for beta thalassemia, 7% for BTT, and 5% for G6PD deficiency in India. The carrier state for beta thalassemia (7%) is more prevalent than sickle cell anemia (5%).

The highest prevalence of SCD was observed in the Southern region (6%), whereas the lowest was in the Eastern region (1%). Global prevalence of SCD in 2021 was estimated at 7.74 million individuals, accounting for 1% of the world’s population, which marks a 41.4% increase from 5.46 million in 2000. This increase is likely due to population expansion in the Caribbean as well as in the western and central sub-Saharan African regions. Sub-Saharan Africa bears the highest burden, accounting for 80% of SCD cases worldwide.[103] India ranks second, contributing to approximately 15% of SCD-affected newborns worldwide, translating over 42,000 affected infants annually.[104]

Malaria has been a leading contributor to premature death for the past 5,000 years, particularly in Africa, where it accounts for 95% of global malaria cases and deaths and has exerted the strongest known selective pressure on the human genome, thereby giving a survival advantage for the population with HBB-βS variant in malaria-endemic areas.[,106,107] In certain regions of sub-Saharan Africa known for malaria endemicity, the carrier rates are as high as 25%.[108] The prevalence of SCT was highest in Central India (15%). SCT, the carrier state for SCD, affects roughly 5% of the global community. Within India, the geographical differences in SCD as well as trait could be attributed partly to the endemicity of malaria and partly to the population types, as states like Madhya Pradesh, Maharashtra, Gujarat, Odisha, and Kerala have significant tribal populations with higher HbS gene frequencies. Certain communities, such as the Bhils, Madias, Pawaras, and Pardhans, exhibit higher carrier rates.[109] SCD is relatively rare in the Northeastern region, other hemoglobinopathies predominance such as hemoglobin E (23.9%) in Assam.[36]

Differences in healthcare infrastructure and diagnostic facility access could also result in differences in reported prevalence. Regions with well-established screening initiatives for SCD, such as western and central parts of India, report higher prevalence of carrier states due to better detection rates.[44] The estimated pooled prevalence of beta-thalassemia disease in India is 6%, with considerable regional variation, whereas it was 7% for BTT. The Eastern part of India exhibited the highest prevalence of the disease (19%), whereas the Central region reported the lowest (0%). However, the geographical trend for the beta-thalassemia trait was different, with the highest prevalence reported in Maharashtra (14%), Central India. Although this observation is suggestive of a screening gap, many studies have not been conducted in this area in order to strongly conclude this. Prevalence of other hemoglobinopathies like HbE is known to be high in northeastern regions.[36]

G6PD deficiency was found in 5% of participants. The highest prevalence was observed in the Northeastern region at 11%, whereas the lowest was in the Northern region at 1%. The variation in prevalence might be linked to ethnic and genetic factors, as well as endogamous marriages, influencing the distribution of G6PD deficiency in India.[111] It is estimated that nearly 330 million people, accounting for 4.1% of the global inhabitants, have G6PD deficiency worldwide.[112]

Since the meta-regression analyses indicate trends in prevalence over time, changes in screening programs, improved diagnostics, and demographic shifts could also be the reasons for careful consideration. Studies have shown that increased awareness about the diseases, coupled with prenatal screening have led to better identification and management of these disorders, potentially affecting reported prevalence rates.[112]

A study conducted in Mali examined the interaction between SCT and G6PD deficiency in the context of malaria protection. The findings indicated that children possessing both SCT and the G6PD polymorphism did not exhibit increased protection against severe malaria compared to those with only one of these traits. In fact, female children with both traits showed a higher susceptibility to severe malaria (odds ratio = 15; P = 0.003) compared to those with SCT alone. This observation implies a possible negative interaction between the two conditions, reducing the overall protective effect against malaria.[113]

In the current study, G6PD deficiency is commonly observed in the Northeastern region, whereas SCT and disease are in Southern and Central India. It appears that G6PD deficiency and sickle cell anemia, both the trait and the disease forms, show high prevalence in geographically distinct areas, as though they complement each other in providing protective effects against malaria. However, the other reason for the spatial distribution of the disease could be endogamy, the practice of marrying within specific communities or ethnic groups can lead to a higher concentration of the respective genes in certain regions. This endogamy practice could also potentially lead to an increase in the prevalence of autosomal recessive diseases.[110]

State-wise and region-wise subgroup analyses reveal considerable geographic variations in the prevalence of these hemoglobinopathies. These variations may be attributed to differences in genetic backgrounds, consanguineous marriages, and regional environmental factors, as well as differential screening practices relevant to the context.

The National Sickle Cell Anemia Elimination Mission, under Ministry of Tribal Affairs, launched in 2023, aimed to lower the prevalence of SCD by universal screening of almost 7 crore individuals under 40 years of age in high-prevalence areas. It targets to eliminate the disease by the year 2047. Screening has to be full-fledged in order to achieve the same, especially in tribal areas. In order to reduce the incidence of new cases, preimplantation genetic diagnosis (PGD), in couples where both are carriers, need to be implemented. The practical, social, and ethical consequences of restricting the births of births of children with a genetic disorder, pertaining to reproductive autonomy, often tend to be overlooked.[114]

Studies highlight the impending need of integrating thalassemia screening into regular health assessments, especially in high-prevalence areas, to identify carriers early. This would be helpful for couples intending to marry, make informed decisions about future pregnancy outcomes in terms of the risk of bearing a child with disease.[115] Research also underscores the role of genetic counseling in educating carriers about reproductive options and voluntary genetic testing, thereby reducing the incidence of severe thalassemia cases.[114] PGD, at a subsidized cost in high burden areas, could be implemented since these diseases mainly affect the socially disadvantaged population for whom cost will be a solid determining factor for screening.[116] Since these hemoglobinopathies contribute to shortened survival, it is justified to use universal screening in high-burden areas.[103,117,118]

Strengths and limitations

Despite providing comprehensive prevalence estimates, the current study has some limitations. The high degree of heterogeneity across studies suggests that variations in study designs and population characteristics might have influenced the results. In addition, the presence of publication bias indicates a need for future studies to include more unpublished and gray literature to obtain more balanced estimates. Future research should focus on longitudinal studies to assess trends in prevalence as well as the impact of interventions over time.

CONCLUSIONS AND IMPLICATION

This systematic review and meta-analysis offer critical perspectives about the prevalence and distribution of sickle cell anemia, SCT, beta-thalassemia disease, beta-thalassemia trait, and G6PD deficiency in India. The findings reveal significant regional variations and project the importance of targeted screening and prevention strategies. Addressing the burden of these conditions through early diagnosis, genetic counseling, and public health interventions is crucial in reducing morbidity and improving health outcomes. The study findings highlight the need for region-specific strategies for managing hemoglobinopathies. Given the high burden of SCD, beta-thalassemia, and G6PD deficiency in certain regions, targeted genetic screening, and counseling programs should be prioritized. Prenatal screening and carrier detection programs can help in early diagnosis and management, reducing disease burden.

Conflicts of interest

There are no conflicts of interest.

Acknowledgment

The authors acknowledge the expert guidance and resources provided by the Global Centre for Evidence Synthesis (GCES), Chandigarh, and the Technical Resource Centre, Centre for Evidence-based Guidelines at the Department of Community Medicine, All India Institute of Medical Sciences, Nagpur, India, for undertaking the systematic review.

Funding Statement

Nil.

Figure S1

Leave out analysis for prevalence of sickle cell anaemia

AAM-25-1625_Suppl1.tif (1.2MB, tif)
Figure S2

Meta regression of prevalence of sickle cell anaemia on year of publication

AAM-25-1625_Suppl2.tif (921.1KB, tif)
Figure S3

DOI plot for publication bias assessment

AAM-25-1625_Suppl3.tif (765.3KB, tif)
Figure S4

Forest plot showing the pooled prevalence of sickle cell anaemia trait

AAM-25-1625_Suppl4.tif (952.3KB, tif)
Figure S5

Leave out analysis for prevalence of sickle call anaemia trait

Figure S6

Forest plot showing state wise subgroup analysis of prevalence of sickle cell anaemia

AAM-25-1625_Suppl6.tif (846.5KB, tif)
Figure S7

Forest plot showing region wise subgroup analysis of prevalence of sickle cell anaemia trait

AAM-25-1625_Suppl7.tif (1.1MB, tif)
Figure S8

Meta regression of prevalence of sickle cell anaemia trait on year of publication

AAM-25-1625_Suppl8.tif (930.3KB, tif)
Figure S9

DOI plot for publication bias assessment for assessment of prevalence of sickle cell anaemia trait

AAM-25-1625_Suppl9.tif (819.3KB, tif)
Figure S10

Forest plot showing the pooled prevalence of beta thalassemia

AAM-25-1625_Suppl10.tif (822.1KB, tif)
Figure S11

Leave out analysis for prevalence of beta thalassemia

AAM-25-1625_Suppl11.tif (883.7KB, tif)
Figure S12

Forest plot showing state wise subgroup analysis of prevalence of beta thalassemia

AAM-25-1625_Suppl12.tif (590.6KB, tif)
Figure S13

Forest plot showing region wise subgroup analysis of prevalence of beta thalassemia

Figure S14

Meta regression of prevalence of beta thalassemia on year of publication

AAM-25-1625_Suppl14.tif (920.2KB, tif)
Figure S15

DOI plot for publication bias assessment

AAM-25-1625_Suppl15.tif (765.5KB, tif)
Figure S16

Forest plot showing the pooled prevalence of beta thalassemia trait

Figure S17

Leave out analysis for prevalence of beta thalassemia trait

Figure S18

Forest plot showing state wise subgroup analysis of prevalence of beta thalassemia trait

AAM-25-1625_Suppl18.tif (860.7KB, tif)
Figure S19

Forest plot showing region wise subgroup analysis of prevalence of beta thalassemia trait

Figure S20

Meta regression of prevalence of beta thalassemia trait on year of publication

Figure S21

DOI plot for publication bias assessment

AAM-25-1625_Suppl21.tif (797.9KB, tif)
Figure S22

Forest plot showing the pooled prevalence of glucose 6 phosphate deficiency

AAM-25-1625_Suppl22.tif (569.1KB, tif)
Figure S23

Leave out analysis for prevalence of glucose 6 phosphate deficiency

AAM-25-1625_Suppl23.tif (648.7KB, tif)
Figure S24

Forest plot showing state wise subgroup analysis of prevalence of glucose 6 phosphate deficiency

AAM-25-1625_Suppl24.tif (846.7KB, tif)
Figure S25

Forest plot showing region wise subgroup analysis of prevalence of glucose 6 phosphate deficiency

AAM-25-1625_Suppl25.tif (892.9KB, tif)
Figure S26

Meta regression of prevalence of glucose 6 phosphate deficiency on year of publication

AAM-25-1625_Suppl26.tif (758.4KB, tif)
Figure S27

DOI plot for publication bias assessment

AAM-25-1625_Suppl27.tif (801.7KB, tif)

REFERENCES

  • 1.Colah RB, Gorakshakar A. Control of thalassemia in India. Thalass Rep. 2014;4:1955. [Google Scholar]
  • 2.Urade BP. Sickle cell gene (HbS) scenario in tribal India. J Health Med Inform. 2012;3:1–6. [Google Scholar]
  • 3.Rai V, Kumar P. Epidemiological study of glucose-6-phosphate dehydrogenase deficiency in scheduled caste population of India. J Anthropol 2012. 2012:984180. [Google Scholar]
  • 4.Shamim MA, Gandhi AP, Dwivedi P, Padhi BK. How to perform meta-analysis in R: A simple yet comprehensive guide. [[Last accessed on 2024 Dec 14]];Evidence. 2023 1:93–113. Available from: https://the.evidencejournals.com/index.php/j/article/view/6 . [Google Scholar]
  • 5.Gandhi AP, Shamim MA, Padhi BK. Steps in undertaking meta-analysis and addressing heterogeneity in meta-analysis. [[Last accessed on 2024 Dec 14]];Evidence. 2023 1:78–92. Available from: https://the.evidencejournals.com/index.php/j/article/view/7 . [Google Scholar]
  • 6.Karapurkar S, Ghildiyal R, Shah N, Keshwani R, Sharma S. Early detection of glomerular dysfunction and renal tubulopathy in children with sickle cell disease in India. J Trop Pediatr. 2023;69:fmad019. doi: 10.1093/tropej/fmad019. [DOI] [PubMed] [Google Scholar]
  • 7.Maji SK, Dolai TK, Pradhan S, Maity A, Mandal S, Mondal T, et al. Implications of population screening for thalassemias and hemoglobinopathies in rural areas of West Bengal, India: Report of a 10-year study of 287,258 cases. Hemoglobin. 2020;44:432–7. doi: 10.1080/03630269.2020.1831530. [DOI] [PubMed] [Google Scholar]
  • 8.Balgir RS, Murmu B, Dash BP. Hereditary hemolytic disorders among the Ashram school children in Mayurbhanj district of Orissa. The Journal of the Association of Physicians of India. 1999;47:987–90. [PubMed] [Google Scholar]
  • 9.Joshi SR, Patel RZ, Patel HR, Sukumar S, Colah RB. High prevalence of G6PD deficiency in Vataliya Prajapati community in western India. Haematologia. 2001;31:57–60. doi: 10.1163/15685590151092715. [DOI] [PubMed] [Google Scholar]
  • 10.Sukumar S, Mukherjee MB, Colah RB, Mohanty D. Molecular basis of G6PD deficiency in India. Blood Cells, Molecules, and Diseases. 2004;33:141–5. doi: 10.1016/j.bcmd.2004.06.003. [DOI] [PubMed] [Google Scholar]
  • 11.Sinha S, Kumar A, Gupta V, Kumar S, Singh VP, Raman R. Haemoglobinopathies–thalassaemias and abnormal haemoglobins in eastern Uttar Pradesh and adjoining districts of neighbouring states. Current science. 2004;87:775–80. [Google Scholar]
  • 12.Balgir RS. The spectrum of haemoglobin variants in two scheduled tribes of Sundargarh district in north-western Orissa, India. Annals of Human Biology. 2005;32:560–73. doi: 10.1080/03014460500228741. [DOI] [PubMed] [Google Scholar]
  • 13.Jawahirani A, Mamtani M, Das K, Rughwani V, Kulkarni H. Prevalence of β-thalassaemia in subcastes of Indian Sindhis: Results from a two-phase survey. Public Health. 2007;121:193–8. doi: 10.1016/j.puhe.2006.10.017. [DOI] [PubMed] [Google Scholar]
  • 14.Mulchandani DV, Fulare MB, Zodpey SP, Vasudeo ND. Prevalence and some epidemiological factors of beta thalassaemia trait in Sindhi community of Nagpur City, India. Indian journal of public health. 2008;52:11–5. [PubMed] [Google Scholar]
  • 15.Patel J, Patel A, Patel J, Kaur A, Patel V. Prevelance of haemoglobinophaties in Gujarat, India: A cross-sectional study. Internet J Hematol. 2009:5. [Google Scholar]
  • 16.Mandot S, Khurana VL, Sonesh JK. Sickle cell anemia in Garasia tribals of Rajasthan. Indian Pediatrics. 2009;46:239–40. [PubMed] [Google Scholar]
  • 17.Choubisa SL. Sickle cell haemoglobin, thalassaemia and G-6-PD enzyme deficiency genes in Garasiya tribe inhabited malaria endemic areas of Sirohi District, Rajasthan (India) J Commun Dis. 2009;41:13–8. [PubMed] [Google Scholar]
  • 18.Panda A, Praveen B, Bisht SS. Clinical and pathological status of haemoglobinopathies among pregnant women in southern Orissa. Indian Journal of Biotechnology. 2009;8:456–7. [Google Scholar]
  • 19.Kaur G, Srivastav J, Jain S, Chawla D, Chavan BS, Atwal R, et al. Preliminary report on neonatal screening for congenital hypothyroidism, congenital adrenal hyperplasia and glucose-6-phosphate dehydrogenase deficiency: a Chandigarh experience. The indian journal of pediatrics. 2010;77:969–73. doi: 10.1007/s12098-010-0150-x. [DOI] [PubMed] [Google Scholar]
  • 20.Patra PK, Tripathi S, Khodiar P, Sablania P, Keshari JR, Dalla AR. Epidemiology of sickle cell disease in state of Chhattisgarh. Indian Journal of Public Health Research & Development. 2010;1:64–8. [Google Scholar]
  • 21.Madan N, Sharma S, Sood SK, Colah R. Frequency of β-thalassemia trait and other hemoglobinopathies in northern and western India. Indian journal of human genetics. 2010;16:16. doi: 10.4103/0971-6866.64941. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Colah R, Gorakshakar A, Phanasgaonkar S, D’Souza E, Nadkarni A, Surve R, et al. Epidemiology of β-thalassaemia in Western India: mapping the frequencies and mutations in sub-regions of Maharashtra and Gujarat. British journal of haematology. 2010;149:739–47. doi: 10.1111/j.1365-2141.2010.08131.x. [DOI] [PubMed] [Google Scholar]
  • 23.Dolai TK, Dutta S, Bhattacharyya M, Ghosh MK. Prevalence of hemoglobinopathies in rural Bengal, India. Hemoglobin. 2012;36:57–63. doi: 10.3109/03630269.2011.621007. [DOI] [PubMed] [Google Scholar]
  • 24.Achoubi N, Asghar M, Saraswathy KN, Murry B. Prevalence of β-thalassemia and hemoglobin E in two migrant populations of Manipur, North East India. Genetic Testing and Molecular Biomarkers. 2012;16:1195–200. doi: 10.1089/gtmb.2011.0373. [DOI] [PubMed] [Google Scholar]
  • 25.Jain BB, Roy RN, Ghosh S, Ghosh T, Banerjee U, Bhattacharya SK. Screening for thalassemia and other hemoglobinopathies in a tertiary care hospital of West Bengal: implications for population screening. Indian journal of public health. 2012;56:297–300. doi: 10.4103/0019-557X.106419. [DOI] [PubMed] [Google Scholar]
  • 26.Mondal B, Maiti S, Biswas BK, Ghosh D, Paul S. Prevalence of hemoglobinopathy, ABO and rhesus blood groups in rural areas of West Bengal, India. Journal of research in medical sciences: the official journal of Isfahan University of Medical Sciences. 2012;17:772. [PMC free article] [PubMed] [Google Scholar]
  • 27.Patel AG, Shah AP, Sorathiya SM, Gupte SC. Hemoglobinopathies in South Gujarat population and incidence of anemia in them. Indian journal of human genetics. 2012;18:294. doi: 10.4103/0971-6866.107979. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Gunjal Sandeep S, Narlawar Uday W, Humne Arun Y, Chaudhari Vijaya L. Prevalence of sickle cell disorder and anaemia in tribal school students from central India. International Journal of Collaborative Research on Internal Medicine & Public Health. 2012;4:1321–9. [Google Scholar]
  • 29.Gorakshakar A, Sathe P, Colah R, Nadkarni A, Ghosh K. Hemoglobin Showa-Yakushiji: a common β thalassemia mutation among the Agri community from western India. Genetic Testing and Molecular Biomarkers. 2012;16:302–5. doi: 10.1089/gtmb.2011.0158. [DOI] [PubMed] [Google Scholar]
  • 30.Bisoi S, Chakraborty S, Chattopadhyay D, Biswas B, Ray S. Glucose-6-phosphate dehydrogenase screening of babies born in a tertiary care hospital in West Bengal. Indian journal of public health. 2012;56:146–8. doi: 10.4103/0019-557X.99908. [DOI] [PubMed] [Google Scholar]
  • 31.Vasaikar M, Kanthikar S, Chavan S. Spectrum of hemoglobinopathies diagnosed by HPLC in high prevalence area of north Maharashtra. International Journal of Pharma and Bio Sciences. 2012;3:B690–697. [Google Scholar]
  • 32.Verma P, Singh S, Krishna A, Ali W, Tiwari S. Prevalence of Heamoglobin Variants, ABO and Rhesus Blood Groups in Northern Uttar Pradesh, India. Biomedical Research. 2013;24:377–82. [Google Scholar]
  • 33.Meena LP, Kumar K, Singh VK, Bharti A, Rahman SK, Tripathi K. Study of mutations in β-thalassemia trait among blood donors in Eastern Uttar Pradesh. Journal of Clinical and Diagnostic Research: JCDR. 2013;7:1394. doi: 10.7860/JCDR/2013/5456.3150. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Kulkarni P, Masthi NR, Niveditha SR, Suvarna R. The Prevalence of the Beta Thalassemia Trait among the Pregnant Women who attended the ANC Clinic in a PHC, by using the NESTROF Test in Bangalore, Karnataka. Journal of clinical and diagnostic research: JCDR. 2013;7:1414. doi: 10.7860/JCDR/2013/5286.3149. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Baxi A, Manila K, Kadhi P, Heena B. Carrier screening for β thalassemia in pregnant Indian women: experience at a single center in Madhya Pradesh. Indian journal of Hematology and Blood transfusion. 2013;29:71–4. doi: 10.1007/s12288-012-0165-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Mohanty D, Colah RB, Gorakshakar AC, Patel RZ, Master DC, Mahanta J, et al. Prevalence of β-thalassemia and other haemoglobinopathies in six cities in India: a multicentre study. [[Last accessed on 2025 Feb 27]];J Community Genet [Internet] 2013 4:33–42. doi: 10.1007/s12687-012-0114-0. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3537975/ [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Patel J, Patel B, Gamit N, Serjeant GR. Screening for the sickle cell gene in Gujarat, India: a village-based model. Journal of Community Genetics. 2013;4:43–7. doi: 10.1007/s12687-012-0116-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Maji SK, Mandal PK, Bera R, Dolai TK. The Prevalence and Characterization of ²-Thalassemia Trait by Using High-Performance Liquid Chromatography Among the Rural Population in West Bengal, India. Thalassemia Reports. 2014;4:2188. [Google Scholar]
  • 39.Samal J, Meshram FA. A cross sectional study on sickle cell disease among backward communities of Gadchiroli, Maharashtra, India. Indian Journal of Public Health Research & Development. 2014;5:117–20. [Google Scholar]
  • 40.Purohit P, Dehury S, Patel S, Patel DK. Prevalence of deletional alpha thalassemia and sickle gene in a tribal dominated malaria endemic area of eastern India. International Scholarly Research Notices 2014. 2014:745245. doi: 10.1155/2014/745245. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Mohanty D, Gorakshakar AC, Colah RB, Patel RZ, Master DC, Mahanta J, et al. Interaction of iron deficiency anemia and hemoglobinopathies among college students and pregnant women: a multi center evaluation in India. Hemoglobin. 2014;38:252–7. doi: 10.3109/03630269.2014.913517. [DOI] [PubMed] [Google Scholar]
  • 42.Shrikhande AV, Arjunan A, Agarwal A, Dani A, Tijare J, Gettig E, et al. Prevalence of the βS gene among scheduled castes, scheduled tribes and other backward class groups in Central India. Hemoglobin. 2014;38:230–5. doi: 10.3109/03630269.2014.931287. [DOI] [PubMed] [Google Scholar]
  • 43.Ratan S, Sharma D, Chawla S, Kumar S. Screening for Glucose-6-Phosphate Deficiency in Newborns of a Tertiary Hospital. Indian Journal of Public Health Research & Development. 2014;5:167–71. [Google Scholar]
  • 44.Putchen DD, Kulkarni S, Nanjundarao SS, Bhat DG, Venkatachala PK, Prasad SR. Retrospective study on the distribution of hemoglobinopathies in Karnataka-A laboratory experience. Indian J Pathol Microbiol. 2024;67:585–9. doi: 10.4103/ijpm.ijpm_893_22. [DOI] [PubMed] [Google Scholar]
  • 45.Balgir RS. Prevalence of hemolytic anemia and hemoglobinopathies among the pregnant women attending a tertiary hospital in central India. Thalassemia Reports. 2015;5:4644. [Google Scholar]
  • 46.Mohanty D, Mukherjee MB, Colah RB, Wadia M, Ghosh K, Chottray GP, et al. Spectrum of hemoglobinopathies among the primitive tribes: a multicentric study in India. Asia Pacific Journal of Public Health. 2015;27:NP562–71. doi: 10.1177/1010539513480231. [DOI] [PubMed] [Google Scholar]
  • 47.Panigrahi S, Patra PK, Khodiar PK. The screening and morbidity pattern of sickle cell anemia in Chhattisgarh. Indian Journal of Hematology and Blood Transfusion. 2015;31:104–9. doi: 10.1007/s12288-014-0407-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Teli AB, Deori R, Saikia SP. Haemoglobinopathies and β-thalassaemia among the tribals working in the tea gardens of Assam, India. J Clin Diagn Res. 2016:22. doi: 10.7860/JCDR/2016/22010.9002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Singh MP, Gupta RB, Yadav R, Sharma RK, Shanmugam R. Prevalence of α+-thalassemia in the scheduled tribe and scheduled caste populations of Damoh District in Madhya Pradesh, Central India. Hemoglobin. 2016;40:285–8. doi: 10.3109/03630269.2016.1170031. [DOI] [PubMed] [Google Scholar]
  • 50.Buch A, Iqbal B, Bordawekar R, Jain A, Jariwala P, Rathod H. Patterns of hemoglobinopathies diagnosed by high-performance liquid chromatography in and around Pune (Western Maharashtra, India): A pilot study. Journal of medical society. 2016;30:111–5. [Google Scholar]
  • 51.Chakrabarti S, Mandal K, Pathak S, Patra A, Pal S. Haemoglobinopathies among the tribal and non-tribal antenatal mothers in a tertiary care hospital of rural West Bengal, India. Bangladesh Journal of Medical Science. 2016;15:90–4. [Google Scholar]
  • 52.Chandrashekhar C, Bandaru K. Incidence of G6PD Defi ciency in a Tertiary Care Hospital in South India. J Nepal Paediatr Soc. 2016;36:141–2. [Google Scholar]
  • 53.Iqbal MS, Tabassum A, Chatura KR. Spectrum of thalassemia and hemoglobinopathies in a tertiary care diagnostic center. Journal of Advanced Clinical and Research Insights. 2016;3:161–5. [Google Scholar]
  • 54.Mondal SK, Mandal S. Prevalence of thalassemia and hemoglobinopathy in eastern India: a 10-year high-performance liquid chromatography study of 119,336 cases. Asian journal of transfusion science. 2016;10:105–10. doi: 10.4103/0973-6247.175424. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Gupta S, Gupta A, Raina B, Khajuria A. Prevalence and pattern of anaemias in children at ASCOMS & hospital Jammu. JK Sci. 2017;19:76–80. [Google Scholar]
  • 56.Suresh Babu TV, Saritha, Ashwini S, Shantaram M. Pre-marital screening tests of β-thalassemia trait in Dakshina Kannada population of Karnataka. Int J Pharmcy Pharm Sci. 2017;9:268–72. [Google Scholar]
  • 57.Warghade S, Britto J, Haryan R, Dalvi T, Bendre R, Chheda P, et al. Prevalence of hemoglobin variants and hemoglobinopathies using cation-exchange high-performance liquid chromatography in central reference laboratory of India: A report of 65779 cases. Journal of laboratory physicians. 2018;10:073–9. doi: 10.4103/JLP.JLP_57_17. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Ghosh M, Basu K, Karmakar S, Biswas A, Sengupta M, Chatterjee U, et al. Thalassaemia carrier detection during antenatal period: Single centre experience from Eastern India. J Clin Diagn Res. 2019;13:EC01–5. [Google Scholar]
  • 59.Tripathi P, Agarwal S, Muthuswamy S. Prevalence and Genetic Characterization of Glucose-6-Phosphate Dehydrogenase Deficiency in Anemic Subjects from Uttar Pradesh, India. J Pediatr Genet. 2019;8:47–53. doi: 10.1055/s-0039-1677729. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.Gorakshakar AC, Breganza PV, Colaco SP, Shaikh RF, Bohra MY, Sawant PM, et al. Rare β- and δ-Globin Gene Mutations in the Pathare Prabhus: Original Inhabitants of Mumbai, India. Hemoglobin. 2018;42:297–301. doi: 10.1080/03630269.2018.1544909. [DOI] [PubMed] [Google Scholar]
  • 61.Jain P, Gupta M, Dua S, Marwah N, Gill M, Sen R. Prevalence of Various Hemoglobinopathies-An Experience from Tertiary Care Centre. Dicle Tip Dergisi. 2019;46:269–76. [Google Scholar]
  • 62.Devendra R, Gupta V, Shanmugam R, Singh MPSS, Patel P, Valecha N, et al. Prevalence and spectrum of mutations causing G6PD deficiency in Indian populations. Infection Genetics Evol. 2020;86:104597. doi: 10.1016/j.meegid.2020.104597. [DOI] [PubMed] [Google Scholar]
  • 63.Saha S, Ghosh S, Basu K, Bhattacharyya M. Prevalence of β-haemoglobinopathies in Eastern India and development of a novel formula for carrier detection. J Hematop. 2020;13:159–64. [Google Scholar]
  • 64.Devendra R, Gupta V, Biradar SS, Bhat P, Hegde S, Hoti SL, et al. G6PD A- is the major cause of G6PD deficiency among the Siddis of Karnataka, India. Ann Hum Biol. 2020;47:55–8. doi: 10.1080/03014460.2019.1699954. [DOI] [PubMed] [Google Scholar]
  • 65.Achoubi N, Asghar M, Wahengbam AK, Singh SJ, Saraswathy KN, Murry B. Prevalence and Molecular Characterization of Glucose-6Phosphate Dehydrogenase Deficiency among Brahmins and Muslims of Manipur, India. Indian Journal of Public Health Research & Development. 2020;11:278–82. [Google Scholar]
  • 66.Chourasia S, Kumar R, Singh MPSS, Vishwakarma C, Gupta AK, Shanmugam R. High Prevalence of Anemia and Inherited Hemoglobin Disorders in Tribal Populations of Madhya Pradesh State, India. Hemoglobin. 2020;44:391–6. doi: 10.1080/03630269.2020.1848859. [DOI] [PubMed] [Google Scholar]
  • 67.Verma J, Roy P, Thomas DC, Jhingan G, Singh A, Bijarnia-Mahay S, et al. Newborn Screening for Congenital Hypothyroidism, Congenital Adrenal Hyperplasia, and Glucose-6-Phosphate Dehydrogenase Deficiency for Improving Health Care in India. J Pediatr Intensive Care. 2020;9:40–4. doi: 10.1055/s-0039-1698424. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 68.Dharmarajan S, Pawar A, Bhide P, Kar A. Undiagnosed haemoglobinopathies among pregnant women attending antenatal care clinics in Pune, India. J Community Genet. 2021;12:337–44. doi: 10.1007/s12687-021-00505-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69.Patel S, Priya R, Padhi P, Naik T, Abraham J, Nanda R, et al. Prevalence of Congenital Hypothyroidism and G6PD Deficiency in Newborns in a Tertiary Care Hospital of Central India. J Neonatal. 2021;35:5–9. [Google Scholar]
  • 70.Mishra MN, Sangma B, George DM, John L, George KC. Hematological indices and hemoglobin high-pressure liquid chromatography for diagnosis of hemoglobinopathies in a secondary care center in North-East India. Int Med. 2021;2:330–7. [Google Scholar]
  • 71.Saha J, Panja A, Nayek K. The Prevalence of HBB Mutations among the Transfusion-Dependent and Non Transfusion-Dependent Hb E/β-Thalassemia Children in a Tertiary Center of West Bengal, India. Hemoglobin. 2021;45:157–62. doi: 10.1080/03630269.2021.1933023. [DOI] [PubMed] [Google Scholar]
  • 72.Basumatary N, Baruah D, Sarma PK, Deka M, Sarmah J. Prevalence And Alteration In Haematological Parameters Of Glucose-6-Phosphate Dehydrogenase Deficient Proto-Australoid Population Of Malaria Endemic Himalayan Belt Of Assam, India. Explor Anim Med Res. 2021;11:43–8. [Google Scholar]
  • 73.Tripathi S, Mishra N, Kumar A. Prevalence of Sickle Cell Anemia in Association of Plasmodium falciparum from Rajnandgaon, India. Annals of Nutritional Disorders & Therapy Open. 2021;8:1067. [Google Scholar]
  • 74.Ghosh S, Chakrabarti S, Bhattacharyya M. Prenatal Screening and Diagnosis of ß-Thalassemia in India: Is ARMS-PCR Enough? Indian J Hematol Blood Transfus. 2021;37:448–52. doi: 10.1007/s12288-020-01370-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 75.Cherian S, Singh P, Patil S, Bhandarkar P, Jadhav VR. Spectrum of Haemoglobinopathies Detected on Antenatal Screening and Diagnostic Work-up in an Urban Healthcare Set-up: A Retrospective Study. J Clin Diagn Res. 2022;16:EC21–5. [Google Scholar]
  • 76.Dixit S, Das A, Rana R, Khuntia HK, Ota AB, Pati S, et al. A community based study on haemoglobinopathies and G6PD deficiency among particularly vulnerable tribal groups in hard-to-reach malaria endemic areas of Odisha, India: implications on malaria control. Malar J. 2022;21:340. doi: 10.1186/s12936-022-04358-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 77.Patel RK, Mamatha S, Manjhi R, Dutta P, Pothal S, Haran A. Demographic and Clinicoradiological Presentation of Tuberculosis in Patients with Sickle Cell Haemoglobinopathy: A Cross-sectional Study. Journal of Clinical and Diagnostic Research. 2022;16:22–5. [Google Scholar]
  • 78.Mukherjee S, Mallige A, Chowdhry A, Devgan A, Singh B, Mukherjee B, et al. Neonatal Screening for Glucose-6-Phosphate Dehydrogenase (G6PD) Deficiency in Eastern India. J Nepal Paediatr Soc. 2022;41:408–12. [Google Scholar]
  • 79.Sabitha Rani SS, Vamshidhar IS, Bangaru S, John NA, John J. A study of spectrum of sickle cell anemia and thalassemia in a teaching institute of South India. Niger J Clin Pr. 2022;25:490–5. doi: 10.4103/njcp.njcp_1742_21. [DOI] [PubMed] [Google Scholar]
  • 80.Mohanty SS, Purohit A, Anand PK, Huda RK, Sharma AK. Burden of sickle cell disease in tribal students in Maa-Baadi institutions in southern Rajasthan - A pilot study. Indian J Med Res. 2022;156:269–74. doi: 10.4103/ijmr.ijmr_3195_21. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 81.Mohanty SS, Parihar S, Huda RK, Toteja GS, Sharma AK. Prevalence of sickle cell anemia, β-thalassemia and glucose-6-phosphate dehydrogenase deficiency among the tribal population residing in the Aravali hills of Sirohi region of Rajasthan state. Clin Epidemiol Glob Health. 2022;13:100916. [Google Scholar]
  • 82.Sinha KK, Ranjan R, Murari K. Assessment of the Clinico-Epidemiological Profile of Thalassemia Patients in a Tertiary Care Hospital in Bihar Region. Intl J Pharm Clin Res. 2022;14:600–4. [Google Scholar]
  • 83.Vachhani NA, Vekariya DJ, Colah RB, Kashiyani HN, Nandani SL. Spectrum of β-Thalassemia and Other Hemoglobinopathies in the Saurashtra Region of Gujarat, India: Analysis of a Large Population Screening Program. Hemoglobin. 2022;46:285–9. doi: 10.1080/03630269.2022.2142608. [DOI] [PubMed] [Google Scholar]
  • 84.Mukhopadhyay D, Chakraborty A, Bagdi S, Samaddar A, Pathak S. Pattern of hemoglobinopathies in females attending in antenatal clinic with special reference to the role of RBC parameters in beta-thalassemia carrier detection. Natl J Physiol Pharm Pharmacol. 2022;12:2167–71. [Google Scholar]
  • 85.Asati AA, Sahu M, Uike S, Tiwari P. Distribution of hemoglobinopathies in pediatric population: A tertiary care experience. J Cardiovasc Res. 2022;13:1997–2005. [Google Scholar]
  • 86.Singh K, Patel KP, Singh RP, Khare AK. Study of sickle cell anemia in high-incidence tribal area belt in Madhya Pradesh-Shahdol district. NeuroQuantology. 2022;20:3974–80. [Google Scholar]
  • 87.Thakkar M, Shah H, Shah U, Parmar P, Solanki S. Prevalence of Haemoglobinopathy among Young College Students in Anand-Gujarat: A Premarital Screening Program for Carrier Detection of Hemoglobin Disorders. Indian Journal of Public Health Research & Development. 2023;14:210–5. [Google Scholar]
  • 88.Kavitha A, Kavita V, Mukilarasi Incidence Of Hemoglobinopathies In South Indian Population – 4 Year Study. Ann Pathol Lab Med. 2023;10:A47–52. [Google Scholar]
  • 89.Narang V, Jain A, Grover S, Soni A, Narang M, Taneja A. Prevalence and spectrum of haemoglobinopathies in females of reproductive age group- A first tertiary care center experience in Punjab, North India. Indian J Pathol Microbiol. 2023;66:564–7. doi: 10.4103/ijpm.ijpm_2_22. [DOI] [PubMed] [Google Scholar]
  • 90.Mahajan S, Dhule S, Taksande RV. Prevalence and profile of beta-thalassemia: A prospective analysis at a tertiary care center. Asian J Pharm Clin Res. 2023;16:174–6. [Google Scholar]
  • 91.Dawar A, Mujalde VS, Chopda AK, Khare AK, Ahirwar T. Study to how sickle cell anemia affects the tribal area of Central India population. J Popul Ther Clin Pharmacol. 2023;30:759–65. [Google Scholar]
  • 92.Minal T, Hitesh S, Prakash P, Singh NKR. Screening of haemoglobinopathies by high performance liquid chromatography and its molecular characterization using amplification refractory mutation system and direct DNA sequencing techniques among college students in central Gujarat. J Krishna Inst Med Sci Univ. 2023;12:20–34. [Google Scholar]
  • 93.Parmar JKS, Babariya BJ, Gupta D, Babariya MJ, Agrawal P, Doshi C. Usefulness of HPLC & Hematological Parameters in Laboratory for Evaluation of Abnormal Hemoglobin Variants in Western-Tribal Part of India. Intl J Pharm Clin Res. 2023;15:110–5. [Google Scholar]
  • 94.Baruah D, Basumatary MN, Barua A, Sarmah J. Haemoglobinopathy: A Major Concern for Adolescents of Kokrajhar District of Assam. Int J Toxicol Pharmacol Res. 2023;13:1–6. [Google Scholar]
  • 95.Sampagar A, Patil NG, Mahantashetti NS, Patil H, Hukkeri M, Kangle R, et al. Feasibility and Cost Analysis of Antenatal Screening and Diagnosis of Hemoglobinopathies: A Prospective Study. Perinatology. 2023;24:26–33. [Google Scholar]
  • 96.Gupta T, Gupta T, Yadav D, Gupta M. The Need for Mandatory Newborn Screening in India: A Retrospective Analysis of 3-Year Data From a Multispecialty Private Hospital in Uttar Pradesh, India. Perinatology. 2023;24:7–14. [Google Scholar]
  • 97.Rao MH, Edara A, Reddy YS, Rekapalli S. Incidence and clinical profile of hemoglobinopathies in children: a hospital-based study in Gayatri Vidya Parishad Health Care and Medical Technology. J Assoc Physicians India. 2023;14:1713–40. [Google Scholar]
  • 98.Setia S, Bagga PK, Sodhi MK, Saini JS. Spectrum of haemoglobinopathies in children by high-performance liquid chromatography (HPLC) in a tertiary care hospital. J Cardiovasc Res. 2023;14:119–25. [Google Scholar]
  • 99.Arora D, Gupta P, Rai S, Iyengar S. Study of Spectrum of Hemoglobinopathies in Pediatric Age Group Using Hematological Indices and Confirmation by High Performance Liquid Chromatography in A Tertiary Care Centre in Central India-A Prospective Study. J Cardiovasc Res. 2023;14:300–5. [Google Scholar]
  • 100.Shaw J, Ray R, Bhattacharjee S, Dasgupta AK, Bhattacharyya M. Pattern of hemoglobinopathy among the young tribes of West Bengal: A completely different scenario from Rest of India. Indian J Public Health. 2023;67:159–61. doi: 10.4103/ijph.ijph_766_22. [DOI] [PubMed] [Google Scholar]
  • 101.Singh N, Chowdhury N, Bahadur A, Ahuja S, Arathi K, Jeladharan R, et al. Thalassemia and hemoglobinopathy screening in women attending antenatal clinic at a tertiary care center in Uttarakhand, India: a re-look at the laboratory parameters mandating high-performance liquid chromatography workup. Cureus. 2023;15((6)):e40667. doi: 10.7759/cureus.40667. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 102.Singh E, Shivwanshi LR, Kumar A. A positive correlation between mutated gene of sickle cell anemia and glucose-6-phosphate dehydrogenase among gond tribes of Chhattisgarh, India. Mutat Res. 2024;828:111849. doi: 10.1016/j.mrfmmm.2023.111849. [DOI] [PubMed] [Google Scholar]
  • 103.Global, regional, and national prevalence and mortality burden of sickle cell disease, 2000–2021 | Institute for Health Metrics and Evaluation [Internet] [[Last accessed on 2025 Feb 19]]. Available from: https://www.healthdata.org/research-analysis/library/global-regional-and-national-prevalenceand-mortality-burden-sickle-cell .
  • 104.Raman V, Seshadri T, Joice SV, Srinivas PN. Sickle cell disease in India: a scoping review from a health systems perspective to identify an agenda for research and action. [[Last accessed on 2025 Feb 19]];BMJ Glob Health [Internet] 2021 6((2)) doi: 10.1136/bmjgh-2020-004322. Available from: https://gh.bmj.com/content/6/2/e004322 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 105.Fact sheet about malaria [Internet] [[Last accessed on 2025 Feb 27]]. Available from: https://www.who.int/news-room/fact-sheets/detail/malaria .
  • 106.Kariuki SN, Williams TN. Human genetics and malaria resistance. Hum Genet. 2020;139:801–11. doi: 10.1007/s00439-020-02142-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 107.Esoh K, Wonkam A. Evolutionary history of sickle-cell mutation: implications for global genetic medicine. [[Last accessed on 2025 Feb 27]];Hum Mol Genet [Internet] 2021 30:R119–28. doi: 10.1093/hmg/ddab004. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8117455/ [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 108.Sickle Cell Disease | WHO | Regional Office for Africa [Internet] 2025. [[Last accessed on 2025 Feb 24]]. Available from: https://www.afro.who.int/health-topics/sickle-cell-disease .
  • 109.Colah RB, Mukherjee MB, Martin S, Ghosh K. Sickle cell disease in tribal populations in India. [[Last accessed on 2025 Feb 19]];Indian J Med Res [Internet] 2015 141:509–15. doi: 10.4103/0971-5916.159492. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4510747/ [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 110.Bajaj A, Mathew S, Vellarikkal SK, Sivadas A, Bhoyar RC, Joshi K, et al. Genomics of rare genetic diseases—experiences from India. [[Last accessed on 2025 Feb 24]];Hum Genomics [Internet] 2019 13((1)):52. Available from:. doi: 10.1186/s40246-019-0215-5. https://doi.org/10.1186/s40246-019-0215-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 111.Nkhoma ET, Poole C, Vannappagari V, Hall SA, Beutler E. The global prevalence of glucose-6-phosphate dehydrogenase deficiency: A systematic review and meta-analysis. Blood Cells Mol Dis. 2009;42:267–78. doi: 10.1016/j.bcmd.2008.12.005. [DOI] [PubMed] [Google Scholar]
  • 112.Giardina PJ, Rivella S. Thalassemia syndromes. In: Hoffman R, et al., editors. Hematology: Basic Principles and Practice. 6th ed. Elsevier; 2013. pp. p. 476–503. [Google Scholar]
  • 113.Guindo A, Traore K, Diakite S, Wellems TE, Doumbo OK, Diallo DA. An evaluation of concurrent G6PD (A−) deficiency and sickle cell trait in Malian populations of children with severe or uncomplicated P. falciparum malaria. [[Last accessed on 2025 Feb 19]];Am J Hematol [Internet] 2011 86((9)):795–6. doi: 10.1002/ajh.22093. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4795173/ [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 114.Petrou M. Genetic counselling. In: Prevention of Thalassaemias and Other Haemoglobin Disorders: Volume 1: Principles [Internet] 2nd edition [Internet] Thalassaemia International Federation. 2013. [[Last accessed on 2025 Feb 24]]. Available from: https://www.ncbi.nlm.nih.gov/books/NBK190461/ [PubMed]
  • 115.Thiyagarajan A, Bhattacharya S, Sharma N, Srivastava A, Dhar DK. Need for a universal thalassemia screening programme in India? A public health perspective. [[Last accessed on 2025 Feb 24]];J Fam Med Prim Care [Internet] 2019 8:1528–32. doi: 10.4103/jfmpc.jfmpc_90_19. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6559078/ [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 116.Lemke T, Rüppel J. Social dimensions of preimplantation genetic diagnosis: a literature review. [[Last accessed on 2025 Feb 24]];New Genet Soc [Internet] 2019 38:80–112. Available from: https://doi.org/10.1080/14636778.2018.1549983 . [Google Scholar]
  • 117.Platt OS, Brambilla DJ, Rosse WF, Milner PF, Castro O, Steinberg MH, et al. Mortality in sickle cell disease. Life expectancy and risk factors for early death. N Engl J Med. 1994;330:1639–44. doi: 10.1056/NEJM199406093302303. [DOI] [PubMed] [Google Scholar]
  • 118.Tantiworawit A, Kamolsripat T, Piriyakhuntorn P, Rattanathammethee T, Hantrakool S, Chaiadisaksopha C, et al. Survival and Causes of Death in Patients with Alpha and Beta-Thalassemia. [[Last accessed on 2025 Feb 24]];Blood [Internet] 2023 142:5250. Available from: https://doi.org/10.1182/blood-2023-179942. [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Figure S1

Leave out analysis for prevalence of sickle cell anaemia

AAM-25-1625_Suppl1.tif (1.2MB, tif)
Figure S2

Meta regression of prevalence of sickle cell anaemia on year of publication

AAM-25-1625_Suppl2.tif (921.1KB, tif)
Figure S3

DOI plot for publication bias assessment

AAM-25-1625_Suppl3.tif (765.3KB, tif)
Figure S4

Forest plot showing the pooled prevalence of sickle cell anaemia trait

AAM-25-1625_Suppl4.tif (952.3KB, tif)
Figure S5

Leave out analysis for prevalence of sickle call anaemia trait

AAM-25-1625_Suppl5.tif (1MB, tif)
Figure S6

Forest plot showing state wise subgroup analysis of prevalence of sickle cell anaemia

AAM-25-1625_Suppl6.tif (846.5KB, tif)
Figure S7

Forest plot showing region wise subgroup analysis of prevalence of sickle cell anaemia trait

AAM-25-1625_Suppl7.tif (1.1MB, tif)
Figure S8

Meta regression of prevalence of sickle cell anaemia trait on year of publication

AAM-25-1625_Suppl8.tif (930.3KB, tif)
Figure S9

DOI plot for publication bias assessment for assessment of prevalence of sickle cell anaemia trait

AAM-25-1625_Suppl9.tif (819.3KB, tif)
Figure S10

Forest plot showing the pooled prevalence of beta thalassemia

AAM-25-1625_Suppl10.tif (822.1KB, tif)
Figure S11

Leave out analysis for prevalence of beta thalassemia

AAM-25-1625_Suppl11.tif (883.7KB, tif)
Figure S12

Forest plot showing state wise subgroup analysis of prevalence of beta thalassemia

AAM-25-1625_Suppl12.tif (590.6KB, tif)
Figure S13

Forest plot showing region wise subgroup analysis of prevalence of beta thalassemia

AAM-25-1625_Suppl13.tif (1.2MB, tif)
Figure S14

Meta regression of prevalence of beta thalassemia on year of publication

AAM-25-1625_Suppl14.tif (920.2KB, tif)
Figure S15

DOI plot for publication bias assessment

AAM-25-1625_Suppl15.tif (765.5KB, tif)
Figure S16

Forest plot showing the pooled prevalence of beta thalassemia trait

AAM-25-1625_Suppl16.tif (1.2MB, tif)
Figure S17

Leave out analysis for prevalence of beta thalassemia trait

AAM-25-1625_Suppl17.tif (1.4MB, tif)
Figure S18

Forest plot showing state wise subgroup analysis of prevalence of beta thalassemia trait

AAM-25-1625_Suppl18.tif (860.7KB, tif)
Figure S19

Forest plot showing region wise subgroup analysis of prevalence of beta thalassemia trait

AAM-25-1625_Suppl19.tif (1.4MB, tif)
Figure S20

Meta regression of prevalence of beta thalassemia trait on year of publication

AAM-25-1625_Suppl20.tif (906KB, tif)
Figure S21

DOI plot for publication bias assessment

AAM-25-1625_Suppl21.tif (797.9KB, tif)
Figure S22

Forest plot showing the pooled prevalence of glucose 6 phosphate deficiency

AAM-25-1625_Suppl22.tif (569.1KB, tif)
Figure S23

Leave out analysis for prevalence of glucose 6 phosphate deficiency

AAM-25-1625_Suppl23.tif (648.7KB, tif)
Figure S24

Forest plot showing state wise subgroup analysis of prevalence of glucose 6 phosphate deficiency

AAM-25-1625_Suppl24.tif (846.7KB, tif)
Figure S25

Forest plot showing region wise subgroup analysis of prevalence of glucose 6 phosphate deficiency

AAM-25-1625_Suppl25.tif (892.9KB, tif)
Figure S26

Meta regression of prevalence of glucose 6 phosphate deficiency on year of publication

AAM-25-1625_Suppl26.tif (758.4KB, tif)
Figure S27

DOI plot for publication bias assessment

AAM-25-1625_Suppl27.tif (801.7KB, tif)

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