Skip to main content
Download PDF

Sickle cell trait in São Tomé e Príncipe: a population-based prevalence study in women of reproductive age

BMC Public Health volume 24, Article number: 850 (2024) Cite this article

Abstract

Background

Sickle Cell Disorder is Africa’s most prevalent genetic disease. Yet, it remains a neglected condition, with high mortality under-five, and a lack of population-based studies in the region. This is the first of its kind in São Tomé e Príncipe, aiming to estimate the prevalence of sickle cell trait and other haemoglobin variants in women of reproductive age and its associated factors.

Methods

We conducted a cluster survey in 35 neighbourhoods. Haemoglobin was assessed through point-of-care capillary electrophoresis or high-performance liquid chromatography, and sociodemographic data through questionnaires. The weighted prevalence of sickle cell trait (HbAS) and HbC carriers was estimated with a 95% confidence interval (95% CI). We calculated weighted prevalence ratios (95% CI) through robust Poisson regression for its association with age and individual and collective genetic heritage.

Findings

The prevalence of sickle cell trait in women of reproductive age in São Tomé e Príncipe (n = 376) was 13.45% (95% CI: 9.05-19.00). The prevalence of HbC carriers was 8.00% (95% CI: 4.71-12.00). Older age and speaking Forro or Angolar were positively associated with having sickle cell trait.

Interpretation

The prevalence of sickle cell trait in São Tomé e Príncipe ranks high in the West African region. The country should follow international guidelines, implementing newborn screening and comprehensive healthcare management.

Peer Review reports

Background

Sickle cell disorder (SCD) is an autosomal recessive condition, characterised by a mutation in the haemoglobin β-chain gene (HBB:c.20 A > T) that alters chain structures into Haemoglobin S (HbS). When subjected to hypoxia, HbS polymerises and forms fibrous precipitates, transforming the usual discoid, flexible red blood cell structure into a rigid, sickle-shaped structure [1]. Heterozygotic, sickle cell trait (SCT) is a benign condition assumed to confer protection against malaria and, therefore, an evolutionary advantage in endemic regions [2]. Contrarily, in homozygosity (HbSS) or compound heterozygotic forms (e.g. HbSC, HbSD, HbSbetatal), SCD is associated with high morbidity, vaso-occlusive crises with episodes of severe pain, anaemia, susceptibility to infection due to functional asplenia and ischaemic events [3].

Without proper clinical support, as observed in some of the African countries where it is most prevalent, the median survival of these patients is less than five years [4]. On the other hand, a prompt diagnosis and follow-up can reduce under-five mortality up to 10 times [5] with patients registering average life expectancies above 50 years [6]. Early case identification and implementation of comprehensive health care management is thus a fundamental strategy to tackle the disease and improve patients’ lives [7, 8]. The identification of parental SCT also allows timely genetic counselling when available. In addition to proven health gains, these strategies are highly cost-effective [9].

SCD is the most prevalent genetic disorder in Africa, where more than 1000 babies are born every day with the disease and 38,403 deaths from SCD were recorded in 2019, a 26% increase from 2000 [10]. In some sub-Saharan regions, SCT can be present in up to 30% of the population [2] and be responsible for more than 4% of all under-five mortality [11]. However, SCD has been constantly neglected by national and international agents, with insufficient funding and research on the disease and its impact and a lack of population-based studies [12]. São Tomé e Príncipe, the archipelagic country in the Gulf of Guinea, still lacks official numbers on carriers and patients. Three previous hospital-based studies point to an SCT prevalence between 13.00% [13], 14.10% (plus 1.92% of Hemoglobin C) [14] and 19.40% [15]. A revision of the 2021 Global Burden of Disease estimated that SCD was the 5th cause of under-five mortality in the country [16].

São Tomé e Príncipe has no newborn screening or other public health policy towards SCD. This is the first nationwide study in the country, and one of the first in Sub-Saharan Africa, aiming to estimate the prevalence of SCT and other haemoglobin variants among women of reproductive age and its associations with age and individual and collective genetic heritage. As a secondary objective, we intend to assess literacy on SCD (sickle cell knowledge) and its association with sickle cell status or relatives with SCD. We believe this can be an essential first step to designing future health plans to improve SCD patients in São Tomé e Príncipe and widen knowledge of the disease in Sub-Saharan Africa.

Methods

Study design and setting

This cross-sectional study was conducted in the country’s two islands, São Tomé and Região Autónoma do Príncipe, and consisted of an on-site collection of capillary blood and structured questionnaires. Data were collected between 10 and 30 April 2023; the remaining laboratory and statistical analyses were conducted in May of the same year.

Participants

The study’s target population was women of reproductive age (15–49 years) living in São Tomé e Príncipe. We limited our analysis to this group as they would benefit the most from the results since the identified carriers could receive genetic counselling and test future offspring. As inclusion criteria, we considered women of this age, living in São Tomé e Príncipe. We excluded participants who had received a blood transfusion in the last three months, who were on a short or medium-term stay in the country (e.g. tourists, emigrants or expatriates), women who were sisters or mothers/daughters identified as such, or who had a previous clinical diagnosis of SCD and were referred to us by healthcare professionals to get the laboratory confirmation. In this last case, the test was conducted but we excluded them from the study to avoid a positive selection bias.

Due to the inability to conduct a nationwide simple random sampling, we employed a two-stage cluster sampling, with neighbourhoods as primary sampling units. The sampling process is detailed in Supplementary File 1.

Study size

To calculate the sample size, we used the total number of women aged between 15 and 65 in São Tomé e Príncipe, identified in the 2012 Census: 48,983 women [17]. Assuming an expected frequency of 10.00%, consistent with previous studies, and for a 95% confidence interval (95% CI), this implied a minimum sample size of 138 women [18]. Based on analogous studies in the same region, we also assumed a design effect of 2.00 [19] and considered a refusal to participate rate of 10.00% [20]. Thus, we defined a minimum recruitment target of 304 women to achieve a minimum sample size of 276 participants. This value was employed in the clusters mentioned above.

Data sources/measurements

We used a drop of blood to conduct capillary electrophoresis, using the point-of-care device Lab001 (ARKRAY Inc, Japan). Whenever it was not possible to perform this technique, we collected the samples on Guthrie filter paper, later analysed in Laboratório de Eritropatologia, at Centro Hospitalar e Universitário de Coimbra, Portugal, through high-performance liquid chromatography (HPLC) in the VARIANT II Haemoglobin Testing System (Bio-Rad Laboratories Inc., Hercules, CA, USA and through Multiplex Ligation-dependent Probe Amplification (MLPA) and Sanger sequencing to confirm dubious diagnoses.

Participants were also asked to answer a questionnaire. One team member read every question to ensure answers would not be affected by poor reading abilities, and always in the company of local volunteers who explained the questions if needed, avoiding interpretation issues and information bias.

Variables

The main outcome was the woman’s HBB genotype, categorized as (1) “SCT” if she had a HbAS genotype, defined as a proportion of HbS between 20% and 45%; (2) “SCD” if she had an HBSS genotype, a pathological heterozygous combination of HbS with another haemoglobin variant (e.g. HbC, HbD), or an HBAS genotype if the proportion of HbA was below 30% (e.g. beta-thalassemia plus patients); (3) “HbAC” if she had a heterozygous combination of HbC and HbA, defined as a proportion of HbC between 20% and 45%; (4) “normal” if she had a HbAA genotype, defined as a proportion of HbA above 70% without any haemoglobin variant.

As independent variables, we collected sociodemographic characteristics of the participants: age, civil status, nationality, education level (knowing how to read or write) and past childbirths. As a proxy of collective genetic heritage, we asked if they spoke any local dialects: Forro, Angolar, Lunguie or Cape Verdian Creole. For individual genetic heritage, if they identified any relative with the disorder.

Quantitative variables were categorized into groups. Age was considered as seven intervals of five years (15–19 to 45–49); and pregnancies were considered as nulliparous (0), primiparous (1), multiparous (2–4) or grand multipara (> 4). Local dialects were also grouped as none, only Forro, only Angular and other combinations, including Lunguie. The variable of identifying any relative with SCD was grouped as (1) none; (2) a direct descendent, i.e. son(s) or daughter(s); (3) “ascendant 1” - father, mother, or sibling or nephew (as both sibling and nephew with SCD imply a direct ascendant with at least SCT); (4) “ascendant 2” - a grandparent or a first-degree uncle/aunt or cousin; and (5) second-degree uncle/aunt or cousin.

Finally, to assess of sickle cell knowledge, we used the naming of SCD (“bone disease” or sickle cell disease), the knowledge about the associated symptoms and how SCD was transmitted from one person to another.

Statistical methods

All the statistical analyses were conducted using R version 4.1.0 [21].

First, we evaluated the suitability of the sampling design by calculating the intraclass correlation coefficient (ICC) using the R package “fishmethods” [22] and then the design effect, using the equation Deff = 1 + ICC(n– 1), where n is the average number of subjects sampled per cluster.

For sample characterisation, we calculated the absolute and relative frequencies. Even if the sampling was proportional to the size of each neighbourhood, and to address difficulties in recruitment, we adjusted the analysis considering each sample weight, calculated as the ratio between each neighbourhood population and the number of participants selected in that neighbourhood. The adjustment was conducted using the R package “survey” [23]. We then calculated the weighted prevalence of all the outcomes and the corresponding 95% CI. Haemoglobin proportions were represented using a boxplot to illustrate the dispersion of HbF in participants without Hb variants and with SCT, and of HbS in those with SCT.

The HW_TEST software [24] gave us the expected prevalence of each genotype based on the weighted tri-allelic frequencies, assuming a Hardy-Weinberg equilibrium (HWE) of the three alelles (A, S and C) at birth. We then tested the HWE with Chi-square without correction, and alpha set at 5%.

To assess if the age group, the spoken local dialect or an identified relative with SCD was associated with a higher risk of having SCT, we calculated the weighted prevalence ratio (PR) and corresponding 95% CI, through the robust Poisson method [25], using the “sandwich” package [26]. The association between a better knowledge of sickle cell and having SCT, or a relative with SCD, was also assessed using the same method.

Results

A total of 376 women were enrolled in this study, representing 35 neighbourhoods of the seven health districts of São Tomé e Príncipe. Supplementary File 1 presents detailed information on neighbourhood selection and the number of participants per cluster. The ICC for the presence of HbS was 0.02, thus implying a Design Effect of 1.25. The anonymised dataset is available according to the requirements in the data statement.

Table 1 Characteristics of the participants: absolute and relative for both sample and (weighted) population
Full size table

Table 1 presents sample and weighted frequencies. Most participants (67.98%) were between 20 and 39 years old. The low numbers registered below 19 years old were mainly due to the need for informed consent by the legal representative of underaged volunteers, usually absent at the collection site. Most women were single (82.71%), even though some advised they were partnered, as São Tomé e Príncipe polygamy and informal marriages are common practices. Most of them also knew how to read and write, and all of them were of São Tomense nationality, with a few having double nationality. The most spoken dialect was forro and around a quarter of the women only spoke Portuguese.

All the blood samples were processed successfully: 155 by point-of-care capillary electrophoresis and 221 by HPLC. The analysis of haemoglobin proportions according to sickle cell status for the point-of-care samples is presented in Supplementary File 2.

Prevalence of SCT and other haemoglobinopathies

The weighted allele frequencies were 8.71% for the HbS allele and 3.99% for the HbC allele. Table 2 presents the prevalence of each genotype. The district with a higher proportion of participants with SCT was Região Autónoma do Príncipe, with 25.00% (5/20), followed by Lembá and Me-Zochi with 16.67% (5/30; 20/120), Água Grande with 14.17% (17/120), Caué with 13.33% (4/30), Cantagalo with 11.54% (3/26) and Lobata with 10.00% (3/30). The expected (weighted) prevalence of newborns with the HbSS genotype is 0.80% (0.40–1.20) and with the HbSC genotype is 0.70% (0.40-1.00). This means that between 8 and 22 of each 1000 newborns will develop SCD. The HWE could not be rejected (p = 0.30).

Table 2 Genotype: absolute and prevalence in the sample and in the population (weighted)
Full size table

Additionally, we tested two participants with an HbAA genotype that presented levels of Foetal Haemoglobin (HbF) above 12%. One had a heterozygous mutation known as Cape Verdean deltabeta deletion [27]and the other a frameshift (HBB:c.126-129delCTTT).

The individual and familiar history of SCD

More than half of the participants (63.03%) didn’t know whether any relative had SCD. Among the 139 recognizing at least one relative with the condition, 32.37% had a grandparent or first-degree uncle or aunt, 39.57% had the mother, father, sibling, or nephew, 13.67% had a son or daughter and 10.07% had a second-degree uncle or aunt. There were also 15 participants reporting that they suffered from SCD. Of these, one had HbS in homozygosity, three had SC haemoglobinopathy, six only had SCT, and five had no single haemoglobin variant of interest. Though we do not ask why the latter assumed the diagnoses, we presume it was due to either a false positive sickling test or to clinical misdiagnoses of pain syndromes.

Factors associated with SCT

Figure 1 presents prevalence ratios of SCT according to age, dialect and having a relative with SCD. Age was significantly associated with having SCT, especially in the groups above 30 years old. The participant’s dialect registered different associations when compared to those who only spoke Portuguese. Speaking Forro or Angolar increased the prevalence by 9.29% (95% CI: 2.35–16.70) and 46.86% (95% CI: 23.54–73.88) respectively while speaking only Cape Verdean or other combinations lowered the prevalence by 55.12% (95% CI: 46.74–65.02) and 82.30% (95% CI: 71.35–94.93). Compared to individuals without any known relatives with SCD, those who had a son or daughter showed the highest probability of having SCT, with a prevalence 806.22% (95% CI: 699.20-925.60) ahigher than the reference group. This probability decreased by a third when the individual only had a mother, father, sibling, or nephew, and remained 113.95% (95% CI: 67.07-173.98) higher in other first-degree relatives.

Fig. 1
figure 1

Prevalence ratios for associations of age, dialect and relative with SCD with having SCT. PR– Prevalence Ratio. CI– Confidence Interval. Descendant - son or daughter; Ascendant 1 - father, mother, sibling or nephew; Ascendant 2 - grandparent or first-degree uncle, aunt or cousin. Reference classes are presented as PR of 1.

Full size image

Sickle cell knowledge

When asked about their knowledge of SCD, only seven participants did not recognize the condition. The most used name was sickle cell disease, “cell disease” or just “cell” (69.65%), 45.26% named it “bone disease” and 14.90% would use both names. As for signs and symptoms, most women associated the disease with pain (79.25%), 14.36% with fatigue and 6.65% with anaemia. Knowledge of the transmission of SCD was scarcer, with 63.56% admitting they didn’t know it and just 28.99% answering it was hereditary, from both mother and father.

When analysing the influence of each sickle cell status on sickle cell knowledge, we observe that people with SCT were 61.48% (95% CI: 59.54–63.48) less probable of being ignorant of SCD transmission, but 131.82% (95% CI: 98.70-170.47) more potential of being unaware of its symptoms. On the other hand, those without relatives with SCD were 67.16% (95% CI: 59.34–75.36) and 148.69% (95% CI: 99.52-209.98) more probable of being ignorant of both transmission and symptoms, respectively.

Discussion

Our study estimated an SCT prevalence of 13.45% (95% CI: 9.05-19.00) among women of reproductive age in São Tomé e Príncipe. Due to its autosomal condition, we don’t expect a different prevalence of SCT according to sex [28]. Thus, we expect that this value corresponds to the estimated prevalence of SCT in the whole population between 15 and 49 years in São Tomé e Príncipe. This prevalence is accordant to the previous studies in the country [13,14,15]. Ranking high in the interval of prevalence found in West Africa [2], this highlights the need for São Tomé e Príncipe to participate in the regional effort to tackle the disease [12].

The prevalence of the HbC carriers, 6.79% (95% CI: 3.77-11.00), though higher than the previously recorded, is the first recorded in a representative sample. The value aligns with the ones observed in neighbouring countries [29]. When looking at the HbSS genotype alone, we also obtained a prevalence − 1.38 (95% CI: 0.25-4.00) - comparable to that of the region [30]. In any case, these are only indicative values, as the study design only considered SCT prevalence. However, they show that future newborn screening strategies should recognize HbC and beta-thalassemia.

The positive association of SCT with age has been previously reported [28] and may be related to its protection against malaria, still endemic in the country, increasing long-term survival. As malaria elimination in São Tomé e Príncipe gets closer, these differences may disappear with time. However, the possibility of an HWE does not reflect the expected survival bias favouring the HbA allele frequency. This may imply a slight positive bias in the selection of SCD patients or an eventual balance between the negative survival bias of SCD and the positive one in SCT (malaria-related). Further research on comparative mortality between genotypes would be useful to clarify this.

Forro and mainly Angolar-speaking people had higher SCT prevalence, while the ones that spoke Cape Verdean creole or combinations of dialects had lower prevalence. This supports the theory of different genetic lineages according to the geographical origin of each ethnic group, as these findings are in line with the lower prevalence found in Cape Verde and higher in the Gulf of Guinea and Angola. On the other hand, it also suggests a genetic difference between Tonga (Cape-Verdean descendants) and Forros which was previously discarded [31]. Studies on the genetic diversity of São Tomé e Príncipe would be required to comprehend the history of its colonisation better.

The overall recognition of SCD was impressive, with most correctly naming the disease and identifying pain as a symptom. It would be useful for future campaigns to clarify the “bone disease” nomenclature and avoid eventual misdiagnoses of osteoarticular syndromes or dengue. Most of the participants correctly identified the transmission of the disease as hereditary, showing that there is a literacy base for health strategies addressing genetic counselling. The positive association of sickle cell status with sickle cell knowledge reveals a vulnerability of the target population to those strategies. The opposite was noted for people without close relatives with SCD and may imply that people without close contact with the disease need to be better informed about SCD. Since previous studies did not include the recognition of signs and symptoms [32], it would be helpful to include these variables in future sickle cell knowledge scales.

The lack of recent demographic data on São Tomé e Príncipe is a limitation of this paper, as current estimates point to a population that almost doubled the one in the last census [33]. This means that the distribution used in the sampling procedure may not correspond to the current one. However, the primary demographic flux was towards the capital (Água Grande district), already overrepresented in our sample as this tendency was already present in 2012.

Besides, when analysing age distribution in our sample, we realised that younger groups are underrepresented since they constitute most of the population but not of our sample. This may be related to the nonrandom selection in the second stage of sampling and the use of schools or health services as collection sites, not frequently used by women this age. Although that may have influenced each location’s results, the collection points choice tended to overcome this bias. This limitation may hinder our population-based estimates, yet we attempted to minimise it using weighted analyses.

Haemoglobin analysis was conducted with highly sensitive and specific tests to avoid misclassification. Data on literacy may also have been subjected to social-desirability bias, but we tended to avoid replications of answers among participants by controlling the interview settings.

Conclusion

The data supports that SCD is a significant health problem in São Tomé e Príncipe that shall be addressed by national strategies such as neonatal screening, updating therapeutic guidelines and capacitation of healthcare workers. Neighbouring countries with lower prevalence of SCT have already addressed the issue, which was also set as a health priority by the Ministers of Health of the Region [10]. Future research on SCD in the country should also focus on younger generations, the quality of life and life expectancy of SCD patients, to better design support strategies. Data shows that there’s a solid base of knowledge encouraging future programmes that involve the community. São Tomé e Príncipe, with its high prevalence of SCT, its scale, organization and culture, has the opportunity to be an example in Sub-Saharan Africa and significantly improve the lives of patients.

Data availability

De-identified data and study protocol can be requested by e-mail to the corresponding author (GQ) and depends on the authorization of Centro de Investigação em Antropologia e Saúde and Centro Nacional de Endemias da República Democrática de São Tomé e Príncipe. The request must include a methodologically sound proposal with the full study protocol. All requests will be evaluated individually by both institutions, with data being available until 5 years following the publication. All shared data will ensure that the rights and privacy of the participants will be safeguarded during the process. The model code is available at https://gitlab.com/drepa-comunidade/sickle-cell-trait-in-stp.

Abbreviations

CI:

Confidence Interval

HWE:

Hardy-Weinberg equilibrium

ICC:

Intraclass Correlation Coefficient

HbA:

Haemoglobin A

HbC:

Haemoglobin C

HbF:

Faetal Haemoglobin

HbS:

Haemoglobin S

HPLC:

High-Performance Liquid Chromatography

MLPA:

Multiplex Ligation-dependent Probe Amplification

PR:

Prevalente Ratio

SCD:

Sickle Cell Disorder

SCT:

Sickle Cell Trait

References

  1. Benz J Jr. Disorders of Hemoglobin. In: Jameson JL, Fauci AS, Kasper DL, Hauser SL, Longo DL, Loscalzo J, editors. Harrison’s principles of Internal Medicine [Internet]. 20th ed. New York, NY: McGraw-Hill Education; 2018.

    Google Scholar 

  2. Piel FB, Patil AP, Howes RE, Nyangiri OA, Gething PW, Williams TN, et al. Global distribution of the sickle cell gene and geographical confirmation of the malaria hypothesis. Nat Commun. 2010;1:104.

    Article  PubMed  Google Scholar 

  3. Serjeant GR. The natural history of Sickle Cell Disease. Cold Spring Harb Perspect Med. 2013;3(10):a011783.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Weatherall D, Akinyanju O, Fucharoen S, Olivieri N, Musgrove P. Inherited disorders of Hemoglobin. In: Jamison DT, Breman JG, Measham AR, Alleyne G, Claeson M, Evans DB, et al. editors. Disease Control priorities in developing countries [Internet]. 2nd ed. Washington (DC): The International Bank for Reconstruction and Development / The World Bank; 2006.

    Google Scholar 

  5. Rahimy MC, Gangbo A, Ahouignan G, Alihonou E. Newborn screening for sickle cell disease in the Republic of Benin. J Clin Pathol. 2009;62(1):46–8.

    Article  CAS  PubMed  Google Scholar 

  6. Lubeck D, Agodoa I, Bhakta N, Danese M, Pappu K, Howard R, et al. Estimated life expectancy and income of patients with Sickle Cell Disease compared with those without Sickle Cell Disease. JAMA Netw Open. 2019;2(11):e1915374.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Lobitz S, Telfer P, Cela E, Allaf B, Angastiniotis M, Backman Johansson C, et al. Newborn screening for sickle cell disease in Europe: recommendations from a pan-european Consensus Conference. Br J Haematol. 2018;183(4):648–60.

    Article  PubMed  Google Scholar 

  8. The Lancet Haematology Commission on sickle. Cell disease: key recommendations. Lancet Haematol. 2023;10(8):e564–7.

    Article  Google Scholar 

  9. McGann PT, Grosse SD, Santos B, de Oliveira V, Bernardino L, Kassebaum NJ, et al. A cost-effectiveness analysis of a pilot neonatal screening program for Sickle Cell Anemia in the Republic of Angola. J Pediatr. 2015;167(6):1314–9.

    Article  PubMed  PubMed Central  Google Scholar 

  10. WHO| Regional Office for Africa [Internet]. 2023 [cited 2023 Jun 6]. African health ministers launch drive to curb sickle cell disease toll. Available from: https://www.afro.who.int/news/african-health-ministers-launch-drive-curb-sickle-cell-disease-toll.

  11. Nnodu OE, Oron AP, Sopekan A, Akaba GO, Piel FB, Chao DL. Child mortality from sickle cell disease in Nigeria: a model-estimated, population-level analysis of data from the 2018 demographic and Health Survey. Lancet Haematol. 2021;8(10):e723–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Sickle-cell disease. A strategy for the WHO African region. Geneva: WHO: World Health Organization Regional Office for Africa;; 2010. Report No.: Report Number AFR/FC60/8.

    Google Scholar 

  13. Vasconcelos A, Sousa S, Bandeira N, Alves M, Papoila AL, Pereira F, et al. Antenatal screenings and maternal diagnosis among pregnant women in Sao Tome & Principe—missed opportunities to improve neonatal health: a hospital-based study. PLOS Glob Public Health. 2022;2(12):e0001444.

    Article  PubMed  PubMed Central  Google Scholar 

  14. de Trovoada M. J. Hemoglobina: técnicas electroforéticas de separação: estudo populacional em amostras de S. Tomé e da região centro de Portugal. [Coimbra]: Universidade de Coimbra; 1994.

    Google Scholar 

  15. Caroça C, Lima J, Campelo P, Carolino E, Caria H, Paço J, et al. Sickle cell trait, Malaria and Sensorineural hearing Loss–A case-control study from São Tomé and Príncipe. Otolaryngol Open Access. 2016;6:1–7.

    Google Scholar 

  16. Thomson AM, McHugh TA, Oron AP, Teply C, Lonberg N, Tella VV et al. Global, regional, and national prevalence and mortality burden of sickle cell disease, 2000–2021: a systematic analysis from the global burden of Disease Study 2021. Lancet Haematol. 2023 Jun 15.

  17. IV Recenseamento Geral da População e da Habitação 2012 (IV RGPH 2012): Resultados Gerais Sobre Localidades [Internet]. Instituto Nacional de Estatística da Républica Democrática de São Tomé e Príncipe.; 2015. Available from: https://www.ine.st/index.php/publicacao/documentos.

  18. Sullivan KM, Dean A, Soe MM, OpenEpi. 2009. OpenEpi: Open Source Epidemiologic Statistics for Public Health. Available from: www.OpenEpi.com.

  19. Engle-Stone R, Williams TN, Nankap M, Ndjebayi A, Gimou MM, Oyono Y, et al. Prevalence of inherited Hemoglobin disorders and relationships with Anemia and Micronutrient Status among children in Yaoundé and Douala, Cameroon. Nutrients. 2017;9(7):693.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Mfutso-Bengo J, Masiye F, Molyneux M, Ndebele P, Chilungo A. Why do people refuse to take part in biomedical research studies? Evidence from a resource-poor area. Malawi Med J J Med Assoc Malawi. 2008;20(2):57–63.

    Google Scholar 

  21. R Core Team. R: A language and environment for statistical computing. [Internet]. Vienna, Austria: R Foundation for Statistical Computing.; 2021. Available from: https://www.R-project.org/.

  22. Gary A. Nelson. fishmethods: Fishery Science Methods and Models. [Internet]. 2023. Available from: https://CRAN.R-project.org/package=fishmethods.

  23. Lumley T. Analysis of Complex Survey samples. J Stat Softw. 2004;9:1–19.

    Article  Google Scholar 

  24. Santos FAB, Lemes RB, Otto PA. HW_TEST, a program for comprehensive HARDY-WEINBERG equilibrium testing. Genet Mol Biol. 2020;43:e20190380.

  25. Santos CAS, Fiaccone RL, Oliveira NF, Cunha S, Barreto ML, do Carmo MBB, et al. Estimating adjusted prevalence ratio in clustered cross-sectional epidemiological data. BMC Med Res Methodol. 2008;8(1):80.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Zeileis A, Köll S, Graham N. Various versatile variances: an object-oriented implementation of clustered covariances in R. J Stat Softw. 2020;95:1–36.

    Article  Google Scholar 

  27. Andersson BAR, Wering MEL, Luo HY, Basran RK, Steinberg MH, Smith HP, et al. Sickle cell disease due to compound heterozygosity for Hb S and a novel 7.7-kb beta-globin gene deletion. Eur J Haematol. 2007;78(1):82–5.

    Article  CAS  PubMed  Google Scholar 

  28. Délicat-Loembet LM, Elguero E, Arnathau C, Durand P, Ollomo B, Ossari S, et al. Prevalence of the Sickle Cell Trait in Gabon: a nationwide study. Infect Genet Evol. 2014;25:52–6.

    Article  PubMed  Google Scholar 

  29. Piel FB, Howes RE, Patil AP, Nyangiri OA, Gething PW, Bhatt S, et al. The distribution of haemoglobin C and its prevalence in newborns in Africa. Sci Rep. 2013;3:1671.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Brown M. Manson’s tropical diseases. Lancet Infect Dis. 2009;9(7):407–8.

    Article  PubMed Central  Google Scholar 

  31. Trovoada Mj, Tavares L, Gusmão L, Alves C, Abade A, Amorim A, et al. Dissecting the genetic history of São Tomé E Príncipe: a new window from Y-Chromosome biallelic markers. Ann Hum Genet. 2007;71(1):77–85.

    Article  CAS  PubMed  Google Scholar 

  32. Gilpin-Macfoy F, Perilla MJ, Koehly LM. Variability in sickle cell knowledge by sickle cell status. J Genet Couns. 2023.

  33. Sao Tome and Principe Overview.: Development news, research, data| World Bank [Internet]. [cited 2023 Jun 6]. Available from: https://www.worldbank.org/en/country/saotome/overview.

Download references

Acknowledgements

We thank Hidalgo Afonso and Gelson Vila Nova for the technical support in Região Autónoma do Príncipe and São Tomé territory, respectively. We also thank Rita Aguiar and Vasco Pessoa Jorge for lending their home and car during the whole stay of GQ and CB in São Tomé e Príncipe. For the support in the definition of the neighbourhoods as PSU we thank Idálio Luís and NGouabi Tiny da Trindade from Instituto Nacional de Estatística. The initial support and conversations with Joabi Nascimento, from Instituto de Pesquisa Clínica Carlos Borborema, about population-based prevalence studies were also fundamental to developing our methodology. Finally, we thank the generosity and support of the administration of Hospital Dr. Ayres de Menezes, all the healthcare workers who helped us during fieldwork and the people of São Tomé e Príncipe.

Funding

CIAS-UC (FCT: UIDB/00283/2020), APPDH and Forum Haematologico. This work was funded by CIAS - supported by FCT - Foundation for Science and Technology, I.P., within the scope of the project UIDB/00283/2020CIAS-UC - who paid for the ARKRAY® Lab001 and reagents for all the laboratory work, the flight and travel expenses of CB, the flight to Príncipe of GQ and publishing fees. APPDH paid for the flights of GQ to São Tomé. Forum Hematologico de Coimbra paid for the impression of forms and informed consent.

Author information

Authors and Affiliations

  1. Centro de Investigação em Antropologia e Saúde (CIAS), Universidade de Coimbra, Coimbra, Portugal

    Guilherme Queiroz, Licínio Manco & Celeste Bento

  2. NOVA National School of Public Health, Public Health Research Centre, Comprehensive Health Research Center, CHRC, NOVA University Lisbon, Av. Padre Cruz, Lisboa, 1600 − 560, Portugal

    Guilherme Queiroz & Andreia Leite

  3. Unidade de Saúde Pública, Unidade Local de Saúde da Região de Aveiro, Aveiro, Portugal

    Guilherme Queiroz

  4. Hospital Dr. Ayres de Menezes, São Tomé, São Tomé and Príncipe

    Celdidy Monteiro

  5. Departamento de Ciências da Vida, Universidade de Coimbra, Coimbra, Portugal

    Licínio Manco

  6. Unidade Funcional Hematologia Molecular, Unidade Local de Saúde de Coimbra, Coimbra, Portugal

    Luís Relvas & Celeste Bento

  7. Centro Nacional de Endemias, São Tomé, São Tomé and Príncipe

    Maria de Jesus Trovoada

  8. Department of Epidemiology, Instituto Nacional de Saúde Doutor Ricardo Jorge (INSA), Lisbon, Portugal

    Andreia Leite

Authors
  1. Guilherme Queiroz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Celdidy Monteiro
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Licínio Manco
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Luís Relvas
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Maria de Jesus Trovoada
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Andreia Leite
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Celeste Bento
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

GQ and CB conceptualized the study, CM and MJT helped to adapt it to the context of São Tomé e Príncipe, and along with AL and MJT designed the methodology. GQ collected data in Região Autónoma do Príncipe and, with CM and CB, in São Tomé. CB, LM and LR were responsible for all laboratory analyses in Portugal. GQ and AL did the statistical analysis. GQ wrote the paper, with editing from AL, and all authors participated in the revision process.

Corresponding author

Correspondence to Guilherme Queiroz.

Ethics declarations

Ethics approval and consent to participate

This study was approved by the Ethics Committee of São Tomé e Príncipe, identified as CESIC Case PC022_2022.

As the study is a population-based screening study, the research team decided to study only women of childbearing age, since they represent the group that can most benefit from a possible carrier diagnosis. Participants diagnosed as HbS carriers were guaranteed genetic counselling by Associação Filhos da Meia Lua Vermelha and, if possible, testing of their newborns. If the offspring is diagnosed with SCD at birth, clinical follow-up from the very first moment can improve the prognosis and quality of life of the patient. This ensures that the health information received by the participant corresponds to the proper response.

All participants signed an informed consent form before participating in the study. In the case of underage women, the consent was signed by a legal representative.

All data were anonymised by the first author and shared with the team in an aggregate form. Individual files and biological material are archived and protected in CIAS servers for 5 years, being destroyed thereafter.

Competing interests

The authors GQ and CB are associates of Associação Portuguesa de Pais e Doentes com Hemoglobinopatias, and CM is head of Associação Filhos da Meia Lua Vermelha. CM and MJT work in Centro Nacional de Endemias da´República Democrática de São Tomé e Príncipe. LM and LR have no conflicts of interest. Their personal and professional involvement in the fight for sickle cell disease patients did not collide but enforced the methodological rigour and transparency of this study. The authors alone are responsible for the views expressed in this publication, and they do not necessarily represent the decisions, policies, or views of the government of República Democrática de São Tomé e Príncipe. CB has received honoraria from Arkray for a talk in a webinar. AL has received honoraria from Pfizer for a presentation at a scientific event.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1:

Cluster sampling

Supplementary Material 2:

Hemoglobin proportion analysis

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Queiroz, G., Monteiro, C., Manco, L. et al. Sickle cell trait in São Tomé e Príncipe: a population-based prevalence study in women of reproductive age. BMC Public Health 24, 850 (2024). https://doi.org/10.1186/s12889-024-17761-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12889-024-17761-1

Keywords

BMC Public Health

ISSN: 1471-2458

Contact us