Our keyword searches yielded a total of 868 unique potential sources of data on α-thalassaemia prevalence and/or genetic diversity in 10 Southeast Asian countries: Brunei Darussalam, Cambodia, Indonesia, Lao People’s Democratic Republic, Malaysia, Myanmar, Philippines, Singapore, Thailand and Vietnam (Figure 1—figure supplement 1). A further 74 potential data sources were identified by one of the authors (SE) from local Thai journals and independently double-checked for inclusion into the study (CH). Of all these sources, 75 met our inclusion criteria and were included in the final database. Due to some sources reporting estimates for more than one population, data were available for 106 individual population samples: 58 from the online literature search and 48 from the local literature. A detailed description of the database is provided in Appendix 5.

Forty-six surveys provided data on α-thalassaemia gene frequency alone, two provided data only relating to genetic diversity and 58 provided data on both. Four surveys were reported at the national level (two from Malaysia, one from Singapore and one from Thailand), and were retained for the regional analysis. The spatial and temporal distributions of identified surveys are shown in Figure 1—figure supplement 2. The country for which the highest number of surveys was published in the international literature (i.e. excluding surveys obtained through local searches) was Thailand. No published surveys were identified for Brunei Darussalam or the Philippines. Within countries, surveys predominated in certain areas. For instance, in Thailand, the northern and northeastern parts of the country contained >75% of all surveys. Data for southern Thailand came exclusively from Thai journals (n = 4) (Figure 1—figure supplement 2). The total number of individuals sampled was 133,649 (the population of the region is estimated to be 647,483,729 in 2017), with a mean sample size of 1261. Further details on the final database are provided in Appendix 5.

Prevalence surveys varied considerably with regards to the α-thalassaemia alleles and/or genotypes that were tested for or reported upon; whilst some reported allele frequencies for α⁰-, α⁺- and α^ND-thalassaemia, others provided data for only one or two of these. To maximise use of the available allele frequency data, whilst avoiding the incorporation of potentially biased estimates for overall α-thalassaemia allele frequency, we generated separate maps for each of the three major forms of α-thalassaemia – that is, α⁰-, α⁺- and α^ND-thalassaemia (Figure 1A,B and C, respectively). α⁰-thalassaemia was the most extensively studied form (n = 97), followed by α^ND-thalassaemia (n = 49) and then α⁺-thalassaemia (n = 47).

Figure 1 with 3 supplements see all

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Figure 1—source data 1

Source data for Figure 1A,a map of the observed α⁰-thalassaemia allele frequencies in the database.

Data were obtained through a review of the published literature using a rigorous inclusion/exclusion protocol. In the figure, a spatial jitter of up to 0.30 latitude and longitude decimal degree coordinates was applied to allow visualisation of spatially duplicated data points.

https://doi.org/10.7554/eLife.40580.006

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Figure 1—source data 2

Source data for Figure 1B,a map of the observed α⁺-thalassaemia allele frequencies in the database.

Data were obtained through a review of the published literature using a rigorous inclusion/exclusion protocol. In the figure, a spatial jitter of up to 0.3⁰ latitude and longitude decimal degree coordinates was applied to allow visualisation of spatially duplicated data points.

https://doi.org/10.7554/eLife.40580.007

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Figure 1—source data 3

Source data for Figure 1C,a map of the observed α^ND-thalassaemia allele frequencies in the database.

Data were obtained through a review of the published literature using a rigorous inclusion/exclusion protocol. In the figure, a spatial jitter of up to 0.3⁰ latitude and longitude decimal degree coordinates was applied to allow visualisation of spatially duplicated data points.

https://doi.org/10.7554/eLife.40580.008

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Data for Thailand and its neighbouring countries (Cambodia, Lao PDR, Malaysia and Myanmar) formed the evidence-base for a Bayesian geostatistical model and are presented in Figure 2—figure supplement 1. The total number of data points available for α⁰-, α⁺- and α^ND-thalassaemia was 88, 37 and 42, respectively. The data were used to generate 1 km x 1 km maps of allele frequencies of α⁰-, α⁺- and α^ND-thalassaemia in Thailand (Figure 2). One hundred realisations of the model were performed to generate a posterior predictive distribution (PPD) for each 1 km x 1 km pixel. The mean of the PPD is displayed, along with the 95% credible interval as a measure of model uncertainty.

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The maps for α⁰- and α⁺-thalassaemia indicate clear spatial heterogeneity in allele frequencies, with ranges of 0.57–4.46% and 2.43–15.03%, respectively (Figure 2A,B). Heterogeneity is greatest in the north of the country. For α⁰-thalassaemia, while large parts of the northernmost provinces of Chiang Rai, Phayao and Nan have predicted allele frequencies of up to 2%, allele frequencies for the neighbouring provinces of Chiang Mai, Lampang and Phrae are often twice as high (see Figure 2—figure supplement 2 for a reference map of Thailand provinces). The allele is also predicted at frequencies of up to 4% in the northeast of the country, along a belt across most of the north of the country and in Chonburi and Rayong provinces in central Thailand. Allele frequencies below 1% are predicted throughout the southern zone. α⁺-thalassaemia has its highest predicted allele frequencies across the whole of the north and northeastern zones. Predicted allele frequencies of α^ND-thalassaemia range between 1.57% and 1.65% only.

Model uncertainty is greatest in areas where data are scarce (e.g. southern Thailand and along the border with Myanmar) or where there is heterogeneity in the available data (e.g. in Chiang Mai and the surrounding area). Overall, uncertainties are higher for α⁺-thalassaemia than for α⁰-thalssaemia or α^ND-thalassaemia, which is partly due to the wider range of observed frequencies for this form. For α⁰-thalassaemia, the highest level of uncertainty is 9% and is found in Chumphon and Ranong provinces in southern Thailand and Kanchanaburi and Tak in the westernmost part of the country. For α⁺-thalassaemia, the highest uncertainty (up to 23%) is observed in the northeastern zone and in the north. Uncertainty for α^ND-thalassaemia is patchy and ranged from 1.5% in central and northern Thailand to 2.5% in southern and northeastern Thailand. The results of the 10-fold cross-validation procedure reveal an average mean absolute error of the predictions of 0.93%, 4.10% and 2.30%, for α⁰-, α⁺- and α^ND-thalassaemia, respectively. The average correlation between the observed and predicted values is 0.74 (0.62–0.83), 0.71 (0.49–0.85) and 0.47 (0.17–0.69), respectively.

Estimates of the number of newborns born with a severe form of α-thalassaemia (i.e. Hb Bart’s hydrops fetalis and HbH disease) in Thailand in 2020 were generated by pairing our allele frequency predictions to high-resolution demographic data for the country. We estimate that the number of Hb Bart’s hydrops fetalis births in the country will be 423 (CI: 184–761) in 2020. The number of new cases of HbH disease is estimated to be 3,172, including 2674 (CI: 1,296–4,491) deletional and 498 (CI: 237–947) non-deletional cases. The highest absolute burden of hydrops fetalis is predicted in Bangkok City (57 [CI: 13–151]) (Figure 2—figure supplement 2), with its high population density, and Udon Thani (23 [CI: 6–66]) in the northeastern zone, where some of the highest α⁰-thalassaemia allele frequencies are predicted. Other provinces with a comparatively high burden include: Chiang Mai in the north of the country; Khon Kaen, Sakon Nakhon and Ubon Ratchathani in the northeast; and Chon Buri, Samut Prakan and Nonthaburi close to Bangkok City. The estimated number of hydrops fetalis births in these provinces range between 10 and 19. For HbH disease, the highest burden is predicted in northeast Thailand for both the deletional and non-deletional forms. Bangkok City is predicted to have the highest burden of HbH disease (301 [CI: 94–639] for deletional HbH disease and 68 [CI: 25–148] for non-deletional HbH disease).

To directly compare estimates generated using our methodology with those previously published by Modell and Darlison, we also calculated estimates using population and birth rate data for 2003 (Appendix 3). Modell and Darlison estimated 1017 and 2515 births to be affected by Hb Bart’s hydrops fetalis and HbH disease, respectively, in 2003. Using population data from the same year paired with our model-based maps, and assuming no consanguinity, we estimate 709 and 5469 newborns to be born with Hb Bart’s hydrops fetalis and HbH disease in the country. As Modell and Darlison included a population coefficient of consanguinity (F) in their calculations, we examined the effect that this would have on our estimates. We found that they do not change considerably (951 and 5,409), when a value of F of 0.1, a high value for the region (www.consang.net), is incorporated. Our estimates are therefore consistent with those by Modell and Darlison for Hb Bart’s hydrops fetalis. However, they suggest that the burden of HbH disease in Thailand may have previously been underestimated. Moreover, whilst Modell and Darlison did not estimate the burden of non-deletional forms of HbH disease, our estimates suggest that 15% of the 5469 neonatal cases were of non-deletional types, which are usually associated with more severe phenotypes.

In our database for all of Southeast Asia, the number of surveys that tested for all three forms of α-thalassaemia was 40. Amongst these, the overall α-thalassaemia gene frequency ranged from 0% in populations from peninsular Malaysia to 35.4% in Preah Vihar, Cambodia (Munkongdee et al., 2016). A higher allele frequency of 49% was also reported in the So ethnic group from Khammouane Province in Lao PDR, although the sample size for this study was small (n = 50) (Sengchanh et al., 2005). Appendix 5—table 2 shows the range of allele frequencies observed for the different α-thalassaemia forms (α⁰-, α⁺- and α^ND-thalassaemia) in each country.

For α⁰-thalassaemia, the highest allele frequencies were observed in Thailand (Figure 1A, Figure 1—source data 1) In Lao PDR, surveys along the Lao PDR-Thailand border near Vientiane reported allele frequencies between 4.03% and 7.28%, whilst the survey among the aforementioned So ethnic group reported an absence of the α⁰-thalassaemia allele. The highest reported allele frequencies in Cambodia and Vietnam were 1.10% and 2.66%, respectively, with the majority of studies reporting even lower frequencies. However, data were sparse in the two countries (n = 4 in each). Allele frequencies of up to 1.53% were observed in southern Thailand, whilst the few surveys carried out in Myanmar (n = 1), Malaysia (n = 11) and Singapore (n = 2) reported allele frequencies of around 1%. In Malaysia, the highest allele frequency of α⁰-thalassaemia was 1.92% from a study carried out in newborns in Kuala Lumpur, the capital city (Alauddin et al., 2017).

α⁺-thalassaemia, the most prevalent form, reached allele frequencies of 26% in Cambodia (Figure 1B, Figure 1—source data 2) (Munkongdee et al., 2016). The surveys revealed a clear north-to-south decline in the distribution of α⁺-thalassaemia across the region, with a single high allele frequency estimate of 16.8% observed in Sabah in Malaysian Borneo (Tan et al., 2010). High allele frequencies (≥10%) were observed in all four surveys in Cambodia. In Vietnam the reported α⁺-thalassaemia allele frequency ranged from 1.59 to 14.4%.

The observed allele frequency of α^ND-thalassaemia ranged between 0% in various locations across Malaysia and 16.25% in central Peninsular Malaysia (Figure 1C, Figure 1—source data 3). Within Thailand, the highest reported allele frequencies of around 7% were observed in Khon Kaen in the northeast and Chachoengsao in central Thailand. In Cambodia and Vietnam, α^ND-thalassaemia allele frequencies of up to 8% and 14.3% were reported, respectively, in surveys in which α⁰-thalassaemia was found to be absent, whilst in other parts of these countries, the two forms were found to co-exist at similar allele frequencies (e.g. around 2.5% in Binh Phuoc and Khanh Hoa provinces in Vietnam).

Maps of the genetic diversity of α-thalassaemia across Southeast Asia are shown in Figures 3–6. Figure 3 (Figure 3—source data 1) displays surveys that included all three α-thalassaemia forms (α⁰-, α⁺- and α^ND-thalassaemia), allowing relative proportions of each of the forms to be calculated without giving specific variant details. Figures 4–6 (Figure 4—source data 1, Figure 5—source data 1, Figure 6—source data 1) display surveys that provided information on the frequencies of specific α-thalassaemia variants (e.g. --^SEA, -α^3.7, etc.). For these, the variants that were tested for differ between surveys. Some surveys are included in both Figure 3 and Figures 4–6. For the latter figures, the region has been divided to improve visualisation of the data.

Figure 3

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Figure 3—source data 1

Source data for Figure 3, a map showing the proportions of α0-, α+- and αND-thalassaemia in Southeast Asia.

Reported allele frequencies (converted to percentages here) and sample size were used to calculate the number of chromosomes bearing each form of α-thalassaemia. Sample size was multiplied by two to obtain the total number of chromosomes in the study sample. In total, 40 surveys included genetic diversity information for the three α-thalassaemia forms.

https://doi.org/10.7554/eLife.40580.015

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Figure 4

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Figure 4—source data 1

Source data for Figure 4, a map showing the proportions of specific α-thalassaemia variants in Thailand.

'NA' indicates those variants that were not tested for in the survey. In Thailand, 29 surveys included genetic diversity information for specific α-thalassaemia variants.

https://doi.org/10.7554/eLife.40580.017

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Figure 5

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Figure 5—source data 1

Source data for Figure 5, a map showing the proportions of specific α-thalassaemia variants in Cambodia, Lao PDR, Myanmar and Vietnam.

'NA' indicates those variants that were not tested for in the survey. In total, 13 surveys included genetic diversity information for specific α-thalassaemia variants in these countries.

https://doi.org/10.7554/eLife.40580.013

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Figure 6

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Figure 6—source data 1

Source data for Figure 6, a map showing the proportions of specific α-thalassaemia variants in Indonesia, Malaysia and Singapore.

'NA' indicates those variants that were not tested for in the survey. In total, 14 surveys included genetic diversity information for specific α-thalassaemia variants in these countries.

https://doi.org/10.7554/eLife.40580.019

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α⁺-thalassaemia most consistently constituted the highest proportion of α-thalassaemia, although there were some surveys in which α^ND-thalassaemia was the predominant form (e.g. central Vietnam and in parts of Malaysia). In Figure 3, areas where the observed relative proportion of α⁰-thalassaemia was greatest include: Chiang Mai and Phayao provinces in north Thailand, Kalasin in northeast Thailand, Vientiane in Lao PDR, Kuala Lumpur and Selangor in Malaysia, Singapore and Jakarta in Indonesia. The α⁰-thalassaemia allele was absent in the survey from Malaysian Borneo as well as in central Vietnam and central Lao PDR. In certain areas, α⁰- and α^ND-thalassaemia together accounted for the majority of α-thalassaemia (e.g. ~75% in Kalasin in Thailand,~60% in Kuala Lumpur and Jakarta and ~53% in Khon Kaen and Chachoensao in Thailand and Vientiane in Lao PDR). Some of these areas also correspond to where the highest allele frequencies of these alleles are found, for example, northeast Thailand and the Thailand-Lao PDR border.

Among surveys that tested for specific α-thalassaemia variants, the most commonly tested variant throughout the region was --^SEA (n = 44), followed by -α^3.7 (n = 36) (Figures 4–6). Most of the studies in Thailand only tested for a subset of the mutations considered in this study; only one survey in Bangkok City tested for the whole suite. More than in other countries, surveys in Thailand tested specifically for α⁰- or α^ND-thalassaemia mutations (n = 7 and 9, respectively).

Throughout the region, --^SEA was the dominant α⁰-thalassaemia mutation, and in the majority of surveys -α^3.7 was the dominant α⁺-thalassaemia and Hb CS the dominant α^ND-thalassaemia mutation. The only exceptions were in Java in Indonesia, where -α^3.7 and -α^4.2 were found at similar frequencies and in Kelantan in Malaysia, where Hb Adana was the only α^ND-thalassaemia mutation identified. The -(α)^20.5 and --^MED mutations were not detected in any of the surveys, whilst the --^FIL mutation was found in 2 of the 16 surveys in which it was tested for and --^THAI in 9 of the 31 surveys in which it was included. Consistent with Figure 3, α⁰-thalassaemia variants contributed minimally to α-thalassaemia mutations in Myanmar, Cambodia and Vietnam but were found at higher frequencies in surveys along the Thailand-Lao PDR border. In Vietnam, Malaysia, Indonesia and Singapore, the frequency of α⁰-thalassaemia varied considerably, with it being absent in some areas and a predominant form in others. This is also true for α^ND-thalassaemia.

The model-based maps for Thailand presented here are, to our knowledge, the first spatially continuous maps of the distribution of α-thalassaemia in any country. Our newborn estimates represent the first evidence-based estimates of specific forms of α-thalassaemia disease amongst newborns since 2003 (although the study in which they were reported was published in 2008) (Modell and Darlison, 2008) and the first estimates at sub-national level. Importantly, whilst there are currently no estimates of the number of stillbirths that will occur in Thailand in 2020, our estimate of the number of Hb Bart’s hydrops fetalis births represents more than 10% of the 3697 stillbirths estimated for 2015 (Blencowe et al., 2016).

Comparisons between previous newborn estimates and those generated in this study using our updated database and 2003 demographic data revealed an almost two-fold difference for deletional HbH disease (2515 compared to 4694 in the present study). Reasons for such discrepancies most likely relate to: (i) differences in the inclusion criteria used in the generation of our map and therefore our calculation of newborn estimates, (ii) the quantity of survey data used, and iii) the statistical methods employed. For instance, spatial specificity was not a consideration in the study by Modell and Darlison, who used a single allele frequency estimate extrapolated to the whole country. As such, the newborn calculations in the present study represent a methodological advance over previous efforts to assess the burden of α-thalassaemia. We related fine-scale allele frequency data to birth count data of equally high resolution, allowing location-specific estimates to be generated that could be aggregated to province level. Moreover, the use of model-based maps in our calculations enabled the measurement of uncertainty in our predictions. Finally, by including allele frequency data on α^ND-thalassaemia, we were able to estimate the burden of the more severe non-deletional HbH disease.

Our newborn estimates for 2020 are considerably lower than those for 2003. This reduction is due to the lower number of births in Thailand in 2020 as a result of a decreasing birth rate and population size (World population prospects, 2017). It would be interesting to quantify how improvements in the prevention of thalassaemias will affect these estimates in the future.

Our descriptive maps represent the first detailed cartographic representations of α-thalassaemia allele frequency estimates in Southeast Asia, which take into account the specific geographical location of the surveys in which they were observed. Until now, available maps (e.g. Figure 1—figure supplement 3) provided only a crude overview of overall α-thalassaemia gene frequency, without any distinction between different α-thalassaemia forms, and extrapolated to the entire region, thereby masking sub-national and even international, variation in allele frequencies (Piel and Weatherall, 2014).

The maps are broadly consistent with early narrative reviews of the gene frequency of α-thalassaemia in the region (Fucharoen and Winichagoon, 1987), showing a clear north-to-south trend of decreasing allele frequencies of α⁰- and α⁺-thalassaemia and a patchier distribution of α^ND-thalassaemia. However, our maps also demonstrate a severe lack of data on the allele frequency of α-thalassaemia across large parts of Southeast Asia, including in Myanmar, northern Lao PDR, northern Vietnam, Indonesia, Philippines and Brunei. This impedes our ability to assess the fine-scale burden of α-thalassaemia, making efficient public health planning for its control difficult, and limits our ability to track progress in the prevention and management of the disorder.

The pattern of genetic diversity observed in this study indicates variable distributions of mild and severe α-thalassaemia forms. Reasons for this are unclear. However, high variant heterogeneity has been observed for other genetic disorders (e.g. G6PD deficiency) in Southeast Asia, (Howes et al., 2013) which might suggest a similar underlying cause. In their global study, Howes et al. noted that G6PD variants were most diverse in East Asia and the West Pacific, where P. falciparum parasites show strong population structure with lower genetic relatedness between populations in the region. Indeed, P. falciparum has been shown to display genetically structured populations within Thailand alone. (Pumpaibool et al., 2009) It is possible that the evolutionary dynamics between P. falciparum and haemoglobin variants, including α-thalassaemia, are more complex than we currently appreciate.

The observed spatial distributions of the different α-thalassaemia forms and variants has important implications for the design of newborn screening programmes with regards to the preferred diagnostic algorithm and allocation of treatment and management service provision. Areas with the highest proportions of co-occurring severe α-thalassaemia forms (i.e. α⁰-thalassaemia and α^ND-thalassaemia) may experience a higher prevalence of the severe non-deletional form of HbH disease. Furthermore, the predominance of Hb CS in surveys from Malaysia and Vietnam suggests that the health burden of α-thalassaemia in these areas may be greater than previously thought. Hb CS is a mutation at the termination codon of the α2-globin gene, which, in a normal individual, accounts for three-quarters of overall α-globin production (Liebhaber and Kan, 1981; Orkin et al., 1981). As a result, α2-globin gene mutations, such as Hb CS, tend to cause a more severe phenotype (Chui, 2005).

The reliability of the model-based maps is intrinsically linked to the quality, quantity and spatial coverage of the data upon which the models are based. We were unable to generate continuous maps for the whole of the Southeast Asian region as data were sparse in large areas. Whilst we are aware that unpublished surveys are likely to be available for most countries of the region, obtaining local data for all of the countries was beyond the scope of this study. Nevertheless, we have demonstrated that substantial additional survey data can be identified in locally published sources and, as a result, highlighted the enormous value of future collaborations to collate local data in other regions.

For Thailand, limitations relating to data sparsity, uneven survey distributions and heterogeneity in allele frequency can be quantified in the presented uncertainty intervals. Areas where there is little data or where observed allele frequencies are highly heterogeneous within a small geographical area will have more uncertain predictions, whilst a large amount of data for which there is little heterogeneity will lead to more precise predictions. We identified a lack of data in the southern part of Thailand, which is reflected in larger uncertainty estimates. Other predictions with high associated uncertainty include those along the Thailand-Myanmar border, particularly the southern tail of Myanmar, where no α-thalassaemia prevalence surveys are found. This highlights the arbitrary nature of country borders in mapping studies.

Spatial smoothing is an important component of most geostatistical models. For the modelling approach used here, the range function (i.e. the extent of spatial autocorrelation) is defined by a parameter within the SPDE framework and takes a prior distribution. The smoothing in the approximate posterior therefore balances over- and under-fitting and is necessary to ensure that the model predicts adequately without fitting the idiosyncrasies of the data. As a result, the model does not predict allele frequencies that fully reflect heterogeneity between nearby surveys. Although extensive variation in allele frequencies between different ethnic groups in similar geographic locations has been observed in Thailand (Kulaphisit et al., 2017) and other countries (e.g. Sri Lanka), this could not be reflected in our predicted allele frequencies. For example, allele frequencies of around 3.65% for the Hb CS mutation have been reported in the Khmer ethnic group in Surin and Buriram provinces, whilst our model predicts maximum allele frequencies of 1.65% here. This smoothing process can similarly explain why the highest observed α^ND-thalassaemia frequency of 7% in Khon Kaen was not reproduced in the predicted maps. In fact, our model-based predictions for α^ND-thalassaemia are remarkably homogenous and the average correlation between the observed and predicted frequencies is low (0.47). This is because the close-range heterogeneity in the observed data, coupled with the absence of a long-range trend in frequency (as is observed for α⁰- and α⁺-thalassaemia), makes it difficult for the model to discern a signal.

It is likely that other factors influence the allele frequencies of the different α-thalassaemia forms, but have not been considered in this mapping study, including ethnicity, consanguinity, historic rates of malaria (both Plasmodium falciparum and P. vivax) (Douglas et al., 2012) and population migration patterns. Furthermore, there is bound to be uncertainty in the geolocation of some of the surveys included in the study due to the lack of details published or available. This uncertainty could not be accounted for. Finally, whilst the inclusion criterion of molecular methods should help to improve the reliability of allele frequency estimates, they are not 100% sensitive (Old and Henderson, 2010) and do not cover all possible α-thalassaemia mutations, which may lead to some error in the reported allele frequencies.

Whilst we have calculated the burden of α-thalassaemia in terms of the number newborns born with severe forms in 2020, there are other aspects of the disease burden that would be worth considering pending the availability of more data, for example, milder-forms and their coinheritance with β-thalassaemia, DALY losses from α-thalassaemia, maternal complications (some of which can be life-threatening) (Chui, 2005; Ratanasiri et al., 2009), psychological effects and, in the case of HbH disease, survival data allowing the calculation of all-age population estimates. Furthermore, the estimates presented here do not include compound disorders, such as EA Bart’s and EF Bart’s diseases (HbH disease with heterozygous and homozygous forms of β^E, another clinically important structural β-globin variant, respectively), and therefore remain underestimates of the overall burden of α-thalassaemia disorders in Thailand (Galanello, 2013). Finally, the visualisation of our burden estimates are subject to the modifiable area unit problem, whereby the presentation of estimates at the province level likely masks pockets of high burden (Wong, 2009).

The allele frequency, distribution and genetic variant profile of α-globin forms is only a part of their epidemiological complexity. An improved understanding of the natural history of α-thalassaemia and the factors that modify its clinical outcome will be imperative for establishing better estimates of its burden. This is particularly pertinent in the Southeast Asian region, where the disorder co-exists with β-thalassaemia, including the commonest haemoglobin variant, Hb E. Many studies have shown a positive epistatic interaction between α- and β-thalassaemia, whereby their co-inheritance results in the amelioration of the associated blood disorder (Fucharoen and Weatherall, 2012; Viprakasit et al., 2004).

A detailed assessment of current knowledge on the allele frequency of α-thalassaemia and the magnitude of its health burden is needed to develop suitable prevention and control programmes. This study provides a detailed overview of the existing data on the gene frequency and genetic diversity of α-thalassaemia in Southeast Asia. We show that our knowledge of the accurate allele frequency and distribution of this highly complex disease remains somewhat limited. Because of the remarkable geographic heterogeneities in the gene frequency of α-thalassemia, interventions have to be tailored to the specific characteristics of the local population (e.g., prevalence of the disorder in the population, ethnic makeup, and consanguinity) and the local health care system As the epidemiological transition in these countries continues (Weatherall, 2011; Bundhamcharoen et al., 2011; Dhillon et al., 2012), it will become increasingly important to regularly update regional and national maps of α-thalassaemia gene frequency and newborn estimates such that health and demographic changes can be properly quantified (Piel and Weatherall, 2014). Our findings provide a baseline for such endeavours.

A comprehensive search of three major online bibliographic databases (PubMed, ISI Web of Knowledge and Scopus) was performed to identify published surveys of α-thalassaemia prevalence and/or genetic diversity in Southeast Asia (Figure 7). The 10 member states of the Association of Southeast Asian Nations (ASEAN) were used to define the region under study and include: Brunei Darussalam, Cambodia, Indonesia, Lao PDR, Malaysia, Myanmar, Philippines, Singapore, Thailand and Vietnam (Figure 1—figure supplement 1). In addition, for Thailand, articles published in national journals (in Thai) – not included in international bibliographic databases – were manually searched for local surveys. Consistent and pre-defined sets of inclusion criteria for prevalence/allele frequency data and genetic diversity data, outlined in the Appendix 1, were used to identify relevant surveys. Data extracted from Thai journals were independently validated against the inclusion criteria by two of the authors (SE and CH).

Figure 7

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We employed a Bayesian geostatistical framework to model the allele frequencies of α⁰-, α⁺- and α^ND-thalassaemia, respectively, in Thailand, where a substantially higher number of surveys were identified. We included data from Thailand and its neighbouring countries (Myanmar, Lao PDR, Cambodia and Malaysia) in order to preclude the possibility of a border effect. Three surveys that were reported only at the national level (one in Thailand and two in Malaysia) were excluded for this part of the analysis. Only geographical location was included as a predictor of α-thalassaemia allele frequency (Figure 7).

For each of the three main forms of α-thalassaemia, a model was fitted using a Bayesian Stochastic Partial Differential Equation (SPDE) approach with Integrated Nested Laplace Approximation (INLA) algorithms developed by Rue et al. (Rue et al., 2009), available in an R-package (www.r-inla.org). The observed allele frequency data were transformed through an empirical logit to facilitate approximation by the Gaussian likelihood. The fitted model was then used to generate predictions at a resolution of 1 km x 1 km for α-thalassaemia allele frequencies for all unsampled locations in Thailand. Uncertainty estimates, measured as the 95% credible interval, for the predictions were calculated using 100 conditionally simulated realisations of the model to generate a posterior predictive distribution (PPD) for each 1 km x 1 km pixel. Full details of the modelling process and model validation procedures, which involved a 10-fold cross validation, are provided in Appendix 2.

To generate estimates of the annual number of newborns affected by Hb Bart’s hydrops fetalis syndrome (--/--) and deletional and non-deletional HbH disease (-α/-- and αα^ND/--, respectively) in Thailand in 2020, we paired the predicted allele frequency maps generated using our Bayesian geostatistical framework with high-resolution birth count data. First, we combined the three allele frequency maps to estimate the frequency of each genotype in each pixel, assuming Hardy-Weinberg proportions for a four-allele system (Equation 1 and Table 1) (Hardy, 1908; Weinberg, 1908).

where, p is the allele frequency of α a⁰-thalassaemia (--), q is the allele frequency of α⁺-thalassaemia (-α), r is the allele frequency of α^ND-thalassaemia (αα^ND) and s is the allele frequency of the wild-type α-globin haplotype (αα).

To calculate birth counts, the 2015–2020 crude birth rate for Thailand was downloaded from the 2017 United Nations (UN) world population prospects (World population prospects, 2017) and multiplied with a high-resolution predicted 2020 population surface, adjusted to UN population estimates, obtained from the WorldPop project (www.worldpop.org.uk, last accessed 23 January 2018) (Tatem, 2017). The predicted genotype frequencies were then paired with the birth count data over 100 conditionally simulated realisations of the geostatistical model and areal estimates at province level calculated, together with 95% credible intervals; their calculation is described in Appendix 3. We also applied our maps to 2003 demographic data, and incorporated consanguinity into our calculations (Table 1), (Vieira et al., 2013) in order to more directly compare estimates generated using our method with previous estimates (Modell and Darlison, 2008). We used the online global database of consanguinity estimates (www.consang.net) to identify an upper limit for the coefficient of consanguinity for Thailand (F = 0.1) (Bittles and Black, 2015). However, due to important variations of this coefficient between ethnic groups and the lack of reliable or high-resolution data for consanguinity, we chose not to include it in our main calculations.

Cartographic representations of the identified prevalence surveys were generated using ArcGIS 10.4.1 (ESRI Inc, Redlands, CA, USA). The descriptive maps reflect the spatial distribution of the prevalence surveys, along with their respective sample sizes and observed α⁰-, α⁺- and α^ND-thalassaemia allele frequencies. Other features of the database, including the temporal distribution of the surveys, the identity of the populations studied (e.g. community, pregnant women, newborns, etc.) and the contribution of local Thai surveys to the evidence-base, were also examined.

Maps of the genetic diversity of α-thalassaemia across Southeast Asia were also generated (Figure 6). Given the heterogeneity in the reporting of different α-thalassaemia genotypes, we divided the genetic diversity data into two subtypes: (i) those surveys that only distinguished between the different α-thalassaemia forms (α⁰-, α⁺-, and α^ND-thalassaemia), and (ii) those surveys that contained detailed count data for a range of common mutations. We focused on the 11 mutations that are most commonly reported in Southeast Asia or are part of standard multiplex polymerase chain reaction (PCR) methods: -α^3.7, -α^4.2, --^SEA, --^THAI, --^MED, --^FIL, -(α)^20.5, Hb Adana (HBA2:c.179G > A), Hb CS (HBA2:c.427T > C). Hb Paksé (HBA2:c.429A > T), Hb Quong Sze (HbA2:c.377T > C) (Liu et al., 2000). An ‘Other’ category was used for other, rarer α-thalassaemia mutations. For the first data subtype, only surveys that tested for all three α-thalassaemia forms were mapped and the relative proportions of the different forms in the study sample were displayed using pie charts. For the latter, the same approach to that used in Howes et al. (2013) was used; the variant proportions were displayed using bar charts in which all variants that were explicitly tested for were included on the x-axis (Howes et al., 2013). This allowed information regarding the suite of variants that were tested for in the survey to be displayed as well as unambiguous representation of the absence of a variant in the study sample.

Continuous maps of the allele frequencies of α⁰-, α⁺- and α^ND-thalassaemia were generated for Thailand only. This is because there was considerably more data for Thailand than for any of the other countries in the region, in part due to the inclusion of data from local journals. Data from Thailand and all of its neighbouring countries (Myanmar, Lao PDR, Cambodia and Malaysia) were used in this part of the analysis to preclude the possibility of a border effect on the predicted allele frequencies. Surveys that were reported only at the national level were excluded. To generate three separate maps of α-thalassaemia allele frequency, data on each α-thalassaemia form were extracted from the database and used as input for three separate models. The observed allele frequency data were transformed through an empirical logit, which, for databases ≥ 20 surveys, can be well approximated by a Gaussian likelihood.(Diggle and Ribeiro, 2007)

The goal of the geostatistical analysis was to estimate model parameters and generate predictions of α-thalassaemia allele frequencies in Thailand at fine spatial resolution. We employed, using the ‘r-inla’ package, an easily available Bayesian geostatistical framework involving a stochastic partial differential equation (SPDE) approach(Lindgren et al., 2011) with an integrated nested Laplace approximation (INLA)(Rue et al., 2009) algorithm for Bayesian inference. The theoretical principles of this approach have been described in detail elsewhere.(Lindgren et al., 2011; Rue et al., 2017; Cameletti et al., 2013) A brief overview is provided below.

Let Y be the observed data, which in this study is the set of allele frequency estimates in Thailand and neighbouring countries. Each value y_i represents the allele frequency y at location i. In general, we can assume that Y is generated by an underlying Gaussian process, denoted as S(x), such that any evaluations of S(x) are multivariate normal distributed with a given mean and covariance function. Therefore, S(x) can be interpreted as a continuously indexed Gaussian field (GF), whereby the effect at each location has a multivariate normal as

where, y denotes allele frequency, x is the Gaussian random effect on y, n represents the number of spatially unique data points, i represents location and θ denotes the hyperparameters of x (i.e. mean μ and dispersion parameter k). Observations y are assumed to be conditionally independent, given x and θ.

The first law of geography states that: ‘everything is related to everything else, but near things are more related to distant things.”(Tobler, 1970) This phenomenon is termed spatial dependency, or autocorrelation, and its inclusion in spatial models can greatly improve predictive performance. Several methods for defining the covariance between spatial points exist; here we use the Matérn class of covariance function in which the correlation function is defined based on the Euclidean distance between locations.(Krainski et al., 2017) Specifically, the stationary Matérn covariance function assumes that if we have two pairs of points separated by the same Euclidean distance h, both pairs have same correlation, with correlation monotonically decreasing as a function of h.

Matrix algebra operations on a continuously indexed GF are computationally intensive to model. To overcome this, we use a finite element solution of an SPDE approximation to the Matérn covariance function, resulting in a finite dimensional Gaussian Markov random field (GMRF). The finite element representation represents S(x) by means of a basis function representation defined on a triangulation of the domain under study, represented as:(Lindgren et al., 2011)

where, G is the number of vertices in the triangulation, ψ_g(·) are piecewise polynomial basis functions on each triangle (Moraga et al., 2015). The SPDE approach replaces the Matérn covariance function’s dense covariance matrix by a sparse reduced rank matrix. Following the generation of the GMRF prior, the posterior distribution is approximated by an Integrate Nested Laplace Approximation (INLA), which uses a combination of analytical approximation and numerical integration methods to approximate the posterior distribution at each point in the GMRF. The model’s fully Bayesian hierarchical formulation is as follows:

where, again, Y denotes allele frequency (the observation variable), S denotes the underlying Gaussian field, θ denotes a vector of hyperparameters and Q is the sparse precision matrix.

One hundred conditional simulations of the model were performed, whereby all of the pixels in the map were jointly simulated such that spatial autocorrelation between the pixels was accounted for.(Gething et al., 2010) This generated PPDs of allele frequency for each pixel in a 1 km x 1 km grid of Thailand, which were then used to calculate the mean and 95% Bayesian credible intervals for the predicted allele frequencies.

Given the relatively small number of surveys in each dataset, the predictive ability of the model for each form of α-thalassaemia was assessed using a 10-fold cross validation procedure. For each form of α-thalassaemia, the original dataset was randomly partitioned into 10 equal-sized data subsets. In each of 10 separate cross-validation experiments, one of the data subsets was retained as the test set whilst the remaining nine data subsets were used as the training set. The disparity between the model predictions and the observed allele frequencies in the test set was quantified for each cross-validation iteration using: (i) the mean absolute error (MAE), defined as the average magnitude of errors in the predicted values, and (ii) the correlation. The average across the ten cross-validation experiments was then calculated.

R code for the geostatistical model was generated by SB and is available on request. The model was implemented in R using the ‘r-inla’ package, while the predicted allele frequency and disease burden maps were generated in ArcMap.

Newborn estimates of Hb Bart’s hydrops fetalis and HbH disease (deletional and non-deletional) were generated by pairing our predicted allele frequency PPDs with population and birth rate data. First, the number of births in each pixel of a 1 km x 1 km grid of Thailand were calculated by multiplying high-resolution 2020 population count data obtained from the WorldPop project website (www.worldpop.org.uk) with the national birth rate. In the UN World Population Prospects 2017 Revision, birth rates were provided as low-, medium- and high-fertility variant projections for 5 year periods. To obtain 2020 estimates of the number of births in Thailand, we used the average of the two 5 year periods 2015–2020 and 2020–2025.

One-hundred realisations of the geostatistical models for α⁰-, α⁺- and α^ND-thalassaemia allele frequency were run in parallel. For each realisation of the models, the estimated genotype frequencies for Hb Bart’s hydrops fetalis disease (--/--), deletional HbH disease (-α/--) and non-deletional HbH disease (αα^ND/--) were calculated using the predicted allele frequencies, assuming Hardy-Weinberg proportions for a four-allele system (see Equation 1 in the main text). We also examined the effect that the inclusion of consanguinity into the Hardy-Weinberg equations would have on the estimates (see Table 1 in the main text). Genotype frequencies were then multiplied by the number of births in the corresponding 1 km x 1 km pixel of the birth count map described above. A PPD for the number of births in each pixel was therefore generated, from which point estimates and uncertainty around these estimates were computed.

The lower bounds of our credible intervals were calculated using the 2.5^th percentile for genotype frequency estimates and the low-fertility variant for birth count, whilst the higher bounds were calculated using the 97.5^th percentile for genotype frequency estimates and birth count data based on the high-fertility variant.

In order to compare the geostatistical method employed in this study with methods used to generate previous α-thalassaemia disease estimates, we obtained 2003 population count data from the Global Rural-Urban Mapping Project (GRUMP) and paired this with the same birth rate as that used in Modell and Darlison (17 births per 1000 population; Modell and Darlison, 2008). The same method described above was used; however, no uncertainty intervals were calculated as no corresponding low- and high-fertility variants for birth rate were reported in their study.

From a clinical perspective, the most important distinction between different α-thalassaemia alleles is the number of α-globin genes affected and whether the mutation is of the deletional or non-deletional type – i.e. the distinction between α⁰-, α⁺- and α^ND-thalassaemia.(Piel and Weatherall, 2014) However, molecular diagnosis of α-thalassaemia is mutation-specific and as such the profile of α-thalassaemia mutations that are found in a population determines which mutations are tested for in screening programmes. A better understanding of the genetic diversity patterns of α-thalassaemia at the mutation level therefore has important implications for programme design.(Old and Henderson, 2010; Galanello, 2013) In this study, we include surveys that report on genetic diversity at both levels of specificity.

For the descriptive analysis of the relative proportions of each of the three major forms of α-thalassaemia (α⁰-, α⁺ and α^ND-thalassaemia), only surveys which tested for all three forms were included. This was to avoid misinterpretation of untested forms as being absent. The relative proportions of the different forms were represented using pie charts for which the denominator was the number of α-thalassaemia alleles in the study. The size of the pie charts reflects sample size on the log scale to allow clear visualisation of the data points. A jitter of 0.5-1^o latitude and longitude decimal degree coordinates was applied to each survey to avoid the overlapping of surveys carried out in the same location.

The subset of surveys that reported on specific α-thalassaemia mutations indicated that 11 variants were commonly tested for in the Southeast Asia region: single deletion mutations, -α^3.7 and -α^4.2; non-deletion mutations, Hb Adana, Hb Constant Spring, Hb Paksé and Hb Quong Sze; and double deletion mutations, (α)20.5, FIL, MED, SEA and THAI. Other non-deletion mutations such as Hb Q-Thailand and initiation codon mutations were tested for in very few surveys (n = 13) and were grouped together into a single category as ‘Other’. Surveys were mapped spatially using bar charts to depict variant proportions. All variants that were tested for in a survey were represented along the x-axis of the corresponding chart. Empty spaces are therefore indicative of the variant being absent in the sample. To allow clear visualisation of the bar charts, data for Thailand were displayed separately, whilst the remaining Southeast Asian countries were divided into two maps: (i) Myanmar, Lao PDR, Cambodia and Vietnam in the north, and (ii) Malaysia, Indonesia and Singapore in the south. Bar charts could not be placed at their precise geographical location and so surveys were mapped as points and connected to their corresponding bar chart by a black line.

Among the surveys included in the final geodatabase, α⁰-thalassaemia was the most extensively studied form (n = 97), followed by α^ND-thalassaemia (n = 50) and then α⁺-thalassaemia (n = 47). The majority of studies (95.3%) were carried out after 1995 and in particular from 2005 onwards (78.3%) (Figure 1—figure supplement 2). In terms of the population groups sampled, 21.7% of surveys were community-based, 59.4% were carried out amongst selected but unbiased population groups (e.g. pregnant women, husbands of pregnant women, neonates and cord blood) and 8.5% in opt-out volunteers. One (<1%) survey providing only genetic diversity data was carried out in individuals known to have α-thalassaemia. Twelve per cent of surveys provided no detailed information on the sampling methodology but were judged to be unbiased (based on other information provided in the article or personal communication with the corresponding author) and were included. Sample size ranged from 4 to 55,796. Small sample sizes (e.g. n = 4) came from surveys that were carried out across multiple geographic locations and the α-thalassaemia frequencies reported separately for each. Appendix 5—table 1 provides a breakdown of the survey features for the overall database and for prevalence surveys and genetic variant surveys, separately.

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

© 2019, Hockham et al.

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