Heterogeneity in the Estimated Prevalence of Huntington’s Disease

Heterogeneity in the Estimated Prevalence of Huntington’s Disease | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Heterogeneity in the Estimated Prevalence of Huntington’s Disease View ORCID Profile Alexander Thompson , View ORCID Profile Oliver Quarrell , View ORCID Profile Mark Strong doi: https://doi.org/10.1101/2025.04.09.25325546 Alexander Thompson 1 Yorkshire and Humber School of Public Health , Leeds, United Kingdom 2 School of Medicine and Population Health, University of Sheffield , Sheffield, United Kingdom MPH Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Alexander Thompson For correspondence: alexander.thompson7{at}nhs.net Oliver Quarrell 3 Sheffield Children’s Hospital , Sheffield, United Kingdom MD, FRCP Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Oliver Quarrell Mark Strong 2 School of Medicine and Population Health, University of Sheffield , Sheffield, United Kingdom PhD, CStat Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Mark Strong Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract There is significant variation in estimates of Huntington’s Disease (HD) prevalence in different settings. Despite this heterogeneity, there continues to be much interest in quantitatively synthesizing prevalence estimates to produce global or regional pooled prevalence values. This systematic review was undertaken to describe and assess the sources of heterogeneity in estimated prevalence values, and to consequently evaluate the validity of pooled prevalence values produced. Observational studies from which a prevalence estimate (point or period) or cumulative incidence of HD could be calculated between 1993-2024 were sought from Medline and Embase databases. Features of included studies are described and evaluated, with sources of heterogeneity discussed. A meta-regression was conducted including predictor variables: continent, median age of population, number of years since 1993, case ascertainment method, and Healthcare Access and Quality Index score. A total of 43 studies met the criteria for inclusion in the review. Significant clinical and methodological heterogeneity between the included studies is described, including differences in case definitions and ascertainment methods, and in the estimates of disease burden calculated. There were differences in the estimated point prevalence between regions and population groups within regions, while the estimated point prevalence was shown to be increasing over time since 1993. Wide prediction intervals in the overall pooled point prevalence (95% prediction interval: 0.32 – 37.55 cases per 100,000), and the European pooled point prevalence (95% prediction interval: 1.64 – 19.18 cases per 100,000), indicate the scale of heterogeneity between studies and settings. Such heterogeneity precludes valid extrapolation of findings to settings in which HD prevalence is unknown, and greatly limits the utility of pooled prevalence values. Introduction Huntington’s disease (HD) is a genetic neurological disorder with autosomal dominant inheritance. 1 It produces a wide spectrum of clinical symptoms and signs, including motor, cognitive, and behavioral features. 2 The HTT gene responsible for HD was identified and described in 1993. 3 In HD patients there is an expanded CAG trinucleotide repeat within the gene, encoding an abnormally long and toxic polyglutamine sequence. 4 Average CAG repeat lengths in the general population have been described as between 16-20. 5 Intermediate alleles, which may expand to pathogenic range on transmission from parents to offspring, have between 27-35 CAG repeats. 36-39 CAG repeats are associated with incomplete penetrance of HD, while 40 or more CAG repeats invariably leads to disease. Historically, the diagnosis of HD was solely clinical, with patients identified on the basis of motor abnormalities (typically chorea), and cognitive and neuropsychiatric features, in conjunction with a family history of HD. 2 Since the identification of the mutant HTT gene in 1993, the clinical diagnosis typically includes genetic confirmation of the expanded CAG repeat sequence. Prior to genetic testing it is likely that de novo cases of disease in patients without a family history, thought to represent around 5-8% of diagnoses, were missed. 6 In recent years much interest has been given to describing the burden of HD in different populations. 7 – 11 HD can have a significant health and social care impact on both affected individuals and families, and the wider health service. 12 – 13 The accurate description and prediction of changes in the burden of the disease in a population is therefore of public health significance. In epidemiological terms the burden of a disease can be described using different measures. In the HD literature, these have typically included point prevalence (the proportion of the population with the disease at a single point in time), period prevalence (the total number of individuals with a diagnosis at any time during a period, divided by the total population over that period), and cumulative incidence (the total number of new cases during a period, divided by the total population over that period). These provide related but distinct measures of disease burden. In this manuscript we use the term ‘prevalence’ broadly to describe disease burden in a population. Where specific estimates are referenced, we specify the measure of disease burden used where possible. Meta-analysis refers to the statistical combination of results from multiple studies to produce a pooled estimate. 14 While historically meta-analyses have been used to pool results of multiple randomized controlled trials to produce an overall estimate of treatment effect, in recent years the methodology has been increasingly applied to studies measuring the prevalence of disease. 15 The summary value reported is a pooled prevalence estimate – a weighted average – of all of the prevalence estimates synthesized. Indeed, in a recent systematic review and meta-analysis the ‘global prevalence’ of HD was reported as 4.88 per 100,000 (95% confidence interval (CI): 3.38-7.06). 10 However, the apparently high heterogeneity in the prevalence of HD (described by large I 2 values) between different populations, and at different times, raises questions about the validity and meaningfulness of such a quantitative synthesis. 16 Such levels of heterogeneity in the context of studies measuring treatment effects would ordinarily preclude statistical synthesis. 14 High values of I 2 are ubiquitous in meta-analyses of prevalence. 15 Observational studies of disease prevalence typically have large sample sizes (often the whole population), resulting in precise estimates of prevalence with low sampling error. Since the I 2 statistic is a proportional value describing the proportion of heterogeneity due to variance in underlying true prevalence, it follows that as the sampling error tends towards zero the I 2 statistic tends towards 100%. This is irrespective of whether true variation is large or small in absolute terms. Consequently, caution has been advised in excluding pooled estimates on the basis of large I 2 values alone. 17 Prediction intervals have been suggested as a more conservative method of incorporating uncertainty into a meta-analysis where true heterogeneity is expected. 18 A recent meta-analysis in Latin America produced a regional prediction interval, although the authors advised caution in interpretation since the interval covered a large range. 11 Clinical heterogeneity in the estimated prevalence of HD in different populations results from multiple factors. The underlying genetics of the population under investigation has been shown to be correlated with disease prevalence, including: mean CAG repeat length, presence of different genetic haplotypes, and CCG polymorphisms. 19 – 21 Further, while HD can occur at almost any age the majority of patients develop HD between 35-55 years. Therefore, the age-distribution of the population has implications for the prevalence of disease. Since the estimated prevalence of HD is purportedly increasing in parts of the world, the date of any prevalence studies may impact on prevalence values reported. 22 Methodological heterogeneity may also impact on estimates of disease prevalence. Countries with less developed healthcare services, or countries with higher stigma around disease diagnosis, may diagnose fewer cases. 23 Additionally, study designs benefitting from an active case finding component, in which secondary cases are actively sought, may capture more cases than designs dependent on administrative data alone. 24 Recent studies have made use of administrative healthcare records to identify HD cases. 22 In the absence of case validation, this may lead to the inclusion of miscoded or misdiagnosed HD cases. 25 Factors contributing to heterogeneity in the estimated prevalence can be elucidated by means of a meta-regression. A meta-regression regresses the outcome variable on various predictor variables associated with each study. Its use to explore heterogeneity is common in meta-analysis designs. 26 In this study we aim to explore qualitatively and quantitatively, the clinical and methodological heterogeneity between included studies. Heterogeneity is visualized in a forest plot of included studies and summarized by I 2 values and prediction intervals. Contributing factors to heterogeneity are described and quantitatively evaluated in a meta-regression analysis. Methods The methodology followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) protocols checklist. This systematic review is registered on Prospero: CRD42024605294. Study Selection We conducted a systematic search of Medline and Embase databases using index terms specific to the prevalence and epidemiology of HD. The search was produced using minor alterations to a search conducted in a previous review (supplementary material). 10 Two authors (AT/OQ) conducted all stages of the literature review independently. Studies were progressed through the title and abstract screening stage to full record review if they examined either case numbers or prevalence of HD in a defined geographical area. All discrepant selections at title and abstract screening were progressed to the next stage of review for both authors. At full record review studies were included if: (1) there was sufficient information to calculate a measure of disease burden (point prevalence; period prevalence; cumulative incidence) and binomial confidence interval (at least 2/3 of: the proportional value case numbers/population size, population size, or case numbers), (2) the date of the measure of disease burden was 1993 or later, (3) the full paper was available in English, (4) the population size was at least 100,000, and (5) values reported were based on observational data in a defined geographical region or a representative sample of that region. Papers were excluded if they were a conference abstract or if they reported a mathematically modelled value. The 1993 date limit was set since this followed the identification of the mutant HTT gene, after which time testing for the gene became more routine in clinical practice. The 100,000-population size minimum was set since, given the rarity of HD, reports of disease burden in populations below this size would provide unreliable estimates. This approach is similar to previous reviews. 8 Review papers and included articles had their references searched for further papers. Discrepancies in final study selection were discussed between three authors (AT/OQ/MS) until consensus on inclusion was reached. Referencing software Zotero 6.0.37 was used. Data Extraction Manual data extraction for all studies was undertaken independently by two authors (AT/OQ). Data extracted included: date of study, number of cases, population size, case ascertainment method, case definition, and location (including continent). Where one of the ‘number of cases’ or the ‘total population size’ was not available, this was extrapolated from back-calculation. Where back-calculation was not possible, but there were reliable external sources of population size estimates (e.g. census records), these external sources were used. For studies presenting multiple point prevalence values at different time points, the case numbers and population size at each time point was extracted. Studies were grouped into three categories for the estimate of HD burden: point prevalence, period prevalence, or cumulative incidence. Case ascertainment methods were broadly categorized into ‘passive’ and ‘passive and active case finding’. Data extracted was supplemented with online searches for the median age of the population studied and the Healthcare Access Quality Index (HAQI) score of the country. The HAQI score is an index measure which scores countries on a range from 0 – 100 based on both the quality of the healthcare provided and the ease of access for the general population. Where the population of interest was a region within a country, the median age for that country was applied. Any discrepancies in data extraction were discussed between all authors until consensus reached. Study details are available in the supplementary material. Risk of Bias We assessed the risk of bias of each study using the Joanna Brigg’s Institute (JBI) critical appraisal tool for prevalence studies. 27 This tool scores studies on their sampling, diagnostic, and statistical methodologies. Scores for each study are available in the supplementary material. Data Analysis All studies were included within the comparative qualitative and descriptive analysis. For the quantitative data synthesis (meta-analysis and meta-regression), only a smaller subsection of these studies were included. Studies were included in the quantitative data synthesis if: (1) the estimate of disease burden calculated was a point prevalence, and (2) it was the most recent study within that region. Only including the most recent studies avoided the issue of patients being ‘counted twice’. Data analysis and synthesis was undertaken using the ‘meta’ package in R version 2023.12.1+402. Studies are visually presented in a forest plot grouped by region. Studies were synthesized using a random effect meta-analysis (generalized linear mixed model (GLMM)) and 95% prediction intervals were produced. Prediction intervals describe the range of values within which 95% of similar studies are expected to lie. They are more conservative than confidence intervals since they incorporate study heterogeneity into the range described. Where no heterogeneity is present, prediction intervals coincide with confidence intervals. 28 Prediction intervals for regional subgroups are only presented where n ≥ 10. 14 The meta-regression was conducted using predictor variables agreed between authors a priori. These included: continent of study, number of years since 1993, HAQI score, median age of population, and case ascertainment method. As discussed in the introduction, these variables have been shown to be related to overall HD prevalence within a population. Unlike conventional regression analysis, meta-regression incorporates two additional error terms to account for sampling error in studies, and between-study heterogeneity. Meta-regression was conducted using the weighted least squares method. Sensitivity Analysis The meta-analysis and meta-regression were repeated with differences in the definition of geographical regions (using the world bank classification) and excluding studies relying on administrative diagnostic codes for HD diagnosis. Results Literature Search Medline and Embase databases were searched on 28 th October 2024 ( Fig. 1 ). A total of 3092 citations were identified, 2409 in Embase and 684 in Medline. 673 of these records were excluded as duplicates. Titles were examined for relevance to HD prevalence, with 2000 records excluded at this stage. The abstracts of 419 records were reviewed, with 288 excluded. 131 records had their full texts examined, with 93 records excluded. Main reasons for exclusion included: insufficient information to calculate a prevalence value and binomial confidence interval (n=44), paper not available in English (n=15), and the paper being an abstract only (n=13). Other reasons included: paper being a review (n=9), a pre-1993 prevalence date (n=5), estimate of disease burden based on previously published data (n=5), population size <100,000 (n=1), and calculated prevalence based on a non-representative sample (n=1). Review papers and included records had their references searched for papers not identified through the search strategy, yielding five additional citations. A total of 43 records were included for analysis. A paper using insurance records in the USA was excluded from our analysis since the author’s acknowledged it was not a representative sample of the population. 29 A study on a cluster of HD cases in the Minas Gerais state in Brazil was excluded since the population size was <100,000. 30 Vishnevetsky et al’s 2023 study, while focused on juvenile HD in Peru, also reported numbers of adult HD patients and so was included. 31 Details of the included studies are shown in table 1 . Download figure Open in new tab Figure 1: Flowchart of screening and selection process of final papers for inclusion in the systematic review. *Other= population size <100,000 (n=1) and a non-representative sample of a geographical region (n=1). View this table: View inline View popup Table 1: Huntington’s Disease Prevalence Studies Study Heterogeneity Most included studies were undertaken in Europe (24/43; ∼56%). Asia and South America had six and five included studies respectively, Africa and North America both had three included studies, while Oceania had only two included studies. There was variation in how cases were sourced. Most studies used medical records for at least part of their case sourcing (27/43; ∼63%). A HD register was used in seven included studies. Five studies used diagnostic codes from insurance records, while six studies included records from genetic laboratories. 14 studies benefitted from an active case finding component, typically searching for secondary cases within families. 61 In one study the source of cases was unclear. 32 There was variation in how HD cases were defined. Just over half of included studies incorporated clinical features with genetic confirmation into their case definition (23/43; ∼53%). Thirteen studies used administrative codes alone to identify HD diagnoses – of these only two detailed attempts to validate the diagnosis. Case validation in these studies involved clinical review of medical records with HD diagnostic read codes to confirm the diagnosis. 35 , 49 An insurance-based study in Israel did limit their source of administrative records to either a neurologist diagnosis, or a chronic diagnosis, to improve the specificity of their case identification. 60 In three studies the case definition was unclear. Most studies included used the whole population size to describe the ‘population at risk’. However, an administrative based study in the UK, an insurance-based study in Israel, and a study in Croatia, included only adults aged ≥18 years of age, 22 , 32 , 60 while a Canadian study included only adults ≥21 years. 12 The prevalence of juvenile HD is known to be far lower than adult-onset. 50 There was differential inclusion of incident and/or prevalent HD cases in the numerator of the disease burden estimates between studies, as well as whether efforts were made to exclude deceased patients on the prevalence date. A point prevalence value could be calculated in most studies (34/43; ∼79%). Period prevalence could be calculated in four of the studies. The period prevalence range varied between two and 18 years. Most of the remaining studies (5/43; ∼12%) appeared to include only incident cases diagnosed over the study period. The range of study periods for which cumulative incidence rates were reported was between 9-33 years. In two studies it was unclear how the prevalence value reported had been produced. 32 , 69 There was variation in study quality as assessed using the JBI critical appraisal tool. 27 Relatively few studies dropped points for their sample frame (6/43), sampling method (8/43) or sample size (42/43). Larger numbers of studies dropped points on the validity of methods used to diagnose the condition (29/43) – for instance, relying on administrative codes alone – and whether they clearly stated the numerator and denominator in their reporting of results (33/43). Meta-analysis Study heterogeneity can be visualized in the forest plot in figure 2. Only studies for which a point prevalence value could be calculated were included in the quantitative synthesis and forest plot. Additionally, older studies were excluded where a more recent study in a geographical area was available. In the UK, Furby et al.’s 2022 study was preferred to Kounidas et al.’s 2021 study in Scotland, despite it having an older prevalence date, since it covered a broader and more representative sample of the whole UK population. 22 , 46 Studies in other devolved nations in the UK were excluded since the CPRD database used in Furby et al.’s study includes practices based in all devolved nations. The final number of populations included in the quantitative synthesis was n=24. Significant heterogeneity was present both within and between regional subgroups. The overall pooled 95% prediction interval for all studies ranged between 0.32 to 37.55 HD cases per 100,000. Large I 2 values were reported for all regional subgroups, with a wide 95% prediction interval in Europe (1.64, 19.18). Prediction intervals in other regions are not presented since the subgroup sample size was insufficient. Given the significant heterogeneity present we do not report an overall pooled point prevalence value, nor pooled point prevalence values for the regional subgroups. The 95% confidence interval of the pooled prevalence value (2.19, 5.46) is presented as the checked lines on the forest plot. Only 10/24 included studies had 95% CIs that overlapped with the 95% CI of the pooled estimate, emphasizing the heterogeneity between study findings. There is a large range of point prevalence values reported within European populations. Studies in Iceland and Finland reported low point prevalence values (0.96 and 0.98 per 100,000 respectively), 32 , 36 while higher point prevalence values were reported in small studies in Italy and Malta, 41 , 43 and a large administrative-based study in Sweden. 48 The other continents had relatively few studies for inclusion. Asian studies included three in South-East Asia (Japan, South Korea, Taiwan) with similar reported point prevalence values (0.39-0.65 per 100,000), 62 – 64 while two Asian studies in the Middle Eastern region had higher point prevalence values reported (4.36-7.36 per 100,000). 60 – 61 In North America a study based on the Navajo Nation found no cases of HD, 55 while studies in Canada found high point prevalence values similar to those reported in some of the higher prevalence areas in Europe. 12 , 54 Download figure Open in new tab Figure 2: Forest plot presenting HD point prevalence estimates grouped by region. Error bars are 95% confidence intervals. Checked lines are the lower and upper 95% confidence limits for the pooled point prevalence estimate. Download figure Open in new tab Figure 3: Point prevalence estimates over time. Error bars are 95% confidence intervals. Meta-regression Table 2 presents the meta-regression analysis. Both Asian and South American based studies had a significantly reduced point prevalence compared to the European reference group. Prevalence in the Asian based studies were estimated at about a quarter of the European reference group, while South American studies were around a twentieth. Studies in Oceania and North America were not statistically significantly different compared to Europe. View this table: View inline View popup Download powerpoint Table 2: Meta-regression Results Each year post-1993 was associated with approximately a 7% increase in the point prevalence of HD. The HAQI scores of the study countries was not associated with differences in the point prevalence of HD, and neither was the median age of the underlying population. While our model co-variates explained a significant proportion of the heterogeneity between studies (R 2 = 0.62), nearly 40% of the heterogeneity between studies remained unexplained. Prevalence over time For some geographical areas there were studies estimating point prevalence values at different points in time. Changes in prevalence over time in these areas are presented in figure 3. The estimated point prevalence appears to have risen over time in: Taiwan, the UK, and the Alberta region of Canada. 12 , 22 , 50 – 52 , 65 Four studies (the Douglas et al. and Evans et al. studies were combined to include both juvenile and adult-onset HD) using administrative primary care datasets are included within the UK region, with each study claiming to be based on a representative sample of the UK population. It is notable that in 2004 there is little to no overlap in the UK prevalence estimates between studies, despite apparent similarity of methods and large sample sizes. Further, while Furby et al.’s 2022 study is consistently lower than the other UK HD prevalence estimates presented, since this study actually reports a prevalence in only the ≥18-years-old adult population, we would have expected this study to estimate larger prevalence values than the other UK studies included. 22 There is little apparent change in estimated prevalence between the time points measured in: Scotland (Northern region), South Korea, Finland, and the Navarre region of Spain. 25 , 35 , 36 , 47 , 63 To understand changes in the estimated prevalence of disease over time, it is important to consider changes in both the incidence and mortality of disease over the same period. Of the papers presented in figure 3 only Vicente et al.’s 2021 study presented contemporaneous incidence and mortality data. 47 Five of the included studies did present data on incidence alone. 12 , 22 , 52 , 63 , 65 Increasing point prevalence mirrors increasing incidence in Taiwan. 65 However, in the UK and Canada apparent rises in the estimated point prevalence values occurred against a background of fluctuating incidence levels, indicating that improvements in patient longevity may have driven the changes. 12 , 22 Sensitivity Analysis The meta-analysis and meta-regression were repeated with an alternative regional grouping (World Bank classification). We also repeated the meta-analysis and meta-regression excluding studies based on administrative data alone (supplementary material). Significant heterogeneity remained, with large regional I 2 values and wide overall and European (or ‘Europe and Central Asia’) 95% prediction intervals. Adjusting the region definitions to the World Bank classification meant that none of the adjusted meta-regression coefficients were statistically significant at the 5% level. When administrative code-based studies were excluded the adjusted meta-regression coefficients were grossly similar, with South American and Asian studies being associated with lower prevalence of HD, while each additional year post-1993 was associated with a 7% increase in HD prevalence. However, in this model increases in the HAQI score were associated with decreases in the prevalence of HD. Discussion Heterogeneity between regions The large heterogeneity between studies in different regions is demonstrated by the wide overall pooled prediction interval incorporating all studies. The overall 95% prediction interval ranged between 0.32-37.55 HD cases per 100,000 – an over 100-fold difference in possible point prevalence values. While prediction intervals had been suggested as a mechanism for more conservatively synthesizing studies where true heterogeneity is expected, 17 the overall prediction interval produced in the context of such heterogeneity is too wide to be meaningful. The adjusted meta-regression analysis demonstrated reduced HD point prevalence within both the South American and Asian continents, compared with the European reference group (around a quarter in Asian based studies; a twentieth in South American based studies). Lesser HD prevalence in Asian populations is widely recognized and likely reflects a combination of genetic factors: protective genetic haplotypes, reduced mean CAG repeat length in the general population, and CCG polymorphisms. 71 The small sample of South American studies (n=2) limits conclusions based on this result. Heterogeneity within regions Heterogeneity between studies in the same region is demonstrated by large regional I 2 values (all >90%). The prediction interval for the European subgroup ranged between 1.64-19.48 HD cases per 100,000. This significant within region study heterogeneity indicates that ‘lines on a map’ may be a poor proxy for genetic similarity between populations within defined geographic regions. Indeed, Gordon et al.’s 2016 paper in the Navajo Nation in the US found no cases of HD, while papers based in Canada reported point prevalence values closer to the high prevalence populations in Europe. 12 , 54 – 55 While these populations co-exist in the geographically defined North American continent, genetic differences between the different races studied will contribute to the differences in point prevalence estimates reported. Further, where HD burden is broken down by race, as in Baine et al.’s 2016 paper reporting HD cases in South Africa, racial differences are a much more significant predictor of genetic similarity, and consequently HD burden, than geographical area. 70 An approach which pooled racially disparate populations into overall regional populations would lose important nuance in differences in HD prevalence between different racial groups. This is perhaps clearest in the ‘Asian’ studies within this systematic review. While studies based in South-East Asia reported largely similar point prevalence values (Taiwan, South Korea, and Japan), 62 – 63 , 65 when combined with the Israel and Oman studies in the Middle East under the ‘Asia’ umbrella, the heterogeneity dramatically increases. 60 – 61 Heterogeneity over time There is heterogeneity in point prevalence values observed at different times within our inclusion window. The adjusted meta-regression analysis demonstrated a positive association between the number of years since 1993 (the start of our inclusion window) and the prevalence date. On average, for each year after 1993 there was a 7% increase in the estimated HD point prevalence. Increasing estimates over time is also demonstrated in the included studies reporting point prevalence values at different time points (figure 3). In Taiwan, the UK, and the Alberta region of Canada, there are clear increases in the estimated point prevalence over time. Increasing estimates of point prevalence over time was not universal: studies in Spain and South Korea, did not demonstrate increases. 45 , 63 Suggested factors driving increases in the estimated point prevalence include an increase in expected survival of HD patients from diagnosis, and reduced stigma around disease diagnosis. 23 Additionally, where studies are repeated in the same region at different time points an increase in point prevalence may be noted due to improved case ascertainment over time (for instance, diagnosing secondary cases in families with established heterozygosity for the expanded HTT allele). Moreover, widening access to genetic testing may have facilitated further diagnoses. While increases in disease prevalence can follow increases in disease incidence, Furby et al.’s 2022 paper in the UK reported unchanged HD incidence rates over the 19-year study period. 22 It is also recognized that some under ascertainment on the prevalence date may occur due to the presence of symptomatic individuals who have not yet been diagnosed. Attempts to address this cause of under ascertainment have been made by using a prevalence date just before, or early in, the survey period. 44 , 66 Patients symptomatic on the prevalence date but not diagnosed until later in the study period would therefore be included. We recommend that studies reporting a single estimated point prevalence value use remote prevalence dates where possible or if using estimates over time, include contemporaneous data on incidence and mortality. Heterogeneity in estimates of disease burden There are significant differences in the estimates of HD burden that can be calculated from studies reporting HD cases. Of the 43 studies included within this systematic review, four could be used to produce estimates of period prevalence, while estimates of the cumulative incidence of HD cases could be calculated in five studies. Point prevalence values could be calculated in 34 studies. For valid quantitative synthesis, these distinctions in estimate types matter. Period prevalence values include all cases (prevalent and incident) during a time period, without excluding any cases that died during the study period. Consequently, the estimated number of cases is inflated (since deaths are not removed) and the prevalence value is also inflated. This is exemplified in Shaw’s 2022 Canadian study reporting yearly point prevalence values for the period 2014-2019, and a period prevalence value across the same six years. 12 While the yearly point prevalence values range around 8 – 9 / 100,000, the period prevalence value reported over the same period is about a third higher at 12.15 / 100,000. Conversely, cumulative incidence values may provide either inflated or deflated approximations of point prevalence. Although case numbers are reduced since prevalent cases are not included, failing to exclude deceased patients will inflate case numbers reported. Consequently, whether or not a cumulative incidence estimate is greater or lesser than a point prevalence estimate depends on the length of the study period. Brief study periods, such as Bouhouche et al.’s 2022 Morocco based study will likely produce cumulative incidence values less than the ‘true’ point prevalence, since most cases in the region will be prevalent and therefore missed, while only few incident cases would be expected to have died during the study period. 69 It is concerning that previous meta-analyses of HD prevalence have quantitatively synthesized studies reporting estimates of HD burden across all three categories described, without distinction or comment. 10 , 11 We recommend that studies intending to report a measure of burden of HD make clear the estimate type reported. Heterogeneity in case ascertainment There are significant differences between studies in how HD cases were ascertained. Thirteen of the included studies used administrative read codes alone to find HD cases. There is concern in the literature around the validity of using administrative codes for this purpose, which is dependent on the quality of the clinical coding. 47 Indeed, inflation of case numbers due to false positives in administrative codes may be significant. Sipilä et al.’s 2015 study in Finland incorporated clinical review of medical records into their case ascertainment process. 35 Of 399 records identified as having an HD diagnostic code, only 207 cases were validated as HD following chart review. Further, four UK based studies using administrative records from primary care databases all reported differing point prevalence estimates for the same time periods. 22 , 50 – 52 While the rarity of HD makes traditional door-to-door survey methods for case finding impractical in most settings, it is important that the use of routine administrative data in case finding is rigorous and validated. Further attempts to assess and improve the validity of administrative data in epidemiological studies of HD prevalence should be a priority for future research. Incorporating multiple methods of ascertainment into the study design provides a mechanism for both validating cases (through triangulation of evidence), and also for identifying cases that would otherwise have been missed. 47 , 54 Heterogeneity in defining the population at risk Differences in how the population at risk of HD is defined, may also lead to inflation of the estimated HD point prevalence in some contexts. Within this systematic review three papers included only cases of adult-onset HD, and therefore included only the adult population within the population at risk. 12 , 22 , 60 Since juvenile onset HD makes up only a small fraction of overall HD cases, ‘adult-only’ point prevalence values would be expected to be higher than the estimated prevalence in the whole population. 72 Strengths and Limitations This systematic review benefits from the large number of studies that have been undertaken since the identification of the mutant HTT allele in 1993. This enables meaningful evaluation of the different sources of heterogeneity between studies and settings. In comparison to previous reviews which speculate qualitatively on the contribution of different factors to heterogeneity, 7 our meta-regression approach enables quantification of the relative contribution of different factors to HD prevalence. However, caution must be advised in interpreting our meta-regression co-efficients. The reduced sample size for our quantitative analysis (n=24) means our regression model may be underpowered to find a true association where one exists, while model overfitting may also occur. Nevertheless, statistically significant predictors of the point prevalence of HD in our adjusted meta-regression model included a lesser point prevalence of HD in South American and Asian populations compared with the European reference population, and an increase in the point prevalence of HD in the years after 1993. These findings are epidemiologically plausible and are consistent with findings in other studies. 9 – 11 , 71 Larger sample sizes may have been possible if we had been able to include non-English studies within this review. The lack of non-English studies also meant studies based in Europe made up a large proportion of our sample (24/43). This European bias further limits the use of overall pooled prevalence estimates, which would be more reflective of the European population group rather than the global population. Previous systematic reviews of HD prevalence that included studies published in other languages had larger numbers of studies for analysis. 8 , 11 Conclusion This systematic review describes the significant heterogeneity between studies used to estimate the burden of HD in different settings. Sources of clinical heterogeneity include differences between regions and between different population groups within regions, and differences in HD prevalence over time. Sources of methodological heterogeneity include differences in case definitions and case ascertainment methodologies, as well as differences in the estimates of disease burden produced. Meta-analysis, in the context of such heterogeneity, produces invalid pooled prevalence values that lack meaning and are inappropriate for extrapolation to contexts in which HD prevalence is unknown. While prediction intervals have been suggested as a mechanism for incorporating heterogeneity in estimates of HD prevalence to produce a range of possible prevalence values in similar studies, the prediction intervals produced here are too wide to be meaningful for researchers and implementers of health policy. Data Availability Statement The data supporting the findings of this study can be found in the supplementary material of this article. Financial disclosures AT, OQ and MS have no relevant disclosures. Author Roles A. Conception of research; B. Literature Search; C. Data Extraction D. Statistical Analysis; E. Writing of first draft; F. Review and feedback. 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Share Heterogeneity in the Estimated Prevalence of Huntington’s Disease Alexander Thompson , Oliver Quarrell , Mark Strong medRxiv 2025.04.09.25325546; doi: https://doi.org/10.1101/2025.04.09.25325546 Share This Article: Copy Citation Tools Heterogeneity in the Estimated Prevalence of Huntington’s Disease Alexander Thompson , Oliver Quarrell , Mark Strong medRxiv 2025.04.09.25325546; doi: https://doi.org/10.1101/2025.04.09.25325546 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Epidemiology Subject Areas All Articles Addiction Medicine (567) Allergy and Immunology (863) Anesthesia (297) Cardiovascular Medicine (4411) Dentistry and Oral Medicine (443) Dermatology (380) Emergency Medicine (606) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1505) Epidemiology (15205) Forensic Medicine (30) Gastroenterology (1119) Genetic and Genomic Medicine (6574) Geriatric Medicine (666) Health Economics (994) Health Informatics (4511) Health Policy (1365) Health Systems and Quality Improvement (1608) Hematology (537) HIV/AIDS (1263) Infectious Diseases (except HIV/AIDS) (15903) Intensive Care and Critical Care Medicine (1103) Medical Education (620) Medical Ethics (144) Nephrology (666) Neurology (6573) Nursing (345) Nutrition (998) Obstetrics and Gynecology (1139) Occupational and Environmental Health (954) Oncology (3319) Ophthalmology (968) Orthopedics (369) Otolaryngology (420) Pain Medicine (435) Palliative Medicine (129) Pathology (662) Pediatrics (1689) Pharmacology and Therapeutics (691) Primary Care Research (710) Psychiatry and Clinical Psychology (5422) Public and Global Health (9205) Radiology and Imaging (2191) Rehabilitation Medicine and Physical Therapy (1367) Respiratory Medicine (1191) Rheumatology (593) Sexual and Reproductive Health (709) Sports Medicine (529) Surgery (709) Toxicology (99) Transplantation (288) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9feb28bebc50df94',t:'MTc3OTI3ODE5Nw=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();