Prevalence And Spectrum of Congenital Heart Disease in Nepal: A Systematic Review and Meta-Analysis

Prevalence And Spectrum of Congenital Heart Disease in Nepal: A Systematic Review and Meta-Analysis | 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 Prevalence And Spectrum of Congenital Heart Disease in Nepal: A Systematic Review and Meta-Analysis View ORCID Profile Sandip Pandey , View ORCID Profile Apil Tiwari , View ORCID Profile Alisha Bhattarai , View ORCID Profile Anu Timalsina , Asmit Pandey , View ORCID Profile Aakash Neupane , View ORCID Profile Deepak Jung Subedi doi: https://doi.org/10.1101/2025.09.20.25336248 Sandip Pandey 1 B.P. Koirala Institute of Health Sciences (BPKIHS) , Dharan, Nepal Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sandip Pandey For correspondence: dr.sandip.pandey5{at}gmail.com Apil Tiwari 1 B.P. Koirala Institute of Health Sciences (BPKIHS) , Dharan, Nepal Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Apil Tiwari Alisha Bhattarai 2 Nepal Medical College , Kathmandu, Nepal Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Alisha Bhattarai Anu Timalsina 3 Patan Academy of Health Sciences , Lalitpur, Nepal Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Anu Timalsina Asmit Pandey 3 Patan Academy of Health Sciences , Lalitpur, Nepal Find this author on Google Scholar Find this author on PubMed Search for this author on this site Aakash Neupane 1 B.P. Koirala Institute of Health Sciences (BPKIHS) , Dharan, Nepal Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Aakash Neupane Deepak Jung Subedi 4 College of Medical Sciences , Bharatpur, Nepal Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Deepak Jung Subedi Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Background The true population burden of congenital heart disease in Nepal remains uncertain due to the blending of community- and hospital-based data. We aimed to synthesize congenital heart disease prevalence estimates, quantify heterogeneity, and explore the impact of the study setting and design on reported rates. Methods Following PRISMA guidelines, we searched PubMed, Embase, ScienceDirect, and Nepal-specific journals through May 1, 2025 (PROSPERO CRD420251125950). Eligible observational studies reported an extractable numerator of congenital heart disease cases and a denominator within community/school, neonatal (births or consecutive admissions), or hospital/referral (including consecutive echocardiography) frames. Pooled prevalence used binomial GLMM on the logit scale (PLOGIT; random effects; method.tau = ML); fallback was inverse-variance PLOGIT with REML and Hartung-Knapp CIs. We reported 95% CIs, 95% prediction intervals (PIs), and I^2/τ^2. The prespecified subgroups were community (CO) vs. hospital/referral (H) and Kathmandu Valley vs. sub-urban/mixed. Lesion outcomes (ASD, VSD) were pooled as shares among CHD and population prevalence; studies with multi-lesion reporting (>100% summed percentages) were excluded from pooling. Exploratory meta- regression (Knapp-Hartung) evaluated log□□(sample size) Results Nine studies (N = 79,595; CHD cases = 462) met the inclusion criteria. Overall pooled prevalence was 8.9 per 1,000 (95% CI 3.0 - 26.1; PI 0.2 - 338.9), with I^2 ≈ 99% and τ^2 ≈ 2.76 (logit). Hospital/referral series showed higher descriptive prevalence than community (25.64 vs. 3.81 per 1,000), but in meta-regression, the hospital effect attenuated and was not significant after adjusting for log□□(n) (OR ≈ 1.32, 95% CI 0.21 - 8.15). Larger studies reported a lower prevalence (OR per 10-fold increase ≈ 0.10, 95% CI 0.04 - 0.27). By geography, the pooled prevalence was 5.69/1,000 in Kathmandu Valley versus 15.46/1,000 in suburban/mixed settings. Lesion patterns differed by setting: Atrial Septal Defect share was higher in the community and Ventricular Septal Defect share was higher in hospitals. Population-scale estimates were ASD 1.18/1,000 (CO) vs 4.93/1,000 (H) and VSD 0.38/1,000 (CO) vs 6.68/1,000 (H); all lesion analyses showed wide uncertainty. Conclusions The prevalence of Congenital Heart Disease in Nepal is highly context-dependent and dominated by the frame and study size. Hospital/referral data are not generalizable to the population prevalence. Community-based estimates are more informative for planning. Given the extreme heterogeneity and wide PIs, the pooled figures should be interpreted with caution. The risk of bias was moderate to high, and the certainty was very low. Background Congenital heart disease (CHD) refers to structural abnormalities of the heart or great vessels that are present at birth. These defects arise from incomplete or abnormal development of the fetal heart during early pregnancy. CHD encompasses a wide spectrum of conditions, ranging from simple, asymptomatic lesions that may resolve spontaneously to complex, life-threatening malformations requiring immediate medical or surgical intervention( 1 ). The severity and clinical presentation of CHD vary significantly depending on the specific defect, its hemodynamic impact, and the presence of associated anomalies. Early diagnosis and appropriate management are crucial for improving outcomes and reducing morbidity and mortality associated with these conditions( 2 ). Globally, CHD represents a substantial public health challenge, affecting approximately 8 per 1,000 live births. The global birth prevalence of CHD has shown a progressive increase over recent decades, reaching an estimated 9.41 per 1,000 live births during 2010-2017 ( 3 ). While advancements in medical and surgical care have significantly improved survival rates in high- income countries, the burden of CHD remains disproportionately high in low- and middle- income countries (LMICs). These regions often face significant challenges, including limited access to diagnostic facilities, specialized healthcare professionals, and advanced treatment options, leading to higher rates of complications and mortality( 4 ). Within South Asia, the epidemiological landscape of CHD presents unique complexities. South Asia had the highest number of CHD cases globally, with India leading in absolute numbers( 5 ). The prevalence of CHD in north-central India was found to be 19.14 per 1,000 individuals, with ventricular septal defect being the most common defect( 6 ).Similarly, a retrospective analysis in the UK reported a higher estimated prevalence of CHD requiring hospital admission in Asian infants compared to non-Asian infants( 7 ). Global data from 1990 to 2021 revealed that South Asia had the highest number of CHD cases among children under five years old( 5 ). The challenges in this region are compounded by socioeconomic disparities, inadequate healthcare infrastructure, and a lack of comprehensive screening programs. These factors contribute to delayed diagnosis and suboptimal management, further exacerbating the impact of CHD on affected individuals and healthcare systems. In Nepal, the true epidemiological burden of CHD remains largely undefined due to various systemic limitations. A significant portion of the population lives below the poverty line, and access to essential diagnostic services, such as echocardiography, is severely limited, particularly in rural areas. Existing data on CHD prevalence in Nepal are fragmented and often derived from single-center studies or limited regional surveys. For example, a systematic review and meta- analysis reported the prevalence of CHD in Nepal to be 0.7%, while other reports suggest a prevalence of 10.2% births( 8 ) ( 9 ). These discrepancies highlight the urgent need for a more comprehensive and updated assessment. The lack of robust, nationally representative data hinders effective public health planning, resource allocation, and the development of targeted interventions for prevention, early detection, and management of CHD. Therefore, this updated meta-analysis aims to address these critical gaps by re-evaluating the prevalence of CHD in Nepal, integrating the most current available data, and providing a more nuanced understanding of the factors influencing its reported rates. Specifically, this study seeks to: 1) synthesize pooled prevalence (2) explain heterogeneity (3) quantify setting, sample size, geography (4) offer planning implications. Methods Protocol and registration The study protocol was registered with PROSPERO 2025 (CRD420251125950). During the review we made two amendments: the date limit for eligibility was extended to May 1, 2025, and the risk-of-bias tool was changed from NOS to JBI. All other procedures followed the registered protocol. Search strategy and study selection Following PRISMA, we aimed to estimate pooled congenital heart disease (CHD) prevalence across populations and settings, and to examine setting effects. We systematically searched PubMed, Embase, ScienceDirect, and Nepal-specific journals (e.g., Journal of Nepal Medical Association , Nepalese Heart Journal ) for studies published up to May 1, 2025. The query combined terms for CHD and epidemiology with the country filter: (“congenital heart disease” OR CHD OR “cardiac anomalies” OR “birth defects”) AND (prevalence OR epidemiology) AND (Nepal) NOT (“case reports”[Publication Type] OR “case series”[Title/Abstract] OR “meta-analysis”[Publication Type] OR “systematic review”[Publication Type]). There were no language restrictions. Results were exported to Rayyan for de-duplication. Two reviewers screened titles/abstracts independently; disagreements were resolved by a third reviewer. Full texts were assessed against pre-specified criteria. Study selection is shown in the PRISMA flow diagram. Eligibility criteria We included observational studies reporting CHD prevalence with an extractable numerator (CHD cases) and denominator (sample size) within a defined frame: (i) community/school screenings, (ii) neonatal cohorts (live births or consecutive admissions), and (iii) hospital/referral cohorts, including consecutive echocardiography series. Denominators were, respectively, all screened individuals, all live births or all consecutive neonatal admissions, and all consecutive encounters (or all consecutive echo patients) within the study window. We excluded RHD-only reports, case reports/series without denominators, convenience samples restricted to suspected CHD, studies without a clearly enumerated denominator, non-Nepal data, and duplicates/overlaps. No language or age restrictions were applied at search. Data extraction and quality assessment Two reviewers independently extracted: first author, year, setting and region/province, sampling frame (e.g., admitted neonates, schoolchildren, hospital/echo patients), age range, sample size (n), CHD cases, and lesion-level proportions for atrial septal defect (ASD) and ventricular septal defect (VSD). We coded: Site from the dataset mapping (CO = community, H = hospital/referral); inconsistent casing/whitespace were normalized and non-CO/H rows flagged. Urbanicity as Kathmandu Valley vs sub-urban/mixed using the region field. Age stratum (neonates/infants, children, general). Where only lesion percentages were reported, we reconstructed counts as round(% × CHD). Studies reporting multiple lesions per patient are excluded from patient-level pooling of lesion shares and lesion-specific population prevalence. Risk of bias assessment Two reviewers independently applied the JBI Critical Appraisal Checklist for Prevalence Studies (sampling frame/method, size, setting description, coverage, case identification/measurement, analysis, response rate/handling). Disagreements were resolved by discussion. We did not compute summary scores; studies were categorized qualitatively as low (≥7 “Yes”), moderate ( 4 – 6 ), or high (≤3). Domain-level judgments and overall categories are presented in Table 1 . View this table: View inline View popup Table 1. Characteristics of included studies. Columns: Author; Year; Setting (CO/H); Frame; Region/Province; Sample size (n); CHD cases; CHD per 1,000; Diagnostic method; Lesion reporting (patient-level vs “of lesions”). *multi-label lesion reporting; excluded from lesion share/population pooling . Data synthesis and statistical analysis Effect size and pooling model Prevalence was analyzed on the logit scale. The primary model was a binomial GLMM via meta::metaprop with: sm="PLOGIT"; study-level random effects; method.tau="ML". Zero-cell handling: GLMM used the binomial likelihood without continuity corrections; a small addition was enabled only for degenerate strata (all 0% or all 100%) using incr=0.5, method.incr="if0all". If GLMM was unsupported, we used inverse-variance PLOGIT with REML for τ 2 and Hartung– Knapp (HK) CIs. Estimates were back-transformed and also expressed per 1,000 persons. We report 95% CIs, 95% prediction intervals (PIs), and heterogeneity (I 2 , τ 2 on the logit scale). Subgroups and lesion outcomes Prespecified subgroups were site (CO vs H) and geography (Kathmandu Valley vs sub- urban/mixed). Lesion outcomes were synthesized as: Share among CHD (ASD or VSD cases ÷ CHD cases), patient-level only; Population prevalence (ASD or VSD cases ÷ n), patient-level only. Studies with multi-lesion reporting (>100% summed percentages) were analyzed descriptively as “of lesions” and excluded from these patient-level pools. Multi-lesion lesion studies were exludedfrom lesion shares & lesion population prevalence. Meta-regression (exploratory) To explore heterogeneity, we fit random-effects meta-regressions (metafor::rma) on the logit scale with Knapp–Hartung adjustment. The primary moderator was log10(sample size) (small-study effects). Site (H vs CO) was examined unadjusted and adjusted for log10(n); publication year was explored in sensitivity models when data permitted. A uniform continuity correction (+0.5 events, +1 total) was applied to compute logits/variances. Results are reported as log-odds and odds ratios (OR) with 95% CIs; bubble plots illustrate fitted relationships. Given k≈9, models are hypothesis-generating. Sensitivity analyses We compared GLMM vs inverse-variance + HK, performed leave-one-out influence analyses, and generated influence/Baujat plots (where k≥3). Funnel/Egger tests were not performed due to small k; potential small-study effects were assessed via the log10(n) moderator. We summarize pooled ranges from leave-one-out analyses and note whether any single study materially shifted estimates or heterogeneity. Certainty of evidence Certainty for prevalence outcomes was graded using GRADE tailored to prevalence, considering risk of bias, inconsistency/heterogeneity, imprecision (including PIs), indirectness, and publication bias (not formally tested due to small k). Overall certainty was typically low to very low. Software implementation Analyses were conducted in R using meta and metafor (data wrangling dplyr; figures ggplot2). Forests (zoomed per 1,000 axes), subgroup forests, meta-regression bubbles, and scatterplots were exported to PDF. Results Study selection and characteristics We identified 27 records (25 databases, 2 other sources). After removing 8 duplicates, 17 titles/abstracts were screened and 8 were excluded. We assessed 9 full texts; 7 met criteria. Two studies Gautam NC (2021)( 10 ) and Joshi A(2016)( 11 ) were excluded because they were descriptive study without denominators for evaluation. Study characteristics Nine studies met inclusion (N = 79,595; CHD cases = 462), spanning community/school screenings and hospital/referral cohorts. Frames were coded as community (CO) v hospital/referral (H) and by geography (Kathmandu Valley vs sub-urban/mixed). Studies with multi-lesion reporting (percentages summing >100%) were retained for overall CHD prevalence but excluded from patient-level lesion pooling ( Table 1 ). Overall prevalence The random-effects GLMM (logit scale) yielded an overall CHD prevalence of 8.90 per 1,000 (95% CI 3.00–26.11). Heterogeneity was extreme (I 2 ≈ 99.2%, τ 2 ≈ 2.765 [logit]), with very wide PIs, indicating substantial between-study dispersion ( Figure 2 ). Download figure Open in new tab Figure 1: PRISMA Flow Diagram. Schematic overview of the study identification, screening, eligibility assessment, and inclusion process. Download figure Open in new tab Figure 2: Forest plot of overall CHD prevalence (per 1,000, zoomed). I 2 and τ 2 displayed; PI reported in Table 2 . View this table: View inline View popup Table 2. Pooled CHD prevalence by subgroup. Columns: Subgroup | k | Pooled per 1,000 | 95% CI | 95% PI | I 2 (%) | τ 2 (logit). Note: Estimates are back-transformed from the logit scale; GLMM primary, Inverse+HK fallback; PI shows between-study dispersion. Subgroup analyses By study setting Pooled prevalence was higher descriptively in hospital/referral vs community cohorts: 25.64 per 1,000 (95% CI 5.40–113.16; I 2 ≈ 99.4%, τ 2 ≈ 2.558) vs 3.81 per 1,000 (95% CI 1.40–10.35; I 2 ≈ 98.8%, τ 2 ≈ 1.275), respectively ( Figures 3a – 3b ; Table 2 ). In exploratory meta-regression adjusting for log (sample size) (and year where possible), the hospital effect attenuated and was not statistically significant, while the inverse association with study size persisted. Download figure Open in new tab Figure 3a. CHD prevalence — Community (per 1,000, zoomed). I 2 and τ 2 displayed; PI in Table 2 . Download figure Open in new tab Figure 3b. CHD prevalence — Hospital (per 1,000, zoomed). I 2 and τ 2 displayed; PI in Table 2 . By geography Pooled prevalence in Kathmandu Valley (urban) was 5.69 per 1,000 (95% CI 1.29–24.77; I 2 ≈ 99.4%, τ 2 ≈ 2.845), compared with 15.46 per 1,000 (95% CI 3.74–61.70; I 2 ≈ 98.9%, τ 2 ≈ 2.108) in sub-urban/mixed strata; PIs were wide in both ( Figures 4a / 4b ). Download figure Open in new tab Figure 4a. CHD prevalence, Urban (Kathmandu Valley) (per 1,000, zoomed). I 2 and τ 2 displayed. Download figure Open in new tab Figure 4b. CHD prevalence , Sub-urban/mixed (per 1,000, zoomed). I 2 and τ 2 displayed. Download figure Open in new tab Figure 5a. VSD share among CHD, Community (proportion, zoomed). I 2 and τ 2 displayed. Download figure Open in new tab Figure 5b. VSD share among CHD, Hospital (proportion, zoomed). I 2 and τ 2 displayed. Download figure Open in new tab Figure 6. Overall Baujat plot (contribution to heterogeneity vs influence; no single dominant outlier). Lesion-level results Shares among CHD (patient-level only) Lesion distributions differed by setting. VSD share pooled ∼22–24% (low heterogeneity; I 2 ≈ 12.9%, τ 2 = 0) in community vs ∼38% (substantial heterogeneity; I 2 ≈ 81.1%, τ 2 ≈ 0.434) in hospital cohorts ( Figures 4a – 4b ; Table 3 ). ASD share was higher in community and lower in hospital, consistent with case-mix and referral patterns ( Table 3 ). View this table: View inline View popup Download powerpoint Table 3. ASD and VSD share among CHD by setting (patient-level only). Columns: Site | Lesion | k | Pooled % | 95% CI | 95% PI | I 2 (%) | τ 2 (logit). Footnote: Multi-lesion studies excluded from patient-level pooling. Population prevalence of lesions (patient-level only) Population-scale lesion estimates mirrored the site gradient. VSD pooled very low in community vs higher in hospital cohorts, with wide uncertainty ( Table 4 ). ASD estimates showed a similar pattern (higher in hospital than community), also with wide PIs ( Table 4 ). View this table: View inline View popup Table 4. Population prevalence of ASD and VSD by setting (per 1,000; patient-level only). Columns: Site | Lesion | k | Pooled per 1,000 | 95% CI | 95% PI | I 2 (%) | τ 2 (logit). Footnote: Multi-lesion (“of lesions”) studies excluded from patient-level pooling. Meta-regression and small-study effects Exploratory random-effects meta-regression (Knapp–Hartung) identified a strong inverse association between log (sample size) and prevalence (OR ≈ 0.10–0.12 per 10-fold increase). The hospital vs community effect was large but imprecise unadjusted and attenuated to non- significance after adjusting for log (n); year effects were negligible. Leave-one-out checks altered slope magnitude/direction in some models (small k), but conclusions were unchanged ( Table 5 ). View this table: View inline View popup Table 5 Random effects meta regression (Knapp–Hartung) View this table: View inline View popup Table 6: JBI domain summary by setting View this table: View inline View popup Table 7: Summary of Findings (GRADE) for key outcomes Sensitivity analyses Overall leave-one-out runs showed modest variation in the pooled estimate and τ 2 ; conclusions were unchanged. The overall Baujat plot indicated dispersed contributions to heterogeneity without a single dominant outlier ( Figs. 7 –8) Download figure Open in new tab Figure 7. Overall leave-one-out analysis (pooled estimate and τ 2 across omissions; conclusions unchanged). Baujat plots showed that contributions to heterogeneity were diffuse across strata; in overall analyses, Chapagain 2017 had a high heterogeneity contribution (but not singularly decisive), while in other strata single studies occasionally contributed more (e.g., Dongol 2018 or Shah 2008 in sub-urban) ( Figure 7 ). Leave-one-out analyses did not identify a single decisive study in any stratum. For All studies, τ 2 ranged approximately 2.01–3.11, I 2 remained ∼98.8–99.3%, and the pooled prevalence shifted modestly; community, hospital, urban, and sub-urban strata showed τ 2 ranges of roughly 0.65– 1.59, 2.09–2.47, 0.72–3.54, and 0.59–2.79, respectively, with I 2 remaining very high in all ( Figure 7 ). These diagnostics reinforce that conclusions are robust to single-study omission despite extreme heterogeneity. Agreement between GLMM and Inverse-variance + Hartung–Knapp across All/CO/H and Urban/Sub-urban strata further supports robustness; qualitative interpretations were unchanged. Risk of bias assessment Body-of-evidence appraisal with the JBI prevalence checklist indicated moderate-to-high risk of bias, driven by non-population sampling frames and uneven coverage in hospital/referral cohorts, and by inconsistent ascertainment across designs. Certainty of Evidence Given extreme heterogeneity and imprecision, GRADE certainty for prevalence outcomes was judged very low, with lesion-share estimates ranging low to very low depending on subgroup. Discussion This meta-analysis offers a clear, data-driven account of CHD in Nepal and explains why the estimates diverge so widely across studies. The principal finding was that CHD is not rare, but the quoted number depends heavily on where cases are ascertained and how studies are designed( 19 ). Interpretation of Principal Findings Three key observations anchor this evidence. First, the setting dominated the signal. The seven- fold difference between community (3.81/1,000) and hospital (25.64/1,000) estimates is the expected footprint of referral bias, case-mix, and more intensive diagnostic testing in clinical settings( 20 ). Second, geography adds to the dispersion. The higher pooled prevalence in suburban/mixed regions (15.46/1,000) versus Kathmandu Valley (5.69/1,000) likely reflects a combination of differing access to echocardiography, screening intensity, and study design choices rather than a single geographic effect. Third, lesion patterns differ by setting. The ASD- dominant profile in community screening and the higher VSD representation in hospitals are consistent with the referral of symptomatic or auscultation-positive lesions, highlighting how case-finding pathways shape the observed epidemiological picture of congenital heart disease. The extreme statistical heterogeneity (I 2 ≈99%) is not a flaw but a reflection of the real-world diversity in sampling frames, eligibility windows, and ascertainment methods( 21 ) ( 22 ). Our meta-regression clarifies these drivers. The large unadjusted effect of the hospital setting attenuated and lost precision after adjusting for the sample size. In contrast, the robust inverse association between sample size and reported prevalence across all models (OR ∼0.10–0.11) is a classic sign of small-study effects. This is precisely what is expected when smaller, often hospital-based studies with intensive, targeted echocardiography report large effects, whereas larger, population-based surveys with less sensitive screening report more conservative estimates. This pattern is highly suggestive of publication and design-related biases, indicating that the overall pooled estimate is likely inflated. The lack of a secular trend suggests that improvements in detection technology alone do not explain the observed spread of the disease( 23 ). Global congenital heart disease (CHD) prevalence has increased to 9.41 per 1000 live births in recent decades, largely due to improved detection of mild lesions rather than true epidemiological change. Regional variation is marked, with the lowest rates in Africa (2.32/1000) and the highest in Asia (9.34/1000) as per study done by Liu et al. ( 24 ). In Malaysia, prevalence reached 6.7/1000, with a high proportion of severe lesions( 25 ). Nepali studies show similar patterns but with greater variability. Referral-based and hospital studies report higher prevalence and predominantly acyanotic CHD. In contrast, community- based school surveys report lower prevalence, ranging from 1.3/1000 in Kathmandu valley to 3.92/1000 in central Nepal( 14 ). Such heterogeneity reflects differences in study design, diagnostic capacity, and setting, paralleling trends seen in other low- and middle-income countries. Overall, Nepal’s prevalence estimates fall within global ranges but underscore the need for standardized, population-based surveillance and improved detection outside urban centers. Implications for Research and Policy The core implication is that a single pooled prevalence is a poor summary of the policy. Instead, context-rich ranges must be used for the analysis. For Public Health Planning: Burden estimates should be anchored in community data, while hospital figures should be treated as upper bounds indicative of clinical case-mix and service demand. There is a clear need to strengthen the detection capacity outside urban centers through equitable access to echocardiography, standardized screening protocols, and integrated referral pathways. For Future Research: To improve comparability, future studies must clearly define their sampling frame, use standardized ascertainment protocols, report complete lesion- specific counts, and be adequately powered. A coordinated national program of population-based CHD surveillance using harmonized echocardiography criteria is urgently needed to generate reliable and decision-relevant data. Strengths and Limitations The strengths of this review include its comprehensive search, use of advanced statistical models (GLMM), predefined stratification, lesion-level synthesis, and meta-regression that quantifies key sources of bias. Despite very high heterogeneity and low certainty, our analyses using GLMM, wide prediction intervals, and multiple robustness checks demonstrate that study frame and size are the dominant drivers of reported CHD prevalence in Nepal, and community-based estimates remain the most informative anchor for public health planning. Limitations are inherent to the primary evidence base, including a small number of studies in some strata, mixed sampling frames, reconstructed lesion counts, and persistent, residual heterogeneity. The risk of bias was generally moderate to high, and the certainty of evidence (GRADE) for prevalence estimates was very low. Conclusion In conclusion, the prevalence of CHD in Nepal is heterogeneous. It is highest in referral settings, lowest in community sampling, and strongly modulated by the study size and ascertainment intensity. An appropriate summary for policy is not a single number but a framework of stratified estimates that acknowledges this context. Investing in standardized, population-based detection, especially outside the Kathmandu Valley, is crucial to establish the true prevalence, shorten diagnostic delays, and enable rational planning of Nepal’s cardiac care services. Data Availability All data produced in the present study are available upon reasonable request to the authors Abbreviations ASD Atrial septal defect CHD Congenital heart disease CI Confidence interval CO Community (study setting) CRediT Contributor Roles Taxonomy GLMM Generalized linear mixed model GRADE Grading of Recommendations, Assessment, Development and Evaluations H Hospital or referral (study setting) HK Hartung-Knapp (adjustment for random-effects CIs) I 2 Higgins’ I-squared heterogeneity statistic JBI Joanna Briggs Institute KV Kathmandu Valley (urban stratum) LMIC(s) Low- and middle-income country(ies) LOO Leave-one-out (sensitivity analysis) ML Maximum likelihood (estimator for tau-squared in GLMM) N Total pooled sample size across studies n Sample size within a single study OR Odds ratio PI Prediction interval PLOGIT Logit transformation for proportions PPI Patient and public involvement PRISMA Preferred Reporting Items for Systematic Reviews and Meta-Analyses PROSPERO International Prospective Register of Systematic Reviews REML Restricted maximum likelihood (estimator for tau-squared) RHD Rheumatic heart disease RoB Risk of bias SoF Summary of Findings (GRADE table) tau-squared (τ 2 ) Between-study variance in random-effects meta-analysis VSD Ventricular septal defect Declarations Ethics approval and consent to participate Not applicable. This meta-analysis is based on the synthesis of previously published data and does not involve direct human or animal participation. Consent for publication Not applicable. This manuscript does not contain any individual person’s data in any form. Availability of data and materials Derived tables/figures (CSV/Excel/PDF) and R code are available on request to the corresponding author. Competing interests The authors declare that they have no competing interests. Funding The authors received no specific funding for this work. Authors’ contributions Authors’ contributions (CRediT): Conceptualization/Methodology/Guarantor - Sandip Pandey; Screening/Data curation - Apil Tiwari, Alisha Bhattarai; Risk of bias - Anu Timalsina; Data extraction/Figures - Asmit Pandey; Formal analysis/Visualization - Aakash Neupane; Supervision/Methodology-Deepak Jung Subedi; Writing - original draft - Sandip Pandey; Writing - review & editing - all authors. 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