Predictors of time to diagnosis in monogenic neurological disorders from a referral center in Peru

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We estimated time to diagnosis (TTD) and associated factors in 248 adult individuals with MNDs from a national neurogenetic referral center in Peru. A questionnaire-based study was conducted from February 2024 to March 2025, assessing demographics, clinical features, and diagnostic timelines. The mean TTD in this cohort was 8.5 years, with a health system delay (HSD) of 4.3 years. Factors associated with longer TTD included employment status, healthcare facility, and restricted access to physicians. Distance to the diagnostic center showed no association with TTD, and geospatial analysis showed no global or local clustering, suggesting diagnostic delays are primarily driven by systemic and social rather than geographic factors. Future initiatives should address these barriers to enable earlier diagnosis and improve prognostic outcomes for patients with MNDs. Health sciences/Health care Health sciences/Medical research Health sciences/Neurology Biological sciences/Neuroscience Figures Figure 1 Figure 2 Figure 3 Introduction The care of individuals with rare diseases (RDs) requires long-term and multidisciplinary coordination, inclusive of comprehensive clinical assessment and genomic testing to obtain a definitive diagnosis. RDs are individually rare but collectively common - with reported geographic prevalence fewer than 1 in 2,000 people reported in World Health Organization (WHO) regions 1,2 , < 5 in 10,000 in Europe, and 1 in 100,000 in Peru 3,4 - and face substantial health-related barriers, often limiting access to care and full participation in society. At least 80% of RDs are genetic in origin, with over ~6500 entities, mainly classified as monogenic, of which approximately 17% are neurological disorders 2,5 . Monogenic neurological disorders (MNDs) are rare and complex diseases affecting the nervous system and its networks, with a prevalence of approximately 90 per 100,000 inhabitants 6 . Most repeat expansion disorders are MNDs, including Huntington's disease (HD), many inherited ataxias, and myotonic dystrophy type 1 (DM1) 7 . Specific genetic assessments are required for the diagnosis of these disorders such as motif repeat estimation based on PCR-related tests, or short or long-read sequencing 8 . A notable proportion of MNDs are caused by single nucleotide variants (SNVs) with clinical exome sequencing yields ranging from 27-32.7% 9,10 . A further 10-20% are caused by copy number variants (CNVs) 11 , which are generally captured by microarrays, targeted Sanger or short-read sequencing, including whole-exome sequencing (WES), or whole-genome sequencing (WGS) for confirmatory diagnosis 12,13 . The wide range of diagnostic modalities can add complexity to the clinical workup and the subsequent return of results 14 . Genetic diseases often face prolonged time to diagnosis due to clinical, sociodemographic, and economic factors. Time to diagnosis (TTD) is the period of time between the first medical consultation and the confirmed diagnosis, reflecting both patient delays and health system delays (HSD) 15 . In RDs, it can range from 4 to 10 years from age at onset to confirmatory genetic diagnosis 15,16 . Some studies performed in MNDs, estimate a TTD of about 5 years for monogenic diseases 17 . The International Rare Disorders Association has proposed a goal of one year total time to diagnose RDs by 2027 18 . Most studies analyzing TTD in RDs are performed in upper and upper-middle-income countries, in contrast to very few published studies from developing countries 17,19 . Peru, a Latin American middle-income country, faces significant difficulties in accessing healthcare services, which may negatively impact the TTD for MNDs and other rare disorders 20 . The public healthcare system in Peru has a national referral center for neurogenetic disease care that provides both outpatient consultations and genetic testing for some MNDs, based in Lima, the capital city. Over the past decade, this center has evolved its genetic testing capabilities, transitioning from in-house single-gene testing approaches to deliver genetic reports through next-generation sequencing (NGS), including WES or WGS provided by the iHope philanthropic program 21 , significantly expanding its diagnostic capacity. Despite this progress, delays in diagnosis remain frequent, underscoring the need to identify underlying factors affecting TTD. We aim to estimate TTD and associated factors in a cohort of people living with MNDs evaluated at this specialized tertiary neurogenetic center in Peru. Results A total of 248 participants (221 families) were enrolled in the study. Repeat expansion disorders with HD (52.8%), DM1 (18.5%) and inherited ataxias (11.2%) were the most frequent diagnoses in the cohort. Among HD cases, the phenotype distribution included three juvenile cases ( 50 years). The remaining “Other” group comprised a heterogeneous cluster of approximately 20 different MNDs. The genetic diagnostic techniques applied in our cohort included PCR based procedures (207, 83.47%), mainly for repeat expansion diseases, gene-targeted tests (4, 1.61%), WES (5, 2.02%) and WGS (32, 12.90%). Detailed data by diagnosis, including the proportion of participants for each genetic disorder is listed in the Supplementary information. Global characteristics of the population, including demographics and social determinants may impact the time to various clinical diagnostic milestones (first symptom, first medical evaluation, clinical diagnosis, and genetic diagnosis) are described in Table 1 and Table 2, respectively. In addition, we describe the change of selected social determinants of health over time, including health insurance, medical center type, and occupation over time ( Figure 1) . Geographically, most of the participants were located in Lima, the capital city (72%) (Figure 2). Overall, the TTD within this cohort was 8.5 ± 8.4 years, median 6 [IQR 2-12] and range 0-47 years. The mean HSD was 4.3 ± 6.6 years, median 2 [IQR 1-6] and range 0-45 years. The overall TTD, HSD, together with other milestone periods are shown in Figure 3 . The multivariable linear regression showed that a larger number of physicians involved in care, earlier decades of symptom onset, healthcare center type other than national hospital at first medical evaluation, unemployed/retired status at the time of genetic diagnosis, and diagnosis through WES/WGS were significantly associated with longer TTD; whereas unemployed/retired status at first symptom were significantly associated with shorter TTD (Table 3). We identified predictors associated with longer HSD (Table 4) which included the number of physicians involved in care and the decade of symptom onset were associated with longer delays, while health professionals involved at first evaluation, and positive family history were associated with shorter HSD. Pearson correlation and linear regression were used to evaluate whether greater geographic distance from residence at symptom onset to the referral center was associated with longer TTD. These analyses showed no significant association, indicating that patients living farther away did not experience longer TTD. Spatial lag and spatial error models were applied to assess whether TTD values were influenced by those of nearby locations. These models detected statistically significant spatial dependence. However, this statistical dependence did not translate into recognizable geographic patterns. Moran’s I, which assesses high or low TTD values cluster across the study area, showed no evidence of global spatial autocorrelation. Local Gi* analysis, which identifies specific geographic areas with consistently longer or shorter TTD, similarly revealed no localized clusters. Geodesic correlation, which directly evaluates whether individuals living closer to one another have more similar TTD values, showed no significant association (Table 5) . Discussion Our study identified an overall mean TTD of 8.5 ± 8.4 years among 248 cases affected with MNDs. Number of physicians, decade of symptom onset, unemployment rate and healthcare center type were identified as the most significant predictors of the global TTD. Individuals residing in Lima and other major cities have easier access to specialized care and genetic testing compared to those living in more distant regions, where geographical, socio-economic, and cultural barriers are predominant, such as the high Andean communities or the Amazon Jungle 22 . Rural health centers are frequently limited in infrastructure, resources and specialized personnel necessary for timely diagnosis and treatment, which exacerbates health disparities 23 . The TTD in our cohort was longer compared to other Spanish and European cohorts. The TTD in our study was 8.5 ± 8.4 years, longer than the TTD reported in the Spanish Rare Diseases Patient Registry, where the average time to diagnosis was 6.18 years (median 2, IQR 0.2-7.5) 16 , and almost twice the time reported by the EURODIS Rare Barometer conducted across 41 European Union countries 15 . In our study, 33.5% of the cases had a TTD of 10 years or more - a notable difference in diagnosis assessments - reflecting disparities in healthcare access in contrast with the 21% of cases found in the Spanish RD cohort 16 . The extended TTD in our cohort underscores persistent systemic barriers that continue to hinder timely diagnostic confirmation of RDs. We found a HSD of 4.3 ± 6.6 years, similar to the 4.3 years reported in the EURODIS rare barometer for the same period of time 15,24 . This is likely due to limited access to specialized healthcare services and diagnostics 25 . For example, one participant with ataxia with oculomotor apraxia - recently published as a case report - had a TTD period of over 40 years. During this period, they received multiple misdiagnoses before being diagnosed through a philanthropic program that provided access to proper genetic testing 26 . The TTD varies depending on the specific monogenic neurogenetic disorder. In this cohort, HD was the most frequently diagnosed neurogenetic condition (52.8%), followed by DM1 (18.1%) and inherited ataxias (12.9%). Notably, this tertiary neurogenetics center specializes in repeat expansion disorders, including HD, DM1, and dominant ataxias 27,28 . For the HD group, the TTD was 5.9 ± 5.9 years (median: 4 years, IQR: 2–7.5, range 0-31), consistent with data from the European Huntington’s Disease Network (EHDN) registry 29 , and a recent preliminary report from the Latin American Enroll-HD global registry 30 . The annual cost of HD for the Peruvian population was estimated at approximately USD 1.2 million in 2015, including about USD 85,000 per year in direct healthcare expenditures. Consequently, the financial burden primarily falls on patients and their families, as they personally cover 98.2% of direct healthcare-related expenses, resulting in substantial out-of-pocket costs to access medical care and specialized treatment 31 . In the DM1 group, the TTD was 8.9 ± 8.2 years (median: 9 years, IQR: 2–14), which aligns reasonably well with findings from an earlier U.S. cohort of DM1, reporting a TTD of 7.3 ± 8.2 years 32 . For inherited ataxias, the TTD averaged 9.3 ± 9.8 years (median: 6 years, IQR: 2.8–11, range 0-38). Historically, prior to the 2000s, a TTD of approximately 6 years was reported for ataxias 33 . However, likely due to the increasing use of molecular diagnostic tools, disorders such as Friedreich’s ataxia (FRDA) have shown a TTD as short as 2 years 34 . Given the genetic heterogeneity of ataxias, particularly for spinocerebellar ataxias (SCAs) and rarer variants (e.g., SCA11, SCA13, SCA14), previous studies have reported broader TTDs ranging from 3 to 45 years, with a median of 18.1 years 35 . For other miscellaneous monogenic neurogenetic disorders, the TTD was 15.7 ± 10.1 years (median: 15 years, IQR: 8–19, range 1-47). This group, representing 12.9% of our cohort, includes several early-onset cases with particularly long diagnostic odysseys, which were diagnosed by WEG/WGS, mostly through international philanthropic programs like iHope or other collaborative networks 21 . Unemployment status is significantly associated with TTD in the Peruvian MNDs cohort. As shown in Figure 1 , unemployment status increases with disease progression, possibly related to the effect of the MND on motor, cognitive and functional capacity 36 . We found that being unemployed at the time of genetic testing was a predictor of longer TTD compared to those with employment. This may be due to the financial constraints unemployment imposes, limiting access to healthcare services. Although Peru’s universal healthcare system (SIS) provides coverage, patients and families still face significant out-of-pocket expenses 37 . To mitigate these challenges, social programs oriented to specific vulnerable populations have been implemented to provide partial support to vulnerable populations with disabilities 38 . In contrast, unemployment at the onset of the first MND-related symptom was associated with a shorter TTD. This could reflect the fact that unemployed individuals in the early stages of disease have more time and flexibility to seek medical care. However, other factors such as differences in social support, health literacy, or psychosocial stressors related to unemployment that were not analyzed in this study could also influence this association 39 . Patients first evaluated in a primary care center tended to experience longer TTD. This pattern may be explained by systemic healthcare challenges such as resource centralization in the capital and major regional cities, bureaucratic referral systems, and a lack of interoperability between healthcare subsystems 40 . Additionally, 66.1% of our participants were initially misdiagnosed at the first medical evaluation (Table 2) , presumedly due to limited training opportunities in rare disorders in these centers. To address this gap, the Ministry of Health has recently launched pilot initiatives to provide training on rare disorders for primary care professionals through the ENSAP academic program 41 . In contrast, a subgroup of HD patients received timely clinical and genetic diagnoses at primary care centers through a sustainable extramural program led by a tertiary neurological center. This program included a mobile clinic that provided specialized outpatient consultations, sample collection for genetic testing, and on-site genetic counseling 42 . Furthermore, 9.2% of patients in our cohort consulted more than five physicians before receiving a diagnosis. A higher number of physician consultations was associated with both a longer TTD and a longer healthcare-seeking duration. This finding may reflect the high misdiagnosis rate of RDs, potentially driven by the deficiencies in healthcare organization for patient referral to specialized facilities and by the difficulties they face when traveling to distant centers, but could also indicate the complexity of establishing a strong patient-physician relationship when navigating a RD 23,43 . Additionally, many rare diseases are frequently misdiagnosed as psychiatric conditions, delaying accurate diagnosis and treatment 44 . The limited consultation time available to healthcare professionals may represent another factor affecting quality of care, which varies depending on the specialty. The causal variant type for MND affects TTD in our cohort.Since our center is the national referral center and central molecular lab for these disorders in Peru, the most prevalent diseases in our cohort were repeat expansion disorders such as HD, DM1 and inherited ataxias 28 . Each specific repeat expansion disorder encompasses particular diagnostic challenges. Early or late-onset HD presentations differ from classic adult-onset forms, and are more challenging to diagnose, usually contributing to longer TTD 45,46 ; however, we could not replicate this observation given the limited sample size in the juvenile (under 20 years) HD subgroup within our cohort (only three cases). The specific molecular tool employed differs based on the type of genetic variant causing a MND and the availability of each technique, thus in this cohort the first implemented techniques were PCR-based genotyping procedures, with manual sizing models using gel electrophoresis further evolving to capillary electrophoresis, as well as MLPA/RFLP techniques and sanger sequencing. There are not short read sequencing technologies available at this referral center, but the implementation of collaborative diagnostic programs like iHope have increased the capabilities for genetic testing in this vulnerable population 21 . The advancement of genetic testing services in Peru, along with global collaborative efforts, has contributed to a shorter TTD. As shown in Table 3 , patients who developed symptoms before 2010 experienced significantly longer TTD, possibly due to the gradual expansion of genetic testing availability. Although clinical evaluations for neurogenetic diseases in Peru were first established in 1995 27 , genetic testing capabilities progressed more slowly. For instance, while the genetic cause of HD was identified in 1993, Peru’s first HD genetic test was introduced in 2004 using an in-house PCR-based method, which was later refined and transitioned to capillary electrophoresis by 2017 47,48 . A partnership in 2014 with a Brazilian institution enabled the diagnosis of SCAs and FRDA, and facilitated technology transfer, allowing the local center to diagnose at least six different SCAs by 2017 49 . The genetic testing for FRDA became available in Peru in 2023. More recently, the implementation of the iHope philanthropic program, which provides free WGS and WES for individuals with early-onset neurogenetic disorders, has facilitated the diagnosis of many previously unsolved cases. Some patients had been awaiting a definitive diagnosis for decades before benefiting from this initiative 50,21 .In contrast to previous publications, GIS analysis did not show significant associations 51 . Paradoxically, geographic disparities in access to specialized health care, a very factor we aimed to explore, may limit the detection of spatial effects, as individuals outside the capital may be underdiagnosed and thus underrepresented in our cohort. Strengths and Limitations. This study provides valuable insights into an underrepresented population, and helps address a diagnostic gap that has not been previously characterized. However, important limitations must be acknowledged. Although data were obtained from a national and unique genetics testing reference center for MNDs, they derive from a single institution and may not fully represent the broader Peruvian population. Pediatric cases were excluded, as our center primarily serves adults. We enrolled all available patients who were willing to participate, which limited a proper sample size calculation analysis and introduced the possibility of selection bias. Limited diagnostic resources and the absence of standardized screening for rare diseases may have led to underdiagnosis of other neurogenetic conditions in Peru. Lastly, diagnostic intervals were measured in years, which limited the detection of nuanced variations in diagnostic delay. Conclusion In this Peruvian cohort of 248 individuals affected by monogenic neurological disorders, we identified a prolonged mean time to diagnosis of 8.5 ± 8.4 years. Diagnostic delay was primarily associated with the healthcare system and socio-economic factors, including the number of physicians consulted, decade of symptom onset, unemployment status and type of healthcare center. In contrast to other reports, geographic disparities were not observed, as no statistically significant differences in time to diagnosis were found according to geographic location or distance from the main diagnostic center. These findings remain consistent with barriers commonly reported in resource-limited settings, such as constrained diagnostic capacity outside major urban centers and insufficient implementation of existing regulatory frameworks. The establishment of a national network for neurogenetic and rare disease care coordinated by specialized hub centers, alongside quality-assured genetic testing strategies supported by multidisciplinary teams and sustained public investment in essential diagnostic infrastructure, may improve diagnostic efficiency. Additional priorities include strengthening training in neurogenetics and genetic counseling, implementing national clinical guidelines, and fostering strategic international collaborations, such as iHope, to promote more equitable access in these regions. Methods A cross-sectional questionnaire-based study was conducted from February 2024 to March 2025 at the Neurogenetics Research Center, Instituto Nacional de Ciencias Neurológicas (NRC-INCN), a national reference center for neurogenetics in Peru. After an appropriate informed consent, an adapted and revised 60-question questionnaire was applied to individuals older than 18 years of age of both sexes affected by genetically confirmed neurogenetic diseases. From a total of 1313 patients followed up in the studied period (497 of them with confirmatory genetic diagnosis), a total of 248 participants were invited to participate during follow-up consultations, and all of them, after appropriate informed consent process, were enrolled. Data were collected using paper-based questionnaires administered to participants, in person or remotely, depending on preference and availability. Upon completion, all responses were entered into REDCap (Research Electronic Data Capture) for data storage and management. Setting. The study was conducted at NRC-INCN, a tertiary care public institution specializing in neurogenetics, located in Lima, the capital city of the country. Peru, a middle-income country in South America with nearly 33 million people, exhibits significant population centralization in Lima, which hosts over one-third of its residents 52 . This concentration is also reflected in the distribution of health services, as most tertiary public institutions offering genetic testing are located in the capital. This remains fragmented into four public and one private subsystems that operate independently, resulting in access, coverage and quality of care disparities 53–55 . This fragmentation and unequal distribution of healthcare services not only limits access to specialized diagnostics but also affects the availability of centers dedicated to rare diseases 25 . Peru presents significant disparities in health coverage with an average of 16.8 physicians per 10,000 inhabitants, but its number decreased drastically in regions with higher poverty rates and lower health investment 55 . The universal health insurance (SIS) coverage was progressively implemented by the government in 2019, progressively covering more than 70% of the Peruvian population 56,57 . In this context, the NRC-INCN offers specialized outpatient consultations and genetic testing for neurogenetic disorders. In-house genetic testing for repeat expansion disorders is available, including HD (2004), SCAs (2015), and DM1 (2015). Diagnostic capacity has improved through joint initiatives and philanthropic support, such as the iHope program 50,58 . The questionnaire. A preliminary questionnaire was adapted from previous instruments assessing diagnostic delay in Peruvian tuberculosis patients 59 , and determinants of delayed diagnosis in hereditary ataxias 60 . A local panel of 3 experts in neurogenetics and rare disorders reviewed the draft questionnaire. The revised version was piloted in a sample of 10 individuals for cultural and linguistic adaptation. The revised and adapted questionnaire consisted of a set of 60 structured questions organized into the following sections: a) Demographics: gender, age, education level, and location. b) Diagnosis and clinical features: age of symptom onset, age at a clinical and molecular diagnosis, code of the disease (ICD-10, OMIM, ORPHANET). c) Epidemiological information: health insurance, employment, civil status, disability level, family income, etc. And d) Diagnostic-related dates, including date of the first symptom, first consultation, clinical diagnosis and genetic diagnosis. Ethical aspects . This study was approved by the local ethics committee at Universidad Cientifica del Sur (131-CIEI-CIENTIFICA-2023) and INCN (001-2024-CIEI-INCN). All participants provided written informed consent. We confirm that we have read the journal’s position on issues involved in ethical publication and affirm that this work is consistent with those guidelines. Outcome and study variables The main outcome variable is Time to diagnosis (TTD), defined as the number of years between the first related symptom and genetic confirmation of the diagnosis. We also explored other relevant milestones, including time from first symptom to first ever medical evaluation related to the rare disease, and time from first symptom to clinical diagnosis. In addition, we included the time from the first medical evaluation to the genetic diagnosis as a surrogate of HSD 15 . Misdiagnosis was defined as the percentage of patients whose initial clinical diagnosis differed from the genetic test results. Statistical analysis We used descriptive statistics to evaluate the characteristics of the study population. For categorical variables, we used total counts and percentages, and for continuous variables we used medians and interquartile range (IQR) and mean and standard deviation (SD). Multivariable logistic, linear, and Cox regression were used to generate prediction models for the outcome time to diagnosis. Continuous outcomes were log-transformed before fitting the regressions and back-transformed by exponentiation when reporting the results. Models were chosen using bidirectional stepwise procedures with the Akaike Information Criteria (AIC) or the Bayesian Information Criteria (BIC) when stricter penalization was needed (i.e. less than five events). A set of “candidate” variables were chosen a priori based on clinical relevance by experts in the field. Furthermore, we conducted sensitivity analyses for other time periods, as well as differentiating by specific diagnosis, and by geographical location. Alluvial plots were generated to visualize transitions between key variables across relevant timepoints. Geospatial analysis (GIS) was conducted using the Ubigeo codes of the participants at the different diagnosis timepoints, and administrative boundaries as well as other geographic data obtained from publicly available files ( https://www.gob.pe/inei/ ). Ubigeo codes were transformed into spatial points. Spatial associations were evaluated through spatial lag and spatial error models, and we examined the significance of spatial parameters (ρ for lag, λ for error). Clustering and hotspot analysis were conducted using the Moran’s I and Getis-Ord Gi* statistics. To assess the effect of geodesic distance from patients’ location to the neurogenetic center, we computed Pearson correlation and fit a linear regression. Analyses and GIS figures were conducted using R software version 4.5.0. Declarations Data availability The datasets analyzed in the study are available upon reasonable request to the corresponding author. Due to ethical and privacy restrictions, certain sensitive data cannot be shared publicly. Code availability The code used for statistical analysis and data management in this study is available upon reasonable request to the corresponding author. R version 4.5.0 was used for data processing and analysis. Acknowledgments This study was supported by Universidad Cientifica del Sur , Fondo Semilla Docente (003-DGIDI-CIENTIFICA-2023). We express our gratitude to the participants and their families for their invaluable contribution to this research, and the Neurogenetics Research Center at the Instituto Nacional de Ciencias Neurológicas for their logistics support. Genetic testing was provided at no-cost to 37 cases in this cohort through the iHope program. Author contributions MI-M. and MC-O. conceptualized and designed the study. MS-B., RY-C., and BT-P. coordinated data collection and participated in patient recruitment and clinical evaluations. M.G.-C. and MI-M. performed the statistical analysis. MI-M., MC-O., MS-B., RAY-C., BT-P., M.G.-C., AR-V., ES-C., PG-D., AM-P., AA., EV., and RT. contributed to data selection and manuscript preparation. MI-M., MC-O., AR-V., and ES-C. contributed their expertise in genetic diagnosis and methodology. MC-O. served as the senior author, supervised the study, and critically revised the manuscript. All authors reviewed and approved the final version of the manuscript. Competing Interests Maryenela Illanes-Manrique, Mario Cornejo-Olivas, Robinson Yrene-Cubas, Milagros Galecio-Castillo, Andrea Rivera-Valdivia, Elison Sarapura-Castro, Pilar Girón-Davila, Alid Manrique-Palomino, and Alonso Abad declare no competing interests. Data from a subgroup of participants with Huntington’s disease were generated as part of an undergraduate dissertation conducted by Midiam Silva-Bullón and Brylianna Toledo-Pacheco, with a corresponding manuscript currently accepted for publication at a Peruvian journal. Ryan Taft, Akanchha Kesari, and Erin Venti were employees of and stockholders in Illumina, Inc. at the time of this investigation. Additionally, the iHope program was funded exclusively by Illumina, Inc. through March 2024 and in part by Illumina, Inc. thereafter. Declaration of generative AI and AI-assisted technologies During the preparation of this manuscript, the authors used ChatGPT to support language editing and improve clarity and readability. All content was subsequently reviewed and revised by the authors, who take full responsibility for the accuracy and integrity of the final published work. References The Lancet Global Health, null. The landscape for rare diseases in 2024. Lancet Glob. 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Observing Huntington’s Disease: the European Huntington’s Disease Network’s REGISTRY. PLoS Curr. 2 , RRN1184 (2010). Mendizabal, A., Ogilvie, A. C., Bordelon, Y., Perlman, S. L. & Brown, A. Racial Disparities in Time to Huntington Disease Diagnosis in North America. Neurol. Clin. Pract. 14 , e200344 (2024). Silva-Paredes, G., Urbanos-Garrido, R. M., Inca-Martinez, M., Rabinowitz, D. & Cornejo-Olivas, M. R. Economic burden of Huntington’s disease in Peru. BMC Health Serv. Res. 19 , 1017 (2019). Hilbert, J. E. et al. Diagnostic odyssey of patients with myotonic dystrophy. J. Neurol. 260 , 2497–2504 (2013). Leone, M. et al. [The diagnostic course in patients with hereditary ataxias and hereditary spastic paraparesis]. Minerva Med. 83 , 421–426 (1992). Indelicato, E. et al. Onset features and time to diagnosis in Friedreich’s Ataxia. Orphanet J. Rare Dis. 15 , 198 (2020). Németh, A. H. et al. 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Bonadonna, L. V., Saunders, M. J., Guio, H., Zegarra, R. & Evans, C. A. Socioeconomic and Behavioral Factors Associated with Tuberculosis Diagnostic Delay in Lima, Peru. Am. J. Trop. Med. Hyg. 98 , 1614–1623 (2018). Dos Santos Pinheiro, J. et al. Diagnostic Delay of Hereditary Ataxias in Brazil: the Case of Machado-Joseph Disease. Cerebellum Lond. Engl. 22 , 348–354 (2023). Tables Table 1. Population characteristics Characteristics No. (%) / Median [IQR] Age at interview 46 [36-56.0] Female 126 (50.8%) Marital status Single Married/cohabiting Others (widow, divorced) 132 (53.2%) 72 (29.0%) 44 (17.7%) Occupation Student Unemployed Part-time employed Fully employed Homemaker Retired 12 (4.8%) 113 (45.6%) 28 (11.3%) 34 (13.7%) 35 (14.1%) 26 (10.5%) Years of education 11 [11-14] Healthcare Insurance Universal health insurance (SIS) Social health insurance (ESSALUD) Police/Military forces Private 179 (72.2%) 58 (23.4%) 2 (0.8%) 9 (3.6%) Household income# (PEN=0.27 USD) 2748,6 - 6690,8 PEN 1,479.0 - 2,748.6 PEN 810.9 - 1,479.0 PEN < 810.9 PEN Others 34 (13.7%) 73 (29.4%) 86 (34.7%) 44 (17.7%) 11 (4.4%) Number of Household Members 4 [3-5] A family member with the MNDs 177 (71.4%) Number of affected family members 2 [1-4] Number of participants who migrated to obtain a diagnosis 68 (27.4%) Diagnosis Huntington Disease Myotonic Dystrophy type 1 Inherited ataxias* Others** 131 (52.8%) 46 (18.1%) 28 (12.9%) 43 (16.1%) Genetic testing methodology PCR based MLPA/RFLP NGS/WGS Unknown 201 (81%) 5 (2%) 41 (16.5%) 1 (0.4%) #INEI.Peruvian household income classification considers 4 levels. * SCAs (SCA2, MJD/SCA3, SCA8, SCA10), Friedreich’s ataxia (FRDA), Autosomal Recessive Spastic Ataxia of Charlevoix-Saguenay (ARSACS), Ataxia- telangiectasia. ** X-Linked Adrenoleukodystrophy, Spinal Muscular Atrophy, Primary Coenzyme Q10 deficiency, Limb Girdle Muscular Dystrophy, Dopa-responsive dystonia, Early-Onset primary dystonia, Becker Muscular Dystrophy, Facioscapulohumeral Muscular Dystrophy, Myoclonic Epilepsy with Ragged Red Fibers (MERRF), Myopathy, Hereditary spastic paraplegia, Primary mitochondrial diseases, Fahr syndrome, Leigh syndrome, Neurodegeneration with Ataxia, Dystonia, and Gaze Palsy (NADGP ) , Noonan syndrome, inherited disorders of Sulfur Amino Acid (SAA). *** Abbr.: PEN: Peruvian Sol; USD United States Dollar. MLPA, Multiplex Ligation-dependent Probe Amplification; RFLP, Restriction Fragment Length Polymorphism; NGS, next-generation sequencing; WGS, whole genome sequencing; PCR, Polymerase Chain Reaction. Table 2. Population characteristics at each milestone. Characteristics median [IQR] or n (%) At first symptom First medical evaluation Clinical Diagnosis Genetic Diagnosis Age, years 36 [21 - 46] 38 [27 - 48] 40 [31 - 51] 41 [32 - 52] Year 2013 [2007 - 2017] 2017 [2012 - 2021] 2019 [2016 - 2022] 2021 [2017 - 2023] Medical insurance SIS 141 (56.9%) 160 (64.5%) 173 (69.8%) 177 (71.4%) ESSALUD 77 (31.0%) 73 (29.4%) 60 (24.2%) 59 (23.8%) Private 11 (4.4%) 12 (4.8%) 13 (5.2%) 9 (3.6%) Police/Military forces 2 (0.8%) 2 (0.8%) 2 (0.8%) 2 (0.8%) Other 17 (6.9%) 1 (0.4%) 0 (0.0%) 0 (0.0%) Residency setting Rural 25 (10.1%) 22 (8.9%) 15 (6.0%) 15 (6.0%) Urban 223 (89.9%) 226 (91.1%) 233 (94.0%) 233 (94.0%) Employment Student 6 (2.4%) 4 (1.6%) 3 (1.2%) 18 (7.3%) Full-time 90 (36.3%) 75 (30.2%) 54 (21.8%) 46 (18.5%) Part-time 106 (42.7%) 88 (35.5%) 81 (32.7%) 44 (17.7%) Unemployed 45 (18.1%) 80 (32.3%) 108 (43.5%) 98 (39.5%) Retired 1 (0.4%) 1 (0.4%) 1 (0.4%) 13 (5.2%) Homemaker NA NA 1 (0.4%) 29 (11.7%) Medical Center category Private NA 41 (16.5%) 11 (4.4%) 3 (1.2%) Public NA 207 (83.5%) 237 (95.6%) 245 (98.8%) Type of Medical Center Primary care center NA 72 (29.0%) 1 (0.4%) 0 (0.0%) Private care centers NA 26 (10.5%) 7 (2.8%) 0 (0.0%) Regional hospital NA 79 (31.9%) 12 (4.8%) 1 (0.4%) National hospital NA 71 (28.6%) 228 (91.9%) 247 (99.6%) Care provider Specialist, physician NA 158 (63.7%) 246 (99.2%) 248 (100%) Primary care physician NA 90 (36.3%) 2 (0.8%) 0 (0.0%) Misdiagnosis NA 164 (66.1%) 2 (0.8%) NA Abbr.: IQR, interquartile range; SIS: Universal Health Insurance; ESSALUD: Social Health Insurance; NA: non-applicable. Other: Newborn, Infant. Table 3. Predictors of time to diagnosis. Predictor Adjusted Ratio (95% CI) p -value Employment status at first symptom Unemployed/retired 0.7 (0.5 - 0.9) <0.002 Partial employment (including homemaker, student) 1.0 (0.9 - 1.3) 0.649 Full employment Ref. Ref. Employment status at the time of genetic diagnosis Unemployed/retired 1.4 (1.1 - 1.7) <0.009 Partial employment 1.2 (1.0 - 1.5) 0.209 Full employment Ref. Ref. Molecular genetic technique MLPA/RFLP 0.8 (0.5 - 1.5) 0.504 NGS/WGS 1.4 (1.1 - 1.8) 0.001 PCR-based Ref. Ref. Number of physicians 1.1 (1.0 - 1.1) <0.001 Decade of symptom onset <2000 9.1 (6.3 - 13.1) <0.001 2001 - 2010 3.7 (2.8 - 5.0) <0.001 2011 - 2020 2.0 (1.6 - 2.7) 2020 Ref. Ref. Healthcare center type at first medical evaluation Primary care center 1.2 (1.0 - 1.5) 0.037 Private care center 1.4 (1.1 - 1.9) 0.012 Regional hospital 1.3 (1.1 - 1.6) 0.006 National hospital Ref. Ref. Abbr.: MLPA, Multiplex Ligation-dependent Probe Amplification; RFLP, Restriction Fragment Length Polymorphism; NGS, next-generation sequencing; WGS, whole genome sequencing; PCR, Polymerase Chain Reaction. Table 4. Predictors of HSD Predictor Adjusted Ratio (95% CI) p -value Health professional at first evaluation 0.8 (0.7 - 1.0) 0.073 Positive family history 0.8 (0.6 - 1.0) 0.041 Influence by family history 1.3 (1.0 - 1.6) 0.056 Molecular genetic technique MLPA/RFLP 0.7 (0.3 - 1.4) 0.279 NGS/WGS 2.2 (1.7 - 2.9) <0.001 PCR-based Ref. Ref. Number of physicians 1.1 (1.0 - 1.2) <0.001 Decade of symptom onset <2000 4.1 (2.7 - 6.5) <0.001 2001 - 2010 2.2 (1.5 - 3.1) 2020 Ref. Ref. Healthcare center type at first medical evaluation Primary care center 1.3 (1.0 - 1.7) 0.071 Private care center 1.1 (0.8 - 1.6) 0.579 Regional hospital 1.4 (1.0 - 1.8) 0.023 National hospital Ref. Ref. Abbr.: MLPA, Multiplex Ligation-dependent Probe Amplification; RFLP, Restriction Fragment Length Polymorphism; NGS, next-generation sequencing; WGS, whole genome sequencing; PCR, Polymerase Chain Reaction. Table 5. Spatial dependence and geodesic associations with TTD. Analysis Effect (95% CI) p -value Spatial Lag Model ρ = −1.06 (−2.05 to −0.07) 0.037 Spatial Error Model λ = −1.06 (−2.05 to −0.37) 0.037 Moran’s I test I = −0.013 0.808 Geodesic Correlation r = −0.05 (−0.17 to 0.08) 0.433 Linear Regression exp(β) = 0.99 (0.99 to 1.00) 0.463 Local Gi* Analysis Non significant Additional Declarations Competing interest reported. Maryenela Illanes-Manrique, Mario Cornejo-Olivas, Robinson Yrene-Cubas, Milagros Galecio-Castillo, Andrea Rivera-Valdivia, Elison Sarapura-Castro, Pilar Girón-Davila, Alid Manrique-Palomino, and Alonso Abad declare no competing interests. Data from a subgroup of participants with Huntington’s disease were generated as part of an undergraduate dissertation conducted by Midiam Silva-Bullón and Brylianna Toledo-Pacheco, with a corresponding manuscript currently accepted for publication at a Peruvian journal. Ryan Taft, Akanchha Kesari, and Erin Venti were employees of and stockholders in Illumina, Inc. at the time of this investigation. Additionally, the iHope program was funded exclusively by Illumina, Inc. through March 2024 and in part by Illumina, Inc. thereafter. Supplementary Files Supplementaryinformation.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8570556","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":578984316,"identity":"c3da8b4d-a8d5-43c3-b404-9f55829431cc","order_by":0,"name":"Maryenela Illanes-Manrique","email":"","orcid":"","institution":"Universidad Cientifica del Sur","correspondingAuthor":false,"prefix":"","firstName":"Maryenela","middleName":"","lastName":"Illanes-Manrique","suffix":""},{"id":578984317,"identity":"e0ba51d7-1098-4db5-bafe-2477c4bddce5","order_by":1,"name":"Midiam Silva-Bullon","email":"","orcid":"","institution":"Universidad Cientifica del Sur","correspondingAuthor":false,"prefix":"","firstName":"Midiam","middleName":"","lastName":"Silva-Bullon","suffix":""},{"id":578984318,"identity":"bd8ff9c2-6c78-40eb-9041-e2b3992de278","order_by":2,"name":"Robinson Yrene-Cubas","email":"","orcid":"","institution":"Universidad Cientifica del Sur","correspondingAuthor":false,"prefix":"","firstName":"Robinson","middleName":"","lastName":"Yrene-Cubas","suffix":""},{"id":578984319,"identity":"0a4cc044-4b36-4f5e-a27e-b18c9eaeb32e","order_by":3,"name":"Brylianna Toledo-Pacheco","email":"","orcid":"","institution":"Universidad Cientifica del Sur","correspondingAuthor":false,"prefix":"","firstName":"Brylianna","middleName":"","lastName":"Toledo-Pacheco","suffix":""},{"id":578984320,"identity":"8fbd3c28-a359-4fd3-8f4e-29db8b6ce862","order_by":4,"name":"Milagros Galecio-Castillo","email":"","orcid":"","institution":"Universidad Cientifica del Sur","correspondingAuthor":false,"prefix":"","firstName":"Milagros","middleName":"","lastName":"Galecio-Castillo","suffix":""},{"id":578984321,"identity":"82fb5a28-2aa6-443f-8a55-cd41a554a85f","order_by":5,"name":"Andrea Rivera-Valdivia","email":"","orcid":"","institution":"Universidad Cientifica del Sur","correspondingAuthor":false,"prefix":"","firstName":"Andrea","middleName":"","lastName":"Rivera-Valdivia","suffix":""},{"id":578984322,"identity":"95180162-609b-40cd-b520-41173ae76598","order_by":6,"name":"Elison Sarapura-Castro","email":"","orcid":"","institution":"Universidad Cientifica del Sur","correspondingAuthor":false,"prefix":"","firstName":"Elison","middleName":"","lastName":"Sarapura-Castro","suffix":""},{"id":578984323,"identity":"7fb69099-9734-48a3-8d6c-d6a4ba5cd350","order_by":7,"name":"Pilar Girón-Dávila","email":"","orcid":"","institution":"Universidad Cientifica del Sur","correspondingAuthor":false,"prefix":"","firstName":"Pilar","middleName":"","lastName":"Girón-Dávila","suffix":""},{"id":578984324,"identity":"15fddbc5-cb8f-49d2-82f0-9865da82dff1","order_by":8,"name":"Alid Manrique-Palomino","email":"","orcid":"","institution":"Universidad Cientifica del Sur","correspondingAuthor":false,"prefix":"","firstName":"Alid","middleName":"","lastName":"Manrique-Palomino","suffix":""},{"id":578984325,"identity":"07be0fb9-3eea-4523-bece-1bebcea5b9ab","order_by":9,"name":"Alonso Abad","email":"","orcid":"","institution":"Universidad Cientifica del Sur","correspondingAuthor":false,"prefix":"","firstName":"Alonso","middleName":"","lastName":"Abad","suffix":""},{"id":578984326,"identity":"0ec32f86-1343-4e2f-99d2-e6fd6f7833ea","order_by":10,"name":"Erin T. 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Natural regions are color-coded: Coast (yellow), Highlands (brown), and Jungle (green). A substantial clustering is evident in Lima and surrounding coastal areas, highlighted in the inset. This inset emphasizes the high density of cases in the capital city, suggesting centralization of diagnostic services. There are not overt differences in diagnosis trajectory length among natural regions, despite geographic, healthcare access, and referral barriers compared with coastal regions.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8570556/v1/6b244e24cf899b5768aa82be.jpg"},{"id":101792294,"identity":"676dcb99-69d9-4047-8bad-89fba3337920","added_by":"auto","created_at":"2026-02-03 16:11:42","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":37510,"visible":true,"origin":"","legend":"\u003cp\u003eAbbr.: TTD, time to diagnosis; HSD, Health System Delay.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTime to diagnosis (TTD) and Health system delayed (HSD) \u003c/strong\u003eDiagnostic timeline from symptom onset to genetic diagnosis in the study cohort (n = 248), including first medical evaluation and clinical diagnosis. Mean age ± SD and ranges are shown at each timepoint.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8570556/v1/38b8be0d500735d45af3b421.jpg"},{"id":101792437,"identity":"7bffbd23-2a09-4dbe-a57d-7e6258040702","added_by":"auto","created_at":"2026-02-03 16:12:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1736209,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8570556/v1/93d31c35-427c-4910-992a-eb3f810f8916.pdf"},{"id":101792330,"identity":"2866e447-e271-4adc-8133-df25c1f61880","added_by":"auto","created_at":"2026-02-03 16:11:53","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17053,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-8570556/v1/a518e3fb6f4f38c0277c7785.docx"}],"financialInterests":"Competing interest reported. Maryenela Illanes-Manrique, Mario Cornejo-Olivas, Robinson Yrene-Cubas, Milagros Galecio-Castillo, Andrea Rivera-Valdivia, Elison Sarapura-Castro, Pilar Girón-Davila, Alid Manrique-Palomino, and Alonso Abad declare no competing interests. Data from a subgroup of participants with Huntington’s disease were generated as part of an undergraduate dissertation conducted by Midiam Silva-Bullón and Brylianna Toledo-Pacheco, with a corresponding manuscript currently accepted for publication at a Peruvian journal. Ryan Taft, Akanchha Kesari, and Erin Venti were employees of and stockholders in Illumina, Inc. at the time of this investigation. Additionally, the iHope program was funded exclusively by Illumina, Inc. through March 2024 and in part by Illumina, Inc. thereafter.","formattedTitle":"Predictors of time to diagnosis in monogenic neurological disorders from a referral center in Peru","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe care of individuals with rare diseases (RDs) requires long-term and multidisciplinary coordination, inclusive of comprehensive clinical assessment and genomic testing to obtain a definitive diagnosis. RDs are individually rare but collectively common - with reported geographic prevalence fewer than 1 in 2,000 people reported in World Health Organization (WHO) regions\u003csup\u003e1,2\u003c/sup\u003e, \u0026lt; 5 in 10,000 in Europe, and 1 in 100,000 in Peru\u003csup\u003e3,4\u003c/sup\u003e - and face substantial health-related barriers, often limiting access to care and full participation in society. At least 80% of RDs are genetic in origin, with over ~6500 entities, mainly classified as monogenic, of which approximately 17% are neurological disorders\u003csup\u003e2,5\u003c/sup\u003e. Monogenic neurological disorders (MNDs) are rare and complex diseases affecting the nervous system and its networks, with a prevalence of approximately 90 per 100,000 inhabitants\u003csup\u003e6\u003c/sup\u003e. Most repeat expansion disorders are MNDs, including Huntington\u0026apos;s disease (HD), many inherited ataxias, and myotonic dystrophy type 1 (DM1)\u003csup\u003e7\u003c/sup\u003e. Specific genetic assessments are required for the diagnosis of these disorders such as motif repeat estimation based on PCR-related tests, or short or long-read sequencing\u003csup\u003e8\u003c/sup\u003e. A notable proportion of MNDs are caused by single nucleotide variants (SNVs) with clinical exome sequencing yields ranging from 27-32.7%\u003csup\u003e9,10\u003c/sup\u003e. A further 10-20% are caused by copy number variants (CNVs)\u003csup\u003e11\u003c/sup\u003e, which are generally captured \u0026nbsp;by microarrays, targeted Sanger or short-read sequencing, including \u0026nbsp;whole-exome sequencing (WES), or whole-genome sequencing (WGS) for confirmatory diagnosis\u003csup\u003e12,13\u003c/sup\u003e. The wide range of diagnostic modalities can add complexity to the clinical workup and the subsequent return of results\u003csup\u003e14\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGenetic diseases often face prolonged time to diagnosis due to clinical, sociodemographic, and economic factors. Time to diagnosis (TTD) is the period of time between the first medical consultation and the confirmed diagnosis, reflecting both patient delays and health system delays (HSD)\u003csup\u003e15\u003c/sup\u003e. In RDs, it can range \u0026nbsp;from 4 to 10 years from age at onset to confirmatory genetic diagnosis\u003csup\u003e15,16\u003c/sup\u003e. Some studies performed in MNDs, estimate a TTD of about 5 years for monogenic diseases\u003csup\u003e17\u003c/sup\u003e. The International Rare Disorders Association has proposed a goal of one year total time to diagnose RDs by 2027\u003csup\u003e18\u003c/sup\u003e. Most studies analyzing TTD in RDs are performed in upper and upper-middle-income countries, in contrast to very few published studies from developing countries\u003csup\u003e17,19\u003c/sup\u003e. Peru, a Latin American middle-income country, faces significant difficulties in accessing healthcare services, which may negatively impact the TTD for MNDs and other rare disorders\u003csup\u003e20\u003c/sup\u003e. \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe public healthcare system in Peru has a national referral center for neurogenetic disease care that provides both outpatient consultations and genetic testing for some MNDs, based in Lima, the capital city. Over the past decade, this center has evolved its genetic testing capabilities, transitioning from in-house single-gene testing approaches to deliver genetic reports through \u0026nbsp;next-generation sequencing (NGS), including WES or WGS provided by the iHope philanthropic program\u003csup\u003e21\u003c/sup\u003e, significantly expanding its diagnostic capacity. Despite this progress, delays in diagnosis remain frequent, underscoring the need to identify underlying factors affecting TTD. We aim to estimate TTD and associated factors in a cohort of people living with MNDs evaluated at this specialized tertiary neurogenetic center in Peru.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 248 participants (221 families) were enrolled in the study. Repeat expansion disorders with HD (52.8%), DM1 (18.5%) and inherited ataxias (11.2%) were the most frequent diagnoses in the cohort. Among HD cases, the phenotype distribution included three juvenile cases (\u003cu\u003e\u0026lt;\u003c/u\u003e20 years), 91 classical adult onset, and 37 late-onset cases (\u003cu\u003e\u0026gt;\u003c/u\u003e50 years). The remaining \u0026ldquo;Other\u0026rdquo; group comprised a heterogeneous cluster of approximately 20 different MNDs. The genetic diagnostic techniques applied in our cohort included PCR based procedures (207, 83.47%), mainly for repeat expansion diseases, gene-targeted tests (4, 1.61%), WES (5, 2.02%) and WGS (32, 12.90%). Detailed data by diagnosis, including the proportion of participants for each genetic disorder is listed in the \u003cstrong\u003eSupplementary information.\u003c/strong\u003e\u0026nbsp; Global characteristics of the population, including demographics and social determinants may impact the time to various clinical diagnostic milestones (first symptom, first medical evaluation, clinical diagnosis, and genetic diagnosis) are described in \u003cstrong\u003eTable 1\u003c/strong\u003e and \u003cstrong\u003eTable 2,\u003c/strong\u003e respectively. In addition, we describe the change of selected social determinants of health over time, including health insurance, medical center type, and occupation over time (\u003cstrong\u003eFigure 1)\u003c/strong\u003e. Geographically, most of the participants were located in Lima, the capital city (72%) \u003cstrong\u003e(Figure 2).\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOverall, the TTD within this cohort was 8.5 \u0026plusmn; 8.4 years, median 6 [IQR 2-12] and range 0-47 years. The mean HSD was 4.3 \u0026plusmn; 6.6 years, median 2 [IQR 1-6] and range 0-45 years. The overall TTD, HSD, together with other milestone periods are shown in\u003cstrong\u003e\u0026nbsp;Figure 3\u003c/strong\u003e. The multivariable linear regression showed that a larger \u0026nbsp;number of physicians involved in care, earlier decades of symptom onset, healthcare center type other than national hospital at first medical evaluation, unemployed/retired status at the time of genetic diagnosis, and diagnosis through WES/WGS were significantly associated with longer TTD; whereas unemployed/retired status at first symptom were significantly associated with shorter TTD \u003cstrong\u003e(Table 3).\u0026nbsp;\u003c/strong\u003eWe identified predictors associated with longer HSD\u003cstrong\u003e\u0026nbsp;(Table 4)\u0026nbsp;\u003c/strong\u003ewhich included the number of physicians involved in care and the decade of symptom onset were associated with longer delays, while health professionals involved at first evaluation, and positive family history were associated with shorter HSD. Pearson correlation and linear regression were used to evaluate whether greater geographic distance from residence at symptom onset to the referral center was associated with longer TTD. These analyses showed no significant association, indicating that patients living farther away did not experience longer TTD. Spatial lag and spatial error models were applied to assess whether TTD values were influenced by those of nearby locations. These models detected statistically significant spatial dependence. However, this statistical dependence did not translate into recognizable geographic patterns. Moran\u0026rsquo;s I, which assesses high or low TTD values cluster across the study area, showed no evidence of global spatial autocorrelation. Local Gi* analysis, which identifies specific geographic areas with consistently longer or shorter TTD, similarly revealed no localized clusters. Geodesic correlation, which directly evaluates whether individuals living closer to one another have more similar TTD values, showed no significant association \u003cstrong\u003e(Table 5)\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study identified an overall mean TTD of 8.5 ± 8.4 years among 248 cases affected with MNDs. Number of physicians, decade of symptom onset, unemployment rate and healthcare center type were identified as the most significant predictors of the global TTD. Individuals residing in Lima and other major cities have easier access to specialized care and genetic testing compared to those living in more distant regions, where geographical, socio-economic, and cultural barriers are predominant, such as the high Andean communities or the Amazon Jungle\u003csup\u003e22\u003c/sup\u003e. Rural health centers are frequently limited in infrastructure, resources and specialized personnel necessary for timely diagnosis and treatment, which exacerbates health disparities\u003csup\u003e23\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe TTD in our cohort was longer compared to other Spanish and European cohorts. \u0026nbsp;The TTD in our study was \u0026nbsp;8.5 ± 8.4 years, longer than the TTD reported in the Spanish Rare Diseases Patient Registry, where the average time to diagnosis was 6.18 years (median 2, IQR 0.2-7.5)\u003csup\u003e16\u003c/sup\u003e, and almost twice the time reported by the EURODIS Rare Barometer conducted across 41 European Union countries\u003csup\u003e15\u003c/sup\u003e. In our study, 33.5% of the cases had a TTD \u0026nbsp;of 10 years or more - a notable difference in diagnosis assessments - reflecting disparities in healthcare access in contrast with the 21% of cases found in the Spanish RD cohort\u003csup\u003e16\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe extended TTD in our cohort underscores persistent systemic barriers that continue to hinder timely diagnostic confirmation of RDs. We found a HSD of 4.3 ± 6.6 years, similar to the 4.3 years reported in the EURODIS rare barometer for the same period of time\u003csup\u003e15,24\u003c/sup\u003e. This is likely due to limited access to specialized healthcare services and diagnostics\u003csup\u003e25\u003c/sup\u003e. For example, one participant with ataxia with oculomotor apraxia - recently published as a case report - had a TTD period of over 40 years. During this period, they received multiple misdiagnoses before being diagnosed through a philanthropic program that provided access to proper genetic testing\u003csup\u003e26\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe TTD varies depending on the specific monogenic neurogenetic disorder.\u0026nbsp;\u0026nbsp;In this cohort, HD was the most frequently diagnosed neurogenetic condition (52.8%), followed by DM1 (18.1%) and inherited ataxias (12.9%). Notably, this tertiary neurogenetics center specializes in repeat expansion disorders, including HD, DM1, and dominant ataxias\u003csup\u003e27,28\u003c/sup\u003e. For the HD group, the TTD was 5.9 ± 5.9 years (median: 4 years, IQR: 2–7.5, range 0-31), consistent with data from the European Huntington’s Disease Network (EHDN) registry\u003csup\u003e29\u003c/sup\u003e, and a recent preliminary report from the Latin American Enroll-HD global registry\u003csup\u003e30\u003c/sup\u003e. The annual cost of HD for the Peruvian population was estimated at approximately USD 1.2 million in 2015, including about USD 85,000 per year in direct healthcare expenditures. Consequently, the financial burden primarily falls on patients and their families, as they personally cover 98.2% of direct healthcare-related expenses, resulting in substantial out-of-pocket costs to access medical care and specialized treatment\u003csup\u003e31\u003c/sup\u003e. In the DM1 group, the TTD was 8.9 ± 8.2 years (median: 9 years, IQR: 2–14), which aligns reasonably well with findings from an earlier U.S. cohort of DM1, reporting a TTD of 7.3 ± 8.2 years\u003csup\u003e32\u003c/sup\u003e. For inherited ataxias, the TTD averaged 9.3 ± 9.8 years (median: 6 years, IQR: 2.8–11, range 0-38). Historically, prior to the 2000s, a TTD of approximately 6 years was reported for ataxias\u003csup\u003e33\u003c/sup\u003e. However, likely due to the increasing use of molecular diagnostic tools, disorders such as Friedreich’s ataxia (FRDA) have shown a TTD as short as 2 years\u003csup\u003e34\u003c/sup\u003e. Given the genetic heterogeneity of ataxias, particularly for spinocerebellar ataxias (SCAs) and rarer variants (e.g., SCA11, SCA13, SCA14), previous studies have reported broader TTDs ranging from 3 to 45 years, with a median of 18.1 years\u003csup\u003e35\u003c/sup\u003e. For other miscellaneous monogenic neurogenetic disorders, the TTD was 15.7 ± 10.1 years (median: 15 years, IQR: 8–19, range 1-47). This group, representing 12.9% of our cohort, includes several early-onset cases with particularly long diagnostic odysseys, which were diagnosed by WEG/WGS, mostly through international philanthropic programs like iHope or other collaborative networks\u003csup\u003e21\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUnemployment status is significantly associated with TTD in the Peruvian MNDs cohort.\u0026nbsp;As shown in \u003cstrong\u003eFigure 1\u003c/strong\u003e, unemployment status increases with disease progression, possibly related to the effect of the MND on motor, cognitive and functional capacity\u003csup\u003e36\u003c/sup\u003e. We found that being unemployed at the time of genetic testing was a predictor of longer TTD compared to those with employment. This may be due to the financial constraints unemployment imposes, limiting access to healthcare services. Although Peru’s universal healthcare system (SIS) provides coverage, patients and families still face significant out-of-pocket expenses\u003csup\u003e37\u003c/sup\u003e. To mitigate these challenges, social programs oriented to specific vulnerable populations have been implemented to provide partial support to vulnerable populations with disabilities\u003csup\u003e38\u003c/sup\u003e. In contrast, unemployment at the onset of the first MND-related symptom was associated with a shorter TTD. This could reflect the fact that unemployed individuals in the early stages of disease have more time and flexibility to seek medical care. However, other factors such as differences in social support, health literacy, or psychosocial stressors related to unemployment that were not analyzed in this study could also influence this association\u003csup\u003e39\u003c/sup\u003e.\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePatients first evaluated in a primary care center tended to experience longer TTD. This pattern may be explained by systemic healthcare challenges such as resource centralization in the capital and major regional cities, bureaucratic referral systems, and a lack of interoperability between healthcare subsystems\u003csup\u003e40\u003c/sup\u003e. Additionally, 66.1% of our participants were initially misdiagnosed at the first medical evaluation \u003cstrong\u003e(Table 2)\u003c/strong\u003e, presumedly due to limited training opportunities in rare disorders in these centers. To address this gap, the Ministry of Health has recently launched pilot initiatives to provide training on rare disorders for primary care professionals through the ENSAP academic program\u003csup\u003e41\u003c/sup\u003e. In contrast, a subgroup of HD patients received timely clinical and genetic diagnoses at primary care centers through a sustainable extramural program led by a tertiary neurological center. This program included a mobile clinic that provided specialized outpatient consultations, sample collection for genetic testing, and on-site genetic counseling\u003csup\u003e42\u003c/sup\u003e. Furthermore, 9.2% of patients in our cohort consulted more than five physicians before receiving a diagnosis. A higher number of physician consultations was associated with both a longer TTD and a longer healthcare-seeking duration. This finding may reflect the high misdiagnosis rate of RDs, potentially driven by the deficiencies in healthcare organization for patient referral to specialized facilities and by the difficulties they face when traveling to distant centers, but could also indicate the complexity of establishing a strong patient-physician relationship when navigating a RD\u003csup\u003e23,43\u003c/sup\u003e. Additionally, many rare diseases are frequently misdiagnosed as psychiatric conditions, delaying accurate diagnosis and treatment\u003csup\u003e44\u003c/sup\u003e. The limited consultation time available to healthcare professionals may represent another factor affecting quality of care, which varies depending on the specialty.\u003c/p\u003e\n\u003cp\u003eThe causal variant type for MND affects TTD in our cohort.Since our center is the national referral center and central molecular lab for these disorders in Peru, the most prevalent diseases in our cohort were repeat expansion disorders such as HD, DM1 and inherited ataxias\u003csup\u003e28\u003c/sup\u003e. Each specific repeat expansion disorder encompasses particular diagnostic challenges. Early or late-onset HD presentations differ from classic adult-onset forms, and are more challenging to diagnose, usually contributing to longer TTD\u003csup\u003e45,46\u003c/sup\u003e; however, we could not replicate this observation given the limited sample size in the juvenile (under 20 years) HD subgroup within our cohort (only three cases). The specific molecular tool employed differs based on the type of genetic variant causing a MND and the availability of each technique, thus in this cohort the first implemented techniques were PCR-based genotyping procedures, with manual sizing models using gel electrophoresis further evolving to capillary electrophoresis, as well as MLPA/RFLP techniques and sanger sequencing. There are not short read sequencing technologies available at this referral center, but the implementation of collaborative diagnostic programs like iHope have increased the capabilities for genetic testing in this vulnerable population\u003csup\u003e21\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe advancement of genetic testing services in Peru, along with global collaborative efforts, has contributed to a shorter TTD. As shown in \u003cstrong\u003eTable 3\u003c/strong\u003e, patients who developed symptoms before 2010 experienced significantly longer TTD, possibly due to the gradual expansion of genetic testing availability. Although clinical evaluations for neurogenetic diseases in Peru were first established in 1995\u003csup\u003e27\u003c/sup\u003e, genetic testing capabilities progressed more slowly. For instance, while the genetic cause of HD was identified in 1993, Peru’s first HD genetic test was introduced in 2004 using an in-house PCR-based method, which was later refined and transitioned to capillary electrophoresis by 2017\u003csup\u003e47,48\u003c/sup\u003e. A partnership in 2014 with a Brazilian institution enabled the diagnosis of SCAs and FRDA, and facilitated technology transfer, allowing the local center to diagnose at least six different SCAs by 2017\u003csup\u003e49\u003c/sup\u003e. The genetic testing for FRDA became available in Peru in 2023. More recently, the implementation of the iHope philanthropic program, which provides free WGS and WES for individuals with early-onset neurogenetic disorders, has facilitated the diagnosis of many previously unsolved cases. Some patients had been awaiting a definitive diagnosis for decades before benefiting from this initiative\u003csup\u003e50,21\u003c/sup\u003e.In contrast to previous publications, GIS analysis did not show significant associations\u003csup\u003e51\u003c/sup\u003e. Paradoxically, geographic disparities in access to specialized health care, a very factor we aimed to explore, may limit the detection of spatial effects, as individuals outside the capital may be underdiagnosed and thus underrepresented in our cohort.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrengths and Limitations.\u0026nbsp;\u003c/strong\u003eThis study provides valuable insights into an underrepresented population, and helps address a diagnostic gap that has not been previously characterized. However, important limitations must be acknowledged. Although data were \u0026nbsp;obtained \u0026nbsp; from a national and unique genetics testing reference center for MNDs, they derive from a single institution and may not fully represent the broader Peruvian population. Pediatric cases were excluded, as our center primarily serves adults. We enrolled all available patients who were willing to participate, which limited a proper sample size calculation analysis and introduced the possibility of selection bias. Limited diagnostic resources and the absence of standardized screening for rare diseases may have led to underdiagnosis of other neurogenetic conditions in Peru. Lastly, diagnostic intervals were measured in years, which limited the detection of nuanced variations in diagnostic delay.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this Peruvian cohort of 248 individuals affected by monogenic neurological disorders, we identified a prolonged mean time to diagnosis of 8.5 ± 8.4 years. Diagnostic delay was primarily associated with the healthcare system and socio-economic factors, including the number of physicians consulted, decade of symptom onset, unemployment status and type of healthcare center. In contrast to other reports, geographic disparities were not observed, as no statistically significant differences in time to diagnosis were found according to geographic location or distance from the main diagnostic center. These findings remain consistent with barriers commonly reported in resource-limited settings, such as constrained diagnostic capacity outside major urban centers and insufficient implementation of existing regulatory frameworks. The establishment of a national network for neurogenetic and rare disease care coordinated by specialized hub centers, alongside quality-assured genetic testing strategies supported by multidisciplinary teams and sustained public investment in essential diagnostic infrastructure, may improve diagnostic efficiency. Additional priorities include strengthening training in neurogenetics and genetic counseling, implementing national clinical guidelines, and fostering strategic international collaborations, such as iHope, to promote more equitable access in these regions.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eA cross-sectional questionnaire-based study was conducted from February 2024 to March 2025 at the Neurogenetics Research Center, \u003cem\u003eInstituto Nacional de Ciencias Neurológicas\u003c/em\u003e (NRC-INCN), a national reference center for neurogenetics in Peru. After an appropriate informed consent, an adapted and revised 60-question questionnaire was applied to individuals older than 18 years of age of both sexes affected by genetically confirmed neurogenetic diseases. From a total of 1313 patients followed up in the studied period (497 of them with confirmatory genetic diagnosis), a total of 248 participants were invited to participate during follow-up consultations, and all of them, after appropriate informed consent process, were enrolled. Data were collected using paper-based questionnaires administered to participants, in person or remotely, depending on preference and availability. Upon completion, all responses were entered into REDCap (Research Electronic Data Capture) for data storage and management.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSetting.\u003c/strong\u003e The study was conducted at NRC-INCN, a tertiary care public institution specializing in neurogenetics, located in Lima, the capital city of the country. Peru, a middle-income country in South America with nearly 33 million people, exhibits significant population centralization in Lima, which hosts over one-third of its residents\u003csup\u003e52\u003c/sup\u003e. This concentration is also reflected in the distribution of health services, as most tertiary public institutions offering genetic testing are located in the capital. This remains fragmented into four public and one private subsystems that operate independently, resulting in access, coverage and quality of care disparities\u003csup\u003e53–55\u003c/sup\u003e. This fragmentation and unequal distribution of healthcare services not only limits access to specialized diagnostics but also affects the availability of centers dedicated to rare diseases\u003csup\u003e25\u003c/sup\u003e. Peru presents significant disparities in health coverage with an average of 16.8 physicians per 10,000 inhabitants, but its number decreased drastically in regions with higher poverty rates and lower health investment\u003csup\u003e55\u003c/sup\u003e.\u0026nbsp;The universal health insurance (SIS) coverage was progressively implemented by the government in 2019, progressively covering more than 70% of the Peruvian population\u003csup\u003e56,57\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this context, the NRC-INCN offers specialized outpatient consultations and genetic testing for neurogenetic disorders. In-house genetic testing for repeat expansion disorders is available, including HD (2004), SCAs (2015), and DM1 (2015). Diagnostic capacity has improved through joint initiatives and philanthropic support, such as the iHope program\u003csup\u003e50,58\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe questionnaire.\u0026nbsp;\u003c/strong\u003eA preliminary questionnaire was adapted from previous instruments assessing diagnostic delay in Peruvian tuberculosis patients\u003csup\u003e59\u003c/sup\u003e, and determinants of delayed diagnosis in hereditary ataxias\u003csup\u003e60\u003c/sup\u003e. A local panel of 3 experts in neurogenetics and rare disorders reviewed the draft questionnaire. The revised version was piloted in a sample of 10 individuals for cultural and linguistic adaptation. The revised and adapted questionnaire consisted of a set of 60 structured questions organized into the following sections: a) Demographics: gender, age, education level, and location. b) Diagnosis and clinical features: age of symptom onset, age at a clinical and molecular diagnosis, code of the disease (ICD-10, OMIM, ORPHANET). c) Epidemiological information: health insurance, employment, civil status, disability level, family income, etc. And d) Diagnostic-related dates, including date of the first symptom, first consultation, clinical diagnosis and genetic diagnosis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical aspects\u003c/strong\u003e. This study was approved by the local ethics committee at \u003cem\u003eUniversidad Cientifica del Sur\u003c/em\u003e (131-CIEI-CIENTIFICA-2023) and INCN (001-2024-CIEI-INCN). All participants provided written informed consent. We confirm that we have read the journal’s position on issues involved in ethical publication and affirm that this work is consistent with those guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcome and study variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe main outcome variable is Time to diagnosis (TTD), defined as the number of years between the first related symptom \u0026nbsp; and genetic confirmation of the diagnosis. We also explored other relevant milestones, including time from first symptom to first ever medical evaluation related to the rare disease, and time from first symptom to clinical diagnosis. In addition, we included the time from the first medical evaluation to the genetic diagnosis as a surrogate of HSD\u003csup\u003e15\u003c/sup\u003e.\u0026nbsp;Misdiagnosis was defined as the percentage of patients whose initial clinical diagnosis differed from the genetic test results.\u003c/p\u003e\n\u003ch2\u003eStatistical analysis\u003c/h2\u003e\n\u003cp\u003eWe used descriptive statistics to evaluate the characteristics of the study population. For categorical variables, we used total counts and percentages, and for continuous variables we used medians and interquartile range (IQR) and mean and standard deviation (SD). Multivariable logistic, linear, and Cox regression were used to generate prediction models for the outcome time to diagnosis. Continuous outcomes were log-transformed before fitting the regressions and back-transformed by exponentiation when reporting the results. Models were chosen using bidirectional stepwise procedures with the Akaike Information Criteria (AIC) or the Bayesian Information Criteria (BIC) when stricter penalization was needed (i.e. less than five events). A set of “candidate” variables were chosen \u003cem\u003ea priori\u0026nbsp;\u003c/em\u003ebased on clinical relevance by experts in the field. Furthermore, we conducted sensitivity analyses for other time periods, as well as differentiating by specific diagnosis, and by geographical location. Alluvial plots were generated to visualize transitions between key variables across relevant timepoints.\u003c/p\u003e\n\u003cp\u003eGeospatial analysis (GIS) was conducted using the Ubigeo codes of the participants at the different diagnosis timepoints, and administrative boundaries as well as other geographic data obtained from publicly available files (\u003cu\u003ehttps://www.gob.pe/inei/\u003c/u\u003e). Ubigeo codes were transformed into spatial points. Spatial associations were evaluated through spatial lag and spatial error models, and we examined the significance of spatial parameters (ρ for lag, λ for error). Clustering and hotspot analysis were conducted using the Moran’s I and Getis-Ord Gi* statistics. To assess the effect of geodesic distance from patients’ location to the neurogenetic center, we computed Pearson correlation and fit a linear regression. Analyses and GIS figures were conducted using R software version 4.5.0.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eData availability\u003c/h2\u003e\n\u003cp\u003eThe datasets analyzed in the study are available upon reasonable request to the corresponding author. Due to ethical and privacy restrictions, certain sensitive data cannot be shared publicly.\u003c/p\u003e\n\u003ch2\u003eCode availability\u003c/h2\u003e\n\u003cp\u003eThe code used for statistical analysis and data management in this study is available upon reasonable request to the corresponding author. R version 4.5.0 was used for data processing and analysis.\u003c/p\u003e\n\u003ch2\u003eAcknowledgments\u003c/h2\u003e\n\u003cp\u003eThis study was supported by \u003cem\u003eUniversidad Cientifica del Sur\u003c/em\u003e, \u003cem\u003eFondo Semilla Docente\u0026nbsp;\u003c/em\u003e(003-DGIDI-CIENTIFICA-2023). We express our gratitude to the participants and their families for their invaluable contribution to this research, and the Neurogenetics Research Center at the \u003cem\u003eInstituto Nacional de Ciencias Neurológicas\u003c/em\u003e for their logistics support. Genetic testing was provided at no-cost to 37 cases in this cohort through the iHope program.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAuthor contributions\u003c/h2\u003e\n\u003cp\u003eMI-M. and MC-O. conceptualized and designed the study. MS-B., RY-C., and BT-P. coordinated data collection and participated in patient recruitment and clinical evaluations. M.G.-C. and MI-M. performed the statistical analysis. MI-M., MC-O., MS-B., RAY-C., BT-P., M.G.-C., AR-V., ES-C., PG-D., AM-P., AA., EV., and RT. contributed to data selection and manuscript preparation. MI-M., MC-O., AR-V., and ES-C. contributed their expertise in genetic diagnosis and methodology. MC-O. served as the senior author, supervised the study, and critically revised the manuscript. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003ch2\u003eCompeting Interests\u003c/h2\u003e\n\u003cp\u003eMaryenela Illanes-Manrique, Mario Cornejo-Olivas, Robinson Yrene-Cubas, Milagros Galecio-Castillo, Andrea Rivera-Valdivia, Elison Sarapura-Castro, Pilar Girón-Davila, Alid Manrique-Palomino, and Alonso Abad declare no competing interests. Data from a subgroup of participants with Huntington’s disease were generated as part of an undergraduate dissertation conducted by Midiam Silva-Bullón and Brylianna Toledo-Pacheco, with a corresponding manuscript currently accepted for publication at a Peruvian journal. Ryan Taft, Akanchha Kesari, and Erin Venti were employees of and stockholders in Illumina, Inc. at the time of this investigation. Additionally, the iHope program was funded exclusively by Illumina, Inc. through March 2024 and in part by Illumina, Inc. thereafter.\u003c/p\u003e\n\u003ch2\u003eDeclaration of generative AI and AI-assisted technologies\u003c/h2\u003e\n\u003cp\u003eDuring the preparation of this manuscript, the authors used ChatGPT to support language editing and improve clarity and readability. All content was subsequently reviewed and revised by the authors, who take full responsibility for the accuracy and integrity of the final published work.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eThe Lancet Global Health, null. The landscape for rare diseases in 2024. \u003cem\u003eLancet Glob. 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Dis. \u003c/em\u003e\u003cstrong\u003e4\u003c/strong\u003e, 99\u0026ndash;105 (2015).\u003c/li\u003e\n\u003cli\u003eA novel gene containing a trinucleotide repeat that is expanded and unstable on Huntington\u0026rsquo;s disease chromosomes. The Huntington\u0026rsquo;s Disease Collaborative Research Group. \u003cem\u003eCell \u003c/em\u003e\u003cstrong\u003e72\u003c/strong\u003e, 971\u0026ndash;983 (1993).\u003c/li\u003e\n\u003cli\u003eBlanco, S. \u003cem\u003eet al.\u003c/em\u003e Use of capillary electrophoresis for accurate determination of CAG repeats causing Huntington disease. An oligonucleotide design avoiding shadow bands. \u003cem\u003eScand. J. Clin. Lab. Invest. \u003c/em\u003e\u003cstrong\u003e68\u003c/strong\u003e, 577\u0026ndash;584 (2008).\u003c/li\u003e\n\u003cli\u003eVieira, T. A. \u003cem\u003eet al.\u003c/em\u003e Information and Diagnosis Networks - tools to improve diagnosis and treatment for patients with rare genetic diseases. \u003cem\u003eGenet. Mol. Biol. \u003c/em\u003e\u003cstrong\u003e42\u003c/strong\u003e, 155\u0026ndash;164 (2019).\u003c/li\u003e\n\u003cli\u003eBazalar-Montoya, J. \u003cem\u003eet al.\u003c/em\u003e Clinical genome sequencing in patients with suspected rare genetic disease in Peru. \u003cem\u003eNPJ Genomic Med. \u003c/em\u003e\u003cstrong\u003e9\u003c/strong\u003e, 51 (2024).\u003c/li\u003e\n\u003cli\u003eBest, S., Vidic, N., An, K., Collins, F. \u0026amp; White, S. M. A systematic review of geographical inequities for accessing clinical genomic and genetic services for non-cancer related rare disease. \u003cem\u003eEur. J. Hum. Genet. \u003c/em\u003e\u003cstrong\u003e30\u003c/strong\u003e, 645\u0026ndash;652 (2022).\u003c/li\u003e\n\u003cli\u003eHuarachi, L. A., Lozano-Zanelly, G., Acosta, J., Huarachi, C. A. \u0026amp; Moya-Salazar, J. Inequality in the distribution of resources and health care in the poverty quintiles: Evidence from Peruvian comprehensive health insurance 2018-2019. \u003cem\u003eElectron. J. Gen. Med. \u003c/em\u003e\u003cstrong\u003e21\u003c/strong\u003e, em568 (2024).\u003c/li\u003e\n\u003cli\u003eYpanaqu\u0026eacute;-Luyo, P. \u0026amp; Martins, M. Uso de los servicios de salud ambulatorios en la poblaci\u0026oacute;n peruana. \u003cem\u003eRev. Peru. Med. Exp. Salud Publica \u003c/em\u003e\u003cstrong\u003e32\u003c/strong\u003e, 464\u0026ndash;470 (2015).\u003c/li\u003e\n\u003cli\u003eMinisterio de Salud. \u003cem\u003eInformaci\u0026oacute;n de recursos humanos en el sector salud, Per\u0026uacute; 2023\u003c/em\u003e. (Ministerio de Salud, Lima, Per\u0026uacute;, 2024).\u003c/li\u003e\n\u003cli\u003eMinisterio de Salud. \u003cem\u003eAn\u0026aacute;lisis de Situaci\u0026oacute;n de Salud Del Per\u0026uacute;, 2021\u003c/em\u003e. (Ministerio de Salud, Lima, Per\u0026uacute;, 2023).\u003c/li\u003e\n\u003cli\u003ePer\u0026uacute;. Ministerio de Salud. Decreto de Urgencia N.\u003csup\u003eo\u003c/sup\u003e 017-2019. (2019).\u003c/li\u003e\n\u003cli\u003ePer\u0026uacute;. Ministerio de Salud. Minsa cubri\u0026oacute; m\u0026aacute;s de 90.9 millones de atenciones de asegurados SIS en 2024. (2025).\u003c/li\u003e\n\u003cli\u003eThorpe, E. \u003cem\u003eet al.\u003c/em\u003e The impact of clinical genome sequencing in a global population with suspected rare genetic disease. \u003cem\u003eAm. J. Hum. Genet. \u003c/em\u003e\u003cstrong\u003e111\u003c/strong\u003e, 1271\u0026ndash;1281 (2024).\u003c/li\u003e\n\u003cli\u003eBonadonna, L. V., Saunders, M. J., Guio, H., Zegarra, R. \u0026amp; Evans, C. A. Socioeconomic and Behavioral Factors Associated with Tuberculosis Diagnostic Delay in Lima, Peru. \u003cem\u003eAm. J. Trop. Med. Hyg. \u003c/em\u003e\u003cstrong\u003e98\u003c/strong\u003e, 1614\u0026ndash;1623 (2018).\u003c/li\u003e\n\u003cli\u003eDos Santos Pinheiro, J. \u003cem\u003eet al.\u003c/em\u003e Diagnostic Delay of Hereditary Ataxias in Brazil: the Case of Machado-Joseph Disease. \u003cem\u003eCerebellum Lond. Engl. \u003c/em\u003e\u003cstrong\u003e22\u003c/strong\u003e, 348\u0026ndash;354 (2023).\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Population characteristics\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"622\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;No. (%) / Median [IQR]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAge at interview\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e46 [36-56.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Female\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e126 (50.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003cp\u003eMarried/cohabiting\u003c/p\u003e\n \u003cp\u003eOthers (widow, divorced)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e132 (53.2%)\u003c/p\u003e\n \u003cp\u003e72 (29.0%)\u003c/p\u003e\n \u003cp\u003e44 (17.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eOccupation\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eStudent\u003c/p\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003cp\u003ePart-time employed\u003c/p\u003e\n \u003cp\u003eFully employed\u003c/p\u003e\n \u003cp\u003eHomemaker\u003c/p\u003e\n \u003cp\u003eRetired\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12 (4.8%)\u003c/p\u003e\n \u003cp\u003e113 (45.6%)\u003c/p\u003e\n \u003cp\u003e28 (11.3%)\u003c/p\u003e\n \u003cp\u003e34 (13.7%)\u003c/p\u003e\n \u003cp\u003e35 (14.1%)\u003c/p\u003e\n \u003cp\u003e26 (10.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eYears of education\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11 [11-14]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHealthcare Insurance\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eUniversal health insurance (SIS)\u003c/p\u003e\n \u003cp\u003eSocial health insurance (ESSALUD)\u003c/p\u003e\n \u003cp\u003ePolice/Military forces\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ePrivate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e179 (72.2%)\u003c/p\u003e\n \u003cp\u003e58 (23.4%)\u003c/p\u003e\n \u003cp\u003e2 (0.8%)\u003c/p\u003e\n \u003cp\u003e9 (3.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHousehold income# (PEN=0.27 USD)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e2748,6 - 6690,8 PEN\u003c/p\u003e\n \u003cp\u003e1,479.0 - 2,748.6 PEN\u003c/p\u003e\n \u003cp\u003e810.9 - 1,479.0 PEN\u003c/p\u003e\n \u003cp\u003e\u0026lt; 810.9 PEN\u003c/p\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e34 (13.7%)\u003c/p\u003e\n \u003cp\u003e73 (29.4%)\u003c/p\u003e\n \u003cp\u003e86 (34.7%)\u003c/p\u003e\n \u003cp\u003e44 (17.7%)\u003c/p\u003e\n \u003cp\u003e11 (4.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of Household Members\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4 [3-5]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eA family member with the MNDs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e177 (71.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of affected family members\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2 [1-4]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of participants who \u0026nbsp;migrated to obtain a diagnosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e68 (27.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDiagnosis\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eHuntington Disease\u003c/p\u003e\n \u003cp\u003eMyotonic Dystrophy type 1\u003c/p\u003e\n \u003cp\u003eInherited ataxias*\u003c/p\u003e\n \u003cp\u003eOthers**\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;131 (52.8%)\u003c/p\u003e\n \u003cp\u003e46 (18.1%)\u003c/p\u003e\n \u003cp\u003e28 (12.9%)\u003c/p\u003e\n \u003cp\u003e43 (16.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGenetic testing methodology\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ePCR based\u003c/p\u003e\n \u003cp\u003eMLPA/RFLP\u003c/p\u003e\n \u003cp\u003eNGS/WGS\u003c/p\u003e\n \u003cp\u003eUnknown\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;201 (81%)\u003c/p\u003e\n \u003cp\u003e5 (2%)\u003c/p\u003e\n \u003cp\u003e41 (16.5%)\u003c/p\u003e\n \u003cp\u003e1 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e#INEI.Peruvian household income classification considers 4 levels.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e* SCAs (SCA2, MJD/SCA3, SCA8, SCA10), Friedreich\u0026rsquo;s ataxia (FRDA), Autosomal Recessive Spastic Ataxia of Charlevoix-Saguenay (ARSACS), Ataxia- telangiectasia.\u003c/p\u003e\n\u003cp\u003e** X-Linked Adrenoleukodystrophy, \u0026nbsp;Spinal Muscular Atrophy, Primary Coenzyme Q10 deficiency, Limb Girdle Muscular Dystrophy, Dopa-responsive dystonia, Early-Onset primary dystonia, Becker Muscular Dystrophy, Facioscapulohumeral Muscular Dystrophy, Myoclonic Epilepsy with Ragged Red Fibers \u0026nbsp;(MERRF), Myopathy, Hereditary spastic paraplegia, Primary mitochondrial diseases, Fahr syndrome, Leigh syndrome, Neurodegeneration with Ataxia, Dystonia, and Gaze Palsy (NADGP ) , Noonan syndrome, inherited disorders of Sulfur Amino Acid (SAA).\u003c/p\u003e\n\u003cp\u003e***\u003c/p\u003e\n\u003cp\u003eAbbr.: PEN: Peruvian Sol; USD United States Dollar. MLPA, Multiplex Ligation-dependent Probe Amplification; RFLP, Restriction Fragment Length Polymorphism; NGS, next-generation sequencing; WGS, whole genome sequencing; PCR, Polymerase Chain Reaction.\u003c/p\u003e\n\u003cp\u003eTable 2. Population characteristics at each milestone.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"641\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Characteristics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003emedian [IQR] or n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAt first symptom\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFirst medical evaluation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eClinical Diagnosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGenetic Diagnosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAge, years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36 [21 - 46]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38 [27 - 48]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e40 [31 - 51]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e41 [32 - 52]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003cp\u003e[2007 - 2017]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2017 [2012 - 2021]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003cp\u003e[2016 - 2022]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2021 [2017 - 2023]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedical insurance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e141 (56.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e160 (64.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e173 (69.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e177 (71.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eESSALUD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e77 (31.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e73 (29.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e60 (24.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e59 (23.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePrivate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11 (4.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12 (4.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13 (5.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9 (3.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePolice/Military forces\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17 (6.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidency setting\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25 (10.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22 (8.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15 (6.0%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15 (6.0%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e223 (89.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e226 (91.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e233 (94.0%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e233 (94.0%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmployment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStudent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6 (2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (1.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;3 (1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;18 (7.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFull-time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e90 (36.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e75 (30.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e54 (21.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e46 (18.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePart-time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e106 (42.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e88 (35.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e81 (32.7%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e44 (17.7%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e45 (18.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e80 (32.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e108 (43.5%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e98 (39.5%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRetired\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13 (5.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHomemaker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (0.4%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e29 (11.7%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedical Center category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePrivate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e41 (16.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11 (4.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePublic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e207 (83.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e237 (95.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e245 (98.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of Medical Center\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePrimary care center\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e72 (29.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePrivate care centers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26 (10.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRegional hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e79 (31.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12 (4.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNational hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e71 (28.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e228 (91.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e247 (99.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCare provider\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSpecialist, physician\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e158 (63.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e246 (99.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e248 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePrimary care physician\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e90 (36.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMisdiagnosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e164 (66.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbr.: IQR, interquartile range; SIS: Universal Health Insurance; ESSALUD: Social Health Insurance; NA: non-applicable. Other: Newborn, Infant.\u003c/p\u003e\n\u003cp\u003eTable 3. Predictors of time to diagnosis.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"625\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted Ratio (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmployment status at first symptom\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnemployed/retired\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.7 (0.5 - 0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePartial employment (including homemaker, student)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.0 (0.9 - 1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.649\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFull employment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmployment status at the time of genetic diagnosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnemployed/retired\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.4 (1.1 - 1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.009\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePartial employment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.2 (1.0 - 1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFull employment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMolecular genetic technique\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMLPA/RFLP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.8 (0.5 - 1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.504\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNGS/WGS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.4 (1.1 - 1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePCR-based\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of physicians\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.1 (1.0 - 1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDecade of symptom onset\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.1 (6.3 - 13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2001 - 2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.7 (2.8 - 5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2011 - 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.0 (1.6 - 2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealthcare center type at first medical evaluation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePrimary care center\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.2 (1.0 - 1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePrivate care center\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.4 (1.1 - 1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.012\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRegional hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.3 (1.1 - 1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNational hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbr.: MLPA, Multiplex Ligation-dependent Probe Amplification; RFLP, Restriction Fragment Length Polymorphism; NGS, next-generation sequencing; WGS, whole genome sequencing; PCR, Polymerase Chain Reaction.\u003c/p\u003e\n\u003cp\u003eTable 4. Predictors of HSD\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"622\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted Ratio (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealth professional at first evaluation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.8 (0.7 - 1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.073\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePositive family history\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.8 (0.6 - 1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.041\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInfluence by family history\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.3 (1.0 - 1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.056\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMolecular genetic technique\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMLPA/RFLP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.7 (0.3 - 1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.279\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNGS/WGS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.2 (1.7 - 2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePCR-based\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of physicians\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.1 (1.0 - 1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDecade of symptom onset\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.1 (2.7 - 6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2001 - 2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.2 (1.5 - 3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2011 - 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.4 (1.0 - 2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealthcare center type at first medical evaluation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePrimary care center\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.3 (1.0 - 1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePrivate care center\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.1 (0.8 - 1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.579\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRegional hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.4 (1.0 - 1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNational hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbr.: MLPA, Multiplex Ligation-dependent Probe Amplification; RFLP, Restriction Fragment Length Polymorphism; NGS, next-generation sequencing; WGS, whole genome sequencing; PCR, Polymerase Chain Reaction.\u003c/p\u003e\n\u003cp\u003eTable 5. Spatial dependence and geodesic associations with TTD.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnalysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEffect (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSpatial Lag Model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026rho; = \u0026minus;1.06 (\u0026minus;2.05 to \u0026minus;0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSpatial Error Model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lambda; = \u0026minus;1.06 (\u0026minus;2.05 to \u0026minus;0.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMoran\u0026rsquo;s I test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI = \u0026minus;0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.808\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGeodesic Correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003er = \u0026minus;0.05 (\u0026minus;0.17 to 0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.433\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLinear Regression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eexp(\u0026beta;) = 0.99 (0.99 to 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.463\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLocal Gi* Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eNon significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8570556/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8570556/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Care for monogenic neurological disorders (MNDs) in resource-limited regions is constrained, resulting in delayed diagnosis. We estimated time to diagnosis (TTD) and associated factors in 248 adult individuals with MNDs from a national neurogenetic referral center in Peru. A questionnaire-based study was conducted from February 2024 to March 2025, assessing demographics, clinical features, and diagnostic timelines. The mean TTD in this cohort was 8.5 years, with a health system delay (HSD) of 4.3 years. Factors associated with longer TTD included employment status, healthcare facility, and restricted access to physicians. Distance to the diagnostic center showed no association with TTD, and geospatial analysis showed no global or local clustering, suggesting diagnostic delays are primarily driven by systemic and social rather than geographic factors. Future initiatives should address these barriers to enable earlier diagnosis and improve prognostic outcomes for patients with MNDs.","manuscriptTitle":"Predictors of time to diagnosis in monogenic neurological disorders from a referral center in Peru","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-03 16:09:35","doi":"10.21203/rs.3.rs-8570556/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7b18bda2-6607-47df-829a-c42182ca3974","owner":[],"postedDate":"February 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":61604594,"name":"Health sciences/Health care"},{"id":61604595,"name":"Health sciences/Medical research"},{"id":61604596,"name":"Health sciences/Neurology"},{"id":61604597,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2026-02-03T16:09:35+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-03 16:09:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8570556","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8570556","identity":"rs-8570556","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}