Knowledge and attitudes about rare diseases in the general population of Peru

Knowledge and attitudes about rare diseases in the general population of Peru | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Knowledge and attitudes about rare diseases in the general population of Peru Hugo Hernán Abarca-Barriga, Rossana Alvariño Tello, María Cristina Laso-Salazar, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4694492/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background. Rare diseases (RD) affect up to 8% of the population. They present with variable and nonspecific phenotypes, and most of these conditions are genetically determined. Few studies have explored the knowledge of rare diseases in the general population. Methods. The present research is a cross-sectional analytical study, where a modified and expert-verified survey was applied. The survey gathered data such as the level of knowledge of rare diseases, age, gender, profession, and years of professional practice; additionally, the attitude towards state financing, and insurance coverage were explored. We describe the absolute and relative frequencies, as well as the range of the proportion of qualitative variables; we assessed the difference in the average of the final score of rare diseases knowledge, multiple linear regression, and the crude and adjusted prevalence ratio based on the level of deficient or sufficient knowledge of rare diseases were determined. Results. The average score of the final RD knowledge is low (53.9; DE = 14.45); where 85.4% have a deficient score. However, there is a positive attitude towards RD regarding financing and coverage (> 80%). The variables that cause a decrease in the final score include not having a profession, older age, having a negative attitude towards insurance coverage, not having a family member with an RD, and having a higher number of years of professional practice. Despite a deficient score on RD knowledge, there is a positive attitude towards coverage and financing of RD. Conclusions. The study reveals a significant knowledge gap about rare diseases (RD) in the general population. Despite the low knowledge levels, there is a notably positive attitude towards the financing and insurance coverage of RD. Factors such as lack of a professional background, older age, negative attitudes towards insurance coverage, absence of family members with an RD, and longer professional practice are associated with lower knowledge scores. These findings highlight the need for targeted educational initiatives to enhance RD awareness while leveraging the positive attitudes towards financial support to advocate for improved healthcare policies and resources for RD patients. knowledge rare diseases attitude general population Figures Figure 1 Background There are different definitions of rare diseases (RD), which vary according to the country; however, the average is 1 per 2,500 inhabitants [ 1 ]. Between 3.5-8% of the world’s population would be affected by an RD, and its etiology is predominantly genetic (39–80%) [ 2 – 4 ]. The number of RD is estimated to be around 7,000, although this number could reach over 10,000 entities, and only 2.4-5% have a specific or personalized treatment, some of which have a high cost [ 4 , 5 ]. To get to the correct diagnosis can take between 5.6 to 7.6 years [ 6 ]. There are few reports about the interest in prioritizing RD in the general population; for example, in Norway, this reaches 24% [ 7 ]. Most publications are on medical professionals; for instance, some health decision-makers (e.g., the National Institute for Clinical Excellence) show little favorable disposition to reimburse medications for RD, with a “special inclination” for the measure if there is sufficient evidence [ 7 ]. Specifically, knowledge of rare diseases among physicians varies widely between different countries (14.4–25%); although it is important to note that the methodologies used are dissimilar [ 8 – 10 ]. On the other hand, in Brazil, different administrative obstacles were observed for patients, their families, and health professionals, which caused a delay in diagnosis and untimely treatment [ 11 ]. There is a negative perception of the costs of treatment of RD, and how research and development play a role in the improvement, discovery, and accessibility of orphan drugs. It is crucial to demystify aspects related to RD with the idea of generating universality, equity, and solidarity in this group of conditions [ 12 ]. The objective of this study was to determine the level of knowledge and attitudes towards rare diseases in the general population in Peru. Methods This was an observational study, cross-sectional analytical. The survey was conducted between January and October 2023, using a Google Form, distributed to the population of Lima. The survey was modified for application to the general population and was validated by medical experts in the field with a Cronbach’s alpha of 0.9 [ 8 ]. There were 27 questions with multiple-choice and dichotomous responses (Material Supplementary 1). The survey was organized into three parts: the first part was general data such as gender, age, profession, years of professional practice, workplace (public/private), and location where the survey was conducted. The second part consisted of 18 questions, such as population frequency, the definition of rare diseases, orphan drugs, etiology, clinical manifestations, the existence of diagnostic tests, local and worldwide treatment, the importance of diagnosis, treatment cost, and local legislation. Questions were assigned a score between 2.5–10 points, which made a total of 100 points. The third part consisted of four attitudinal questions, evaluating aspects such as funds allocation (dichotomous response), insurance coverage (Likert scale), budget allocation regarding common diseases (dichotomous response), and perception as a public health issue (dichotomous response). The dependent variable was the final score obtained, on a scale of 0-100, which was categorized as sufficient (70–100) and deficient (0–69). Independent variables were gender, profession, years of professional practice, age, and attitudinal questions regarding rare diseases. The question about the allocation of public resources for RD was dichotomized into those who believed there should be a greater budget allocation and those who thought there should be a smaller budget. Insurance coverage was divided into those completely in favor and those with a negative or partial opinion. The population residing in Lima is 9,674,755, and for sample size calculation, the following formula was used: n=(N)(z 2 )(p)(q)/((N-1)(E 2 )+(z 2 )(p)(q), assuming a hypothetical frequency of 50% of people with good knowledge and a confidence interval of 95%, resulting in a sample size of 384. The only inclusion criterion was that they be over 18 years old, and the exclusion criterion was that they be a physician or medical doctor. Responses were directly registered on a Google Form, automatically transferred to a Google spreadsheet, and then coded in a Microsoft Excel sheet. Statistical analysis was conducted using Stata with a significance level of 0.5% and a confidence interval of 95%. Relative frequencies of the qualitative variables were calculated, along with the confidence interval. After determining normality, we calculated the median and interquartile range of age and years of professional practice, as well as the mean and standard deviation (SD) of the knowledge level score. Bivariate analysis was performed using the t-student test between the final score obtained and dichotomous variables. Additionally, a simple linear regression analysis was conducted between the level of knowledge and age, and years of professional practice. Subsequently, the prevalence ratio was calculated between the knowledge level (sufficient/deficient) and attitudinal questions, gender, and survey location. Finally, multiple logistic regression was performed between the score, attitudinal questions, gender, workplace, profession, and age. The project was approved by Instituto de Investigaciones de Ciencias Biomédicas and the Research Vice-Rectory of Universidad Ricardo Palma. Informed consent was obtained from all surveyed individuals. Results Of the 431 individuals surveyed, 424 gave consent. In total, 51.4% (n = 218) of the population was surveyed outside the hospital setting. Of the respondents, 75.2% (n = 319) were women, and 54.9% (n = 233) were professionals. Additionally, 83% (n = 352) worked in both the public and private sectors (Table 1 ). An inverse relationship was observed between age and the level of knowledge (Fig. 1 A), as well as between years of professional practice and the total score obtained in the survey (Fig. 1 B). Table 1 General characteristics, attitudes and level of knowledge of the surveyed population regarding RD. Variable Mean/Median SD/IQR CI 95% Age (median; IQR) 39 15 - Years of professional practice (median; IQR) 14 13 - Level of knowledge (median; SD) 53.98 14.45 52,60 − 55,36 Gender n % IC 95% Female 319 75.2 70.9–79.1 Male 105 24.8 20.9–29.1 Location where the survey was conducted General population 218 48.6 43.8–53.4 In a genetics health center 206 51.4 46.6–56.2 Professional Yes 233 54.9 50.2–59.6 No 191 45.1 40.4–49.8 Workplace Public 72 17.0 13.7–20.9 Private/Public 352 83.0 79.1–86.3 Do you consider rare diseases to be a public health issue? Yes 345 81.4 77.4–84.8 No 79 18.6 15.2–22.6 Do you think the government should allocate a specific fund for rare diseases and orphan drugs? Yes 424 100 - No 0 0 - Do you believe it is important that coverage of these diseases by insurance systems is important? Not important 7 1.7 0,.7-3.4 Slightly important 1 0.2 0.03–1.7 Indiferent 10 2.4 1.3–4.3 Important 27 6.4 4.4–9.1 Very important 379 89.4 86.1–91.9 Do you think it is important to allocate the same budget for rare diseases as for common diseases, considering that the treatment for some rare diseases is more costly? Equal budget for both 140 33,0 28,7–37,7 Higher budget for rare diseases 214 50,5 45,8–55,2 Higher budget for common diseases 39 9,2 6,8–12,4 It is indifferent whether a higher budget is assigned for one or the other 31 7,3 5,2–10,2 Level of knowledge Sufficient 62 14,6 11,6–18,3 Defficient 362 85,4 81,7–88,4 CI = Confidence Interval; SD = Standard Deviation; IQR = Interquartile Range Regarding the State’s attitude towards RD, 81.4% (n = 345) considered that it is a public health issue, and 89.4% (n = 379) believed that insurance coverage of RD was very important (Table 1 ). In a similar sense, 50.5% (n = 214) indicated that a higher should be budget allocated for RD compared to other illnesses (Table 1 ). The level of knowledge of RD was deficient in 85.4% (n = 362). The mean was superior in the professional population, the ones who attend a genetics center, and those who believe that the State should offer coverage for RD in healthcare (Table 2 ). No differences were observed regarding gender, workplace, whether they consider it a public health issue, or if more resources should be allocated to patients with RD (Table 2 ). People without a profession obtained a 9% higher rate of deficient scores in the level of knowledge of RD (Table 3 ). No higher prevalences were observed regarding gender, survey location, working in a state institution, or attitudes toward RD (Table 3 ). The final score on RD knowledge obtained had a variability of 6.7% regarding the variables age, profession, state coverage of RD, gender, workplace, considering RD a public health issue, and budget allocation for RD (Table 4 ). Non-professionals scored 5.8 (2.7–8.8) points less than professionals. Similarly, those indicating that RD should not be covered by insurance obtained a lower score of 6.7 (0.2–13.7); and older age negatively influenced the final score [0.21(0.07–0.3)] (Table 4 ). Table 2 Difference in means of the final score on knowledge of RD in relation to gender, professional practice, survey location, workplace, and attitudes towards rare diseases. Variables Mean SD CI (95%) P value Gender Female 53.7 14.3 52.1–55.3 0.2217 Male 54.9 14.9 52.0-57.8 Professional Yes 56.1 14.6 54.2–58.0 0.0003 No 51.3 13.8 49.3–53.3 Survey location General population 52.7 15.0 50.6–54.7 0.0364 In a genetics health center 55.3 13.9 53.4–57.0 Workplace Private/Public 54.3 14.0 52.8–55.8 0.2008 Public 52.7 16.4 48.8–56.5 Public health issue Yes 54.1 14.6 52.6–55.7 0.3340 No 53.3 13.9 50.2–56.5 Insurance coverage Yes 54.3 14.4 52.9–55.7 0.0166 No/Partially 46.9 14.3 39.7–53.9 Use of public resources Higher resources for RD 53.3 13.8 51.4–55.1 0.1543 Indiferent o less resources for RD 54.7 15.1 52.7–56.8 CI = Confidence interval; SD = Standard deviation Table 3 Prevalence of people with deficient knowledge according to gender and attitudes towards RD Variables aPR CI (95%) P value cPR CI P value Male 0.99 0.90–1.09 0.8370 1.00 0.91–1.11 0.857 Non-professional 1.08 0.99–1.16 0.0556 1.09 1.005–1.189 0.038 General population 1,.2 0.94–1.10 0.6057 1.05 0.97–1.14 0.214 Working in a state institution 1.01 0.91–1.11 0.8467 1.02 0.92–1.14 0.647 Not a public health issue 1.02 0.93–1.13 0.5838 1.01 0.92–1.12 0.752 Should not or should have partial coverage 1.04 0.88–1.23 0.6665 1.06 0.90–1.24 0.492 Use of public resources: Indifferent or less resources for RD 0.96 0.89–1.04 0.3654 0.97 0.89–1.05 0.444 cPR = Crude prevalence ratio; CI = Confidence interval; aPR = Adjusted prevalence ratio Table 4 Effect on the final score of RD knowledge concerning age and attitude towards RD Variables Coefficient Standard error T value P value CI (95%) Constant* 64.6 3.2 20.29 < 0.001 58.3–70.8 Non-professional -5.8 1.6 -3.76 < 0.001 -8.8 - -2.7 General population survey 1.3 1.5 0.91 0.361 -1.5–4.2 Coverage by insurance systems -6.7 3.4 -2.03 0.043 -13.7 - -0.2 Age -0.21 0.07 -3.14 0.002 -0.3 - -0.07 Male gender -0.3 1.6 0.17 0.866 -2.9–3.5 Public workplace -2.2 1.9 -1.16 0.247 -5.8–1.5 You consider that RD are not a public health issue -0.01 1.8 -0.01 0.992 -3.6–3.5 Th estate should allocate less budget 0.46 1.5 0.32 0.752 -2.4–3.3 CI = Confidence interval; * The model explained 6,7% of the variability in the level of knowledge of RD (R-squared = 0,0681) Discussion The surveyed general population has a deficient level of knowledge of rare diseases at 85.4%, with a low average score. In Peru, there are no previous studies exploring attitudes and knowledge of RD in the general population. However, these aspects were previously explored in medical students and physicians, obtaining deficient scores [ 8 ]. This could be due to the healthcare system not promoting and lacking health policies on RD despite having a legal framework since 2014. Other influencing factors include poor access to information or the limited number of healthcare institutions with specialists in this field [ 13 , 14 ]. In Spain, lower knowledge about rare diseases was observed in university students, not related to healthcare, with correct responses ranging from 7.5–82.5% [ 9 ]. All respondents indicated that there should be an exclusive fund for the treatment of RD. Furthermore, more than half indicated the need to allocate a higher budget for RD compared to other illnesses. Unlike studies in the general population in Sweden and Norway, which indicate that 23,9% and 11,2%, respectively, are in favor of this measure [ 7 , 15 ]. Additionally, it was found that most respondents think that insurance systems should cover patients with RD and consider it a public health issue. This positive attitude may be due to the fact that the Peruvian population is one of the most empathetic in the world [ 16 ]. We observed that with increasing age, there was a lower score achieved regarding RD. This could be because the older population has more limited access to digital information or there is an inherent decline in the learning process that is directly proportional to age [ 17 , 18 ]. This result is directly related to what was found in the population with more years of professional practice. The final score achieved was higher in the population attending an institution that serves patients with rare diseases, professionals, and those who indicated that insurance systems should cover RD. Attending an institution with a patient diagnosed with a probable RD makes it more likely that the family or the patient will seek information in advance or know more about these diseases. Additionally, being professionals predicts having greater and better access to information [ 18 ]. Similarly, the prevalence of people with deficient knowledge is higher among non-professionals. In the model to assess the influence of the variables studied in the final knowledge score, the variables that showed negative influence were not being a professional, having a negative attitude towards RD coverage by insurance, and older age. The limitation of the study is that the sample was not randomized, which impedes the generalization of our results; furthermore, the survey was distributed digitally, and therefore individuals could immediately search for information to respond to the survey directly. Additionally, in those cases where the survey was administered directly, the response may have been influenced by the research group. However, the sample size was representative, and the final score showed a normal distribution, leading us to infer that it was representative of the general population in Peru. Despite a significant proportion of the population showing a positive attitude towards RD, and that we are a society with a higher empathy coefficient, we observe in practice that the gap in access to diagnosis (e.g., due to lack of implementation) or treatment opportunity is becoming increasingly evident when compared to countries in the Latin American and global regions [ 19 , 20 ]. Conclusions About 85% of the surveyed individuals had a deficient knowledge of RD. This deficit may be negatively influenced and directly proportional to older age, lack of university education, as well as having a negative attitude toward insurance coverage of RD. While over 80% demonstrated a positive attitude towards increased funding, recognizing it as a public health problem, and supporting coverage for rare diseases, this sentiment does not translate into an improvement in the implementation of genetics services in both public and private institutions. Consequently, there is a lack of opportunity for diagnosis and fair access to approved personalized therapies, which currently exist for rare diseases and often lead to a change in family history and restore social, familial, and economic aspects affected by the presence of chronic rare disease. It is essential to raise awareness through universities about the etiopathogenesis, clinical course, and treatments of rare diseases among medical professionals and the general population. Additionally, conducting qualitative studies to establish the cause of the lack of implementation of a greater number of genetic services and the strengthening of existing ones, as well as identifying the reasons for barriers obstructing access to personalized treatments for RD, is crucial. Declarations Ethics approval and consent to participate. All procedures performed in this study adhered to the ethical standards of the institutional research committee of the Instituto de Investigación de Ciencias Biomédicas and the 1964 Helsinki Declaration and its later amendments. Consent for publication. The written informed consent was obtained prior to administering the survey Availability of data and materials Data such as data supporting the findings of this study are available and may be obtained from the corresponding author upon reasonable request. Competing interest The authors declare no conflict of interest. Funding None Acknowledgements Not applicable. Authors’ Contributions HHAB conceptualized the study. RAT and JLI collected the data. HHAB wrote the draft manuscript. HHAB, MCCM, RAT, JLI, and MCLS edited and reviewed the manuscript for critical content. All authors read and approved the final manuscript. Authors’ information Instituto de Investigación de Ciencias Biomédicas, Universidad Ricardo Palma. Hugo Hernán Abarca-Barriga (HHAB); Jorge La Serna-Infantes (JLI); María del Carmen Castro Mujica (MCCM). Servicio de Genética & Errores Innatos del Metabolismo, Instituto Nacional de Salud del Niño, Breña, Lima, Perú. Hugo Hernán Abarca-Barriga; Rossana Alvariño Tello (RAT); Maria Cristina Laso-Salazar (MCLS) References Richter T, Nestler-Parr S, Babela R, Khan ZM, Tesoro T, Molsen E, et al. Rare Disease Terminology and Definitions—A Systematic Global Review: Report of the ISPOR Rare Disease Special Interest Group. Value Health. 2015;18:906–14. Maiella S, Rath A, Angin C, Mousson F, Kremp O. [Orphanet and its consortium: where to find expert-validated information on rare diseases]. Rev Neurol (Paris). 2013;169(Suppl 1):S3–8. Nguengang Wakap S, Lambert DM, Olry A, Rodwell C, Gueydan C, Lanneau V, et al. Estimating cumulative point prevalence of rare diseases: analysis of the Orphanet database. Eur J Hum Genet. 2019. https://doi.org/10.1038/s41431-019-0508-0 . Ferreira CR. The burden of rare diseases. Am J Med Genet A. 2019;179:885–92. Haendel M, Vasilevsky N, Unni D, Bologa C, Harris N, Rehm H, et al. How many rare diseases are there? Nat Rev Drug Discov. 2020;19:77–8. Rare Disease by the Numbers. Innovation.org. http://innovation.org/about-us/commitment/research-discovery/rare-disease-numbers . Accessed 20 Jul 2019. Desser AS, Gyrd-Hansen D, Olsen JA, Grepperud S, Kristiansen IS. Societal views on orphan drugs: cross sectional survey of Norwegians aged 40 to 67. BMJ. 2010;341. Flores A, Burgos S, Abarca-Barriga H. Knowledge level of medical students and physicians about rare diseases in Lima, Peru. Intractable Rare Dis Res. 2022;11:180–8. Ramalle-Gómara E, Ruiz E, Quiñones C, Andrés S, Iruzubieta J, Gil-de-Gómez J. General knowledge and opinion of future health care and non-health care professionals on rare diseases. J Eval Clin Pract. 2015;21:198–201. Desser AS. Prioritizing treatment of rare diseases: a survey of preferences of Norwegian doctors. Soc Sci Med. 2013;94:56–62. Lopes MT, Koch VH, Sarrubbi-Junior V, Gallo PR, Carneiro-Sampaio M. Difficulties in the diagnosis and treatment of rare diseases according to the perceptions of patients, relatives and health care professionals. Clin (Sao Paulo). 2018;73. Rollet P, Lemoine A, Dunoyer M. Sustainable rare diseases business and drug access: no time for misconceptions. Orphanet J Rare Dis. 2013;8:109. Conoce a tu médico. Colegio Médico del Perú (CMP). https://www.cmp.org.pe/conoce-a-tu-medico/ . Accessed 20 Jul 2019. INEI. Brecha digital. Wiss J, Levin LÅ. Preferences for Prioritizing Patients with Rare Diseases: a Survey of the General Population in Sweden. Value Health. 2014;17:A325–6. Chopik WJ, O’Brien E, Konrath SH. Differences in Empathic Concern and Perspective Taking Across 63 Countries. J Cross-Cult Psychol. 2017;48:23–38. Flores Cueto JJ, Hernandez RM, Garay Argandoña R. Tecnologías de información: Acceso a internet y brecha digital en Perú. Revista Venez de Gerencia: RVG. 2020;25:504–27. Salthouse TA, Atkinson TM, Berish DE. Executive functioning as a potential mediator of age-related cognitive decline in normal adults. J Exp Psychol Gen. 2003;132:566–94. Abarca-Barriga HH, Rodríguez RS. Ampliación del tamizaje de errores innatos del metabolismo en Perú: reporte de caso con trastorno del metabolismo de cobalamina. ACTA Med PERUANA. 2020;37. Fajardo M, Abarca-Barriga. Hugo. La genética y sus implicancias actuales y futuras en la medicina peruana. In: Libro del Bicentenario de la Independencia Nacional 1821–2021. Fondo Editorial Comunicacional. pp. 173–88. Additional Declarations No competing interests reported. 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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-4694492","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":327703878,"identity":"2b13881d-9fe5-4076-8114-8969c81ccd9d","order_by":0,"name":"Hugo Hernán Abarca-Barriga","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIie3RIQsCMRTA8XcM7srE+iz6FWZRDOJXUQSvXDi4ImgwaRHzgl/iumEwOMsVm+CFE2HpgskourNYnNoE9w9vbPBjgwHYbD8YeyxCDyd/bPqfE8K+Ji5+RNro73KYZo32QqpJZZNB1QsYCcevSYcHEYNENdfpqHWgSkFtWTDCU8PD9sEIwZUOR7gTIfUJI5X5O3KVPY7eJdKkV5KrifgJOnM54EhbpLwFNZkZSFoQNlipIadBVFsLRTFVoeSJgWz9Y36+ZF3ubeNzIbJ6dTGMT+H0NQGg7PkjBNVTmgCAlz/vRDmJmdhsNtufdQMtyVUKbrp9bgAAAABJRU5ErkJggg==","orcid":"","institution":"Universidad Ricardo Palma","correspondingAuthor":true,"prefix":"","firstName":"Hugo","middleName":"Hernán","lastName":"Abarca-Barriga","suffix":""},{"id":327703879,"identity":"aafeb1ec-1ad6-4853-be6a-64c10106af60","order_by":1,"name":"Rossana Alvariño Tello","email":"","orcid":"","institution":"Instituto Nacional de Salud del Niño","correspondingAuthor":false,"prefix":"","firstName":"Rossana","middleName":"Alvariño","lastName":"Tello","suffix":""},{"id":327703880,"identity":"bc2f4c7e-5cd4-43a8-a203-6cebe81f4285","order_by":2,"name":"María Cristina Laso-Salazar","email":"","orcid":"","institution":"Instituto Nacional de Salud del Niño","correspondingAuthor":false,"prefix":"","firstName":"María","middleName":"Cristina","lastName":"Laso-Salazar","suffix":""},{"id":327703881,"identity":"a78b6adb-2288-455c-90d8-ebcdd468b31c","order_by":3,"name":"Jorge La Serna-Infantes","email":"","orcid":"","institution":"Universidad Ricardo Palma","correspondingAuthor":false,"prefix":"","firstName":"Jorge","middleName":"La","lastName":"Serna-Infantes","suffix":""},{"id":327703882,"identity":"c7609e72-7e86-440c-90c3-e6f8eaaf821c","order_by":4,"name":"María del Carmen Castro Mujica","email":"","orcid":"","institution":"Universidad Ricardo Palma","correspondingAuthor":false,"prefix":"","firstName":"María","middleName":"del Carmen Castro","lastName":"Mujica","suffix":""}],"badges":[],"createdAt":"2024-07-06 01:10:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4694492/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4694492/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62217071,"identity":"6c926619-0994-49cf-99f5-9115bf952e30","added_by":"auto","created_at":"2024-08-11 11:51:45","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":172983,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e. A decrease in the final score about RD is observed as age increases (p=0.022; R\u003csup\u003e2\u003c/sup\u003e=0.0124) and \u003cstrong\u003e(B) \u003c/strong\u003ethe years of professional practice (p=0,001; R\u003csup\u003e2\u003c/sup\u003e=0,0488).\u003c/p\u003e","description":"","filename":"Figure1A3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4694492/v1/b2b6a250ac7e252a7f5a0340.jpg"},{"id":62218131,"identity":"2eb74680-af1c-42ea-b2de-a1c9d94beeec","added_by":"auto","created_at":"2024-08-11 11:59:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":809287,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4694492/v1/54bc0cac-9ed6-470c-9c74-6e7033db2939.pdf"},{"id":62217070,"identity":"11def4b8-aaf0-4fe5-84d8-c917e1412b5b","added_by":"auto","created_at":"2024-08-11 11:51:45","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":79907,"visible":true,"origin":"","legend":"","description":"","filename":"Materialsupplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-4694492/v1/e6498e6cd991ce644f1d5c3b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Knowledge and attitudes about rare diseases in the general population of Peru","fulltext":[{"header":"Background","content":"\u003cp\u003eThere are different definitions of rare diseases (RD), which vary according to the country; however, the average is 1 per 2,500 inhabitants [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Between 3.5-8% of the world\u0026rsquo;s population would be affected by an RD, and its etiology is predominantly genetic (39\u0026ndash;80%) [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The number of RD is estimated to be around 7,000, although this number could reach over 10,000 entities, and only 2.4-5% have a specific or personalized treatment, some of which have a high cost [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. To get to the correct diagnosis can take between 5.6 to 7.6 years [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere are few reports about the interest in prioritizing RD in the general population; for example, in Norway, this reaches 24% [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMost publications are on medical professionals; for instance, some health decision-makers (e.g., the National Institute for Clinical Excellence) show little favorable disposition to reimburse medications for RD, with a \u0026ldquo;special inclination\u0026rdquo; for the measure if there is sufficient evidence [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Specifically, knowledge of rare diseases among physicians varies widely between different countries (14.4\u0026ndash;25%); although it is important to note that the methodologies used are dissimilar [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. On the other hand, in Brazil, different administrative obstacles were observed for patients, their families, and health professionals, which caused a delay in diagnosis and untimely treatment [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere is a negative perception of the costs of treatment of RD, and how research and development play a role in the improvement, discovery, and accessibility of orphan drugs. It is crucial to demystify aspects related to RD with the idea of generating universality, equity, and solidarity in this group of conditions [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe objective of this study was to determine the level of knowledge and attitudes towards rare diseases in the general population in Peru.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis was an observational study, cross-sectional analytical. The survey was conducted between January and October 2023, using a Google Form, distributed to the population of Lima.\u003c/p\u003e \u003cp\u003eThe survey was modified for application to the general population and was validated by medical experts in the field with a Cronbach\u0026rsquo;s alpha of 0.9 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. There were 27 questions with multiple-choice and dichotomous responses (Material Supplementary 1). The survey was organized into three parts: the first part was general data such as gender, age, profession, years of professional practice, workplace (public/private), and location where the survey was conducted. The second part consisted of 18 questions, such as population frequency, the definition of rare diseases, orphan drugs, etiology, clinical manifestations, the existence of diagnostic tests, local and worldwide treatment, the importance of diagnosis, treatment cost, and local legislation. Questions were assigned a score between 2.5\u0026ndash;10 points, which made a total of 100 points. The third part consisted of four attitudinal questions, evaluating aspects such as funds allocation (dichotomous response), insurance coverage (Likert scale), budget allocation regarding common diseases (dichotomous response), and perception as a public health issue (dichotomous response).\u003c/p\u003e \u003cp\u003eThe dependent variable was the final score obtained, on a scale of 0-100, which was categorized as sufficient (70\u0026ndash;100) and deficient (0\u0026ndash;69). Independent variables were gender, profession, years of professional practice, age, and attitudinal questions regarding rare diseases. The question about the allocation of public resources for RD was dichotomized into those who believed there should be a greater budget allocation and those who thought there should be a smaller budget. Insurance coverage was divided into those completely in favor and those with a negative or partial opinion.\u003c/p\u003e \u003cp\u003eThe population residing in Lima is 9,674,755, and for sample size calculation, the following formula was used: n=(N)(z\u003csup\u003e2\u003c/sup\u003e)(p)(q)/((N-1)(E\u003csup\u003e2\u003c/sup\u003e)+(z\u003csup\u003e2\u003c/sup\u003e)(p)(q), assuming a hypothetical frequency of 50% of people with good knowledge and a confidence interval of 95%, resulting in a sample size of 384. The only inclusion criterion was that they be over 18 years old, and the exclusion criterion was that they be a physician or medical doctor.\u003c/p\u003e \u003cp\u003eResponses were directly registered on a Google Form, automatically transferred to a Google spreadsheet, and then coded in a Microsoft Excel sheet. Statistical analysis was conducted using Stata with a significance level of 0.5% and a confidence interval of 95%. Relative frequencies of the qualitative variables were calculated, along with the confidence interval. After determining normality, we calculated the median and interquartile range of age and years of professional practice, as well as the mean and standard deviation (SD) of the knowledge level score. Bivariate analysis was performed using the t-student test between the final score obtained and dichotomous variables.\u003c/p\u003e \u003cp\u003eAdditionally, a simple linear regression analysis was conducted between the level of knowledge and age, and years of professional practice. Subsequently, the prevalence ratio was calculated between the knowledge level (sufficient/deficient) and attitudinal questions, gender, and survey location. Finally, multiple logistic regression was performed between the score, attitudinal questions, gender, workplace, profession, and age.\u003c/p\u003e \u003cp\u003eThe project was approved by \u003cem\u003eInstituto de Investigaciones de Ciencias Biom\u0026eacute;dicas\u003c/em\u003e and the Research Vice-Rectory of Universidad Ricardo Palma. Informed consent was obtained from all surveyed individuals.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e Of the 431 individuals surveyed, 424 gave consent. In total, 51.4% (n\u0026thinsp;=\u0026thinsp;218) of the population was surveyed outside the hospital setting. Of the respondents, 75.2% (n\u0026thinsp;=\u0026thinsp;319) were women, and 54.9% (n\u0026thinsp;=\u0026thinsp;233) were professionals. Additionally, 83% (n\u0026thinsp;=\u0026thinsp;352) worked in both the public and private sectors (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAn inverse relationship was observed between age and the level of knowledge (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), as well as between years of professional practice and the total score obtained in the survey (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneral characteristics, attitudes and level of knowledge of the surveyed population regarding RD.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean/Median\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD/IQR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCI 95%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (median; IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYears of professional practice (median; IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel of knowledge (median; SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52,60\u0026thinsp;\u0026minus;\u0026thinsp;55,36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003en\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eIC 95%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.9\u0026ndash;79.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.9\u0026ndash;29.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation where the survey was conducted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.8\u0026ndash;53.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn a genetics health center\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.6\u0026ndash;56.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfessional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.2\u0026ndash;59.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.4\u0026ndash;49.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorkplace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.7\u0026ndash;20.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate/Public\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.1\u0026ndash;86.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDo you consider rare diseases to be a public health issue?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.4\u0026ndash;84.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.2\u0026ndash;22.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDo you think the government should allocate a specific fund for rare diseases and orphan drugs?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDo you believe it is important that coverage of these diseases by insurance systems is important?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot important\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,.7-3.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlightly important\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03\u0026ndash;1.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndiferent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3\u0026ndash;4.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImportant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.4\u0026ndash;9.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery important\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86.1\u0026ndash;91.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDo you think it is important to allocate the same budget for rare diseases as for common diseases, considering that the treatment for some rare diseases is more costly?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEqual budget for both\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28,7\u0026ndash;37,7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher budget for rare diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45,8\u0026ndash;55,2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher budget for common diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6,8\u0026ndash;12,4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIt is indifferent whether a higher budget is assigned for one or the other\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5,2\u0026ndash;10,2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel of knowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSufficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11,6\u0026ndash;18,3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81,7\u0026ndash;88,4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eCI\u0026thinsp;=\u0026thinsp;Confidence Interval; SD\u0026thinsp;=\u0026thinsp;Standard Deviation; IQR\u0026thinsp;=\u0026thinsp;Interquartile Range\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRegarding the State\u0026rsquo;s attitude towards RD, 81.4% (n\u0026thinsp;=\u0026thinsp;345) considered that it is a public health issue, and 89.4% (n\u0026thinsp;=\u0026thinsp;379) believed that insurance coverage of RD was very important (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In a similar sense, 50.5% (n\u0026thinsp;=\u0026thinsp;214) indicated that a higher should be budget allocated for RD compared to other illnesses (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe level of knowledge of RD was deficient in 85.4% (n\u0026thinsp;=\u0026thinsp;362). The mean was superior in the professional population, the ones who attend a genetics center, and those who believe that the State should offer coverage for RD in healthcare (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). No differences were observed regarding gender, workplace, whether they consider it a public health issue, or if more resources should be allocated to patients with RD (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePeople without a profession obtained a 9% higher rate of deficient scores in the level of knowledge of RD (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). No higher prevalences were observed regarding gender, survey location, working in a state institution, or attitudes toward RD (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe final score on RD knowledge obtained had a variability of 6.7% regarding the variables age, profession, state coverage of RD, gender, workplace, considering RD a public health issue, and budget allocation for RD (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Non-professionals scored 5.8 (2.7\u0026ndash;8.8) points less than professionals. Similarly, those indicating that RD should not be covered by insurance obtained a lower score of 6.7 (0.2\u0026ndash;13.7); and older age negatively influenced the final score [0.21(0.07\u0026ndash;0.3)] (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifference in means of the final score on knowledge of RD in relation to gender, professional practice, survey location, workplace, and attitudes towards rare diseases.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCI (95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.1\u0026ndash;55.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2217\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.0-57.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfessional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.2\u0026ndash;58.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.3\u0026ndash;53.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurvey location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.6\u0026ndash;54.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0364\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn a genetics health center\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.4\u0026ndash;57.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorkplace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate/Public\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.8\u0026ndash;55.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.8\u0026ndash;56.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic health issue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.6\u0026ndash;55.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3340\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.2\u0026ndash;56.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsurance coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.9\u0026ndash;55.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0166\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo/Partially\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.7\u0026ndash;53.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse of public resources\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher resources for RD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.4\u0026ndash;55.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1543\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndiferent o less resources for RD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.7\u0026ndash;56.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eCI\u0026thinsp;=\u0026thinsp;Confidence interval; SD\u0026thinsp;=\u0026thinsp;Standard deviation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrevalence of people with deficient knowledge according to gender and attitudes towards RD\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaPR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCI (95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ecPR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.90\u0026ndash;1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.91\u0026ndash;1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-professional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99\u0026ndash;1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.005\u0026ndash;1.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.038\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94\u0026ndash;1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.97\u0026ndash;1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorking in a state institution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.91\u0026ndash;1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.92\u0026ndash;1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.647\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot a public health issue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93\u0026ndash;1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.92\u0026ndash;1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShould not or should have partial coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.88\u0026ndash;1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.90\u0026ndash;1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.492\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse of public resources: Indifferent or less resources for RD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.89\u0026ndash;1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.89\u0026ndash;1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.444\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003ecPR\u0026thinsp;=\u0026thinsp;Crude prevalence ratio; CI\u0026thinsp;=\u0026thinsp;Confidence interval; aPR\u0026thinsp;=\u0026thinsp;Adjusted prevalence ratio\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect on the final score of RD knowledge concerning age and attitude towards RD\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCI (95%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e58.3\u0026ndash;70.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-professional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-8.8 - -2.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral population survey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.5\u0026ndash;4.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoverage by insurance systems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.043\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-13.7 - -0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.3 - -0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale gender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.9\u0026ndash;3.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic workplace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-5.8\u0026ndash;1.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYou consider that RD are not a public health issue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.6\u0026ndash;3.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTh estate should allocate less budget\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.4\u0026ndash;3.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eCI\u0026thinsp;=\u0026thinsp;Confidence interval; * The model explained 6,7% of the variability in the level of knowledge of RD (R-squared\u0026thinsp;=\u0026thinsp;0,0681)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":" \u003cp\u003eThe surveyed general population has a deficient level of knowledge of rare diseases at 85.4%, with a low average score. In Peru, there are no previous studies exploring attitudes and knowledge of RD in the general population. However, these aspects were previously explored in medical students and physicians, obtaining deficient scores [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This could be due to the healthcare system not promoting and lacking health policies on RD despite having a legal framework since 2014. Other influencing factors include poor access to information or the limited number of healthcare institutions with specialists in this field [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In Spain, lower knowledge about rare diseases was observed in university students, not related to healthcare, with correct responses ranging from 7.5\u0026ndash;82.5% [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAll respondents indicated that there should be an exclusive fund for the treatment of RD. Furthermore, more than half indicated the need to allocate a higher budget for RD compared to other illnesses. Unlike studies in the general population in Sweden and Norway, which indicate that 23,9% and 11,2%, respectively, are in favor of this measure [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Additionally, it was found that most respondents think that insurance systems should cover patients with RD and consider it a public health issue. This positive attitude may be due to the fact that the Peruvian population is one of the most empathetic in the world [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe observed that with increasing age, there was a lower score achieved regarding RD. This could be because the older population has more limited access to digital information or there is an inherent decline in the learning process that is directly proportional to age [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This result is directly related to what was found in the population with more years of professional practice.\u003c/p\u003e \u003cp\u003eThe final score achieved was higher in the population attending an institution that serves patients with rare diseases, professionals, and those who indicated that insurance systems should cover RD. Attending an institution with a patient diagnosed with a probable RD makes it more likely that the family or the patient will seek information in advance or know more about these diseases. Additionally, being professionals predicts having greater and better access to information [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Similarly, the prevalence of people with deficient knowledge is higher among non-professionals.\u003c/p\u003e \u003cp\u003eIn the model to assess the influence of the variables studied in the final knowledge score, the variables that showed negative influence were not being a professional, having a negative attitude towards RD coverage by insurance, and older age.\u003c/p\u003e\u003cp\u003eThe limitation of the study is that the sample was not randomized, which impedes the generalization of our results; furthermore, the survey was distributed digitally, and therefore individuals could immediately search for information to respond to the survey directly. Additionally, in those cases where the survey was administered directly, the response may have been influenced by the research group. However, the sample size was representative, and the final score showed a normal distribution, leading us to infer that it was representative of the general population in Peru.\u003c/p\u003e \u003cp\u003eDespite a significant proportion of the population showing a positive attitude towards RD, and that we are a society with a higher empathy coefficient, we observe in practice that the gap in access to diagnosis (e.g., due to lack of implementation) or treatment opportunity is becoming increasingly evident when compared to countries in the Latin American and global regions [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e "},{"header":"Conclusions","content":" \u003cp\u003eAbout 85% of the surveyed individuals had a deficient knowledge of RD. This deficit may be negatively influenced and directly proportional to older age, lack of university education, as well as having a negative attitude toward insurance coverage of RD. While over 80% demonstrated a positive attitude towards increased funding, recognizing it as a public health problem, and supporting coverage for rare diseases, this sentiment does not translate into an improvement in the implementation of genetics services in both public and private institutions. Consequently, there is a lack of opportunity for diagnosis and fair access to approved personalized therapies, which currently exist for rare diseases and often lead to a change in family history and restore social, familial, and economic aspects affected by the presence of chronic rare disease.\u003c/p\u003e \u003cp\u003eIt is essential to raise awareness through universities about the etiopathogenesis, clinical course, and treatments of rare diseases among medical professionals and the general population. Additionally, conducting qualitative studies to establish the cause of the lack of implementation of a greater number of genetic services and the strengthening of existing ones, as well as identifying the reasons for barriers obstructing access to personalized treatments for RD, is crucial.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures performed in this study adhered to the ethical standards of the institutional research committee of the Instituto de Investigaci\u0026oacute;n de Ciencias Biom\u0026eacute;dicas and the 1964 Helsinki Declaration and its later amendments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe written informed consent was obtained prior to administering the survey\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData such as data supporting the findings of this study are available and may be obtained from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHHAB conceptualized the study. RAT and JLI collected the data. HHAB wrote the draft manuscript. HHAB, MCCM, RAT, JLI, and MCLS edited and reviewed the manuscript for critical content.\u0026nbsp;All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstituto de Investigaci\u0026oacute;n de Ciencias Biom\u0026eacute;dicas, Universidad Ricardo Palma.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHugo Hern\u0026aacute;n Abarca-Barriga (HHAB); Jorge La Serna-Infantes (JLI); Mar\u0026iacute;a del Carmen Castro Mujica (MCCM).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eServicio de Gen\u0026eacute;tica \u0026amp; Errores Innatos del Metabolismo, Instituto Nacional de Salud del Ni\u0026ntilde;o, Bre\u0026ntilde;a, Lima, Per\u0026uacute;.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHugo Hern\u0026aacute;n Abarca-Barriga; Rossana Alvari\u0026ntilde;o Tello (RAT); Maria Cristina Laso-Salazar (MCLS)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRichter T, Nestler-Parr S, Babela R, Khan ZM, Tesoro T, Molsen E, et al. Rare Disease Terminology and Definitions\u0026mdash;A Systematic Global Review: Report of the ISPOR Rare Disease Special Interest Group. Value Health. 2015;18:906\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaiella S, Rath A, Angin C, Mousson F, Kremp O. [Orphanet and its consortium: where to find expert-validated information on rare diseases]. Rev Neurol (Paris). 2013;169(Suppl 1):S3\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNguengang Wakap S, Lambert DM, Olry A, Rodwell C, Gueydan C, Lanneau V, et al. Estimating cumulative point prevalence of rare diseases: analysis of the Orphanet database. Eur J Hum Genet. 2019. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41431-019-0508-0\u003c/span\u003e\u003cspan address=\"10.1038/s41431-019-0508-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerreira CR. The burden of rare diseases. Am J Med Genet A. 2019;179:885\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaendel M, Vasilevsky N, Unni D, Bologa C, Harris N, Rehm H, et al. How many rare diseases are there? Nat Rev Drug Discov. 2020;19:77\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRare Disease by the Numbers. Innovation.org. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://innovation.org/about-us/commitment/research-discovery/rare-disease-numbers\u003c/span\u003e\u003cspan address=\"http://innovation.org/about-us/commitment/research-discovery/rare-disease-numbers\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 20 Jul 2019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDesser AS, Gyrd-Hansen D, Olsen JA, Grepperud S, Kristiansen IS. Societal views on orphan drugs: cross sectional survey of Norwegians aged 40 to 67. BMJ. 2010;341.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFlores A, Burgos S, Abarca-Barriga H. Knowledge level of medical students and physicians about rare diseases in Lima, Peru. Intractable Rare Dis Res. 2022;11:180\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRamalle-G\u0026oacute;mara E, Ruiz E, Qui\u0026ntilde;ones C, Andr\u0026eacute;s S, Iruzubieta J, Gil-de-G\u0026oacute;mez J. General knowledge and opinion of future health care and non-health care professionals on rare diseases. J Eval Clin Pract. 2015;21:198\u0026ndash;201.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDesser AS. Prioritizing treatment of rare diseases: a survey of preferences of Norwegian doctors. Soc Sci Med. 2013;94:56\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLopes MT, Koch VH, Sarrubbi-Junior V, Gallo PR, Carneiro-Sampaio M. Difficulties in the diagnosis and treatment of rare diseases according to the perceptions of patients, relatives and health care professionals. Clin (Sao Paulo). 2018;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRollet P, Lemoine A, Dunoyer M. Sustainable rare diseases business and drug access: no time for misconceptions. Orphanet J Rare Dis. 2013;8:109.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eConoce a tu m\u0026eacute;dico. Colegio M\u0026eacute;dico del Per\u0026uacute; (CMP). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cmp.org.pe/conoce-a-tu-medico/\u003c/span\u003e\u003cspan address=\"https://www.cmp.org.pe/conoce-a-tu-medico/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 20 Jul 2019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eINEI. Brecha digital.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWiss J, Levin L\u0026Aring;. Preferences for Prioritizing Patients with Rare Diseases: a Survey of the General Population in Sweden. Value Health. 2014;17:A325\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChopik WJ, O\u0026rsquo;Brien E, Konrath SH. Differences in Empathic Concern and Perspective Taking Across 63 Countries. J Cross-Cult Psychol. 2017;48:23\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFlores Cueto JJ, Hernandez RM, Garay Argando\u0026ntilde;a R. Tecnolog\u0026iacute;as de informaci\u0026oacute;n: Acceso a internet y brecha digital en Per\u0026uacute;. Revista Venez de Gerencia: RVG. 2020;25:504\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalthouse TA, Atkinson TM, Berish DE. Executive functioning as a potential mediator of age-related cognitive decline in normal adults. J Exp Psychol Gen. 2003;132:566\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbarca-Barriga HH, Rodr\u0026iacute;guez RS. Ampliaci\u0026oacute;n del tamizaje de errores innatos del metabolismo en Per\u0026uacute;: reporte de caso con trastorno del metabolismo de cobalamina. ACTA Med PERUANA. 2020;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFajardo M, Abarca-Barriga. Hugo. La gen\u0026eacute;tica y sus implicancias actuales y futuras en la medicina peruana. In: Libro del Bicentenario de la Independencia Nacional 1821\u0026ndash;2021. Fondo Editorial Comunicacional. pp. 173\u0026ndash;88.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"knowledge, rare diseases, attitude, general population","lastPublishedDoi":"10.21203/rs.3.rs-4694492/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4694492/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground. \u003c/strong\u003eRare diseases (RD) affect up to 8% of the population. They present with variable and nonspecific phenotypes, and most of these conditions are genetically determined. Few studies have explored the knowledge of rare diseases in the general population.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods.\u003c/strong\u003e The present research is a cross-sectional analytical study, where a modified and expert-verified survey was applied. The survey gathered data such as the level of knowledge of rare diseases, age, gender, profession, and years of professional practice; additionally, the attitude towards state financing, and insurance coverage were explored. We describe the absolute and relative frequencies, as well as the range of the proportion of qualitative variables; we assessed the difference in the average of the final score of rare diseases knowledge, multiple linear regression, and the crude and adjusted prevalence ratio based on the level of deficient or sufficient knowledge of rare diseases were determined.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults.\u003c/strong\u003e The average score of the final RD knowledge is low (53.9; DE = 14.45); where 85.4% have a deficient score. However, there is a positive attitude towards RD regarding financing and coverage (\u0026gt; 80%). The variables that cause a decrease in the final score include not having a profession, older age, having a negative attitude towards insurance coverage, not having a family member with an RD, and having a higher number of years of professional practice. Despite a deficient score on RD knowledge, there is a positive attitude towards coverage and financing of RD.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions.\u003c/strong\u003e The study reveals a significant knowledge gap about rare diseases (RD) in the general population. Despite the low knowledge levels, there is a notably positive attitude towards the financing and insurance coverage of RD. Factors such as lack of a professional background, older age, negative attitudes towards insurance coverage, absence of family members with an RD, and longer professional practice are associated with lower knowledge scores. These findings highlight the need for targeted educational initiatives to enhance RD awareness while leveraging the positive attitudes towards financial support to advocate for improved healthcare policies and resources for RD patients.\u003c/p\u003e","manuscriptTitle":"Knowledge and attitudes about rare diseases in the general population of Peru","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-11 11:51:40","doi":"10.21203/rs.3.rs-4694492/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-16T13:04:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-12T13:34:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-12T13:31:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-07-06T01:09:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0b96dfa9-ec8e-40ec-8135-9f139932d423","owner":[],"postedDate":"August 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-08-11T11:51:41+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-11 11:51:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4694492","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4694492","identity":"rs-4694492","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}