Rural-Urban Disparities in Healthcare Access Among Individuals with Osteoarthritis in Portugal: A Cross-Sectional Analysis of the 2019 National Health Survey

Rural-Urban Disparities in Healthcare Access Among Individuals with Osteoarthritis in Portugal: A Cross-Sectional Analysis of the 2019 National Health Survey | 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 Rural-Urban Disparities in Healthcare Access Among Individuals with Osteoarthritis in Portugal: A Cross-Sectional Analysis of the 2019 National Health Survey Lara Campos, Eduardo Cruz, Baltazar Nunes This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7362024/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Osteoarthritis (OA) is a leading cause of musculoskeletal pain worldwide, with a greater impact on rural populations. Although preliminary findings suggest disparities in healthcare access for rural residents with OA, further research is needed to fully understand this issue. Aim: This study aimed to examine healthcare access disparities between rural and urban areas among individuals with self-reported OA living in Portugal. Methods This study involves a cross-sectional secondary analysis of data from the 2019 Portuguese National Health Interview Survey (NHIS), focusing on individuals who self-reported OA. The prevalence of variables related to healthcare access was estimated, including the frequency of healthcare visits, waiting times for medical care, and the impact of financial factors. The odds ratio (OR) for healthcare access between rural and urban residents, adjusted for confounding, was estimated using multivariable logistic regression models. An age-stratified analysis was also performed. Results A total of 4095 individuals with self-reported OA were included. Confounder-adjusted OR estimates revealed that living in rural areas was associated with a higher likelihood of not accessing physiotherapy treatments (OR 0.680, 95% CI 0.518–0.892), particularly among individuals under 65 years old. Additionally, rural residents were more likely (OR 1.775, 95% CI 1.197–2.633) to experience delays in healthcare services due to distance or transportation issues, with this disparity most pronounced among individuals aged 65 to 79 years. Conclusion These findings highlight disparities in healthcare access between rural and urban residents with OA in Portugal, underscoring the need for targeted interventions to improve healthcare availability and reduce inequities. Epidemiology osteoarthritis rural healthcare access inequities Figures Figure 1 INTRODUCTION Osteoarthritis (OA) is among the leading causes of musculoskeletal pain worldwide, affecting 7.6% of the global population in 2020. This prevalence increased by 132.2% over 30 years and is projected to rise by 60 to 100% by 2050(Steinmetz et al. 2023 ). This upward trend is largely driven by sociodemographic changes such as population ageing, as well as the growing incidence of modifiable risk factors including obesity and sedentary behaviour(O’brien et al. 2020 ). Data from Portugal indicates that the combined prevalence of hip, knee and hand OA ascends to 19.1%(Branco et al. 2016 ) Expectedly, OA is a major contributor to functional impairment and loss of independence, representing the seventh leading cause of disability worldwide after the age of 70(Courties et al. 2024 ). Extensive evidence suggests that OA tends to be more prevalent and disabling among individuals living in rural areas compared to their urban counterparts, with reported prevalence rates ranging from 20.0–21.9% in urban areas and from 26.2–26.9% in rural areas.(Busija et al. 2007 ; Liu et al. 2016 ; Boring et al. 2017 ). This pattern may be partially explained by greater exposure to physically demanding occupations—such as farming and manual labor—which are known to increase the risk of OA(Canetti et al. 2020 ), as well as by older demographic profiles(Busija et al. 2007 ) and higher rates of obesity(Wen et al. 2018 ) commonly found in rural populations, both of which are well-established risk factors for developing knee OA (Busija et al. 2007 ; Dong et al. 2023 ; Yang et al. 2023 ; Courties et al. 2024 ). However, a factor that also contributes significantly to this disparity is the unequal access to and delivery of healthcare services in rural settings (Borkhoff et al. 2011 ; Liu et al. 2022 ). Clinical practice guidelines recommend that the management of OA should begin with core interventions such as patient education, promotion of an active lifestyle, and behavioral strategies including weight loss and regular exercise. When these conservative approaches prove insufficient, pharmacological treatment may be considered, and, in more advanced cases, surgical intervention may be necessary. These treatments are typically delivered by a multidisciplinary team, which may include general practitioners, rheumatologists, orthopaedic surgeons, physiotherapists, and other allied health professionals, depending on the patient’s needs and the stage of disease progression (Bannuru et al. 2019 ; Kolasinski et al. 2020 ; Gibbs et al. 2023 ). Ensuring consistent and timely access to these professionals and services is essential for the effective management of OA and for minimizing its impact on individuals’ quality of life. However, rural inhabitants are frequently in a disadvantaged position with regard to healthcare access. Previous evidence links rural residence with a lower likelihood of visiting qualified healthcare practitioners and seeking care for rheumatologic complaints(Kanungo et al. 2015 ). Specifically in OA populations, rural communities have limited access to general practioners, orthopedic surgeons and physical therapists(Liu et al. 2022 ) In what relates to the core treatments for OA management, individuals living in rural areas are less likely to participate in self-management programs(Eaton et al. 2018 ), receive physical activity counseling(Duca et al. 2019 ), or adopt cognitive-focused coping behaviors(Hollick and Macfarlane 2021 ). Part of this issue can be explained by geography, with research suggesting that individuals residing closer to healthcare facilities and providers generally experience better health outcomes and higher utilization of healthcare services compared to those living further away(Kelly et al. 2016 ; Bühn et al. 2020 ). However, the origin of these disparities is multifactorial, extending beyond geography to include challenges such as social exclusion, limited social connections, restricted awareness, and low income; factors that exacerbate health disparities in rural settings(Haighton et al. 2019 ). In fact, there is evidence that determinants linked to rurality, like age, low education and low socioeconomic status also contribute to inequitable OA care(Abenoja et al. 2023 ). These social inequities undermine public health by increasing the burden of preventable disease, reducing overall population well-being, and straining healthcare systems. Addressing them is not only a matter of social justice but also a necessary step to improve health outcomes at the population level(Marmot and Bell 2012 ; CSDH 2008 ). In Portugal, the National Health Service (NHS) is a publicly funded system that provides universal and mostly free access to both primary and hospital care for all citizens. However, despite this universal coverage, existing data indicate that individuals living in rural areas often face longer waiting times for healthcare services due to geographic distance and transportation barriers(Martinho and Leite 2023 ). They also tend to have reduced access to hospitals(Costa et al. 2020 ) and to primary healthcare facilities(Costa et al. 2020 ), highlighting persistent territorial disparities in healthcare provision. While such disparities in access and service availability are well documented at a general level, there is a notable lack of data specifically addressing how these issues affect individuals living with OA in Portugal. This study aims to investigate differences in healthcare access between rural and urban populations among individuals with self-reported OA living in Portugal. Specifically, this study will measure associations between living in a rural versus urban environment and the frequency of healthcare visits, waiting times for medical care, and financial barriers to healthcare access, among individuals with self-reported OA. METHODS Study design This study follows an analytical, observational, cross-sectional design. It involves a secondary analysis of data from the 2019 Portuguese National Health Interview Survey (NHIS). The study examined differences in healthcare access between individuals living in rural and urban areas in Portugal, restricting the sample to those who self-reported OA in the dataset. The study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for reporting cross-sectional studies(von Elm et al. 2008 ) . Sample The study sample was drawn from the NHIS dataset. NHIS is a population-based survey conducted every five years by Statistics Portugal in partnership with the National Institute of Health Doutor Ricardo Jorge. It collects data to characterize the population of Portugal aged 15 and older, who, during the reference period (16th September 2019–20th December 2019), lived within the national territory, including all regions of mainland Portugal, as well as the Azores and Madeira. The survey focuses on three main domains: health status, healthcare, and health determinants related to lifestyle(Instituto Nacional de Estatística 2019 ). The sample selection in NHIS follows a stratified and multistage sampling scheme based on NUTS II regions[*] or sub-regions. Detailed information on the sampling procedures is available in the official documentation provided by Statistics Portugal(Instituto Nacional de Estatística 2019 ) The 2019 NHIS included a probabilistic sample of 22,191 housing units, encompassing a total of 14,617 individuals. Between September and December 2019, individuals that met the age inclusion criteria were invited to participate either by completing an electronic questionnaire or through a face-to-face interview. For the analysis employed in this study, participants aged 18 and older who self-reported OA in the previous 12 months were included. The relevant question from the NHIS was: “Do you suffer, or have you suffered, from OA during the last 12 months?” The available response options were “yes”, “no”, “prefer not to answer”, and “don't know”. Participants who selected “prefer not to answer” or “don't know” were excluded from the analysis. Participants who were using wheelchairs, bedridden, or had suffered accidents (road, domestic, or leisure), as well as pregnant women or those who were pregnant in the previous 12 months, were also excluded. Variables Characterization of Individuals with OA To characterize individuals who reported having OA, and considering the available evidence regarding factors associated with this condition, rurality and the study outcomes, the analysis included variables across three major domains: 1) demographic factors, 2) clinical factors, and 3) lifestyle factors. Demographic factors included age, sex, marital status, education, professional status, household type, and income. Lifestyle factors comprised body mass index and physical activity variables, such as the number of days spent walking, cycling, engaging in physical exercise, and performing strengthening activities. Clinical factors consisted of having health insurance, the number of reported comorbidities, self-reported health status, pain intensity, and pain interference in usual activities. Definition of Rural and Urban Areas In this study, exposure was defined by the classification of individuals' residence as either rural or urban. NHIS uses a classification system based on the degree of urbanization (DEGURBA), which indicates the character of an area and is used for the collection of European statistical data. DEGURBA combines the population size and the population density thresholds to establish three categories of local administrative units (LAUs): 1) densely populated areas (cities), 2) intermediate density areas (towns and suburbs), and 3) thinly populated areas (rural areas)(Eurostat 2024 ). In this study, LAUs coded as 1 and 2 were considered urban areas, while LAUs coded as 3 were considered rural areas. Outcomes The outcomes of interest in this study focused on healthcare access, specifically through variables measuring the frequency of healthcare visits (visits to the hospital for outpatient health care and consultation with rehabilitation healthcare providers), the existence of waiting times for medical care (non-reasonable waiting for healthcare services and waiting due to distance or transportation) and the impact of financial factors on healthcare access (need for medical consultation or treatment not satisfied due to financial difficulties). A detailed description of all study variables is provided in Online Resource 1 (SM_1). Data Analysis Descriptive statistics, including weighted relative frequencies and observed counts, were used to characterize the comparison groups (rural and urban residents) based on the previously defined demographic, lifestyle, and clinical factors. For the main analysis all variables with more than 5% of missing values were excluded. The association between each outcome and the degree of urbanization was estimated using odds ratios (OR) obtained through complex sample logistic regression models, first as crude estimates and then adjusted for confounding. All variables meeting the criteria for confounding—defined as being associated with both the exposure and the outcome, but not lying on the causal pathway—were included in the model. Given the sufficient number of events, model stability and avoidance of overfitting were ensured according to the rule of 10 events per parameter (Harrell et al. 1996 ). The selected variables were sex, age group, education, marital status, professional status, household type, income, health insurance, and self-reported health status. The previously described analyses were also stratified by age groups to further explore potential heterogeneous associations between rurality and healthcare use across different age groups. Before running the logistic regression models, an exploratory collinearity analysis was conducted to ensure that the independent variables considered had a variance inflation factor (VIF) < 2. Furthermore, a complete case analysis was performed, excluding observations with missing values for any of the variables included in the models. A significance level of p < 0.05 was used to determine statistical significance in all analyzes, and 95% confidence intervals were reported for all OR estimates. Full details of all adjusted regression models are provided in Online Resource 2 (SM_2). Data analysis was conducted using R software (version 4.4.2) and the Survey package, allowing the computation of weighted estimates while accounting for the complex sampling design of the NHIS 2019. Ethical Considerations Ethical approval was not required for this study, as it is based on secondary analysis of fully anonymized and publicly accessible data provided by the NHIS, conducted by Statistics Portugal. The 2019 NHIS complies with Regulation (EC) No 1338/2008, ensuring that statistical confidentiality is maintained. This regulation establishes the rules for managing and sharing health-related data across EU member states, protecting individuals' privacy. In particular, the INS anonymizes personal information in accordance with these standards, ensuring that sensitive data is securely processed and used only for statistical purposes(European Comission 2008 ). RESULTS This study included a total of 4095 Portuguese individuals with self-reported presence of OA, of which 1690 were classified as rural inhabitants and 2405 as urban inhabitants. Figure 1 details the sample selection process for this study. Characterization of individuals with OA according to rural and urban residence Portuguese individuals who self-report having OA were predominantly women, regardless of their area of residence (65% in rural areas and 69% in urban areas). Rural inhabitants with OA tended to be older than their urban counterparts, with 35% aged over 75 years compared to 30% in urban settings. They were also more likely to have less than basic education compared to urban residents (81% vs. 67%). A high proportion of individuals with OA were in an inactive situation, such as retirement or inability to work, independently of the area of residence (72% in rural areas and 65% in urban areas). Regarding income, individuals living in rural areas tended to be more concentrated in the 1st and 2nd income quintiles (54%), compared to those in urban areas (46%), who were more frequently in the 5th quintile (14% vs. 6.1%). Table 1 presents the estimated distribution of sociodemographic characterization variables, stratified by residence area. Table 1: Sociodemographic characteristics of the sample by residence area VARIABLE TOTAL RURAL Unweighted N = 1690 URBAN Unweighted N = 2405 n Unweighted (%Weighted) n Unweighted (%Weighted) n Unweighted (%Weighted) Sex (n = 4095) Male 1217 (32%) 545 (35%) 672 (31%) Female 2878 (8%) 1145 (65%) 1733 (69%) Age Groups (n = 4095) 18–44 18–44 147 (6%) 37 (4%) 110 (7%) 45–54 382 (13%) 142 (12%) 240 (13%) 55–64 893 (23%) 344 (23%) 549 (23%) 65–74 1218 (27%) 477 (26%) 741 (27%) 75–84 1087 (22%) 506 (24%) 581 (21%) 85+ 368 (9%) 184 (11%) 184 (9%) Education (n = 4095) Less than Basic 832 (16%) 451 (23%) 381 (13%) Primary and Lower Secondary Education 2764 (68%) 1129 (69%) 69% 1635 (68%) Upper Secondary Education or Equivalent 256 (8%) 78 (5%) 178 (10%) University or Post-Graduated 243 (8%) 32 (3%) 211 (10%) Marital Status (n = 4091) Single 396 (9%) 139 (7%) 257 (9%) Married 2154 (66%) 925 (68%) 1,229 (65%) Widow(er) 1204 (19%) 532 (20%) 672 (18%) Divorced 337 (7%) 94 (5%) 243 (7%) Household Type (n = 4095) Single person or couple without children 3090 (56%) 1339 (59%) 1751 (54%) Single parent 242 (7%) 77 (6%) 165 (7%) Couple with children 536 (27%) 191 (25%) 345 (27%) Other type 227 (11%) 83 (10%) 144 (11%) Professional Status (n = 4091) With a job 827 (27%) 287 (24%) 540 (28%) Unemployed 176 (5%) 59 (4%) 117 (6%) Retired, unable to work or another inactivity situation 3079 (68%) 1341 (72%) 1738 (65%) Student or on internship 9 (0%) 0 (0%) 9 (1%) Income (n = 4095) 1st Quintile 690 (18%) 283 (20%) 407 (17%) 2nd Quintile 1562 (31%) 743 (34%) 819 (29%) 3rd Quintile 900 (24%) 379 (25%) 521 (23%) 4th Quintile 546 (16%) 204 (14%) 342 (17%) 5th Quintile 397 (12%) 81 (6%) 316 (14%) In relation to lifestyle factors, a large percentage of individuals with OA were overweight or obese and reported lower levels of physical activity engagement. Comparing the two groups, rural residents were more likely to report no physical activity days (88% vs. 80%) and no walking days (45% vs. 36%) per week and were less likely to engage in strengthening exercises (98% vs. 93%). Regarding clinical factors, rural residents with OA reported poorer health status compared to urban residents, with a higher proportion rating their health as poor or very poor (40% vs. 29%). Similarly, rural residents reported higher pain intensity, with a greater proportion experiencing intense or very intense pain in the four weeks before the data collection (37% vs. 32%). Despite these findings, comorbidities were similarly distributed, with no substantial differences between groups. Table 2 presents the distribution of lifestyle and clinical factors among individuals with OA, stratified by rural and urban areas. Access to Healthcare After performing a complete case analysis (excluding subjects with missing data), the study sample for the main analysis consisted of 4073 subjects. Individuals living in rural areas were less likely (16%) than their urban counterparts (24%) to consult a physiotherapist or related professional (OR = 0.68, 95% CI: 0.52–0.89). Likewise, rural residents had a higher likelihood (8,8%) of reporting waiting for a medical appointment or treatment due to distance or transportation issues compared to urban residents (4.5%) (OR = 1.78, 95% CI: 1.20–2.63). Although a slightly higher proportion of rural inhabitants reported non-reasonable waiting for a medical appointment, examination, or treatment in the last 12 months (OR = 1.06, 95% CI: 0.86–1.32), as well as an unmet need for medical consultation or treatment due to financial difficulties (OR = 1.14, 95% CI: 0.86–1.52), no substantial differences were observed between rural and urban populations. The proportion of individuals who reported visits to outpatient healthcare services was evenly distributed. Table 3: Logistic regression analyses of the associations between the study outcomes and the residence area of individuals with OA n / N Unweighted % Weighted Crude OR* Confounder-adjusted OR** p OR (95% CI) p OR (95% CI) Visits to the hospital for outpatient health care in the last 12 months (YES) RURAL 811 / 1684 50% 0.927 0.991 (0.809–1.213) 0.392 0.911 (0.737–1.127) URBAN † 1148 / 2389 50% REF REF Consultation with a physiotherapist, kinesiotherapist, chiropractor or osteopath in the last 12 months (YES) RURAL 237 / 1684 16% < 0.001 0.628 (0.483–0.816) 0.005 0.680 (0.518–0.892) URBAN † 472 / 2389 24% REF REF Non-reasonable waiting for a medical appointment, examination or treatment in the last 12 months (YES) RURAL 595 / 1684 37% 0.406 1.094 (0.406–0.885) 0.584 1.062 (0.855–1.319) URBAN † 800 / 2389 35% REF REF Waiting for a medical appointment, exam or treatment due to distance or transportation in the last 12 months (YES) RURAL 180 / 1884 8.8% 0.001 2.029 (1.338–3.076) 0.004 1.775 (1.197–2.633) URBAN † 145 / 2389 4.5% REF REF Need for medical consultation or treatment not satisfied due to financial difficulties in the last 12 months (YES) RURAL 1400 /1684 84% 0.086 1.275 (0.966–1.684) 0.366 1.142 (0.856–1.524) URBAN † 1970 / 2389 80% REF REF †Reference category *Univariable model for the associations between residence area and study outcomes. **Multivariable model for the associations between residence region and study outcomes adjusted for sex, age group, education, marital status, professional status, household type, income, health insurance and self -reported health status. OR – Odds Ratio CI – Confidence Interval A secondary analysis stratified by age groups revealed that the associations between these outcomes and residence area varied, indicating some heterogeneity across age groups. Regarding access to physiotherapy, this analysis showed that differences between rural and urban residents were most pronounced among individuals under 64 years old (OR = 0.60, CI 0.39–0.92). In contrast, concerning waiting for medical care due to transportation or distance barriers, the most affected age group was 65–79 years, with rural residents being more than twice as likely as their urban counterparts to report this issue (OR = 2.26, CI 1.31–3.90). Table 4 presents the adjusted ORs for the associations between the referred outcomes and the region of residence across different age groups. Table 4: Logistic regression analyses of the associations between the study outcomes and the residence area of individuals with OA stratified by age groups n/N Unweighted % Weighted ADJUSTED MULTIVARIABLE MODEL* p OR (95% CI) Consultation with a physiotherapist, kinesiotherapist, chiropractor or osteopath in the last 12 months (YES) ≤ 64 YEARS RURAL 95/521 19.2% 0.018 0.599 (0.392–0.916) URBAN † 209/893 27% REF 65–79 YEARS RURAL 104/727 17.9% 0.102 0.721 (0.487–1.067) URBAN † 214/1075 24.9% REF ≥ 80 YEARS RURAL 38/436 8.2% 0.443 0.770 (0.394-1,504) URBAN † 49/421 12.9% REF Waiting for a medical appointment, exam or treatment due to distance or transportation in the last 12 months (YES) ≤ 64 YEARS RURAL 48/521 7.3% 0.146 1.677 (0.835–3.367) URBAN † 53/893 4.1% REF 65–79 YEARS RURAL 82/727 11.2% 0.003 2.263 (1.314–3.899) URBAN † 67/1075 5.1% REF ≥ 80 YEARS RURAL 45/436 7.4% 0.542 1.283 (0.576–2.856) URBAN † 30/421 5.5% REF †Reference category *Multivariable model for the associations between residence region and study outcomes adjusted for sex, education, income, self-reported health status, marital status, insurance and household type. CI – Confidence Interval DISCUSSION This study aimed to investigate differences in healthcare access among individuals with OA living in rural and urban areas in Portugal. The findings indicate that rural residents are 32% less likely (OR 0.68, 95% CI 0.52–0.90) to visit a physiotherapist or related professional compared to their urban counterparts. Notably, although a higher proportion of rural inhabitants with OA report poor or very poor health status and intense or very intense pain, this does not appear to be directly linked to access to rehabilitation services. These results align with previous research in Portugal and internationally, which has shown that living in remote geographical areas is associated, on one hand, with higher pain levels(Messier et al., 2024), but, on the other hand, with lower utilization of physiotherapy and other specialized healthcare services among individuals with OA(Costa et al., 2021; Liu et al., 2022 ). This pattern underscores the existence of unmet healthcare needs within these populations. Physiotherapists play a central role in delivering high-value first-line care options for OA management(Briggs et al., 2019), and limited access to physiotherapy treatments may hinder improvements in pain, physical function, and quality of life(Lawford et al., 2024), potentially leading to worse disease progression. Consequently, it is not unexpected that rural individuals exhibit higher rates of total arthroplasty utilization compared to their urban counterparts, as observed in a large cohort of US patients(Hinman et al., 2023). As outlined earlier, this disparity can be attributed to several factors, where geographical distance from healthcare facilities represents a key factor. Previous data had shown that individuals with OA and living in rural areas consider distance the most common reason for not seeking treatment(Bala et al., 2020). Additionally, rural determinants such as lower education and socioeconomic status(Jiang et al., 2021; Wang et al., 2015), also found in this study, influence health-seeking behaviors(Berkman et al., 2011; Kanungo et al., 2015 ) and are directly associated with unmet healthcare needs(Chen et al., 2022; McMaughan et al., 2020). Among rural individuals with OA, a previous study found out a high proportion of unawareness of the beneficial effects of exercise and physiotherapy for condition management(Bala et al., 2020). The association between rural residency and a lower likelihood of visiting a rehabilitation professional was more pronounced among younger individuals (≤ 64 years). This disparity may be partially explained by the types of occupational activities typically undertaken in rural versus urban settings. Working-age individuals in rural areas are more likely to be employed in agricultural, manual, or precarious jobs (Matz et al. 2015 ), which often lack the flexibility or formal provisions—such as paid health leave—required to access healthcare services during working hours. These constraints may hinder their ability to attend physiotherapy or similar appointments. In contrast, individuals working in urban environments are potentially more likely to benefit from structured employment arrangements that facilitate access to health services, and—due to greater proximity and service availability—they typically forfeit less work time when seeking care. The results of this study also indicate that individuals with OA living in rural areas are 78% more likely (OR 1.78, 95% CI 1.20–2.63) to face difficulties in obtaining medical care due to distance or transportation issues, independently of other determinants. This finding aligns with previously presented data, which highlights geographical distance as a major factor in defining healthcare access. While previous research has established the influence of factors such as age, socioeconomic status, education, and income on healthcare access(Abenoja et al., 2023 ; Fontaine et al., 2007; Murphy et al., 2017; Reyes & Katz, 2021), and the study sample revealed a higher proportion of older participants, with fewer years of education, and lower income in the rural group, the confounder-adjusted OR confirmed a strong association between rural residence and reported waiting times for medical appointments, exams, or treatment due to distance or transportation. This result demonstrates that physical distance to healthcare facilities remains an independent predictor of reduced access, placing rural inhabitants at a disadvantaged and unfair position, and further widening the existing gap in healthcare equity. Previous studies had already highlighted the existence of this “distance decay association” in healthcare, where greater geographical distance from healthcare services is linked to lower utilization and poorer health outcomes(Kelly et al., 2016 ; Liu et al., 2022 ). This association was more prominent in the age group between 65 and 79 years. A possible explanation is that younger individuals may have greater access to private transportation or better physical mobility to use public transport. In contrast, in older age groups, more pronounced disability or physical dependence, as well as reduced awareness, can impact health-seeking behavior and decrease proactivity in accessing healthcare services. Regarding the other outcomes considered in the analysis, this study did not found differences in access to hospital outpatient care between rural and urban populations with OA. Outpatient care, which includes services such as consultations and diagnostic tests, often involves a single visit. In contrast, physiotherapy treatments typically require multiple sessions. This could explain the lack of differences observed between the two groups, as rural residents, despite facing access challenges, may be more likely to arrange a one-time visit to a hospital compared to ongoing treatments that necessitate multiple visits. Concerning waiting times for healthcare access, just over one-third of the sample in each group reported experiencing non reasonable waiting times for a medical appointment, examination, or treatment in the previous year, with a slightly higher proportion among rural participants. These results reinforce a well-documented challenge in the Portuguese healthcare system, where 47% of individuals waiting for a first hospital specialty consultation are experiencing waiting times that exceed the maximum guaranteed response times (Conselho das Finanças Públicas, 2024). This finding underscores that long waiting times for medical care are a widespread problem affecting the Portuguese population, regardless of whether they live in rural or urban areas. Similarly, a generally high proportion of individuals with OA reported unmet healthcare needs due to financial constraints. Although this study found a higher concentration of individuals in the lowest income quintiles among rural residents—compared to urban residents, who were more frequently in the highest quintile—these income differences were not fully reflected in the reported financial difficulties in accessing care, as both groups appeared to be similarly affected by economic barriers to healthcare. Once again, this finding is not unexpected, as available data indicate that out-of-pocket payments accounted for 29% of total health expenditure in Portugal in 2021, a percentage significantly higher than the OECD average of 18.4%. Additionally, 10.6% of Portuguese households (compared to an EU average of 5.3%) faced healthcare expenses exceeding 40% of their household budget, which is considered a catastrophic spending on health(OECD, 2023). Considering the overall impact of healthcare costs on Portuguese families, it’s not surprising that no significant differences were found between rural and urban areas, as this seems to be a challenge faced by the entire population. Limitations and Strengths The results of the current study need to be interpreted in the context of its limitations. The cross-sectional nature of the data limits the ability to draw causal inferences. Moreover, the reliance on self-reported measures introduces potential sources of bias, such as recall bias and social desirability bias. Previous research has highlighted a general tendency to under-report chronic disease diagnoses, particularly among older adults and individuals with high BMI, which may lead to an underestimation of symptom burden and barriers to accessing care(Liu et al. 2024 ). Additionally, the potential presence of residual and unmeasured confounding bias should be considered, as there may be important variables that were not available for adjustment in the analysis, but could have influenced the results. Further research in this area should focus on longitudinal evaluations of healthcare access, examining additional factors such as referral patterns, patient and healthcare professionals' beliefs and misconceptions, variations in service and transportation availability, and healthcare-seeking behaviors. This would provide deeper insights into the underlying determinants of healthcare access inequalities. On the other hand, this study presents several strengths. First, the use of a nationally representative survey (INS 2019) ensures robust inferences about the Portuguese population with OA, while the application of survey weights enhances the representativeness of the findings. Additionally, by focusing on healthcare access disparities, the study provides valuable insights into the geographical barriers faced by rural populations, particularly in accessing physiotherapy and other healthcare services. The inclusion of age-group analyses further strengthens the study by revealing age related heterogeneities in healthcare utilization patterns. Importantly, the findings hold significant implications for public health policies, emphasizing the need for alternative strategies—such as digital health solutions—to bridge existing gaps in access. The findings of this study underscore the urgent need for healthcare services and policies to identify or develop alternative methods of delivering high-value treatments to rural populations with OA. Addressing this challenge requires the implementation of strategies that take into account the specific needs and characteristics of these populations, ensuring they have equal opportunities to achieve the same outcomes as urban inhabitants. These strategies can vary, such as enhancing community-based care or improving transportation options. However, in the current era, the role of digital health is undeniably, being emphasized by various health institutions, and being increasingly recognized as a crucial tool in addressing healthcare disparities(World Health Organization, 2021). Significant improvements in clinical and psychosocial outcomes have been widely reported among individuals with chronic musculoskeletal conditions who received various forms of digital interventions. Several systematic reviews have found evidence supporting the effectiveness of digital health interventions in reducing pain intensity, improving functionality and physical performance, enhancing self-management, and promoting better quality of life (Valentijn et al.; Cottrell et al. 2017 ; Thurnheer et al. 2018 ; Pfeifer et al. 2020 ; Chen et al. 2021; Latif-Zade et al. 2021 ; Xie et al. 2021 ; Jirasakulsuk et al. 2022 ; Xiang et al. 2023 ; Baigi et al. 2023 ; Thompson et al. 2023 ). Furthermore, digital interventions have been found to be cost-effective compared with usual care (Fatoye et al. 2020 ; Molina-Garcia et al. 2024 ) and are associated with high levels of satisfaction from both patients and providers.(Amin et al. 2022 ). Although these data are primarily related to urban settings, they provide a promising foundation for extending the benefits of digital health to underserved rural communities. By bridging the gap between urban and rural settings, disparities can be reduced, ensuring that rural populations receive timely and adequate healthcare, regardless of their geographic location. CONCLUSION In conclusion, this study reinforces the existence of healthcare access disparities affecting individuals with OA living in rural areas of Portugal, who experience reduced access to physiotherapy and longer waiting times for services, often due to geographic distance and transportation barriers. The findings suggest that these disparities vary across age groups, potentially highlighting the influence of other factors in healthcare access, particularly in health-seeking behaviors. The results underscore the importance of addressing these challenges through tailored healthcare strategies, including enhancing access to digital health solutions. Ensuring equitable access to care for rural populations is not only crucial for improving health outcomes but also for advancing overall healthcare equity in society. Declarations Acknowledgements We would like to thank Statistics Portugal for providing access to the 2019 Portuguese National Health Interview Survey data. Conflicts of Interest The authors have no competing interests to declare. Ethical Statement This study used anonymized secondary data obtained from the Instituto Nacional de Saúde (INS). As the data were publicly available and did not involve direct interaction with human participants, ethical approval was not required in accordance with national regulations and institutional guidelines. References Abenoja A, Theodorlis M, Ahluwalia V, et al (2023) Strategies to Improve Equitable Access to Early Osteoarthritis Diagnosis and Management: An updated Review. 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MMWR Morb Mortal Wkly Rep 66:527–532. https://doi.org/10.15585/mmwr.mm6620a2 Borkhoff CM, Wieland ML, Myasoedova E, et al (2011) Reaching those most in need: a scoping review of interventions to improve health care quality for disadvantaged populations with osteoarthritis. Arthritis Care Res (Hoboken) 63:39–52. https://doi.org/10.1002/ACR.20349 Branco JC, Rodrigues AM, Gouveia N, et al (2016) Prevalence of rheumatic and musculoskeletal diseases and their impact on health-related quality of life, physical function and mental health in Portugal: Results from EpiReumaPt- a national health survey. RMD Open 2:. https://doi.org/10.1136/rmdopen-2015-000166 Bühn S, Holstiege J, Pieper D (2020) Are patients willing to accept longer travel times to decrease their risk associated with surgical procedures? A systematic review. 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Statistics Explained. https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Territorial_typologies_manual_-_degree_of_urbanisation. Accessed 21 Nov 2024 Fatoye F, Wright JM, Gebrye T (2020) Cost-effectiveness of physiotherapeutic interventions for low back pain: a systematic review. Physiotherapy (United Kingdom) 108:98–107. https://doi.org/10.1016/j.physio.2020.04.010 Gibbs AJ, Gray B, Wallis JA, et al (2023) Recommendations for the management of hip and knee osteoarthritis: A systematic review of clinical practice guidelines. Osteoarthritis Cartilage 31:1280–1292. https://doi.org/10.1016/J.JOCA.2023.05.015 Haighton C, Dalkin S, Brittain K (2019) Health inequalities in ageing in rural and coastal areas Harrell FE, Lee KL, Mark DB (1996) TUTORIAL IN BIOSTATISTICS MULTIVARIABLE PROGNOSTIC MODELS: ISSUES IN DEVELOPING MODELS, EVALUATING ASSUMPTIONS AND ADEQUACY, AND MEASURING AND REDUCING ERRORS. Stat Med 15:361–387 Hollick RJ, Macfarlane GJ (2021) Association of Rural Setting With Poorer Disease Outcomes for Patients With Rheumatic Diseases: Results From a Systematic Review of the Literature. Arthritis Care Res (Hoboken) 73:666–670. https://doi.org/10.1002/acr.24185 Instituto Nacional de Estatística (2019) Documento Metodológico - Inquérito Nacional de Saúde Jirasakulsuk N, Saengpromma P, Khruakhorn S (2022) Real-Time Telerehabilitation in Older Adults With Musculoskeletal Conditions: Systematic Review and Meta-analysis. JMIR Rehabil Assist Technol 9 Kanungo S, Bhowmik K, Mahapatra T, et al (2015) Perceived morbidity, healthcare-seeking behavior and their determinants in a poor-resource setting: observation from India. PLoS One 10:. https://doi.org/10.1371/JOURNAL.PONE.0125865 Kelly C, Hulme C, Farragher T, Clarke G (2016) Are differences in travel time or distance to healthcare for adults in global north countries associated with an impact on health outcomes? A systematic review. https://doi.org/10.1136/bmjopen-2016 Kolasinski SL, Neogi T, Hochberg MC, et al (2020) Foundation Guideline for the Management of Osteoarthritis of the Hand, Hip, and Knee. Arthritis & Rheumatology 72:220–233. https://doi.org/10.1002/art.41142 Latif-Zade T, Tucci B, Verbovetskaya D, et al (2021) Systematic Review Shows Tele-Rehabilitation Might Achieve Comparable Results to Office-Based Rehabilitation for Decreasing Pain in Patients with Knee Osteoarthritis. Medicina (B Aires). https://doi.org/10.3390/medicina57080764 Liu H, Zhao Y, Qiao L, et al (2024) Consistency between self-reported disease diagnosis and clinical assessment and under-reporting for chronic conditions: data from a community-based study in Xi’an, China. Front Public Health 12:. https://doi.org/10.3389/FPUBH.2024.1296939/PDF Liu X, Seidel JE, McDonald T, et al (2022) Rural–Urban Disparities in Realized Spatial Access to General Practitioners, Orthopedic Surgeons, and Physiotherapists among People with Osteoarthritis in Alberta, Canada. International Journal of Environmental Research and Public Health 2022, Vol 19, Page 7706 19:7706. https://doi.org/10.3390/IJERPH19137706 Liu Y, Zhang H, Liang N, et al (2016) Prevalence and associated factors of knee osteoarthritis in a rural Chinese adult population: an epidemiological survey. BMC Public Health 16:1–8. https://doi.org/10.1186/S12889-016-2782-X/TABLES/4 Marmot M, Bell R (2012) Fair society, healthy lives. Public Health 126 Suppl 1:S4–S10. https://doi.org/10.1016/J.PUHE.2012.05.014 Martinho J, Leite A (2023) Where you live matters: how degree of urbanization influences healthcare utilization in Portugal. Eur J Public Health 33:. https://doi.org/10.1093/EURPUB/CKAD160.244 Matz CJ, Stieb DM, Brion O (2015) Urban-rural differences in daily time-activity patterns, occupational activity and housing characteristics. Environmental Health 14:. https://doi.org/10.1186/S12940-015-0075-Y Molina-Garcia P, Mora-Traverso M, Prieto-Moreno R, et al (2024) Effectiveness and cost-effectiveness of telerehabilitation for musculoskeletal disorders: A systematic review and meta-analysis. Ann Phys Rehabil Med 67:. https://doi.org/10.1016/J.REHAB.2023.101791 O’brien P, Bunzli S, Lin I, et al (2020) Tackling the Burden of Osteoarthritis as a Health Care Opportunity in Indigenous Communities-A Call to Action. J Clin Med 9:1–5. https://doi.org/10.3390/JCM9082393 Pfeifer AC, Uddin R, Schröder-Pfeifer P, et al (2020) Mobile Application-Based Interventions for Chronic Pain Patients: A Systematic Review and Meta-Analysis of Effectiveness. J Clin Med 9:1–18. https://doi.org/10.3390/JCM9113557 Steinmetz JD, Culbreth GT, Haile LM, et al (2023) Global, regional, and national burden of osteoarthritis, 1990–2020 and projections to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet Rheumatol 5:e508–e522. https://doi.org/10.1016/s2665-9913(23)00163-7 Thompson D, Rattu S, Tower J, et al (2023) Mobile app use to support therapeutic exercise for musculoskeletal pain conditions may help improve pain intensity and self-reported physical function: a systematic review. J Physiother 69:23–34. https://doi.org/10.1016/J.JPHYS.2022.11.012 Thurnheer SE, Gravestock I, Pichierri G, et al (2018) Benefits of Mobile Apps in Pain Management: Systematic Review. JMIR Mhealth Uhealth 6:. https://doi.org/10.2196/11231 Valentijn PP, Tymchenko L, Jacobson T, et al Digital Health Interventions for Musculoskeletal Pain Conditions: Systematic Review and Meta-analysis of Randomized Controlled Trials. https://doi.org/10.2196/37869 von Elm E, Altman DG, Egger M, et al (2008) The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol 61:344–349. https://doi.org/10.1016/J.JCLINEPI.2007.11.008 Wen M, Fan JX, Kowaleski-Jones L, Wan N (2018) Rural–Urban Disparities in Obesity Prevalence Among Working Age Adults in the United States: Exploring the Mechanisms. American Journal of Health Promotion 32:400–408. https://doi.org/10.1177/0890117116689488 Xiang W, Wang JY, Ji BJ, et al (2023) Effectiveness of Different Telerehabilitation Strategies on Pain and Physical Function in Patients With Knee Osteoarthritis: Systematic Review and Meta-Analysis. J Med Internet Res 25:. https://doi.org/10.2196/40735 Xie S-H, Wang Q, Wang L-Q, et al (2021) Effect of Internet-Based Rehabilitation Programs on Improvement of Pain and Physical Function in Patients with Knee Osteoarthritis: Systematic Review and Meta-analysis of Randomized Controlled Trials. J Med Internet Res 23:e21542. https://doi.org/10.2196/21542 Yang G, Wang J, Liu Y, et al (2023) Burden of Knee Osteoarthritis in 204 Countries and Territories, 1990-2019: Results From the Global Burden of Disease Study 2019. Arthritis Care Res (Hoboken) 75:2489–2500. https://doi.org/10.1002/ACR.25158 Footnotes * Norte, Centro, Lisboa and Vale do Tejo, Alentejo, Algarve, Açores and Madeira Table 2 Table 2 is available in the Supplementary Files section. Additional Declarations The authors declare no competing interests. Supplementary Files SM1.pdf Variables SM1.pdf Variables Table2.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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06:54:03","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":20132,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7362024/v1/c9a925ebc4ca5455a3217b01.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eRural-Urban Disparities in Healthcare Access Among Individuals with Osteoarthritis in Portugal: A Cross-Sectional Analysis of the 2019 National Health Survey\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eOsteoarthritis (OA) is among the leading causes of musculoskeletal pain worldwide, affecting 7.6% of the global population in 2020. This prevalence increased by 132.2% over 30 years and is projected to rise by 60 to 100% by 2050(Steinmetz et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This upward trend is largely driven by sociodemographic changes such as population ageing, as well as the growing incidence of modifiable risk factors including obesity and sedentary behaviour(O\u0026rsquo;brien et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Data from Portugal indicates that the combined prevalence of hip, knee and hand OA ascends to 19.1%(Branco et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eExpectedly, OA is a major contributor to functional impairment and loss of independence, representing the seventh leading cause of disability worldwide after the age of 70(Courties et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eExtensive evidence suggests that OA tends to be more prevalent and disabling among individuals living in rural areas compared to their urban counterparts, with reported prevalence rates ranging from 20.0\u0026ndash;21.9% in urban areas and from 26.2\u0026ndash;26.9% in rural areas.(Busija et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Boring et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This pattern may be partially explained by greater exposure to physically demanding occupations\u0026mdash;such as farming and manual labor\u0026mdash;which are known to increase the risk of OA(Canetti et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), as well as by older demographic profiles(Busija et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and higher rates of obesity(Wen et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) commonly found in rural populations, both of which are well-established risk factors for developing knee OA (Busija et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Dong et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Courties et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHowever, a factor that also contributes significantly to this disparity is the unequal access to and delivery of healthcare services in rural settings (Borkhoff et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e Clinical practice guidelines recommend that the management of OA should begin with core interventions such as patient education, promotion of an active lifestyle, and behavioral strategies including weight loss and regular exercise. When these conservative approaches prove insufficient, pharmacological treatment may be considered, and, in more advanced cases, surgical intervention may be necessary. These treatments are typically delivered by a multidisciplinary team, which may include general practitioners, rheumatologists, orthopaedic surgeons, physiotherapists, and other allied health professionals, depending on the patient\u0026rsquo;s needs and the stage of disease progression (Bannuru et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kolasinski et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gibbs et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Ensuring consistent and timely access to these professionals and services is essential for the effective management of OA and for minimizing its impact on individuals\u0026rsquo; quality of life.\u003c/p\u003e\u003cp\u003eHowever, rural inhabitants are frequently in a disadvantaged position with regard to healthcare access. Previous evidence links rural residence with a lower likelihood of visiting qualified healthcare practitioners and seeking care for rheumatologic complaints(Kanungo et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Specifically in OA populations, rural communities have limited access to general practioners, orthopedic surgeons and physical therapists(Liu et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) In what relates to the core treatments for OA management, individuals living in rural areas are less likely to participate in self-management programs(Eaton et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), receive physical activity counseling(Duca et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), or adopt cognitive-focused coping behaviors(Hollick and Macfarlane \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePart of this issue can be explained by geography, with research suggesting that individuals residing closer to healthcare facilities and providers generally experience better health outcomes and higher utilization of healthcare services compared to those living further away(Kelly et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; B\u0026uuml;hn et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHowever, the origin of these disparities is multifactorial, extending beyond geography to include challenges such as social exclusion, limited social connections, restricted awareness, and low income; factors that exacerbate health disparities in rural settings(Haighton et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In fact, there is evidence that determinants linked to rurality, like age, low education and low socioeconomic status also contribute to inequitable OA care(Abenoja et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThese social inequities undermine public health by increasing the burden of preventable disease, reducing overall population well-being, and straining healthcare systems. Addressing them is not only a matter of social justice but also a necessary step to improve health outcomes at the population level(Marmot and Bell \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; CSDH \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn Portugal, the National Health Service (NHS) is a publicly funded system that provides universal and mostly free access to both primary and hospital care for all citizens. However, despite this universal coverage, existing data indicate that individuals living in rural areas often face longer waiting times for healthcare services due to geographic distance and transportation barriers(Martinho and Leite \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). They also tend to have reduced access to hospitals(Costa et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and to primary healthcare facilities(Costa et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), highlighting persistent territorial disparities in healthcare provision.\u003c/p\u003e\u003cp\u003eWhile such disparities in access and service availability are well documented at a general level, there is a notable lack of data specifically addressing how these issues affect individuals living with OA in Portugal.\u003c/p\u003e\u003cp\u003eThis study aims to investigate differences in healthcare access between rural and urban populations among individuals with self-reported OA living in Portugal. Specifically, this study will measure associations between living in a rural versus urban environment and the frequency of healthcare visits, waiting times for medical care, and financial barriers to healthcare access, among individuals with self-reported OA.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design\u003c/h2\u003e\u003cp\u003eThis study follows an analytical, observational, cross-sectional design.\u003c/p\u003e\u003cp\u003eIt involves a secondary analysis of data from the 2019 Portuguese National Health Interview Survey (NHIS). The study examined differences in healthcare access between individuals living in rural and urban areas in Portugal, restricting the sample to those who self-reported OA in the dataset.\u003c/p\u003e\u003cp\u003e The study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for reporting cross-sectional studies(von Elm et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) .\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSample\u003c/h3\u003e\n\u003cp\u003eThe study sample was drawn from the NHIS dataset.\u003c/p\u003e\u003cp\u003eNHIS is a population-based survey conducted every five years by Statistics Portugal in partnership with the National Institute of Health Doutor Ricardo Jorge. It collects data to characterize the population of Portugal aged 15 and older, who, during the reference period (16th September 2019\u0026ndash;20th December 2019), lived within the national territory, including all regions of mainland Portugal, as well as the Azores and Madeira. The survey focuses on three main domains: health status, healthcare, and health determinants related to lifestyle(Instituto Nacional de Estat\u0026iacute;stica \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe sample selection in NHIS follows a stratified and multistage sampling scheme based on NUTS II regions[*]\u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e\u003c/a\u003e or sub-regions. Detailed information on the sampling procedures is available in the official documentation provided by Statistics Portugal(Instituto Nacional de Estat\u0026iacute;stica \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eThe 2019 NHIS included a probabilistic sample of 22,191 housing units, encompassing a total of 14,617 individuals. Between September and December 2019, individuals that met the age inclusion criteria were invited to participate either by completing an electronic questionnaire or through a face-to-face interview.\u003c/p\u003e\u003cp\u003eFor the analysis employed in this study, participants aged 18 and older who self-reported OA in the previous 12 months were included. The relevant question from the NHIS was: \u0026ldquo;Do you suffer, or have you suffered, from OA during the last 12 months?\u0026rdquo; The available response options were \u0026ldquo;yes\u0026rdquo;, \u0026ldquo;no\u0026rdquo;, \u0026ldquo;prefer not to answer\u0026rdquo;, and \u0026ldquo;don't know\u0026rdquo;. Participants who selected \u0026ldquo;prefer not to answer\u0026rdquo; or \u0026ldquo;don't know\u0026rdquo; were excluded from the analysis. Participants who were using wheelchairs, bedridden, or had suffered accidents (road, domestic, or leisure), as well as pregnant women or those who were pregnant in the previous 12 months, were also excluded.\u003c/p\u003e\n\u003ch3\u003eVariables\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eCharacterization of Individuals with OA\u003c/h2\u003e\u003cp\u003eTo characterize individuals who reported having OA, and considering the available evidence regarding factors associated with this condition, rurality and the study outcomes, the analysis included variables across three major domains: 1) demographic factors, 2) clinical factors, and 3) lifestyle factors. Demographic factors included age, sex, marital status, education, professional status, household type, and income. Lifestyle factors comprised body mass index and physical activity variables, such as the number of days spent walking, cycling, engaging in physical exercise, and performing strengthening activities. Clinical factors consisted of having health insurance, the number of reported comorbidities, self-reported health status, pain intensity, and pain interference in usual activities.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDefinition of Rural and Urban Areas\u003c/h3\u003e\n\u003cp\u003eIn this study, exposure was defined by the classification of individuals' residence as either rural or urban.\u003c/p\u003e\u003cp\u003eNHIS uses a classification system based on the degree of urbanization (DEGURBA), which indicates the character of an area and is used for the collection of European statistical data. DEGURBA combines the population size and the population density thresholds to establish three categories of local administrative units (LAUs): 1) densely populated areas (cities), 2) intermediate density areas (towns and suburbs), and 3) thinly populated areas (rural areas)(Eurostat \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn this study, LAUs coded as 1 and 2 were considered urban areas, while LAUs coded as 3 were considered rural areas.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eOutcomes\u003c/h2\u003e\u003cp\u003eThe outcomes of interest in this study focused on healthcare access, specifically through variables measuring the frequency of healthcare visits (visits to the hospital for outpatient health care and consultation with rehabilitation healthcare providers), the existence of waiting times for medical care (non-reasonable waiting for healthcare services and waiting due to distance or transportation) and the impact of financial factors on healthcare access (need for medical consultation or treatment not satisfied due to financial difficulties).\u003c/p\u003e\u003cp\u003eA detailed description of all study variables is provided in Online Resource 1 (SM_1).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eData Analysis\u003c/h2\u003e\u003cp\u003eDescriptive statistics, including weighted relative frequencies and observed counts, were used to characterize the comparison groups (rural and urban residents) based on the previously defined demographic, lifestyle, and clinical factors.\u003c/p\u003e\u003cp\u003eFor the main analysis all variables with more than 5% of missing values were excluded.\u003c/p\u003e\u003cp\u003eThe association between each outcome and the degree of urbanization was estimated using odds ratios (OR) obtained through complex sample logistic regression models, first as crude estimates and then adjusted for confounding.\u003c/p\u003e\u003cp\u003eAll variables meeting the criteria for confounding\u0026mdash;defined as being associated with both the exposure and the outcome, but not lying on the causal pathway\u0026mdash;were included in the model. Given the sufficient number of events, model stability and avoidance of overfitting were ensured according to the rule of 10 events per parameter (Harrell et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). The selected variables were sex, age group, education, marital status, professional status, household type, income, health insurance, and self-reported health status.\u003c/p\u003e\u003cp\u003eThe previously described analyses were also stratified by age groups to further explore potential heterogeneous associations between rurality and healthcare use across different age groups.\u003c/p\u003e\u003cp\u003eBefore running the logistic regression models, an exploratory collinearity analysis was conducted to ensure that the independent variables considered had a variance inflation factor (VIF)\u0026thinsp;\u0026lt;\u0026thinsp;2. Furthermore, a complete case analysis was performed, excluding observations with missing values for any of the variables included in the models.\u003c/p\u003e\u003cp\u003eA significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was used to determine statistical significance in all analyzes, and 95% confidence intervals were reported for all OR estimates.\u003c/p\u003e\u003cp\u003e Full details of all adjusted regression models are provided in Online Resource 2 (SM_2).\u003c/p\u003e\u003cp\u003eData analysis was conducted using R software (version 4.4.2) and the Survey package, allowing the computation of weighted estimates while accounting for the complex sampling design of the NHIS 2019.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEthical Considerations\u003c/h3\u003e\n\u003cp\u003eEthical approval was not required for this study, as it is based on secondary analysis of fully anonymized and publicly accessible data provided by the NHIS, conducted by Statistics Portugal.\u003c/p\u003e\n\u003cp\u003eThe 2019 NHIS complies with Regulation (EC) No 1338/2008, ensuring that statistical confidentiality is maintained. This regulation establishes the rules for managing and sharing health-related data across EU member states, protecting individuals\u0026apos; privacy. In particular, the INS anonymizes personal information in accordance with these standards, ensuring that sensitive data is securely processed and used only for statistical purposes(European Comission \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThis study included a total of 4095 Portuguese individuals with self-reported presence of OA, of which 1690 were classified as rural inhabitants and 2405 as urban inhabitants.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e details the sample selection process for this study.\u003c/p\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eCharacterization of individuals with OA according to rural and urban residence\u003c/h2\u003e\n \u003cp\u003ePortuguese individuals who self-report having OA were predominantly women, regardless of their area of residence (65% in rural areas and 69% in urban areas).\u003c/p\u003e\n \u003cp\u003eRural inhabitants with OA tended to be older than their urban counterparts, with 35% aged over 75 years compared to 30% in urban settings. They were also more likely to have less than basic education compared to urban residents (81% vs. 67%).\u003c/p\u003e\n \u003cp\u003eA high proportion of individuals with OA were in an inactive situation, such as retirement or inability to work, independently of the area of residence (72% in rural areas and 65% in urban areas). Regarding income, individuals living in rural areas tended to be more concentrated in the 1st and 2nd income quintiles (54%), compared to those in urban areas (46%), who were more frequently in the 5th quintile (14% vs. 6.1%). Table\u0026nbsp;1 presents the estimated distribution of sociodemographic characterization variables, stratified by residence area.\u003c/p\u003e\n \u003cdiv\u003e\n \u003cp\u003eTable 1: Sociodemographic characteristics of the sample by residence area\u003c/p\u003e\n \u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVARIABLE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTOTAL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRURAL\u003c/p\u003e\n \u003cp\u003eUnweighted N\u0026thinsp;=\u0026thinsp;1690\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eURBAN\u003c/p\u003e\n \u003cp\u003eUnweighted N\u0026thinsp;=\u0026thinsp;2405\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en Unweighted (%Weighted)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en Unweighted (%Weighted)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en Unweighted (%Weighted)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex (n\u0026thinsp;=\u0026thinsp;4095)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1217 (32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e545 (35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e672 (31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2878 (8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1145 (65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1733 (69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge Groups (n\u0026thinsp;=\u0026thinsp;4095)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u0026ndash;44\u003c/p\u003e\n \u003cp\u003e18\u0026ndash;44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e147 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e110 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u0026ndash;54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e382 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e142 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e240 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55\u0026ndash;64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e893 (23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e344 (23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e549 (23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65\u0026ndash;74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1218 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e477 (26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e741 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75\u0026ndash;84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1087 (22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e506 (24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e581 (21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e368 (9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e184 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e184 (9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation (n\u0026thinsp;=\u0026thinsp;4095)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLess than Basic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e832 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e451 (23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e381 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimary and Lower Secondary Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2764 (68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1129 (69%)\u003c/p\u003e\n \u003cp\u003e69%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1635 (68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUpper Secondary Education or Equivalent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e256 (8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e178 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUniversity or Post-Graduated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e243 (8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e211 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital Status (n\u0026thinsp;=\u0026thinsp;4091)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e396 (9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e139 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e257 (9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2154 (66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e925 (68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,229 (65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWidow(er)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1204 (19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e532 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e672 (18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e337 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e243 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHousehold Type (n\u0026thinsp;=\u0026thinsp;4095)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSingle person or couple without children\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3090 (56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1339 (59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1751 (54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSingle parent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e242 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e165 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCouple with children\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e536 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e191 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e345 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e227 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e144 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eProfessional Status (n\u0026thinsp;=\u0026thinsp;4091)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWith a job\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e827 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e287 (24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e540 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e176 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e117 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRetired, unable to work or another inactivity situation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3079 (68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1341 (72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1738 (65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStudent or on internship\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncome (n\u0026thinsp;=\u0026thinsp;4095)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1st Quintile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e690 (18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e283 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e407 (17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2nd Quintile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1562 (31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e743 (34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e819 (29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3rd Quintile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e900 (24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e379 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e521 (23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4th Quintile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e546 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e204 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e342 (17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5th Quintile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e397 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e316 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eIn relation to lifestyle factors, a large percentage of individuals with OA were overweight or obese and reported lower levels of physical activity engagement.\u003c/p\u003e\n \u003cp\u003eComparing the two groups, rural residents were more likely to report no physical activity days (88% vs. 80%) and no walking days (45% vs. 36%) per week and were less likely to engage in strengthening exercises (98% vs. 93%).\u003c/p\u003e\n \u003cp\u003eRegarding clinical factors, rural residents with OA reported poorer health status compared to urban residents, with a higher proportion rating their health as poor or very poor (40% vs. 29%). Similarly, rural residents reported higher pain intensity, with a greater proportion experiencing intense or very intense pain in the four weeks before the data collection (37% vs. 32%). Despite these findings, comorbidities were similarly distributed, with no substantial differences between groups.\u003c/p\u003e\n \u003cdiv\u003e\n \u003cp\u003e\u003cspan\u003eTable 2 presents the distribution of lifestyle and clinical factors among individuals with OA, stratified by rural and urban areas.\u003c/span\u003e\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eAccess to Healthcare\u003c/h2\u003e\n \u003cp\u003eAfter performing a complete case analysis (excluding subjects with missing data), the study sample for the main analysis consisted of 4073 subjects.\u003c/p\u003e\n \u003cp\u003eIndividuals living in rural areas were less likely (16%) than their urban counterparts (24%) to consult a physiotherapist or related professional (OR\u0026thinsp;=\u0026thinsp;0.68, 95% CI: 0.52\u0026ndash;0.89). Likewise, rural residents had a higher likelihood (8,8%) of reporting waiting for a medical\u003c/p\u003e\n \u003cp\u003eappointment or treatment due to distance or transportation issues compared to urban residents (4.5%) (OR\u0026thinsp;=\u0026thinsp;1.78, 95% CI: 1.20\u0026ndash;2.63).\u003c/p\u003e\n \u003cp\u003eAlthough a slightly higher proportion of rural inhabitants reported non-reasonable waiting for a medical appointment, examination, or treatment in the last 12 months (OR\u0026thinsp;=\u0026thinsp;1.06, 95% CI: 0.86\u0026ndash;1.32), as well as an unmet need for medical consultation or treatment due to financial difficulties (OR\u0026thinsp;=\u0026thinsp;1.14, 95% CI: 0.86\u0026ndash;1.52), no substantial differences were observed between rural and urban populations. The proportion of individuals who reported visits to outpatient healthcare services was evenly distributed.\u003c/p\u003e\n \u003cdiv\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003eTable 3: Logistic regression analyses of the associations between the study outcomes and the residence area of individuals with OA\u003c/div\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003en / N\u003c/p\u003e\n \u003cp\u003eUnweighted\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003cp\u003eWeighted\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCrude OR*\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eConfounder-adjusted OR**\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003e\u003cstrong\u003eVisits to the hospital for outpatient health care in the last 12 months (YES)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRURAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e811 / 1684\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.991 (0.809\u0026ndash;1.213)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.911 (0.737\u0026ndash;1.127)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eURBAN\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1148 / 2389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003e\u003cstrong\u003eConsultation with a physiotherapist, kinesiotherapist, chiropractor or osteopath in the last 12 months (YES)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRURAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e237 / 1684\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.628 (0.483\u0026ndash;0.816)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.680 (0.518\u0026ndash;0.892)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eURBAN\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e472 / 2389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-reasonable waiting for a medical appointment, examination or treatment in the last 12 months (YES)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRURAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e595 / 1684\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.094 (0.406\u0026ndash;0.885)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.062 (0.855\u0026ndash;1.319)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eURBAN\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e800 / 2389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003e\u003cstrong\u003eWaiting for a medical appointment, exam or treatment due to distance or transportation in the last 12 months (YES)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRURAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e180 / 1884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.029 (1.338\u0026ndash;3.076)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.775 (1.197\u0026ndash;2.633)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eURBAN\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e145 / 2389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003e\u003cstrong\u003eNeed for medical consultation or treatment not satisfied due to financial difficulties in the last 12 months (YES)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRURAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1400 /1684\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.275 (0.966\u0026ndash;1.684)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.142 (0.856\u0026ndash;1.524)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eURBAN\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1970 / 2389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u0026dagger;Reference category\u003c/p\u003e\n \u003cp\u003e*Univariable model for the associations between residence area and study outcomes.\u003c/p\u003e\n \u003cp\u003e**Multivariable model for the associations between residence region and study outcomes adjusted for sex, age group, education, marital status, professional status, household type, income, health insurance and self -reported health status.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eOR \u0026ndash; Odds Ratio\u003c/p\u003e\n \u003cp\u003eCI \u0026ndash; Confidence Interval\u003c/p\u003e\n \u003cp\u003eA secondary analysis stratified by age groups revealed that the associations between these outcomes and residence area varied, indicating some heterogeneity across age groups.\u003c/p\u003e\n \u003cp\u003eRegarding access to physiotherapy, this analysis showed that differences between rural and urban residents were most pronounced among individuals under 64 years old (OR\u0026thinsp;=\u0026thinsp;0.60, CI 0.39\u0026ndash;0.92). In contrast, concerning waiting for medical care due to transportation or distance barriers, the most affected age group was 65\u0026ndash;79 years, with rural residents being more than twice as likely as their urban counterparts to report this issue (OR\u0026thinsp;=\u0026thinsp;2.26, CI 1.31\u0026ndash;3.90).\u003c/p\u003e\n \u003cdiv\u003eTable 4 presents the adjusted ORs for the associations between the referred outcomes and the region of residence across different age groups.\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003eTable 4: Logistic regression analyses of the associations between the study outcomes and the residence area of individuals with OA stratified by age groups\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tabc\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003en/N\u003c/p\u003e\n \u003cp\u003eUnweighted\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003cp\u003eWeighted\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eADJUSTED MULTIVARIABLE MODEL*\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eConsultation with a physiotherapist, kinesiotherapist, chiropractor or osteopath in the last 12 months (YES)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026le;\u0026thinsp;64 YEARS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRURAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95/521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.599 (0.392\u0026ndash;0.916)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eURBAN\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e209/893\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e65\u0026ndash;79 YEARS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRURAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104/727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.721 (0.487\u0026ndash;1.067)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eURBAN\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e214/1075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;\u003cstrong\u003e80 YEARS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRURAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38/436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.770 (0.394-1,504)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eURBAN\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49/421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eWaiting for a medical appointment, exam or treatment due to distance or transportation in the last 12 months (YES)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026le;\u0026thinsp;64 YEARS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRURAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48/521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.677 (0.835\u0026ndash;3.367)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eURBAN\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53/893\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e65\u0026ndash;79 YEARS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRURAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82/727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.263 (1.314\u0026ndash;3.899)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eURBAN\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67/1075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;\u003cstrong\u003e80 YEARS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRURAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45/436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.283 (0.576\u0026ndash;2.856)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eURBAN\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30/421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u0026dagger;Reference category\u003c/p\u003e\n \u003cp\u003e*Multivariable model for the associations between residence region and study outcomes adjusted for sex, education, income, self-reported health status, marital status, insurance and \u0026nbsp; household type.\u003c/p\u003e\n \u003cp\u003eCI \u0026ndash; Confidence Interval\u003c/p\u003e\n\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study aimed to investigate differences in healthcare access among individuals with OA living in rural and urban areas in Portugal.\u003c/p\u003e\u003cp\u003eThe findings indicate that rural residents are 32% less likely (OR 0.68, 95% CI 0.52\u0026ndash;0.90) to visit a physiotherapist or related professional compared to their urban counterparts.\u003c/p\u003e\u003cp\u003eNotably, although a higher proportion of rural inhabitants with OA report poor or very poor health status and intense or very intense pain, this does not appear to be directly linked to access to rehabilitation services.\u003c/p\u003e\u003cp\u003eThese results align with previous research in Portugal and internationally, which has shown that living in remote geographical areas is associated, on one hand, with higher pain levels(Messier et al., 2024), but, on the other hand, with lower utilization of physiotherapy and other specialized healthcare services among individuals with OA(Costa et al., 2021; Liu et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis pattern underscores the existence of unmet healthcare needs within these populations.\u003c/p\u003e\u003cp\u003e Physiotherapists play a central role in delivering high-value first-line care options for OA management(Briggs et al., 2019), and limited access to physiotherapy treatments may hinder improvements in pain, physical function, and quality of life(Lawford et al., 2024), potentially leading to worse disease progression. Consequently, it is not unexpected that rural individuals exhibit higher rates of total arthroplasty utilization compared to their urban counterparts, as observed in a large cohort of US patients(Hinman et al., 2023).\u003c/p\u003e\u003cp\u003eAs outlined earlier, this disparity can be attributed to several factors, where geographical distance from healthcare facilities represents a key factor. Previous data had shown that individuals with OA and living in rural areas consider distance the most common reason for not seeking treatment(Bala et al., 2020). Additionally, rural determinants such as lower education and socioeconomic status(Jiang et al., 2021; Wang et al., 2015), also found in this study, influence health-seeking behaviors(Berkman et al., 2011; Kanungo et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and are directly associated with unmet healthcare needs(Chen et al., 2022; McMaughan et al., 2020). Among rural individuals with OA, a previous study found out a high proportion of unawareness of the beneficial effects of exercise and physiotherapy for condition management(Bala et al., 2020).\u003c/p\u003e\u003cp\u003eThe association between rural residency and a lower likelihood of visiting a rehabilitation professional was more pronounced among younger individuals (\u0026le;\u0026thinsp;64 years). This disparity may be partially explained by the types of occupational activities typically undertaken in rural versus urban settings. Working-age individuals in rural areas are more likely to be employed in agricultural, manual, or precarious jobs (Matz et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), which often lack the flexibility or formal provisions\u0026mdash;such as paid health leave\u0026mdash;required to access healthcare services during working hours. These constraints may hinder their ability to attend physiotherapy or similar appointments. In contrast, individuals working in urban environments are potentially more likely to benefit from structured employment arrangements that facilitate access to health services, and\u0026mdash;due to greater proximity and service availability\u0026mdash;they typically forfeit less work time when seeking care.\u003c/p\u003e\u003cp\u003eThe results of this study also indicate that individuals with OA living in rural areas are 78% more likely (OR 1.78, 95% CI 1.20\u0026ndash;2.63) to face difficulties in obtaining medical care due to distance or transportation issues, independently of other determinants. This finding aligns with previously presented data, which highlights geographical distance as a major factor in defining healthcare access. While previous research has established the influence of factors such as age, socioeconomic status, education, and income on healthcare access(Abenoja et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Fontaine et al., 2007; Murphy et al., 2017; Reyes \u0026amp; Katz, 2021), and the study sample revealed a higher proportion of older participants, with fewer years of education, and lower income in the rural group, the confounder-adjusted OR confirmed a strong association between rural residence and reported waiting times for medical appointments, exams, or treatment due to distance or transportation. This result demonstrates that physical distance to healthcare facilities remains an independent predictor of reduced access, placing rural inhabitants at a disadvantaged and unfair position, and further widening the existing gap in healthcare equity. Previous studies had already highlighted the existence of this \u0026ldquo;distance decay association\u0026rdquo; in healthcare, where greater geographical distance from healthcare services is linked to lower utilization and poorer health outcomes(Kelly et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis association was more prominent in the age group between 65 and 79 years.\u003c/p\u003e\u003cp\u003eA possible explanation is that younger individuals may have greater access to private transportation or better physical mobility to use public transport. In contrast, in older age groups, more pronounced disability or physical dependence, as well as reduced awareness, can impact health-seeking behavior and decrease proactivity in accessing healthcare services.\u003c/p\u003e\u003cp\u003eRegarding the other outcomes considered in the analysis, this study did not found differences in access to hospital outpatient care between rural and urban populations with OA. Outpatient care, which includes services such as consultations and diagnostic tests, often involves a single visit. In contrast, physiotherapy treatments typically require multiple sessions. This could explain the lack of differences observed between the two groups, as rural residents, despite facing access challenges, may be more likely to arrange a one-time visit to a hospital compared to ongoing treatments that necessitate multiple visits.\u003c/p\u003e\u003cp\u003eConcerning waiting times for healthcare access, just over one-third of the sample in each group reported experiencing non reasonable waiting times for a medical appointment, examination, or treatment in the previous year, with a slightly higher proportion among rural participants. These results reinforce a well-documented challenge in the Portuguese healthcare system, where 47% of individuals waiting for a first hospital specialty consultation are experiencing waiting times that exceed the maximum guaranteed response times (Conselho das Finan\u0026ccedil;as P\u0026uacute;blicas, 2024). This finding underscores that long waiting times for medical care are a widespread problem affecting the Portuguese population, regardless of whether they live in rural or urban areas.\u003c/p\u003e\u003cp\u003eSimilarly, a generally high proportion of individuals with OA reported unmet healthcare needs due to financial constraints. Although this study found a higher concentration of individuals in the lowest income quintiles among rural residents\u0026mdash;compared to urban residents, who were more frequently in the highest quintile\u0026mdash;these income differences were not fully reflected in the reported financial difficulties in accessing care, as both groups appeared to be similarly affected by economic barriers to healthcare. Once again, this finding is not unexpected, as available data indicate that out-of-pocket payments accounted for 29% of total health expenditure in Portugal in 2021, a percentage significantly higher than the OECD average of 18.4%. Additionally, 10.6% of Portuguese households (compared to an EU average of 5.3%) faced healthcare expenses exceeding 40% of their household budget, which is considered a catastrophic spending on health(OECD, 2023). Considering the overall impact of healthcare costs on Portuguese families, it\u0026rsquo;s not surprising that no significant differences were found between rural and urban areas, as this seems to be a challenge faced by the entire population.\u003c/p\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eLimitations and Strengths\u003c/h2\u003e\u003cp\u003eThe results of the current study need to be interpreted in the context of its limitations.\u003c/p\u003e\u003cp\u003eThe cross-sectional nature of the data limits the ability to draw causal inferences. Moreover, the reliance on self-reported measures introduces potential sources of bias, such as recall bias and social desirability bias. Previous research has highlighted a general tendency to under-report chronic disease diagnoses, particularly among older adults and individuals with high BMI, which may lead to an underestimation of symptom burden and barriers to accessing care(Liu et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAdditionally, the potential presence of residual and unmeasured confounding bias should be considered, as there may be important variables that were not available for adjustment in the analysis, but could have influenced the results. Further research in this area should focus on longitudinal evaluations of healthcare access, examining additional factors such as referral patterns, patient and healthcare professionals' beliefs and misconceptions, variations in service and transportation availability, and healthcare-seeking behaviors. This would provide deeper insights into the underlying determinants of healthcare access inequalities.\u003c/p\u003e\u003cp\u003eOn the other hand, this study presents several strengths. First, the use of a nationally representative survey (INS 2019) ensures robust inferences about the Portuguese population with OA, while the application of survey weights enhances the representativeness of the findings. Additionally, by focusing on healthcare access disparities, the study provides valuable insights into the geographical barriers faced by rural populations, particularly in accessing physiotherapy and other healthcare services. The inclusion of age-group analyses further strengthens the study by revealing age related heterogeneities in healthcare utilization patterns. Importantly, the findings hold significant implications for public health policies, emphasizing the need for alternative strategies\u0026mdash;such as digital health solutions\u0026mdash;to bridge existing gaps in access.\u003c/p\u003e\u003cp\u003eThe findings of this study underscore the urgent need for healthcare services and policies to identify or develop alternative methods of delivering high-value treatments to rural populations with OA. Addressing this challenge requires the implementation of strategies that take into account the specific needs and characteristics of these populations, ensuring they have equal opportunities to achieve the same outcomes as urban inhabitants.\u003c/p\u003e\u003cp\u003eThese strategies can vary, such as enhancing community-based care or improving transportation options. However, in the current era, the role of digital health is undeniably, being emphasized by various health institutions, and being increasingly recognized as a crucial tool in addressing healthcare disparities(World Health Organization, 2021). Significant improvements in clinical and psychosocial outcomes have been widely reported among individuals with chronic musculoskeletal conditions who received various forms of digital interventions. Several systematic reviews have found evidence supporting the effectiveness of digital health interventions in reducing pain intensity, improving functionality and physical performance, enhancing self-management, and promoting better quality of life (Valentijn et al.; Cottrell et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Thurnheer et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Pfeifer et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Chen et al. 2021; Latif-Zade et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Xie et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Jirasakulsuk et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Xiang et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Baigi et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Thompson et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFurthermore, digital interventions have been found to be cost-effective compared with usual care (Fatoye et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Molina-Garcia et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and are associated with high levels of satisfaction from both patients and providers.(Amin et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Although these data are primarily related to urban settings, they provide a promising foundation for extending the benefits of digital health to underserved rural communities. By bridging the gap between urban and rural settings, disparities can be reduced, ensuring that rural populations receive timely and adequate healthcare, regardless of their geographic location.\u003c/p\u003e\u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eIn conclusion, this study reinforces the existence of healthcare access disparities affecting individuals with OA living in rural areas of Portugal, who experience reduced access to physiotherapy and longer waiting times for services, often due to geographic distance and transportation barriers. The findings suggest that these disparities vary across age groups, potentially highlighting the influence of other factors in healthcare access, particularly in health-seeking behaviors.\u003c/p\u003e\u003cp\u003eThe results underscore the importance of addressing these challenges through tailored healthcare strategies, including enhancing access to digital health solutions. Ensuring equitable access to care for rural populations is not only crucial for improving health outcomes but also for advancing overall healthcare equity in society.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank Statistics Portugal for providing access to the 2019 Portuguese National Health Interview Survey data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests to declare.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eEthical Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used anonymized secondary data obtained from the Instituto Nacional de Sa\u0026uacute;de (INS). As the data were publicly available and did not involve direct interaction with human participants, ethical approval was not required in accordance with national regulations and institutional guidelines.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbenoja A, Theodorlis M, Ahluwalia V, et al (2023) Strategies to Improve Equitable Access to Early Osteoarthritis Diagnosis and Management: An updated Review. Arthritis Care Res (Hoboken)\u003c/li\u003e\n\u003cli\u003eAmin J, Ahmad B, Amin S, et al (2022) Review Article Rehabilitation Professional and Patient Satisfaction with Telerehabilitation of Musculoskeletal Disorders: A Systematic Review. https://doi.org/10.1155/2022/7366063\u003c/li\u003e\n\u003cli\u003eBaigi SFM, Kimiafar K, Ghaddaripouri K, et al (2023) The effect of telerehabilitation on improving the physical activity of patients with osteoarthritis: A systematic review. J Educ Health Promot 12:. https://doi.org/10.4103/JEHP.JEHP_1586_22\u003c/li\u003e\n\u003cli\u003eBannuru RR, Osani MC, Vaysbrot EE, et al (2019) OARSI guidelines for the non-surgical management of knee, hip, and polyarticular osteoarthritis. Osteoarthritis Cartilage 27:1578\u0026ndash;1589. https://doi.org/10.1016/j.joca.2019.06.011\u003c/li\u003e\n\u003cli\u003eBoring MA, Hootman JM, Liu Y, et al (2017) Prevalence of Arthritis and Arthritis-Attributable Activity Limitation by Urban-Rural County Classification \u0026mdash; United States, 2015. MMWR Morb Mortal Wkly Rep 66:527\u0026ndash;532. https://doi.org/10.15585/mmwr.mm6620a2\u003c/li\u003e\n\u003cli\u003eBorkhoff CM, Wieland ML, Myasoedova E, et al (2011) Reaching those most in need: a scoping review of interventions to improve health care quality for disadvantaged populations with osteoarthritis. Arthritis Care Res (Hoboken) 63:39\u0026ndash;52. https://doi.org/10.1002/ACR.20349\u003c/li\u003e\n\u003cli\u003eBranco JC, Rodrigues AM, Gouveia N, et al (2016) Prevalence of rheumatic and musculoskeletal diseases and their impact on health-related quality of life, physical function and mental health in Portugal: Results from EpiReumaPt- a national health survey. RMD Open 2:. https://doi.org/10.1136/rmdopen-2015-000166\u003c/li\u003e\n\u003cli\u003eB\u0026uuml;hn S, Holstiege J, Pieper D (2020) Are patients willing to accept longer travel times to decrease their risk associated with surgical procedures? A systematic review. 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ISPRS International Journal of Geo-Information 2020, Vol 9, Page 567 9:567. https://doi.org/10.3390/IJGI9100567\u003c/li\u003e\n\u003cli\u003eCottrell MA, Galea OA, O\u0026rsquo;Leary SP, et al (2017) Real-time telerehabilitation for the treatment of musculoskeletal conditions is effective and comparable to standard practice: a systematic review and meta-analysis. Clin Rehabil 31:625\u0026ndash;638. https://doi.org/10.1177/0269215516645148\u003c/li\u003e\n\u003cli\u003eCourties A, Kouki I, Soliman N, et al (2024) Osteoarthritis year in review 2024: Epidemiology and therapy. 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PLoS One 10:. https://doi.org/10.1371/JOURNAL.PONE.0125865\u003c/li\u003e\n\u003cli\u003eKelly C, Hulme C, Farragher T, Clarke G (2016) Are differences in travel time or distance to healthcare for adults in global north countries associated with an impact on health outcomes? A systematic review. https://doi.org/10.1136/bmjopen-2016\u003c/li\u003e\n\u003cli\u003eKolasinski SL, Neogi T, Hochberg MC, et al (2020) Foundation Guideline for the Management of Osteoarthritis of the Hand, Hip, and Knee. Arthritis \u0026amp; Rheumatology 72:220\u0026ndash;233. https://doi.org/10.1002/art.41142\u003c/li\u003e\n\u003cli\u003eLatif-Zade T, Tucci B, Verbovetskaya D, et al (2021) Systematic Review Shows Tele-Rehabilitation Might Achieve Comparable Results to Office-Based Rehabilitation for Decreasing Pain in Patients with Knee Osteoarthritis. Medicina (B Aires). https://doi.org/10.3390/medicina57080764\u003c/li\u003e\n\u003cli\u003eLiu H, Zhao Y, Qiao L, et al (2024) Consistency between self-reported disease diagnosis and clinical assessment and under-reporting for chronic conditions: data from a community-based study in Xi\u0026rsquo;an, China. Front Public Health 12:. https://doi.org/10.3389/FPUBH.2024.1296939/PDF\u003c/li\u003e\n\u003cli\u003eLiu X, Seidel JE, McDonald T, et al (2022) Rural\u0026ndash;Urban Disparities in Realized Spatial Access to General Practitioners, Orthopedic Surgeons, and Physiotherapists among People with Osteoarthritis in Alberta, Canada. International Journal of Environmental Research and Public Health 2022, Vol 19, Page 7706 19:7706. https://doi.org/10.3390/IJERPH19137706\u003c/li\u003e\n\u003cli\u003eLiu Y, Zhang H, Liang N, et al (2016) Prevalence and associated factors of knee osteoarthritis in a rural Chinese adult population: an epidemiological survey. 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Ann Phys Rehabil Med 67:. https://doi.org/10.1016/J.REHAB.2023.101791\u003c/li\u003e\n\u003cli\u003eO\u0026rsquo;brien P, Bunzli S, Lin I, et al (2020) Tackling the Burden of Osteoarthritis as a Health Care Opportunity in Indigenous Communities-A Call to Action. J Clin Med 9:1\u0026ndash;5. https://doi.org/10.3390/JCM9082393\u003c/li\u003e\n\u003cli\u003ePfeifer AC, Uddin R, Schr\u0026ouml;der-Pfeifer P, et al (2020) Mobile Application-Based Interventions for Chronic Pain Patients: A Systematic Review and Meta-Analysis of Effectiveness. J Clin Med 9:1\u0026ndash;18. https://doi.org/10.3390/JCM9113557\u003c/li\u003e\n\u003cli\u003eSteinmetz JD, Culbreth GT, Haile LM, et al (2023) Global, regional, and national burden of osteoarthritis, 1990\u0026ndash;2020 and projections to 2050: a systematic analysis for the Global Burden of Disease Study 2021. 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JMIR Mhealth Uhealth 6:. https://doi.org/10.2196/11231\u003c/li\u003e\n\u003cli\u003eValentijn PP, Tymchenko L, Jacobson T, et al Digital Health Interventions for Musculoskeletal Pain Conditions: Systematic Review and Meta-analysis of Randomized Controlled Trials. https://doi.org/10.2196/37869\u003c/li\u003e\n\u003cli\u003evon Elm E, Altman DG, Egger M, et al (2008) The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol 61:344\u0026ndash;349. https://doi.org/10.1016/J.JCLINEPI.2007.11.008\u003c/li\u003e\n\u003cli\u003eWen M, Fan JX, Kowaleski-Jones L, Wan N (2018) Rural\u0026ndash;Urban Disparities in Obesity Prevalence Among Working Age Adults in the United States: Exploring the Mechanisms. American Journal of Health Promotion 32:400\u0026ndash;408. https://doi.org/10.1177/0890117116689488\u003c/li\u003e\n\u003cli\u003eXiang W, Wang JY, Ji BJ, et al (2023) Effectiveness of Different Telerehabilitation Strategies on Pain and Physical Function in Patients With Knee Osteoarthritis: Systematic Review and Meta-Analysis. J Med Internet Res 25:. https://doi.org/10.2196/40735\u003c/li\u003e\n\u003cli\u003eXie S-H, Wang Q, Wang L-Q, et al (2021) Effect of Internet-Based Rehabilitation Programs on Improvement of Pain and Physical Function in Patients with Knee Osteoarthritis: Systematic Review and Meta-analysis of Randomized Controlled Trials. J Med Internet Res 23:e21542. https://doi.org/10.2196/21542\u003c/li\u003e\n\u003cli\u003eYang G, Wang J, Liu Y, et al (2023) Burden of Knee Osteoarthritis in 204 Countries and Territories, 1990-2019: Results From the Global Burden of Disease Study 2019. Arthritis Care Res (Hoboken) 75:2489\u0026ndash;2500. https://doi.org/10.1002/ACR.25158\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003cp\u003e\u003cspan\u003e* Norte, Centro, Lisboa and Vale do Tejo, Alentejo, Algarve, A\u0026ccedil;ores and Madeira\u003c/span\u003e"},{"header":"Table 2","content":"\u003cp\u003eTable 2 is available in the Supplementary Files section.\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[{"identity":"9a3f458b-e99c-4112-8905-2399cc192cc5","identifier":"10.13039/501100001871","name":"Fundação para a Ciência e a Tecnologia","awardNumber":"PRT/BD/154509/2022","order_by":0}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Universidade Nova de Lisboa","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"osteoarthritis, rural, healthcare access, inequities","lastPublishedDoi":"10.21203/rs.3.rs-7362024/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7362024/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e\u003cp\u003eOsteoarthritis (OA) is a leading cause of musculoskeletal pain worldwide, with a greater impact on rural populations. Although preliminary findings suggest disparities in healthcare access for rural residents with OA, further research is needed to fully understand this issue.\u003c/p\u003e\u003ch2\u003eAim:\u003c/h2\u003e\u003cp\u003eThis study aimed to examine healthcare access disparities between rural and urban areas among individuals with self-reported OA living in Portugal.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis study involves a cross-sectional secondary analysis of data from the 2019 Portuguese National Health Interview Survey (NHIS), focusing on individuals who self-reported OA. The prevalence of variables related to healthcare access was estimated, including the frequency of healthcare visits, waiting times for medical care, and the impact of financial factors. The odds ratio (OR) for healthcare access between rural and urban residents, adjusted for confounding, was estimated using multivariable logistic regression models. An age-stratified analysis was also performed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eA total of 4095 individuals with self-reported OA were included. Confounder-adjusted OR estimates revealed that living in rural areas was associated with a higher likelihood of not accessing physiotherapy treatments (OR 0.680, 95% CI 0.518\u0026ndash;0.892), particularly among individuals under 65 years old. Additionally, rural residents were more likely (OR 1.775, 95% CI 1.197\u0026ndash;2.633) to experience delays in healthcare services due to distance or transportation issues, with this disparity most pronounced among individuals aged 65 to 79 years.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThese findings highlight disparities in healthcare access between rural and urban residents with OA in Portugal, underscoring the need for targeted interventions to improve healthcare availability and reduce inequities.\u003c/p\u003e","manuscriptTitle":"Rural-Urban Disparities in Healthcare Access Among Individuals with Osteoarthritis in Portugal: A Cross-Sectional Analysis of the 2019 National Health Survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-14 06:53:45","doi":"10.21203/rs.3.rs-7362024/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5779bd7c-70f9-4b91-bf74-1547bfd07d65","owner":[],"postedDate":"August 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":53087167,"name":"Epidemiology"}],"tags":[],"updatedAt":"2025-08-14T06:53:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-14 06:53:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7362024","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7362024","identity":"rs-7362024","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}