ABS0830 FAMILY SIZE AND ITS PREDICTORS IN RHEUMATIC DISEASES: A SYNDEMIC ANALYSIS FROM THE CoVAD-3 STUDY
Notice bibliographique
Résumé
Background: Family size amongst patients diagnosed with rheumatic diseases (RMD) is frequently reduced. Understanding the complex interplay of social, psychological, and disease-related factors influencing family planning decisions remains crucial for developing targeted interventions. Objectives: The study compared family size between gender-matched respondents with RMD and healthy controls (HC), and to identify factors associated with family size. Methods: A cross-sectional analysis of the CoVAD-3 survey employed a syndemic framework to examine family size determinants. Variables were categorised into three thematic domains [Figure 1A]: social (educational status, human development index (HDI) which was classified as very high, high, medium, or low, and caregiving responsibilities), disease-related (disease duration, multimorbidity patterns, medication use), and psychological (life satisfaction, resilience, mental health, loneliness). Family size was assessed through biological children counts. Statistical analysis included bivariate comparisons (chi-square, Mann-Whitney, Student t-tests) and multivariate regression models adjusted for age and ethnicity. Syndemic clustering was evaluated through thematic analysis. Results: The respondents (n = 3,026) [Figure 1B] had a male-to-female ratio of 1:3.15, with 2,349 having RMD and 677 being HC. The respondents [Table 1] demonstrated distinct gender patterns [Figure 1C], with female RMD having more biological children than male RMD [median=1 (IQR=0-2) vs median=0 (IQR=0-2), p<0.001]. Within the social domain, higher HDI (p<0.001) predicted fewer children in females. Disease-related factors showed that longer disease duration negatively impacted family size (females: β=-0.072, p=0.020; males: β=-0.137, p=0.009)). In the psychological domain, life satisfaction (females: β=0.095, p<0.001; males: β=0.193, p<0.001) positively correlated with family size for both genders while higher resilience (β = 0.059, p=0.023) positively correlated for females. Mental health concerns and loneliness showed negative associations in females only (β=-0.107, p=0.002; β=-0.067, p=0.012). Conclusion: This syndemic analysis reveals complex interactions between gender and thematic factors affecting family size in RMD. The findings support the development of multifaceted, gender-specific interventions addressing family planning needs within the broader context of rheumatic disease management. Based on the evidence, the authors propose integrating early family planning counselling with RMD care, providing psychological support focusing on resilience building, and social support systems targeting caregiving needs. Healthcare policy adaptation for regions with varying HDI and gender-specific reproductive health programmes are recommended to address identified barriers. REFERENCES: NIL . TABLES Figure 1A. Variables analyzed B. Workflow diagram depicting the inclusion of participants responses C. Variables demonstrating significant gender patterns in RMD.Abbreviations: UCLA: University of California, Los Angeles; PROMIS: Patient-Reported Outcomes Measurement Information System; SWLS: Satisfaction with life scale; VAS: Visual Analogue Scale; APGAR: adaptation, partnership, growth, affection, and resolve; HDI: Human development index; RMD: Rheumatic disease. Table 1Characteristics of the cohort.Female (n=2297)Male (n=729)VariablesRMD (n=1865)HC (n=432)p-value*RMD (n=484)HC (n=245)p-value*A. Basic demographic characteristicsAge overall, mean (SD)50.77 (14.10)42.12 (12.78)<0.00151.57 (13.38)44.04 (11.77)<0.001Heterosexual, n (%)1591 (85.30)363 (84.02)0.281444 (91.73)218 (88.97)<0.001Caucasian Ethnicity, n (%)1068 (57.26)195 (45.13)<0.001146 (30.16)65 (26.53)0.307HDI of country of current residence, n (%)a. Very highb. Highc. Mediumd. Low1375 (73.72)285 (15.28)123 (6.59)33 (1.76)164 (37.96)144 (33.33)112 (25.92)7 (1.62)<0.001178 (36.77)54 (11.57)245 (50.61)2 (0.41)68 (27.75)47 (19.18)127 (51.83)3 (1.22)0.005Graduate education and above, n (%)537 (28.79)167 (38.65)<0.00182 (16.94)74 (30.20)<0.001B. Disease specific factorsDisease duration, median (IQR)10.00(4.00-18.00)--13.38 (5.00-18.25)--Presence of Disease Activity, n (%)150 (8.04)--264 (5.45)--Patient Global Disease Activity Score, mean (SD)4.85 (2.45)--4.94 (2.09)--Patient Global Disease Damage Score, mean (SD)5.28 (2.55)--4.94 (2.09)--Comorbidities, n (%)a.Basic multimorbidityb.Complex multimorbidityc.Autoimmune multimorbidity1159 (62.14)587 (31.47)463 (24.82)113 (26.15)55 (7.92)-<0.001<0.001-160 (33.05)93 (19.21)54 (11.15)32 (13.06)28 (11.42)-<0.0010.008-Functional Comorbidity Index Score, mean [SD]1.80 (1.65)0.55 (0.87)<0.0010.897 (1.30)0.27 (0.61)0.002C. Personal factorsMarried or living as married, n (%)1226 (65.73)275 (63.65)0.413425 (87.81)198 (80.81)0.011Caring responsibilities, n (%)648 (35.33)171 (40.42)0.05117 (24.47)69 (28.51)0.243Recreational habit, n (%)1.Smoking status2.Consumes alcohol1486 (79.67)1025 (54.96)371 (85.88)281 (65.04)0.003<0.001241 (49.79)362 (74.79)149 (60.81)182 (74.28)0.0050.882* p-value was assessed by χ² test for categorical variables and by Student t-test or Mann-Whitney U test for normally and skewed distributed continuous variables respectively .Abbreviations: RMD: Rheumatic disease; HC: Healthy Controls; SD: Standard deviations; IQR: interquartile range; HDI: Human development index Acknowledgements: Patient research partners: Peter Boyd, Linda Kobert, Paula Jordan, Kirtida Oza, Dr. Ingrid De Groot, Allison Foss, Celia Meyer, Karin Blomkvist Sporre, Annika Broberg Lavén, Veronica Fatura, Ailsa Bosworth, Malak Aburas, Silvia Aguilera, Rachel Bromley Patient Support Groups: Cure JM, JCR, CYPLER, EULAR PARE, Myositis Support and Understanding, Myositis UK, The Myositis Association, ARCH Network, ArLAR, Young GRAPPA, APLAR myositis SIG, Myasthenia Gravis Association, Wolverhampton PSG, Patients Alliance for Rheumatic Diseases (PARD), SSc UK, Conquer Myasthenia Gravis, Myasthenia Gravis Association of Western PA, The MG Holistic Society, MG Ohio, Myasthenia Gravis Foundation of Michigan,MIHRA, EULAR Rehfap, Rodney Jansen (Myositis Canada), AAAA, NRAS, National Association for SLE, TMA Michigan Support Group Co-leader, TMA Adelante Affinity Group Co-leader, Myasthenia Gravis Foundation of America, MIHRA, EULAR Reproductive Health and Family Planning (ReHFaP), Rodney Jansen (Myositis Canada), Asociacion Miastenia de Espana, Conquer MG, Associazione Italiana Miastenia, Associazione Miastenia, EU-MGA, Hellenic Myasthenia Association, MG Holistic Society, MG Japan, MG Ohio, MG Society of Canada, Myasthenia Gravis Association, Myasthenia Gravis Association of Western PA, Myasthenia Gravis Foundation of America, Myasthenia Gravis Foundation of Bulgaria, Myasthenia Gravis Foundation of Michigan, MyAware, Netherlands MG Association, Stowarzyszenie Miastenia Gravis Face to Face, Mission Arthritis India (MAI), Ankylosing Spondylitis Welfare Society (ASWS), StandForAS, Scleroderma India. Disclosure of Interests: Manali Sarkar: None declared, Laura Andreoli: None declared, Luis Fernando Perez: None declared, Manasik Mamoun: None declared, Sreoshy Saha: None declared, Nelly Ziade NZ has received speaker fees, advisory board fees, and research grants from Pfizer, Roche, Abbvie, Eli Lilly, NewBridge, Sanofi-Aventis, Boehringer Ingelheim, Janssen, and Pierre Fabre; none are related to this manuscript, NZ has received speaker fees, advisory board fees, and research grants from Pfizer, Roche, Abbvie, Eli Lilly, NewBridge, Sanofi-Aventis, Boehringer Ingelheim, Janssen, and Pierre Fabre; none are related to this manuscript, Tsvetelina Velikova TV has received speaker honoraria from Pfizer and AstraZeneca, non-related to the current manuscript, Ioannis Parodis I.P. has received research funding and/or honoraria from Amgen, AstraZeneca, Aurinia Pharmaceuticals, Elli Lilly and Company, Gilead Sciences, GlaxoSmithKline, Janssen Pharmaceuticals, Novartis and F. Hoffmann-La Roche AG; none are related to this manuscript, Karen Cheng KC is an employee of Sobi working on projects unrelated to this manuscript, Jasmine Parihar: None declared, Vikas Agarwal: None declared, Latika Gupta: None declared, Vincenzo Venerito: None declared. © The Authors 2025. This abstract is an open access article published in Annals of Rheumatic Diseases under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Neither EULAR nor the publisher make any representation as to the accuracy of the content. The authors are solely responsible for the content in their abstract including accuracy of the facts, statements, results, conclusion, citing resources etc.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».