{"doi":"10.3389/fpubh.2025.1645486","title":"Sociodemographic inequalities in multimorbidity and lifestyle risk factors among young adults: a national population-based study","abstract":"<jats:sec><jats:title>Background</jats:title><jats:p>Multimorbidity is an increasing worldwide concern, disproportionately impacting disadvantaged populations. Existing studies have mainly focused on older adults, leaving a gap in understanding how sociodemographic inequalities influence the prevalence and risk factors for multimorbidity among younger individuals. This study aims to explore sociodemographic disparities in the prevalence of multimorbidity and its behavioral risk factors among young Brazilian adults.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>This cross-sectional study utilized data from the 2019 National Health Survey, involving a representative sample of individuals aged 20–50 years (<jats:italic>n</jats:italic> = 48,890). Sociodemographic variables (such as gender, age, race, education, socioeconomic status, marital status, health insurance, and region of residence) and behavioral factors (smoking, alcohol use, physical activity, and eating habits) were examined in relation to multimorbidity. Logistic regression models were used to explore the associations between the selected variables and multimorbidity.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>Of the 48,890 participants, 18% reported multimorbidity. Sociodemographic disparities were identified, with higher prevalence among women, older adults, individuals with lower education levels, and those with health insurance. Conversely, participants from the North, Northeast, and Central-West regions were associated with lower prevalence rates. The prevalence of multiple behavioral risk factors (four or more) was greater among those with lower education, lower socioeconomic status, and residents of the South and North regions. Older and married individuals exhibited a lower prevalence of concurrent risk factors.</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>Sociodemographic factors such as age, gender, education, marital status, health insurance, and region were associated to multimorbidity and the co-occurrence of multiple behavioral risk factors. These disparities highlight the need for policies to reduce modifiable risk factors and promote equitable healthcare access, especially for socioeconomically disadvantaged groups.</jats:p></jats:sec>","journal":"Frontiers in Public Health","year":2025,"id":633073,"datarank":0.16479184330021646,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"self_citation_contribution":0.16479184330021646,"citation_network_contribution":0.0,"self_endowment_contribution":0.16479184330021646,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":533072,"name":"Luciana Bertoldi Nucci","orcid":"0000-0002-5140-3622","position":1,"is_corresponding":false},{"id":1611551,"name":"André Luiz Monezi Andrade","orcid":"0000-0003-0111-8935","position":2,"is_corresponding":false},{"id":1641201,"name":"Carla Cristina Enes","orcid":null,"position":3,"is_corresponding":false},{"id":1641198,"name":"Mirta Mara Mendonça Tavares","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Sociodemographic inequalities in multimorbidity and lifestyle risk factors among young adults: a national population-based study","abstract":"<jats:sec><jats:title>Background</jats:title><jats:p>Multimorbidity is an increasing worldwide concern, disproportionately impacting disadvantaged populations. Existing studies have mainly focused on older adults, leaving a gap in understanding how sociodemographic inequalities influence the prevalence and risk factors for multimorbidity among younger individuals. This study aims to explore sociodemographic disparities in the prevalence of multimorbidity and its behavioral risk factors among young Brazilian adults.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>This cross-sectional study utilized data from the 2019 National Health Survey, involving a representative sample of individuals aged 20–50 years (<jats:italic>n</jats:italic> = 48,890). Sociodemographic variables (such as gender, age, race, education, socioeconomic status, marital status, health insurance, and region of residence) and behavioral factors (smoking, alcohol use, physical activity, and eating habits) were examined in relation to multimorbidity. Logistic regression models were used to explore the associations between the selected variables and multimorbidity.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>Of the 48,890 participants, 18% reported multimorbidity. Sociodemographic disparities were identified, with higher prevalence among women, older adults, individuals with lower education levels, and those with health insurance. Conversely, participants from the North, Northeast, and Central-West regions were associated with lower prevalence rates. The prevalence of multiple behavioral risk factors (four or more) was greater among those with lower education, lower socioeconomic status, and residents of the South and North regions. Older and married individuals exhibited a lower prevalence of concurrent risk factors.</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>Sociodemographic factors such as age, gender, education, marital status, health insurance, and region were associated to multimorbidity and the co-occurrence of multiple behavioral risk factors. These disparities highlight the need for policies to reduce modifiable risk factors and promote equitable healthcare access, especially for socioeconomically disadvantaged groups.</jats:p></jats:sec>","is_dataset_classified":null,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"41000305","pmcid":"PMC12457391","openalex_id":"https://openalex.org/W4414086531","authors":[],"funders":[],"total_grants":0,"fwci":1.5361,"citation_percentile":0.84422362,"influential_citations":0,"citation_trend":[{"year":2026,"count":2}],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1645486/pdf","host_type":"journal"},{"url":"https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1645486/pdf","host_type":"publisher"},{"url":"https://www.frontiersin.org/articles/10.3389/fpubh.2025.1645486/full","host_type":"publisher"},{"url":"https://doi.org/10.3389/fpubh.2025.1645486","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/41000305","host_type":"repository"},{"url":"https://doaj.org/article/409e73e3fccb43689c648149969b7ea6","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/12457391","host_type":"repository"},{"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC12457391/","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC12457391","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC12457391?pdf=render","host_type":"Europe_PMC"}],"fields_of_study":["Chronic Disease Management Strategies","Primary Care and Health Outcomes","Diabetes Management and Education","Humans","Female","Male","Adult","Cross-Sectional Studies","Middle Aged","Risk Factors","Multimorbidity","Socioeconomic Factors","Life Style","Brazil","Young Adult","Prevalence","Sociodemographic Factors","Health Surveys","Health Status Disparities"],"mesh_terms":["Multimorbidity","Sociodemographic Factors","Adult","Brazil","Cross-Sectional Studies","Female","Health Surveys","Humans","Life Style","Male","Middle Aged","Risk Factors","Socioeconomic Factors","Prevalence","Health Status Disparities","Young Adult"],"keywords":["Multimorbidity","Disadvantaged","Inequality","Marital status","Health care","Health equity","Epidemiology","Behavioral Risk Factor Surveillance System","Socioeconomic status","Socioeconomic Factors","Health Surveys","Lifestyle","Noncommunicable Diseases"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-06T10:53:46.158559Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}