Utilization of Primary Healthcare Services in Patients with Multimorbidity According to Their Risk Level by Adjusted Morbidity Groups: A Cross-Sectional Study in Chamartín District (Madrid).
Jaime Barrio-CortesAlmudena Castaño ReguilloBeatriz Benito-SánchezMaría Teresa Beca-MartínezCayetana Ruiz-ZaldibarPublished in: Healthcare (Basel, Switzerland) (2024)
Patients with multimorbidity have increased and more complex healthcare needs, posing their management a challenge for healthcare systems. This study aimed to describe their primary healthcare utilization and associated factors. A population-based cross-sectional study was conducted in a Spanish basic healthcare area including all patients with chronic conditions, differentiating between having multimorbidity or not. Sociodemographic, functional, clinical and service utilization variables were analyzed, stratifying the multimorbid population by the Adjusted Morbidity Groups (AMG) risk level, sex and age. A total of 6036 patients had multimorbidity, 64.2% being low risk, 28.5% medium risk and 7.3% high risk. Their mean age was 64.1 years and 63.5% were women, having on average 3.5 chronic diseases, and 25.3% were polymedicated. Their mean primary care contacts/year was 14.9 (7.8 with family doctors and 4.4 with nurses). Factors associated with primary care utilization were age (B-coefficient [BC] = 1.15;95% Confidence Interval [CI] = 0.30-2.01), female sex (BC = 1.04; CI = 0.30-1.78), having a caregiver (BC = 8.70; CI = 6.72-10.69), complexity (B-coefficient = 0.46; CI = 0.38-0.55), high-risk (B-coefficient = 2.29; CI = 1.26-3.32), numerous chronic diseases (B-coefficient = 1.20; CI = 0.37-2.04) and polypharmacy (B-coefficient = 5.05; CI = 4.00-6.10). This study provides valuable data on the application of AMG in multimorbid patients, revealing their healthcare utilization and the need for a patient-centered approach by primary care professionals. These results could guide in improving coordination among professionals, optimizing multimorbidity management and reducing costs derived from their extensive healthcare utilization.
Keyphrases
- healthcare
- primary care
- end stage renal disease
- ejection fraction
- newly diagnosed
- mental health
- emergency department
- chronic kidney disease
- diffusion weighted imaging
- magnetic resonance imaging
- type diabetes
- south africa
- peritoneal dialysis
- metabolic syndrome
- adipose tissue
- big data
- risk factors
- health information
- general practice
- machine learning
- patient reported outcomes
- deep learning
- health insurance
- affordable care act
- cervical cancer screening