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Currently submitted to: JMIR Public Health and Surveillance

Date Submitted: Aug 15, 2026
Open Peer Review Period: Aug 18, 2026 - Oct 13, 2026
(currently open for review)

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Adjusted Morbidity Groups Complexity Profiles Among High-Risk Chronic Patients in Primary Care: Population-Based Cross-Sectional Study Using Real-World Data

  • María De La Concepción Martín Trujillo; 
  • Beatriz Benito-Sánchez; 
  • Almudena Castaño Reguillo; 
  • Ana Isabel Villimar Rodríguez; 
  • Andrés Gaspar Castillo Sanz; 
  • Jaime Barrio-Cortes

ABSTRACT

Background:

Population-based risk stratification using routinely collected health data can help identify individuals with greater health and care needs. However, high-risk chronic patients constitute a heterogeneous population, and further characterization of their complexity may support more targeted population health management.

Objective:

To describe the sociodemographic, clinical, functional, pharmacological, and primary health care (PHC) utilization profiles of high-risk chronic patients across quartiles of Adjusted Morbidity Group (AMG) complexity and identify factors independently associated with the highest complexity quartile.

Methods:

This population-based cross-sectional study used routinely collected real-world data from PHC electronic health records and pharmacy dispensing databases of the Madrid Health Service. Adults aged ≥18 years with chronic conditions classified as high-risk according to the AMG stratification tool as of April 30, 2021, were included. Patients were categorized into AMG complexity quartiles, with Q4 representing the highest complexity level. Sociodemographic, clinical, functional, pharmacological, and health care utilization characteristics were compared across complexity levels using descriptive and bivariate analyses. Multivariable logistic regression was used to identify factors independently associated with Q4 complexity.

Results:

A total of 163,188 high-risk chronic patients were included. Compared with patients in the lower complexity quartile, those in Q4 were older (mean age 78.9 vs 75.6 years), had greater functional dependency (31.1% vs 14.2%), immobilization (18.9% vs 8.1%), and caregiver requirements (5.4% vs 2.4%), and had a higher morbidity burden (mean number of conditions 8.93 vs 6.31), medication use (10.0 vs 8.2 medications), and prevalence of polypharmacy (64.3% vs 47.7%). They also had more PHC contacts (29.6 vs 20.8 visits/year). In multivariable analysis, older age (OR 1.024 per year), heart failure (OR 2.874), neoplasm (OR 2.715), chronic kidney disease (OR 2.088), dementia (OR 1.987), caregiver presence (OR 1.393), polypharmacy (OR 1.161), and ≥20 PHC visits/year (OR 1.887) were positively associated with Q4 complexity, whereas female sex (OR 0.846) and greater functional independence (OR 0.669) were associated with lower odds of Q4 complexity (all p<0.001).

Conclusions:

High-risk chronic patients in the highest AMG complexity quartile showed greater multimorbidity, functional impairment, medication burden, and PHC utilization. AMG complexity quartiles add value beyond high-risk status by identifying complex chronic patients with greater care needs and supporting targeted proactive care in PHC settings. These findings highlight the potential of routinely collected electronic health record and pharmacy data, combined with population-based risk stratification systems, as scalable tools for population health analysis and targeted health care planning in primary care.


 Citation

Please cite as:

Martín Trujillo MDLC, Benito-Sánchez B, Castaño Reguillo A, Villimar Rodríguez AI, Castillo Sanz AG, Barrio-Cortes J

Adjusted Morbidity Groups Complexity Profiles Among High-Risk Chronic Patients in Primary Care: Population-Based Cross-Sectional Study Using Real-World Data

JMIR Preprints. 15/08/2026:109694

DOI: 10.2196/preprints.109694

URL: https://preprints.jmir.org/preprint/109694

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