Comorbidity patterns and socioeconomic inequalities in children under 15 with medical complexity: a population-based study

Background Children with medical complexity (CMC) denotes the profile of a child with diverse acute and chronic conditions, making intensive use of the healthcare services and with special health and social needs. Previous studies show that CMC are also affected by the socioeconomic position (SEP) of their family. The aim of this study is to describe the pathologic patterns of CMC and their socioeconomic inequalities in order to better manage their needs, plan healthcare services accordingly, and improve the care models in place. Methods Cross-sectional study with latent class analysis (LCA) of the CMC population under the age of 15 in Catalonia in 2016, using administrative data. LCA was used to define multimorbidity classes based on the presence/absence of 57 conditions. All individuals were assigned to a best-fit class. Each comorbidity class was described and its association with SEP tested. The Adjusted Morbidity Groups classification system (Catalan acronym GMA) was used to identify the CMC. The main outcome measures were SEP, GMA score, sex, and age distribution, in both populations (CMC and non-CMC) and in each of the classes identified. Results 71% of the CMC population had at least one parent with no employment or an annual income of less than €18,000. Four comorbidity classes were identified in the CMC: oncology (36.0%), neurodevelopment (13.7%), congenital and perinatal (19.8%), and respiratory (30.5%). SEP associations were: oncology OR 1.9 in boys and 2.0 in girls; neurodevelopment OR 2.3 in boys and 1.8 in girls; congenital and perinatal OR 1.7 in boys and 2.1 in girls; and respiratory OR 2.0 in boys and 2.0 in girls. Conclusions Our findings show the existence of four different patterns of comorbidities in CMC and a significantly high proportion of lower SEP children in all classes. These results could benefit CMC management by creating more efficient multidisciplinary medical teams according to each comorbidity class and a holistic perspective taking into account its socioeconomic vulnerability.


Model of success
GMA has proven to be a more effective predictive grouping tool than CRG in the Catalan healthcare system, and also allows each individual to be assigned a unique value of complexity, which is not possible in the previous models. The success of the GMA has led the Ministry of Health to consider using it as a reference grouping system for the entire National Health System (Catalan acronym SNS). It has also been approved as a stratification risk tool for the World Health Organisation.

Three levels
GMA uses three levels of information. The first level is the classification of the population into unique morbidity groups that are simultaneously divided into different levels of complexity. In total, the Catalan model identifies seven groups: healthy population; pregnancy and childbirth; patients with acute disease; patients with chronic disease in one system (for example, diabetics); patients with chronic disease in two or three systems (for example, people with diabetes with heart failure and kidney failure); patients with chronic disease in four or more systems (diabetic people with heart failure, kidney failure and osteoarthritis); and patients with active neoplasia. Each of these groups is divided into five levels of severity, except for the healthy group, which has only one level. Combining morbidity and severity, there are a total of 31 groups.
The novelty of the model lies in the second level of information, in which a unique value of complexity is assigned to each individual. This value reflects the healthcare needs that people may have, based on their health problems. In particular, it takes into account the factors associated with multimorbidity (that is, the joint effects that an individual has on different diseases), a situation that is more a norm than an exception among chronic patients.
The third level, derived from the previous one, involves the identification of people with certain relevant health problems with a clinical label. A total of 80 conditions are identified at this level (among which are: diabetes II, COPD, neoplasms, high blood pressure, arthritis, and depression). Clinical etiquette allows for better follow-up of patients with more complex care needs.

Flexibility
The head of the ICS Statistics Office, Monterde, emphasises that, "the main advantage of GMA is that they are flexible; we can adapt them to the clinical and technical needs as appropriate." One of the things that has most appreciated is precisely this flexibility, which allows the model to be adapted to the reality of each region by developing new predictive models. However, this task will be completed at a later point. Currently, the implementation of GMA in the SNS is based on the data collected in the Catalan healthcare system.
Another benefit of implementing GMA as a clinical grouping system is that it saves on the costs of licences for other tools, such as CRG or ACG. The adoption of GMA in the SNS has led Catalan healthcare companies to earn more than €150,000 and save more than €100,000 per year oinICS licences for CRG.

Functionality of morbidity groups
Population stratification and identification of the population at risk Architecture of GMA