Adjusted morbidity groups: a new population morbidity classifier

2 Feb
Foto Emili Vela
Emili Vela

At present, chronic pathologies have become a challenge for health systems in developed countries. The majority of sick people that use health services have multiple morbidity and this increases with age. The presence of multiple morbidity is associated with a greater use of resources for care (both health and social) and a lower quality of life.

In this context, it is necessary to measure multiple morbidity to be able to determine its impact. There are two large sets of measurements of multiple morbidity: on the one hand, a simple count of the diseases (usually chronic) of each person and, on the other, indexes which indicate the burden of an individual’s diseases based on the ranking of pathologies giving each one a differential weighting drawn from clinical criteria provided by groups of experts and/or statistical analysis based on mortality or the utilisation of health services.

The Adjusted Morbidity Groups (AMG) are encompassed in this last group, the only one of these tools developed in Europe on the basis of a public health system, universal in nature and eminently free.

Los grupos de morbilidad ajustados

The characteristics and functioning of the AMG can be found in this article. In a nutshell, we can say that the AMG have been validated statistically, by analysing their explanatory and predictive capacity. In this validation, the AMG have shown better results than other tools in the majority of indicators studied, including those relative to social and health care.

Concordancia y utilidad sistema estratificación

They have also been validated clinically by primary care doctors, both in Catalonia and in the Community of Madrid. The main results of these validations are that the AMG show a good classification of the patient in terms of risk, that this good classification increases with the complexity of the patient, the preference of clinicians for this tool with respect to other tools to classify morbidity and finally, that it is a useful tool for assigning a level of intervention in accordance with the needs of patients.

From 2012, the AMG were developed in the framework of an agreement of collaboration between CatSalut and Catalan Health Institute. Subsequently, they have been implemented at a national level in 13 autonomous communities thanks to an agreement reached between CatSalut and the Ministry of Health, Social and Equality Services. As a result of the implementations done during 2015, 38 million people of the Spanish population have been classified. The final goal of this agreement is to jointly develop a tool to stratify the population and which could be applicable to the entire National Health System by means of adapting the AMG.

Proposals enhanced health risk

Similarly, the AMG are being used in several European projects concerning the stratification and integration of health and social care.

In summary, we can assert that the AMG are a new classifier of morbidity which shows comparable results -at the very least- to those provided by other classifiers on the market. On the other hand, having been developed using the information from our health system (universal and eminently free), it can not only be adapted to new requirements or strategies of our organisations, but also to other health systems as well as to specific areas or populations. Evidence of this last point is that at the moment, together with the Master Plan of Mental Health and Addictions of the Health Department, a specific classifier is being developed for patients with mental health and addiction problems.

Post written by David Monterde (Oficina d’Estadística. Sistemes d’Informació. Institut Català de la Salut), Emili Vela (Àrea d’Atenció Sanitària. Servei Català de la Salut) and Montse Clèries (Àrea d’Atenció Sanitària. Servei Català de la Salut).

Stratification and morbidity database (2n part)

31 Mar
Foto Emili Vela
Emili Vela

(This post is the second part of this post)

A key element for completing the stratification of population in risk groups is information system. It’s necessary to have a database that integrates information collected from different health records and therefore, in order to meet this demand, the population morbidity database was created.

The point is that every day there is more and more emphasis on the need to provide patients with a comprehensive and integrated health and social care, but the analysis and evaluation of this care can not be carried out correctly with fragmented information systems, on the contrary: it must be done starting from the integration of the data these contain.

Population morbidity database structure

The population morbidity database is based on a system of related tables that pivot around the users table, which includes the main data of the insured patient (demographics or health status, to name two examples).

Currently, there are three more tables: the diagnostic, the contact with health services and the pharmacy, but this type of structure relatively easily allows incorporating both information from new records (outpatient clinics, dialysis, respiratory therapy, etc.) and new tables with other relevant information, such as results of clinical findings:

Figure 2: Structure and content of the population morbidity database. The clinical determinations table in gray is not yet implemented.

Taula d'assegurats

The population morbidity database integrates information from the following records:

  • Registro Central de Asegurados – (RCA) (Central Registry of Insured Patients) managed by public relations management of CatSalut. This register basically provides all the information of residence, socio-demographics and health status of the insured patient.
  • Registros del conjunto mínimo básico de datos – (CMBD) (Records of basic minimum data set) managed by CatSalut Division of demand and activity analysis. These records feed both into the diagnoses table and in the contacts tables. There are different registers to collect information from the healthcare lines:
  • Hospitalization (CMBD-HA): information provided by general acute care hospitals (hospital admissions, outpatient surgery, home hospitalization, day hospital) from 2005 to 2014
  • Socio – sanitary (CMBD-SS): information of the care provided by the health centres of internment (long and medium stay and UFISS) and outpatient care equipment (PADES) from 2005-2014.
  • Psychiatric hospitalization (CMBD-SMH): information of the care provided by psychiatric hospitals from 2005-2014.
  • Outpatient Mental Health (CMBD-SMP): information of the care provided by outpatient mental health centres for the period 2005-2014.
  • Primary Care (CMBD-AP) information on the care provided by primary care teams from 2010 to 2014.
  • Emergency (CMBD-UR): information of emergency care (hospital and CUAP) from 2013 to 2014.
  • Pharmacy activity Log (RAF) managed by CatSalut’ management of pharmacy and medicine unit. This record provides all the information about outpatient pharmacy dispensing for the period 2011-2014.
  • Record health services turnover (RF) managed by the Division of care services provision. This record provides information on any activity financed by CatSalut, but that does not rely on a specific record: hospital outpatient clinics, dialysis, home oxygen therapy, rehabilitation or non-emergency medical transport. This record provides information mainly on the contacts table for the period 2011-2014.

The possible uses of the population morbidity database are multiple: the population stratification, the specific analysis of certain health problems (broken femur, IC, COPD,…), the development of population indicators of efficiency in the use of resources, etc.

We can conclude that, for the volume of data that integrates, this database has the characteristics of a structured “big data”, with a considerable capacity for growth and adaptation to new requirements and data sources and offers enormous possibilities for analysis.

Post written by Emili Vela, head of Modules for Tracking Quality Indicators (MSIQ). Health Care Area. CatSalut.