Summary of main findings
In this paper we have studied a selective group of patients with trajectories of high numbers of multiple pathology. This concerned patients who accumulated eleven or more chronic health problems in lifetime as registered by the RNH-general practitioner. This intensive form of multimorbidity was defined as comprising one extreme part of the Gaussian-distribution of multimorbidity. Overall 4,560 subjects were registered with more than ten chronic health problems during their life (MM11+). This group comprises 5% of the RNH-patient population, but accounts for 61,653 (20%) of the 302,808 registered chronic health problems in the RNH population (N = 87,837 subjects). Some health problem clusters as represented in the ICPC chapters are prominent in the MM11 + −patients, in particular the locomotor, cardiovascular, gastro-intestinal, respiratory and metabolic health problems. It is important to note that in total 3,393 (74.4%) of the 4,560 patients develop four or more conditions within one organ system in the course of life. However, most patients have a ‘broad’ or ‘flat’ profile as well: patients accumulate a broad range of conditions in their body systems. Most patients in the MM11+ group accumulate two or more times during their life two or more chronic conditions in one year, with a variety of chronic conditions distributed over the organ systems as classified in the ICPC-chapters (data not shown). About half of the patients have been confronted with a neoplasm diagnosis, be it of a benign or malignant type. Patients with intensive forms of multimorbidity have very often been inflicted during their life by chronic health conditions related to infection, inflammation, and injury.
The presented results show that intensive forms of multimorbidity involve a broad range of organ systems. This indicates that next to specific also more general mechanisms may be at work. The approach used in this study expresses this twofold approach to health problems. On the one hand a specific approach investigating the specific health problems and relating this to the type and number of organ systems involved as represented by the ICPC chapters. On the other hand, a broad, system theory and non-specific approach, which embraces the perspective of general disease susceptibility [28,29,35,36]. Thus, chronic health problems were taken into account, which may affect multiple sites in the body due to strains and pressures “external” from outside the subject, e.g. injuries and infections, and “internal” within the subject, e.g. inflammations and tumours, − although originating in a particular organ system or specific site in the body as referred to by the separate ICPC codes.
Strengths and limitations of the study
This study is the first, as far as known by the authors, investigating the topic of intensive forms of multimorbidity in general practice from a lifetime prevalence perspective. We were able to collect for all included patients the dates of the new diagnosis during their lifetime and to construct the life time trajectories of the accumulation of chronic conditions for each patient individually. The data presented here are the result of the exploratory analysis used as a heuristics for getting a better understanding of the dynamic patterns involved, hence are aggregated at a group level comprising the patients with what we call intensive forms of multimorbidity.
This study suffers from several limitations. Firstly, the study comprises all the limitations inherent in any retrospective design which uses electronic medical records as a data source. We tried to compensate for the weaknesses of this way of data collection and analysis as much as possible as described above. The study is based on electronic patient records comprising various data, but in this study we focused on the diagnosis of the chronic health conditions, so that our analysis of the diagnoses as registered by the GPs would be reliable. The quality of the data is assured by instruction and training sessions, regional consensus groups, quality control experiments, and special software programs, such as an automated thesaurus and automated checking for erroneous or missing entries. Reliability and completeness have been proved previously [42-44]. It is important to stress that in the Netherlands the GPs have comprehensive information on the health status of their patients because GPs are the gatekeeper to other health care facilities, and it is compulsory for all Dutch residents to have health care insurance and to register with a GP. Further, GPs will be informed on a routine basis by clinical specialists in the hospital of the diagnosis and other medical relevant data of their patients.
Therefore we expect this study not to suffer too much from the limitations possibly implicated by underdiagnosis or misclassification, for example misclassification of COPD being diagnosed as asthma or other comparable cases in the diagnosis of chronic health conditions.
However, diagnostic habits may differ between GPs in different countries and regions within countries, due to differences in the level of professional training, the degree of implementation or content of clinical practice guidelines, the use of active protocols for detecting certain diseases, organisational factors, etc. [11,45,46]. For example, Aarts et al.  showed that 3 of the 21 practices involved in the RNH-network diagnosed a relatively high percentage of diabetes patients with depression (ranging from 9.5 – 9.8%), while in 3 of the 21 practices this percentage is lower (ranging from 4.0 to 4.7%). Notably, the GPs were not instructed to systematically screen patients for possible depression or depressive symptoms: which could have led to a lower risk estimate. It seems plausible that GPs differ regarding their inclination to diagnose a depression. After investigating the most important characteristics of the practices, such as geographic place (defined by postal code) of a general practice; total number of diagnosed depressive disorders in the general practice, number and gender of patients, education of the patients, and number and gender of GPs in a practice, the authors were not able to identify any specific characteristics that could explain this effect . This diagnostic variability may have important implications for general practice. In the RNH a large number of GPs is participating and previous studies showed that a minimum number of 25 to 30 GPs is sufficient to take into account the inter-GP variety in coding .
Despite such differences in prevalence, diagnostic habit and health care organisation, relevant similarities of multimorbidity patterns can be found in different European regions, as in the north-east of Spain and the south-east of the Netherlands . In addition, the Dutch general practice holds an outstanding position regarding quality assurance and guideline implementation with respect to other European countries [46,47]. This also endorses the use of primary care electronic medical records for the epidemiologic characterization of multimorbidity. The use of electronic medical records would enable a longitudinal approach to the multimorbidity phenomenon. Understanding the way in which health conditions are associated with one another throughout the lives of individuals, as well as knowing how frequently these diseases appear, will bring about a better understanding of multimorbidity.
Furthermore, a broad spectrum of 335 chronic health problems were analysed. This included codes which enable GPs to register chronic health problems, which they cannot define in a strict way – e.g., the 99-codes -, as the ICPC-classification aims to provide the whole spectrum of health problems in family practice. This study does not intend to measure prevalence of chronic conditions or groups of chronic conditions nor to determine risk factors as such. Lifetime prevalence rates or lifetime risks were not estimated. Instead, the study focuses on the dynamics of life time patterns of multimorbidity in patients. In this study we selected a specific group of patient with a life time prevalence of more than health conditions. We did not take, for example, a cohort of patients who have been identified with similar characteristics of multimorbidity and then followed them forward. Even then, longitudinal analysis accounting for the different temporal aspects, e.g., age, birth and cohort aspects, is complex by itself. We considered it useful to start with a relatively small proportion of the population made up of people with high numbers of health conditions throughout their life, comparing trends and patterns with this selective group and with the other groups of multimorbidity patients (MM4-5, MM6-10). The progression among these subgroups requires a more fine-grained investigation of transitions in lifetime multimorbidity. Such a more dynamic in-depth analysis goes beyond the scope of the current study.
Other limitations have to be noted. The classification scheme of inflammation, infection, tumours and injury must be considered carefully and no biological connection between these biological processes and the occurrence of health problems is established in this study. However, it is worthwhile to see how the accumulation of specific health problems may relate to broader patterns of morbidity. It is generally acknowledged that – apart from socioeconomic and demographic factors – a broader range of host response mechanisms, ranging from genetic factors, biological stress mechanisms and psychosocial processes, e.g. coping styles, social network of the patient, operate at the level of the individual, but have also an important influence at the population level [31,33,34,36].
The analysed patterns are interesting from the perspective of daily care by GPs. The study shows that a certain number of patients accumulate larger series of health problems in life. Although sometimes health problems may be some event in some period of life of a subject, the health problems analysed here are all events only coded and registered by the GP when they are permanent (no recovery expected), chronic (duration longer than 6 months) or recurrent (more than three recurrences within 6 months), or when they have lasting consequences for the functional status or prognosis of the patient. Thus, the health problems concern conditions with great impact on patient’s lives and daily care by GPs. Although these conditions do bear a great impact, it has to be noted that the burden of multimorbidity may differ for patients and their GPs. A patient with malignant cancer, heart failure and renal disease may only have these three comorbid conditions but their severity is significant. On the other hand some other patient may have mild depression, diet controlled diabetes and a dry skin and equally may be labelled as having three comorbid conditions. The intensity of a patient’s multimorbidity may vary on the progressive state of the conditions present and the specific conditions diagnosed [1,18]. The main focus of this study was not to assess severity of conditions and multimorbidity, but to disentangle multimorbidity trajectories and patterns, among those patients with a large number of chronic health problems occurring during life (MM11+).
Comparison with existing literature
Research into multimorbidity is relatively new, mostly encompassing population studies, hospital studies and primary healthcare studies. In recent years multimorbidity has received fortunately increasing attention addressing the issues related prevalence, determinants, consequences and the patterns of multimorbidity, in general as well in different age-groups [1,2,9,26]. This study is unique, as far as known by the authors, for investigating the topic of severe forms of multimorbidity in general practice from a lifetime prevalence perspective. For reasons of investigating both disease-specific and more general patterns of multimorbidity, referring to the literature on general disease susceptibility and psychological and social determinants of multimorbidity , we focused on a broad scope of chronic disease conditions. Most multimorbidity studies select a much smaller set of chronic diseases or regroup diagnoses into specific chronic disease groups, as for example in the Expanded Diagnosis Clusters (EDC) [48,49]. In this study we started from the daily practice of the GP, aiming to include all the chronic conditions as registered in their care for complex patients. Definitions of multimorbidity should both inform and reflect clinical practice. This objective may be difficult to achieve when epidemiology oriented definitions are less inclusive and aim at a limited set of clear-cut criteria. For ‘diseases’ with varying latency or a chronic course, such as multimorbidity, developing a definition depends on decisions regarding which phase to monitor – asymptomatic, early phase, late phase – and on the circumscription of the spectrum of morbidity [50,27]. In our study we made the decision to start from the health problems as addressed by the GPs themselves. Only ICPC codes (ICPC70-99) that correspond to serious or chronic diseases were entered into the database.Higher-level regrouping of diagnoses into diagnosis clusters is foreseen for future research.
Implications for research and daily care
Primary care is characterized by a heterogeneous patient population, which comprises, about 120 patients with intensive forms of multimorbidity per standard practice (of 2350 patients) as is usual in the Netherlands . These patients representing the 5% extreme of the Gaussian distribution cover about 20% of all chronic health conditions. It should be added that it is not a black-white, stationary picture. The subgroup of multimorbidity patients with six to ten chronic conditions, encompass patients who can be indicated to develop rapidly an increasing number of conditions during certain periods in their lives. Further, most of the patients in both multimorbidity groups – i.e. the MM6-10 and MM11+ − exhibit new diagnoses up to the time of the data-collection (July 2010) for this study. In fact, it can be shown (data not presented in results) that almost half of the MM-11+ (N = 2010; 44%) have a new diagnosis added in one year before the data extraction and many ((N = 3815; 83.6%) MM11+ have at least two new diagnoses in the past five-year period (2005–2009) with a mean of 3.35 new diagnoses (95% CI 3.2 - 3.4; median = 3.0). This is to note the intrinsic dynamic pattern of the life time prevalence pattern of the multimorbidity patients. Considering the fact that the diagnoses concern merely the registered chronic health conditions, leaving out the symptoms, medications and other health care activities, this underlines that complex care is an intensive task for the GP. Given the GPs’ expertise in dealing with multimorbidity and their overview of patient’s life, it is due to stress that GPs play an important role in achieving a life time perspective on multimorbidity patterns . Hence, primary care providers are in urgent need of more knowledge on the trajectories of multimorbidity in patient’s life.