Our study revealed that multimorbidity (defined as two or more concurrent chronic health condition in one patient) is more often observed than isolated diseases in Swiss primary care. Multimorbidity was 1.5 times as prevalent (14.5% [95%CI 11.4-17.7%]) as the most common single specific chronic medical condition (hypertension: 9.37% [95%CI 7.40-11.35%]). Without restricting our analysis to chronic medical conditions only, the prevalence estimates of multimorbidity would have been even higher. As expected, prevalence estimates of multimorbidity increased with age, rising more than 20-fold from the youngest (1.70% [1.11-2.29]) to the oldest age group (37.7 [31.2-44.2]) and affecting more than one out of three elderly patients. Although we found similar gender-specific prevalence of multimorbidity for the entire study population, stratified on age groups, elderly women had a prevalence estimates of multimorbidity close to that of men ten years younger (Table 2).
Estimates of prevalence of multimorbidity vary widely in the literature, related to different definitions of multimorbidity (type and number of diseases included in the definition), diverse data sources (questionnaires, medical records, patient charts, and administrative data), and dissimilar patient populations studied [2, 14], making comparisons between studies difficult. However, most estimates from primary care, which relied on practitioners records as data source, reported prevalence rates between 20-30% for the entire population, and 50-90% for the elderly [6, 10, 11, 15, 16], which is roughly twice the prevalence found in our data. The most obvious reason for this low prevalence of multimorbidity in our study is under-coding of chronic health conditions. The participating general practitioners in the FIRE project were initially instructed to code symptoms or diagnoses, which were addressed during consultations or needed special attention, such as a drug prescription. Therefore, chronic conditions such as obesity, hearing loss, or visual impairment, which were not addressed during a patient-physician encounter, were not coded. By accumulating all chronic health conditions coded in an individual patient across the entire observation period, we were able to raise the prevalence rates for specific chronic conditions. However, under-coding remained a problem. For example, based on the ICPC-2 codes T89 (insulin-dependent diabetes mellitus) and T 90 (non-insulin dependent diabetes mellitus), we found an overall prevalence estimate of diabetes mellitus of 3.96% (95%CI 3.22%-4.39%), yet including those patients with a blood glucose >11.1 mmol/L or glycosylated hemoglobin >6.5% (diabetes by definition) and those prescribed anti-diabetic medication, the prevalence rose to 5.02% (95%CI 4.26%-5.88%). Nonetheless, such an internal coding validation was not feasible for all 147 chronic health conditions included in our definition of multimorbidity, therefore, we report our estimates based on ICPC-2 codes alone, acknowledging significant under-coding resulting in an underestimation of the burden of disease.
Not surprisingly, our results confirm the striking age-dependent increase in prevalence of multimorbidity observed in many European [10, 11, 17] and North American [6, 8] studies, a pattern that results due to accumulation of chronic health conditions during the ageing process. The presence of this pattern independent of the definition of multimorbidity used (Figure 2), is reassuring regarding the consistency of our data considering the significant under-coding discussed above.
It is proven that men in industrialized countries die earlier than women, but that women have poorer health than men . This might be possibly due to more illnesses and disabilities in women, which are not life-threatening, and more serious and often deadly conditions in men . As expected, our data show a higher prevalence estimates of potentially deadly chronic health conditions in men (cardiovascular disease, diabetes, and lipid disorders), whereas women suffer more often from less “deadly” chronic conditions (varicose veins, osteoarthritis, osteoporosis, and depression) (Table 1). Conversely, after adjusting for age, we found higher prevalence estimates of multimorbidity in elderly men, which is opposite to common belief and not supported by some recent publications [11, 17, 19]. However, this gender effect was not consistent across all studies [19, 20] and conclusions should be inferred with caution. A specific concern regarding our data that might have contributed to the observed gender effect is under-coding of gynecological problems (many women in Switzerland see their gynecologists independent of consultations with their general practitioners), and under-coding of non-life-threatening chronic health conditions.
Multimorbidity was observed in all age groups, and not only in the elderly, a fact that has been shown in previous studies . This observation can be explained, because young patients are both, very healthy and not seen by primary care physicians at all, or they have serious diseases with co-existing related medical conditions and present with multimorbidity.
Our study has several strengths and limitations. The main strength of our study is a large, robust data set, generated by practitioners trained in coding using the ICPC-2 coding system, which is a validated ambulatory coding system for primary care that includes impairments (e.g., hearing impairment). Limitations are: First, the relative small number of participating practices (63) may reduce the generalizability of our results to the Swiss primary care. Second, our data capture the actual prevalence estimates of multimorbidity in the population that consults general practitioners, and not the prevalence estimates in the general population. Third, our definition of multimorbidity might seem quite arbitrary, because a simple count of chronic condition lacks the information of a specific impact on the health system, e.g., a diabetic patient with coronary heart disease has not the same impact on resources use as an obese patient with hearing impairment; however, because we lacked information about the severity of the conditions, we could not use more precise instruments. Forth, the proportion of elderly with one or more chronic health condition was higher for men than women (Table 2) using the chi square statistic, however, no significant differences between sexes were found using 95% confidence intervals. This was due to wide degree of uncertainty in each estimate because of the cluster sample design. Fifth, the potentially severe underestimation because of under-coding was already discussed above.