Measuring multimorbidity in family practice-a comparison of two methods

Association of lifestyle and sociodemographic factors on multimorbidity: a cross-sectional study in Portugal

Background

Lifestyle factors are widely recognized as modifiers and major risk factors for non-communicable diseases. Previous studies on the prevalence of multimorbidity in Portugal predict an unfavourable reality. The aim of the present study was to analyse 1) the prevalence of multimorbidity in Portugal and 2) the association of individuals’ lifestyles and sociodemographic factors with multimorbidity.

Methods

A cross-sectional, population-wide study was conducted on a representative sample of the general population of Portuguese adults aged ≥ 20 years. Categorical variables were described by their respective absolute and relative frequencies (n (%)). All variables with a p-value < 20% were included in the multiple logistic regression model. The variables were removed one by one in descending order of p-value (p) until the model contained only significant variables. The results are presented using the odds ratio and 95% confidence intervals. P-values ​​ < 5% were considered significant.

Results

The prevalence of multimorbidity was 48.9% (n = 436), and the odds of multimorbidity increased 4% (p < 0.001) for each year of increase in age. Participants with reasonable general health status had higher odds of multimorbidity (Odds ratio (OR) = 3.04; p < 0.001), and those with poor or very poor general health status had even higher odds (OR = 9.14; p < 0.001). Compared to those who never smoked, participants who quit smoking ≥ 1 year presented an increase of 91% (p = 0.005) in the odds of multimorbidity. Individuals with no good-quality sleep, non-moderate screen time, or non-moderate stress level had higher odds of multimorbidity (OR = 1.98; OR = 1.88; OR = 2.22, respectively. p < 0.001).

Conclusions

This study presented a new approach to multimorbidity in Portugal. Population-based, multidimensional lifestyle interventions are needed. It seems necessary to optimize and adjust measures to prevent non-communicable diseases to improve health in Portugal. In the future, longitudinal studies will be an asset to reinforce and clarify these conclusions.

Different definitions of multimorbidity and their effect on prevalence rates: a retrospective study in German general practices

BACKGROUND

Multimorbidity is common among general practice patients and increases a general practitioner's (GP's) workload. But the extent of multimorbidity may depend on its definition and whether a time delimiter is included in the definition or not.

AIMS

The aims of the study were (1) to compare practice prevalence rates yielded by different models of multimorbidity, (2) to determine how a time delimiter influences the prevalence rates and (3) to assess the effects of multimorbidity on the number of direct and indirect patient contacts as an indicator of doctors' workload.

METHODS

This retrospective observational study used electronic medical records from 142 German general practices, covering 13 years from 1994 to 2007. The four models of multimorbidity ranged from a simple definition, requiring only two diseases, to an advanced definition requiring at least three chronic conditions. We also included a time delimiter for the definition of multimorbidity. Descriptive statistics, such as means and correlation coefficients, were applied.

FINDINGS

The annual percentage of multimorbid primary care patients ranged between 84% (simple model) and 16% (advanced model) and between 74% and 13% if a time delimiter was included. Multimorbid patients had about twice as many contacts annually than the remainder. The number of contacts were different for each model, but the ratio remained similar. The number of contacts correlated moderately with patient age (r = 0.35). The correlation between age and multimorbidity increased from model to model up to 0.28 while the correlations between contacts and multimorbidity varied around 0.2 in all four models.

CONCLUSION

Multimorbidity seems to be less prevalent in primary care practices than usually estimated if advanced definitions of multimorbidity and a temporal delimiter are applied. Although multimorbidity increases in any model a doctor's workload, it is especially the older person with multiple chronic diseases who is a challenge for the GP.

Multimorbidity, polypharmacy, and mortality in older patients with pacemakers

BACKGROUND

The prevalence of multimorbidity and polypharmacy and its association with all-cause mortality in older patients with pacemakers are largely unknown. We aimed to clarify the prevalence of multimorbidity and polypharmacy, and its association with all-cause mortality in patients ≥75 years of age with pacemakers.

METHODS

We retrospectively investigated 256 patients aged ≥75 years (mean age 84.0 ± 5.3 years; 45.7% male) with newly implanted pacemakers. The study endpoint was all-cause mortality ("with events"). Multimorbidity was defined as a Charlson Comorbidity Index ≥3. Polypharmacy was defined as the use of ≥5 medications.

RESULTS

During the follow-up period (median, 3.1 years), 60 all-cause deaths were reported. The Charlson Comorbidity Index (2.9 ± 1.9 vs. 1.7 ± 1.7, p < .001) and prevalence of multimorbidity (56.7% vs. 26.0%, p < .001) were significantly higher in deceased patients than in survivors. The number of drugs (6.9 ± 3.0 vs. 5.9 ± 3.3, p = .03) and the prevalence of polypharmacy (78.3% vs. 63.8%, p = .04) were significantly higher in patients with events than in those without events. The event-free survival rate was significantly higher among patients without multimorbidity than in those with multimorbidity (log-rank, p < .001), and was also significantly higher among patients without polypharmacy than in those with polypharmacy (log-rank, p < .001). Multimorbidity (hazard ratio [HR]: 3.21; 95% confidence interval [CI]: 1.85-5.58; p < .001) and polypharmacy (HR: 1.97; 95% CI: 1.03-3.77; p = .04) were independent predictors of all-cause mortality.

CONCLUSIONS

Multimorbidity and its associated polypharmacy, which are common in the older population, are prevalent in patients with pacemakers and are independent predictors of poor prognosis.

Multimorbidity in Hospitalized Patients Admitted to General Practice Departments and Its Implications for the General Practice Healthcare System: A Four-Year Longitudinal Study in China

Objective: China and many developing countries has placed high expectations on the general practice healthcare system in terms of lowering medical costs and improving the health status of the multimorbid population in recent years. However, the prevalence of multimorbidity among inpatients attending the general practice department of hospitals and its policy implications are largely unknown. The current study aimed to analyze the prevalence of comorbidities among inpatients attending the general practice department of the tertiary Grade-A Hospitals in China, and put forward evidence-based policy recommendations.

Methods: Between December 2016 and November 2020, 351 registered general practitioners from 27 tertiary hospitals were selected, and their direct admissions were evaluated. The rate and composition ratio were used for descriptive analysis of the clinical and epidemiological characteristics of multimorbidity. A backward stepwise algorithm was used to explore independent variables. The absence of multicollinearity and plausible interactions among variables were tested to ensure the robustness of the logistic regression model. The pyramid diagram was used to show the link between gender and the involved human body system in multimorbidity.

Results: Multimorbidity was present in 93.1% of the 64, 395 patients who were admitted directly. Multimorbidity was significantly more prevalent in patients aged 45–59 years (OR=3.018, 95% CI=1.945–4.683), 60–74 years (OR = 4.349, 95% CI = 2.574–7.349), ≥75 years (OR = 7.804, 95% CI = 3.665–16.616), and those with body mass index (BMI) ≥ 28 kg/m² (OR = 3.770, 95% CI = 1.453–9.785). The circulatory system was found to be the most commonly involved human body system in multimorbidity, accounting for 79.2% (95% CI = 78.8–79.5%) of all cases. Significant gender inequity was further observed in the involved human body system in multimorbidity.

Conclusion: Multimorbidity is likely common among the inpatients attending the general practice department of hospitals in China and many developing countries, with significant gender inequity in the involved human body systems. Effective countermeasures include establishing a GP-PCIC multimorbidity prevention and control model and enhancing the multimorbidity of elderly and obese patients at both the clinical and healthy lifestyle levels. The diagnosis and treatment capabilities of GPs on the circulatory, endocrine, metabolic, digestive, and respiratory systems should be prioritized.

Adapting the definition of multimorbidity - development of a locality-based consensus for selecting included Long Term Conditions

BACKGROUND

Defining multimorbidity has proved elusive in spite of attempts to standardise definitions. For national studies, a broad definition is required to capture national diversity. For locally based studies, the definition may need to reflect demographic and morbidity patterns. We aimed to define multimorbidity for an inner city, multi-ethnic, deprived, young age community typical of many large cities.

METHODS

We used a scoping literature review to identify the international literature, standards and guidelines on Long Term Condition (LTC) definitions for inclusion in our multimorbidity definition. Consensus was categorised into high, medium or low consensus, depending on the number of literature sources citing each LTC. Findings were presented to a workshop consisting of local health service stakeholders who were asked to select LTCs for inclusion in a second stage review. In the second stage, each LTC was tested against seven evaluation domains: prevalence, impact, preventability, treatment burden, progression to multiple LTCs, impact on younger people, data quality. These domains were used to create 12 target criteria. LTC rankings according to consensus group and target criteria scores were presented to a second workshop for a final decision about LTC inclusion.

RESULTS

The literature review identified 18 literature sources citing 86 LTCs: 11 were excluded because they were LTC clusters. The remainder were allocated into consensus groupings: 13 LTCs were 'high consensus' (cited by ≥ 11 sources); 15 were 'medium consensus' (cited by 5-10 sources); 47 were 'low consensus' (cited by < 5 sources). The first workshop excluded 31 LTCs. The remaining 44 LTCs consisted of: 13 high consensus LTCs, all with high target score (score 6-12); 15 medium consensus LTCs, 11 with high target scores; 16 low consensus LTCs, 6 with high target scores. The final workshop selected the 12 high consensus conditions, 12 medium consensus LTCs (10 with high target scores) and 8 low consensus LTCs (3 with high target scores), producing a final selection of 32 LTCs.

CONCLUSIONS

Redefining multimorbidity for an urban context ensures local relevance but may diminish national generalisability. We describe a detailed LTC selection process which should be generalisable to other contexts, both local and national.

The socio-economic determinants of multimorbidity among the elderly population in Trinidad and Tobago

OBJECTIVE

To estimate the prevalence of multimorbidity and investigate the socioeconomic factors that are associated with multimorbidity among persons 70 years and older in Trinidad and Tobago.

DESIGN AND METHODS

The data were obtained from a nationally representative comprehensive cross-sectional survey conducted in 2014 among elderly persons in the targeted age group. The prevalence of multimorbidity among the elderly population was estimated. A logit model was utilized to determine the socioeconomic characteristics that are associated with multimorbidity in the elderly.

RESULTS

The results of the study show that multimorbidity in the elderly population is strongly associated with age, ethnicity, lower education, smoking history, no physical activity and being female. An interesting finding is that elderly persons in the richest quintile are in general, more prone to multimorbidity.

CONCLUSION

The findings suggest that interventions to reduce multimorbidity among the elderly population must encourage greater levels of physical activity, provide education on the risk factors of multimorbidity, and discourage smoking.