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Background The perceptions of general practitioners (GP), cardiologists and pharmacists on multidisciplinary heart failure (HF) care were studied before. However, geriatricians are often overlooked in HF research, despite the high prevalence of HF in the elderly. Therefore, we investigated how geriatricians perceive their role in multidisciplinary HF care. Methods This study is a qualitative semi-structured interview study with geriatricians, working in Flanders, Belgium. Purposive sampling was performed, and interviews were conducted until data saturation was reached. The QUAGOL method was used for data analysis. Results Thirteen geriatricians were interviewed. They reported to feel confident about HF management and see themselves as the guardians of the patient during hospitalization. Regarding care organization during the hospitalization phase, striking differences were reported in triage at the emergency department (ED). Geriatricians were satisfied with the collaboration with the cardiologists and valued their role, although they reported differences in vision about dealing with geriatric HF patients. Regarding transmural care organization, follow-up after hospitalization was valued highly to prevent rehospitalization but most geriatricians did not see this as their responsibility. They mostly passed on the follow-up to the GP and the cardiologist. Some did take this follow-up into their own hands in various forms because of the high rehospitalization rate and many suggested ways to improve the organization of multidisciplinary HF care. Advance care planning was seen as an important aspect of geriatrics, yet they expected more involvement from both the cardiologist and the GP in this matter. Conclusions Based on these results, our study highlights three recommendations for optimizing the care of geriatric HF patients. First, the development of standardized methods to triage patients at the ED, in combination with geriatric-cardiologist co-management to ensure that each patient receives appropriate in-hospital care. Secondly, structured and closer transitional follow-up to limit readmission rates. Lastly, inclusion of advance care planning as a mandatory component in every HF program.

ObjectivesThe present study investigated (1) trends in the prevalence and incidence of knee osteoarthritis over a 20-year period (1996–2015); (2) trends in multimorbidity and (3) trends in drug prescriptions.DesignRegistry-based study.SettingPrimary healthcare, Flanders, Belgium.ParticipantsData were collected from Intego, a general practice-based morbidity registration network. In the study period between 1996 and 2015, data from 440 140 unique patients were available.Outcome measuresTrends in prevalence and incidence rate of knee osteoarthritis were computed using joinpoint regression analysis. The mean disease count was calculated to assess trends in multimorbidity. In addition, the number of drug prescriptions was identified by the Anatomical Therapeutic Chemical Classification code and trends were equally recorded with joinpoint regression.ResultsThe total age-standardised prevalence of knee osteoarthritis increased from 2.0% in 1996 to 3.6% in 2015. An upward trend was observed with an average annual percentage change (AAPC) of 2.5 (95% CI 2.2 to 2.9). In 2015, the prevalence rates in the 10 year age groups from the 45–54 years age group onwards were 3.1%, 5.6%, 9.0% and 13.9%, to reach 15.0% in people aged 85 years and older. The incidence remained stable with 3.75‰ in 2015 (AAPC=−0.5, 95% CI −1.4 to 0.5). The mean disease count significantly increased from 1.63 to 2.34 (p<0.001) for incident cases with knee osteoarthritis. Finally, we observed a significantly positive trend in the overall prescription of acetaminophen (AAPC=6.7, 95% CI 5.6 to 7.7), weak opioids (AAPC=4.0, 95% CI 0.9 to 7.3) and glucosamine (AAPC=8.6, 95% CI 2.4 to 15.1). Oral non-steroidal anti-inflammatory drugs were most prescribed, with a prevalence rate of 29.8% in 2015, but remained stable during the study period (AAPC=0.0, 95% CI −1.1 to 1.1).ConclusionsIncreased prevalence, multimorbidity, and number of drug prescriptions confirm an increased burden of knee osteoarthritis. In future, these trends can be used to prioritise initiatives for improvement in care.

Background The Short Physical Performance Battery (SPPB) is a well-established tool to assess lower extremity physical performance status. Its predictive ability for all-cause mortality has been sparsely reported, but with conflicting results in different subsets of participants. The aim of this study was to perform a meta-analysis investigating the relationship between SPPB score and all-cause mortality. Methods Articles were searched in MEDLINE, the Cochrane Library, Google Scholar, and BioMed Central between July and September 2015 and updated in January 2016. Inclusion criteria were observational studies; >50 participants; stratification of population according to SPPB value; data on all-cause mortality; English language publications. Twenty-four articles were selected from available evidence. Data of interest (i.e., clinical characteristics, information after stratification of the sample into four SPPB groups [0–3, 4–6, 7–9, 10–12]) were retrieved from the articles and/or obtained by the study authors. The odds ratio (OR) and/or hazard ratio (HR) was obtained for all-cause mortality according to SPPB category (with SPPB scores 10–12 considered as reference) with adjustment for age, sex, and body mass index. Results Standardized data were obtained for 17 studies ( n  = 16,534, mean age 76 ± 3 years). As compared to SPPB scores 10–12, values of 0–3 (OR 3.25, 95%CI 2.86–3.79), 4–6 (OR 2.14, 95%CI 1.92–2.39), and 7–9 (OR 1.50, 95%CI 1.32–1.71) were each associated with an increased risk of all-cause mortality. The association between poor performance on SPPB and all-cause mortality remained highly consistent independent of follow-up length, subsets of participants, geographic area, and age of the population. Random effects meta-regression showed that OR for all-cause mortality with SPPB values 7–9 was higher in the younger population, diabetics, and men. Conclusions An SPPB score lower than 10 is predictive of all-cause mortality. The systematic implementation of the SPPB in clinical practice settings may provide useful prognostic information about the risk of all-cause mortality. Moreover, the SPPB could be used as a surrogate endpoint of all-cause mortality in trials needing to quantify benefit and health improvements of specific treatments or rehabilitation programs. The study protocol was published on PROSPERO (CRD42015024916).

Acute coronary syndrome (ACS) and acute myocardial infarction (AMI) account for a large portion of cardiovascular deaths. Signs and symptoms for these syndromes, such as chest pain, are non-specific and can be caused by a variety of non-cardiac conditions, especially in low-prevalence settings such as general practice. The diagnostic value of these signs and symptoms can be assessed using diagnostic meta-analyses, but the last one dates from 2012. We performed a diagnostic meta-analysis in accordance with PRISMA guidelines. We searched PubMed, Embase and CENTRAL from 2006 to 2024. We included studies that assessed the diagnostic accuracy of thirteen different signs and symptoms. We divided patients into two subgroups (AMI and ACS) on which analysis was performed independently. We selected 24 articles for inclusion. Our analysis indicates that signs and symptoms have a limited role in the diagnosis of AMI or ACS. The most useful (highest diagnostic odds ratios, DOR) in the diagnosis of AMI were pain radiating to both arms (DOR 2.95 (95%CI 1.57-5.06)), absence of chest wall tenderness (DOR 3.51 (95%CI 1.64-6.61)), pain radiating to the right arm (DOR 5.17 (95%CI 1.77-11.9)) and sweating (DOR 5.75 (95%CI 2.51-11.4)). For ACS these were pain radiating to the right arm (DOR 3.9 (95%CI 0.7-12.6)) and absence of chest wall tenderness (DOR 7.73 (95%CI 2.19-19.8)). We report the accuracy of thirteen signs and symptoms in the diagnosis of AMI and ACS. These can be useful to calibrate general practitioners' diagnostic assessment of chest pain in primary care settings.

The aim of this paper was to describe the time trends in the prevalence of multimorbidity and polypharmacy in Flanders (Belgium) between 2000 and 2015, while controlling for age and sex. Data were available from Intego, a Flemish-Belgian general practice-based morbidity registration network. The practice population between 2000 and 2015 was used as the denominator, representing a mean of 159,946 people per year. Age and gender-standardised prevalence rates were used for the trends of multimorbidity and polypharmacy in the total population and for subgroups. Joinpoint regression analyses were used to analyse the time trends and breaks in trends, for the entire population as well as for specific age and sex groups. Overall, in 2015, 22.7% of the population had multimorbidity, while the overall prevalence of polypharmacy was 20%. Throughout the study period the standardised prevalence rate of multimorbidity rose for both sexes and in all age groups. The largest relative increase in multimorbidity was observed in the younger age groups (up to the age of 50 years). The prevalence of polypharmacy showed a significant increase between 2000 and 2015 for all age groups except the youngest (0-25 years). For all adult age groups multimorbidity and polypharmacy are frequent, dynamic over time and increasing. This asks for both epidemiological and interventional studies to improve the management of the resulting complex care.

The Covid-19 pandemic had a tremendous impact on healthcare but uncertainty remains about the extent to which primary care provision was affected. Therefore, this paper aims to assess the impact on primary care provision and the evolution of the incidence of disease during the first year of the Covid-19 pandemic in Flanders (Belgium). Care provision was defined as the number of new entries added to a patient's medical history. Pre-pandemic care provision (February 1, 2018-January 31, 2020) was compared with care provision during the pandemic (February 1, 2020-January 31, 2021). A large morbidity registry (Intego) was used. Regression models compared the effect of demographic characteristics on care provision and on acute and chronic diagnoses incidence both prior and during the pandemic. During the first year of the Covid-19 pandemic, overall care provision increased with 9.1% (95%CI 8.5%;9.6%). There was an increase in acute diagnoses of 5.1% (95%CI 4.2%;6.0%) and a decrease in the selected chronic diagnoses of 12.8% (95% CI 7.0%;18.4%). Obesity was an exception with an overall incidence increase. The pandemic led to strong fluctuations in care provision that were not the same for all types of care and all demographic groups in Flanders. Relative to other groups in the population, the pandemic caused a reduction in care provision for children aged 0-17 year and patients from a lower socio-economic situation. This paper strengthened the claim that Covid-19 should be considered as a syndemic instead of a pandemic. During the first Covid-19 year, overall care provision and the incidence of acute diagnoses increased, whereas chronic diseases' incidence decreased, except for obesity diagnoses which increased. More granular, care provision and chronic diseases' incidence decreased during the lockdowns, especially for people with a lower socio-economic status. After the lockdowns they both returned to baseline.

The compound occurrence of extreme heat and the COVID-19 pandemic may have increased mortality risk beyond the impact of each factor alone. However, the interaction between these two risk factors and their combined effect on mortality has not been adequately quantified. We conducted a time-stratified case-crossover analysis of daily all-cause mortality, minimum temperature, and COVID-19 case counts in Flanders over the period 2018–2021. We applied a distributed lag nonlinear model (DLNM) with a conditional quasi-Poisson regression to estimate the cumulative effects (lag 0–14 days) of extreme heat. The relative risk (RR) was quantified at the 99th percentile (P99) of minimum temperature compared to 50th percentile (P50). To assess effect modification, we used a binary interaction approach (pre-COVID-19 and during COVID-19) and a linear interaction approach. We used a 15-day moving average of daily confirmed cases and centered it at three reference points representing 25th, 75th and 95th percentiles of the distribution, corresponding to low, mild and high intensity levels, respectively. We observed that during the COVID-19 pandemic, the risk of mortality associated with extreme heat was significantly elevated (RR = 1.55; 95% CI: 1.18–2.02), compared to a weaker and non-significant association during pre-COVID-19 (RR = 1.07; 95% CI: 0.72–1.58). The heat-mortality curve showed a sharper increase during the pandemic, specifically above the 14 °C (P50). An increased association was observed together with rising COVID-19 incidence. On days when COVID-19 intensity was low, the relative risk (RR) of heat-related mortality was 1.18 (95% CI: 0.64–2.18). This risk increased under moderate incidence, with an RR of 1.95 (95% CI: 1.1.03–3.70), and rose markedly during high cases, reaching an RR of 3.57 (95% CI: 1.10-11.61). Our findings suggest an increased risk of heat-related mortality during the COVID-19 pandemic, especially as COVID-19 transmission intensified.

Background The use of Electronic Health Records (EHR) data in clinical research is incredibly increasing, but the abundancy of data resources raises the challenge of data cleaning. It can save time if the data cleaning can be done automatically. In addition, the automated data cleaning tools for data in other domains often process all variables uniformly, meaning that they cannot serve well for clinical data, as there is variable-specific information that needs to be considered. This paper proposes an automated data cleaning method for EHR data with clinical knowledge taken into consideration. Methods We used EHR data collected from primary care in Flanders, Belgium during 1994–2015. We constructed a Clinical Knowledge Database to store all the variable-specific information that is necessary for data cleaning. We applied Fuzzy search to automatically detect and replace the wrongly spelled units, and performed the unit conversion following the variable-specific conversion formula. Then the numeric values were corrected and outliers were detected considering the clinical knowledge. In total, 52 clinical variables were cleaned, and the percentage of missing values (completeness) and percentage of values within the normal range (correctness) before and after the cleaning process were compared. Results All variables were 100% complete before data cleaning. 42 variables had a drop of less than 1% in the percentage of missing values and 9 variables declined by 1–10%. Only 1 variable experienced large decline in completeness (13.36%). All variables had more than 50% values within the normal range after cleaning, of which 43 variables had a percentage higher than 70%. Conclusions We propose a general method for clinical variables, which achieves high automation and is capable to deal with large-scale data. This method largely improved the efficiency to clean the data and removed the technical barriers for non-technical people.

Background Since 2014, Belgium’s Superior Health Council has recommended pneumococcal vaccination for adults aged 19–85 years at increased risk for pneumococcal diseases with a specific vaccine administration sequence and timing. Currently, Belgium has no publicly funded adult pneumococcal vaccination program. This study investigated the seasonal pneumococcal vaccination trends, evolution of vaccination coverage and adherence to the 2014 recommendations. Methods INTEGO is a general practice morbidity registry in Flanders (Belgium) that represents 102 general practice centres and comprised over 300.000 patients in 2021. A repeated cross-sectional study was performed for the period between 2017 and 2021. Using adjusted odds ratios computed via multiple logistic regression, the association between an individual’s characteristics (gender, age, comorbidities, influenza vaccination status and socioeconomic status) and schedule-adherent pneumococcal vaccination status was assessed. Results Pneumococcal vaccination coincided with seasonal flu vaccination. The vaccination coverage in the population at risk decreased from 21% in 2017 to 18.2% in 2018 and then started to increase to 23.6% in 2021. Coverage in 2021 was highest for high-risk adults (33.8%) followed by 50- to 85-year-olds with comorbidities (25.5%) and healthy 65- to 85-year-olds (18.7%). In 2021, 56.3% of the high-risk adults, 74.6% of the 50+ with comorbidities persons, and 74% of the 65+ healthy persons had an adherent vaccination schedule. Persons with a lower socioeconomic status had an adjusted odds ratio of 0.92 (95% Confidence Interval (CI) 0.87–0.97) for primary vaccination, 0.67 (95% CI 0.60–0.75) for adherence to the recommended second vaccination if the 13-valent pneumococcal conjugate vaccine was administered first and 0.86 (95% CI 0.76–0.97) if the 23-valent pneumococcal polysaccharide vaccine was administered first. Conclusion Pneumococcal vaccine coverage is slowly increasing in Flanders, displaying seasonal peaks in sync with influenza vaccination campaigns. However, with less than one-fourth of the target population vaccinated, less than 60% high-risk and approximately 74% of 50 + with comorbidities and 65+ healthy persons with an adherent schedule, there is still much room for improvement. Furthermore, adults with poor socioeconomic status had lower odds of primary vaccination and schedule adherence, demonstrating the need for a publicly funded program in Belgium to ensure equitable access.

Background In case-control studies most algorithms allow the controls to be sampled several times, which is not always optimal. If many controls are available and adjustment for several covariates is necessary, matching without replacement might increase statistical efficiency. Comparing similar units when having observational data is of utter importance, since confounding and selection bias is present. The aim was twofold, firstly to create a method that accommodates the option that a control is not resampled, and second, to display several scenarios that identify changes of Odds Ratios (ORs) while increasing the balance of the matched sample. Methods The algorithm was derived in an iterative way starting from the pre-processing steps to derive the data until its application in a study to investigate the risk of antibiotics on colorectal cancer in the INTEGO registry (Flanders, Belgium). Different scenarios were developed to investigate the fluctuation of ORs using the combination of exact and varying variables with or without replacement of controls. To achieve balance in the population, we introduced the Comorbidity Index (CI) variable, which is the sum of chronic diseases as a means to have comparable units for drawing valid associations. Results This algorithm is fast and optimal. We simulated data and demonstrated that the run-time of matching even with millions of patients is minimal. Optimal, since the closest controls is always captured (using the appropriate ordering and by creating some auxiliary variables), and in the scenario that a case has only one control, we assure that this control will be matched to this case, thus maximizing the cases to be used in the analysis. In total, 72 different scenarios were displayed indicating the fluctuation of ORs, and revealing patterns, especially a drop when balancing the population. Conclusions We created an optimal and computationally efficient algorithm to derive a matched case-control sample with and without replacement of controls. The code and the functions are publicly available as an open source in an R package. Finally, we emphasize the importance of displaying several scenarios and assess the difference of ORs while using an index to balance population in observational data.