Validity and reliability of the Katz-15 scale to measure unfavorable health outcomes in community-dwelling older people

Geriatrics in primary car

Valuing Healthcare Goods and Services: A Systematic Review and Meta-Analysis on the WTA-WTP Disparity

Objective: The objective of this systematic review was to review the available evidence on the disparity between willingness to accept (WTA) and willingness to pay (WTP) for healthcare goods and services. Methods: A tiered approach consisting of (1) a systematic review, (2) an aggregate data meta-analysis, and (3) an individual participant data meta-analysis was used. MEDLINE, EMBASE, Scopus, Scisearch, and Econlit were searched for articles reporting both WTA and WTP for healthcare goods and services. Individual participant data were requested from the authors of the included studies. Results: Thirteen papers, reporting WTA and WTP from 19 experiments/subgroups, were included in the review. The WTA/WTP ratios reported in these papers, varied from 0.60 to 4.01, with means of 1.73 (median 1.31) for 15 estimates of the mean and 1.58 (median 1.00) for nine estimates of the median. Individual data obtained from six papers, covering 71.2% of the subjects included in the review, yielded an unadjusted WTA/WTP ratio of 1.86 (95% confidence interval 1.52–2.28) and a WTA/WTP ratio adjusted for age, sex, and income of 1.70 (95% confidence interval 1.42–2.02). Income category and age had a statistically significant effect on the WTA/WTP ratio. The approach to handling zero WTA and WTP values has a considerable impact on the WTA/WTP ratio found. Conclusions and Implications: The results of this study imply that losses in healthcare goods and services are valued differently from gains (ratio > 1), but that the degree of disparity found depends on the method used to obtain the WTA/WTP ratio, including the approach to zero responses. Irrespective of the method used, the ratios found in our meta-analysis are smaller than the ratios found in previous meta-analyses

Reduced parenchymal cerebral blood flow is associated with greater progression of brain atrophy: the SMART-MR study

Global cerebral hypoperfusion may be involved in the aetiology of brain atrophy; however, long-term longitudinal studies on this relationship are lacking. We examined whether reduced cerebral blood flow was associated with greater progression of brain atrophy. Data of 1165 patients (61 +/- 10 years) from the SMART-MR study, a prospective cohort study of patients with arterial disease, were used of whom 689 participated after 4 years and 297 again after 12 years. Attrition was substantial. Total brain volume and total cerebral blood flow were obtained from magnetic resonance imaging scans and expressed as brain parenchymal fraction (BPF) and parenchymal cerebral blood flow (pCBF). Mean decrease in BPF per year was 0.22% total intracranial volume (95% CI: -0.23 to -0.21). Mean decrease in pCBF per year was 0.24 ml/min per 100 ml brain volume (95% CI: -0.29 to -0.20). Using linear mixed models, lower pCBF at baseline was associated with a greater decrease in BPF over time (p = 0.01). Lower baseline BPF, however, was not associated with a greater decrease in pCBF (p = 0.43). These findings indicate that reduced cerebral blood flow is associated with greater progression of brain atrophy and provide further support for a role of cerebral blood flow in the process of neurodegeneration.Neuro Imaging Researc

Lower respiratory tract infection in the community: associations between viral aetiology and illness course

Objectives: This study determined associations between respiratory viruses and subsequent illness course in primary care adult patients presenting with acute cough and/or suspected lower respiratory tract infection.Methods: A prospective European primary care study recruited adults with symptoms of lower respiratory tract infection between November 2007 and April 2010. Real-time in-house polymerase chain reaction (PCR) was performed to test for six common respiratory viruses. In this secondary analysis, symptom severity (scored 1 = no problem, 2 = mild, 3 = moderate, 4 = severe) and symptom duration were compared between groups with different viral aetiologies using regression and Cox proportional hazard models, respectively. Additionally, associations between baseline viral load (cycle threshold (Ct) value) and illness course were assessed.Results: The PCR tested positive for a common respiratory virus in 1354 of the 2957 (45.8%) included patients. The overall mean symptom score at presentation was 2.09 (95% confidence interval (CI) 2.07-2.11) and the median duration until resolution of moderately bad or severe symptoms was 8.70 days (interquartile range 4.50-11.00). Patients with influenza virus, human metapneumovirus (hMPV), respiratory syncytial virus (RSV), coronavirus (CoV) or rhinovirus had a significantly higher symptom score than patients with no virus isolated (0.07-0.25 points or 2.3-8.3% higher symptom score). Time to symptom resolution was longer in RSV infections (adjusted hazard ratio (AHR) 0.80, 95% CI 0.65-0.96) and hMPV infections (AHR 0.77, 95% CI 0.62-0.94) than in infections with no virus isolated. Overall, baseline viral load was associated with symptom severity (difference 0.11, 95% CI 0.06-0.16 per 10 cycles decrease in Ct value), but not with symptom duration.Conclusions: In healthy, working adults from the general community presenting at the general practitioner with acute cough and/or suspected lower respiratory tract infection other than influenza impose an illness burden comparable to influenza. Hence, the public health focus for viral respiratory tract infections should be broadened. (C) 2020 Published by Elsevier Ltd on behalf of European Society of Clinical Microbiology and Infectious Diseases.Molecular basis of virus replication, viral pathogenesis and antiviral strategie

Diagnostic and prognostic models: applications and methods

Prediction modelling, both diagnostic and prognostic, has become a major topic in clinical research and practice. Traditionally, clinicians intuitively combine and judge the documented patient information, on e.g. risk factors and test results, to implicitly assess the probability or risk of having (in diagnostic estimations) or developing (in prognostic estimations) for certain diseases or outcomes. In this thesis, various clinical prediction models, both diagnostic and prognostic, were developed using empirical data. First, a prediction model of major depressive disorder in primary care patients was proposed. This model allows primary care physicians to select patients at high risk for this disorder. Second, prediction modelling was used to identify the best prognostic predictors for classifying the herpes zoster patients at highest risk for the development of postherpetic neuralgia. Third, prognostic predictors for short term and long term complications in patients with a pacemaker were studied. Even though we identified several effective predictors for the last two applications, these models have to be evaluated further before the results can be applied in practice. In recent years there has been an increasing interest in the methodology of prediction research. This research resulted in methodological recommendations for the design and analysis for prediction studies. The reporting of design and analysis issues and to what extent recommendations were followed in recently published prediction research was studied in a systematic review. Positive aspects of prediction research were the presence of relatively many prospective designs, adequate description of predictors and good reporting of the selection of predictors. Improvement is notably needed in blinded assessment of predicted outcomes, handling of continuous predictors, the investigation predictor interactions, reporting on statistical power, the reporting of the amount of missing data, the presentation of the results of multivariable analysis, and the methods used to quantify and validate the predictive performance of prediction models. Alternative methods for the development of prediction models with a limited effective sample size were also evaluated. The first method was the reduction of the number of predictors with principal components analysis. This method showed similar performance as other advanced methods for model development. If a strong predictor dominates the predictive performance, however, principal components analysis may not be a good option for variable reduction. The second method was aimed at dichotomous outcomes that were derived from a continuous variable. Here, the development of prediction models for the dichotomous versus the continuous outcome was evaluated. The two types of models showed a very similar performance in data with a large effective sample size. In smaller samples, the analysis of continuous outcomes is recommended to prevent development of too optimistic and overfitted prediction models

Diagnostic and prognostic models: applications and methods

Prediction modelling, both diagnostic and prognostic, has become a major topic in clinical research and practice. Traditionally, clinicians intuitively combine and judge the documented patient information, on e.g. risk factors and test results, to implicitly assess the probability or risk of having (in diagnostic estimations) or developing (in prognostic estimations) for certain diseases or outcomes. In this thesis, various clinical prediction models, both diagnostic and prognostic, were developed using empirical data. First, a prediction model of major depressive disorder in primary care patients was proposed. This model allows primary care physicians to select patients at high risk for this disorder. Second, prediction modelling was used to identify the best prognostic predictors for classifying the herpes zoster patients at highest risk for the development of postherpetic neuralgia. Third, prognostic predictors for short term and long term complications in patients with a pacemaker were studied. Even though we identified several effective predictors for the last two applications, these models have to be evaluated further before the results can be applied in practice. In recent years there has been an increasing interest in the methodology of prediction research. This research resulted in methodological recommendations for the design and analysis for prediction studies. The reporting of design and analysis issues and to what extent recommendations were followed in recently published prediction research was studied in a systematic review. Positive aspects of prediction research were the presence of relatively many prospective designs, adequate description of predictors and good reporting of the selection of predictors. Improvement is notably needed in blinded assessment of predicted outcomes, handling of continuous predictors, the investigation predictor interactions, reporting on statistical power, the reporting of the amount of missing data, the presentation of the results of multivariable analysis, and the methods used to quantify and validate the predictive performance of prediction models. Alternative methods for the development of prediction models with a limited effective sample size were also evaluated. The first method was the reduction of the number of predictors with principal components analysis. This method showed similar performance as other advanced methods for model development. If a strong predictor dominates the predictive performance, however, principal components analysis may not be a good option for variable reduction. The second method was aimed at dichotomous outcomes that were derived from a continuous variable. Here, the development of prediction models for the dichotomous versus the continuous outcome was evaluated. The two types of models showed a very similar performance in data with a large effective sample size. In smaller samples, the analysis of continuous outcomes is recommended to prevent development of too optimistic and overfitted prediction models

Beta-blockers may reduce mortality and risk of exacerbations in patients with chronic obstructive pulmonary disease

Background: Physicians avoid the use of beta-blockers in patients with chronic obstructive pulmonary disease (COPD) and concurrent cardiovascular disease because of concerns about adverse pulmonary effects We assessed the long-term effect of beta-blocker use on survival and exacerbations in patients with COPD Methods: An observational cohort study using data from the electronic medical records of 23 general practices in the Netherlands The data Included standardized information about daily patient contacts, diagnoses, and drug prescriptions Results: In total, the study included 2230 patients 45 years and older with an incident or prevalent diagnosis of COPD between 1996 and 2006 The mean (SD) age of the patients with COPD was 648 (11.2) years at the start of the study, and 53% of the patients were male During a mean (SD) follow-up of 7.2 (2 8) years, 686 patients (30.8%) died and 1055 (47.3%) had at least I exacerbation of COPD. The crude and adjusted hazard ratios with Cox regression analysis of beta-blocker use for mortality were 0.70 (95% confidence interval [CI], 0.59-0.84) and 0.68 (95% Cl, 0.56-0.83), respectively The crude and adjusted hazard ratios for exacerbation of COPD were 0.73 (95% Cl, 0.63-0.83) and 0.71(95% Cl, 0.600 83), respectively The adjusted hazard ratios with the propensity score methods were even lower Subgroup analyses revealed that patients with COPD but without overt cardiovascular disease had similar results Conclusion: Treatment with beta-blockers may reduce the risk of exacerbations and improve survival in patients with COPD, possibly as a result of dual cardiopulmonary protective properties

Risk of recurrent acute lower urinary tract infections and prescription pattern of antibiotics in women with and without diabetes in primary care

Aim. To investigate diabetes characteristics associated with the risk of recurrent lower UTIs and the antibiotic prescription pattern. Methods. In an exploratory retrospective study involving 7063 women aged >= 30 years, we studied the incidence of recurrent UTI (relapses and reinfection) in women with (n = 340) and without diabetes (n = 6618). Multivariable logistic regression and multilevel multinomial logistic analyses were used to determine the adjusted associations between diabetes characteristics and recurrent UTI [odds ratio (OR); 95% confidence interval (CI)] and the influence of diabetes on the pattern of antibiotic prescriptions for UTI, respectively. Results. Relapses and reinfections were reported in 7.1% and 15.9% of women with diabetes versus 2.0% and 4.1% of women without diabetes. There was an independent higher risk of recurrent UTI in women with diabetes compared with women without diabetes (OR 2.0; 95% CI 1.4-2.9). Women taking oral blood glucose-lowering medication (OR 2.1; 95% CI 1.2-3.5) or insulin (OR 3.0; 95% CI 1.7-5.1) or who had had diabetes for >= 5 years (OR 2.9; 95% CI 1.9-4.4) or who had retinopathy (OR 4.1; 95% CI 1.9-9.1) were at risk of recurrent UTI. The pattern of antibiotic prescriptions for UTI was not influenced by diabetes. Conclusions. Women with diabetes >= 5 years or with glucose-lowering medication or with retinopathy have an increased risk of recurrent UTI. Diabetes itself does not seem to influence the antibiotic prescription pattern. Research focusing on effective antibiotic treatment of UTI in women at risk of recurrence is needed and may help limit the development of antibiotic resistance

A clinical prediction rule for detecting major depressive disorder in primary care:the PREDICT-NL study

BACKGROUND: Major depressive disorder often remains unrecognized in primary care. OBJECTIVE: Development of a clinical prediction rule using easily obtainable predictors for major depressive disorder in primary care patients. METHODS: A total of 1046 subjects, aged 18-65 years, were included from seven large general practices in the center of The Netherlands. All subjects were recruited in the general practice waiting room, irrespective of their presenting complaint. Major depressive disorder according to Diagnostic and Statistical Manual of Mental Disorders, Fourth Text Revision edition criteria was assessed with the Composite International Diagnostic Interview. Candidate predictors were gender, age, educational level, being single, number of presented complaints, presence of non-somatic complaints, whether a diagnosis was assigned, consultation rate in past 12 months, presentation of depressive complaints or prescription of antidepressants in past 12 months, number of life events in past 6 months and any history of depression. RESULTS: The first multivariable logistic regression model including only predictors that require no confronting depression-related questions had a reasonable degree of discrimination (area under the receiver operating characteristic curve or concordance-statistic (c-statistic) = 0.71; 95% Confidence Interval (CI): 0.67-0.76). Addition of three simple though more depression-related predictors, number of life events and history of depression, significantly increased the c-statistic to 0.80 (95% CI: 0.76-0.83). After transforming this second model to an easily to use risk score, the lowest risk category (sum score < 5) showed a 1% risk of depression, which increased to 49% in the highest category (sum score > or = 30). CONCLUSION: A clinical prediction rule allows GPs to identify patients-irrespective of their complaints-in whom diagnostic workup for major depressive disorder is indicated