Cost of diagnosing dementia in a German memory clinic

The Diagnostic Pathway Experiences of People Living with Rare Dementia and Their Family Caregivers: A Cross-Sectional Mixed Methods Study Using Qualitative and Economic Analyses

The pathways for receiving a diagnosis of a rare type of dementia are poorly understood. Diagnostic challenges decrease access to relevant health promotion activities and post-diagnostic support. This study was focused on pathways experienced by people affected by rare dementia in Wales, United Kingdom (UK), considering the practical, emotional, and economic consequences. Semi-structured interviews were completed with 10 people affected by rare dementia across Wales, UK (nine family caregivers and one person living with rare dementia). The interview data were subject to a thematic analysis and a bottom-up costing approach was used to cost the pathway journeys. Five transitional points occurred across the diagnostic pathway (initial contact, initial referral, further referrals-provider, further referrals-private, and diagnosis) alongside two further themes (i.e., involved in the diagnostic process and disputes between stakeholders). The timeliness of the diagnosis was perceived to often be subject to 'luck', with access to private healthcare a personal finance option to expedite the process. Higher economic costs were observed when, in retrospect, inappropriate referrals were made, or multiple referrals were required. The confusion and disputes relating to individual diagnostic pathways led to further emotional burdens, suggesting that higher economic costs and emotional consequences are interlinked. Clearer diagnostic pathways for rare dementia may prevent unnecessary service contacts, waiting times, and associated distress. Prioritising appropriate and timely service contacts leads to diagnosis and support to families and enables people to increase control over their health. Appropriate diagnostic pathways may be less costly and reduce costs for families.

Urine biomarkers for Alzheimer's disease: A new opportunity for wastewater-based epidemiology?

While Alzheimer's disease (AD) diagnosis, management, and care have become priorities for healthcare providers and researcher's worldwide due to rapid population aging, epidemiologic surveillance efforts are currently limited by costly, invasive diagnostic procedures, particularly in low to middle income countries (LMIC). In recent years, wastewater-based epidemiology (WBE) has emerged as a promising tool for public health assessment through detection and quantification of specific biomarkers in wastewater, but applications for non-infectious diseases such as AD remain limited. This early review seeks to summarize AD-related biomarkers and urine and other peripheral biofluids and discuss their potential integration to WBE platforms to guide the first prospective efforts in the field. Promising results have been reported in clinical settings, indicating the potential of amyloid β, tau, neural thread protein, long non-coding RNAs, oxidative stress markers and other dysregulated metabolites for AD diagnosis, but questions regarding their concentration and stability in wastewater and the correlation between clinical levels and sewage circulation must be addressed in future studies before comprehensive WBE systems can be developed.

Patient-related benefits of amyloid PET imaging in dementia: Rationale and design of the German randomized coverage with evidence development study ENABLE

The utility of amyloid positron emission tomography (PET) for the etiological diagnosis of dementia and its impact on functional status of patients in routine care are currently unclear. Here, we describe the design of ENABLE, a randomized controlled two-armed coverage with evidence development (CED) study in Germany. Approximately 1126 patients with mild to moderate dementia of unclear etiology will be randomly assigned to either an amyloid PET or a no amyloid PET group. Patients will be followed-up for 24 months. The study has been registered at the German Clinical Trials Register (https://drks.de/search/de/trial/DRKS00030839) with the registration code DRKS00030839. The primary endpoint of ENABLE is the ability to perform functional activities of daily living at 18 months. Secondary endpoints include change in diagnosis, diagnostic confidence, and cognitive and clinical outcomes of patients. We expect that the CED study ENABLE will inform about patient relevant effects of amyloid PET in routine care. Furthermore, we anticipate that ENABLE will support physicians' and payers' decisions on provision of health care for patients with dementia.

Highlights

Study design focuses on the usefulness of amyloid positron emission tomography (PET) in routine care.Study design addresses the patient-relevant effect of amyloid PET.Patient representatives were involved in the creation of the study design.The study will help improve routine care for people with dementia.

Healthcare Costs in the Year Before and After Alzheimer's Disease Diagnosis: A Danish Register-Based Matched Cohort Study

BACKGROUND

Alzheimer's disease (AD) carries a significant economic burden, with costs peaking around the time of diagnosis. However, the cost of diagnosis, including the time leading up to it, has not been studied thoroughly. Furthermore, regionalized healthcare structure could result in differences in the pre-diagnostic costs for people with suspected AD.

OBJECTIVE

This study set out to estimate the excess healthcare costs before and after AD diagnosis compared to a matched non-AD population and to investigate regional variation in AD healthcare costs in Denmark.

METHODS

We used a register-based cohort of 25,523 matched pairs of new cases of AD and non-AD controls. The healthcare costs included costs on medication, and inpatient-, outpatient-, and primary care visits. Generalized estimating equations were employed to estimate the excess healthcare cost attributable to diagnosing AD, and the variation in costs across regions.

RESULTS

Mean excess costs attributable to AD were € 3,284 and € 6,173 in the year before and after diagnosis, respectively. Regional differences in healthcare costs were identified in both the AD and control groups and were more pronounced in patients with AD (PwAD). The variation over time in regional inequality between PwAD and their controls was identified.

CONCLUSION

PwAD incur higher healthcare costs across all cost categories in the year before and after diagnosis. Regional differences in healthcare utilization by PwAD may reveal potential variation in access to healthcare. These findings suggest that a more standardized and targeted diagnostic process may help reduce costs and variation in access to healthcare.

[The Cost of Early Diagnosis of Cognitive Decline in German Memory Clinics]

Dementias are expensive diseases: the net annual cost in European healthcare is about € 28.000 per case with a strong stage dependency, of which medical care accounts for about 19%. Diagnostic costs, on the other hand, account for only a small proportion of the total costs. With changes in the guidelines, biomarker tests are becoming increasingly important. At present, the concrete economic impact of biomarker-based diagnosis is largely unknown. To determine the actual costs of diagnostic procedures based on guidelines, we conducted a survey among the members of the German Memory Clinic Network (DNG). From 15 expert centres, the staff engagement time for all procedures was collected. Based on the individual engagement times of the different professions, the total of personnel costs for diagnostics was calculated using current gross personnel costs. The total sum of diagnostic costs (personnel plus procedures) was calculated for three different scenarios e. g. € 633,97 for diagnostics without biomarkers, € 1.214,90 for diagnostics with CSF biomarkers and € 4.740,58 € for diagnostics with FDG- plus Amyloid-PET. In addition, the actual diagnostic costs of the current practice in expert memory clinics were estimated, taking into account personnel costs, costs for the different procedures and the frequency of their use across all patients. This results in total average costs of € 1.394,43 per case as the mean across all centres (personnel costs € 351,72, costs for diagnostic procedures € 1.042,71). The results show that state-of-the-art diagnosis of dementia and pre-dementia states, such as mild cognitive impairment (MCI) requires financial resources, which are currently not fully reimbursed in Germany. The need for a biomarker-based etiological diagnosis of dementia and pre-dementia states will increase, due to availability of disease-modifying treatments. Therefore, the current gap of reimbursement must be filled by new models of compensation.

Impact of Area Deprivation Index on the Performance of Claims-Based Risk-Adjustment Models in Predicting Health Care Costs and Utilization

Traditionally, risk-adjustment models do not address the characteristics of minority populations, such as race or socioeconomic status. This study aimed to evaluate the added value of place-based social determinants on risk-adjustment models in explaining health care costs and utilization. Statewide commercial claims from the Maryland Medical Care Database were used, including 1,150,984 Maryland residents aged 18 to 63 with ≥6 months enrollment in 2013 and 2014. Area Deprivation Index (ADI) was assigned to individuals through zip code. The authors examined the addition of ADI to predictive models of concurrent and prospective costs and utilization; linear regression was adopted for costs and logistic regression for utilization markers. Performance measures included R² for costs (total, pharmacy, and medical costs) and the area under the curve (AUC) for utilization (being top 5% top users, having any hospitalization, having any emergency room [ER] visit, having any avoidable ER visit, and having any readmission). All performance measures were derived from the bootstrapping analysis with 200 iterations. Study subjects were ∼48% male with a mean age of ∼41 years. Adding ADI to the demographics or claims-based models generally did not improve performance except in predicting the probability of having any ER or any avoidable ER visit; for example, AUC of avoidable ER visits increased significantly from .610 to .613 when using ADI rank deciles in claims-based models. Future research should focus on patients with a higher need for social services, assess more granular place-based determinants (eg, Census block group), and evaluate the added value of individual social variables.

Nuclear medical imaging as part of dementia diagnostics in psychiatric day-care clinics and inpatient care settings

BACKGROUND

Current guidelines support the use of nuclear medical imaging (NMI) techniques for differential diagnostics of certain cases of dementia.

AIMS

We aimed at studying the association between using NMI and the accuracy of dementia diagnoses. Additionally, we evaluated the effect of conducting NMI on the duration of hospital treatment.

METHODS

This study was based on data collected according to §21 of the German hospital remuneration law, including relevant diagnostic and procedural codes for NMI in dementia patients. In total, more than 7.2 million cases treated in German psychiatric and somatic hospitals between 2015 and 2017 were included. Associations between the frequency of NMI and the accuracy of dementia diagnoses in terms of specific vs. unspecific diagnostic codes were analyzed using Fischer's exact test.

RESULTS

In total, 351,106 cases with a dementia diagnosis were encoded during the study period. NMI was performed in 1.03% or 0.15% of all patients with dementia in psychiatric or somatic clinics, respectively. In psychiatric clinics, the proportion of unspecific dementia diagnoses decreased from 20.86% in 2015 to 17.73% in 2017. NMI was mainly performed within psychiatric day-care settings. Interestingly, patients receiving NMI stayed shorter within day-care settings (8.1 ± 16.0 days) compared to inpatient settings (38.3 ± 44.7 days).

CONCLUSIONS

Nuclear medical imaging is often performed in psychiatric day-care settings. Further studies are warranted to understand the predictive diagnostic value of NMI in dementia diagnosis compared with clinical, CSF and structural imaging in different healthcare settings.

Association Between the Use of Antihyperglycemic Drugs and Dementia Risk: A Case-Control Study

BACKGROUND

There is a conflicting literature on the association between the use of antihyperglycemic drugs and dementia risk.

OBJECTIVE

The goal of this case-control study was to analyze the association between the use of antihyperglycemic drugs and dementia risk in patients followed in general practices in Germany.

METHODS

This study included patients with type 2 diabetes mellitus who had received a first dementia diagnosis in 972 general practices in Germany between January 2013 and December 2017 (index date). Controls without dementia were matched (1:1) to cases by age, gender, index year, and physician. Two multivariate regression models were used to study the association between the use of antihyperglycemic drugs and dementia risk. Model 1 included all antihyperglycemic drugs prescribed to patients regardless of the prescription duration, whereas Model 2 only included the longest therapy prescribed to each patient.

RESULTS

There were 8,276 diabetes patients with dementia and 8,276 diabetes patients without dementia included in this study. In Model 1, glitazones were associated with a decreased dementia risk (odds ratio [OR] = 0.80), whereas insulin was associated with an increased risk of developing the condition (OR = 1.34). In Model 2, metformin, prescribed as monotherapy (OR = 0.71) or as dual therapy with sulfonylureas (OR = 0.90), was associated with a decrease in the likelihood of subsequently being diagnosed with dementia. By contrast, the combination of basal insulin and bolus insulin (OR = 1.47) and premix insulin (OR = 1.33) were risk factors for dementia.

CONCLUSION

Metformin and glitazones were negatively associated with dementia, while insulin was positively associated with dementia.

A Novel Ensemble-Based Machine Learning Algorithm to Predict the Conversion From Mild Cognitive Impairment to Alzheimer's Disease Using Socio-Demographic Characteristics, Clinical Information, and Neuropsychological Measures

Background: Despite the increasing availability in brain health related data, clinically translatable methods to predict the conversion from Mild Cognitive Impairment (MCI) to Alzheimer's disease (AD) are still lacking. Although MCI typically precedes AD, only a fraction of 20-40% of MCI individuals will progress to dementia within 3 years following the initial diagnosis. As currently available and emerging therapies likely have the greatest impact when provided at the earliest disease stage, the prompt identification of subjects at high risk for conversion to AD is of great importance in the fight against this disease. In this work, we propose a highly predictive machine learning algorithm, based only on non-invasively and easily in-the-clinic collectable predictors, to identify MCI subjects at risk for conversion to AD. Methods: The algorithm was developed using the open dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI), employing a sample of 550 MCI subjects whose diagnostic follow-up is available for at least 3 years after the baseline assessment. A restricted set of information regarding sociodemographic and clinical characteristics, neuropsychological test scores was used as predictors and several different supervised machine learning algorithms were developed and ensembled in final algorithm. A site-independent stratified train/test split protocol was used to provide an estimate of the generalized performance of the algorithm. Results: The final algorithm demonstrated an AUROC of 0.88, sensitivity of 77.7%, and a specificity of 79.9% on excluded test data. The specificity of the algorithm was 40.2% for 100% sensitivity. Conclusions: The algorithm we developed achieved sound and high prognostic performance to predict AD conversion using easily clinically derived information that makes the algorithm easy to be translated into practice. This indicates beneficial application to improve recruitment in clinical trials and to more selectively prescribe new and newly emerging early interventions to high AD risk patients.