Usability and feasibility of PreventS-MD web app for stroke prevention.

BACKGROUND: Most strokes and cardiovascular diseases (CVDs) are potentially preventable if their risk factors are identified and well controlled. Digital platforms, such as the PreventS-MD web app (PreventS-MD) may aid health care professionals (HCPs) in assessing and managing risk factors and promoting lifestyle changes for their patients. METHODS: This is a mixed-methods cross-sectional two-phase survey using a largely positivist (quantitative and qualitative) framework. During Phase 1, a prototype of PreventS-MD was tested internationally by 59 of 69 consenting HCPs of different backgrounds, age, sex, working experience, and specialties using hypothetical data. Collected comments/suggestions from the study HCPs in Phase 1 were reviewed and implemented. In Phase 2, a near-final version of PreventS-MD was developed and tested by 58 of 72 consenting HCPs using both hypothetical and real patient (n = 10) data. Qualitative semi-structured interviews with real patients (n = 10) were conducted, and 1 month adherence to the preventive recommendations was assessed by self-reporting. The four System Usability Scale (SUS) groups of scores (0-50 unacceptable; 51-68 poor; 68-80.3 good; >80.3 excellent) were used to determine usability of PreventS-MD. FINDINGS: Ninety-nine HCPs from 27 countries (45% from low- to middle-income countries) participated in the study, and out of them, 10 HCPs were involved in the development of PreventS before the study, and therefore were not involved in the survey. Of the remaining 89 HCPs, 69 consented to the first phase of the survey, and 59 of them completed the first phase of the survey (response rate 86%), and 58 completed the second phase of the survey (response rate 84%). The SUS scores supported good usability of the prototype (mean score = 80.2; 95% CI [77.0-84.0]) and excellent usability of the final version of PreventS-MD (mean score = 81.7; 95% CI [79.1-84.3]) in the field. Scores were not affected by the age, sex, working experience, or specialty of the HCPs. One-month follow-up of the patients confirmed the high level of satisfaction/acceptability of PreventS-MD and (100%) adherence to the recommendations. INTERPRETATION: The PreventS-MD web app has a high level of usability, feasibility, and satisfaction by HCPs and individuals at risk of stroke/CVD. Individuals at risk of stroke/CVD demonstrated a high level of confidence and motivation in following and adhering to preventive recommendations generated by PreventS-MD.

Te Oranga O Te Roro: kaumatua perspectives on the development of a mobile app for mate wareware (dementia) awareness.

AIM: Mate wareware (dementia) presents a significant social and economic burden for Maori in Aotearoa New Zealand. Previous literature has highlighted the need to improve health literacy for Maori regarding the causes and management of mate wareware, yet there is a lack of Maori-centred educational resources. It was determined that a mobile phone application (app) could meet this need and that early consultation with Maori was required to ensure the digital solution would be culturally safe and relevant. METHOD: This study explored the perspectives of kaumatua (Maori elders) regarding how to cater the mate wareware mobile app to Maori. Through a qualitative approach based on Kaupapa Maori principles, two focus groups were held with 15 kaumatua. Focus group data were thematically analysed. RESULTS: The analysis identified four themes related to the content of the proposed app and its design features. "Information about mate wareware" and "Caregiver support" were prominent themes that kaumatua prioritised for inclusion in the proposed app. To ensure uptake, kaumatua emphasised that the "Access" and "Appeal" of the proposed app should be considered. CONCLUSION: The findings have informed the design of the Mate Wareware app and should be considered when developing other digital health interventions for Maori.

Filter and Wrapper Stacking Ensemble (FWSE): a robust approach for reliable biomarker discovery in high-dimensional omics data.

Selecting informative features, such as accurate biomarkers for disease diagnosis, prognosis and response to treatment, is an essential task in the field of bioinformatics. Medical data often contain thousands of features and identifying potential biomarkers is challenging due to small number of samples in the data, method dependence and non-reproducibility. This paper proposes a novel ensemble feature selection method, named Filter and Wrapper Stacking Ensemble (FWSE), to identify reproducible biomarkers from high-dimensional omics data. In FWSE, filter feature selection methods are run on numerous subsets of the data to eliminate irrelevant features, and then wrapper feature selection methods are applied to rank the top features. The method was validated on four high-dimensional medical datasets related to mental illnesses and cancer. The results indicate that the features selected by FWSE are stable and statistically more significant than the ones obtained by existing methods while also demonstrating biological relevance. Furthermore, FWSE is a generic method, applicable to various high-dimensional datasets in the fields of machine intelligence and bioinformatics.

Enhancing precision of the Telephone Interview for Cognitive Status-Modified (TICS-M) using the Rasch model.

The Telephone Interview for Cognitive Status-modified (TICS-M) is a well-established and widely used screening instrument for dementia and assessment of global cognitive function in older people. This study aimed to evaluate the psychometric properties of the TICS-M and to enhance the accuracy of the instrument using Rasch methodology. Partial Credit Rasch model was applied to the TICS-M scores. The sample selected for Rasch analysis consisted of 432 participants aged 70-90 years (M = 78.85, SD = 4.73) including 195 males (237 females), and 132 (30.56%) of whom were diagnosed with dementia after the baseline assessment. Initial analysis indicated good reliability of the TICS-M assessment scores, but there were three misfitting items and local dependency issues. Combining locally dependent and misfitting items into super-items achieved the best Rasch model fit for the TICS-M. This modification improved reliability of the assessment scores and resulted in no misfitting items, no local dependency, strict unidimensionality, and invariance across individual factors such as participants age, sex, diagnosis, and in-person neuropsychological assessment scores. Satisfying Rasch model expectations allowed for creation of a transformation table to convert raw TICS-M scores into interval-level data, which improves precision of the instrument. In summary, the TICS-M assessment scores demonstrated excellent reliability as reflected by Person Separation Index (PSI = 0.86) and met expectations of the unidimensional Rasch model after minor adjustments. The ordinal-to-interval transformation table can be used to increase accuracy of the TICS-M without altering its current format. These findings contribute to more accurate assessments of cognitive decline in older people and screening for conditions such as dementia. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Using network analysis to validate domains of the modified telephone interview for cognitive status.

BACKGROUND: The modified Telephone Interview for Cognitive Status (TICS-M) is a widely used tool for assessing global cognitive functions and screening for cognitive impairments. The tool was conceptualised to capture various cognitive domains, but the validity of such domains has not been investigated against comprehensive neuropsychological assessments tools. Therefore, this study aimed to explore the associations between the TICS-M domains and neuropsychological domains to evaluate the validity of the TICS-M domains using network analysis. MATERIALS AND METHODS: A longitudinal research design was used with a large sample of older adults (aged above 70 years; n = 1037 at the baseline assessment) who completed the TICS-M and comprehensive neuropsychological assessments biennially. We applied network analysis to identify unique links between the TICS-M domains and neuropsychological test scores. RESULTS: At baseline, there were weak internal links between the TICS-M domains. The TICS-M memory and language domains were significantly related to their corresponding neuropsychological domains. The TICS-M attention domain had significant associations with executive function and visuospatial abilities. The TICS-M orientation domain was not significantly associated with any of the five neuropsychological domains. Despite an attrition of almost 50% at wave four, weak internal links between the TICS-M domains and most associations between TICS-M and neuropsychological domains that were found initially, remained stable at least over two waves within the 6-year period. CONCLUSIONS: This study supports the overall structural validity of the TICS-M screener in assessing enduring global cognitive function. However, separate TICS-M cognitive domains should not be considered equivalent to the analogous neuropsychological domains.

Investigation of social and cognitive predictors in non-transition ultra-high-risk' individuals for psychosis using spiking neural networks.

Finding predictors of social and cognitive impairment in non-transition Ultra-High-Risk individuals (UHR) is critical in prognosis and planning of potential personalised intervention strategies. Social and cognitive functioning observed in youth at UHR for psychosis may be protective against transition to clinically relevant illness. The current study used a computational method known as Spiking Neural Network (SNN) to identify the cognitive and social predictors of transitioning outcome. Participants (90 UHR, 81 Healthy Control (HC)) completed batteries of neuropsychological tests in the domains of verbal memory, working memory, processing speed, attention, executive function along with social skills-based performance at baseline and 4 × 6-month follow-up intervals. The UHR status was recorded as Remitters, Converters or Maintained. SNN were used to model interactions between variables across groups over time and classify UHR status. The performance of SNN was examined relative to other machine learning methods. Higher interaction between social and cognitive variables was seen for the Maintained, than Remitter subgroup. Findings identified the most important cognitive and social variables (particularly verbal memory, processing speed, attention, affect and interpersonal social functioning) that showed discriminative patterns in the SNN models of HC vs UHR subgroups, with accuracies up to 80%; outperforming other machine learning models (56-64% based on 18 months data). This finding is indicative of a promising direction for early detection of social and cognitive impairment in UHR individuals that may not anticipate transition to psychosis and implicate early initiated interventions to stem the impact of clinical symptoms of psychosis.

A Pilot Study of Application of the Stroke Riskometer Mobile App for Assessment of the Course and Clinical Outcomes of COVID-19 among Hospitalized Patients.

INTRODUCTION: Early determination of COVID-19 severity and health outcomes could facilitate better treatment of patients. Different methods and tools have been developed for predicting outcomes of COVID-19, but they are difficult to use in routine clinical practice. METHODS: We conducted a prospective cohort study of inpatients aged 20-92 years, diagnosed with COVID-19 to determine whether their individual 5-year absolute risk of stroke at the time of hospital admission predicts the course of COVID-19 severity and mortality. The risk of stroke was determined by the Stroke Riskometer mobile application. RESULTS: We examined 385 patients hospitalized with COVID-19 (median age 61 years). The participants were categorized based on COVID-19 severity: 271 (70.4%) to the "not severe" and 114 (29.6%) to the "severe" groups. The median risk of stroke the next day after hospitalization was significantly higher among patients in the severe group (2.83, 95% CI: 2.35-4.68) versus the not severe group (1.11, 95% CI: 1.00-1.29). The median risk of stroke and median systolic blood pressure (SBP) were significantly higher among non-survivors (12.04, 95% CI: 2.73-21.19) and (150, 95% CI: 140-170) versus survivors (1.31, 95% CI: 1.14-1.52) and (134, 95% CI: 130-135), respectively. Those who spent more than 2.5 h a week on physical activity were 3.1 times more likely to survive from COVID-19. Those who consumed more than one standard alcohol drink a day, or suffered with atrial fibrillation, or had poor memory were 2.5, 2.3, and 2.6 times more likely not to survive from COVID-19, respectively. CONCLUSIONS: High risk of stroke, physical inactivity, alcohol intake, high SBP, and atrial fibrillation are associated with severity and mortality of COVID-19. Our findings suggest that the Stroke Riskometer app could be used as a simple predictive tool of COVID-19 severity and mortality.

Tobacco use among Kola Sámi, the indigenous people of the Murmansk region, Russia: A cross-sectional study.

Russia is among the top ten nations in terms of smoking prevalence. Little is known about smoking rates among Indigenous Peoples in Russia. Our aim was to assess the prevalence of tobacco and nicotine product use among Kola peninsula Sámi. An exploratory cross-sectional survey was conducted to determine tobacco or nicotine product use among 505 Sámi people (about 30% of the whole Sámi population of Russia). Over 60% of participants had tried tobacco or nicotine products. Median age of first use was 15 years, with cigarettes being the most frequent first product tried and the most common type of product used currently. About a third of participants used a tobacco or nicotine product at least occasionally; 25% (predominantly males) smoked at least occasionally with 23.8% smoking daily. Of participants who smoked, 52.5% scored medium and 44.2% scored high on the Heaviness of Smoking Index. Seventeen percent of participants smoked formerly but not currently. Like some other Indigenous Peoples, Kola Sámi in Russia have a higher smoking prevalence than the average among the Russian population. Interest in a smoking cessation mobile app designed for the Sámi population suggests that such an intervention could help to reduce this inequity.

A novel approach to investigate depression symptoms in the aging population using generalizability theory.

As depression is common in older people and confers significant risk for dementia, its accurate assessment is essential. The 15-item Geriatric Depression Scale (GDS-15) is a widely used assessment tool for measuring depression in aged populations, and its psychometric properties have been recently improved using Rasch analysis. However, its temporal reliability and ability to distinguish between dynamic and enduring symptoms of depression have not been examined using the appropriate methodology. Generalizability theory (G theory) is a suitable method to distinguish between enduring and dynamic symptoms of depression and to evaluate the reliability of the GDS-15 scores and sources of measurement error. We applied G theory to the longitudinal GDS-15 data of 354 participants from the Sydney Memory and Ageing Study, collected biennially over 10 years, from individuals aged 70 years and older. The GDS-15 demonstrated strong reliability and generalizability of its test scores in measuring enduring symptoms of depression (Ga = 0.82, Gr = 0.90) across the sample population and occasions, and indicated that depression scores significantly increased over time. In addition, three identified dynamic symptoms of depression, namely helplessness, hopelessness, and loss of interest in activities, did not affect the overall reliability of the GDS-15. Thus, the GDS-15 is a reliable measure for assessing enduring symptoms of depression and can be used to evaluate the efficacy of depression treatments and monitor depression levels over time in older adults. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Machine learning, artificial intelligence and the prediction of dementia.

PURPOSE OF REVIEW: Artificial intelligence and its division machine learning are emerging technologies that are increasingly applied in medicine. Artificial intelligence facilitates automatization of analytical modelling and contributes to prediction, diagnostics and treatment of diseases. This article presents an overview of the application of artificial intelligence in dementia research. RECENT FINDINGS: Machine learning and its branch Deep Learning are widely used in research to support in diagnosis and prediction of dementia. Deep Learning models in certain tasks often result in better accuracy of detection and prediction of dementia than traditional machine learning methods, but they are more costly in terms of run times and hardware requirements. Both machine learning and Deep Learning models have their own strengths and limitations. Currently, there are few datasets with limited data available to train machine learning models. There are very few commercial applications of machine learning in medical practice to date, mostly represented by mobile applications, which include questionnaires and psychometric assessments with limited machine learning data processing. SUMMARY: Application of machine learning technologies in detection and prediction of dementia may provide an advantage to psychiatry and neurology by promoting a better understanding of the nature of the disease and more accurate evidence-based processes that are reproducible and standardized.

Clinical investigation of dynamic and enduring aspects of global cognition in aged population.

BACKGROUND: A major issue in evaluating the cognitive status of ageing populations is a clear distinction between enduring and dynamic aspects of global cognition necessary for evaluating risks of dementia and effectiveness of preventive interventions. MATERIALS AND METHODS: Generalizability Theory was applied to investigate dynamic and enduring aspects of global cognition using longitudinal data over 10 years of follow-up. Measures included the Mini-Mental Status Examination (MMSE) and the Telephone Interview for Cognitive Status-modified (TICS-M). The sample (N = 238) included 154 females, mean age = 76.54 years, SD = 3.94 from the Sydney Memory and Ageing Study. RESULTS: The MMSE measured dynamic and enduring aspects of cognition to a comparable degree with 56% of variance explained by enduring aspects and 44% by dynamic aspects and showed low sensitivity/high specificity in detecting dementia. A shortened version of the MMSE (MMSE-D8) better captured dynamic aspects of cognition after removing three items less sensitive to change. The TICS-M predominantly measured enduring aspects of cognition (72%) with the remaining 28% due to dynamic aspects and displayed high sensitivity/high specificity for dementia screening. CONCLUSIONS: The MMSE measures both dynamic and enduring cognitive aspects and is suitable for general clinical assessments, while the MMSE-D8 can be used to monitor transitory changes of global cognition over time. The TICS-M is more useful for measuring enduring features of cognition and screening for dementia. Our findings highlight the value of generalizability theory to distinguish dynamic and enduring features of cognition, which may contribute to preventive interventions and monitoring cognitive ability over time.

General hospital specialists' attitudes toward psychiatry: a cross-sectional survey in seven countries.

OBJECTIVE: Psychiatric comorbidities are common in physical illness and significantly affect health outcomes. Attitudes of general hospital doctors toward psychiatry are important as they influence referral patterns and quality of care. Little is known about these attitudes and their cultural correlates. The aim of this study was to identify attitudes toward psychiatry among general hospital specialists in relation to culture of the practice setting and other clinician factors (gender, age, seniority and specialty). METHODS: A cross-sectional, descriptive study was carried out in seven countries (New Zealand, China, Sri Lanka, Russia, Israel, Brazil, the Netherlands). Data were collected from senior medical staff of various disciplines using an updated version of Mayou and Smith's (1986) self-administered questionnaire. RESULTS: A total of 889 hospital doctors participated. While favourable attitudes toward both psychiatric consultation and management were endorsed by a majority, significant differences were also observed between countries. Subgroup differences were mostly confined to gender, acuity of practice setting and specialty. For example, female doctors in Russia (χ2=7.7, p=0.0056), China (χ2=9.2, p=0.0025) and the Netherlands (χ2=5.7, p=0.0174) endorsed more positive attitudes compared with their male counterparts, but this gender effect was not replicated in the total sample. Chronic care specialists were overall more inclined to manage patients' emotional problems compared with those working in acute care (χ2=70.8, p (adjusted)<0.0001), a significant finding seen also in individual countries (China, New Zealand, the Netherlands, Russia). Physicians were more favourably disposed toward psychiatry compared with other specialists, especially surgeons, in all countries except Israel. CONCLUSIONS: This study adds to evidence for the association of medical attitudes with individual clinician factors and demonstrates that the influence of these factors varies by country. Understanding these issues may help to overcome barriers and improve quality of care provided to general hospital patients.

Enhancing precision of the 16-item Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE-16) using Rasch methodology.

OBJECTIVE: This study aimed to investigate psychometric properties and enhance precision of the 16-item Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE-16) up to interval-level scale using Rasch methodology. DESIGN: Partial Credit Rasch model was applied to the IQCODE-16 scores using longitudinal data spanning 10 years of biennial follow-up. SETTING: Community-dwelling older adults aged 70-90 years and their informants, living in Sydney, Australia, participated in the longitudinal Sydney Memory and Ageing Study (MAS). PARTICIPANTS: The sample included 400 participants of the MAS aged 70 years and older, 109 out of those were diagnosed with dementia 10 years after the baseline assessment. MEASUREMENTS: The IQCODE-16. RESULTS: Initial analysis indicated excellent reliability of the IQCODE-16, Person Separation Index (PSI) = 0.92, but there were four misfitting items and local dependency issues. Combining locally dependent items into four super-items resulted in the best Rasch model fit with no misfitting or locally dependent items, strict unidimensionality, strong reliability, and invariance across person factors such as participants' diagnosis and relationship to their informants, as well as informants' age and sex. This permitted the generation of conversion algorithms to transform ordinal scores into interval data to enhance precision of measurement. CONCLUSIONS: The IQCODE-16 demonstrated strong reliability and satisfied expectations of the unidimensional Rasch model after minor modifications. Ordinal-to-interval transformation tables published here can be used to increase accuracy of the IQCODE-16 without altering its current format. These findings could contribute to enhancement of precision in assessing clinical conditions such as cognitive decline in older people.

Personalised predictive modelling with brain-inspired spiking neural networks of longitudinal MRI neuroimaging data and the case study of dementia.

BACKGROUND: Longitudinal neuroimaging provides spatiotemporal brain data (STBD) measurement that can be utilised to understand dynamic changes in brain structure and/or function underpinning cognitive activities. Making sense of such highly interactive information is challenging, given that the features manifest intricate temporal, causal relations between the spatially distributed neural sources in the brain. METHODS: The current paper argues for the advancement of deep learning algorithms in brain-inspired spiking neural networks (SNN), capable of modelling structural data across time (longitudinal measurement) and space (anatomical components). The paper proposes a methodology and a computational architecture based on SNN for building personalised predictive models from longitudinal brain data to accurately detect, understand, and predict the dynamics of an individual's functional brain state. The methodology includes finding clusters of similar data to each individual, data interpolation, deep learning in a 3-dimensional brain-template structured SNN model, classification and prediction of individual outcome, visualisation of structural brain changes related to the predicted outcomes, interpretation of results, and individual and group predictive marker discovery. RESULTS: To demonstrate the functionality of the proposed methodology, the paper presents experimental results on a longitudinal magnetic resonance imaging (MRI) dataset derived from 175 older adults of the internationally recognised community-based cohort Sydney Memory and Ageing Study (MAS) spanning 6 years of follow-up. SIGNIFICANCE: The models were able to accurately classify and predict 2 years ahead of cognitive decline, such as mild cognitive impairment (MCI) and dementia with 95% and 91% accuracy, respectively. The proposed methodology also offers a 3-dimensional visualisation of the MRI models reflecting the dynamic patterns of regional changes in white matter hyperintensity (WMH) and brain volume over 6 years. CONCLUSION: The method is efficient for personalised predictive modelling on a wide range of neuroimaging longitudinal data, including also demographic, genetic, and clinical data. As a case study, it resulted in finding predictive markers for MCI and dementia as dynamic brain patterns using MRI data.

Cross-cultural validation of the stroke riskometer using generalizability theory.

The Stroke Riskometer mobile application is a novel, validated way to provide personalized stroke risk assessment for individuals and motivate them to reduce their risks. Although this app is being used worldwide, its reliability across different countries has not yet been rigorously investigated using appropriate methodology. The Generalizability Theory (G-Theory) is an advanced statistical method suitable for examining reliability and generalizability of assessment scores across different samples, cultural and other contexts and for evaluating sources of measurement errors. G-Theory was applied to the Stroke Riskometer data sampled from 1300 participants in 13 countries using two-facet nested observational design (person by item nested in the country). The Stroke Riskometer demonstrated strong reliability in measuring stroke risks across the countries with coefficients G relative and absolute of 0.84, 95%CI [0.79; 0.89] and 0.82, 95%CI [0.76; 0.88] respectively. D-study analyses revealed that the Stroke Riskometer has optimal reliability in its current form in measuring stroke risk for each country and no modifications are required. These results suggest that the Stroke Riskometer's scores are generalizable across sample population and countries permitting cross-cultural comparisons. Further studies investigating reliability of the Stroke Riskometer over time in longitudinal study design are warranted.

Trends in tobacco smoking and smoking cessation in Russia with a focus on Indigenous populations: A narrative review.

Introduction: Tobacco smoking is one of the main preventable causes of illness and premature death. Globally, more than 7 million people die annually from diseases associated with smoking, and this number is projected to increase to 8 million per year by 2030. Wide disparities in smoking prevalence exist by gender, age, socioeconomic status, rurality and ethnicity. In several countries, smoking is disproportionately high among the Indigenous populations. Objective: This review assesses the prevalence and harm of smoking and current trends in smoking cessation among the diverse multi-ethnic populations of Russia, with a particular emphasis on Indigenous populations. Data sources: We systematically searched health, nursing, social science and grey literature databases and bibliographies for relevant studies. Search strings combined keywords related to smoking prevalence and smoking cessation with keywords related to Russia and the Indigenous populations of Russia. Study selection: Studies were included if they were published between 1 January 2005 and 14 October 2020, and if they reported prevalence of tobacco smoking and/or activities and outcomes of a smoking cessation programme or ban in the Russian Federation. Conclusions: Tobacco smoking is significant in the entire Russian population, a higher prevalence of smoking in Indigenous populations compared to the dominant Russian (Slavic) ethnic group is common. Smoking prevalence data for most of the Indigenous ethnic groups of Russia remains unclear. Tobacco control interventions for Indigenous groups are underdeveloped even though they have the potential to deliver proportionately greater reduction in smoking harm.

New avenue for the geriatric depression scale: Rasch transformation enhances reliability of assessment.

BACKGROUND: Depression is a common problem in older adults. The 15-item Geriatric Depression Scale (GDS-15) is a widely used psychometric tool for measuring depression in the elderly, but its psychometric properties have not been yet rigorously investigated. The aim was to evaluate psychometric properties of the GDS-15 and improve precision of the instrument by applying Rasch analysis and deriving conversion tables for transformation of raw scores into interval level data. METHODS: The data was extracted from the prospective cohort Sydney Memory and Ageing Study of initially not demented individuals aged 70 years and older. The GDS-15 items scores of 212 participants (47.2% males) were analysed using the dichotomous Rasch model. RESULTS: Initially poor reliability of the GDS-15, Person Separation Index (PSI) = 0.68, was improved by combining locally dependent items into seven super-items. These modifications improved reliability of the GDS-15 (PSI = 0.78) and resulted in the best Rasch model fit (χ2(28)=37.72, p = =0.104), strict unidimensionality and scale invariance across personal factors such as gender, diagnostic and language background. LIMITATIONS: Presence of participants with cognitive impairment may be a potential limitation. CONCLUSIONS: Reliability and psychometric characteristics of the GDS-15 were improved by minor modifications and now satisfy expectations of the unidimensional Rasch model. By using Rasch transformation tables published here psychiatrists, psychologists and researchers can transform GDS raw scores into interval-level data, which improves reliability of the GDS-15 without the need to modify its original response format. These findings increase accuracy of clinical psychometric assessments, leading to more precise diagnosis of depression in the elderly.