Risk Measures Research Articles

A combined integer-valued autoregressive process with actuarial applications

This paper proposes a modification of a combined integer-valued autoregressive (CINAR) process based on binomial thinning, which is instrumental in modeling higher-order dependence between the number of claims in an insurance portfolio. The modified CINAR process is more general and enjoys stationarity and flexibility in higher-order serial dependence modeling. Two actuarial applications of the proposed process in risk theory and credibility model are explored. As an application to risk theory, we derive the distribution of aggregate claims under a discrete-time collective risk model and examine the effect of high-order dependence on the tail-related risk measures of the aggregate claims. Next, we apply the modified CINAR process to account for the unobserved gamma heterogeneity in determining the dynamics of the predictive credibility premium. A real data analysis shows that our approach provides a superior pattern to the predictive premium calculation when compared to the outcomes of several alternative models.

Risk parity portfolio optimization under heavy‐tailed returns and dynamic correlations

Risk parity portfolio optimization, using expected shortfall as the risk measure, is investigated when asset returns are fat‐tailed and heteroscedastic with regime switching dynamic correlations. The conditional return distribution is modeled by an elliptical multi‐variate generalized hyperbolic distribution, allowing for fast parameter estimation via an expectation‐maximization algorithm, and a semi‐closed form of the risk contributions. A new method for efficient computation of non‐Gaussian risk parity weights sidesteps the need for numerical simulations or Cornish–Fisher‐type approximations. Accounting for fat‐tailed returns, the risk parity allocation is less sensitive to volatility shocks, thereby generating lower portfolio turnover, in particular during market turmoils such as the global financial crisis or the COVID shock. While risk parity portfolios are rather robust to the misuse of the Gaussian distribution, a sophisticated time series model can improve risk‐adjusted returns, strongly reduces drawdowns during periods of market stress and enables to use a holistic risk model for portfolio and risk management.

Analysis of the Implementation of Risk Management to Minimize the Risk of Bad Credit at the Nias Rural Development Savings and Loans Cooperative (KSP3) Gunungsitoli Branch

This study aims to determine the application of risk management carried out by the Nias Rural Development Savings and Loan Cooperative (KSP3) Gunungsitoli Branch in minimizing the risk of bad loans. In this study, the type of research used is a descriptive type with a qualitative approach. Data collection techniques are used through observation, interviews and documentation. To obtain information, the researcher conducted direct interviews with 6 informants, namely branch heads, 2 employees and 3 members of the Nias Rural Development Savings and Loan Cooperative (KSP3) Gunungsitoli Branch. The data analysis techniques used in qualitative research include transcripts of interview results, data reduction, data presentation and conclusion drawn. The results of the study show that the implementation of risk management carried out by the Nias Rural Development Savings and Loan Cooperative (KSP3) Gunungsitoli Branch is carried out through a series of stages, namely risk identification, risk measurement, risk monitoring and risk control. The implementation of risk management to minimize the risk of bad loans has been effectively implemented by KSP3 Nias Gunungsitoli Branch as evidenced by the NPL (Non-Performing Loan) rate from 2020-2022 which is still below 5%.

Modeling the distribution of jet fuel price returns based on fat-tail stable Paretian distribution.

Jet fuel plays a crucial role as an essential energy source in aerospace and aviation operations. The recent increase in fuel prices has presented airlines with the new challenge of managing jet fuel costs to ensure consistent cash flow and minimize operational uncertainties. The conventional risk prediction models used by airlines often assume that risks are normally distributed according to the classical Central Limit Theorem, which can lead to under-hedging. This paper proposes an innovative approach using the stable Paretian model to analyze the price return of jet fuel in large samples. It comprehensively compares the fitting effect of the stable Paretian distribution with that of the normal distribution based on specific criteria and non-parametric significance tests. Furthermore, it investigates the accuracy of risk measures such as Value at Risk (VaR) and Conditional Value at Risk (CVaR) predicted by both models. In addition to comparing differences in VaR between predicted values and actual values, this paper provides a more comprehensive comparison of risk measures under rolling window forecast situation. Results suggest that despite indistinguishable results in VaR backtest, the stable Paretian distribution has a overall better fitting effect as well as a less biased predicted CVaR based on the AIC of -14099.46, BIC of -14110.98, p = 0.58 in Kolmogorov-Smirnov test and p = 0.46(0.92) in the 0.01(0.05) significance level of Expected Shortfall Regression Test. This might be explained by its ability to capture asset return dynamics while maintaining shape stability with few parameters. This research can provide valuable insights for guiding airlines' risk management decisions. its ability to capture asset return dynamics while maintaining shape stability with few parameters. This research can provide valuable insights for guiding airlines' risk management decisions.

The Application of Calculus in Credit Default Swaps: Pricing, Risk Assessment, and Market Implications

This paper aims at the investigation of one of the derivatives, namely from the group of Credit Default Swaps, with the help of calculus and, more particularly, differential equations and integrals. The plan is to show how these calculative instruments may be utilized to price and risk measure CDS and to thereby illustrate the utility of such models in mitigating financial risks. The paper is structured as follows: In Section 2, ‘The mechanics and functionality of CDS’, the focus is brought on the specifics of construction of CDS contracts, and the matter of their pricing. Valuation of CDS is described in section three where differential equations and integrals are used in the mathematical modeling of these instruments. Section 4 also focuses on the connection between CDS and market stability; this section also looks at the positive and negative effects of CDS on the financial markets. Finally, Section 5 provides conclusion and recommendation for the subsequent research.

Exploring the association between phase angle of bioimpedance at 50 kHz and cardiovascular risk

BackgroundCardiovascular diseases are characterized by chronic inflammation, leading to increased inflammatory markers that can cause cell damage and death. Phase angle has emerged as a marker of cellular health. It is considered a prognostic factor in various acute and chronic conditions. However, few studies have examined its association with cardiovascular disease risk measures. This study aims to investigate the relationship between phase angle, the general Framingham risk score, and the HEARTS cardiovascular risk score.MethodsThis cross-sectional study included a convenience sample of adult patients of 2 primary health care services. Phase angle was measured using multifrequency bioimpedance analysis at 50 kHz. The risk of cardiovascular events was calculated using the Framingham and HEARTS risk scores. Statistical analysis included generalized linear regression models, unadjusted and adjusted according to sex and age, to determine the association between scores, risk factors, and phase angle.ResultsThe study included 164 individuals with a mean age 52.2 (SD 17.9). According to the HEARTS score, low-risk patients had higher phase angle values than those with high or very high risk [ß = -0.57 (95% CI -0.95; -0.19), P = 0.003]. Framingham scores showed a trend toward significance for higher mean phase angle values in low-risk than high-risk patients [ß = -0.43 (95% CI -0.88 to 0.02), P = 0.06].ConclusionPhase angle values were lower in high and very high-risk patients than in low-risk patients, which shows that phase angle is a promising risk predictor for patients with cardiovascular diseases.

Financial Determinants of Adoption of Agricultural Technologies in Irish Potato Production in Ol Kalou Sub County, Kenya: Application of Multinomial Logistic Regression

Irish potato is a significant source of food and income for many households worldwide. The demand for the crop has steadily increased around the globe. Kenyan Irish potato farmers have the potential to produce about 30 tonnes per Ha. However, small-scale Irish farmers in the country realize low output ranging from 4-8 tonnes per Ha due to limited uptake of agricultural technologies. This study aimed to analyze the financial factors influencing the uptake of agricultural technologies in Irish potato production in Ol Kalou Sub County. The agricultural technologies under investigation were chemical fertilizer, certified seeds, fungicides, and farm machinery. Production and innovation diffusion theories guided the study. Descriptive cross-sectional research design was used to obtain data from a study population of 21,942 smallholder Irish potato farmers in Ol Kalou Sub County. A multiple-stage sampling technique was employed to give a sample size of 385 respondents small scale Irish potato farmers, where data was collected through a semi-structured questionnaire. Data collected was analyzed using multinomial logistic regression through SPSS version 28 and STATA version 17. The model indicated that off-farm income, access to credit, and production risk had a positive and significant influence on adopting agricultural technologies. Conversely, production cost had a negative and significant influence on the adoption of agricultural technologies, while the availability of subsidies had no significant influence. The Marginal effect analysis showed that the availability of off-farm income increased adoption by 22.00%, the increase in credit increased adoption by 2.00%, the availability of mitigation measures of production risk increased adoption by 19.00%, while the increase in production cost decreased adoption by 7.00%. The study concluded that off-farm income, credit facilities, production risk and cost of production influence the adoption of agricultural technologies. The study recommended that the government should develop policy regulations such as diversification of income, grants that reduce production costs, credit incentives, and crop insurance.

Interaction between subclinical carotid atherosclerosis endotype and biological sex: a study from IMPROVE cohort

Abstract Background Carotid-intima media thickness (c-IMT) is a measure of subclinical carotid atherosclerosis and a predictor of future atherosclerotic cardiovascular events (ASCVD). We have recently generated, using an artificial intelligence approach, four endotypes (E) of subclinical atherosclerosis, i.e. subgroups of individuals predisposed to both c-IMT progression and ASCVD. Each E is defined by a set of demographic, clinical, and molecular data and characterize individuals with a low (E1), intermendiate (E2), high (E3) and very high (E4) cardiovascular risk profile. We observed an unbalanced distribution of males and females across the 4 endotypes, with a higher prevalence of female in the low risk E1. Purpose To estimate the interaction between biological sex and the 4 endotypes on measures of c-IMT progression and ASCVD risk. Methods We performed our study on 3121 individuals from the IMPROVE cohort, which includes participants with at least three ASCVD risk factors. We estimated the interaction between biological sex and the endotypes generated in the IMPROVE on (1) c-IMT and carotid plaque measurements after 30 months of follow-up (c-IMTfastest-progr, Area of plaquesbulb-progr) by linear regression (β, SE) and (2) with the 3-year ASCVD risk by Cox [hazard ratio (HR), 95% confidence interval (CI)] regression model. Crude estimates were adjusted by batch effect for biomarker measurement, geographical region and/or baseline ultrasonographic measures. Adjusted models further included ASCVD common risk factors. Results As shown in Figure 1, we did not observe any significant interaction between endotypes and biological sex on 30-month carotid ultrasound progression measures, and 3-year-ASCVD risk. In both the crude and adjusted models on c-IMTfastest-progr, Area of plaquesbulb-progr, and 3-year ASCVD, the p-value for interaction ranged from 0.18 to 0.98. While E4 was associated with the strongest cardiovascular risk profile in both males and females. Conclusions Our results indicate that the association between endotypes and progression of subclinical atherosclerosis and ASCVD risk mirrors a specific risk profile not entirely explained by differences in biological sex.

Mortality related to left ventricular ejection fraction and obstructive coronary artery disease in non-ST-segment elevation myocardial infarction

Abstract Background Extent of coronary artery disease (CAD) and the severity of reduced left ventricular ejection fraction (LVEF) have both been associated with the prognosis after non-ST-segment elevation myocardial infarction (NSTEMI). However, the contemporary mortality associated with the extent of obstructive CAD over a range of LVEF remains uncertain. Purpose The aim of this study was to assess the impact of CAD and LVEF on all-cause death following a first-time NSTEMI. Methods We included consecutive first-time NSTEMI patients undergoing coronary angiography from 2010 to 2021 in Western Denmark. Patients were stratified by extent of obstructive vessel disease (VD; 1, 2, or 3VD) and LVEF (≥51%, 41-50%, or 10-40%). Patients with previous myocardial infarction, percutaneous coronary intervention, or coronary artery bypass grafting were excluded. The primary outcome was all-cause death. We calculated 5-year cumulative incidence proportions (CIP) as a measure of absolute risk and adjusted hazard ratios (HR). Results A total of 8,770 patients with first-time NSTEMI and obstructive CAD were examined with coronary angiography. Two-thirds were male and median age was 68 years (Q1-Q3: 58-78). The increase in mortality associated with decreasing LVEF was greater than observed by extent of CAD (Table). Thus, the 5-year mortality increased stepwise by extent of CAD from 14% for 1VD to 31% for 3VD (adjusted HR 1.35, 95% CI:1.19-1.54), but from 12% for LVEF ≥51% to 40% for LVEF 10-40% (adjusted HR 2.32, 95% CI: 2.04-2.65). When combining LVEF and extent of CAD, an increase in 5-year mortality was found from 9% for 1VD and LVEF ≥51% to 46% for 3VD and LVEF 10-40% (risk difference of 37.1%; Figure). Correspondingly, using 1VD and LVEF ≥51% as reference, the adjusted HRs increased gradually with extent of CAD and decline in LVEF to 3.05 (95% CI: 2.51-3.70) for patients with 3VD and LVEF 10-40% (Table). Conclusion In a contemporary all-comers cohort of first-time NSTEMI patients, increasing extent of CAD and declining LVEF are both associated with a gradual increase in 5-year mortality. Our data suggest that the level of LVEF reduction was more strongly associated with 5-year mortality than CAD extent.

Prediction of the burden and characteristics of coronary plaques by cardiorespiratory fitness

Abstract Background Cardiorespiratory fitness (CRF) and coronary plaque burden are both important predictors of mortality. Although mortality of coronary heart disease (CHD) is reduced, prevalence remains high and there is a need to improve early detection and preventive measures. How CRF relates to coronary plaque burden and composition in the general population is largely unknown. Purpose The aim was to study the relationship between CRF and the total coronary plaque burden, including subgroups of coronary plaques, in subjects without known CHD. Methods A random sample of participants from a large population-based echocardiographic reference range study aged 55-70 at baseline was included. Coronary plaque burden was assessed, on average, 4.0 years after the baseline evaluation using coronary computed tomography angiography (CCTA). The burden and characteristics of the coronary plaques were evaluated using a validated, commercially available, semi-automatic software that assessed all coronary territories including the left main, left anterior descending, circumflex, and right arteries. Coronary plaques were classified as dense calcified plaques, plaques with necrotic core, fibrous, and fibrous-fatty plaques. Baseline measures included cardiovascular risk factor assessment and measurement of peak oxygen consumption (VO2peak) assessed by treadmill cardiopulmonary exercise testing. Linear regression models were used to assess the impact of VO2peak on coronary plaque burden. In Model 1, we adjusted for sex and age, while Model 2 also included adjustments for non-HDL cholesterol, HbA1c, systolic blood pressure, smoking status, and body mass index. Results Of 845 invited to undergo CCTA, 707 (51% females) underwent CT scanning. Of these, 699 scans had acceptable quality for further analyses. Table 1 shows baseline characteristics and Table 2 shows the associations of VO2peak with coronary plaque burden. In the fully adjusted Model 2, there was a significant negative association of VO2peak with total plaque burden (β -0.18, R2 15%, p<0.001), while the association was not significant in Model 1 (p=0.54). Similarly, higher VO2peak significantly predicted a lower burden of dense calcified plaques, plaques with necrotic core, and fibrofatty plaques in both models (R2 3-17%, p≤0.01). The results were consistent whether the coronary plaques were evaluated across the entire coronary arteries (down to a diameter of 1.5 mm) or solely in the proximal segments. However, the predictive power of the models was relatively low with R2 of maximum 17%. Conclusion In a population without known CHD, CRF explained a modest part of the variation in coronary plaque burden. Considering CRF in risk prediction may enhance coronary artery disease prevention beyond traditional risk factors.

Distribution of the Culex pipiens mosquito in mainland Portugal: a geospatial modelling study

Abstract Background The mosquito Culex pipiens is a vector for several pathogens, such as the West Nile virus. As the most abundant mosquito in Portugal, understanding its spatial distribution can support vector-borne diseases control and robust public health planning, given the mainland’s favourable climate for this vector. Information on its spatial distribution at a national level is limited, especially its association with climate variables. Thus we aimed to predict the suitability of Culex pipiens distribution in mainland Portugal, considering climate factors. Methods We used presence records for adult and larvae of Culex pipiens mosquitoes sampled across mainland Portugal from January 2017 to October 2023, as part of the Portuguese Vector Surveillance Network program (REVIVE). Between May-October, in random locations and throughout the year in point-of-entry sites, adult mosquito collection used CDC light traps and BG-Sentinel traps, and larvae collection used dippers in breeding sites. Sampling bias was corrected by filtering presence sites, retaining one record per each cell grid of 1 km2. Climatic data (including temperature, precipitation and elevation) were used as predictors in a maximum entropy (MaxEnt) model of the Culex pipiens geographical distribution. Results From the dataset of 6859 records, 1416 unique sites were obtained after filtering. Suitable habitats seem to be mainly distributed on the coast, particularly in Minho, Porto and Lisbon metropolitan areas, and Tagus Valley, with precipitation as the most important predictor. Conclusions Most West Nile Virus case reports come from the south of Portugal, but the potential distribution of Culex pipiens covers the whole mainland territory, with seemingly higher distribution on the north coast. However, bias due to convenience sampling could be present. West Nile vector surveillance should be a priority across all regions to accurately assess transmission risk and implement effective control measures. Key messages • The suitability of Culex pipiens distribution covers the entire mainland of Portugal, with higher presence in the north. • The distribution of Culex pipiens in the country points towards a need for developing region-specific strategies regarding West Nile Virus prevention and control.

Left atrial function for predicting atrial fibrillation: a machine learning approach

Abstract Background Common risk scores for atrial fibrillation (AF) are based on clinical risk factors, and little is known about the contribution of left atrial (LA) function parameters to risk assessment. Purpose To determine whether a machine learning-based model that includes LA function parameters outperforms a commonly used risk scores, the CHARGE-AF, in predicting AF. Methods Participants in the Atherosclerosis Risk in Communities (ARIC) Study (a community-based cohort study in the USA) who attended visit 5 (2011-2013) and without a history of AF were included in this analysis. Incident AF was ascertained from ECG during study visits, hospitalization discharge codes, and death certificates. Random survival forest methodology was used to build a prediction model for AF; candidate variables included traditional clinical risk factors and measures of LA structure and function. Dataset was randomly split into a training (75%) and testing (25%) cohort. For each variable, importance was measured based on the reduction in Harrell’s c-statistic if the variable was randomly permuted prior to obtaining predicted values. Variables significantly associated with AF were included in the final model. Model calibration was tested using the Greenwood-Nam-D'Agostino (GND) test. Discrimination ability of our model was assessed in the testing cohort using Harrell’s c-statistic and compared with that of the CHARGE-AF model. Results 4,909 participants (59% female, 22% Black, mean age 75 years) were included. During a median follow-up of 7.0 years (1st-3rd quartile: 5.4-7.8), 650 participants developed AF. Variables significantly associated with incident AF were race, triglycerides, systolic blood pressure, coronary heart disease, age, LA maximal volume index, NT-proBNP, and LA reservoir strain (Figure 1). LA reservoir strain was the variable most strongly associated with AF. The final model showed good discrimination (c-statistic (95% CI): 0.734 (0.698-0.768)). The GND test did not detect statistically significant miscalibration (GND p=0.08). Compared with our model, CHARGE-AF had lower discrimination ability (c-statistic (95% CI): 0.635 (0.594-0.677)) and the difference was statistically significant (difference in c-statistic (95% CI): 0.099 (0.043-0.152)). Conclusion A machine learning-based model with few variables including LA function outperforms a commonly used risk model in predicting AF. Incorporation of LA functional assessment may help inform clinicians regarding individual risk of AF.

Wellbeing at the Edges of Ownership: The Impacts of Housing Tenure, Social Capital, and Institutions

This paper presents a three-country analysis of the relationship between housing, indicators of social capital, and life satisfaction (a proxy for wellbeing) using cross-sectional data from national surveys in Australia, the UK and the US. Addressing a key policy debate we construct, for each jurisdiction, models of life satisfaction as a function of housing and social capital, with a view to better targeting policy interventions to achieve wellbeing outcomes. First, we show that the wellbeing premium associated with homeownership remains resilient despite housing market instability early in the 21st century. Second, we find that while social capital is an important independent positive influence on life satisfaction, its effect is weaker than that of housing tenure. Third, we include two measures of housing risk (repayment risk and investment risk). These position mortgagors on a continuum across the edges of owner-occupation, placing them closer to, or further from, the ‘ideal’ of outright ownership. The impact of these measures varies across the three countries, raising questions about the way life satisfaction at the edges of ownership may be shaped by institutional forces.

Entropy-Based Volatility Analysis of Financial Log-Returns Using Gaussian Mixture Models.

Volatility in financial markets refers to the variation in asset prices over time. High volatility indicates increased risk, making its evaluation essential for effective risk management. Various methods are used to assess volatility, with the standard deviation of log-returns being a common approach. However, this implicitly assumes that log-returns follow a Gaussian distribution, which is not always valid. In this paper, we explore the use of (differential) entropy to evaluate the volatility of financial log-returns. Estimation of entropy is obtained using a Gaussian mixture model to approximate the underlying density of log-returns. Following this modeling approach, popular risk measures such as Value at Risk and Expected Shortfall can also be computed. By integrating Gaussian mixture modeling and entropy into the analysis of log-returns, we aim to provide a more accurate and robust framework for assessing financial volatility and risk measures.

Association Between Early-Life and Premorbid Measurements of Body Composition and Risk of Motor Neuron Disease: A Prospective Cohort Study in the UK Biobank.

The objective of this study was to investigate the association between developmental and premorbid body composition measurements and the risk of motor neuron disease (MND). We performed a cohort study in the UK Biobank to assess the association of developmental body metrics and premorbid body composition measures (using 28 measurements and 7 patterns of body composition) with the risk of MND. Among participants with longitudinal measures, we compared the changes in body composition over time between individuals who later developed MND and those who remained free of MND. Among the 412,691 individuals included in this study, 549 people received an MND diagnosis during the follow-up visit. Higher birth weight was associated with an increased risk of MND among individuals born over 4 kg (hazard ratio [HR] per kg increase = 2.21, 95% confidence interval [CI] = 1.38-3.55), and taller adult height was associated with an increased risk of MND (HR per 5 cm increase = 1.10, 95% CI = 1.03-1.17). We observed that measures of elevated fat mass were associated with a lower risk of MND more than 5 years before diagnosis. A higher "leg-dominant fat distribution" pattern was associated with an increased risk whereas higher "muscle strength" was associated with a reduced risk of MND 5 years before diagnosis. Longitudinal analyses indicated a faster decline in measures of fat mass and muscle strength, as well as a shift in fat distribution from arm to leg dominant, among individuals who later developed MND, compared with others. Body composition at early and middle age may be indicative of the risk of MND development. ANN NEUROL 2024.

Is firm-level political risk priced in the corporate bond market?

We investigate whether political risk is priced in the cross-section of corporate bond returns by using a text-based measure of firm-level political risk. We document a positive and significant political risk premium after controlling for bond and firm characteristics, conventional risk factors, and exposure to aggregate economic policy uncertainty. Bonds with higher political and credit risk, as well as smaller, more illiquid, and longer maturity corporate bonds exhibit a larger political risk premium. Time-series analysis indicates that monetary policy shocks and common shocks in the equity and bond market exhibit a statistically significant and positive association with the political risk premium. Our findings reveal the importance of idiosyncratic political risk beyond common risk factors and aggregate economic policy uncertainty.

Developing Independent Living Support for Older Adults Using Internet of Things and AI-Based Systems: Co-Design Study.

The number of older people with unmet health care and support needs is increasing substantially due to the challenges facing health care systems worldwide. There are potentially great benefits to using the Internet of Things coupled with artificial intelligence to support independent living and the measurement of health risks, thus improving quality of life for the older adult population. Taking a co-design approach has the potential to ensure that these technological solutions are developed to address specific user needs and requirements. The aim of this study was to investigate stakeholders' perceptions of independent living and technology solutions, identify stakeholders' suggestions on how technology could assist older adults to live independently, and explore the acceptability and usefulness of a prototype Internet of Things solution called the NEX system to support independent living for an older adult population. The development of the NEX system was carried out in 3 key phases with a strong focus on diverse stakeholder involvement. The initial predesign exploratory phase recruited 17 stakeholders, including older adults and family caregivers, using fictitious personas and scenarios to explore initial perceptions of independent living and technology solutions. The subsequent co-design and testing phase expanded this to include a comprehensive web-based survey completed by 380 stakeholders, encompassing older adults, family caregivers, health care professionals, and home care support staff. This phase also included prototype testing at home by 7 older adults to assess technology needs, requirements, and the initial acceptability of the system. Finally, in the postdesign phase, workshops were held between academic and industry partners to analyze data collected from the earlier stages and to discuss recommendations for the future development of the system. The predesign phase revealed 3 broad themes: loneliness and technology, aging and technology, and adopting and using technology. The co-design phase highlighted key areas where technology could assist older adults to live independently: home security, falls and loneliness, remote monitoring by family members, and communication with clients. Prototype testing revealed that the acceptability aspects of the prototype varied across technology types. Ambient sensors and voice-activated assistants were described as the most acceptable technology by participants. Last, the postdesign analysis process highlighted that ambient sensors have the potential for automatic detection of activities of daily living, resulting in key recommendations for future developments and deployments in this area. This study demonstrates the significance of incorporating diverse stakeholder perspectives in developing solutions that support independent living. Additionally, it emphasizes the advantages of prototype testing in home environments, offering crucial insights into the real-world experiences of users interacting with technological solutions.

Turkish adaptation of the antenatal risk questionnaire-revised: study of validity and reliability

ObjectiveThis study sought to translate the Antenatal Risk Questionnaire-Revised (ANRQ-R) into Turkish and evaluate its psychometric properties for assessing psychosocial vulnerabilities among Turkish-speaking pregnant women.MethodsThe ANRQ-R was translated into Turkish following standard linguistic adaptation procedures. Psychometric properties were then examined using a cross-sectional study design, involving 156 pregnant women recruited from antenatal clinics in Turkey. Participants completed the Turkish ANRQ-R and the Edinburgh Postnatal Depression Scale (EPDS). Statistical analyses, including intraclass correlation coefficients (ICC) for test-retest reliability, Pearson correlation analysis for item-total test correlations, and Receiver Operating Characteristic (ROC) analysis for diagnostic accuracy, were conducted.ResultsThe ANRQ-R Turkish version showed high test-retest reliability with an ICC of 0.888 for the total score. Moderate to good ICCs were obtained for individual Likert-type items (0.572–0.849). Criterion-related validity was established via moderate correlations with the EPDS and its anxiety subscale EPDS-3 A (r = 0.537 and r = 0.431, respectively). ROC analysis demonstrated good discriminatory power (Area Under the Curve = 0.75) with an optimal cut-off score of 17, yielding 78% sensitivity and 65% specificity for identifying potential depression cases.ConclusionsThis study provides preliminary evidence of the overall reliability of the Turkish version of the ANRQ-R as a measure of psychosocial risk among Turkish-speaking women during pregnancy. Despite some limitations in item-level internal consistency indicators, integration of the ANRQ-R into routine antenatal care could enhance early identification and intervention strategies, potentially improving maternal health outcomes. Future research should aim to further validate the scale across diverse populations and settings, using a diagnostic tool as the reference standard.

Identification of allele-specific KIV-2 repeats and impact on Lp(a) measurements for cardiovascular disease risk

The abundance of Lp(a) protein holds significant implications for the risk of cardiovascular disease (CVD), which is directly impacted by the copy number (CN) of KIV-2, a 5.5 kbp sub-region. KIV-2 is highly polymorphic in the population and accurate analysis is challenging. In this study, we present the DRAGEN KIV-2 CN caller, which utilizes short reads. Data across 166 WGS show that the caller has high accuracy, compared to optical mapping and can further phase approximately 50% of the samples. We compared KIV-2 CN numbers to 24 previously postulated KIV-2 relevant SNVs, revealing that many are ineffective predictors of KIV-2 copy number. Population studies, including USA-based cohorts, showed distinct KIV-2 CN, distributions for European-, African-, and Hispanic-American populations and further underscored the limitations of SNV predictors. We demonstrate that the CN estimates correlate significantly with the available Lp(a) protein levels and that phasing is highly important.

Short Communication: On the Separability of Vector-Valued Risk Measures

Short Communication: On the Separability of Vector-Valued Risk Measures