Hearing loss is a modifiable risk factor for dementia in older adults. Whether hearing aid use can delay the onset of cognitive decline is unknown. Participants in this study (aged 62–82 years) were assessed before and 18 months after hearing aid fitting on hearing, cognitive function, speech perception, quality of life, physical activity, loneliness, isolation, mood, and medical health. At baseline, multiple linear regression showed hearing loss and age predicted significantly poorer executive function performance, while tertiary education predicted significantly higher executive function and visual learning performance. At 18 months after hearing aid fitting, speech perception in quiet, self-reported listening disability and quality of life had significantly improved. Group mean scores across the cognitive test battery showed no significant decline, and executive function significantly improved. Reliable Change Index scores also showed either clinically significant improvement or stability in executive function for 97.3% of participants, and for females for working memory, visual attention and visual learning. Relative stability and clinically and statistically significant improvement in cognition were seen in this participant group after 18 months of hearing aid use, suggesting that treatment of hearing loss with hearing aids may delay cognitive decline. Given the small sample size, further follow up is required.

Function is an important marker of health throughout the life course, however, in low-and-middle-income-countries, little is known about the burden of functional impairment as women transition from pregnancy to the first year post-partum. Leveraging longitudinal data from 960 women participating in the Share Child Cohort in Pakistan, this study sought to (1) characterize functional trajectories over time among women in their perinatal period and (2) assess predictors of chronic poor functioning following childbirth. We used a group-based trajectory modeling approach to examine maternal patterns of function from the third trimester of pregnancy through 12 months post-partum. Three trajectory groups were found: persistently well-functioning (51% of women), poor functioning with recovery (39% of women), and chronically poor functioning (10% of women). When compared to mothers in the highest functioning group, psychosocial characteristics (e.g., depression, stress, and serious life events) were significantly associated with sustained poor functioning one-year following child-birth. Mothers living in nuclear households were more likely to experience chronic poor functioning. Higher education independently predicted maternal function recovery, even when controlling for psychosocial characteristics. Education, above and beyond socio-economic assets, appears to play an important protective role in maternal functional trajectories following childbirth. Public health implications related to maternal function and perinatal mental health are discussed.

The class of spectrally positive Lévy processes is a frequent choice for modelling loss processes in areas such as insurance or operational risk. Dependence between such processes (e.g. between different lines of business) can be modelled with Lévy copulas. This approach is a parsimonious, efficient and flexible method which provides many of the advantages akin to distributional copulas for random variables. Literature on Lévy copulas seems to have primarily focussed on bivariate processes. When multivariate settings are considered, these usually exhibit an exchangeable dependence structure (whereby all subset of the processes have an identical marginal Lévy copula). In reality, losses are not always associated in an identical way, and models allowing for non-exchangeable dependence patterns are needed. In this paper, we present an approach which enables the development of such models. Inspired by ideas and techniques from the distributional copula literature we investigate the procedure of nesting Archimedean Lévy copulas. We provide a detailed analysis of this construction, and derive conditions under which valid multivariate (nested) Lévy copulas are obtained. Our results are discussed and illustrated, notably with an example of model fitting to data.

This paper is concerned with dependency between business segments in the non-life insurance industry. When considering the business of an insurance company at the aggregate level, dependence structures can have a major impact in several areas of Enterprise Risk Management, such as in claims reserving and capital modelling. The accurate estimation of the diversification benefits related to the dependence structures between lines of business (LoBs) is crucial for (i) capital efficiency, as one should avoid holding unnecessarily high levels of capital, and (ii) solvency of the insurance company, as an underestimation, on the other hand, may lead to insufficient capitalisation and safety. There seems to be a great deal of preconception as to how dependent insurance claims should be. Often, presence of dependence is taken as a given and rarely discussed or challenged, perhaps because of the lack of extensive datasets to be publicly analysed. In this paper, we take a different approach, and consider how much correlation some real datasets actually display (the Meyers–Shi dataset from the USA, and the AUSI dataset from Australia). We develop a simple theoretical framework that enables us to explain how and why correlations can be illusory (and what we mean by that). We show with some real examples that, sometimes, most (if not all) of the correlation can be “explained” by an appropriate methodology. Two major conclusions stem from our analysis. 1.In any attempt to measure cross-LoB correlations, careful modelling of the data needs to be the order of the day. The exercise will not be well served by rough modelling, such as the use of simple chain ladders, and may indeed result in the prescription of excessive risk margins and/or capital margins. 2.Such empirical evidence as examined in the paper reveals cross-LoB correlations that vary only in the range zero to very modest. There is little evidence in favour of the high correlation assumed in some jurisdictions. The evidence suggests that these assumptions derived from either poor modelling or a misconception of the cross-LoB dependencies relevant to the purpose to which they are applied.

US intelligence analysts must weigh up relevant evidence to assess the probability of their conclusions, and express this reasoning clearly in written reports for decision-makers. Typically, they work alone with no special analytic tools, and sometimes succumb to common probabilistic and causal reasoning errors. So, the US government funded a major research program (CREATE) for four large academic teams to develop new structured, collaborative, software-based methods that might achieve better results. Our team's method (BARD) is the first to combine two key techniques: constructing causal Bayesian network models (BNs) to represent analyst knowledge, and small-group collaboration via the Delphi technique. BARD also incorporates compressed, high-quality online training allowing novices to use it, and checklist-inspired report templates with a rudimentary AI tool for generating text explanations from analysts' BNs. In two prior experiments, our team showed BARD's BN-building assists probabilistic reasoning when used by individuals, with a large effect (Glass' Δ 0.8) (Cruz et al., 2020), and even minimal Delphi-style interactions improve the BN structures individuals produce, with medium to very large effects (Glass' Δ 0.5-1.3) (Bolger et al., 2020). This experiment is the critical test of BARD as an integrated system and possible alternative to business-as-usual for intelligence analysis. Participants were asked to solve three probabilistic reasoning problems spread over 5 weeks, developed by our team to test both quantitative accuracy and susceptibility to tempting qualitative fallacies. Our 256 participants were randomly assigned to form 25 teams of 6-9 using BARD and 58 individuals using Google Suite and (if desired) the best pen-and-paper techniques. For each problem, BARD outperformed this control with very large to huge effects (Glass' Δ 1.4-2.2), greatly exceeding CREATE's initial target. We conclude that, for suitable problems, BARD already offers significant advantages over both business-as-usual and existing BN software. Our effect sizes also suggest BARD's BN-building and collaboration combined beneficially and cumulatively, although implementation differences decreased performances compared to Cruz et al. (2020), so interaction may have contributed. BARD has enormous potential for further development and testing of specific components and on more complex problems, and many potential applications beyond intelligence analysis.

INTRODUCTION: Early childhood interventions primarily focus on the mother-child relationship, but grandmothers are often critical in childcare in low-resource settings. Prior research is mixed on how grandmother involvement influences child outcomes and there is a paucity of research on grandmother caregiving in low-income and middle-income countries. We examined the role of grandmother involvement on child growth and development in the first 2 years of life cross sectionally and longitudinally in rural Pakistan. METHODS: We used data from the Bachpan Cohort, a longitudinal birth cohort in rural Pakistan. Maternally reported grandmother involvement in daily instrumental and non-instrumental caregiving was collected at 3 and 12 months. A summed score was created and categorised into non-involved, low and high. Outcomes included 12-month and 24-month child growth, 12-month Bayley Scales of Infant and Toddler Development and 24-month Ages and Stages Questionnaire-Socioemotional. We used multivariable generalised linear models to estimate mean differences (MD) at 12 months (n=727) and 24 months (n=712). Inverse probability weighting was used to account for missingness and sampling. RESULTS: In our sample, 68% of children lived with a grandmother, and most grandmothers were involved in caregiving. Greater 3-month grandmother involvement was positively associated with 12-month weight z-scores; however, greater involvement was associated with lower 24-month weight z-scores. High 12-month grandmother involvement was associated with improved 12-month cognitive (MD=0.38, 95% CI -0.01 to 0.76), fine motor skills (MD=0.45, 95% CI 0.08 to 0.83) and 24-month socioemotional development (MD=-17.83, 95% CI -31.47 to -4.19). No meaningful associations were found for length z-scores or language development. CONCLUSION: In rural Pakistan, grandmothers provide caregiving that influences early child development. Our findings highlight the complex relationship between grandmother involvement and child weight, and suggest that grandmothers may positively promote early child cognitive, fine motor and socioemotional development. Understanding how grandmother involvement affects child outcomes in early life is necessary to inform caregiving interventions.

In the classical optimal dividends problem, dividend decisions are allowed to be made at any point in time — according to a continuous strategy. Depending on the surplus process that is considered and whether dividend payouts are bounded or not, optimal strategies are generally of a band, barrier or threshold type. In reality, while surpluses change continuously, dividends are generally paid on a periodic basis. Because of this, the actuarial literature has recently considered strategies where dividends are only allowed to be distributed at (random) discrete times — according to a periodic strategy. In this paper, we focus on the Brownian risk model. In this context, the optimal continuous and periodic strategies have previously been shown (independently of one another) to be of barrier type. For the first time, we consider a model where both strategies are used. In such a hybrid strategy, decisions are allowed to be made either at any time (continuously), or periodically at a lower cost. This proves optimal in some cases. We also determine under which combination of parameters a pure continuous, pure periodic or hybrid (including both continuous and periodic dividend payments) barrier strategy is optimal. Interestingly, the hybrid strategy lies in-between periodic and continuous strategies, which provides some interesting insights. Results are illustrated.

The accurate estimation of outstanding liabilities of an insurance company is an essential task. This is to meet regulatory requirements, but also to achieve efficient internal capital management. Over the recent years, there has been increasing interest in the utilisation of insurance data at a more granular level, and to model claims using stochastic processes. So far, this so-called ‘micro-level reserving’ approach has mainly focused on the Poisson process. In this paper, we propose and apply a Cox process approach to model the arrival process and reporting pattern of insurance claims. This allows for over-dispersion and serial dependency in claim counts, which are typical features in real data. We explicitly consider risk exposure and reporting delays, and show how to use our model to predict the numbers of Incurred-But-Not-Reported (IBNR) claims. The model is calibrated and illustrated using real data from the AUSI data set.

Stochastic loss reserving with dependence has received increased attention in the last decade. A number of parametric multivariate approaches have been developed to capture dependence between lines of business within an insurer’s portfolio. Motivated by the richness of the Tweedie family of distributions, we propose a multivariate Tweedie approach to capture cell-wise dependence in loss reserving. This approach provides a transparent introduction of dependence through a common shock structure. In addition, it also has a number of ideal properties, including marginal flexibility, transparency, and tractability including moments that can be obtained in closed form. Theoretical results are illustrated using both simulated data sets and a real data set from a property-casualty insurer in the US.

Because of the profitable nature of risk businesses in the long term, de Finetti suggested that surplus models should allow for cash leakages, as otherwise the surplus would unrealistically grow (on average) to infinity. These leakages were interpreted as ‘dividends’. Subsequent literature on actuarial surplus models with dividend distribution has mainly focussed on dividend strategies that either maximise the expected present value of dividends until ruin or lead to a probability of ruin that is less than one (see Albrecher and Thonhauser, Avanzi for reviews). An increasing number of papers are directly interested in modelling dividend policies that are consistent with actual practice in financial markets. In this short note, we review the corporate finance literature with the specific aim of fleshing out properties that dividend strategies should ideally satisfy, if one wants to model behaviour that is consistent with practice.

This paper describes an approach to making fiscal policy decisions based on probabilistic statements on the likely occurrence of events as specified in a rules‐based framework for making fiscal adjustments. The event probability forecasts are obtained from a simple time series econometric model of the key variables influencing debt dynamics (interest rates, output and debt itself). The approach is applied to data for ten developed countries for 1956–2016 and the analysis demonstrates the importance of accommodating international linkages in forecasting, noting that failure to do so would have led to excessive fiscal cutbacks and austerity in recent years.

This paper describes a fiscal database for Australia including measures of government spending, revenue, deficits, debt and various sub‐aggregates as initially published and subsequently revised. The data vintages are collated from various sources and provide a comprehensive description of the Australian fiscal environment as experienced in real time. Methods are described which exploit the richness of the real‐time data sets, and they are illustrated through an analysis of the extent to which stated fiscal plans are realised in practice and through the estimation of fiscal multipliers which draw a distinction between policy responses and policy initiatives. We find predictable differences between plans and actual fiscal policy and a larger multiplier for policy initiatives than for implementation errors.