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OBJECTIVES: To evaluate the association between the Drug Burden Index (DBI), a measure of a person's total exposure to anticholinergic and sedative medications that includes principles of dose-response and maximal effect and is associated with impaired physical function in community-dwelling older people, and falls in residents of residential aged care facilities (RACFs). DESIGN: Data were drawn from participants in a randomized controlled trial that investigated falls and fractures. SETTING: RACFs in Sydney, Australia. PARTICIPANTS: Study participants (N=602; 70.9% female) were recruited from 51 RACFs. Mean age was 85.7 ± 6.4, and mean DBI was 0.60 ± 0.66. MEASUREMENTS: Medication history was obtained on each participant. Drugs were classified as anticholinergic or sedative and a DBI was calculated. Falls were measured over a 12-month period. Comorbidity, cognitive impairment (Mini-Mental State Examination) and depression (Geriatric Depression Scale) were determined. RESULTS: There were 998 falls in 330 individuals during a follow-up period of 574.2 person-years, equating to an average rate of 1.74 falls per person-year. The univariate negative binomial regression model for falls showed incidence rate ratios of 1.69 (95% confidence interval (CI)=1.22-2.34) for low DBI ( Language: en

PurposeThe solvency of insurance companies is closely related to the policyholders, and consequently regulators in Taiwan pay considerable attention to this area. Several studies have demonstrated a close correlation among capital structure, operational risk and profitability. This study seeks to provide evidence regarding the influence of capital structure and operational risk on profitability of the life insurance industry in Taiwan.Design/methodology/approachStructural equation modeling, which involves factor‐analysis and path‐analysis, is used to justify the relationship among capital structure, operational risk and profitability. Adding the macroeconomic latent variable to the model as a control variable demonstrates that the macroeconomic latent variable positively influences capital structure, operational risk and profitability.FindingsThe study leads to four key findings. First, according to the empirical result, the research model has excellent goodness‐of‐fit. That is to say, using multiple financial indices suitably measures the specific financial factors. Second, the capital structure exerts a negative and significant effect on operational risk. Third, there is no reciprocal relationship but a one‐way effect between capital structure and operational risk. Fourth, the operational risk exerts a negative and significant effect on profitability.Practical implicationsThe empirical result shows the profitability decreased with the higher equity ratio. Hence, the regulatory organizations must urge insurance companies to effectively diversify their investments and employ risk avoidance strategies. Effective use of hedging and diversifying will help to divide risk and create financial revenue.Originality/valueThe study proposes that the government should loosen investment restrictions and develop other instruments to assist risk‐based capital in checking the financial condition of insurance companies.

Likelihood profiles for arbitrary functions of the model parameters are useful for constructing likelihood ratio confidence intervals, diagnosing linear approximation intervals, suggesting linearizing transforms, and many other purposes. This article investigates a simple integration method for producing accurate approximations to likelihood profiles that avoid problems associated with producing exact profiles. A basic theorem guarantees that the approximation can achieve any desired precision. In many cases the method requires no more than adding a few lines of code to that required to produce the maximum likelihood estimate of the parameter vector. Standard methods for computing likelihood profiles are based on solving a sequence of constrained maximum likelihood problems. Each problem generates a point on the profile. An earlier article proposed an integration method that generates the entire profile directly by solving a differential equation. That method, however, requires the Hessian of the log-like...

Objectives: To investigate longitudinal associations between changes in brain structure and gait decline. Design: Longitudinal. Setting: Population-based Tasmanian Study of Cognition and Gait. Participants: Two hundred twenty-five individuals aged 60 to 86 (mean age 71.4 ± 6.8) randomly selected from the electoral roll with baseline and follow-up data. Measurements: Volumes of gray matter, white matter, hippocampi, and white matter lesions (WML) were estimated using automated segmentation from magnetic resonance imaging (MRI). Gait variables were measured using a computerized walkway. Linear regression was used to estimate the association between change in brain MRI measures and change in gait. Time between measurements, age, sex, BMI, education level, total intracranial volume, baseline infarcts, and medical history were used as baseline covariates. Results: Mean follow-up was 30.6 months. White matter atrophy was associated with a decline in gait speed (P = .001), step length (P = .005), and cadence (P = .001). WML progression was associated with a decline in gait speed (P = .04), and its association with decline in step length was stronger with greater baseline age (P for interaction = .04). Hippocampal atrophy was associated with a decline in gait speed (P = .006) and step length (P = .001). Total gray matter atrophy was associated with decline in cadence in those with cerebral infarcts (P for interaction = .02). Conclusion: These are the first longitudinal data demonstrating the relative contributions of brain atrophy and WML progression to gait decline in older people. Effect modification according to age and infarcts suggests a contribution of reduced physiological and brain reserve. Interventions targeting brain health may be important in preventing mobility decline in older people. © 2013, The American Geriatrics Society.

We investigate a natural extension of the profile t plot of Bates and Watts to a general parametric function g(θ) of the parameters θ in a general maximum likelihood analysis. Although the basic purpose of the extension, called the signed root deviance profile (SRDP), is to construct likelihood ratio (LR) confidence intervals for g(θ), it has various other applications that significantly extend its usefulness. The tangent to the plot of the SRDP at the maximum likelihood estimate ĝ of g(θ) gives the linear approximation (LA) interval based on the observed information matrix. The plot may be used as a diagnostic tool to compare LA and LR intervals and to suggest transformations of g(θ) whose LA intervals when inverted are close to the LR intervals for g(θ). The standard way to construct any profile is through repeated optimizations, but problems associated with nonlinear constraints can make this difficult. An alternative method based on integration is presented that avoids these problems. An exam...

We investigate linear approximation (LA) confidence intervals for functions g(θ) of the parameters θ in a nonlinear regression model. These intervals are almost universally used and generally perform well, but at times have poor coverage probabilities. Using gradient direction plots, we identify transformations of g(θ) that lead to more accurate LA intervals. These include power transformations, whose effectiveness is demonstrated in a variety of nonlinear regression problems via a simulation study. Finally, we show how to use profile t plots and bias indices to suggest transforms to improve LA intervals. The idea is to find a monotone transformation T such that the linearization confidence interval for T(g(θ)) has coverage probability close to its nominal value and then invert this interval to give an accurate interval for g(θ). The transformation T is obtained from a gradient direction plot that may be thought of as an attempt to view the graph of the estimator ĝ of g(θ) in two dimensions. We d...

We investigate linear approximation (LA) confidence intervals for functions g(θ) of the parameters θ in a nonlinear regression model. These intervals are almost universally used and generally perform well, but at times they have poor coverage probabilities. A diagnostic plot and index are developed to detect these failures. We show how these diagnostics may be used to estimate coverage probabilities and these are used to calibrate the diagnostics. The performance of the coverage probability estimates in a variety of nonlinear regression problems is investigated via simulation; for these problems, they work quite well. Conditions are identified under which the estimates are exact. Finally, we discuss the use of the profile t plot and asymmetry and bias indices as diagnostics for LA intervals and show how to calibrate them in terms of coverage probabilities.

BACKGROUND: Quantification of the disease burden caused by different risks informs prevention by providing an account of health loss different to that provided by a disease-by-disease analysis. No complete revision of global disease burden caused by risk factors has been done since a comparative risk assessment in 2000, and no previous analysis has assessed changes in burden attributable to risk factors over time. METHODS: We estimated deaths and disability-adjusted life years (DALYs; sum of years lived with disability [YLD] and years of life lost [YLL]) attributable to the independent effects of 67 risk factors and clusters of risk factors for 21 regions in 1990 and 2010. We estimated exposure distributions for each year, region, sex, and age group, and relative risks per unit of exposure by systematically reviewing and synthesising published and unpublished data. We used these estimates, together with estimates of cause-specific deaths and DALYs from the Global Burden of Disease Study 2010, to calculate the burden attributable to each risk factor exposure compared with the theoretical-minimum-risk exposure. We incorporated uncertainty in disease burden, relative risks, and exposures into our estimates of attributable burden. FINDINGS: In 2010, the three leading risk factors for global disease burden were high blood pressure (7·0% [95% uncertainty interval 6·2-7·7] of global DALYs), tobacco smoking including second-hand smoke (6·3% [5·5-7·0]), and alcohol use (5·5% [5·0-5·9]). In 1990, the leading risks were childhood underweight (7·9% [6·8-9·4]), household air pollution from solid fuels (HAP; 7·0% [5·6-8·3]), and tobacco smoking including second-hand smoke (6·1% [5·4-6·8]). Dietary risk factors and physical inactivity collectively accounted for 10·0% (95% UI 9·2-10·8) of global DALYs in 2010, with the most prominent dietary risks being diets low in fruits and those high in sodium. Several risks that primarily affect childhood communicable diseases, including unimproved water and sanitation and childhood micronutrient deficiencies, fell in rank between 1990 and 2010, with unimproved water and sanitation accounting for 0·9% (0·4-1·6) of global DALYs in 2010. However, in most of sub-Saharan Africa childhood underweight, HAP, and non-exclusive and discontinued breastfeeding were the leading risks in 2010, while HAP was the leading risk in south Asia. The leading risk factor in Eastern Europe, most of Latin America, and southern sub-Saharan Africa in 2010 was alcohol use; in most of Asia, North Africa and Middle East, and central Europe it was high blood pressure. Despite declines, tobacco smoking including second-hand smoke remained the leading risk in high-income north America and western Europe. High body-mass index has increased globally and it is the leading risk in Australasia and southern Latin America, and also ranks high in other high-income regions, North Africa and Middle East, and Oceania. INTERPRETATION: Worldwide, the contribution of different risk factors to disease burden has changed substantially, with a shift away from risks for communicable diseases in children towards those for non-communicable diseases in adults. These changes are related to the ageing population, decreased mortality among children younger than 5 years, changes in cause-of-death composition, and changes in risk factor exposures. New evidence has led to changes in the magnitude of key risks including unimproved water and sanitation, vitamin A and zinc deficiencies, and ambient particulate matter pollution. The extent to which the epidemiological shift has occurred and what the leading risks currently are varies greatly across regions. In much of sub-Saharan Africa, the leading risks are still those associated with poverty and those that affect children. FUNDING: Bill and Melinda Gates Foundation.