Claims databases consisting of routinely collected longitudinal records of medical expenditures are increasingly utilized for estimating expected medical costs of patients with a specific condition. Survival data of the patients of interest are usually highly censored, and observed expenditures are incomplete. In this study, we propose a survival-adjusted estimator for estimating mean lifetime costs, which integrates the product of the survival function and the mean cost function over the lifetime horizon. The survival function is estimated by a new algorithm of rolling extrapolation, aided by external information of age- and sex-matched referents simulated from national vital statistics. The mean cost function is estimated by a weighted average of mean expenditures of patients in a number of months prior to their death, of which the number could be determined by observed costs in their final months, and the weights depend on extrapolated hazards. We evaluate the performance of the proposed approach in comparison with that of a popular method using simulated data under various scenarios and 2 cohorts of intracerebral hemorrhage and ischemic stroke patients with a maximum follow-up of 13 years and conclude that our new method estimates the mean lifetime costs more accurately.

Epidemiological studies have shown that particulate matter (PM) air pollution is associated with cardiovascular mortality and morbidity, especially for particles with aerodynamic diameters under 2.5 microm (PM(2.5)). Recent studies have revealed an association between PM pollution and autonomic functions including heart rate (HR), blood pressure (BP), and heart-rate variability. However, the association and linking mechanisms have not been clearly demonstrated in animal studies. Utilizing a novel approach that employs a mixed-effects model to overcome the problems of variations in diseased animals and circadian cycles, we have previously demonstrated an association between concentrated PM(2.5) and changes of HR and BP in pulmonary hypertensive rats. The objective of this study is to test the plausibility of this methodology and to demonstrate the particle effects under different pathophysiology. The feasibility of cardiac contractility (measured as QA interval, QAI) as an indicator for PM toxicology was also explored. Four spontaneously hypertensive (SH) rats were repeatedly exposed to concentrated PM(2.5) during spring and summer. The mass concentration of particles during the 5 h of exposure was 202.0 +/- 68.8 (mean +/- SE) and 141.0 +/- 54.9 microg/m(3) for spring and summer experiments, respectively. During spring exposures, the maximum increase of HR and mean BP noted at the end of exposure were 51.6 bpm (p <.001) and 8.7 mm Hg (p =.002), respectively. The maximum decrease of QAI noted at the same time was 1.6 ms (p =.001). Though a similar pattern was demonstrated during summer exposures, the responses were less prominent. We conclude that concentrated PM(2.5) may increase HR and mean BP and decrease QAI in SH rats. Our results also show that QAI may be used as an indicator in PM toxicology.

BackgroundInstead of traditional statistical models for large spatial areas and weekly or monthly temporal units, what public health workers urgently need is a timely risk prediction method for small areas. This risk prediction would provide information for early warning, target surveillance and intervention.MethodsDaily dengue cases in the 457 urban villages of Kaohsiung City, Taiwan from 2009 to 2012 were used for model development and evaluation. There were in total 2,997 confirmed dengue cases during this period. A logistic regression model was fitted to the daily incidents occurring in the villages for the past 30 days. The fitted model was then used to predict the incidence probabilities of dengue outbreak for the villages the next day. A percentile of the 457*30 fitted incidence probabilities was chosen to determine a cut-point for issuing the alerts. The covariates included three different levels of spatial effect, and with four lag time periods. The population density and the meteorological conditions were also included for the prediction.ResultsThe performance of the prediction models was evaluated on 122 consecutive days from September 1 to December 31, 2012. With the 80th percentile threshold, the median sensitivity was 83% and the median false positive rate was 23%. We found that most of the coefficients of the predictors of having cases at the same village in the previous 14 days were positive and significant for the 122 daily updated models. The estimated coefficients of population density were significant during the peak of the epidemic in 2012.ConclusionsThe proposed method can provide near real-time dengue risk prediction for a small area. This can serve as a useful decision making tool for front-line public health workers to control dengue epidemics. The precision of the spatial and temporal units can be easily adjusted to different settings for different cities.

ObjectivesThis paper examines how people express personal mood concurrently with those connected with them by one or two degrees of separation.DesignParticipatory cohort study.SettingOnline contact diary.Participants133 participants kept online diaries for 7 months in 2014, which included 127 455 contacts with 12 070 persons.Main outcome measuresDiary keepers rated a contacted person’s mood during each specific contact, as well as the strength of ties between any pairs of such contacted persons. Such rich information about ties and contacts enable us to construct a complete contact network for each diary keeper, along with the network members’ mood and tie strength. We calculate one’s overall mood by that person’s average mood score during the study period and take the shortest path between any given pair of contacted persons as the degree of separation. We further assume that two connecting persons in a contact network have made contact with each other during the study period, which allows us to examine whether and how personal moods occur concurrently within these contact networks.ResultsUsing mixed-effects models while controlling for covariates at individual, tie and contact levels, we show that personal mood score positively and significantly correlates with the average mood among those directly tied to the person. The same effect remains positive and significant for those connected to the person by two degrees, although the effect size is reduced by about one-half. The mood of anyone separated by more than two degrees is statistically irrelevant.ConclusionsApplying network perspectives and rich data at both tie and contact levels to inquiries about subjective well-being, the current study sheds new light on how an improved diary approach can help explain the sophisticated ways in which individuals express their personal moods concurrently during social interactions in everyday life, contact by contact.

We propose a new stepwise regression algorithm with a simple stopping rule for the identification of influential predictors and interactions among a huge number of variables in various statistical models. Like conventional stepwise regression, at each forward selection step, a variable is included in the current model if the test statistic of the enlarged model with the predictor against the current model has the minimum p-value among all the candidates and is smaller than a predetermined threshold. Instead of using conventional information types of criteria, the threshold is determined by a lower percentile of the beta distribution. We conducted extensive simulation studies to evaluate the performance of the proposed algorithm for various statistical models and found it to be very competitive and robust compared with several popular high-dimensional variable selection methods.