[Show abstract][Hide abstract] ABSTRACT: Background
Behavioural weight loss programs are effective first-line treatments for obesity and are recommended by the US Preventive Services Task Force. Gaining an understanding of intervention components that are found helpful by different demographic groups can improve tailoring of weight loss programs. This paper examined the perceived helpfulness of different weight loss program components.Methods
Participants (n = 236) from the active intervention conditions of the Practice-based Opportunities for Weight Reduction (POWER) Hopkins Trial rated the helpfulness of 15 different components of a multicomponent behavioural weight loss program at 24-month follow-up. These ratings were examined in relation to demographic variables, treatment arm and weight loss success.ResultsThe components most frequently identified as helpful were individual telephone sessions (88%), tracking weight online (81%) and coach review of tracking (81%). The component least frequently rated as helpful was the primary care providers' general involvement (50%). Groups such as older adults, Blacks and those with lower education levels more frequently reported intervention components as helpful compared with their counterparts.DiscussionWeight loss coaching delivered telephonically with web support was well received. Findings support the use of remote behavioural interventions for a wide variety of individuals.
[Show abstract][Hide abstract] ABSTRACT: Objective
In behavioral studies of weight loss programs, participants typically receive interventions free of charge. Understanding an individual's willingness to pay (WTP) for weight loss programs could be helpful when evaluating potential funding models. This study assessed WTP for the continuation of a weight loss program at the end of a weight loss study.MethodsWTP was assessed with monthly coaching contacts at the end of the two-year Hopkins POWER trial. Interview-administered questionnaires determined the amount participants were willing to pay for continued intervention. Estimated maximum payment was calculated among those willing to pay and was based on quantile regression adjusted for age, body mass index, race, sex, household income, treatment condition, and weight change at 24 months.ResultsAmong the participants (N = 234), 95% were willing to pay for continued weight loss intervention; the adjusted median payment was $45 per month. Blacks had a higher adjusted median WTP ($65/month) compared to Non-Blacks ($45/month), P = 0.021.ConclusionsA majority of participants were willing to pay for a continued weight loss intervention with a median monthly amount that was similar to the cost of commercial weight loss programs.
[Show abstract][Hide abstract] ABSTRACT: Objective
To evaluate the 3-year incremental cost-effectiveness of fluocinolone acetonide implant versus systemic therapy for the treatment of noninfectious intermediate, posterior, and panuveitis.
Randomized, controlled, clinical trial.
Patients with active or recently active intermediate, posterior, or panuveitis enrolled in the Multicenter Uveitis Steroid Treatment Trial.
Data on cost and health utility during 3 years after randomization were evaluated at 6-month intervals. Analyses were stratified by disease laterality at randomization (31 unilateral vs 224 bilateral) because of the large upfront cost of the implant.
Main Outcome Measures
The primary outcome was the incremental cost-effectiveness ratio (ICER) over 3 years: The ratio of the difference in cost (in United States dollars) to the change in quality-adjusted life-years (QALYs). Costs of medications, surgeries, hospitalizations, and regular procedures (e.g., laboratory monitoring for systemic therapy) were included. We computed QALYs as a weighted average of EQ-5D scores over 3 years of follow-up.
The ICER at 3 years was $297 800/QALY for bilateral disease, driven by the high cost of implant therapy (difference implant - systemic [Δ]: $16 900; P < 0.001) and the modest gains in QALYs (Δ = 0.057; P = 0.22). The probability of the ICER being cost-effective at thresholds of $50 000/QALY and $100 000/QALY was 0.003 and 0.04, respectively. The ICER for unilateral disease was more favorable, namely, $41 200/QALY at 3 years, because of a smaller difference in cost between the 2 therapies (Δ = $5300; P = 0.44) and a larger benefit in QALYs with the implant (Δ = 0.130; P = 0.12). The probability of the ICER being cost-effective at thresholds of $50 000/QALY and $100 000/QALY was 0.53 and 0.74, respectively.
Fluocinolone acetonide implant therapy was reasonably cost-effective compared with systemic therapy for individuals with unilateral intermediate, posterior, or panuveitis but not for those with bilateral disease. These results do not apply to the use of implant therapy when systemic therapy has failed or is contraindicated. Should the duration of implant effect prove to be substantially >3 years or should large changes in therapy pricing occur, the cost-effectiveness of implant versus systemic therapy would need to be reevaluated.
[Show abstract][Hide abstract] ABSTRACT: We address estimation of intervention effects in experimental designs in which (a) interventions are assigned at the cluster level; (b) clusters are selected to form pairs, matched on observed characteristics; and (c) intervention is assigned to one cluster at random within each pair. One goal of policy interest is to estimate the average outcome if all clusters in all pairs are assigned control versus if all clusters in all pairs are assigned to intervention. In such designs, inference that ignores individual level covariates can be imprecise because cluster-level assignment can leave substantial imbalance in the covariate distribution between experimental arms within each pair. However, most existing methods that adjust for covariates have estimands that are not of policy interest. We propose a methodology that explicitly balances the observed covariates among clusters in a pair, and retains the original estimand of interest. We demonstrate our approach through the evaluation of the Guided Care program.
[Show abstract][Hide abstract] ABSTRACT: Websites and phone apps are increasingly used to track weights during weight loss interventions, yet the longitudinal accuracy of these self-reported weights is uncertain.
Journal of Medical Internet Research 07/2014; 16(7):e173. DOI:10.2196/jmir.3332 · 3.43 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Prospective pregnancy studies are a valuable source of longitudinal data on menstrual cycle length. However, care is needed when making inferences of such renewal processes. For example, accounting for the sampling plan is necessary for unbiased estimation of the menstrual cycle length distribution for the study population. If couples can enroll when they learn of the study as opposed to waiting for the start of a new menstrual cycle, then due to length-bias, the enrollment cycle will be stochastically larger than the general run of cycles, a typical property of prevalent cohort studies. Furthermore, the probability of enrollment can depend on the length of time since a woman's last menstrual period (a backward recurrence time), resulting in selection effects. We focus on accounting for length-bias and selection effects in the likelihood for enrollment menstrual cycle length, using a recursive two-stage approach wherein we first estimate the probability of enrollment as a function of the backward recurrence time and then use it in a likelihood with sampling weights that account for length-bias and selection effects. To broaden the applicability of our methods, we augment our model to incorporate a couple-specific random effect and time-independent covariate. A simulation study quantifies performance for two scenarios of enrollment probability when proper account is taken of sampling plan features. In addition, we estimate the probability of enrollment and the distribution of menstrual cycle length for the study population of the Longitudinal Investigation of Fertility and the Environment Study.
[Show abstract][Hide abstract] ABSTRACT: In places where malaria transmission is unstable or is transmitted under hypoendemic conditions, there are periods where limited foci of cases still occur and people become infected. These residual "hot spots" are likely reservoirs of the parasite population and so are fundamental to the seasonal spread and decline of malaria. It is, therefore, important to understand the ecological conditions that permit vector mosquitoes to survive and forage in these specific areas. Features such as local waterways and vegetation, as well as local ecology, particularly nocturnal temperature, humidity, and vegetative sustainability, are important for modeling local mosquito behavior. Vegetation around a homestead likely provides refuge for outdoor resting of these insects and may be a risk factor for malaria transmission. Analysis of this vegetation can be done using satellite information and mapping programs, such as Google Earth, but manual quantification is difficult and can be tedious and subjective. A more objective method is required.
Vegetation cover in the environment is reasonably static, particularly in and around homesteads. In order to evaluate and enumerate such information, ImageJ, an image processing software, was used to analyse Google Earth satellite imagery. The number of plants, total amount of vegetation around a homestead and its percentage of the total area were calculated and related to homesteads where cases of malaria were recorded.
Preliminary results were obtained from a series of field trials carried out in South East Zambia in the Choma and Namwala districts from a base at the Macha District Hospital.
This technique is objective, clear and simple to manipulate and has potential application to determine the role that vegetation proximal to houses may play in affecting mosquito behaviour, foraging and subsequent malaria incidence.
[Show abstract][Hide abstract] ABSTRACT: Background:
Several studies demonstrating that central line-associated bloodstream infections (CLABSIs) are preventable prompted a national initiative to reduce the incidence of these infections.
We conducted a collaborative cohort study to evaluate the impact of the national "On the CUSP: Stop BSI" program on CLABSI rates among participating adult intensive care units (ICUs). The program goal was to achieve a unit-level mean CLABSI rate of less than 1 case per 1,000 catheter-days using standardized definitions from the National Healthcare Safety Network. Multilevel Poisson regression modeling compared infection rates before, during, and up to 18 months after the intervention was implemented.
A total of 1,071 ICUs from 44 states, the District of Columbia, and Puerto Rico, reporting 27,153 ICU-months and 4,454,324 catheter-days of data, were included in the analysis. The overall mean CLABSI rate significantly decreased from 1.96 cases per 1,000 catheter-days at baseline to 1.15 at 16-18 months after implementation. CLABSI rates decreased during all observation periods compared with baseline, with adjusted incidence rate ratios steadily decreasing to 0.57 (95% confidence intervals, 0.50-0.65) at 16-18 months after implementation.
Coincident with the implementation of the national "On the CUSP: Stop BSI" program was a significant and sustained decrease in CLABSIs among a large and diverse cohort of ICUs, demonstrating an overall 43% decrease and suggesting the majority of ICUs in the United States can achieve additional reductions in CLABSI rates.
Infection Control and Hospital Epidemiology 01/2014; 35(1):56-62. DOI:10.1086/674384 · 4.18 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Genome-wide association studies (GWAs) have identified thousands of DNA loci associated with a variety of traits. Statistical inference is almost always based on single marker hypothesis tests of association and the respective p-values with Bonferroni correction. Since commercially available genomic arrays interrogate hundreds of thousands or even millions of loci simultaneously, many causal yet undetected loci are believed to exist because the conditional power to achieve a genome-wide significance level can be low, in particular for markers with small effect sizes and low minor allele frequencies and in studies with modest sample size. However, the correlation between neighboring markers in the human genome due to linkage disequilibrium (LD) resulting in correlated marker test statistics can be incorporated into multi-marker hypothesis tests, thereby increasing power to detect association. Herein, we establish a theoretical benchmark by quantifying the maximum power achievable for multi-marker tests of association in case-control studies, achievable only when the causal marker is known. Using that genotype correlations within an LD block translate into an asymptotically multivariate normal distribution for score test statistics, we develop a set of weights for the markers that maximize the non-centrality parameter, and assess the relative loss of power for other approaches. We find that the method of Conneely and Boehnke (2007) based on the maximum absolute test statistic observed in an LD block is a practical and powerful method in a variety of settings. We also explore the effect on the power that prior biological or functional knowledge used to narrow down the locus of the causal marker can have, and conclude that this prior knowledge has to be very strong and specific for the power to approach the maximum achievable level, or even beat the power observed for methods such as the one proposed by Conneely and Boehnke (2007).
Frontiers in Genetics 12/2013; 4:252. DOI:10.3389/fgene.2013.00252
[Show abstract][Hide abstract] ABSTRACT: Our retrospective analysis of the Michigan Keystone intensive care unit (ICU) collaborative demonstrated that adult ICUs could achieve and sustain a zero rate of ventilator-associated pneumonia (VAP) for a considerable number of ventilator and calendar months. Moreover, the results highlight the importance of adjustment for ventilator-days before comparing VAP-free time among ICUs.
Infection Control and Hospital Epidemiology 07/2013; 34(7):740-3. DOI:10.1086/670989 · 4.18 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Statistical approaches to evaluate interactions between single nucleotide polymorphisms (SNPs) and SNP-environment interactions are of great importance in genetic association studies, as susceptibility to complex disease might be related to the interaction of multiple SNPs and/or environmental factors. With these methods under active development, algorithms to simulate genomic data sets are needed to ensure proper type I error control of newly proposed methods and to compare power with existing methods. In this paper we propose an efficient method for a haplotype-based simulation of case-parent trios when the disease risk is thought to depend on possibly higher-order epistatic interactions or gene-environment interactions with binary exposures.
Human Heredity 03/2013; 75(1):12-22. DOI:10.1159/000348789 · 1.47 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Purpose:
To evaluate effects of two behavioral weight-loss interventions (in-person, remote) on health-related quality of life (HRQOL) compared to a control intervention.
Four hundred and fifty-one obese US adults with at least one cardiovascular risk factor completed five measures of HRQOL and depression: MOS SF-12 physical component summary (PCS) and mental component summary; EuroQoL-5 dimensions single index and visual analog scale; PHQ-8 depression symptoms; and PSQI sleep quality scores at baseline and 6 and 24 months after randomization. Change in each outcome was analyzed using outcome-specific mixed-effects models controlling for participant demographic characteristics.
PCS-12 scores over 24 months improved more among participants in the in-person active intervention arm than among control arm participants (P < 0.05, ES = 0.21); there were no other statistically significant treatment arm differences in HRQOL change. Greater weight loss was associated with improvements in most outcomes (P < 0.05 to < 0.0001).
Participants in the in-person active intervention improved more in physical function HRQOL than participants in the control arm did. Greater weight loss during the study was associated with greater improvement in all PRO except for sleep quality, suggesting that weight loss is a key factor in improving HRQOL.
Quality of Life Research 03/2013; 22(9). DOI:10.1007/s11136-013-0363-3 · 2.49 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Purpose:
Normative biopsychosocial stressors that occur during entry into adolescence can affect school performance.As a set of resources for adapting to life's challenges, good health may buffer a child from these potentially harmful stressors. This study examined the associations between health (measured as well-being, functioning, symptoms, and chronic conditions) and school outcomes among children aged 9-13 years in 4th-8th grades.
We conducted a prospective cohort study of 1,479 children from 34 schools followed from 2006 to 2008. Survey data were obtained from children and their parents, and school records were abstracted. Measures of child self-reported health were dichotomized to indicate presence of a health asset. Outcomes included attendance, grade point average, state achievement test scores, and child-reported school engagement and teacher connectedness.
Both the transition into middle school and puberty had independent negative influences on school outcomes. Chronic health conditions that affected children's functional status were associated with poorer academic achievement. The number of health assets that a child possessed was positively associated with school outcomes. Low levels of negative stress experiences and high physical comfort had positive effects on teacher connectedness, school engagement, and academic achievement, whereas bullying and bully victimization negatively affected these outcomes. Children with high life satisfaction were more connected with teachers, more engaged in schoolwork, and earned higher grades than those who were less satisfied.
As children enter adolescence, good health may buffer them from the potentially negative effects of school and pubertal transitions on academic success.
Journal of Adolescent Health 02/2013; 52(2):186-194. DOI:10.1016/j.jadohealth.2012.06.019 · 3.61 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The susceptibility of older adults to the health effects of air pollution is well-recognized. Advanced age may act as a partial surrogate for conditions associated with aging. The authors investigated whether gerontologic frailty (a clinical health status metric) modified the association between ambient level of ozone or particulate matter with an aerodynamic diameter less than 10 µm and lung function in 3,382 older adults using 7 years of follow-up data (1990-1997) from the Cardiovascular Health Study and its Environmental Factors Ancillary Study. Monthly average pollution and annual frailty assessments were related to up to 3 repeated measurements of lung function using cumulative summaries of pollution and frailty histories that accounted for duration as well as concentration. Frailty history was found to modify long-term associations of pollutants with forced vital capacity. For example, the decrease in forced vital capacity associated with a 70-ppb/month greater cumulative sum of monthly average ozone exposure was 12.3 mL (95% confidence interval: 10.4, 14.2) for a woman who had spent the prior 7 years prefrail or frail as compared with 4.7 mL (95% confidence interval: 3.8, 5.6) for a similar woman who was robust during all 7 years (interaction P < 0.001).
American journal of epidemiology 07/2012; 176(3):214-23. DOI:10.1093/aje/kws001 · 5.23 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Genotype imputation has become a standard option for researchers to expand their genotype datasets to improve signal precision and power in tests of genetic association with disease. In imputations for family-based studies however, subjects are often treated as unrelated individuals: currently, only BEAGLE allows for simultaneous imputation for trios of parents and offspring; however, only the most likely genotype calls are returned, not estimated genotype probabilities. For population-based SNP association studies, it has been shown that incorporating genotype uncertainty can be more powerful than using hard genotype calls. We here investigate this issue in the context of case-parent family data. We present the statistical framework for the genotypic transmission-disequilibrium test (gTDT) using observed genotype calls and imputed genotype probabilities, derive an extension to assess gene-environment interactions for binary environmental variables, and illustrate the performance of our method on a set of trios from the International Cleft Consortium. In contrast to population-based studies, however, utilizing the genotype probabilities in this framework (derived by treating the family members as unrelated) can result in biases of the test statistics toward protectiveness for the minor allele, particularly for markers with lower minor allele frequencies and lower imputation quality. We further compare the results between ignoring relatedness in the imputation and taking family structure into account, based on hard genotype calls. We find that by far the least biased results are obtained when family structure is taken into account and currently recommend this approach in spite of its intense computational requirements.
[Show abstract][Hide abstract] ABSTRACT: In observational studies of treatments or interventions, propensity score (PS) adjustment is often useful for controlling bias in estimation of treatment effects. Regression on PS is used most often and can be highly efficient, but it can lead to biased results when model assumptions are violated. The validity of stratification on PS depends on fewer model assumptions, but this approach is less efficient than regression adjustment when the regression assumptions hold. To investigate these issues, we compare stratification and regression adjustments in a Monte Carlo simulation study. We consider two stratification approaches: equal frequency strata and an approach that attempts to choose strata that minimize the mean squared error (MSE) of the treatment effect estimate. The regression approach that we consider is a generalized additive model (GAM) that estimates treatment effect controlling for a potentially nonlinear association between PS and outcome. We find that under a wide range of plausible data generating distributions the GAM approach outperforms stratification in treatment effect estimation with respect to bias, variance, and thereby MSE. We illustrate each approach in an analysis of insurance plan choice and its relation to satisfaction with asthma care.
Health Services and Outcomes Research Methodology 03/2012; 12(1). DOI:10.1007/s10742-012-0080-3