Epidemiologic perspectives & innovations : EP+I最新文献

Background: We suggest that the need to account for systematic error may explain the apparent lack of agreement among studies of maternal dietary methylmercury exposure and neuropsychological testing outcomes in children, a topic of ongoing debate.

Methods: These sensitivity analyses address the possible role of systematic error on reported associations between low-level prenatal exposure to methylmercury and neuropsychological test results in two well known, but apparently conflicting cohort studies: the Faroe Islands Study (FIS) and the Seychelles Child Development Study (SCDS). We estimated the potential impact of confounding, selection bias, and information bias on reported results in these studies using the Boston Naming Test (BNT) score as the outcome variable.

Results: Our findings indicate that, assuming various degrees of bias (in either direction) the corrected regression coefficients largely overlap. Thus, the reported effects in the two studies are not necessarily different from each other.

Conclusion: Based on our sensitivity analysis results, it is not possible to draw definitive conclusions about the presence or absence of neurodevelopmental effects due to in utero methylmercury exposure at levels reported in the FIS and SCDS.

Longitudinal studies are helpful in understanding how subtle associations between factors of interest change over time. Our goal is to apply statistical methods which are appropriate for analyzing longitudinal data to a repeated measures epidemiological study as a tutorial in the appropriate use and interpretation of random effects models. To motivate their use, we study the association of alcohol consumption on markers of HIV disease progression in an observational cohort. To make valid inferences, the association among measurements correlated within a subject must be taken into account. We describe a linear mixed effects regression framework that accounts for the clustering of longitudinal data and that can be fit using standard statistical software. We apply the linear mixed effects model to a previously published dataset of HIV infected individuals with a history of alcohol problems who are receiving HAART (n = 197). The researchers were interested in determining the effect of alcohol use on HIV disease progression over time. Fitting a linear mixed effects multiple regression model with a random intercept and random slope for each subject accounts for the association of observations within subjects and yields parameters interpretable as in ordinary multiple regression. A significant interaction between alcohol use and adherence to HAART is found: subjects who use alcohol and are not fully adherent to their HIV medications had higher log RNA (ribonucleic acid) viral load levels than fully adherent non-drinkers, fully adherent alcohol users, and non-drinkers who were not fully adherent. Longitudinal studies are increasingly common in epidemiological research. Software routines that account for correlation between repeated measures using linear mixed effects methods are now generally available and straightforward to utilize. These models allow the relaxation of assumptions needed for approaches such as repeated measures ANOVA, and should be routinely incorporated into the analysis of cohort studies.

Background: Little is known about the characteristics of people who do and do not agree to the long-term storage and use of their biological materials, or about potential biases that may be introduced as a result of differential consent. More specifically, concerns about tissue storage and use are especially relevant among population groups for whom blood and other biological materials are culturally significant, such as Indigenous Australians. Using data from a 2003-2005 study of 1,004 Indigenous Australians, we examined participants' choices regarding long-term storage of excess blood for possible use in future studies.

Results: Overall, 55% of participants agreed to long-term storage. Among 854 participants with a fasting blood sample and completed questionnaire, consent for storage was more likely among those aged 45+ years than those 15-44 (odds ratio (OR) = 1.55, 95% confidence interval (CI): 1.14, 2.11), and was similar for males and females. After adjustment for age and other covariates using logistic regression, consent was more likely for never smokers than current smokers (OR = 1.48, 95% CI: 1.04, 2.10), those reporting any non-Indigenous grandparent(s) (OR = 2.07, 95% CI: 1.50, 2.85), and those whose consent form was administered/witnessed by an Indigenous staff member (OR 1.43, 95% CI: 1.05, 1.94). Consent for long-term storage was associated with only small differences (generally less than +/- 5%) in the results of assays performed on all participants' blood samples as part of the baseline health examination.

Conclusion: These data show that consent for blood storage among these research participants was neither rare nor universal. It was associated with some socio-demographic/cultural factors but not with blood biochemistry. Decisions about requesting or giving consent for storage and later use of tissue samples must recognize a number of important, and potentially competing, ethical and logistical considerations.

Population estimates are generally drawn from one point in time to study disease trends over time; changes in population characteristics over time are usually not assessed and included in the study design. We evaluated whether population characteristics remained static and assessed the degree of population shifts over time. The analysis was based on the New York State 1990 and 2000 census data with adjustments for changes in geographic boundaries. Differences in census tract information were quantified by calculating the mean, median, standard deviation, and the percent of change for each population characteristic. Between 1990 and 2000, positive and negative fluctuations in population size created a U-shaped bimodal pattern of population change which increased the disparities in demographics and socioeconomic status for many census tracts. While 268 (10%) census tracts contracted by 10%, twice as many census tracts (21%, N = 557) grew at least 10%. Notably, the non-Hispanic African-American population grew 10% or more in 152 tracts. Although there were overall reductions in working class and undereducated populations and gains in incomes, most census tracts experienced growing income inequalities and an increased poverty rate. These changes were most pronounced in urban census tracts. Differences in population characteristics in a decade showed growing disparities in demographics and socioeconomic status. This study elucidates that important population shifts should be taken into account when conducting longitudinal research.

Long term studies of childhood cancer survivors are hampered by difficulties in tracking young adult participants. After performing a National Death Index (NDI) search we sought to identify which factors best predicted a match among known decedents from the National Wilms Tumor Study (NWTS) and to determine if record linkage could substitute for missing follow-up in a cohort of NWTS survivors. To our knowledge, this is the first study to compare passive mortality follow-up using the NDI to active follow-up of a childhood and young adult population. Records for 984 known decedents and 3,406 subjects whose January 1, 2002 vital status was unknown were sent to the NDI in June 2003. In April 2005 NWTS follow-up records were used to reassess January 1, 2002 vital status. Matches were established for 709 of 789 known decedents (sensitivity 89.9%) with a date of death between 1979 and 2001, the calendar period covered by the NDI at the time of the search. No matches were identified among 1,052 subjects known to be alive in 2002 (specificity 100%). Factors associated with decreased sensitivity were an unknown social security number (sensitivity 87.8%), Hispanic ethnicity (76.4%) and foreign birth (56.5%). For 2,351 subjects with 2002 vital status unknown who had 13,166 pre 2002 person-years of missing observation, only 18 deaths were ascertained by the NDI whereas 79.3 were expected based on NWTS mortality data. Mortality analyses based strictly on NDI search results and those based on NWTS follow-up augmented with NDI search results yielded inflated estimates of the 15 year survival rate when compared with estimates based on NWTS active follow-up. Match rates were comparable to those observed in adult populations. Since the same selection factors were likely associated with NDI failure to match and NWTS loss to follow-up, use of the NDI to fill in missing follow-up data appears unwarranted.

Background: Power for assessing interactions during data analysis is often poor in epidemiologic studies. This is because epidemiologic studies are frequently powered primarily to assess main effects only. In light of this, some investigators raise the Type I error rate, thereby increasing power, when testing interactions. However, this is a poor analysis strategy if the study is chronically under-powered (e.g. in a small study) or already adequately powered (e.g. in a very large study). To demonstrate this point, this study quantified the gain in power for testing interactions when the Type I error rate is raised, for a variety of study sizes and types of interaction.

Methods: Power was computed for the Wald test for interaction, the likelihood ratio test for interaction, and the Breslow-Day test for heterogeneity of the odds ratio. Ten types of interaction, ranging from sub-additive through to super-multiplicative, were investigated in the simple scenario of two binary risk factors. Case-control studies of various sizes were investigated (75 cases & 150 controls, 300 cases & 600 controls, and 1200 cases & 2400 controls).

Results: The strategy of raising the Type I error rate from 5% to 20% resulted in a useful power gain (a gain of at least 10%, resulting in power of at least 70%) in only 7 of the 27 interaction type/study size scenarios studied (26%). In the other 20 scenarios, power was either already adequate (n = 8; 30%), or else so low that it was still weak (below 70%) even after raising the Type I error rate to 20% (n = 12; 44%).

Conclusion: Relaxing the Type I error rate did not usefully improve the power for tests of interaction in many of the scenarios studied. In many studies, the small power gains obtained by raising the Type I error will be more than offset by the disadvantage of increased "false positives". I recommend investigators should not routinely raise the Type I error rate when assessing tests of interaction.

This paper critically discusses two previous studies concerned with predictions of HIV/AIDS in the United States and Japan during the early 1990s. Although the study in the US applied a historical theory, assuming normal distribution for the epidemic curve, the underlying infection process was not taken into account. In the Japan case, the true HIV incidence was estimated using the coverage ratio of previously diagnosed/undiagnosed HIV infections among AIDS cases, the assumptions of which were not supported by a firm theoretical understanding. At least partly because of failure to account for underlying mechanisms of the disease and its transmission, both studies failed to yield appropriate predictions of the future AIDS incidence. Further, in the Japan case, the importance of consistent surveillance data was not sufficiently emphasized or openly discussed and, because of this, revision of the AIDS reporting system has made it difficult to determine the total number of AIDS cases and apply a backcalculation method. Other widely accepted approaches can also fail to provide perfect predictions. Nevertheless, wrong policy direction could arise if we ignore important assumptions, methods and input data required to answer specific questions. The present paper highlights the need for appropriate assessment of specific modeling purposes and explicit listing of essential information as well as possible solutions to aid relevant policy formulation.

Societies are facing challenges as the public health burden increases in tandem with population aging. Local information systems are needed that would allow a continuous monitoring of the incidence and effectiveness of treatments. This study investigates the possibilities of routinely collected administrative data as a data source for hip fracture incidence monitoring in Finland. The study demonstrates that a straightforward use of register data results in biased estimates for the numbers of hip fractures. An interpretation of hip fractures from the population aging point of view offers an alternative perspective for hip fracture incidence calculation. This enables development of a generalizable method for probabilistic detection of starting points of hip fracture care episodes. Several risk factor and risk population extraction techniques required in register-based data analyses are also demonstrated. Finally, it is shown that empirical evidence suggests that hip fracture incidence is proportional to population level disability prevalence. In conclusion, Finnish administrative data makes it possible to derive data for rather detailed population level risk factor stratification. Certain limitations of register-based data can be partly avoided by synthesizing data-sensitive methodological solutions during the analysis process.

Injury-related mortality rate estimates are often analyzed under the assumption that case counts follow a Poisson distribution. Certain types of injury incidents occasionally involve multiple fatalities, however, resulting in dependencies between cases that are not reflected in the simple Poisson model and which can affect even basic statistical analyses. This paper explores the compound Poisson process model as an alternative, emphasizing adjustments to some commonly used interval estimators for population-based rates and rate ratios. The adjusted estimators involve relatively simple closed-form computations, which in the absence of multiple-case incidents reduce to familiar estimators based on the simpler Poisson model. Summary data from the National Violent Death Reporting System are referenced in several examples demonstrating application of the proposed methodology.

Objective: To examine the generalizability of two National Institutes of Health (NIH)-funded double-blind randomized placebo-controlled clinical trials in patients with chronic Lyme disease and to determine whether selection factors resulted in the unfavorable outcomes.

Design: Epidemiologic review of the generalizability of two trials conducted by Klempner et al. This paper considers whether the study group was representative of the general chronic Lyme disease population.

Results: In their article in The New England Journal of Medicine, Klempner et al. failed to discuss the limitations of their clinical trials. This epidemiologic review argues that their results are not generalizable to the overall Lyme disease population. The treatment failure reported by the authors may be the result of enrolling patients who remained ill after an average of 4.7 years and an average of 3 previous courses of treatment. The poor outcome cited in these trials may be explained by having selected patients who had undergone delayed treatment or multiple treatments unsuccessfully. These selection factors were not addressed by the studies' authors, nor have they been discussed by reviewers. The trials have been over-interpreted by the NIH and widely publicized in a press release. The results have been extrapolated to other groups of Lyme disease patients by commentators, by a case discussant in an influential medical journal, and by health insurance companies to deny antibiotic treatment.

Conclusion: The Klempner et al. trials are assumed to be internally valid based on a Randomized Control Trial (RCT) design. However, this review argues that the trials have limited generalizability beyond the select group of patients with characteristics like those in the trial. Applying the findings to target populations with characteristics that differ from those included in these trials is inappropriate and may limit options for chronic Lyme disease patients who might benefit from antibiotic treatment.