A method to compute multiplicity corrected confidence intervals for odds ratios and other relative effect estimates.

Epidemiological studies commonly test multiple null hypotheses. In some situations it may be appropriate to account for multiplicity using statistical methodology rather than simply interpreting results with greater caution as the number of comparisons increases. Given the one-to-one relationship that exists between confidence intervals and hypothesis tests, we derive a method based upon the Hochberg step-up procedure to obtain multiplicity corrected confidence intervals (CI) for odds ratios (OR) and by analogy for other relative effect estimates. In contrast to previously published methods that explicitly assume knowledge of P values, this method only requires that relative effect estimates and corresponding CI be known for each comparison to obtain multiplicity corrected CI

Validation of a Parkinson disease predictive model in a population-based study

Parkinson disease (PD) has a relatively long prodromal period that may permit early identification to reduce diagnostic testing for other conditions when patients are simply presenting with early PD symptoms, as well as to reduce morbidity from fall-related trauma. Earlier identification also could prove critical to the development of neuroprotective therapies. We previously developed a PD predictive model using demographic and Medicare claims data in a population-based case-control study. The area under the receiver-operating characteristic curve (AUC) indicated good performance. We sought to further validate this PD predictive model. In a randomly selected, population-based cohort of 115,492 Medicare beneficiaries aged 66–90 and without PD in 2009, we applied the predictive model to claims data from the prior five years to estimate the probability of future PD diagnosis. During five years of follow-up, we used 2010–2014 Medicare data to determine PD and vital status and then Cox regression to investigate whether PD probability at baseline was associated with time to PD diagnosis. Within a nested case-control sample, we calculated the AUC, sensitivity, and specificity. A total of 2,326 beneficiaries developed PD. Probability of PD was associated with time to PD diagnosis (p<0.001, hazard ratio = 13.5, 95% confidence interval (CI) 10.6–17.3 for the highest vs. lowest decile of probability). The AUC was 83.3% (95% CI 82.5%–84.1%). At the cut point that balanced sensitivity and specificity, sensitivity was 76.7% and specificity was 76.2%. In an independent sample of additional Medicare beneficiaries, we again applied the model and observed good performance (AUC = 82.2%, 95% CI 81.1%–83.3%). Administrative claims data can facilitate PD identification within Medicare and Medicare-aged samples

Immunosuppressants and risk of Parkinson disease

We performed a population-based case-control study of United States Medicare beneficiaries age 60-90 in 2009 with prescription data (48,295 incident Parkinson disease cases and 52,324 controls) to examine the risk of Parkinson disease in relation to use of immunosuppressants. Inosine monophosphate dehydrogenase inhibitors (relative risk = 0.64; 95% confidence interval 0.51-0.79) and corticosteroids (relative risk = 0.80; 95% confidence interval 0.77-0.83) were both associated with a lower risk of Parkinson disease. Inverse associations for both remained after applying a 12-month exposure lag. Overall, this study provides evidence that use of corticosteroids and inosine monophosphate dehydrogenase inhibitors might lower the risk of Parkinson disease

A comparison of prediction approaches for identifying prodromal Parkinson disease

Identifying people with Parkinson disease during the prodromal period, including via algorithms in administrative claims data, is an important research and clinical priority. We sought to improve upon an existing penalized logistic regression model, based on diagnosis and procedure codes, by adding prescription medication data or using machine learning. Using Medicare Part D beneficiaries age 66-90 from a population-based case-control study of incident Parkinson disease, we fit a penalized logistic regression both with and without Part D data. We also built a predictive algorithm using a random forest classifier for comparison. In a combined approach, we introduced the probability of Parkinson disease from the random forest, as a predictor in the penalized regression model. We calculated the receiver operator characteristic area under the curve (AUC) for each model. All models performed well, with AUCs ranging from 0.824 (simplest model) to 0.835 (combined approach). We conclude that medication data and random forests improve Parkinson disease prediction, but are not essential

Severity of parkinsonism associated with environmental manganese exposure

BACKGROUND: Exposure to occupational manganese (Mn) is associated with neurotoxic brain injury, manifesting primarily as parkinsonism. The association between environmental Mn exposure and parkinsonism is unclear. To characterize the association between environmental Mn exposure and parkinsonism, we performed population-based sampling of residents older than 40 in Meyerton, South Africa (N = 621) in residential settlements adjacent to a large Mn smelter and in a comparable non-exposed settlement in Ethembalethu, South Africa (N = 95) in 2016-2020. METHODS: A movement disorders specialist examined all participants using the Unified Parkinson Disease Rating Scale motor subsection part 3 (UPDRS3). Participants also completed an accelerometry-based kinematic test and a grooved pegboard test. We compared performance on the UPDRS3, grooved pegboard, and the accelerometry-based kinematic test between the settlements using linear regression, adjusting for covariates. We also measured airborne PM RESULTS: Mean PM CONCLUSIONS: Environmental airborne Mn exposures at levels substantially lower than current occupational exposure thresholds in the United States may be associated with clinical parkinsonism

Risk of Brain Tumors in Children and Susceptibility to Organophosphorus Insecticides: The Potential Role of Paraoxonase (PON1)

Prior research suggests that childhood brain tumors (CBTs) may be associated with exposure to pesticides. Organophosphorus insecticides (OPs) target the developing nervous system, and until recently, the most common residential insecticides were chlorpyrifos and diazinon, two OPs metabolized in the body through the cytochrome P450/paraoxonase 1 (PON1) pathway. To investigate whether two common PON1 polymorphisms, C-108T and Q192R, are associated with CBT occurrence, we conducted a population-based study of 66 cases and 236 controls using DNA from neonatal screening archive specimens in Washington State, linked to interview data. The risk of CBT was nonsignificantly increased in relation to the inefficient PON1 promoter allele [per PON1(-108T) allele, relative to PON1(-108CC): odds ratio (OR) = 1.4; 95% confidence interval (CI), 1.0–2.2; p-value for trend = 0.07]. Notably, this association was strongest and statistically significant among children whose mothers reported chemical treatment of the home for pests during pregnancy or childhood (per PON1(-108T) allele: among exposed, OR = 2.6; 95% CI, 1.2–5.5; among unexposed, OR = 0.9; 95% CI, 0.5–1.6) and for primitive neuroectodermal tumors (per PON1(-108T) allele: OR = 2.4; 95% CI, 1.1–5.4). The Q192R polymorphism, which alters the structure of PON1 and influences enzyme activity in a substrate-dependent manner, was not associated with CBT risk, nor was the PON1(C-108T/Q192R) haplotype. These results are consistent with an inverse association between PON1 levels and CBT occurrence, perhaps because of PON1’s ability to detoxify OPs common in children’s environments. Larger studies that measure plasma PON1 levels and incorporate more accurate estimates of pesticide exposure will be required to confirm these observations

Water quality monitoring records for estimating tap water arsenic and nitrate: a validation study

<p>Abstract</p> <p>Background</p> <p>Tap water may be an important source of exposure to arsenic and nitrate. Obtaining and analyzing samples in the context of large studies of health effects can be expensive. As an alternative, studies might estimate contaminant levels in individual homes by using publicly available water quality monitoring records, either alone or in combination with geographic information systems (GIS).</p> <p>Methods</p> <p>We examined the validity of records-based methods in Washington State, where arsenic and nitrate contamination is prevalent but generally observed at modest levels. Laboratory analysis of samples from 107 homes (median 0.6 μg/L arsenic, median 0.4 mg/L nitrate as nitrogen) served as our "gold standard." Using Spearman's rho we compared these measures to estimates obtained using only the homes' street addresses and recent and/or historical measures from publicly monitored water sources within specified distances (radii) ranging from one half mile to 10 miles.</p> <p>Results</p> <p>Agreement improved as distance decreased, but the proportion of homes for which we could estimate summary measures also decreased. When including all homes, agreement was 0.05-0.24 for arsenic (8 miles), and 0.31-0.33 for nitrate (6 miles). Focusing on the closest source yielded little improvement. Agreement was greatest among homes with private wells. For homes on a water system, agreement improved considerably if we included only sources serving the relevant system (ρ = 0.29 for arsenic, ρ = 0.60 for nitrate).</p> <p>Conclusions</p> <p>Historical water quality databases show some promise for categorizing epidemiologic study participants in terms of relative tap water nitrate levels. Nonetheless, such records-based methods must be used with caution, and their use for arsenic may be limited.</p