Has the National Do Not Call Registry Helped or Hurt State-Level Response Rates?: A Time Series Analysis

By the end of the initial registration period on August 31,2003, the National Do Not Call Registry (DNC Registry) had registeredmore than 50 million telephone numbers. Approximately 18 monthslater that number had increased to more than 91 million. Theimpact of the DNC Registry on survey response rates, however,is largely unknown. Some researchers speculate that the registrycould make it easier to distinguish between telephone surveyinterviewers and telemarketers. Other researchers argue thata significant portion of DNC registrants may not make such distinctionsand would prefer instead to reduce all unsolicited calls frommarketers and interviewers alike. Case outcomes from nearly4.5 million telephone numbers called between January 1, 2002,and June 30, 2005, as part of the Behavioral Risk Factor SurveillanceSystem were analyzed. Using trend analyses and autoregressiveintegrated moving average (ARIMA) time series modeling, we assessedthe impact of the DNC Registry on state-level monthly responserates in 47 states. Our findings indicate that once pre-DNCRegistry trends in response rates and other potential covariatesare accounted for, the national Do Not Call rules have had nosignificant impact on state-level response rates in either apositive or negative direction.     

Proposals for 2001 samples of anonymized records: An assessment of disclosure risk

In 1991 Marsh and co-workers made the case for a sample of anonymized records (SAR) from the 1991 census of population. The case was accepted by the Office for National Statistics (then the Office of Population Censuses and Surveys) and a request was made by the Economic and Social Research Council to purchase the SARs. Two files were released for Great Britain—a 2% sample of individuals and a 1% sample of households. Subsequently similar samples were released for Northern Ireland. Since their release, the files have been heavily used for research and there has been no known breach of confidentiality. There is a considerable demand for similar files from the 2001 census, with specific requests for a larger sample size and lower population threshold for the individual SAR. This paper reassesses the analysis of Marsh and co-workers of the risk of identification of an individual or household in a sample of microdata from the 1991 census and also uses alternative ways of assessing risks with the 1991 SARs. The results of both the reassessment and the new analyses are reassuring and allow us to take the 1991 SARs as a base-line against which to assess proposals for changes to the size and structure of samples from the 2001 census.     

Modeling developmental data using U-shaped threshold dose-response curves

This paper develops threshold models for developmental toxicity data. The distinguishing feature of these threshold models is their flexibility in modeling data below threshold with a U-shaped function if the data warrants. The method is applied to actual data from a developmental study which exhibits U-shaped behavior in early dose groups. Results from a simulation study demonstrate the flexibility of the threshold model to pick up on U-shaped trends in the data. In addition, the simulation study reveals important considerations in design of developmental studies.     

A Parametric Model for Detecting Hormetic Effects in Developmental Toxicity Studies

Hormetic effects have been observed at low exposure levels based on the dose-response pattern of data from developmental toxicity studies. This indicates that there might actually be a reduced risk of exhibiting toxic effects at low exposure levels. Hormesis implies the existence of a threshold dose level and there are dose-response models that include parameters that account for the threshold. We propose a function that introduces a parameter to account for hormesis. This function is a subset of the set of all functions that could represent a hormetic dose-response relationship at low exposure levels to toxic agents. We characterize the overall dose-response relationship with a piecewise function that consists of a hormetic u-shape curve at low dose levels and a logistic curve at high dose levels. We apply our model to a data set from an experiment conducted at the National Toxicology Program (NTP). We also use the beta-binomial distribution to model the litter response data. It can be seen by observing the structure of these data that current experimental designs for developmental studies employ a limited number of dose groups. These designs may not be satisfactory when the goal is to illustrate the existence of hormesis. In particular, increasing the number of low-level doses improves the power for detecting hormetic effects. Therefore, we also provide the results of simulations that were done to characterize the power of current designs in detecting hormesis and to demonstrate how this power can be improved upon by altering these designs with the addition of only a few low exposure levels.     

Expanded learning time and opportunities: key principles, driving perspectives, and major challenges

If expanded learning is going to make a real difference, then three key principles must inform how communities overcome challenges and assure equitable access to learning opportunities.     

Urban bird community composition influenced by size of urban green spaces,presence of native forest,and urbanization

Urban Ecosystems - Understanding what determines wildlife species richness and community composition in urban green spaces is important for wildlife management and urban planning. Numerous studies...     

Simplified estimation of the parameters of exponential distribution from samples censored in the middle

This paper presents the small sample optimum choice of k ≤n + r₁ ? r₂ + 1) order statistics for the best linear unbiased estimates (BLUES) of the parameters μ and σ or σ alone ( μ known) when the sample is Type II censored in the middle retaining only r₁ lower and n - r₂ + 1 upper order statistics. For n = 3(1)10, k = 2(1)4, r₁ = O(1) (n?2) and r₂ = (r₁ +2) (l)n, the optimum ranks, the coefficients of the BLUEs have been presented in Table I