A New Approach for Managing Bovine Tuberculosis: Veterinary Services’ Proposed Action Plan

Bovine tuberculosis (TB) is a serious disease with animal health, public health, and international trade consequences. The cooperative Federal-State-industry effort to eradicate bovine TB from cattle in the United States has made significant progress since the program’s inception in 1917. However, the goal of eradication remains elusive. This proposed action plan presents Veterinary Services’ (VS’) current thinking about changes we are considering for the TB program to address our current challenges. This action plan will: 1. Reduce the introduction of TB into the U.S. national herd from imported animals and wildlife by: o Applying additional requirements to cattle imports from Mexico o Enhancing efforts to mitigate risks from wildlife 2. Enhance TB surveillance by: o Crafting a comprehensive national surveillance plan o Accelerating diagnostic test development to support surveillance 3. Increase options for managing TB-affected herds by: o Conducting epidemiological investigations and assessing individual herd risk o Applying whole-herd depopulation judiciously and developing alternative control strategies o Applying animal identification (ID) standards to meet animal ID needs 4. Modernize the regulatory framework to allow VS to focus resources where the disease exists 5. Transition the TB program from a State classification system to a science-based zoning approach to address disease risk To succeed, this new approach will require VS’ continued partnership with State animal health and wildlife officials, other Federal agencies, industry, international partners, academia, and other stakeholders. Successful partnerships will allow us to use available resources efficiently to achieve program objectives and protect our nation’s herd. Implementation of the VS proposed action plan will benefit Federal and State animal health officials, the regulated industries, and producers by allowing a more rapid response that employs up-to-date science and can adapt rapidly to changing situations.

An Assessment of Local Peoples Opinions of Community Conservation Initiatives in Relation to Livelihood Strategies in Kenya

Abstract This paper analyzed the changing livelihood strategies in Kenya, and their cultural impacts via a literature review. I then combined this understanding with the data I collected while in Kenya to examine the opinions local people have of community conservation initiatives, based on their changing livelihood strategies. I expected to find that the following factors would have an affect on the opinions local community members have of community conservation initiatives: livelihood strategy, gender, ethnicity, whether or not they believe the distribution of benefits coming from wildlife conservation is equitable, what issues they would like to see improved within community conservation initiatives, and their overall satisfaction with community conservation initiatives. Through correlation tests done using SPSS (Statistical Package for Social Scientists) I found that all five of these factors do influence the perceptions local community members have of community conservation initiatives within the Amboseli region in Kenya.

Evaluating Measurement Invariance with Censored Ordinal Data: A Monte Carlo Comparison of Alternative Model Estimators and Scales of Measurement

Evaluations of measurement invariance provide essential construct validity evidence. However, the quality of such evidence is partly dependent upon the validity of the resulting statistical conclusions. The presence of Type I or Type II errors can render measurement invariance conclusions meaningless. The purpose of this study was to determine the effects of categorization and censoring on the behavior of the chi-square/likelihood ratio test statistic and two alternative fit indices (CFI and RMSEA) under the context of evaluating measurement invariance. Monte Carlo simulation was used to examine Type I error and power rates for the (a) overall test statistic/fit indices, and (b) change in test statistic/fit indices. Data were generated according to a multiple-group single-factor CFA model across 40 conditions that varied by sample size, strength of item factor loadings, and categorization thresholds. Seven different combinations of model estimators (ML, Yuan-Bentler scaled ML, and WLSMV) and specified measurement scales (continuous, censored, and categorical) were used to analyze each of the simulation conditions. As hypothesized, non-normality increased Type I error rates for the continuous scale of measurement and did not affect error rates for the categorical scale of measurement. Maximum likelihood estimation combined with a categorical scale of measurement resulted in more correct statistical conclusions than the other analysis combinations. For the continuous and censored scales of measurement, the Yuan-Bentler scaled ML resulted in more correct conclusions than normal-theory ML. The censored measurement scale did not offer any advantages over the continuous measurement scale. Comparing across fit statistics and indices, the chi-square-based test statistics were preferred over the alternative fit indices, and ΔRMSEA was preferred over ΔCFI. Results from this study should be used to inform the modeling decisions of applied researchers. However, no single analysis combination can be recommended for all situations. Therefore, it is essential that researchers consider the context and purpose of their analyses.