We investigated the temporal associations between the severity of foot lesions caused by footrot (FR) and the severity of lameness in sheep. Sixty sheep from one farm were monitored for five weeks. The locomotion of each sheep was scored once each week using a validated numerical rating scale of 0-6. All feet were then examined, FR was the only foot lesion observed; the severity of FR lesions was recorded on a scale from 0 to 4. Sheep had a locomotion score > 0 on 144/298 observations. FR lesions were present on at least one foot on 83% of observations of lame sheep but also present on 27% of observations where sheep were not lame; 95% of these sheep with a lesion but not lame had FR score 1. The results from a linear mixed model with locomotion score as the outcome were that the mean (95% CI) locomotion score of 0.28 (0.02, 0.53) in sheep with no lesions increased by 0.35 (0.05, 0.65) in sheep with FR score 1 or 2 and by 1.55 (1.13, 1.96) in sheep with FR score > 2 at the time of the observation; indicating that as the severity of the lesion increased, the severity of lameness increased. One week before an FR score > 2 was clinically apparent, sheep had a locomotion score 0.81 (0.37, 1.24) higher than sheep that did not have an FR score > 2 in the subsequent week. One week after treatment with intramuscular antibacterials the locomotion score of lame sheep reduced by 1.00 (0.50, 1.49). Our results indicate a positive association between severity of FR lesions and locomotion score and indicate that some non-lame and mildly lame sheep have footrot lesions. Treatment of even those mildly lame will facilitate healing and probably reduce the spread of infection to other sheep in the same group.

The impact of trust on economic performance has been widely explored, but the reasons for its variability across countries are not well understood. We analyse the effect of the quality of government at the regional level on individual generalized trust in a multi-country context across regions in Europe. Social phenomena are often subnational and a number of public services are provided at a subnational level; the trust of individuals living in the same country may, therefore, differ by region depending also on the quality of the local government. As a proxy of the quality of institutions, we use the European Quality of Government Index, calculated at the regional level over 27 European Union (EU) countries. The analysis conducted on data extracted from the European Social Survey 2012 refers to 142 regions from 15 EU member states. Considering the clustered nature of the data, a multilevel approach is used. The findings show that living in a region with high-quality local government positively influences individual trust. This positive association survives the inclusion of several contextual regional variables.

Corruption is a global problem. Despite the importance of this theme, a shortage of theoretical models in both psychology and related areas that favor its understanding and investigation is noted. Due to this scarcity of theoretical models, in addition to the need to systematize studies on the topic, this theoretical article aims to describe the Analytical Model of Corruption (AMC) as an interdisciplinary and multilevel proposal aimed at corruption analysis. To achieve this goal, the concept of corruption was analyzed using related phenomena as reference. Similarities and differences in corruption have been identified with dishonest behavior and unethical behavior. Subsequently, theoretical models on corruption identified in the literature were presented, and their main characteristics and limitations were pointed out. After describing the models, the AMC was presented and its advantages over the previous models were discussed. Finally, it was concluded that the AMC could be configured as a theoretical model that guides interdisciplinary studies on corruption, allowing for a more complete analysis compared to previous theoretical models identified in the literature.

The interval between successive pregnancies (birth interval) is one of the main indexes used to evaluate the health of a mother and her child. This study evaluated birth intervals in Iran using data from the Iranian Multiple Indicators Demographic and Health Survey (IrMIDHS) conducted in 2010–2011. A total of 20,093 married Iranian women aged 15–54 years from the whole country constituted the study sample. Based on the nature of sampling and the unobserved population heterogeneity for birth intervals in each city and province, a multilevel survival frailty model was applied. Data were analysed for women’s first three birth intervals. The median first and second birth intervals were 30.3 and 39.7 months respectively. Higher education, Caesarean delivery, contraceptive use and exposure to public mass media were found to decrease the hazard rate ratio (HRR) of giving birth. Meanwhile, higher monthly income increased the hazard of giving birth. The results suggest that public mass media can play an effective role in encouraging women to have the recommended birth interval. Furthermore, increasing family income could encourage Iranian couples to decrease the time to their next birth.

This paper presents a unilevel and multilevel approach for the analysis and meta-analysis of single-case experiments (SCEs). We propose a definition of SCEs and derive the specific features of SCEs’ data that have to be taken into account when analysing and meta-analysing SCEs. We discuss multilevel models of increasing complexity and propose alternative and complementary techniques based on probability combining and randomisation test wrapping. The proposed techniques are demonstrated with real-life data and corresponding R code.

Research examining racial/ethnic disparities in pollution exposure often relies on cross-sectional data. These analyses are largely insensitive to exposure trends and rarely account for broader contextual dynamics. To provide a more comprehensive assessment of racial-environmental inequality over time, we combine the 1990 to 2009 waves of the Panel Study of Income Dynamics (PSID) with spatially- and temporally-resolved measures of nitrogen dioxide (NO2) and particulate matter (PM2.5 and PM10) in respondents’ neighborhoods, as well as census data on the characteristics of respondents’ metropolitan areas. Results based on multilevel repeated measures models indicate that Blacks and Latinos are, on average, more likely to be exposed to higher levels of NO2, PM2.5, and PM10 than Whites. Despite nationwide declines in levels of pollution over time, racial and ethnic disparities persist and cannot be fully explained by individual-, household-, or metropolitan-level factors.

Typically most studies of individual employees perceptions of the work place adopt multiple regression models (ordinary least squares [OLS]) which ignore inherent clustering in their data. However, such an approach does not supply unbiased and accurate answers to research questions. This study intends to simulate three data alternatives – weighted, disaggregated (individual level), and aggregated (organizational level) using the OLS and multilevel models to compare the results of different research designs. To answer the research questions, the current study investigates the impact of individual and organizational factors on job satisfaction, using a 2000 USA National Partnership for Reinventing Government survey. This study presents the methodological misuse and measurement errors of the previous research and presents guidelines for future research.

It is common for professional associations and regulators to combine the claims experience of several insurers into a database known as an “intercompany” experience data set. In this paper, we analyze data on claim counts provided by the General Insurance Association of Singapore, an organization consisting of most of the general insurers in Singapore. Our data comes from the financial records of automobile insurance policies followed over a period of nine years. Because the source contains a pooled experience of several insurers, we are able to study company effects on claim behavior, an area that has not been systematically addressed in either the insurance or the actuarial literatures.

We analyze this intercompany experience using multilevel models. The multilevel nature of the data is due to: a vehicle is observed over a period of years and is insured by an insurance company under a “fleet” policy. Fleet policies are umbrella-type policies issued to customers whose insurance covers more than a single vehicle. We investigate vehicle, fleet and company effects using various count distribution models (Poisson, negative binomial, zero-inflated and hurdle Poisson). The performance of these various models is compared; we demonstrate how our model can be used to update a priori premiums to a posteriori premiums, a common practice of experience-rated premium calculations. Through this formal model structure, we provide insights into effects that company-specific practice has on claims experience, even after controlling for vehicle and fleet effects.

Objectives: To identify and assess the effects of general practitioner and patient characteristics on global adherence to pharmacotherapeutic guidelines.

Methods: In a cross-sectional study in the northern Netherlands, a two-level multilevel model was applied to patients (n=269,067) in 190 practices with a total of 251 general practitioners. The dependent variable was the global adherence to pharmacotherapeutic guidelines as a measure of good prescribing in a general practice setting.

Results: The mean global adherence was 82%. Two general practice variables, organization form and degree of urbanization influenced the global adherence, whereas all patient variables (age and gender) and the patient-related prescription characteristics (costs, volume, different ATC-codes) were significant predictors for the global adherence. The total explained variance was 28%.

Conclusions: Patient characteristics have a greater influence on prescribing behavior than general practitioner characteristics.We thank the health care insurance organization (Regionaal Ziekenfonds Groningen [RZG]) for supplying the data set. We also thank Eddie Bruin from the health care insurance organization for his assistance with data collection and Tom Snijders for data analysis and statistical advice.