The use of Poisson regression in the sociological study of suicide

This paper explains how Poisson regression can be used in studies in which the dependent variable describes the number of occurrences of some rare event such as suicide. After pointing out why ordinary linear regression is inappropriate for treating dependent variables of this sort, we go on to present the basic Poisson regression model and show how it fits in the broad class of generalized linear models. Then we turn to discussing a major problem of Poisson regression known as overdispersion and suggest possible solutions, including the correction of standard errors and negative binomial regression. The paper ends with a detailed empirical example, drawn from our own research on suicide.

Loan quality, ownership and efficiency of Indian banks : A bootstrap truncated regression approach

The Drivers of Wage Inequality Across Europe: A Recentered Influence Function Regression Approach

This study analyzes the impact of individual characteristics as well as occupation and industry on male wage inequality in nine European countries. Unlike previous studies, we consider regression models for five inequality measures and employ the recentered influence function regression method proposed by Firpo et al. (2009) to test directly the influence of covariates on inequality. We conclude that there is heterogeneity in the effects of covariates on inequality across countries and throughout wage distribution. Heterogeneity among countries is more evident in education and experience whereas occupation and industry characteristics as well as holding a supervisory position reveal more similar effects. Our results are compatible with the skill biased technological change, rapid rise in the integration of trade and financial markets as well as explanations related to the increase of the remunerative package of top executives.

Mammographic mass-screening and future breast cancer burden in Finland

A population-based early detection program for breast cancer has been in progress in Finland since 1987. According to regulations during the study period 1987-2001, free of charge mammography screening was offered every second year to women aged 50-59 years. Recently, the screening service was decided to be extended to age group 50-69. However, the scope of the program is still frequently discussed in public and information about potential impacts of mass-screening practice changes on future breast cancer burden is required. The aim of this doctoral thesis is to present methodologies for taking into account the mass-screening invitation information in breast cancer burden predictions, and to present alternative breast cancer incidence and mortality predictions up to 2012 based on scenarios of the future screening policy. The focus of this work is not on assessing the absolute efficacy but the effectiveness of mass-screening, and, by utilizing the data on invitations, on showing the estimated impacts of changes in an existing screening program on the short-term predictions. The breast cancer mortality predictions are calculated using a model that combines incidence, cause-specific and other cause survival on individual level. The screening invitation data are incorporated into modeling of breast cancer incidence and survival by dividing the program into separate components (first and subsequent rounds and years within them, breaks, and post screening period) and defining a variable that gives the component of the screening program. The incidence is modeled using a Poisson regression approach and the breast cancer survival by applying a parametric mixture cure model, where the patient population is allowed to be a combination of cured and uncured patients. The patients risk to die from other causes than breast cancer is allowed to differ from that of a corresponding general population group and to depend on age and follow-up time. As a result, the effects of separate components of the screening program on incidence, proportion of cured and the survival of the uncured are quantified. According to the predictions, the impacts of policy changes, like extending the program from age group 50-59 to 50-69, are clearly visible on incidence while the effects on mortality in age group 40-74 are minor. Extending the screening service would increase the incidence of localized breast cancers but decrease the rates of non-localized breast cancer. There were no major differences between mortality predictions yielded by alternative future scenarios of the screening policy: Any policy change would have at the most a 3.0% reduction on overall breast cancer mortality compared to continuing the current practice in the near future.

Use of Poisson spatiotemporal regression models for the Brazilian Amazon Forest: malaria count data

INTRODUÇÃO: A malaria é uma doença endêmica na região da Amazônia Brasileira, e a detecção de possíveis fatores de risco pode ser de grande interesse às autoridades em saúde pública. O objetivo deste artigo é investigar a associação entre variáveis ambientais e os registros anuais de malária na região amazônica usando métodos bayesianos espaço-temporais. MÉTODOS: Utilizaram-se modelos de regressão espaço-temporais de Poisson para analisar os dados anuais de contagem de casos de malária entre os anos de 1999 a 2008, considerando a presença de alguns fatores como a taxa de desflorestamento. em uma abordagem bayesiana, as inferências foram obtidas por métodos Monte Carlo em cadeias de Markov (MCMC) que simularam amostras para a distribuição conjunta a posteriori de interesse. A discriminação de diferentes modelos também foi discutida. RESULTADOS: O modelo aqui proposto sugeriu que a taxa de desflorestamento, o número de habitants por km² e o índice de desenvolvimento humano (IDH) são importantes para a predição de casos de malária. CONCLUSÕES: É possível concluir que o desenvolvimento humano, o crescimento populacional, o desflorestamento e as alterações ecológicas associadas a estes fatores estão associados ao aumento do risco de malária. Pode-se ainda concluir que o uso de modelos de regressão de Poisson que capturam o efeito temporal e espacial em um enfoque bayesiano é uma boa estratégia para modelar dados de contagem de malária.

Association between risk behaviors and adiposity indicators in adolescents from Southern Brazil: A methodological approach

The aim of this study was to analyze the association between risk behaviors and adiposity indicators in adolescents and to discuss some methodological aspects related to this relationship. We evaluated 1,321 adolescents (55.2% female) aged 10-16 years. Relative body fat (%fat) by measurement of triceps and subscapular skinfold thickness and waist circumference (WC) were used as total and central adiposity indicators, respectively. Physical inactivity, time spent in front of the TV, the consumption of soda and/or chocolate, alcohol, and tobacco smoking were analyzed as risk behaviors. Information about the socioeconomic status (categorized into three levels) and nutritional status of the mother (overweight or normal weight) were used as adjustment factors in the analyses of prevalence ratio (PR) of the outcomes and their associated 95% confidence intervals (95% CI). The chi-square test and Poisson regression were used for statistical analyses. Low associations were found between risk behaviors and adiposity indicators. Tobacco smoking was the most positively correlated behavior with adiposity in girls (%fat: PR = 1.61; 95% CI = 1.04-2.47; WC: PR = 1.90; 95% CI = 1.17-3.08) and in adolescents whose mothers were normal weight (%fat: PR = 2.31; 95% CI = 1.33-4.03; WC: PR: 2.31; CI: 1.19-4.46). Additionally, as an important methodological issue, we highlighted the assessment of risk behaviors in adolescents as crucial to producing more robust evidence on the subject. Of the investigated behaviors, we concluded that tobacco smoking is the behavior most associated with adiposity indicators.

Towards greater accuracy in individual-tree mortality regression

Background mortality is an essential component of any forest growth and yield model. Forecasts of mortality contribute largely to the variability and accuracy of model predictions at the tree, stand and forest level. In the present study, I implement and evaluate state-of-the-art techniques to increase the accuracy of individual tree mortality models, similar to those used in many of the current variants of the Forest Vegetation Simulator, using data from North Idaho and Montana. The first technique addresses methods to correct for bias induced by measurement error typically present in competition variables. The second implements survival regression and evaluates its performance against the traditional logistic regression approach. I selected the regression calibration (RC) algorithm as a good candidate for addressing the measurement error problem. Two logistic regression models for each species were fitted, one ignoring the measurement error, which is the “naïve” approach, and the other applying RC. The models fitted with RC outperformed the naïve models in terms of discrimination when the competition variable was found to be statistically significant. The effect of RC was more obvious where measurement error variance was large and for more shade-intolerant species. The process of model fitting and variable selection revealed that past emphasis on DBH as a predictor variable for mortality, while producing models with strong metrics of fit, may make models less generalizable. The evaluation of the error variance estimator developed by Stage and Wykoff (1998), and core to the implementation of RC, in different spatial patterns and diameter distributions, revealed that the Stage and Wykoff estimate notably overestimated the true variance in all simulated stands, but those that are clustered. Results show a systematic bias even when all the assumptions made by the authors are guaranteed. I argue that this is the result of the Poisson-based estimate ignoring the overlapping area of potential plots around a tree. Effects, especially in the application phase, of the variance estimate justify suggested future efforts of improving the accuracy of the variance estimate. The second technique implemented and evaluated is a survival regression model that accounts for the time dependent nature of variables, such as diameter and competition variables, and the interval-censored nature of data collected from remeasured plots. The performance of the model is compared with the traditional logistic regression model as a tool to predict individual tree mortality. Validation of both approaches shows that the survival regression approach discriminates better between dead and alive trees for all species. In conclusion, I showed that the proposed techniques do increase the accuracy of individual tree mortality models, and are a promising first step towards the next generation of background mortality models. I have also identified the next steps to undertake in order to advance mortality models further.

Assessing the impacts of grazing levels on bird density in woodland habitat: a Bayesian approach using expert opinion

Many studies on birds focus on the collection of data through an experimental design, suitable for investigation in a classical analysis of variance (ANOVA) framework. Although many findings are confirmed by one or more experts, expert information is rarely used in conjunction with the survey data to enhance the explanatory and predictive power of the model. We explore this neglected aspect of ecological modelling through a study on Australian woodland birds, focusing on the potential impact of different intensities of commercial cattle grazing on bird density in woodland habitat. We examine a number of Bayesian hierarchical random effects models, which cater for overdispersion and a high frequency of zeros in the data using WinBUGS and explore the variation between and within different grazing regimes and species. The impact and value of expert information is investigated through the inclusion of priors that reflect the experience of 20 experts in the field of bird responses to disturbance. Results indicate that expert information moderates the survey data, especially in situations where there are little or no data. When experts agreed, credible intervals for predictions were tightened considerably. When experts failed to agree, results were similar to those evaluated in the absence of expert information. Overall, we found that without expert opinion our knowledge was quite weak. The fact that the survey data is quite consistent, in general, with expert opinion shows that we do know something about birds and grazing and we could learn a lot faster if we used this approach more in ecology, where data are scarce. Copyright (c) 2005 John Wiley & Sons, Ltd.

Socio-environmental factors and suicide in Queensland, Australia

Suicide has drawn much attention from both the scientific community and the public. Examining the impact of socio-environmental factors on suicide is essential in developing suicide prevention strategies and interventions, because it will provide health authorities with important information for their decision-making. However, previous studies did not examine the impact of socio-environmental factors on suicide using a spatial analysis approach. The purpose of this study was to identify the patterns of suicide and to examine how socio-environmental factors impact on suicide over time and space at the Local Governmental Area (LGA) level in Queensland. The suicide data between 1999 and 2003 were collected from the Australian Bureau of Statistics (ABS). Socio-environmental variables at the LGA level included climate (rainfall, maximum and minimum temperature), Socioeconomic Indexes for Areas (SEIFA) and demographic variables (proportion of Indigenous population, unemployment rate, proportion of population with low income and low education level). Climate data were obtained from Australian Bureau of Meteorology. SEIFA and demographic variables were acquired from ABS. A series of statistical and geographical information system (GIS) approaches were applied in the analysis. This study included two stages. The first stage used average annual data to view the spatial pattern of suicide and to examine the association between socio-environmental factors and suicide over space. The second stage examined the spatiotemporal pattern of suicide and assessed the socio-environmental determinants of suicide, using more detailed seasonal data. In this research, 2,445 suicide cases were included, with 1,957 males (80.0%) and 488 females (20.0%). In the first stage, we examined the spatial pattern and the determinants of suicide using 5-year aggregated data. Spearman correlations were used to assess associations between variables. Then a Poisson regression model was applied in the multivariable analysis, as the occurrence of suicide is a small probability event and this model fitted the data quite well. Suicide mortality varied across LGAs and was associated with a range of socio-environmental factors. The multivariable analysis showed that maximum temperature was significantly and positively associated with male suicide (relative risk [RR] = 1.03, 95% CI: 1.00 to 1.07). Higher proportion of Indigenous population was accompanied with more suicide in male population (male: RR = 1.02, 95% CI: 1.01 to 1.03). There was a positive association between unemployment rate and suicide in both genders (male: RR = 1.04, 95% CI: 1.02 to 1.06; female: RR = 1.07, 95% CI: 1.00 to 1.16). No significant association was observed for rainfall, minimum temperature, SEIFA, proportion of population with low individual income and low educational attainment. In the second stage of this study, we undertook a preliminary spatiotemporal analysis of suicide using seasonal data. Firstly, we assessed the interrelations between variables. Secondly, a generalised estimating equations (GEE) model was used to examine the socio-environmental impact on suicide over time and space, as this model is well suited to analyze repeated longitudinal data (e.g., seasonal suicide mortality in a certain LGA) and it fitted the data better than other models (e.g., Poisson model). The suicide pattern varied with season and LGA. The north of Queensland had the highest suicide mortality rate in all the seasons, while there was no suicide case occurred in the southwest. Northwest had consistently higher suicide mortality in spring, autumn and winter. In other areas, suicide mortality varied between seasons. This analysis showed that maximum temperature was positively associated with suicide among male population (RR = 1.24, 95% CI: 1.04 to 1.47) and total population (RR = 1.15, 95% CI: 1.00 to 1.32). Higher proportion of Indigenous population was accompanied with more suicide among total population (RR = 1.16, 95% CI: 1.13 to 1.19) and by gender (male: RR = 1.07, 95% CI: 1.01 to 1.13; female: RR = 1.23, 95% CI: 1.03 to 1.48). Unemployment rate was positively associated with total (RR = 1.40, 95% CI: 1.24 to 1.59) and female (RR=1.09, 95% CI: 1.01 to 1.18) suicide. There was also a positive association between proportion of population with low individual income and suicide in total (RR = 1.28, 95% CI: 1.10 to 1.48) and male (RR = 1.45, 95% CI: 1.23 to 1.72) population. Rainfall was only positively associated with suicide in total population (RR = 1.11, 95% CI: 1.04 to 1.19). There was no significant association for rainfall, minimum temperature, SEIFA, proportion of population with low educational attainment. The second stage is the extension of the first stage. Different spatial scales of dataset were used between the two stages (i.e., mean yearly data in the first stage, and seasonal data in the second stage), but the results are generally consistent with each other. Compared with other studies, this research explored the variety of the impact of a wide range of socio-environmental factors on suicide in different geographical units. Maximum temperature, proportion of Indigenous population, unemployment rate and proportion of population with low individual income were among the major determinants of suicide in Queensland. However, the influence from other factors (e.g. socio-culture background, alcohol and drug use) influencing suicide cannot be ignored. An in-depth understanding of these factors is vital in planning and implementing suicide prevention strategies. Five recommendations for future research are derived from this study: (1) It is vital to acquire detailed personal information on each suicide case and relevant information among the population in assessing the key socio-environmental determinants of suicide; (2) Bayesian model could be applied to compare mortality rates and their socio-environmental determinants across LGAs in future research; (3) In the LGAs with warm weather, high proportion of Indigenous population and/or unemployment rate, concerted efforts need to be made to control and prevent suicide and other mental health problems; (4) The current surveillance, forecasting and early warning system needs to be strengthened, to trace the climate and socioeconomic change over time and space and its impact on population health; (5) It is necessary to evaluate and improve the facilities of mental health care, psychological consultation, suicide prevention and control programs; especially in the areas with low socio-economic status, high unemployment rate, extreme weather events and natural disasters.

Modeling crash outcome probabilities at rural intersections: Application of hierarchical binomial logistic models

It is important to examine the nature of the relationships between roadway, environmental, and traffic factors and motor vehicle crashes, with the aim to improve the collective understanding of causal mechanisms involved in crashes and to better predict their occurrence. Statistical models of motor vehicle crashes are one path of inquiry often used to gain these initial insights. Recent efforts have focused on the estimation of negative binomial and Poisson regression models (and related deviants) due to their relatively good fit to crash data. Of course analysts constantly seek methods that offer greater consistency with the data generating mechanism (motor vehicle crashes in this case), provide better statistical fit, and provide insight into data structure that was previously unavailable. One such opportunity exists with some types of crash data, in particular crash-level data that are collected across roadway segments, intersections, etc. It is argued in this paper that some crash data possess hierarchical structure that has not routinely been exploited. This paper describes the application of binomial multilevel models of crash types using 548 motor vehicle crashes collected from 91 two-lane rural intersections in the state of Georgia. Crash prediction models are estimated for angle, rear-end, and sideswipe (both same direction and opposite direction) crashes. The contributions of the paper are the realization of hierarchical data structure and the application of a theoretically appealing and suitable analysis approach for multilevel data, yielding insights into intersection-related crashes by crash type.

Efficiency and productivity of Singapore’s manufacturing sector 2001-2010: An analysis using Simar and Wilson’s (2007) bootstrapped truncated approach

This paper seeks to explain the lagging productivity in Singapore’s manufacturing noted in the statements of the Economic Strategies Committee Report 2010. Two methods are employed: the Malmquist productivity to measure total factor productivity change and Simar and Wilson’s (J Econ, 136:31–64, 2007) bootstrapped truncated regression approach. In the first stage, the nonparametric data envelopment analysis is used to measure technical efficiency. To quantify the economic drivers underlying inefficiencies, the second stage employs a bootstrapped truncated regression whereby bias-corrected efficiency estimates are regressed against explanatory variables. The findings reveal that growth in total factor productivity was attributed to efficiency change with no technical progress. Most industries were technically inefficient throughout the period except for ‘Pharmaceutical Products’. Sources of efficiency were attributed to quality of worker and flexible work arrangements while incessant use of foreign workers lowered efficiency.

Efficiency and productivity of Singapore's manufacturing sector 2001-2010 : an analysis using bootstrapped truncated approach

This paper seeks to explain the lagging productivity in Singapore’s manufacturing noted in the statements of the Economic Strategies Committee Report 2010. Two methods are employed: the Malmquist productivity to measure total factor productivity (TFP) change and Simar and Wilson’s (2007) bootstrapped truncated regression approach which first derives bias-corrected efficiency estimates before being regressed against explanatory variables to help quantify sources of inefficiencies. The findings reveal that growth in total factor productivity was attributed to efficiency change with no technical progress. Sources of efficiency were attributed to quality of worker and flexible work arrangements while the use of foreign workers lowered efficiency.

Technical efficiency of mainstream airlines and low-cost carriers : New evidence using bootstrap data envelopment analysis truncated regression

Between 2001 and 2005, the US airline industry faced financial turmoil while the European airline industry entered a period of substantive deregulation. Consequently, this opened up opportunities for low-cost carriers to become more competitive in the market. To assess airline performance and identify the sources of efficiency in the immediate aftermath of these events, we employ a bootstrap data envelopment analysis truncated regression approach. The results suggest that at the time the mainstream airlines needed to significantly reorganize and rescale their operations to remain competitive. In the second-stage analysis, the results indicate that private ownership, status as a low-cost carrier, and improvements in weight load contributed to better organizational efficiency.

A two-arm cluster randomized control trial to determine the effectiveness of a pressure ulcer prevention bundle for critically ill patients

Purpose This study tested the effectiveness of a pressure ulcer (PU) prevention bundle in reducing the incidence of PUs in critically ill patients in two Saudi intensive care units (ICUs). Design A two-arm cluster randomized experimental control trial. Methods Participants in the intervention group received the PU prevention bundle, while the control group received standard skin care as per the local ICU policies. Data collected included demographic variables (age, diagnosis, comorbidities, admission trajectory, length of stay) and clinical variables (Braden Scale score, severity of organ function score, mechanical ventilation, PU presence, and staging). All patients were followed every two days from admission through to discharge, death, or up to a maximum of 28 days. Data were analyzed with descriptive correlation statistics, Kaplan-Meier survival analysis, and Poisson regression. Findings The total number of participants recruited was 140: 70 control participants (with a total of 728 days of observation) and 70 intervention participants (784 days of observation). PU cumulative incidence was significantly lower in the intervention group (7.14%) compared to the control group (32.86%). Poisson regression revealed the likelihood of PU development was 70% lower in the intervention group. The intervention group had significantly less Stage I (p = 002) and Stage II PU development (p = 026). Conclusions Significant improvements were observed in PU-related outcomes with the implementation of the PU prevention bundle in the ICU; PU incidence, severity, and total number of PUs per patient were reduced. Clinical Relevance Utilizing a bundle approach and standardized nursing language through skin assessment and translation of the knowledge to practice has the potential to impact positively on the quality of care and patient outcome.