Estimates of influenza-associated excess mortality by three regression models in Shanxi Province during 2013-2017 / 中华预防医学杂志

Objective@#Using three models too estimate excess mortality associated with influenza of Shanxi Province during 2013-2017.@*Methods@#Mortality data and influenza surveillance data of 11 cities of Shanxi Province from the 2013-2014 through 2016-2017 were used to estimate influenza-associated all cause deaths, circulatory and respiratory deaths and respiratory deaths. Three models were used: (i) Serfling regression, (ii)Poisson regression, (iii)General line model.@*Results@#The total reported death cases of all cause were 157 733, annual death cases of all cause were 39 433, among these cases, male cases 93 831 (59.50%), cases above 65 years old 123 931 (78.57%). Annual influenza-associated excess mortality, for all causes, circulatory and respiratory deaths, respiratory deaths were 8.62 deaths per 100 000, 6.33 deaths per 100 000 and 0.68 deaths per 100 000 estimated by Serfling model, respectively; and 21.30 deaths per 100 000, 16.89 deaths per 100 000 and 2.14 deaths per 100 000 estimated by General line model, respectively; and 21.76 deaths per 100 000, 17.03 deaths per 100 000 and 2.05 deaths per 100 000, estimated by Poisson model, respectively. Influenza-related excess mortality was higher in people over 75 years old; influenza-associated excess mortalityfor all causes, circulatory and respiratory deaths, respiratory deaths were 259.67 deaths per 100 000, 229.90 deaths per 100 000 and 32.63 deaths per 100 000, estimated by GLM model, respectively; and 269.49 deaths per 100 000, 233.69 deaths per 100 000 and 31.27 deaths per 100 000, estimated by Poisson model,respectively.@*Conclusion@#Excess mortality associated with influenza mainly caused by A (H3N2), Influenza caused the most associated death amongold people.

Leaf count overdispersion in coffee seedlings / Superdispersão relacionado a contagem de folhas em mudas de cafeeiro

ABSTRACT: Coffee crops play an important role in Brazilian agriculture, with a high level of social and economic participation resulting from the jobs created in the supply chain and from the income obtained by producers and the revenue generated for the country from coffee bean export. In coffee plant growth, leaves have a determinant role in higher production; therefore, the leaf count per plant provides relevant information to producers for adequate crop management, such as foliar fertilizer applications. To describe count data, the Poisson model is the most commonly employed model; when count data show overdispersion, the negative binomial model has been determined to be more adequate. The objective of this study was to compare the fitness of the Poisson and negative binomial models to data on the leaf count per plant in coffee seedlings. Data were collected from an experiment with a randomized block design with 30 treatments and three replicates and four plants per plot. Data from only one treatment, in which the number of leaves was counted over time, were employed. The first count was conducted on 8 April 2016, and the other counts were performed 18, 32, 47, 62, 76, 95, 116, 133, and 153 days after the first evaluation, for a total of ten measurements. The fitness of the models was assessed based on deviance values and simulated envelopes for residuals. Results of fitness assessment indicated that the Poisson model was inadequate for describing the data due to overdispersion. The negative binomial model adequately fitted the observations and was indicated to describe the number of leaves of coffee plants. Based on the negative binomial model, the expected relative increase in the number of leaves was 0.9768% per day.

RESUMO: A cultura do café desempenha papel relevante na agricultura do Brasil, com expressiva participação social e econômica tanto pelos empregos gerados na cadeia produtiva, bem como pela renda obtida pelos produtores e pelas divisas geradas para o país na exportação do grão. No crescimento das plantas de café, as folhas desempenham papel decisivo para que tenha maior produção, portanto a contagem do número de folhas por planta fornece informações importantes aos produtores para o manejo adequado da cultura como, por exemplo, a aplicação de adubações foliares. Em geral, na descrição de dados obtidos por contagem, o modelo mais utilizado é o Poisson, sendo que quando os dados apresentam superdispersão, o modelo Binomial Negativo tem se mostrado mais adequado. O objetivo deste trabalho foi comparar o ajuste dos modelos de Poisson e Binomial Negativo em dados de contagens do número de folhas por planta em mudas do cafeeiro. Os dados foram obtidos de um experimento usando o delineamento em blocos casualizados com trinta tratamentos e três repetições com quatro plantas por parcela. Foram utilizados os dados de apenas um tratamento no qual foi feita a contagem do número de folhas ao longo do tempo. A primeira avaliação foi feita em 8 de abril de 2016 e as demais aos 18, 32, 47, 62, 76, 95, 116, 133 e 153 dias após a primeira avaliação, totalizando dez medidas. A adequação dos mesmos foi verificada com base nos valores da Deviance e no envelope simulado para os resíduos. Os resultados do ajuste indicaram que o modelo Poisson foi inadequado para descrição dos dados devido a superdispersão. O modelo Binomial Negativo se ajustou adequadamente e foi indicado para descrever o número de folhas das plantas do cafeeiro. Com base no modelo Binomial Negativo o aumento relativo esperado para o número de folhas foi de 0,9768% para cada dia.

Model Comparison and Factors Associated with Quality of Life of Type 2 Diabetic Patients: Gender Differentials.

Background: Type 2 diabetes mellitus (T2DM) disease has become public health concern, because of its increasing rate worldwide especially in developing countries. Previous studies have used statistical methods like multiple regression and correlation to show factors associated with Quality of life (QoL) assessed by SF-36 despite the scoring nature of the items. This study aimed at identifying best model and factors associated with gender differentials in QoL among T2DM. Methods: This cross-sectional study recruited T2DM from Diabetes Care Centre of a teaching hospital, South-western, Nigeria. The models considered were Poisson Model with log link function and square-root link function. The model selection criteria used was Akaike Information Criterion (AIC). The model with the smaller AIC was considered to be better. Results: The AIC values for Poisson model with log and square-root link functions for Physical Component Summary (PCS) were 1713 and 1708.3, Mental Component Summary (MCS): 1482.2 and 1480.7, QoL: 2359.7 and 235.8 respectively. Age and diastolic blood pressure had significant negative association with PCS, MCS and QoL in both gender (p<0.05), while occupation and education had significant positive association with PCS, MCS and QoL more in male than female. BMI of normal weight had significant reduction in PCS and QoL of female, whereas this had significant increase in the MCS of male. Conclusion: Poisson model with square-root link function was of better fit to model QoL in T2DM. The significant positive effect of occupation and education on QoL and its domains was more in male than female.

Statistical estimations forPlasmodium vivaxmalaria in South Korea

Objective:To calculate the numbers of weekly infections and prevalence of malaria, and to predict future trend of malaria incidences in South Korea.Methods:Weekly incidences of malaria for 13 years from the period 2001-2013 in South Korea were analyzed. The back-calculation equations were used with incubation period distributions. The maximum likelihood estimation for Poisson model was also used. The confidence intervals of the estimates were obtained by a bootstrap method. A regression model for time series of malaria incidences over 13 years was fitted by the non-linear least squares method, and used to predict futuretrend. Results:The estimated infection curve is narrower and more concentrated in the summer than in the incidence distribution. Infection started around the 19th week and was over around the 41st week. The maximum weekly infection 110 was obtained at the 29th week. The prevalence at the first week was around 496 persons, the minimum number was 366 at 22nd week, and the maximum prevalence was 648 at 34th week. Prevalence drops in late spring with people that falling ill and had had long incubation periods and rose in the summer with new infections. Our future forecast based on the regression model was that an increase at year 2014 compared to 2013 may reach a peak (at maximum about 70 weekly cases) at year 2015, with a decreasing trend after then.Conclusions:This work shows that back-calculation methods could work well in estimating the infection rates and the prevalence of malaria. The obtained results can be useful in establishing an efficient preventive program for malaria infection. The method presented here can be used in other countries where incidence data and incubation period are available.

Statistical estimations for Plasmodium vivax malaria in South Korea

Objective: To calculate the numbers of weekly infections and prevalence of malaria, and to predict future trend of malaria incidences in South Korea. Methods: Weekly incidences of malaria for 13 years from the period 2001-2013 in South Korea were analyzed. The back-calculation equations were used with incubation period distributions. The maximum likelihood estimation for Poisson model was also used. The confidence intervals of the estimates were obtained by a bootstrap method. A regression model for time series of malaria incidences over 13 years was fitted by the non-linear least squares method, and used to predict futuretrend. Results: The estimated infection curve is narrower and more concentrated in the summer than in the incidence distribution. Infection started around the 19th week and was over around the 41st week. The maximum weekly infection 110 was obtained at the 29th week. The prevalence at the first week was around 496 persons, the minimum number was 366 at 22nd week, and the maximum prevalence was 648 at 34th week. Prevalence drops in late spring with people that falling ill and had had long incubation periods and rose in the summer with new infections. Our future forecast based on the regression model was that an increase at year 2014 compared to 2013 may reach a peak (at maximum about 70 weekly cases) at year 2015, with a decreasing trend after then. Conclusions: This work shows that back-calculation methods could work well in estimating the infection rates and the prevalence of malaria. The obtained results can be useful in establishing an efficient preventive program for malaria infection. The method presented here can be used in other countries where incidence data and incubation period are available.

Statistical estimations for Plasmodium vivax malaria in South Korea

OBJECTIVE@#To calculate the numbers of weekly infections and prevalence of malaria, and to predict future trend of malaria incidences in South Korea.@*METHODS@#Weekly incidences of malaria for 13 years from the period 2001-2013 in South Korea were analyzed. The back-calculation equations were used with incubation period distributions. The maximum likelihood estimation for Poisson model was also used. The confidence intervals of the estimates were obtained by a bootstrap method. A regression model for time series of malaria incidences over 13 years was fitted by the non-linear least squares method, and used to predict futuretrend.@*RESULTS@#The estimated infection curve is narrower and more concentrated in the summer than in the incidence distribution. Infection started around the 19th week and was over around the 41st week. The maximum weekly infection 110 was obtained at the 29th week. The prevalence at the first week was around 496 persons, the minimum number was 366 at 22nd week, and the maximum prevalence was 648 at 34th week. Prevalence drops in late spring with people that falling ill and had had long incubation periods and rose in the summer with new infections. Our future forecast based on the regression model was that an increase at year 2014 compared to 2013 may reach a peak (at maximum about 70 weekly cases) at year 2015, with a decreasing trend after then.@*CONCLUSIONS@#This work shows that back-calculation methods could work well in estimating the infection rates and the prevalence of malaria. The obtained results can be useful in establishing an efficient preventive program for malaria infection. The method presented here can be used in other countries where incidence data and incubation period are available.

The Effect of Rear Seatbelt Advocacy and Law Enforcement in Reducing Injuries among Passenger Vehicle Occupants in Malaysia

The increase in car usage due to economic prosperity has led to increase in occupant injuries. One way to reduce the injuries encountered by road accident victims is by implementing the rear seatbelt (RSB) law. Rear seatbelt wearing has been proven to save lives. In Malaysia, the implementation of the restraint system for front occupant has started in the 70’s. However, the rear seatbelt enforcement law only came in 2009, after six months of an advocacy program. Prior to the introduction of the rear seatbelt law, rear seatbelt wearing rate was rather low, started to increase gradually during the advocacy period and jumped to the highest level after two month of the enforcement. This paper attempts to assess the effectiveness of the rear seatbelt intervention in reducing injuries among passenger car occupants in Malaysia using the generalized linear model (GLM). In GLM procedure, the dependent variable is the number of people from passenger vehicles that sustained severe and slight injuries, for the study period. The study period selected covers six months before implementation, six months during advocacy program, and six months after the law is implemented. The independent variables considered are enforcement and balik kampung activities (both are dummy variables) and time effect. Our results suggest that RSB intervention (p-value= 0.0001) had significantly reduced the number of people sustained serious and slight injuries by about 20%. The implementation of change in the RSB law has benefited not only in reducing the number of injuries but also result to great impact to the health outcomes.

Air Pollution and Daily Mortality in Seoul / 예방의학회지

OBJECTIVES: To examine the relationship between air pollution exposure and mortality in Seoul for the years of 1991-1995. METHODS: Daily counts of death were analyzed by general additive Poisson model, with adjustment for effects of secular trend, seasonal factor, day of the week, heat wave, temperature, and humidity. Pollution variables were ozone, nitrogen dioxide, total suspended particles(TSP), and sulfur dioxide. RESULTS: Daily death counts were associated with ozone(1 day before), nitrogen dioxide(1 day before), TSP(2 days before), sulfur dioxide(2 days before). The association with ozone was most statistically significant and independent of other air pollutants. Increase of 100 ppb in ozone was associated with 6%(95% CI= 2%-10%) increase in the daily number of death. This effect was greater in persons aged 65 and older. The relative risks of death from respiratory disease and cardiovascular disease were greater than for all-cause mortality in each pollutant. After ozone level exceeds 25 ppb, the dose-response relationship between mortality and ozone was almost linear. However, the effect of TSP, sulfur dioxide, and nitrogen dioxide on mortality might be confounded with each other. CONCLUSION: Daily variations in air pollution within the range currently occurring in Seoul might have an adverse effect on daily mortality.