Effects of temperature variation on suicide in five U.S. counties, 1991–2001

Effects of weather variables on suicide are well-documented, but there is still little consistency among the results of most studies. Nevertheless, most studies show a peak in suicides during the spring season, and this is often attributed to increased temperatures. The purpose of this study is to test the relationship between monthly temperature and monthly suicide, independent of months or seasons, for five counties located across the United States. Harmonic analysis shows that four of the five counties display some seasonal components in the suicide data. However, simple linear regression shows no correlation between suicide and temperature, and discriminant analysis shows that monthly departure from mean annual suicide rates is not a useful tool for identifying months with temperatures that are colder or warmer than the annual average. Therefore, it appears that the seasonality of suicides is due to factors other than temperature.     

Suicide seasonality: complex demodulation as a novel approach in epidemiologic analysis

Background

Seasonality of suicides is well-known and nearly ubiquitous, but recent evidence showed inconsistent patterns of decreasing or increasing seasonality in different countries. Furthermore, strength of seasonality was hypothesized to be associated with suicide prevalence. This study aimed at pointing out methodological difficulties in examining changes in suicide seasonality.

Methododology/Principal Findings

The present study examines the hypothesis of decreasing seasonality with a superior method that allows continuous modeling of seasonality. Suicides in Austria (1970–2008, N = 67,741) were analyzed with complex demodulation, a local (point-in-time specific) version of harmonic analysis. This avoids the need to arbitrarily split the time series, as is common practice in the field of suicide seasonality research, and facilitates incorporating the association with suicide prevalence. Regression models were used to assess time trends and association of amplitude and absolute suicide numbers. Results showed that strength of seasonality was associated with absolute suicide numbers, and that strength of seasonality was stable during the study period when this association was taken into account.

Conclusion/Significance

Continuous modeling of suicide seasonality with complex demodulation avoids spurious findings that can result when time series are segmented and analyzed piecewise or when the association with suicide prevalence is disregarded.     

Applications of additive semivarying coefficient models: monthly suicide data from Hong Kong

A semivarying coefficient model for the monthly numbers of suicides in Hong Kong is developed and a new estimation procedure for estimating the parametric component is proposed. The estimators are examined in a small simulation study and fitted to monthly suicide data to estimate a nonparametric long-term trend and parametric seasonal and socioeconomic effects. Fitting the model detected interpretable structure in the data that is consistent with that driving public health policy. While exploratory, the analysis motivates the collection of more detailed data and the development of more sophisticated models to help determine target groups and strategies to reduce the suicide rate in Hong Kong.     

In humans, the seasonal variation in poly-unsaturated fatty acids is related to the seasonal variation in violent suicide and serotonergic markers of violent suicide

BACKGROUND: Depression is accompanied by a depletion of n-3 poly-unsaturated fatty acids (PUFAs). There is also a negative correlation between suicide and fish-oil intake (rich in n-3 PUFAs) across different countries. Both depression and suicide show a seasonal variation and are related to disorders in the serotonergic system. AIMS: The present study was carried out to determine if there is a seasonal variation in the PUFA fractions in serum phospholipids and whether there are significant relationships between lowered n-3 PUFA status and the seasonal variation in the number of suicide deaths and serotonergic markers of suicide. METHODS: We took monthly blood samples during 1 calendar year from 23 healthy volunteers and analyzed the PUFA composition in serum phospholipids and related those data to the annual variation in the mean weekly number of suicides for Belgium and the Bmax [3H]-paroxetine binding to platelets in the same 23 subjects. RESULTS: Significant annual rhythms were detected in the long-chain PUFAs only, i.e. arachidonic acid (C20: 4n-6; AA), eicosapentaenoic acid (C20: 5n-3; EPA), and docosahexaenoic acid (C22: 6n-3; DHA). There was a significant correlation between the changes over the last 2 weeks in AA and EPA and the mean weekly number of violent, but not nonviolent, suicide deaths in Belgium. There was a significant correlation between the PUFAs, AA and DHA, and the Bmax [3H]-paroxetine binding to platelets. CONCLUSIONS: Our results show that there is a true seasonality in long-chain PUFAs, such as AA, EPA and DHA. The results suggest that the seasonality in PUFAs may be related to the incidence of violent suicide and the expression of the serotonin transporter complex.     

Seasonal Variation of Suicide in Brazil

Most of what is known about the seasonal variation in suicide rate originates from studies conducted in the northern hemisphere; very few studies have been done in the southern hemisphere. The purpose of the present study was to explore the possibility that in Brazil, the seasonal variation of suicides is a function of photoperiod. This was accomplished by analyzing monthly suicide data for a 12 yr period (1979 to 1990), within latitudes ranging from 2°N to 33°S. Single cosinor analyses with periods of 12 or 6 months were applied to time series of monthly total and suicidal deaths, separated by gender and state. Significant spring or early summer peaks of suicide were found only in the south of Brazil for both men and women, except for the latter in one state. These peaks did not coincide with those found for total deaths, which occurred in the autumn or winter in all areas. No significant six‐month period was found. In the present study, the chance of a suicide was typically 10–17% higher during the peak period than during the other months of the year. Although this moderate seasonal effect might not be sufficient to justify planning large scale prophylactic interventions, those dealing with patients who have suicide ideation should be aware of this high risk time.     

Seasonal variation of suicide in Brazil

Most of what is known about the seasonal variation in suicide rate originates from studies conducted in the northern hemisphere; very few studies have been done in the southern hemisphere. The purpose of the present study was to explore the possibility that in Brazil, the seasonal variation of suicides is a function of photoperiod. This was accomplished by analyzing monthly suicide data for a 12 yr period (1979 to 1990), within latitudes ranging from 2°N to 33°S. Single cosinor analyses with periods of 12 or 6 months were applied to time series of monthly total and suicidal deaths, separated by gender and state. Significant spring or early summer peaks of suicide were found only in the south of Brazil for both men and women, except for the latter in one state. These peaks did not coincide with those found for total deaths, which occurred in the autumn or winter in all areas. No significant six-month period was found. In the present study, the chance of a suicide was typically 10-17% higher during the peak period than during the other months of the year. Although this moderate seasonal effect might not be sufficient to justify planning large scale prophylactic interventions, those dealing with patients who have suicide ideation should be aware of this high risk time.     

Seasonality of retinal detachment incidence and its associations with climate: an 11-year nationwide population-based study

This study aimed to examine the seasonal variability of retinal detachment (RD) in Taiwan by using an 11-yr nationwide population database. This study also investigated the association of weather conditions, i.e., ambient temperature, relative humidity, rainfall, monthly hours of sunshine, and atmospheric pressure, with RD. Data were retrospectively collected from the Taiwan National Health Insurance Research Database. The study sample included 23 718 RD hospitalizations between January 1999 and December 2009. The incidence rate of RD/100 000 people over the 132 months was computed according to sex and age groupings of <20, 20-39, 40-59, and ≥60 yrs. Then, the association between climatic factors and the monthly RD incidence rate was examined. The ARIMA (autoregressive integrated moving average) method was also employed to test the seasonality of RD incidence rates and their association with climatic factors. The annual RD incidence rates were between 7.8 and 10.8 cases/100 000 people during the study period. A fairly similar seasonal pattern of monthly RD incidence rates was apparent for males and females and males and females combined. Rates were highest August through October, decreasing in November, and lowest in February. After adjusting for time, trend, and month, the ARIMA regression models for the male, female, and males and females combined consistently revealed the monthly RD incidence rate was significantly and positively associated with ambient temperature, but negatively associated with atmospheric pressure. The authors conclude that the monthly RD incidence rates were significantly associated with seasonality. The monthly RD incidence rates were positively associated with ambient temperature and negatively associated with atmospheric pressure.     

Does diurnal temperature range influence seasonal suicide mortality? Assessment of daily data of the Helsinki metropolitan area from 1973 to 2010

Several studies show a peak in suicide rates during springtime and suggest differences in the seasonal variation of suicides. However, the seasonal distribution of the temperature impact on suicide is less clear. This study investigated the relationship between diurnal temperature range (DTR) on suicide mortality. Daily temperature and suicide data for Helsinki were analyzed for the period of 1973–2010 inclusive. Overall, DTR reached its maximum during the spring from mid-April to mid-June, which is also the season with highest suicide mortality in the study region. Specifically, the seasonal timing and maxima for both DTR and suicides vary from year to year. Time series analysis of DTR and suicide records revealed a significant (P?<?0.01) correlation between the springtime DTR maxima and suicide rates for males. No similar association could be found for females. These results provide evidence that a higher springtime DTR could be linked statistically to a higher seasonal suicide rate each spring, whereas the exact timing of the DTR peak did not associate with the seasonal suicide rate. A possible mechanism behind the springtime association between the DTR and suicides originates from brown adipose tissue (BAT) over-activity. Activation of BAT through the winter improves cold tolerance at the cost of heat tolerance. This might trigger anxiety and psychomotor agitation, affecting mood in a negative way. As a hypothesis, the compromised heat tolerance is suggested to increase the risk of death from suicide.     

Paraquat Prohibition and Change in the Suicide Rate and Methods in South Korea

The annual suicide rate in South Korea is the highest among the developed countries. Paraquat is a highly lethal herbicide, commonly used in South Korea as a means for suicide. We have studied the effect of the 2011 paraquat prohibition on the national suicide rate and method of suicide in South Korea. We obtained the monthly suicide rate from 2005 to 2013 in South Korea. In our analyses, we adjusted for the effects of celebrity suicides, and economic, meteorological, and seasonal factors on suicide rate. We employed change point analysis to determine the effect of paraquat prohibition on suicide rate over time, and the results were verified by structural change analysis, an alternative statistical method. After the paraquat prohibition period in South Korea, there was a significant reduction in the total suicide rate and suicide rate by poisoning with herbicides or fungicides in all age groups and in both genders. The estimated suicide rates during this period decreased by 10.0% and 46.1% for total suicides and suicides by poisoning of herbicides or fungicides, respectively. In addition, method substitution effect of paraquat prohibition was found in suicide by poisoning by carbon monoxide, which did not exceed the reduction in the suicide rate of poisoning with herbicides or fungicides. In South Korea, paraquat prohibition led to a lower rate of suicide by paraquat poisoning, as well as a reduction in the overall suicide rate. Paraquat prohibition should be considered as a national suicide prevention strategy in developing and developed countries alongside careful observation for method substitution effects.     

A low footprint olive grove weather forecasting using a single-layered seasonal attention encoder-decoder model

Weather forecasting is essential in various applications such as olive smart farming. Farmers use the predicted weather data to take appropriate actions with the aim of increasing the crop production. Many deep learning models have been developed for tackling such a problem. However, olive groves are located in remote areas with no Internet connectivity, therefore these models are not applicable as they require either powerful processors or communication with cloud servers for inference. In this work, we propose a deep learning encoder-decoder model that uses a seasonal attention mechanism for time series forecasting of weather variables. The proposed model is non-complex, yet more powerful, compared to the more complex models in the literature. We use this model as the core of a framework that preprocess the training and testing data, train the model, and deploy the model on a resource-constrained microcontroller. Using real-life weather datasets of Spanish, Greek, and Chinese weather stations, we prove that the proposed model achieves a higher prediction accuracy compared to the existing literature. More specifically, the achieved prediction mean absolute error (MAE) is 2.13 °C and root mean squared error (RMSE) is 2.64 °C. This outstanding accuracy performance is achieved with the model requiring only 37.6 kB of memory for storing the model parameters with a total memory requirement of 50.1 kB. Since the model is relatively non-complex, we implement it on the Raspberry Pi Pico platform which has a very low cost with minimal power consumption compared to other embedded platforms. We also build a prototype and test it to verify the model's ability to achieve the target objective in real-life scenarios.     

Time series analysis models for estimation of greenhouse gas emitted by different sectors in Turkey

Due to the increasing global warming in the world, analyzing greenhouse gas emissions is a crucial issue. This study has examined greenhouse gas emissions in Turkey according to energy sector, industrial processes sector, agriculture sector and waste sector. Then, time series analysis models are used to estimate greenhouse gas emissions based on sectors. Models' performances are tested using mean error, mean absolute error and root mean square error. The results show that forecasting models have a good potential to estimate the national greenhouse gas emissions for different sector within a reasonable error. The study results will help organize and estimate the national greenhouse gas emissions inventory.     

Seasonality of Retinal Detachment Incidence and Its Associations With Climate: An 11-Year Nationwide Population-Based Study

This study aimed to examine the seasonal variability of retinal detachment (RD) in Taiwan by using an 11-yr nationwide population database. This study also investigated the association of weather conditions, i.e., ambient temperature, relative humidity, rainfall, monthly hours of sunshine, and atmospheric pressure, with RD. Data were retrospectively collected from the Taiwan National Health Insurance Research Database. The study sample included 23 718 RD hospitalizations between January 1999 and December 2009. The incidence rate of RD/100 000 people over the 132 months was computed according to sex and age groupings of <20, 20–39, 40–59, and ≥60 yrs. Then, the association between climatic factors and the monthly RD incidence rate was examined. The ARIMA (autoregressive integrated moving average) method was also employed to test the seasonality of RD incidence rates and their association with climatic factors. The annual RD incidence rates were between 7.8 and 10.8 cases/100 000 people during the study period. A fairly similar seasonal pattern of monthly RD incidence rates was apparent for males and females and males and females combined. Rates were highest August through October, decreasing in November, and lowest in February. After adjusting for time, trend, and month, the ARIMA regression models for the male, female, and males and females combined consistently revealed the monthly RD incidence rate was significantly and positively associated with ambient temperature, but negatively associated with atmospheric pressure. The authors conclude that the monthly RD incidence rates were significantly associated with seasonality. The monthly RD incidence rates were positively associated with ambient temperature and negatively associated with atmospheric pressure. (Author correspondence: henry11111@tmu.edu.tw)     

Generalized Seasonal Autoregressive Integrated Moving Average Models for Count Data with Application to Malaria Time Series with Low Case Numbers

Introduction

With the renewed drive towards malaria elimination, there is a need for improved surveillance tools. While time series analysis is an important tool for surveillance, prediction and for measuring interventions’ impact, approximations by commonly used Gaussian methods are prone to inaccuracies when case counts are low. Therefore, statistical methods appropriate for count data are required, especially during “consolidation” and “pre-elimination” phases.

Methods

Generalized autoregressive moving average (GARMA) models were extended to generalized seasonal autoregressive integrated moving average (GSARIMA) models for parsimonious observation-driven modelling of non Gaussian, non stationary and/or seasonal time series of count data. The models were applied to monthly malaria case time series in a district in Sri Lanka, where malaria has decreased dramatically in recent years.

Results

The malaria series showed long-term changes in the mean, unstable variance and seasonality. After fitting negative-binomial Bayesian models, both a GSARIMA and a GARIMA deterministic seasonality model were selected based on different criteria. Posterior predictive distributions indicated that negative-binomial models provided better predictions than Gaussian models, especially when counts were low. The G(S)ARIMA models were able to capture the autocorrelation in the series.

Conclusions

G(S)ARIMA models may be particularly useful in the drive towards malaria elimination, since episode count series are often seasonal and non-stationary, especially when control is increased. Although building and fitting GSARIMA models is laborious, they may provide more realistic prediction distributions than do Gaussian methods and may be more suitable when counts are low.     

The Mexican Cycle of Suicide: A National Analysis of Seasonality, 2000-2013

Introduction

Suicide is a complex and multifactorial phenomenon with growing importance to public health. An increase in its occurrence has been observed in Mexico over the past 10 years. The present article analyzes the secular trend in suicide at the national level between the years 2000 and 2013.

Materials and Methods

All suicides during the study period (n = 64,298, of which 82.11% were men) were characterized using a spectral decomposition of the time series and a wavelet analysis to evaluate the effect of seasonal changes, type of area (urban versus rural) and sex.

Results

A seasonal pattern was observed with statistically significant cycles every 12 months, where peaks were identified in May but only for men in urban zones as of the year 2007. In addition, specific days of the year were found to have a higher frequency of suicides, which coincided with holidays (New Year, Mother’s Day, Mexican Independence Day and Christmas).

Conclusion

A wavelet analysis can be used to decompose complex time series. To the best of our knowledge, this is the first application of this technique to the study of suicides in developing countries. This analysis enabled identifying a seasonal pattern among urban men in Mexico. The identification of seasonal patterns can help to create primary prevention strategies, increase the dissemination of crisis intervention strategies and promote mental health. These strategies could be emphasized during specific periods of the year and directed towards profiles with a higher risk.     

Parametric and semiparametric approaches to testing for seasonal trend in serial count data

We present two tests for seasonal trend in monthly incidence data. The first approach uses a penalized likelihood to choose the number of harmonic terms to include in a parametric harmonic model (which includes time trends and autogression as well as seasonal harmonic terms) and then tests for seasonality using a parametric bootstrap test. The second approach uses a semiparametric regression model to test for seasonal trend. In the semiparametric model, the seasonal pattern is modeled nonparametrically, parametric terms are included for autoregressive effects and a linear time trend, and a parametric bootstrap test is used to test for seasonality. For both procedures, a null distribution is generated under a null Poisson model with time trends and autoregression parameters.We apply the methods to skin melanoma incidence rates collected by the surveillance, epidemiology, and end results (SEER) program of the National Cancer Institute, and perform simulation studies to evaluate the type I error rate and power for the two procedures. These simulations suggest that both procedures are alpha-level procedures. In addition, the harmonic model/bootstrap test had similar or larger power than the semiparametric model/bootstrap test for a wide range of alternatives, and the harmonic model/bootstrap test is much easier to implement. Thus, we recommend the harmonic model/bootstrap test for the analysis of seasonal incidence data.     

SEASONALITY OF PNEUMONIA ADMISSIONS AND ITS ASSOCIATION WITH CLIMATE: AN EIGHT-YEAR NATIONWIDE POPULATION-BASED STUDY

The aim of the study was to examine seasonal variability in monthly admissions for community-acquired pneumonia (CAP) in Taiwan. Our study sample comprised 477,541 pneumonia patients in Taiwan between 1998 and 2005, inclusive. Results showed a fairly consistent seasonal pattern of pneumonia admissions, regardless of sex and age, and for the groups combined. Seasonal trends showed a peak in hospitalizations from January through April, followed by a sharp decrease in May and a trough from August through October. The auto-regressive integrated moving average (ARIMA) test found significant seasonality for all age and sex groups and for the whole sample (all p?<?0.001). After adjusting for seasonality, month, and trends, the ARIMA regression models revealed that the monthly pneumonia admissions rate was significantly associated with ambient temperature, for the total sample, for female groups, and for the 65–74 and?≥75 age groups (all p?<?0.01). A 1°C decrease in ambient temperature was associated with roughly a 0.03 increase in monthly pneumonia admissions rate (per 10,000 people) for the entire sample. We conclude the monthly pneumonia admissions rate was significantly associated with seasonality, and was higher in periods with low ambient temperatures. (Author correspondence: henry11111@tmu.edu.tw)     

Comparison of Two Hybrid Models for Forecasting the Incidence of Hemorrhagic Fever with Renal Syndrome in Jiangsu Province,China

Background

Cases of hemorrhagic fever with renal syndrome (HFRS) are widely distributed in eastern Asia, especially in China, Russia, and Korea. It is proved to be a difficult task to eliminate HFRS completely because of the diverse animal reservoirs and effects of global warming. Reliable forecasting is useful for the prevention and control of HFRS.

Methods

Two hybrid models, one composed of nonlinear autoregressive neural network (NARNN) and autoregressive integrated moving average (ARIMA) the other composed of generalized regression neural network (GRNN) and ARIMA were constructed to predict the incidence of HFRS in the future one year. Performances of the two hybrid models were compared with ARIMA model.

Results

The ARIMA, ARIMA-NARNN ARIMA-GRNN model fitted and predicted the seasonal fluctuation well. Among the three models, the mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of ARIMA-NARNN hybrid model was the lowest both in modeling stage and forecasting stage. As for the ARIMA-GRNN hybrid model, the MSE, MAE and MAPE of modeling performance and the MSE and MAE of forecasting performance were less than the ARIMA model, but the MAPE of forecasting performance did not improve.

Conclusion

Developing and applying the ARIMA-NARNN hybrid model is an effective method to make us better understand the epidemic characteristics of HFRS and could be helpful to the prevention and control of HFRS.     

A Hybrid Model for Predicting the Prevalence of Schistosomiasis in Humans of Qianjiang City,China

Backgrounds/Objective

Schistosomiasis is still a major public health problem in China, despite the fact that the government has implemented a series of strategies to prevent and control the spread of the parasitic disease. Advanced warning and reliable forecasting can help policymakers to adjust and implement strategies more effectively, which will lead to the control and elimination of schistosomiasis. Our aim is to explore the application of a hybrid forecasting model to track the trends of the prevalence of schistosomiasis in humans, which provides a methodological basis for predicting and detecting schistosomiasis infection in endemic areas.

Methods

A hybrid approach combining the autoregressive integrated moving average (ARIMA) model and the nonlinear autoregressive neural network (NARNN) model to forecast the prevalence of schistosomiasis in the future four years. Forecasting performance was compared between the hybrid ARIMA-NARNN model, and the single ARIMA or the single NARNN model.

Results

The modelling mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the ARIMA-NARNN model was 0.1869×10⁻⁴, 0.0029, 0.0419 with a corresponding testing error of 0.9375×10⁻⁴, 0.0081, 0.9064, respectively. These error values generated with the hybrid model were all lower than those obtained from the single ARIMA or NARNN model. The forecasting values were 0.75%, 0.80%, 0.76% and 0.77% in the future four years, which demonstrated a no-downward trend.

Conclusion

The hybrid model has high quality prediction accuracy in the prevalence of schistosomiasis, which provides a methodological basis for future schistosomiasis monitoring and control strategies in the study area. It is worth attempting to utilize the hybrid detection scheme in other schistosomiasis-endemic areas including other infectious diseases.     

Morbidity Rate Prediction of Dengue Hemorrhagic Fever (DHF) Using the Support Vector Machine and the Aedes aegypti Infection Rate in Similar Climates and Geographical Areas

BackgroundIn the past few decades, several researchers have proposed highly accurate prediction models that have typically relied on climate parameters. However, climate factors can be unreliable and can lower the effectiveness of prediction when they are applied in locations where climate factors do not differ significantly. The purpose of this study was to improve a dengue surveillance system in areas with similar climate by exploiting the infection rate in the Aedes aegypti mosquito and using the support vector machine (SVM) technique for forecasting the dengue morbidity rate.ConclusionsThe infection rates of the Ae. aegypti female mosquitoes and larvae improved the morbidity rate forecasting efficiency better than the climate parameters used in classical frameworks. We demonstrated that the SVM-R-based model has high generalization performance and obtained the highest prediction performance compared to classical models as measured by the accuracy, sensitivity, specificity, and mean absolute error (MAE).     

Statistical modelling and forecasting annual sugarcane production in India: Using various time series models

This paper proposes a comparison of various time series forecasting models to forecast annual data on sugarcane production over 63 years from 1960 to 2022. In this research, the Mean Forecast Model, the Naive Model, the Simple Exponential Smoothing Model, Holt's model, and the Autoregressive Integrated Moving Average time series models have all been used to make effective and accurate predictions for sugarcane. Different scale-dependent error forecasting techniques and residual analysis have been used to examine the forecasting accuracy of these time series models. SE of Residuals, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Akaike's Information Criterion (AIC) are used to analyse the forecast's accuracy. The best model has been selected based on the predictions with the lowest value, according to the three-performance metrics of RMSE, MAE, and AIC. The estimated sugarcane production shows an increasing trend for the next 10 years and is projected to be 37,763.38 million tonnes in the year 2032. Further, empirical results support the plan and execution of viable strategies to advance sugarcane production in India to fulfil the utilisation need of the increasing population and further improve food security.