A Population-Level Study Concerning the Assumed Association Between Suicide Rates and Antidepressant Consumption in Hungary

BACKGROUND

It has been claimed that the advent of modern antidepressants has reduced the suicide rate.

AIMS

To examine the correlation between the suicide rate and the prescription of antidepressants.

METHOD

A dynamic regression was employed to analyze a 73-month-long, monthly time series between 2010 and 2016 in Hungary. The independent variable was the Defined Daily Dose value for the number of antidepressant (AD) prescriptions filled each month.

RESULTS

The models failed to show a significant association between the prescription of antidepressants and age- and sex-specific monthly suicide rates.

CONCLUSIONS

The prescription of antidepressants in Hungary has had no impact on suicide rates.

Prediction of early-onset colorectal cancer mortality rates in the United States using machine learning

INTRODUCTION

The current study, focusing on a significant US (United States) colorectal cancer (CRC) burden, employs machine learning for predicting future rates among young population.

METHODS

CDC WONDER data from 1999 to 2022 was analyzed for CRC-related mortality in patients younger than 56 years. Temporal trends in age-adjusted mortality rates (AAMRs) were assessed via Joinpoint software. Future mortality rates were forecasted using an optimal Autoregressive Integrated Moving Average (ARIMA) model.

RESULTS

From 1999 to 2022, we observed 150,908 deaths with CRC listed as the underlying cause, predominantly in males, with an upward trend in AAMR. The ARIMA model projects an increase in CRC mortality by 2035, estimating an average annual percent change (AAPC) of 1.3% overall, 1% for females, and 1.5% for males.

CONCLUSION

Our study findings emphasize the need for more robust preventive measures to reduce future CRC mortality among younger population. These results have significant implications for public health policies, particularly for males under 56, and underscore the importance of early screening and lifestyle modifications.

Early Warning and Prediction of Scarlet Fever in China Using the Baidu Search Index and Autoregressive Integrated Moving Average With Explanatory Variable (ARIMAX) Model: Time Series Analysis

BACKGROUND

Internet-derived data and the autoregressive integrated moving average (ARIMA) and ARIMA with explanatory variable (ARIMAX) models are extensively used for infectious disease surveillance. However, the effectiveness of the Baidu search index (BSI) in predicting the incidence of scarlet fever remains uncertain.

OBJECTIVE

Our objective was to investigate whether a low-cost BSI monitoring system could potentially function as a valuable complement to traditional scarlet fever surveillance in China.

METHODS

ARIMA and ARIMAX models were developed to predict the incidence of scarlet fever in China using data from the National Health Commission of the People's Republic of China between January 2011 and August 2022. The procedures included establishing a keyword database, keyword selection and filtering through Spearman rank correlation and cross-correlation analyses, construction of the scarlet fever comprehensive search index (CSI), modeling with the training sets, predicting with the testing sets, and comparing the prediction performances.

RESULTS

The average monthly incidence of scarlet fever was 4462.17 (SD 3011.75) cases, and annual incidence exhibited an upward trend until 2019. The keyword database contained 52 keywords, but only 6 highly relevant ones were selected for modeling. A high Spearman rank correlation was observed between the scarlet fever reported cases and the scarlet fever CSI (rs=0.881). We developed the ARIMA(4,0,0)(0,1,2)(12) model, and the ARIMA(4,0,0)(0,1,2)(12) + CSI (Lag=0) and ARIMAX(1,0,2)(2,0,0)(12) models were combined with the BSI. The 3 models had a good fit and passed the residuals Ljung-Box test. The ARIMA(4,0,0)(0,1,2)(12), ARIMA(4,0,0)(0,1,2)(12) + CSI (Lag=0), and ARIMAX(1,0,2)(2,0,0)(12) models demonstrated favorable predictive capabilities, with mean absolute errors of 1692.16 (95% CI 584.88-2799.44), 1067.89 (95% CI 402.02-1733.76), and 639.75 (95% CI 188.12-1091.38), respectively; root mean squared errors of 2036.92 (95% CI 929.64-3144.20), 1224.92 (95% CI 559.04-1890.79), and 830.80 (95% CI 379.17-1282.43), respectively; and mean absolute percentage errors of 4.33% (95% CI 0.54%-8.13%), 3.36% (95% CI -0.24% to 6.96%), and 2.16% (95% CI -0.69% to 5.00%), respectively. The ARIMAX models outperformed the ARIMA models and had better prediction performances with smaller values.

CONCLUSIONS

This study demonstrated that the BSI can be used for the early warning and prediction of scarlet fever, serving as a valuable supplement to traditional surveillance systems.

Analyzing physiological signals recorded with a wearable sensor across the menstrual cycle using circular statistics

This study aims to identify the most significant features in physiological signals representing a biphasic pattern in the menstrual cycle using circular statistics which is an appropriate analytic method for the interpretation of data with a periodic nature. The results can be used empirically to determine menstrual phases. A non-uniform pattern was observed in ovulating subjects, with a significant periodicity (p< 0.05) in mean temperature, heart rate (HR), Inter-beat Interval (IBI), mean tonic component of Electrodermal Activity (EDA), and signal magnitude area (SMA) of the EDA phasic component in the frequency domain. In contrast, non-ovulating cycles displayed a more uniform distribution (p> 0.05). There was a significant difference between ovulating and non-ovulating cycles (p< 0.05) in temperature, IBI, and EDA but not in mean HR. Selected features were used in training an Autoregressive Integrated Moving Average (ARIMA) model, using data from at least one cycle of a subject, to predict the behavior of the signal in the last cycle. By iteratively retraining the algorithm on a per-day basis, the mean temperature, HR, IBI and EDA tonic values of the next day were predicted with root mean square error (RMSE) of 0.13 ± 0.07 (C°), 1.31 ± 0.34 (bpm), 0.016 ± 0.005 (s) and 0.17 ± 0.17 (μS), respectively.

Comparison of statistical models to predict age-standardized cancer incidence in Switzerland

This study compares the performance of statistical methods for predicting age-standardized cancer incidence, including Poisson generalized linear models, age-period-cohort (APC) and Bayesian age-period-cohort (BAPC) models, autoregressive integrated moving average (ARIMA) time series, and simple linear models. The methods are evaluated via leave-future-out cross-validation, and performance is assessed using the normalized root mean square error, interval score, and coverage of prediction intervals. Methods were applied to cancer incidence from the three Swiss cancer registries of Geneva, Neuchatel, and Vaud combined, considering the five most frequent cancer sites: breast, colorectal, lung, prostate, and skin melanoma and bringing all other sites together in a final group. Best overall performance was achieved by ARIMA models, followed by linear regression models. Prediction methods based on model selection using the Akaike information criterion resulted in overfitting. The widely used APC and BAPC models were found to be suboptimal for prediction, particularly in the case of a trend reversal in incidence, as it was observed for prostate cancer. In general, we do not recommend predicting cancer incidence for periods far into the future but rather updating predictions regularly.

A potential tool for predicting epidemic trends and outbreaks of scrub typhus based on Internet search big data analysis in Yunnan Province, China

Introduction

Scrub typhus, caused by Orientia tsutsugamushi, is a neglected tropical disease. The southern part of China is considered an important epidemic and conserved area of scrub typhus. Although a surveillance system has been established, the surveillance of scrub typhus is typically delayed or incomplete and cannot predict trends in morbidity. Internet search data intuitively expose the public's attention to certain diseases when used in the public health area, thus reflecting the prevalence of the diseases.

Methods

In this study, based on the Internet search big data and historical scrub typhus incidence data in Yunnan Province of China, the autoregressive integrated moving average (ARIMA) model and ARIMA with external variables (ARIMAX) model were constructed and compared to predict the scrub typhus incidence.

Results

The results showed that the ARIMAX model produced a better outcome than the ARIMA model evaluated by various indexes and comparisons with the actual data.

Conclusions

The study demonstrates that Internet search big data can enhance the traditional surveillance system in monitoring and predicting the prevalence of scrub typhus and provides a potential tool for monitoring epidemic trends of scrub typhus and early warning of its outbreaks.

Empirical Evaluation of Alternative Time-Series Models for COVID-19 Forecasting in Saudi Arabia

COVID-19 is a disease-causing coronavirus strain that emerged in December 2019 that led to an ongoing global pandemic. The ability to anticipate the pandemic's path is critical. This is important in order to determine how to combat and track its spread. COVID-19 data is an example of time-series data where several methods can be applied for forecasting. Although various time-series forecasting models are available, it is difficult to draw broad theoretical conclusions regarding their relative merits. This paper presents an empirical evaluation of several time-series models for forecasting COVID-19 cases, recoveries, and deaths in Saudi Arabia. In particular, seven forecasting models were trained using autoregressive integrated moving average, TBATS, exponential smoothing, cubic spline, simple exponential smoothing Holt, and HoltWinters. The models were built using publicly available daily data of COVID-19 during the period of 24 March 2020 to 5 April 2021 reported in Saudi Arabia. The experimental results indicate that the ARIMA model had a smaller prediction error in forecasting confirmed cases, which is consistent with results reported in the literature, while cubic spline showed better predictions for recoveries and deaths. As more data become available, a fluctuation in the forecasting-accuracy metrics was observed, possibly due to abrupt changes in the data.

An epidemiological study of brucellosis on mainland China during 2004-2018

Brucellosis has re-emerged in China in recent years, resulting in an increasing health burden and economic losses for humans and the livestock industries. This study integrated data from human and livestock brucellosis surveillance systems to explore the changing epidemiology of brucellosis from 2004 to 2018 in China. A total of 524,980 human cases of brucellosis were reported, with the average annual incidence in humans being significantly higher for the period 2012-2018 than for 2004-2011 (3.3 vs. 1.9 per 100,000 residents). An autoregressive integrated moving average (ARIMA) model predicted an upward trend in the monthly incidence of brucellosis in humans in 2019 and 2020. Characteristics including being male, aged 45-54 years, working in the livestock industries, and residing in the northern provinces of China increased the risk of people contracting brucellosis. The percentage of provinces with infected people increased from 67.7% (21/31) in 2004 to all provinces in 2018. A total of 29,115 outbreaks were reported in livestock from 2004 to 2018, with 443,883 seropositive animals although only 381,224 (85.9%) of these were culled. The monthly incidence of brucellosis in humans was strongly positively correlated (r = .539, p < .001) with the number of outbreaks of brucellosis in livestock reported 3 months prior to the human cases. At the provincial level, the annual incidence of brucellosis in humans was significantly positively correlated with the sheep population (r = .786, p < .01). In conclusion, brucellosis in humans and livestock has been spreading in mainland China in the past decade. A more active surveillance of brucellosis in both livestock and humans in China should be coordinated and adjusted by adopting an evidence-based 'One Health' approach, particularly in high-risk regions and livestock industries.

Three Statistical Approaches for Assessment of Intervention Effects: A Primer for Practitioners

INTRODUCTION

Statistical methods to assess the impact of an intervention are increasingly used in clinical research settings. However, a comprehensive review of the methods geared toward practitioners is not yet available.

METHODS AND MATERIALS

We provide a comprehensive review of three methods to assess the impact of an intervention: difference-in-differences (DID), segmented regression of interrupted time series (ITS), and interventional autoregressive integrated moving average (ARIMA). We also compare the methods, and provide illustration of their use through three important healthcare-related applications.

RESULTS

In the first example, the DID estimate of the difference in health insurance coverage rates between expanded states and unexpanded states in the post-Medicaid expansion period compared to the pre-expansion period was 5.93 (95% CI, 3.99 to 7.89) percentage points. In the second example, a comparative segmented regression of ITS analysis showed that the mean imaging order appropriateness score in the emergency department at a tertiary care hospital exceeded that of the inpatient setting with a level change difference of 0.63 (95% CI, 0.53 to 0.73) and a trend change difference of 0.02 (95% CI, 0.01 to 0.03) after the introduction of a clinical decision support tool. In the third example, the results from an interventional ARIMA analysis show that numbers of creatinine clearance tests decreased significantly within months of the start of eGFR reporting, with a magnitude of drop equal to -0.93 (95% CI, -1.22 to -0.64) tests per 100,000 adults and a rate of drop equal to 0.97 (95% CI, 0.95 to 0.99) tests per 100,000 per adults per month.

DISCUSSION

When choosing the appropriate method to model the intervention effect, it is necessary to consider the structure of the data, the study design, availability of an appropriate comparison group, sample size requirements, whether other interventions occur during the study window, and patterns in the data.

Comparison of autoregressive integrated moving average model and generalised regression neural network model for prediction of haemorrhagic fever with renal syndrome in China: a time-series study

OBJECTIVES

Haemorrhagic fever with renal syndrome (HFRS) is a serious threat to public health in China, accounting for almost 90% cases reported globally. Infectious disease prediction may help in disease prevention despite some uncontrollable influence factors. This study conducted a comparison between a hybrid model and two single models in forecasting the monthly incidence of HFRS in China.

DESIGN

Time-series study.

SETTING

The People's Republic of China.

METHODS

Autoregressive integrated moving average (ARIMA) model, generalised regression neural network (GRNN) model and hybrid ARIMA-GRNN model were constructed by R V.3.4.3 software. The monthly reported incidence of HFRS from January 2011 to May 2018 were adopted to evaluate models' performance. Root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were adopted to evaluate these models' effectiveness. Spatial stratified heterogeneity of the time series was tested by month and another GRNN model was built with a new series.

RESULTS

The monthly incidence of HFRS in the past several years showed a slight downtrend and obvious seasonal variation. A total of four plausible ARIMA models were built and ARIMA(2,1,1) (2,1,1)₁₂ model was selected as the optimal model in HFRS fitting. The smooth factors of the basic GRNN model and the hybrid model were 0.027 and 0.043, respectively. The single ARIMA model was the best in fitting part (MAPE=9.1154, MAE=89.0302, RMSE=138.8356) while the hybrid model was the best in prediction (MAPE=17.8335, MAE=152.3013, RMSE=196.4682). GRNN model was revised by building model with new series and the forecasting performance of revised model (MAPE=17.6095, MAE=163.8000, RMSE=169.4751) was better than original GRNN model (MAPE=19.2029, MAE=177.0356, RMSE=202.1684).

CONCLUSIONS

The hybrid ARIMA-GRNN model was better than single ARIMA and basic GRNN model in forecasting monthly incidence of HFRS in China. It could be considered as a decision-making tool in HFRS prevention and control.

The Impact of Alcohol Restriction on Hospital and Emergency Department Service Utilizations in Two Remote Towns in the Kimberley Region of Western Australia

Background: In a remote region of Western Australia, Kimberley, residents have nearly twice the State average per capita consumption of alcohol, four and a half times the level of alcohol-related hospitalizations and nearly three times the level of alcohol-related deaths. This study aimed to evaluate the long term effects of alcohol sale restrictions on health service utilization in two remote towns in Kimberley.

Methods: Sale of high strength packaged alcohol was restricted in Fitzroy Crossing and Halls Creek since October 2007 and May 2009, respectively. Alcohol-related Emergency Department (ED) attendances and hospitalizations utilized by local residents before and after the intervention between 2003 and 2013 was compared by using yearly rates (/1,000 person-years) and interrupted time series analysis with Autoregressive Integrated Moving Average (ARIMA) modeling. The Western Australia specific aetiological fractions (AAFs) were applied to hospital inpatient data for estimation of the proportion of hospital separations attributable to alcohol.

Results: In Fitzroy Crossing, there was a significant reduction of over 40% on rates (/1,000 person-years) of alcohol-related acute hospitalizations (54.2 [95% CI: 53.8–54.7] vs. 31.7 [31.4–32.1]) and ED attendances (534.1[532.8–535.5] vs. 294.5 [293.5–295.4]). In Halls Creek, there was a significant reduction of over 50% on rates (/1,000 person-years) of alcohol- related acute hospitalizations (17.7 [17.6–17.8] vs. 8.0 [7.9–8.1]) and ED attendance (248.4 [247.9–248.9] vs. 111.1[110.8–111.5]). Domestic violence and injury related hospitalization rates were also reduced by over 20% in both towns.

Conclusions: The total restriction of selling high strength alcohol through a community driven process has shown to be effective in reducing alcohol-related health service utilization in post-intervention period. Continue monitoring is required to address new emerging issues. Future research on health service utilization related to alcohol by using interrupted time series analysis incorporating ARIMA modeling and applying AAFs are recommended for evaluating alcohol-related interventions.

Yearly variations of demographic indices and mortality data in Italy from 1901 to 2008 as related to the caloric intake

The aim of the present study was to evaluate, by Join Point regression method, the yearly variations in demographic indices and mortality data in Italy from 1901 to 2008, as related to the caloric intake. The relationships between mortality and caloric intake were studied by time series. The results showed that, from 1901 to 2008, the Italian population grew from 32.5 to 59.6 millions; the live births rates decreased from 31.8 to 10.1‰ (males) and from 33.3 to 9.0‰ (females); the infant mortality rates fell from 184.1 to 3.7‰ (males) and from 149.4 to 3.2‰ (females); males and females gained 35.7 and 40.6 years in life expectancy at birth, respectively. In 1901 the 61% of deaths occurred in the youngest, whereas in 2008 the elderly accounted for the 80%. In 1901, in terms of age-adjusted data, other and undefined causes overcame the specific causes of death, whose rank was: respiratory, digestive, infectious, cardiovascular, cerebrovascular, cancers, accidents, endocrine, and nervous system diseases. In 2008, undefined causes ranked 3rd (males) and 4th (females), while cancers became the leading cause of death, followed by cardiovascular, cerebrovascular, accidental, respiratory, endocrine, digestive, nervous system, and infectious diseases. The caloric intake showed a negative correlation with all-cause mortality, infant mortality, and mortality for a number of specific causes. These patterns reflect the progress in average nutritional status, lifestyle quality, socioeconomic level, and hygienic conditions.