Suicide mortality in the United States following the suicides of Kate Spade and Anthony Bourdain

2020 ◽  
pp. 000486742097684
Author(s):  
Mark Sinyor ◽  
Ulrich S Tran ◽  
David Garcia ◽  
Benedikt Till ◽  
Martin Voracek ◽  
...  
Keyword(s):  
United States ◽  
Confidence Interval ◽  
Natural Experiment ◽  
Moving Average ◽  
The United States ◽  
Suicide Mortality ◽  
Autoregressive Integrated Moving Average ◽  
Public Health Emergencies ◽  
Moving Average Models ◽  
Celebrity Suicide

Objective: The suicides of Kate Spade and Anthony Bourdain, two major American icons, in a span of days in June 2018 represent a unique and tragic natural experiment to characterize associations with actual suicides in the aftermath of celebrity suicides. The aim of this study was to identify changes in suicide counts after their deaths. Methods: Suicide data were obtained from the United States’ Centers for Disease Control and Prevention’s public-use mortality file. A time-series analysis was performed, examining monthly suicide data by age group (⩽19, 20–44, 45–64 and ⩾65 years), for both men and women, for all suicide methods and for hanging versus non-hanging methods, from January 1999 to December 2018. Seasonal autoregressive integrated moving-average models were fitted to the pre-June 2018 period, estimating suicides in subsequent months and identifying deviations from expected values. The volume of Twitter posts about Kate Spade and Anthony Bourdain was used as a proxy of societal attention. Results: Tweets about the celebrities were mainly concentrated in June 2018 and faded quickly in July. Total suicides exceeded the 95% confidence interval for June and approximated the upper limit of the 95% confidence interval in July. Over this 2-month span, there were 418 (95% confidence interval = [184, 652]) more suicides than expected, including 275 (95% confidence interval = [79, 471]) excess suicides in men and 182 (95% confidence interval = [93, 271]) in women. These equate to 4.8%, 4.1% and 9.1% increases above expected counts. There were 392 (95% confidence interval = [271, 514]) excess suicides by hanging, a 14.5% increase, with no significant increase in all other methods combined. Conclusion and Relevance: These findings demonstrate that mortality following celebrity suicides can occur at a similar magnitude to that observed for other public health emergencies. They underscore the urgency for interventions to mitigate imitation effects after celebrity suicide reporting.

scholarly journals Prediction of the COVID-19 Pandemic for the Top 15 Affected Countries: Advanced Autoregressive Integrated Moving Average (ARIMA) Model (Preprint)

2020 ◽  
Author(s):  
Ram Kumar Singh ◽  
Meenu Rani ◽  
Akshaya Srikanth Bhagavathula ◽  
Ranjit Sah ◽  
Alfonso J Rodriguez-Morales ◽  
...  
Keyword(s):  
United States ◽  
United Kingdom ◽  
Death Rate ◽  
Rate Ratio ◽  
Moving Average ◽  
Arima Model ◽  
The United States ◽  
Recovery Ratio ◽  
Autoregressive Integrated Moving Average ◽  
The United Kingdom

BACKGROUND The coronavirus disease (COVID-19) pandemic has affected more than 200 countries and has infected more than 2,800,000 people as of April 24, 2020. It was first identified in Wuhan City in China in December 2019. OBJECTIVE The aim of this study is to identify the top 15 countries with spatial mapping of the confirmed cases. A comparison was done between the identified top 15 countries for confirmed cases, deaths, and recoveries, and an advanced autoregressive integrated moving average (ARIMA) model was used for predicting the COVID-19 disease spread trajectories for the next 2 months. METHODS The comparison of recent cumulative and predicted cases was done for the top 15 countries with confirmed cases, deaths, and recoveries from COVID-19. The spatial map is useful to identify the intensity of COVID-19 infections in the top 15 countries and the continents. The recent reported data for confirmed cases, deaths, and recoveries for the last 3 months was represented and compared between the top 15 infected countries. The advanced ARIMA model was used for predicting future data based on time series data. The ARIMA model provides a weight to past values and error values to correct the model prediction, so it is better than other basic regression and exponential methods. The comparison of recent cumulative and predicted cases was done for the top 15 countries with confirmed cases, deaths, and recoveries from COVID-19. RESULTS The top 15 countries with a high number of confirmed cases were stratified to include the data in a mathematical model. The identified top 15 countries with cumulative cases, deaths, and recoveries from COVID-19 were compared. The United States, the United Kingdom, Turkey, China, and Russia saw a relatively fast spread of the disease. There was a fast recovery ratio in China, Switzerland, Germany, Iran, and Brazil, and a slow recovery ratio in the United States, the United Kingdom, the Netherlands, Russia, and Italy. There was a high death rate ratio in Italy and the United Kingdom and a lower death rate ratio in Russia, Turkey, China, and the United States. The ARIMA model was used to predict estimated confirmed cases, deaths, and recoveries for the top 15 countries from April 24 to July 7, 2020. Its value is represented with 95%, 80%, and 70% confidence interval values. The validation of the ARIMA model was done using the Akaike information criterion value; its values were about 20, 14, and 16 for cumulative confirmed cases, deaths, and recoveries of COVID-19, respectively, which represents acceptable results. CONCLUSIONS The observed predicted values showed that the confirmed cases, deaths, and recoveries will double in all the observed countries except China, Switzerland, and Germany. It was also observed that the death and recovery rates were rose faster when compared to confirmed cases over the next 2 months. The associated mortality rate will be much higher in the United States, Spain, and Italy followed by France, Germany, and the United Kingdom. The forecast analysis of the COVID-19 dynamics showed a different angle for the whole world, and it looks scarier than imagined, but recovery numbers start looking promising by July 7, 2020.

scholarly journals Prediction of the COVID-19 Pandemic for the Top 15 Affected Countries: Advanced Autoregressive Integrated Moving Average (ARIMA) Model

10.2196/19115 ◽  
2020 ◽  
Vol 6 (2) ◽  
pp. e19115 ◽  
Author(s):  
Ram Kumar Singh ◽  
Meenu Rani ◽  
Akshaya Srikanth Bhagavathula ◽  
Ranjit Sah ◽  
Alfonso J Rodriguez-Morales ◽  
...  
Keyword(s):  
United States ◽  
United Kingdom ◽  
Death Rate ◽  
Rate Ratio ◽  
Moving Average ◽  
Arima Model ◽  
The United States ◽  
Recovery Ratio ◽  
Autoregressive Integrated Moving Average ◽  
The United Kingdom

Background The coronavirus disease (COVID-19) pandemic has affected more than 200 countries and has infected more than 2,800,000 people as of April 24, 2020. It was first identified in Wuhan City in China in December 2019. Objective The aim of this study is to identify the top 15 countries with spatial mapping of the confirmed cases. A comparison was done between the identified top 15 countries for confirmed cases, deaths, and recoveries, and an advanced autoregressive integrated moving average (ARIMA) model was used for predicting the COVID-19 disease spread trajectories for the next 2 months. Methods The comparison of recent cumulative and predicted cases was done for the top 15 countries with confirmed cases, deaths, and recoveries from COVID-19. The spatial map is useful to identify the intensity of COVID-19 infections in the top 15 countries and the continents. The recent reported data for confirmed cases, deaths, and recoveries for the last 3 months was represented and compared between the top 15 infected countries. The advanced ARIMA model was used for predicting future data based on time series data. The ARIMA model provides a weight to past values and error values to correct the model prediction, so it is better than other basic regression and exponential methods. The comparison of recent cumulative and predicted cases was done for the top 15 countries with confirmed cases, deaths, and recoveries from COVID-19. Results The top 15 countries with a high number of confirmed cases were stratified to include the data in a mathematical model. The identified top 15 countries with cumulative cases, deaths, and recoveries from COVID-19 were compared. The United States, the United Kingdom, Turkey, China, and Russia saw a relatively fast spread of the disease. There was a fast recovery ratio in China, Switzerland, Germany, Iran, and Brazil, and a slow recovery ratio in the United States, the United Kingdom, the Netherlands, Russia, and Italy. There was a high death rate ratio in Italy and the United Kingdom and a lower death rate ratio in Russia, Turkey, China, and the United States. The ARIMA model was used to predict estimated confirmed cases, deaths, and recoveries for the top 15 countries from April 24 to July 7, 2020. Its value is represented with 95%, 80%, and 70% confidence interval values. The validation of the ARIMA model was done using the Akaike information criterion value; its values were about 20, 14, and 16 for cumulative confirmed cases, deaths, and recoveries of COVID-19, respectively, which represents acceptable results. Conclusions The observed predicted values showed that the confirmed cases, deaths, and recoveries will double in all the observed countries except China, Switzerland, and Germany. It was also observed that the death and recovery rates were rose faster when compared to confirmed cases over the next 2 months. The associated mortality rate will be much higher in the United States, Spain, and Italy followed by France, Germany, and the United Kingdom. The forecast analysis of the COVID-19 dynamics showed a different angle for the whole world, and it looks scarier than imagined, but recovery numbers start looking promising by July 7, 2020.

scholarly journals Application of the Autoregressive Integrated Moving Average Exogenous (ARIMAX) with Calendar Variation Effect Method for Forecasting Chocolate Data in Indonesia and the United States

2022 ◽  
Vol 18 (2) ◽  
pp. 224-236
Author(s):  
Andy Rezky Pratama Syam
Keyword(s):  
United States ◽  
Time Series ◽  
Moving Average ◽  
The United States ◽  
Time Series Regression ◽  
Autoregressive Integrated Moving Average ◽  
Good Ability ◽  
Modeling And Forecasting ◽  
Time Series Method ◽  
Double Exponential Smoothing

Forecasting chocolate consumption is required by producers in preparing the amount of production each month. The tradition of Valentine, Christmas and Eid al-Fitr which are closely related to chocolate makes it impossible to predict chocolate by using the Classical Time Series method. Especially for Eid al-Fitr, the determination follows the Hijri calendar and each year advances 10 days on the Masehi calendar, so that every three years Eid al-Fitr will occur in a different month. Based on this, the chocolate forecasting will show a variation calendar effect. The method used in modeling and forecasting chocolate in Indonesia and the United States is the ARIMAX (Autoregressive Integrated Moving Average Exogenous) method with Calendar Variation effect. As a comparison, modeling and forecasting are also carried out using the Naïve Trend Linear, Naïve Trend Exponential, Double Exponential Smoothing, Time Series Regression, and ARIMA methods. The ARIMAX method with Calendar Variation Effect produces a very precise MAPE value in predicting chocolate data in Indonesia and the United States. The resulting MAPE value is below 10 percent, so it can be concluded that this method has a very good ability in forecasting.

Developing National Genotype-Independent Indicators for Recent Mycobacterium Tuberculosis Transmission Using Pediatric Cases—United States, 2011-2017

2021 ◽  
pp. 003335492098521
Author(s):  
Alexia V. Harrist ◽  
Clinton J. McDaniel ◽  
Jonathan M. Wortham ◽  
Sandy P. Althomsons
Keyword(s):  
United States ◽  
Mycobacterium Tuberculosis ◽  
Statistical Model ◽  
Moving Average ◽  
The United States ◽  
Tuberculosis Transmission ◽  
Average Proportion ◽  
Pediatric Tb ◽  
Case Rate ◽  
Pediatric Tuberculosis

Introduction Pediatric tuberculosis (TB) cases are sentinel events for Mycobacterium tuberculosis transmission in communities because children, by definition, must have been infected relatively recently. However, these events are not consistently identified by genotype-dependent surveillance alerting methods because many pediatric TB cases are not culture-positive, a prerequisite for genotyping. Methods We developed 3 potential indicators of ongoing TB transmission based on identifying counties in the United States with relatively high pediatric (aged <15 years) TB incidence: (1) a case proportion indicator: an above-average proportion of pediatric TB cases among all TB cases; (2) a case rate indicator: an above-average pediatric TB case rate; and (3) a statistical model indicator: a statistical model based on a significant increase in pediatric TB cases from the previous 8-quarter moving average. Results Of the 249 US counties reporting ≥2 pediatric TB cases during 2009-2017, 240 and 249 counties were identified by the case proportion and case rate indicators, respectively. The statistical model indicator identified 40 counties with a significant increase in the number of pediatric TB cases. We compared results from the 3 indicators with an independently generated list of 91 likely transmission events involving ≥2 pediatric cases (ie, known TB outbreaks or case clusters with reported epidemiologic links). All counties with likely transmission events involving multiple pediatric cases were identified by ≥1 indicator; 23 were identified by all 3 indicators. Practice Implications This retrospective analysis demonstrates the feasibility of using routine TB surveillance data to identify counties where ongoing TB transmission might be occurring, even in the absence of available genotyping data.

scholarly journals Longer Residence in the United States is Associated With More Physical Function Limitations in African Immigrant Older Adults

2020 ◽  
pp. 073346482097760
Author(s):  
Manka Nkimbeng ◽  
Yvonne Commodore-Mensah ◽  
Jacqueline L. Angel ◽  
Karen Bandeen-Roche ◽  
Roland J. Thorpe ◽  
...  
Keyword(s):  
United States ◽  
Older Adults ◽  
Confidence Interval ◽  
Physical Function ◽  
Racial Discrimination ◽  
African Immigrants ◽  
The United States ◽  
African Immigrant ◽  
High Discrimination ◽  
The Mean

Acculturation and racial discrimination have been independently associated with physical function limitations in immigrant and United States (U.S.)-born populations. This study examined the relationships among acculturation, racial discrimination, and physical function limitations in N = 165 African immigrant older adults using multiple linear regression. The mean age was 62 years ( SD = 8 years), and 61% were female. Older adults who resided in the United States for 10 years or more had more physical function limitations compared with those who resided here for less than 10 years ( b = −2.62, 95% confidence interval [CI] = [–5.01, –0.23]). Compared to lower discrimination, those with high discrimination had more physical function limitations ( b = −2.51, 95% CI = [–4.91, –0.17]), but this was no longer significant after controlling for length of residence and acculturation strategy. Residing in the United States for more than 10 years is associated with poorer physical function. Longitudinal studies with large, diverse samples of African immigrants are needed to confirm these associations.

scholarly journals Economic Impact from Unrestricted Spread of Potato Cyst Nematodes in Australia

2009 ◽  
Vol 99 (12) ◽  
pp. 1387-1393 ◽  
Author(s):  
M. Hodda ◽  
D. C. Cook
Keyword(s):  
United States ◽  
Confidence Interval ◽  
Economic Impact ◽  
Annual Cost ◽  
The United States ◽  
Economic Consequences ◽  
Potato Cyst Nematodes ◽  
Cyst Nematodes ◽  
Model Predictions ◽  
Alternative Responses

Potato cyst nematodes (PCN) (Globodera spp.) are quarantine pests with serious potential economic consequences. Recent new detections in Australia, Canada, and the United States have focussed attention on the consequences of spread and economic justifications for alternative responses. Here, a full assessment of the economic impact of PCN spread from a small initial incursion is presented. Models linking spread, population growth, and economic impact are combined to estimate costs of spread without restriction in Australia. Because the characteristics of the Australian PCN populations are currently unknown, the known ranges of parameters were used to obtain cost scenarios, an approach which makes the model predictions applicable generally. Our analysis indicates that mean annual costs associated with spread of PCN would increase rapidly initially, associated with increased testing. Costs would then increase more slowly to peak at over AUD$20 million per year ≈10 years into the future. Afterward, this annual cost would decrease slightly due to discounting factors. Mean annual costs over 20 years were $18.7 million, with a 90% confidence interval between AUD$11.9 million and AUD$27.0 million. Thus, cumulative losses to Australian agriculture over 20 years may exceed $370 million without action to prevent spread of PCN and entry to new areas.

The Recent Rise of Suicide Mortality in the United States

2021 ◽  
Vol 43 (1) ◽  
Author(s):  
Gonzalo Martínez-Alés ◽  
Tammy Jiang ◽  
Katherine M. Keyes ◽  
Jaimie L. Gradus
Keyword(s):  
Public Health ◽  
United States ◽  
Suicide Risk ◽  
The United States ◽  
Health Concern ◽  
Suicide Mortality ◽  
Annual Review ◽  
Publication Date ◽  
Suicide Rates ◽  
Subsequent Decrease

Suicide is a major public health concern in the United States. Between 2000 and 2018, US suicide rates increased by 35%, contributing to the stagnation and subsequent decrease in US life expectancy. During 2019, suicide declined modestly, mostly owing to slight reductions in suicides among Whites. Suicide rates, however, continued to increase or remained stable among all other racial/ethnic groups, and little is known about recent suicide trends among other vulnerable groups. This article ( a) summarizes US suicide mortality trends over the twentieth and early twenty-first centuries, ( b) reviews potential group-level causes of increased suicide risk among subpopulations characterized by markers of vulnerability to suicide, and ( c) advocates for combining recent advances in population-based suicide prevention with a socially conscious perspective that captures the social, economic, and political contexts in which suicide risk unfolds over the life course of vulnerable individuals. Expected final online publication date for the Annual Review of Public Health, Volume 43 is April 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals A natural experiment of the consequences of concentrating former prisoners in the same neighborhoods

2015 ◽  
Vol 112 (22) ◽  
pp. 6943-6948 ◽  
Author(s):  
David S. Kirk
Keyword(s):  
United States ◽  
Hurricane Katrina ◽  
Natural Experiment ◽  
Metropolitan Areas ◽  
The United States ◽  
Recidivism Rates ◽  
Geographic Space ◽  
Former Prisoners ◽  
Criminal Opportunities

More than 600,000 prisoners are released from incarceration each year in the United States, and most end up residing in metropolitan areas, clustered within a select few neighborhoods. Likely consequences of this concentration of returning prisoners include higher rates of subsequent crime and recidivism. In fact, one-half of released prisoners return to prison within only 3 y of release. The routine exposure to criminogenic influences and criminal opportunities portends a bleak future for individuals who reside in neighborhoods with numerous other ex-prisoners. Through a natural experiment focused on post-Hurricane Katrina Louisiana, I examine a counterfactual scenario: If instead of concentrating ex-prisoners in geographic space, what would happen to recidivism rates if ex-prisoners were dispersed across space? Findings reveal that a decrease in the concentration of parolees in a neighborhood leads to a significant decrease in the reincarceration rate of former prisoners.

scholarly journals Modeling the Global Dynamic Contagion of COVID-19

2022 ◽  
Vol 9 ◽  
Author(s):  
Lijin Xiang ◽  
Shiqun Ma ◽  
Lu Yu ◽  
Wenhao Wang ◽  
Zhichao Yin
Keyword(s):  
United States ◽  
South Korea ◽  
Feedback Loop ◽  
The United States ◽  
Government Policies ◽  
Global Epidemic ◽  
Public Health Emergencies ◽  
Global Dynamic ◽  
The United Kingdom ◽  
Risk Assessment Mechanism

The COVID-19 infections have profoundly and negatively impacted the whole world. Hence, we have modeled the dynamic spread of global COVID-19 infections with the connectedness approach based on the TVP-VAR model, using the data of confirmed COVID-19 cases during the period of March 23rd, 2020 to September 10th, 2021 in 18 countries. The results imply that, (i) the United States, the United Kingdom and Indonesia are global epidemic centers, among which the United States has the highest degree of the contagion of the COVID-19 infections, which is stable. South Korea, France and Italy are the main receiver of the contagion of the COVID-19 infections, and South Korea has been the most severely affected by the overseas epidemic; (ii) there is a negative correlation between the timeliness, effectiveness and mandatory nature of government policies and the risk of the associated countries COVID-19 epidemic affecting, as well as the magnitude of the net contagion of domestic COVID-19; (iii) the severity of domestic COVID-19 epidemics in the United States and Canada, Canada and Mexico, Indonesia and Canada is almost equivalent, especially for the United States, Canada and Mexico, whose domestic epidemics are with the same tendency; (iv) the COVID-19 epidemic has spread though not only the central divergence manner and chain mode of transmission, but also the way of feedback loop. Thus, more efforts should be made by the governments to enhance the pertinence and compulsion of their epidemic prevention policies and establish a systematic and efficient risk assessment mechanism for public health emergencies.

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