Effect of Maximum Average Temperature on Suicide Rates in California, 2008-2017: Public Health Implications of Climate Change

2020 ◽  
Author(s):  
Mavra Qamar ◽  
Sierra Cheng ◽  
Rebecca Plouffe ◽  
Stephanie Nanos ◽  
David N Fisman ◽  
...  
Keyword(s):  
Public Health ◽  
Climate Change ◽  
Time Series ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Maximum Temperature ◽  
Suicide Rates ◽  
Binomial Regression ◽  
The Relationship ◽  
Heat Events

Abstract BackgroundSuicide prevention is a salient public health responsibility, as it is one of the top ten leading causes of premature mortality in the United States. Risk factors of suicide transcend the individual and societal level as risk can increase based on climatic variables. Previous studies have been country-based. Currently, studies focused solely on regions, provinces, or states, such as California, are limited. The present study holds two purposes: i) to assess the effect of maximum temperature on suicides, and ii) to evaluate the effect of number of monthly heat events on suicide rates, in California from 2008-2017.MethodsThe exposure was measured as the average Californian daily maximum temperature within each month, and the number of monthly heat events, which was calculated as a count of the days exhibiting a >15% increase from the historical monthly temperature. The outcome was measured as California’s monthly suicide rate. Negative binomial regression models assessed the relationship between maximum temperature and suicides, and heat events and suicide. A seasonal decomposition of a time series and auto-correlogram further analyzed the seasonality of suicide and the trend from 2008-2017. ResultsThere were 40,315 deaths by suicide in California between 2008-2017. Negative binomial regression indicated a 6.1% increase in suicide incidence rate ratio (IRR) per 10°F increase in maximum temperature (IRR=1.00590 per 1°F, 95% CI: 1.00387, 1.00793, p<0.0001) and a positive, non-significant association between suicide rates and number of heat events adjusted for month of occurrence (IRR 1.00148 per heat event, 95% CI: 0.99636, 1.00661, p=0.572). The time series analysis and auto-correlogram suggested seasonality of deaths by suicide.ConclusionThe present study provided preliminary evidence that will generate future directions for research. We must seek to further illuminate the relationship of interest and apply our findings to public health interventions that will lower the rates of death by suicide as we are confronted with the effects of climate change.

scholarly journals The Effect of Maximum Average Temperature on Suicide Rates in California, 2008-2017: An Ecological Study

2020 ◽  
Author(s):  
Mavra Qamar ◽  
Sierra Cheng ◽  
Rebecca Plouffe ◽  
Stephanie Nanos ◽  
David N Fisman ◽  
...  
Keyword(s):  
Public Health ◽  
Time Series ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Suicide Rates ◽  
Binomial Regression ◽  
The Relationship ◽  
Heat Events

Abstract Background: Suicide prevention is a salient public health responsibility, as it is one of the top ten leading causes of premature mortality in the United States. Risk factors of suicide transcend the individual and societal level as risk can increase based on climatic variables. Previous studies have been country-based. Currently, studies focused solely on regions, provinces, or states, such as California, are limited. The present study holds two purposes: i) to assess the effect of maximum temperature on suicides, and ii) to evaluate the effect of number of monthly heat events on suicide rates, in California from 2008-2017.Methods: The exposure was measured as the average Californian daily maximum temperature within each month, and the number of monthly heat events, which was calculated as a count of the days exhibiting a >15% increase from the historical monthly temperature. The outcome was measured as California’s monthly suicide rate. Negative binomial regression models assessed the relationship between maximum temperature and suicides, and heat events and suicide. A seasonal decomposition of a time series and auto-correlogram further analyzed the seasonality of suicide and the trend from 2008-2017. Results: There were 40,315 deaths by suicide in California between 2008-2017. Negative binomial regression indicated a 6.1% increase in suicide incidence rate ratio (IRR) per 10°F increase in maximum temperature (IRR=1.00590 per 1°F, 95% CI: 1.00387, 1.00793, p<0.0001) and a positive, non-significant association between suicide rates and number of heat events adjusted for month of occurrence (IRR 1.00148 per heat event, 95% CI: 0.99636, 1.00661, p=0.572). The time series analysis and auto-correlogram suggested seasonality of deaths by suicide.Conclusion: The present study provided preliminary evidence that will generate future directions for research. We must seek to further illuminate the relationship of interest and apply our findings to public health interventions that will lower the rates of death by suicide as we are confronted with the effects of climate change.

scholarly journals Network analysis of firearm movement among US states

2020 ◽  
Author(s):  
Sae Takada ◽  
Kristen Choi ◽  
Shaw Natsui ◽  
Altaf Saadi ◽  
Liza Buchbinder ◽  
...  
Keyword(s):  
Time Series ◽  
Network Analysis ◽  
Negative Binomial ◽  
State Level ◽  
Negative Binomial Regression ◽  
Similar Model ◽  
Cross Sectional ◽  
Us States ◽  
Binomial Regression ◽  
The Relationship

Abstract Background: The movement of firearm across state lines may decrease the effectiveness of state-level firearm laws. Yet how state-level firearm policies affect cross-state movement have not yet been widely explored. This study aims to characterize the interstate movement of firearms and its relationship with state-level firearm policies. Methods: Cross-sectional time series network analysis of interstate firearm movement using Bureau of Alcohol, Tobacco, Firearms, and Explosives firearm trace data (2010 -2017). We constructed the network of firearm movement between 50 states. We used zero-inflated negative binomial regression to estimate the relationship between the number of a state’s firearm laws and number of states for which it was the source of 100 or more firearms, adjusting for state characteristics. We used a similar model to examine the relationship between firearm laws and the number of states for which a given state was the destination of 100 or more firearms.Results: Over the 8-year period, states had an average of 26 (SD 25.2) firearm laws. On average, a state was the source of 100 or more crime-related firearms for 2.2 (SD 2.7) states and was the destination of 100 or more crime-related firearms for 2.2 (SD 3.4) states. Greater number of firearm laws was associated with states being the source of 100 or more firearms to fewer states (IRR0.67 per SD, p<0.001), higher odds of not being a source to any states (aOR1.56 per SD, p<0.001), and states being the destination of 100 or more firearms from more states (IRR1.83 per SD, p<0.001).Conclusions: Restrictive firearm policies are associated with less movement of firearms to other states, but with more movement of firearms from outside states. The effectiveness of state-level firearm-restricting laws is complicated by a network of interstate firearm movement.

scholarly journals Network analysis of firearm movement among US states

2020 ◽  
Author(s):  
Sae Takada ◽  
Kristen Choi ◽  
Shaw Natsui ◽  
Altaf Saadi ◽  
Liza Buchbinder ◽  
...  
Keyword(s):  
Time Series ◽  
Network Analysis ◽  
Negative Binomial ◽  
State Level ◽  
Negative Binomial Regression ◽  
Similar Model ◽  
Cross Sectional ◽  
Us States ◽  
Binomial Regression ◽  
The Relationship

Abstract Background: The movement of firearm across state lines may decrease the effectiveness of state-level firearm laws. Yet how state-level firearm policies affect cross-state movement have not yet been widely explored. This study aims to characterize the interstate movement of firearms and its relationship with state-level firearm policies. Methods : Cross-sectional time series network analysis of interstate firearm movement using Bureau of Alcohol, Tobacco, Firearms, and Explosives firearm trace data (2010 -2017). We constructed the network of firearm movement between 50 states. We used zero-inflated negative binomial regression to estimate the relationship between the number of a state’s firearm laws and number of states for which it was the source of 100 or more firearms, adjusting for state characteristics. We used a similar model to examine the relationship between firearm laws and the number of states for which a given state was the destination of 100 or more firearms. Results : Over the 8-year period, states had an average of 26 (SD 25.2) firearm laws. On average, a state was the source of 100 or more crime-related firearms for 2.2 (SD 2.7) states and was the destination of 100 or more crime-related firearms for 2.2 (SD 3.4) states. Greater number of firearm laws was associated with states being the source of 100 or more firearms to fewer states (IRR0.67 per SD, p<0.001), higher odds of not being a source to any states (aOR1.56 per SD, p<0.001), and states being the destination of 100 or more firearms from more states (IRR1.83 per SD, p<0.001). Conclusions: Restrictive firearm policies are associated with less movement of firearms to other states, but with more movement of firearms from outside states. The effectiveness of state-level firearm-restricting laws is complicated by a network of interstate firearm movement.

scholarly journals Network analysis of firearm movement among US states

2020 ◽  
Author(s):  
Sae Takada ◽  
Kristen Choi ◽  
Shaw Natsui ◽  
Altaf Saadi ◽  
Liza Buchbinder ◽  
...  
Keyword(s):  
Time Series ◽  
Network Analysis ◽  
Negative Binomial ◽  
State Level ◽  
Negative Binomial Regression ◽  
Similar Model ◽  
Cross Sectional ◽  
Us States ◽  
Binomial Regression ◽  
The Relationship

Abstract Background: The movement of firearm across state lines may decrease the effectiveness of state-level firearm laws. Yet how state-level firearm policies affect cross-state movement have not yet been widely explored. This study aims to characterize the interstate movement of firearms and its relationship with state-level firearm policies. Methods: Cross-sectional time series network analysis of interstate firearm movement using Bureau of Alcohol, Tobacco, Firearms, and Explosives firearm trace data (2010 -2017). We constructed the network of firearm movement between 50 states. We used zero-inflated negative binomial regression to estimate the relationship between the number of a state’s firearm laws and number of states for which it was the source of 100 or more firearms, adjusting for state characteristics. We used a similar model to examine the relationship between firearm laws and the number of states for which a given state was the destination of 100 or more firearms.Results: Over the 8-year period, states had an average of 26 (SD 25.2) firearm laws. On average, a state was the source of 100 or more crime-related firearms for 2.2 (SD 2.7) states and was the destination of 100 or more crime-related firearms for 2.2 (SD 3.4) states. Greater number of firearm laws was associated with states being the source of 100 or more firearms to fewer states (IRR0.67 per SD, p<0.001), higher odds of not being a source to any states (aOR1.56 per SD, p<0.001), and states being the destination of 100 or more firearms from more states (IRR1.83 per SD, p<0.001).Conclusions: Restrictive firearm policies are associated with less movement of firearms to other states, but with more movement of firearms from outside states. The effectiveness of state-level firearm-restricting laws is complicated by a network of interstate firearm movement.

scholarly journals NATO between Exclusivity and Inclusivity: Measuring NATO’s Partnerships

2021 ◽  
pp. 1-32
Author(s):  
Branislav Mičko
Keyword(s):  
Human Rights ◽  
Fixed Effects ◽  
International Institutions ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Original Dataset ◽  
Strategic Approach ◽  
Binomial Regression ◽  
The Given ◽  
The Relationship

Building on an original dataset, this article focuses on the interactions between NATO and its declared worldwide partners. It argues that the analysis of these interactions can reveal NATO’s strategic approach to partnerships, but it can also provide a tool for its classification as an organisation that is either exclusive – defined by the focus on defence of its members, or inclusive – emphasising the global protection of democracies and human rights. The relationship between types of interactions and NATO categorisation is estimated using an unconditional negative binomial regression with fixed effects as well as a within-between (hybrid) model. Furthermore, they are illustrated on two brief case studies of Sweden and Japan. The results of the study suggest that NATO engages primarily with countries that are powerful relative to their neighbourhood, even though they are not the most powerful among the partners. The given country’s level of democracy, integration into the international institutions, and stability, do not seem to play any overarching role here.

scholarly journals The Relationship between Near-Repeat Street Robbery and the Environment: Evidence from Malmö, Sweden

2020 ◽  
Vol 9 (4) ◽  
pp. 188
Author(s):  
Markus Rasmusson ◽  
Marco Helbich
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Socioeconomic Indicators ◽  
Law Enforcement Agencies ◽  
Environmental Characteristics ◽  
Binomial Regression ◽  
Using Data ◽  
Near Repeat ◽  
The Relationship ◽  
Crime Event

Near-repeat crime refers to a pattern whereby one crime event is soon followed by a similar crime event at a nearby location. Existing research on near-repeat crime patterns is inconclusive about where near-repeat patterns emerge and which physical and social factors influence them. The present research addressed this gap by examining the relationship between initiator events (i.e., the first event in a near-repeat pattern) and environmental characteristics to estimate where near-repeat patterns are most likely to emerge. A two-step analysis was undertaken using data on street robberies reported in Malmö, Sweden, for the years 2006–15. After determining near-repeat patterns, we assessed the correlations between initiator events and criminogenic places and socioeconomic indicators using a negative binomial regression at a street segment level. Our results show that both criminogenic places and socioeconomic indicators have a significant influence on the spatial variation of initiator events, suggesting that environmental characteristics can be used to explain the emergence of near-repeat patterns. Law enforcement agencies can utilize the findings in efforts to prevent further street robberies from occurring.

scholarly journals Prevalence of SARS-CoV-2 infection among people experiencing homelessness in Toronto during the first wave of the COVID-19 pandemic

2021 ◽  
Author(s):  
Linh Luong ◽  
Michaela Beder ◽  
Rosane Nisenbaum ◽  
Aaron Orkin ◽  
Jonathan Wong ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Infection Rates ◽  
Individual Site ◽  
Site Characteristics ◽  
Increased Risk ◽  
Binomial Regression ◽  
Great Heterogeneity ◽  
The Relationship ◽  
Over Time

Background: People experiencing homelessness are at increased risk of SARS-CoV-2 infection. This study reports the point prevalence of SARS-CoV-2 infection during testing conducted at sites serving people experiencing homelessness in Toronto during the first wave of the COVID-19 pandemic. We also explored the association between site characteristics and prevalence rates. Methods: The study included individuals who were staying at shelters, encampments, COVID-19 physical distancing sites, and drop-in and respite sites and completed outreach-based testing for SARS-CoV-2 during the period April 17 to July 31, 2020. We examined test positivity rates over time and compared them to rates in the general population of Toronto. Negative binomial regression was used to examine the relationship between each shelter-level characteristic and SARS-CoV-2 positivity rates. We also compared the rates across 3 time periods (T1: April 17-April 25; T2: April 26-May 23; T3: May 24-June 25). Results: The overall prevalence of SARS-CoV-2 infection was 8.5% (394/4657). Site-specific rates showed great heterogeneity with infection rates ranging from 0% to 70.6%. Compared to T1, positivity rates were 0.21 times lower (95% CI: 0.06, 0.75) during T2 and 0.14 times lower (95% CI: 0.043, 0.44) during T3. Most cases were detected during outbreak testing (384/394 [97.5%]) rather than active case finding. Interpretation: During the first wave of the pandemic, rates of SARS-CoV-2 infection at sites for people experiencing homelessness in Toronto varied significantly over time. The observation of lower rates at certain sites may be attributable to overall time trends, expansion of outreach-based testing to include sites without known outbreaks and/or individual site characteristics.

scholarly journals Visceral leishmaniasis cyclical trends in Bihar, India – implications for the elimination programme.

2018 ◽  
Vol 2 ◽  
pp. 10 ◽  
Author(s):  
Rinki M Deb ◽  
Michelle C Stanton ◽  
Geraldine M Foster ◽  
Rudra K Das Gupta ◽  
Nupur Roy ◽  
...  
Keyword(s):  
Visceral Leishmaniasis ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Specific Humidity ◽  
Sand Fly ◽  
Maximum Temperature ◽  
Negative Binomial Regression Model ◽  
Case Detection ◽  
Binomial Regression ◽  
Significant Difference

Background:Visceral leishmaniasis (VL) is a vector-borne disease of public health importance in India, with the highest burden of disease in the states of Bihar, Jharkhand, West Bengal and Uttar Pradesh. The disease is currently targeted for elimination (annual incidence to less than one per 10,000 population) using indoor residual spraying, active case detection and treatment. Historically the disease trend in India has been regarded as cyclical with case resurgence characteristically occurring every 15 years.  Understanding this pattern is essential if the VL elimination gains are to be sustained. To better understand the cyclical trends, annual climatic indicators including rainfall, temperature and humidity over time were compared with annual VL case incidence data. Methods:Annual climate data (rainfall, average and maximum temperature and specific humidity) from 1956-2004 were used to identify potential factors influencing VL incidence.  Months relevant to the VL life-cycle were identified and defined (Monsoon, Sand-fly Peak, Pre-Sand-fly Peak and Annual) for analysis. The Kruskall-Wallis test was used to determine significant difference between categorical rainfall and VL incidence, whilst univariate negative binomial regression models were used to determine predictors of disease incidence.Results:The negative binomial regression model showed statistically significant associations (p <0.05) for VL incidence and maximum temperature, and average temperature, when considering annual and pre-sand fly peak time periods. No other associations between humidity, rainfall or temperature and VL incidence were detected (all values p >0.05). Conclusion:The VL programme in Bihar has made significant progress in adopting best practices for improved treatment and vector control, with the aim to achieve VL elimination.  However, open access granular programme data for indoor residual spray activities and case detection is required to fully understand the role of climate in disease transmission and potential resurgence.

The relationship between citations, downloads and alternative metrics in rheumatology publications: a bibliometric study

Rheumatology ◽  
2019 ◽  
Vol 59 (2) ◽  
pp. 277-280 ◽  
Author(s):  
Winnie M Y Chen ◽  
Marwan Bukhari ◽  
Francesca Cockshull ◽  
James Galloway
Keyword(s):  
Negative Binomial ◽  
Positive Association ◽  
Negative Binomial Regression ◽  
Significant Positive Association ◽  
H Index ◽  
Binomial Regression ◽  
Number Of Citations ◽  
The Impact ◽  
The Relationship ◽  
Alternative Metrics

Abstract Objective Scientific journals and authors are frequently judged on ‘impact’. Commonly used traditional metrics are the Impact Factor and H-index. However, both take several years to formulate and have many limitations. Recently, Altmetric—a metric that measures impact in a non-traditional way—has gained popularity. This project aims to describe the relationships between subject matter, citations, downloads and Altmetric within rheumatology. Methods Data from publications in Rheumatology were used. Articles published from 2010 to 2015 were reviewed. Data were analysed using Stata 14.2 (StataCorp, College Station, TX, USA). Correlation between citations, downloads and Altmetric were quantified using linear regression, comparing across disease topics. Relationship between downloads and months since publications were described using negative binomial regression, clustering on individual articles. Results A total of 1460 Basic Science and Clinical Science articles were identified, with the number of citations, downloads and Altmetric scores. There were no correlations between disease topic and downloads (R2 = 0.016, P = 0.03), citations (R2 = 0.011, P = 0.29) or Altmetric (R2 = 0.025, P = 0.02). A statistically significant positive association was seen between the number of citations and downloads (R2 = 0.29, P &lt; 0.001). No correlations were seen between Altmetric and downloads (R2 = 0.028, P &lt; 0.001) or citations (R2 = 0.004, P = 0.445). Conclusion Disease area did not correlate with any of the metrics compared. Correlations were apparent with clear links between downloads and citations. Altmetric identified different articles as high impact compared with citation or download metrics. In conclusion: tweeting about your research does not appear to influence citations.

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