scholarly journals Surveilling Non-Opioid Substance Use: Utilizing Multiple Data Sources in Marion County, Indiana

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
James M. Groh ◽  
Brittany Kasumi Yarnell
Keyword(s):  
Substance Use ◽  
Data Sources ◽  
Prevalence Rates ◽  
Death Rates ◽  
Health Concerns ◽  
Marion County ◽  
Multiple Data ◽  
Ed Visits ◽  
Response Plan ◽  
The Impact

ObjectiveTo assess the prevalence of non-opioid substance use—including cocaine, methamphetamine and “spice”—within Marion County, Indiana and propose response recommendations utilizing a current opioid response plan.IntroductionCocaine, methamphetamine, and “spice” are addictive, non-opioid substances that negatively impact a person’s health through direct and indirect means. Direct health concerns of non-opioid substance use include anxiety, paranoia, seizure, heart attack, stroke, and potentially death while indirect health concerns include the acquisition of disease and infections, particularly sexually transmitted infections (STIs). Substance users experience an increased risk of acquiring STIs since they may exchange sex for substances, use substances within a social setting that may lead to sexual activity, or engage in risky sexual behavior as a result of impaired judgement associated with substance use. The current study evaluated the use of multiple data sources to monitor changes in the rate of cocaine, methamphetamine, and “spice” related emergency department visits as well as cocaine- and methamphetamine-related death rates, within Marion County, Indiana between 2013 and 2017.MethodsTwo data sources were used in this study. First, prevalence rates of non-opioid substance related emergency department (ED) visits were calculated using Marion County (IN) ED data from Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE) between 2013 and 2017. Second, cocaine and methamphetamine death rates were calculated using coroner toxicology data related to Marion County deaths between 2013 and 2017. Cocaine and methamphetamine deaths were defined as any death in which cocaine and methamphetamine was found in the toxicology results, respectively. All rates were calculated per 100,000 and age-adjusted to the 2000 U.S. Census using SAS Enterprise Guide v7.1.ResultsNon-opioid substance related ED visits have persistently risen between 2013 and 2017 (Figure 1). Methamphetamine and “spice” related ED visits exhibited similar prevalence patterns, increasing from 0.99 (0.72, 1.58) to 5.32 (4.67, 6.21) and 0.46 (0.28, 1.00) to 4.13 (3.57, 4.94) per 100,000, respectively, between 2013 and 2016. Cocaine-related ED visits consistently exhibited the highest prevalence rates, ranging from 3.72 (3.17, 4.44) to 23.56 (22.16, 25.11) per 100,000 in 2013 and 2016, respectively. In 2017, all non-opioid substance related ED visits drastically increased to 47.78 (45.79, 49.91), 48.48 (46.48, 50.67), and 42.08 (40.23, 44.13) per 100,000 for cocaine, methamphetamine, and “spice,” respectively. Further, we looked at cocaine- and methamphetamine-related death rates using coroner toxicology results. We found that between 2013 and 2017, the cocaine-related death rate nearly tripled, from 4.82 (4.20, 5.64) per 100,000 in 2013 to 13.01 (11.97, 14.23) per 100,000 in 2017 (Figure 2). Similarly, methamphetamine-related death rates increased from 1.31 (0.99, 1.92) per 100,000 in 2013 to 10.15 (9.25, 11.28) per 100,000 in 2017 (Figure 2). We did not calculate death rates of those who were found to have “spice” in their system at the time of death due to low prevalence.ConclusionsThe increase of non-opioid substance related ED visits in Marion County may indicate that non-opioid substance use—particularly cocaine, methamphetamine, and “spice”—may be an emerging public health issue in Marion County. This growing concern is further supported by the consistent increase in cocaine- and methamphetamine-related death rates. A limitation to our study is the inconsistent reporting of the substance in ED chief complaints and missing fields for discharge diagnoses and triage notes. As such, this inconsistency may have led to an underestimation of the prevalence rates of non-opioid substance related ED visits. The addition of triage notes and more reliable discharge diagnoses in 2017 ultimately culminated in a sharp increase in non-opioid substance related ED visits in 2017.Certain aspects of Marion County Public Health Department’s established opioid response plan may be used to address the growing concern of non-opioid substance use. These aspects include, but are not limited to, engaging community partners, creating a task force, establishing focus groups, and providing resources. While these aspects are critical to effectively respond to non-opioid substance use epidemics, establishing the various components prior to an outbreak enable communities to reduce the impact of such epidemics, if not prevent them from occurring. Additionally, it is important to incorporate participatory aspects into a non-opioid substance response plan such that community members are the driving force to provide context for the impact that non-opioid substance use is having on the community while also offering insight into which interventions would be most effective. 

scholarly journals Investigating the impact of COVID-19 on performance and image enhancing drug use

2021 ◽  
Author(s):  
Matthew Dunn ◽  
Timothy Piatkowski
Keyword(s):  
Mental Health ◽  
Substance Use ◽  
Online Survey ◽  
High Dose ◽  
Convenience Sample ◽  
Health Concerns ◽  
Exercise Habits ◽  
Mental Health Concerns ◽  
Image Enhancing ◽  
The Impact

Abstract Background Emerging research has suggested that the COVID-19 pandemic has had some impact on substance use patterns. The aim of the study was to conduct a rapid survey to assess the impact of COVID-19 on performance and image enhancing drug (PIED) use and training, and any subsequent negative physical or mental health outcomes. Methods During 2020, a convenience sample of 60 PIED consumers (mean age = 26.69; 68.3% located outside Australia) completed a quantitative anonymous online survey exploring how the coronavirus pandemic impacted patterns of PIED use and associated exercise habits. The survey was administered via the Qualtrics platform and distributed online through PIED forums as well as through the investigator's networks. Participants were asked about their PIED use and exercise habits prior to and during restricted movement ‘lockdowns’. Results Pre-COVID, the majority of the sample opted to ‘blast-cruise’ (an initial high dose, followed by a lower maintenance dose; 71.7%, n = 43). During lockdown, (45%, n = 27) reported a change in PIED use as a result of the restrictions. In light of health concerns during COVID-19, a majority of men (60%, n = 36) did not take any extra precautions relating to their PIED use. A subgroup of men ceased using PIEDs completely (16.7%, n = 10) with the majority (80%, n = 8) of that subgroup following post-cycle therapy (PCT) of some kind. Conclusions This study contributes to the emerging literature of the impact of the COVID-19 pandemic on substance use, specifically PIED use among men. The results suggest that the pandemic did influence the choice of PIEDs that participants consumed, although there was little disruption to patterns of exercise, an important aspect of PIED use. Of the men who did cease use completely, the majority reported little issue with PCT access; those who reported difficulty accessing PCT compounds indicated experience some mental health concerns related to ceasing their PIED use. Clinicians and those who come into contact with this group should be alert for any negative physical or mental health concerns resulting from disrupted or ceased PIED use.

Management of Chronic Mental Illnesses and Substance Use Disorders

2019 ◽  
pp. 1-23
Author(s):  
Pallavi Sinha ◽  
Amit Garg ◽  
Prerna Khanna ◽  
Amit Khanna
Keyword(s):  
Substance Abuse ◽  
Mental Illness ◽  
Substance Use ◽  
Substance Use Disorders ◽  
Demographic Characteristics ◽  
Mental Illnesses ◽  
Prevalence Rates ◽  
Factors Affecting ◽  
Impact On Treatment ◽  
The Impact

There is vast literature available about prevalence and pattern of substance use in patients with Chronic mental illness such as Schizophrenia. Many of these studies have also investigated the issues related to the impact of substance use on the course and prognosis of the mental illness and vice versa. The factors affecting the use and its impact on treatment have also been studied to some extent in the Western countries. Estimates of the prevalence of substance abuse in schizophrenia and major affective disorder vary as a function of settings (e.g., community, hospital-in-patient vs. out-patient), demographic characteristics of the sample and assessment methods, with most prevalence rates ranging between15% to 65%. Patients with Chronic Mental illnesses and Substance use disorders are difficult to treat and many models for their management have been proposed. This chapter will deal with some of these issues.

scholarly journals Patient No-Show Predictive Model Development using Multiple Data Sources for an Effective Overbooking Approach

2014 ◽  
Vol 05 (03) ◽  
pp. 836-860 ◽  
Author(s):  
D.A. Hanauer ◽  
Y. Huang
Keyword(s):  
Predictive Model ◽  
Error Rate ◽  
Model Development ◽  
Data Sources ◽  
Validation Dataset ◽  
Multiple Data Sources ◽  
Multiple Data ◽  
Clinic Efficiency ◽  
The Impact ◽  
Patient Waiting

SummaryBackground: Patient no-shows in outpatient delivery systems remain problematic. The negative impacts include underutilized medical resources, increased healthcare costs, decreased access to care, and reduced clinic efficiency and provider productivity.Objective: To develop an evidence-based predictive model for patient no-shows, and thus improve overbooking approaches in outpatient settings to reduce the negative impact of no-shows.Methods: Ten years of retrospective data were extracted from a scheduling system and an electronic health record system from a single general pediatrics clinic, consisting of 7,988 distinct patients and 104,799 visits along with variables regarding appointment characteristics, patient demographics, and insurance information. Descriptive statistics were used to explore the impact of variables on show or no-show status. Logistic regression was used to develop a no-show predictive model, which was then used to construct an algorithm to determine the no-show threshold that calculates a predicted show/no-show status. This approach aims to overbook an appointment where a scheduled patient is predicted to be a no-show. The approach was compared with two commonly-used overbooking approaches to demonstrate the effectiveness in terms of patient wait time, physician idle time, overtime and total cost.Results: From the training dataset, the optimal error rate is 10.6% with a no-show threshold being 0.74. This threshold successfully predicts the validation dataset with an error rate of 13.9%. The proposed overbooking approach demonstrated a significant reduction of at least 6% on patient waiting, 27% on overtime, and 3% on total costs compared to other common flat-overbooking methods.Conclusions: This paper demonstrates an alternative way to accommodate overbooking, accounting for the prediction of an individual patient’s show/no-show status. The predictive no-show model leads to a dynamic overbooking policy that could improve patient waiting, overtime, and total costs in a clinic day while maintaining a full scheduling capacity.Citation: Huang Y, Hanauer D.A. Patient no-show predictive model development using multiple data sources for an effective overbooking approach. Appl Clin Inf 2014; 5: 836–860http://dx.doi.org/10.4338/ACI-2014-04-RA-0026

Abstract 313: Investigation of Substance Use in the Bridge Population

2020 ◽  
Vol 13 (Suppl_1) ◽  
Author(s):  
Rossteen Abbasi ◽  
Rachel H Krallman ◽  
Delaney Feldeisen ◽  
Daniel G Montgomery ◽  
Eva M Kline-Rogers ◽  
...  
Keyword(s):  
Substance Use ◽  
Transitional Care ◽  
Substance Users ◽  
Care Clinic ◽  
Illicit Substance ◽  
Comorbidity Scores ◽  
History Of ◽  
Ed Visits ◽  
Alcohol Users ◽  
The Impact

Background: Alcohol and drug dependence has been linked to increased readmissions and ED visits in some populations. This study investigated the impact of different substance use (SU) on outcomes (ED visit, readmission, death) among hospitalized cardiac patients (pts). Methods: Data on all pts referred to the BRIDGE cardiac transitional care clinic from 2008-2017 were collected. Chart review was conducted on a random selection of pts with a history of SU (n=152) to determine the type of substance used: alcohol, tobacco, illicit substances (i.e. cocaine, narcotics, marijuana) (study conducted prior to Michigan’s legalization of marijuana), or multiple substances. Demographics and outcomes at 30 and 180 days were compared between SU groups. Results: Of 3536 pts, 305 (8.6%) had a history of SU. Compared to those without SU, SU pts were younger (57.3±13.2 v 66.7±14.5 years, p<0.001), male (72.8% v 62.1%, p<0.001), single (62.5% v 38.0%, p<0.001), non-white (21.9% v 15.6%, p=0.005), less likely to attend their BRIDGE appointment (35.7% v 28.3%, p=0.012), had lower Charlson comorbidity scores (CCS) (3.7 v 4.9, p<0.001), and were more likely to visit the ED within 180 days of discharge (44.4% v 38.1%, p=0.033). Of 152 randomly selected SU pts, 57 (37.5%) used alcohol, 20 (13.2%) tobacco, 28 (18.4%) illicit, and 47 (30.9%) multiple substances. Illicit substance users were more likely to be from low SES communities. Despite older age and higher CCS than the other SU groups, alcohol users had fewer 180 day ED visits (p=0.007) and 180 day readmissions (p=0.024) than illicit substance users, as well as fewer 180 day readmissions (p=0.044) than multiple substance users. Conclusion: Compared to the national average (US Department of Health and Human Services), pts referred to BRIDGE appear more likely to have a history of SU (6.4% v 8.6%). Despite being younger and having lower comorbid burden, SU pts in this population had worse outcomes, as seen in prior studies. Additionally, they were less likely to attend a transitional care appointment, putting them at greater risk. Alcohol users, while older and sicker, had the best outcomes among SU pts; illicit substance users had the worst. Further research to identify the causes of these ED visits and readmissions, as well as targeted strategies to improve outcomes in this population are warranted.

scholarly journals High Performance Energy Prediction using Hadoop with Spark

2018 ◽  
Vol 20 ◽  
pp. 02012
Author(s):  
Hung Duong-Ngoc ◽  
Hoan Nguyen-Thanh ◽  
Tam Nguyen-Minh
Keyword(s):  
High Performance ◽  
Weather Forecast ◽  
Machine Learning Algorithms ◽  
Data Sources ◽  
Energy Prediction ◽  
Multiple Data ◽  
Modern Development ◽  
Hadoop Platform ◽  
Mapreduce Model ◽  
The Impact

With the increasingly modern development of the power system. Along with that is the data source collected from them is huge. Combined with other systems such as GIS, MDMS-AMR (Automatic Meter Reading), weather forecast and socio-economic indicators. We consider performing an effective analysis of the data sources in order to understand the evolution, characteristics, and modeling of the power consumption system. Thereby predict future energy trends and build bases for the system model. To implement the issues raised, we appreciate using Hadoop platform for storage and segmentation data, enabling better handle large amounts of data initially. Then, the data was analyzed using the scalable machine learning algorithms - MLib was supported and developed on the Spark/SPARKNET platform. The Hadoop framework has recently evolved to the standard framework implementing the MapReduce model. In this paper, we evaluate Hadoop with Mlib/Sparknet performance in both the traditional model of collocated data and compute services as well as consider the impact of separating out the services. Energy modeling from multiple data sources such large may help to understand the change of the system according to consumer demand for practical, predictable trends of energy in the future and provide the basis for building energy models for similar systems.

scholarly journals P106: Healthcare utilization among homeless and/or substance using adults presenting to the ED

CJEM ◽  
2016 ◽  
Vol 18 (S1) ◽  
pp. S114-S114
Author(s):  
V.V. Puri ◽  
K. Dong ◽  
B.H. Rowe ◽  
S.W. Kirkland ◽  
C. Vandenberghe ◽  
...  
Keyword(s):  
Substance Use ◽  
Risk Factor ◽  
High Risk ◽  
Drug Use ◽  
Acute Care ◽  
Unstable Housing ◽  
Alcohol And Drug Use ◽  
Ed Visits ◽  
The Impact

Introduction: Substance use and unstable housing are associated with heavy use of the Emergency Department (ED). This study examined the impact of substance use and unstable housing on the probability of future ED use. Methods: Case-control study of patients presenting to an urban ED. Patients were eligible if they were unstably housed for the past 30 days, and/or if their chief complaint was related to substance use. Following written informed consent, patients completed a baseline survey and health care use was tracked via electronic medical records for the next six months. Controls were enrolled in a 1:4 ratio. More than 2 ED visits during the follow-up was pre-specified as a measure of excess ED use. Descriptive analyses included proportions and medians with interquartile ranges (IQR). Binomial logistic regression models were used to estimate the impact of housing status, high-risk alcohol use (AUDIT) and drug use (DUDIT), and combinations of these factors on subsequent acute care system contacts (ED visits + admissions). We controlled for age, gender, comorbidities at baseline, and baseline presenting acuity. Results: 41 controls, 46 substance using, 91 unstably housed, and 31 both unstably housed and substance using patients were enrolled (n = 209). Median ED visits during follow up were 0 (IQR: 0-1.0) for controls, 1.0 (IQR: 0-3.3) for substance using, 1.0 (IQR: 0-4.0) for unstably housed and 4 (IQR: 2-12.3) for unstably housed and substance using patients. The median acute care system contacts over the same period was 1.0 (IQR 0-2.0) for controls, 1.0 (IQR: 0-4.0) for substance using, 1.0 (IQR: 0-5.0) for unstably housed and 4.5 (IQR: 2.8-14.3) for unstably housed and substance using patients. Being unstably housed was the factor most strongly associated with having > 2 ED visits (b=3.288, p<0.005) followed by high-risk alcohol and drug use (b=2.149, p<0.08); high risk alcohol use alone was not significantly associated with ED visits (b=1.939, p<0.1). The number of comorbidities present at baseline was a small but statistically significant additional risk factor (b=0.478, p<0.05). The model correctly predicted 70.1% of patients’ ED utilization status. Conclusion: Unstable housing is a substantial risk factor for ED use; high-risk alcohol and drug use, and comorbidities at baseline increased this risk. The intensity of excess ED use was greatest in patients who were unstably housed and substance using.

Management of Chronic Mental Illnesses and Substance Use Disorders

2017 ◽  
pp. 101-122
Author(s):  
Pallavi Sinha ◽  
Amit Garg ◽  
Prerna Khanna ◽  
Amit Khanna
Keyword(s):  
Substance Abuse ◽  
Mental Illness ◽  
Substance Use ◽  
Substance Use Disorders ◽  
Demographic Characteristics ◽  
Mental Illnesses ◽  
Prevalence Rates ◽  
Factors Affecting ◽  
Impact On Treatment ◽  
The Impact

There is vast literature available about prevalence and pattern of substance use in patients with Chronic mental illness such as Schizophrenia. Many of these studies have also investigated the issues related to the impact of substance use on the course and prognosis of the mental illness and vice versa. The factors affecting the use and its impact on treatment have also been studied to some extent in the Western countries. Estimates of the prevalence of substance abuse in schizophrenia and major affective disorder vary as a function of settings (e.g., community, hospital-in-patient vs. out-patient), demographic characteristics of the sample and assessment methods, with most prevalence rates ranging between15% to 65%. Patients with Chronic Mental illnesses and Substance use disorders are difficult to treat and many models for their management have been proposed. This chapter will deal with some of these issues.

scholarly journals Investigating the impact of COVID-19 on performance and image enhancing drug use

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Matthew Dunn ◽  
Timothy Piatkowski
Keyword(s):  
Mental Health ◽  
Substance Use ◽  
Online Survey ◽  
High Dose ◽  
Convenience Sample ◽  
Health Concerns ◽  
Exercise Habits ◽  
Mental Health Concerns ◽  
Image Enhancing ◽  
The Impact

Abstract Background Emerging research has suggested that the COVID-19 pandemic has had some impact on substance use patterns. The aim of the study was to conduct a rapid survey to assess the impact of COVID-19 on performance and image enhancing drug (PIED) use and training, and any subsequent negative physical or mental health outcomes. Methods During 2020, a convenience sample of 60 PIED consumers (mean age = 26.69; 68.3% located outside Australia) completed a quantitative anonymous online survey exploring how the coronavirus pandemic impacted patterns of PIED use and associated exercise habits. The survey was administered via the Qualtrics platform and distributed online through PIED forums as well as through the investigators’ networks. Participants were asked about their PIED use and exercise habits prior to and during restricted movement ‘lockdowns’. Results During pre-COVID, the majority of the sample opted to ‘blast-cruise’ (an initial high dose, followed by a lower maintenance dose; 71.7%, n = 43). During lockdown, 45% (n = 27) reported a change in PIED use as a result of the restrictions. In light of health concerns during COVID-19, a majority of men (60%, n = 36) did not take any extra precautions relating to their PIED use. A subgroup of men ceased using PIEDs completely (16.7%, n = 10) with the majority (80%, n = 8) of that subgroup following post-cycle therapy (PCT) of some kind. Only a small proportion of the sample reported negative mental health issues as a result of PCT access issues. Conclusions This study contributes to the emerging literature of the impact of the COVID-19 pandemic on substance use, specifically PIED use among men. The results suggest that the pandemic did influence the choice of PIEDs that participants consumed, although there was little disruption to patterns of exercise, an important aspect of PIED use. Of the men who did cease use completely, the majority reported little issue with PCT access; those who reported difficulty accessing PCT compounds indicated experience some mental health concerns related to ceasing their PIED use. Clinicians and those who come into contact with this group should be alert for any negative physical or mental health concerns resulting from disrupted or ceased PIED use.

scholarly journals An overview on the correlation between the economic status of a country and the population’s health indicators

2019 ◽  
Vol 13 (1) ◽  
pp. 1058-1069
Author(s):  
Mihai Păunică ◽  
Alexandru Manole ◽  
Cătălina Motofei ◽  
Gabriela-Lidia Tănase
Keyword(s):  
Data Analysis ◽  
Economic Status ◽  
Health Indicators ◽  
Data Sources ◽  
Primary Data ◽  
Processing Power ◽  
Multiple Data ◽  
Depth Analysis ◽  
Macroeconomic Indicators ◽  
The Impact

Abstract In this paper, the authors aim to measure the influence of the macroeconomic indicators that characterize a national economy on some key national health indicators. The purpose is to obtain an updated evaluation of the population medical status, under the impact of either growth or economic decline. The analysis focuses especially on Romania but also on other countries, to be able to investigate comparatively national indicators and trends. Multiple data sources have been used for an in-depth analysis, to fit in an appropriate manner the purpose of the study. Static and econometric data analysis software was applied on primary data, as analysis instruments. The correlation coefficient and the regression were the tools used to obtain the study conclusions. We start from the premise that the potential of statistic data sources, combined with the processing power of the data analysis tools would be able to lead to the best result. We have also reviewed the previous studies performed on the same topic and, along with our data, the obtained conclusions should have a certain value and interest not only for current needs and utilization, but also for future researches. The authors are aware of the direct influence between the macroeconomic status (or performance) indicators and the indicators that characterize the health of the population, and will attempt to measure the intensity of this impact on several key selected domains.

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