Effects of a Community Population Health Initiative on Blood Pressure Control in Latinos.

Background Hypertension remains one of the most important, modifiable cardiovascular risk factors. Yet, the largest minority ethnic group (Hispanics/Latinos) often have different health outcomes and behavior, making hypertension management more difficult. We explored the effects of an American Heart Association-sponsored population health intervention aimed at modifying behavior of Latinos living in Texas. Methods and Results We enrolled 8071 patients, and 5714 (65.7%) completed the 90-day program (58.5 years ±11.7; 59% female) from July 2016 to June 2018. Navigators identified patients with risk factors; initial and final blood pressure ( BP ) readings were performed in the physician\u27s office; and interim home measurements were recorded telephonically. The intervention incorporated home BP monitoring, fitness and nutritional counseling, and regular follow-up. Primary outcomes were change in systolic BP and health-related quality of life. Using a univariate paired-samples pre-post design, we found an average 5.5% (7.6-mm Hg) improvement in systolic BP (139.1 versus 131.5, t=10.32,

Demographic and Survivorship Disparities in Non–muscle-invasive Bladder Cancer in the United States

Objectives To examine survivorship disparities in demographic factors and risk status for non–muscle-invasive bladder cancer (NMIBC), which accounts for more than 75% of all urinary bladder cancers, but is highly curable with early identification and treatment. Methods We used the US National Cancer Institute’s Surveillance, Epidemiology, and End Results registries over a 19-year period (1988-2006) to examine survivorship disparities in age, sex, race/ethnicity, and marital status of patients and risk status classified by histologic grade, stage, size of tumor, and number of multiple primary tumors among NMIBC patients (n=29 326). We applied Kaplan-Meier (K-M) and Cox proportional hazard methods for survival analysis. Results Among all urinary bladder cancer patients, the majority of NMIBCs were in male (74.1%), non-Latino white (86.7%), married (67.8%), and low-risk (37.6%) to intermediate-risk (44.8%) patients. The mean age was 68 years. Survivorship (in median life years) was highest for non-Latino white (5.4 years), married (5.4 years), and low-risk (5.7 years) patients (K-M analysis, p<0.001). We found significantly lower survivorship for elderly, male (female hazard ratio [HR], 0.96), Latino (HR, 1.20), and unmarried (married HR, 0.93) patients. Conclusions Survivorship disparities were ubiquitous across age, sex, race/ethnicity, and marital status groups. Non-white, unmarried, and elderly patients had significantly shorter survivorship. The implications of these findings include the need for a heightened focus on health policy and more organized efforts to improve access to care in order to increase the chances of survival for all patients

Barriers and Disparities in Emergency Medical Services 911 Calls for Stroke Symptoms in the United States Adult Population: 2009 BRFSS Survey

Introduction: This study examines barriers and disparities in the intentions of American citizens, when dealing with stroke symptoms, to call 911. This study hypothesizes that low socioeconomic populations are less likely to call 911 in response to stroke recognition. Methods: The study is a cross-sectional design analyzing data from the Centers for Disease Control’s 2009 Behavioral Risk Factor Surveillance Survey, collected through a telephone-based survey from 18 states and the District of Columbia. The study identified the 5 most evident stroke-warning symptoms based on those given by the American Stroke Association. We conducted appropriate weighting procedures to account for the complex survey design. Results: A total of 131,988 respondents answered the following question: “If you thought someone was having a heart attack or a stroke, what is the first thing you would do?” A majority of those who said they would call 911 were insured (85.1%), had good health (84.1%), had no stroke history (97.3%), had a primary care physician (PCP) (81.4%), and had no burden of medical costs (84.9%). Those less likely to call 911 were found in the following groups: 65 years or older, men, other race, unmarried, less than or equal to high school degree, less than $25,000 family income, uninsured, no PCP, burden of medical costs, fair/poor health, previous history of strokes, or interaction between burden of medical costs and less than$50,000 family income (p\u3c0.0001 by X2 tests). The only factors significantly associated with “would call 911” were age, sex, race/ethnicity, marital status, and previous history of strokes. Conclusion: Barriers and disparities exist among subpopulations of different socioeconomic statuses. This study suggests that some potential stroke victims could have limited access to EMS services. Greater effort targeting certain populations is needed to motivate citizens to call 911. [West J Emerg Med. 2014;15(2):251–259]

Discovering disease-disease associations using electronic health records in The Guideline Advantage (TGA) dataset

Certain diseases have strong comorbidity and co-occurrence with others. Understanding disease-disease associations can potentially increase awareness among healthcare providers of co-occurring conditions and facilitate earlier diagnosis, prevention and treatment of patients. In this study, we utilized the valuable and large The Guideline Advantage (TGA) longitudinal electronic health record dataset from 70 outpatient clinics across the United States to investigate potential disease-disease associations. Specifically, the most prevalent 50 disease diagnoses were manually identified from 165,732 unique patients. To investigate the co-occurrence or dependency associations among the 50 diseases, the categorical disease terms were first mapped into numerical vectors based on disease co-occurrence frequency in individual patients using the Word2Vec approach. Then the novel and interesting disease association clusters were identified using correlation and clustering analyses in the numerical space. Moreover, the distribution of time delay (Δt) between pair-wise strongly associated diseases (correlation coefficients ≥ 0.5) were calculated to show the dependency among the diseases. The results can indicate the risk of disease comorbidity and complications, and facilitate disease prevention and optimal treatment decision-making

Women and ethnoracial minorities with poor cardiovascular health measures associated with a higher risk of developing mood disorder

BACKGROUND: Mood disorders (MDS) are a type of mental health illness that effects millions of people in the United States. Early prediction of MDS can give providers greater opportunity to treat these disorders. We hypothesized that longitudinal cardiovascular health (CVH) measurements would be informative for MDS prediction. METHODS: To test this hypothesis, the American Heart Association\u27s Guideline Advantage (TGA) dataset was used, which contained longitudinal EHR from 70 outpatient clinics. The statistical analysis and machine learning models were employed to identify the associations of the MDS and the longitudinal CVH metrics and other confounding factors. RESULTS: Patients diagnosed with MDS consistently had a higher proportion of poor CVH compared to patients without MDS, with the largest difference between groups for Body mass index (BMI) and Smoking. Race and gender were associated with status of CVH metrics. Approximate 46% female patients with MDS had a poor hemoglobin A1C compared to 44% of those without MDS; 62% of those with MDS had poor BMI compared to 47% of those without MDS; 59% of those with MDS had poor blood pressure (BP) compared to 43% of those without MDS; and 43% of those with MDS were current smokers compared to 17% of those without MDS. CONCLUSIONS: Women and ethnoracial minorities with poor cardiovascular health measures were associated with a higher risk of development of MDS, which indicated the high utility for using routine medical records data collected in care to improve detection and treatment for MDS among patients with poor CVH

Time-series cardiovascular risk factors and receipt of screening for breast, cervical, and colon cancer: The Guideline Advantage

BACKGROUND: Cancer is the second leading cause of death in the United States. Cancer screenings can detect precancerous cells and allow for earlier diagnosis and treatment. Our purpose was to better understand risk factors for cancer screenings and assess the effect of cancer screenings on changes of Cardiovascular health (CVH) measures before and after cancer screenings among patients. METHODS: We used The Guideline Advantage (TGA)-American Heart Association ambulatory quality clinical data registry of electronic health record data (n = 362,533 patients) to investigate associations between time-series CVH measures and receipt of breast, cervical, and colon cancer screenings. Long short-term memory (LSTM) neural networks was employed to predict receipt of cancer screenings. We also compared the distributions of CVH factors between patients who received cancer screenings and those who did not. Finally, we examined and quantified changes in CVH measures among the screened and non-screened groups. RESULTS: Model performance was evaluated by the area under the receiver operator curve (AUROC): the average AUROC of 10 curves was 0.63 for breast, 0.70 for cervical, and 0.61 for colon cancer screening. Distribution comparison found that screened patients had a higher prevalence of poor CVH categories. CVH submetrics were improved for patients after cancer screenings. CONCLUSION: Deep learning algorithm could be used to investigate the associations between time-series CVH measures and cancer screenings in an ambulatory population. Patients with more adverse CVH profiles tend to be screened for cancers, and cancer screening may also prompt favorable changes in CVH. Cancer screenings may increase patient CVH health, thus potentially decreasing burden of disease and costs for the health system (e.g., cardiovascular diseases and cancers)

The diffusion of operators research in management decision making : An analysis of U.S. Healthcare organisations

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Sociodemographic and Clinical Characteristics Associated with Improvements in Quality of Life for Participants with Opioid Use Disorder

Background: The Houston Emergency Opioid Engagement System was established to create an access pathway into long-term recovery for individuals with opioid use disorder. The program determines effectiveness across multiple dimensions, one of which is by measuring the participant’s reported quality of life (QoL) at the beginning of the program and at successive intervals. Methods: A visual analog scale was used to measure the change in QoL among participants after joining the program. We then identified sociodemographic and clinical characteristics associated with changes in QoL. Results: 71% of the participants (n = 494) experienced an increase in their QoL scores, with an average improvement of 15.8 ± 29 points out of a hundred. We identified 10 factors associated with a significant change in QoL. Participants who relapsed during treatment experienced minor increases in QoL, and participants who attended professional counseling experienced the largest increases in QoL compared with those who did not. Conclusions: Insight into significant factors associated with increases in QoL may inform programs on areas of focus. The inclusion of counseling and other services that address factors such as psychological distress were found to increase participants’ QoL and success in recovery

Machine Learning for Predicting Risk of Early Dropout in a Recovery Program for Opioid Use Disorder

Background: An increase in opioid use has led to an opioid crisis during the last decade, leading to declarations of a public health emergency. In response to this call, the Houston Emergency Opioid Engagement System (HEROES) was established and created an emergency access pathway into long-term recovery for individuals with an opioid use disorder. A major contributor to the success of the program is retention of the enrolled individuals in the program. Methods: We have identified an increase in dropout from the program after 90 and 120 days. Based on more than 700 program participants, we developed a machine learning approach to predict the individualized risk for dropping out of the program. Results: Our model achieved sensitivity of 0.81 and specificity of 0.65 for dropout at 90 days and improved the performance to sensitivity of 0.86 and specificity of 0.66 for 120 days. Additionally, we identified individual risk factors for dropout, including previous overdose and relapse and improvement in reported quality of life. Conclusions: Our informatics approach provides insight into an area where programs may allocate additional resources in order to retain high-risk individuals and increase the chances of success in recovery

Developing interagency collaboration to address the opioid epidemic: A scoping review of joint criminal justice and healthcare initiatives.

BACKGROUND: With the current opioid epidemic impacting well over half of all counties across the United States, initiatives that encourage interagency collaboration between first responder organizations appear necessary to comprehensively address this crisis. Police, fire, and emergency medical services (EMS) are in a unique position to identify substance users and provide necessary resources to initiate treatment, yet there is not sufficient evidence of joint collaborative programs between law enforcement/first responders and healthcare providers. METHODS: In this scoping review we examine the current state of joint criminal justice and healthcare interventions, specifically, opioid and substance use pre-arrest initiatives via emergency first responders and police officers. We relied on data from the last 10 years across three major databases to assess the extent of criminal justice (CJ) and healthcare collaborations as a response to individuals with opioid use disorder (OUD). We specifically focused on interventional programs between criminal justice first responders (pre-arrest) and healthcare providers where specific outcomes were documented. RESULTS: We identified only a small number (6) of studies involving interventions that met this criteria, suggesting very limited study of joint interagency collaboration between law enforcement first responders and healthcare providers. Most had small samples, none were in the southern states, and all but one were initiated within the last 5 years. CONCLUSIONS: Although studies describing joint efforts of early intercept criminal justice responses and healthcare interventions were few, existing studies suggest that such programs were effective at improving treatment referral and retention outcomes. Greater resources are needed to encourage criminal justice and healthcare collaboration and policies, making it easier to share data, refer patients, and coordinate care for individuals with OUD