Tailored Risk Reduction Strategies for Preventable Causes of Death

Overseen By Jonathan Feelemyer

Age: 18 - 65

Sex: Any

Trial Phase: Academic

Sponsor: NYU Langone Health

Disqualifiers: Regular care elsewhere, High mortality conditions, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: Unknown

150 Participants Needed

Trial Summary

What is the purpose of this trial?

The purpose of this research study is to investigate if a personalized intervention including parts such as navigation (focus on patient outreach efforts, missed and completed encounters), personalization (individual health benefits) and compensation (value health-related costs borne by patients) will help people reduce their chances of dying from preventable causes, including heart attacks, strokes, drinking alcohol, substance abuse, HIV, and other conditions.

Do I need to stop my current medications for the trial?

The trial information does not specify whether you need to stop taking your current medications. It's best to discuss this with the trial coordinators.

What data supports the effectiveness of the treatment Compensation, Navigation, Personalization for reducing preventable causes of death?

The research highlights the importance of addressing modifiable risks and behavioral factors, which are major contributors to preventable deaths. This suggests that personalized strategies, like those in the treatment, could be effective in reducing these risks by tailoring interventions to individual needs and behaviors. ^{1 2 3 4 5}

Is the treatment generally safe for humans?

The research highlights that adverse events, including those related to medical treatments, can occur and sometimes lead to serious outcomes. However, many adverse events are preventable with careful monitoring and improved medication practices. ^{2 6 7 8 9}

How does this treatment differ from other treatments for preventable causes of death?

This treatment is unique because it uses tailored strategies to reduce risks associated with preventable causes of death, potentially incorporating real-time data on public interest in behavior changes. Unlike standard treatments that may focus on a single cause, this approach considers multiple contributing factors to better address complex health issues. ^{2 10 11 12 13}

Research Team

Ronald S Braithwaite, Braithwaite

Principal Investigator

NYU Langone Health

Eligibility Criteria

This trial is for people aged 35-64 with low income (≤ $38,000), at high risk of heart disease (10-year risk ≥10%), and who drink heavily based on SAMHSA's definition. Participants must be willing to use New York's public health system and can consent in English or Spanish.

Inclusion Criteria

Expected mortality ≥1% per year (based on age, sex, race/ethnicity), with ≥1 of the following contributors: 10-year cardiovascular risk ≥10% (assessed by ASCVD risk tool), Heavy alcohol consumption (defined using SAMHSA binge drinking definition, drinking >4 standard drinks for men and >3 standard drinks for women on same occasion in past month), Willing to be navigated to Health and Hospitals Corporation of New York health system, Ability to provide written informed consent in English or Spanish

I am between 35 and 64 years old.

My annual income is $38,000 or less, adjusted for my family size.

Exclusion Criteria

Receives regular care elsewhere than Health and Hospitals Corporation of New York

I have a life-threatening condition not covered in the study.

Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Intervention

Participants receive the study intervention, composed of navigation, compensation, and personalization, administered by peer navigators trained in Motivational Interviewing techniques

12 months

Regular in-person visits

Follow-up

Participants are monitored for changes in alcohol use, cardiovascular risk, and HIV risk

12 months

Treatment Details

Interventions

• Compensation
• Navigation
• Personalization

Trial Overview The study tests a personalized intervention aimed at reducing death from preventable causes like heart disease, excessive alcohol use, substance abuse, and HIV. It includes patient outreach navigation, individualized health benefits personalization, and covering patients' health-related costs.

Participant Groups

Group I: Low SES Population - Intervention Experimental Treatment 1 Intervention

Participants receive the study intervention, composed of navigation, compensation, and personalization for study participants. The intervention will be administered in person to the participants recruited for the study and will be administered by peer navigators who will be trained on Motivational Interviewing (MINT) techniques.

Group II: Low SES Population - No Intervention Active Control 1 Intervention

Participants receive no intervention components of navigation, compensation, and personalization. Participants will receive their normal medical care through regular doctor visits without any intervention or personalization.

Who Is Running the Clinical Trial?

NYU Langone Health

Lead Sponsor

Trials

1,431

Recruited

838,000+

National Institute on Alcohol Abuse and Alcoholism (NIAAA)

Collaborator

Trials

865

Recruited

1,091,000+

Findings from Research

A new risk prediction model developed using data from 8,241 respondents accurately estimates a person's 10-year mortality risk based on 12 major behavioral and biometric risk factors, showing good predictive ability with an ROC curve of 0.84 for both men and women.

The model highlights that a small percentage of the population (5.1% of men and 5.9% of women aged 30-44) accounts for a significant portion (25%) of mortality risk, indicating that targeted interventions could be effective in improving population health.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Validation of a new predictive risk model: measuring the impact of the major modifiable risks of death for patients and populations. Lim, SS., Carnahan, E., Nelson, EC., et al.[2023]

Search query data from Google indicates that public interest in health-related behaviors like 'weight', 'diet', 'fitness', and 'smoking' peaks in January and declines throughout the year, suggesting a seasonal pattern in health-related searches.

Understanding these patterns can help design more effective public health interventions and campaigns by targeting specific times and locations when interest in these behaviors is highest.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Search query data to monitor interest in behavior change: application for public health. Carr, LJ., Dunsiger, SI.[2021]

From 1999 to 2010, age-adjusted death rates in the U.S. declined for six of the ten leading causes of death, with the most significant decrease seen in cerebrovascular disease (-36.5%), while rates increased for four causes, notably Alzheimer disease (52.4%).

Despite overall declines in death rates, disparities in mortality by sex and race/ethnicity persisted across all causes, highlighting the need for targeted public health interventions to address these inequalities.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Trends in Disparity by Sex and Race/Ethnicity for the Leading Causes of Death in the United States-1999-2010. Chang, MH., Moonesinghe, R., Athar, HM., et al.[2022]

In a study analyzing data from 606,260 patients in Maine, significant regional differences were found in behavioral risks for cardiovascular disease, such as smoking (12.4%-28.6%) and poor diet (43.6%-73.0%), which are strongly linked to cardiovascular mortality.

Despite variations in medical interventions like revascularization, there was no significant association between these procedures and reduced cardiovascular disease mortality, highlighting the importance of focusing on modifiable risk factors for prevention.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Behavioral Risk Factors and Regional Variation in Cardiovascular Health Care and Death. Fairfield, KM., Black, AW., Lucas, FL., et al.[2019]

Tobacco use was identified as the leading cause of death in the United States in 2000, accounting for 435,000 deaths (18.1% of total deaths), highlighting the critical need for smoking prevention and cessation programs.

Poor diet and physical inactivity were also significant contributors, causing 365,000 deaths (15.2%), suggesting that these factors may soon surpass tobacco as the leading cause of mortality, emphasizing the urgency for preventive health measures.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Actual causes of death in the United States, 2000. Mokdad, AH., Marks, JS., Stroup, DF., et al.[2022]

In 2014, the estimated number of potentially preventable deaths from cancer, stroke, and heart disease decreased compared to 2010, indicating progress in public health efforts, with cancer deaths dropping by 25%.

However, deaths from chronic lower respiratory diseases and unintentional injuries increased, highlighting areas that still require attention and targeted prevention strategies to reduce premature deaths.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Potentially Preventable Deaths Among the Five Leading Causes of Death - United States, 2010 and 2014. García, MC., Bastian, B., Rossen, LM., et al.[2017]

Adverse drug events are a major cause of death in US hospitals, ranking as the fourth to sixth leading cause, with fatalities surpassing those from pneumonia and diabetes.

The economic impact of drug-related morbidity and mortality is substantial, estimated at $30 billion annually, potentially exceeding $130 billion in severe cases, highlighting the need for improved monitoring and prevention strategies.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Counting the costs of drug-related adverse events. White, TJ., Arakelian, A., Rho, JP.[2022]

In a study analyzing 2,846,305 deaths in the US, only 0.16% were classified as due to medical adverse events, indicating a significant underreporting of such incidents in current diagnostic coding systems.

Younger individuals, black ethnicity, and those with higher education levels were found to have higher odds of adverse event deaths, suggesting potential disparities in how these events are recorded and recognized.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Medical adverse events in the US 2018 mortality data. Oura, P.[2022]

In 2001, there were approximately 4.3 million visits to healthcare facilities in the U.S. due to adverse drug events (VADEs), indicating a significant patient safety concern.

The study found that VADEs were more common in elderly patients aged 65-74 and in females, highlighting the need for targeted safety measures in these populations.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Ambulatory care visits for treating adverse drug effects in the United States, 1995-2001. Zhan, C., Arispe, I., Kelley, E., et al.[2019]

The study created detailed US mortality rates from 1960 to 1989 using multiple cause-of-death data, revealing that death certificates typically list an average of 2.66 causes, which has increased over the decades.

Multiple-cause analysis identified significant excesses of renal disease and arthritis among granite cutters, highlighting the importance of considering contributory conditions in understanding occupational health risks.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Use of multiple-cause mortality data in epidemiologic analyses: US rate and proportion files developed by the National Institute for Occupational Safety and Health and the National Cancer Institute. Steenland, K., Nowlin, S., Ryan, B., et al.[2022]

In a study of 7740 HIV-positive patients, the 8-year risk of all-cause mortality was found to be 17.5%, with HIV-related mortality accounting for 39% of total mortality risk when using a cause-of-death weighting approach.

The study revealed that using this weighting method highlighted a higher risk of cardiovascular disease among patients with a history of injection drug use, showing an 8% attribution compared to only 4% when considering just the underlying cause.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Estimating a Set of Mortality Risk Functions with Multiple Contributing Causes of Death. Breger, TL., Edwards, JK., Cole, SR., et al.[2022]

Traditional national cause of death statistics often oversimplify complex health issues by reducing them to a single cause, which can overlook important contributing factors.

A new multidimensional approach that includes multiple causes of death from death certificates reveals that many major causes, like diabetes, significantly contribute to deaths beyond their classification as the underlying cause, highlighting the need for more comprehensive data in understanding health patterns.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

New dimensions in cause of death statistics. Chamblee, RF., Evans, MC.[2019]

A qualitative study involving 36 adults with type 2 diabetes explored their reactions to a cardiovascular risk calculator, revealing complex cognitive and emotional responses that may hinder behavior change despite the calculator's intended purpose of motivating healthier choices.

The findings suggest that cardiovascular risk calculators are more effective when used alongside health professionals, who can help interpret the risk information and encourage users to take preventive actions.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Patient reactions to a web-based cardiovascular risk calculator in type 2 diabetes: a qualitative study in primary care. Nolan, T., Dack, C., Pal, K., et al.[2022]

References

1. England pubmed.ncbi.nlm.nih.gov

Validation of a new predictive risk model: measuring the impact of the major modifiable risks of death for patients and populations. [2023]

2. United States pubmed.ncbi.nlm.nih.gov

Search query data to monitor interest in behavior change: application for public health. [2021]

3. United States pubmed.ncbi.nlm.nih.gov

Trends in Disparity by Sex and Race/Ethnicity for the Leading Causes of Death in the United States-1999-2010. [2022]

4. Netherlands pubmed.ncbi.nlm.nih.gov

Behavioral Risk Factors and Regional Variation in Cardiovascular Health Care and Death. [2019]

5. United States pubmed.ncbi.nlm.nih.gov

Actual causes of death in the United States, 2000. [2022]

6. United States pubmed.ncbi.nlm.nih.gov

Potentially Preventable Deaths Among the Five Leading Causes of Death - United States, 2010 and 2014. [2017]

7. New Zealand pubmed.ncbi.nlm.nih.gov

Counting the costs of drug-related adverse events. [2022]

8. United States pubmed.ncbi.nlm.nih.gov

Medical adverse events in the US 2018 mortality data. [2022]

9. Netherlands pubmed.ncbi.nlm.nih.gov

Ambulatory care visits for treating adverse drug effects in the United States, 1995-2001. [2019]

10. United States pubmed.ncbi.nlm.nih.gov

Use of multiple-cause mortality data in epidemiologic analyses: US rate and proportion files developed by the National Institute for Occupational Safety and Health and the National Cancer Institute. [2022]

11. United States pubmed.ncbi.nlm.nih.gov

Estimating a Set of Mortality Risk Functions with Multiple Contributing Causes of Death. [2022]

12. United States pubmed.ncbi.nlm.nih.gov

New dimensions in cause of death statistics. [2019]

13. England pubmed.ncbi.nlm.nih.gov

Patient reactions to a web-based cardiovascular risk calculator in type 2 diabetes: a qualitative study in primary care. [2022]