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Alex J Mitchell Alcohol Detection by Clinician (Aug2012)Alex J Mitchell
Powerpoint slides on detection and identification of alcohol problems (alcohol use disorder) by clinicians.
See related paper:
http://bjp.rcpsych.org/content/201/2/93.abstract
Royalty free for personal use, but please cite with credit to AJMitchell (Leicester)
Prevention of Substance Abuse and Suicide in the Elderly PopulationSande George
Bill Fitzpatrick, Senior Services Coordinator, Lines For Life, presents at the OSRAA Fall Conference 2018.
Incidences of substance abuse and suicide are rising in the older adult population. Learn to identify the warning signs. Discover how you can help. Know where to get help.
In this presentation Dr Jonathan Campion, Director of Public Mental Health and Consultant Psychiatrist, South London and Maudsley NHS Foundation Trust, shows how appropriate public mental health commissioning can prevent mental health problems, promote wellbeing and improve outcomes for services and the people who use them.
Find out more http://mentalhealthpartnerships.com/?p=13135
Alex J Mitchell Alcohol Detection by Clinician (Aug2012)Alex J Mitchell
Powerpoint slides on detection and identification of alcohol problems (alcohol use disorder) by clinicians.
See related paper:
http://bjp.rcpsych.org/content/201/2/93.abstract
Royalty free for personal use, but please cite with credit to AJMitchell (Leicester)
CORONARY ARTERY DISEASE is a modern epidemic in india. due to changes in living conditions and habits its prevalence is increasing day by day . in this presentation i have explained the various risk factors and innovations in diagnosis of CAD. IT is very useful for primary health care physicians and community medicine specialist
This is the ongoing project discussion portion of this class. My pop.docxglennf2
This is the ongoing project discussion portion of this class. My population is geriatric/elderly. The problem is BP...
I will attach previous discussions because it all needs to tie in together
350 words
at least 3 references cited in the discussion.
must be last 5 years
Overview: Dr. Marcia Stanhope (2020) explained that evidence-based public health practice refers to those decisions made by using the best available evidence, data and information systems and program frameworks; engaging community stakeholders in the decision-making process; evaluating the results; and then disseminating that information to those who can use the information.
Practicum Discussion: This week, your assignment will be to incorporate all of the information you have gathered from the community—including the population itself, health data, interviews/conversations with interested community members, and your community assessment, including your Windshield Survey—as well as what you have gathered from scholarly literature to propose measureable interventions. Measureable interventions mean that the results can be measured through some data that could be collected (Stanhope, 2020). This requires thinking in terms of actions and then measuring results. An evaluation of interventions is important to see whether or not they are effective in solving a health care problem. Remember, you will need to use the data you gathered to determine whether or not a problem exists in your community and to then determine whether your interventions might be effective.
Please discuss the following points in your Practicum Discussion:
Identify one evidence-based behavior change that would promote health in your selected population.
Suggest one specific culturally sensitive, evidence-based, measureable intervention to address the health problem for your selected population.
Think in terms of measuring outcomes. What outcomes would you expect to see once the intervention(s) are in place? Be specific.
By Day 4
Post
your response to this Discussion.
Support your response with references from the professional nursing literature.
GOAL of PRACTICUM PROJECT
Overall Purpose for Practicum:
Develop a potential project to improve the health of a specific population of interest or a population at risk.
This practicum is designed to help you develop as a scholar practitioner and health leader to promote positive social change in your own community. In this practicum experience you will focus on
primary prevention
of a health problem in your community (see text for definition.) You already possess the knowledge and skills to help those who are acutely ill. This experience will help learn how to prevent a health problem in a specific population at risk at the
community and system level of care
(see text for definition). Consequently, because you are well aware of how to care for individuals you will now develop leadership and advocacy skills to improve the health of the communi.
GLOBAL HEALTH AND DISEASEChapter 2Chapter 2 OverviewIMatthewTennant613
GLOBAL HEALTH AND DISEASE
Chapter 2
Chapter 2: Overview
Introduction
Burden of Disease
Non communicable Disease
Infectious Disease
The Future of Infectious Disease
Public Health and Healthcare Strategies
Conclusion
Introduction
Development and management
Understanding the environmental or national context
Social and cultural beliefs
The physical environment
The political climate
3
3
Introduction
Understanding the environmental or national context
Economic development
Social structures
Types of diseases present in the population
4
4
Introduction
Influence of population health needs
Distribution of medical resources
Provision of health services
5
5
Introduction
Demands on healthcare systems
Disease prevention
Primary treatment
Secondary treatment
Tertiary treatment
6
6
Introduction
Integration of the healthcare system with public health system
Public health system responsibilities
7
7
Burden of Disease
Measurement of disease
Prevalence
Incidence
Disease specific mortality
Case fatality rate
Mortality rates
8
8
Burden of Disease
Reporting the burden of disease
Disability-adjusted life years (DALY)
Quality-adjusted life years (QALY)
Health expectancy
Healthy life years
Application of cost-benefit analyses
9
9
Burden of Disease
Effect of measurement on appropriation of health resources
Difficulties with collecting health statistics
10
10
Noncommunicable Disease
Heart disease
Cerebrovascular disease
Respiratory infections
HIVAIDS
Chronic pulmonary disease
Perinatal conditions
Diarrheal disease
Tuberculosis
Malaria
Respiratory tract cancers
Top 10 leading causes of death
Noncommunicable Disease
Emergence of noncommunicable disease
Heart disease
Stroke
Cancer
12
12
Noncommunicable Diseases
Emergence of noncommunicable disease
Chronic respiratory disease
Mental illness
Diabetes
13
13
Noncommunicable Disease
Increasing impact on worldwide mortality
Differences between communicable and noncommunicable disease
World Health Organization projection
14
14
Noncommunicable Disease
Risk factors for noncommunicable disease
Lifestyle
Environment
Top ten leading causes of death worldwide
15
15
Noncommunicable Disease
Cardiovascular disease
Forms of disease
Atherosclerotic disease
Non-atherosclerotic disease
16
16
Noncommunicable Disease
Cardiovascular diseases Types
Coronary Artery Disease
Heart Attack
Congenital Heart Disease
Aneurysm
Heart Failure
High Blood Pressure
Stroke
Arrhythmias
17
17
Noncommunicable Disease
Cancer
Risk factors
Preventable risk factors
18
18
Noncommunicable Disease
Factors Known To Increase Cancer Risk
Age: can take decades to develop
Lifestyle: Certain lifestyle choices
Family history: 10% due to inherited condition
Health conditions: Some chronic health conditions can increase risks
Noncommunicable Disease
Factors Known To Increase Cancer Risk
Environment: may contain harmful chemicals
Globalization:
Rising consumption of tobacc ...
Presented by
Salim Chowdhury, MD - Community Care
Curtis Upsher, Jr. MS - Director Community Relations - Community Care
Medicine, Culture, and Spirituality Conference
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This presentation was created as a group project during the Business Analytics course at London Business School.
The goal of this webinar was to help healthcare professionals improve care coordination for patients with advanced illness and to reduce hospital readmissions and length of stay.
Team Lift: Predicting Medication AdherenceNeil Ryan
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We looked at patient data from Medicare part D program released by Centers for Medicare & Medicaid services. We built a prediction model to ascertain whether a patient would be adherent based on a variety of social, economic and behavioral aspects.
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According to TechSci Research report, "India Clinical Trials Market- By Region, Competition, Forecast & Opportunities, 2030F," the India Clinical Trials Market was valued at USD 2.05 billion in 2024 and is projected to grow at a compound annual growth rate (CAGR) of 8.64% through 2030. The market is driven by a variety of factors, making India an attractive destination for pharmaceutical companies and researchers. India's vast and diverse patient population, cost-effective operational environment, and a large pool of skilled medical professionals contribute significantly to the market's growth. Additionally, increasing government support in streamlining regulations and the growing prevalence of lifestyle diseases further propel the clinical trials market.
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Medical Technology Tackles New Health Care Demand - Research Report - March 2...pchutichetpong
M Capital Group (“MCG”) predicts that with, against, despite, and even without the global pandemic, the medical technology (MedTech) industry shows signs of continuous healthy growth, driven by smaller, faster, and cheaper devices, growing demand for home-based applications, technological innovation, strategic acquisitions, investments, and SPAC listings. MCG predicts that this should reflects itself in annual growth of over 6%, well beyond 2028.
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The Promise: CRISPR offers exciting possibilities:
Gene Therapy: Correcting genetic diseases like cystic fibrosis.
Agriculture: Engineering crops resistant to pests and harsh environments.
Research: Studying gene function to unlock new knowledge.
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CA_medicationadherence
1. Do Individual and Neighborhood Level
Poverty Impact Medication Adherence
Among Individuals with High
Cholesterol?
CATHERINE ALLENDE MICHAEL GUSMANO, PHD
RUTGERS UNIVERSITY RUTGERS UNIVERSITY
1
2. Facts about Cardiovascular
Disease
Cardiovascular disease is the number one cause of death in
the United States
• Accounts for nearly 800,000 deaths a year
• 1 out of every 3 deaths in the United States
2
American Heart Association, 2017
3. Two types of lipoproteins: Low-density lipoproteins (LDL) & High-
density lipoproteins (HDL)
Ratio of LDL to HDL is one of the best predictor of cardiovascular
events
Higher levels of LDL, increase the chance of getting cardiovascular
disease
Cardiovascular Disease &
High Cholesterol
3
Lipid Research Clinics Study and the Framingham Heart Study, 1994
4. Non-adherence to Medication
Lowering patient’s cholesterol can
reduce the risk of
o Having a heart attack
o Needing heart bypass surgery
o Angioplasty
o Dying of heart disease
For coronary artery disease, medical
non-adherence was associated with
o 10% to 40% relative increase in risk
of cardiovascular hospitalizations
o 50% to 80% relative increase in
mortality (Ho, 2008)
4
6. Poverty & Medication Adherence
Approximately 57% of high-income individuals with a history of
high cholesterol use statins versus 30% of low-income
respondents (Brown, 2003)
Lower income patients were 26% more likely to become non-
adherent than high income patients (Lemstra, 2012)
6
7. Neighborhood Level Poverty &
Medication Adherence
Area-based social conditions are increasingly becoming recognized as an
important determinant of health and healthcare inequities
Residents of disadvantaged neighborhoods are
less likely to have a usual source of care
more likely to experience unmet need
7
Kirby, 2006
9. Other Possible Explanations for Low
Medical Adherence
Health Factors
Depression is associated with lower medical adherence
Social isolation withdrawal from emotional support (DiMatteo,
2000)
Social Factors
Educational Attainment
Inadequate health literacy skills may lead to impaired
comprehension of medication instruction (Pignone, 2005)
9
11. Filling in the gaps
11
1) Expands research by taking neighborhood level poverty into
account
2) Includes adults younger than 65 years of age
3) Explores inconsistency on past literature
12. Research Questions
1) Does an individual’s poverty level negatively affect adherence to high
cholesterol medication?
2) Does the poverty level of the neighborhood in which a person lives
negatively affect adherence to high cholesterol medication?
3) What are the factors contributing to non-adherence to high cholesterol
medication?
12
13. Hypotheses
1 ) Individuals with incomes below 200% of the federal poverty level are less
likely than individuals with higher incomes to take their high cholesterol
medications.
2) Individuals living in high poverty neighborhoods are less likely than
individuals living in low poverty neighborhoods to take their high
cholesterol medication.
13
14. New York City Community Health Survey
(NYC-CHS), 2014
Health survey that collects data on health behavior and health status of
New York City residents age 18+
Design: cross-sectional telephone survey conducted by the NYC
Department of Health and Mental Hygiene
Sampling method: stratified random sampling
Response rate: 40.5%
14
15. Analytical Sample
Among adults diagnosed high cholesterol by a
health professional
Have you ever been told by a doctor, nurse, or
other health professional that you need to take
medicine for your high cholesterol?”
If respondent answers yes included in
sample
N = 8,565
N = 1,859
N = 1,744
15
16. Medical Adherence
Among those with diagnosed high cholesterol & told they need to
take medication the survey asks:
“Are you currently taking medication to lower your high
cholesterol?”
Yes
No (24.8%)
16
17. Individual Level Poverty
Presented as federal poverty
level percentages (FPL)
<200% FPL (low-income)
≥200% FPL
17
50%50%
Percent Distribution of Poverty
Level Among Adults with High
Cholesterol in NYC, 2014
<200% FPL ≥200% FPL
18. Neighborhood Level Poverty
Measured by the percent of population living below 100%
of the FPL
0% - <10% (low neighborhood poverty)
10% - <20%
20% - <30%
30% - <100% (very high neighborhood poverty)
18
19. 19
21.9%
30.4% 30.7%
17%
0
20
40
60
80
100
0 <10% 10 - <20% 20 - <30% 30 - <100%
%ofsample
Neighborhood Groups by Poverty Level
Percent Distribution of Population Living Below 100%
FPL per Neighborhood Among Adults with High
Cholesterol in NYC, 2014 Very High
Neighborhood
Poverty
Low
Neighborhood
Poverty
22. Analytic Plan
To test the relationship between adherence to high cholesterol medication
and poverty
Chi-Square Test of Independence
To test whether individual and neighborhood level poverty predict medication
adherence
Binary Logistic Regression to determine whether poverty, economic,
health, social and demographic factors are strong predictors of medication
adherence
22
23. 23
1.272* 1.292*
0.746*
0.924*
0.6
0.7
0.8
0.9
1
1.1
1.2
1.3
1.4
<200% medium (10% - <20%) high (20% - <30%) very high (30% - <100%)
OddsRatio
Neighborhood Groups by Poverty Level
Estimated Odds Predicting Medical Non-Adherence by
Individual and Neighborhood Level Poverty, NYC-CHS, 2014
Indv. Level Poverty
Reference (≥200% & low poverty neighborhood); * significant at p<.001
Controlling economic, health, and other sociodemographic factors
26. 26
0.939*
0.787*
0.736*
1.031*
0.6
0.7
0.8
0.9
1
1.1
1.2
1.3
1.4
not married less than high school high school graduate some college
OddsRatio
Educational Attainment
Estimated Odds Predicting Medical Non-Adherence by Social
Factors, NYC-CHS, 2014
Marital Status
Reference (Married & college graduates); *significant at p<.001
Controlling economic and health factors
27. 27
Medical Non-Adherence
Black Non-Hispanic ↑ 1.457*
Hispanic .816* ↓
Asian/PI Non-Hispanic .480* ↓
Other Non-Hispanic ↑ 1.448*
Estimated Odds Predicting Medical Non-
Adherence by Race, NYC-CHS, 2014
Reference (White Non-Hispanic)
*significance at the p<.001 level
28. 28
Medical Non-Adherence
Male .578* ↓
18 – 24 years ↑ 4.726*
25 – 44 years ↑ 7.189*
45 – 64 years ↑ 1.503*
Estimated Odds Predicting Medical Non-
Adherence by Sex and Age, NYC-CHS, 2014
Reference (Females & age 65+)
*significance at the p<.001 level
29. Main Findings
High individual level poverty is significantly associated with higher
odds of non-adherence to cholesterol medication
Neighborhoods with lower levels of poverty are significantly
associated with higher odds of non-adherence
29
30. Main Findings
The following characteristics are associated with having greater odds
of non-adherence to high cholesterol medication
The uninsured
Adults who use the ED as their usual source of care
Adults diagnosed with depression
Black adults
Adults below age 65
30
31. Strengths & Limitations
Strengths
Data taken from NYC
Diversity
Explored neighborhood poverty
alongside individual level
poverty
Limitations
Self - reported data
Limited medical adherence
measure
Cross-sectional
Generalizability
31
32. Implications of Present Study
Health policies to address
Expansion of the ACA (Affordable Care Act)
Delivery systems
Physicians & Pharmacy follow ups
Income Inequalities
32
33. Acknowledgements
Dr. Michael Gusmano
Dr. Jane Miller and Deedee
Theresa Simpson
Dr. Mouzon
Jon Thompson
Project L/EARN Cohort
Friends and Family
National Science Foundation
Rutgers Institute of Health, Health Care Policy and Aging Research
33
38. 38
75.5
74.9
24.5
25.1
0% 20% 40% 60% 80% 100%
≥200%
<200%
IndividualLevelPoverty
Percent Distribution of Medical Adherence by Individual
Level Poverty Among NYC Adults with High Cholesterol,
NYC-CHS, 2014
yes no
Significant at p<.001
Results from a chi-square test
39. 39
76.1 71.6 77.6 76.6
23.9 28.4 22.4 23.4
0%
20%
40%
60%
80%
100%
0-<10% 10 - <20% 20 - <30% 30 - <100%
Percentage(%)
Neighborhood Level Poverty
Percent Distribution of Medical Adherence by
Neighborhood Level Poverty Among NYC Adults with High
Cholesterol, NYC-CHS, 2014
no
yes
Significant at p<.001
Results from a chi-square test
40. Sample Characteristics, (NYC-CHS, 2014)
40
Educational
Attainment
• Less than high school
(21.5%)
• High school graduate (23.3%)
• Some college (21.5%)
• College graduate (30.9%)
Marital Status
• Yes (47.1%)
• No (52.9%)
42. 42
Non-Adherence
Less than High School ↓*
High School Grad ↓*
Some College ↑*
Not Married ↓*
Reference (College Graduate, Married)
* shows significance at the p<.001 level
Estimated Odds Predicting Medical Non-Adherence by
Social Factors, NYC-CHS, 2014
Editor's Notes
Studied the impact of individual and neighborhood level poverty on medical adherence among individuals with high cholesterol
Good afternoon, my name is Catherine and this summer I’m working with Dr. Michael Gusmano from department of health policy at Rutgers School of Public Health. Today I will be sharing with you our summer project where we attempted to find whether
First, I’d like to start off with some facts about CVD
- According to the American Heart Association…cvd accounts for..
Thanks to extensive clinical research, we know that high cholesterol is a driving factor in cvd
- 2 types of proteins that carry cholesterol to the blood… bullet two
- LDL (bad cholesterol)& HDL (good cholesterol)
TRANSITION hc can be controlled with medication
….. Good news is.. reducing LDL levels or high cholesterol is possible with medication !!!
Managing your high cholesterol by taking medication as prescribed are critical components of both preventing and controlling cardiovascular disease
Studies have shown that adherence to medication can reduce your risks of cvd
however not everyone takes their medication which is why medical adherence to statins is a ph issue
TRANSITION To understand the factors that may affect medical adherence we will turn to…
We used this (Andersons…) as our theoretical framework to explore reasons for medication non-adherence
An emergent model of how Environmental, pop and health behaviors influence the health outcomes
we will be focusing on the environmental factors and enabling resources of the population characteristics to
Environmental factors such as
Usual source of care, & access to medical advice,
Enabling resources
Abaility to pay for healthcare, household annual income, Insurance coverage, social support,
Under (1974), environmental factors are hypothesized to affect medical utilization
*previous regarding poverty & medical adherence* has said ….A more recent study found that patients who were of lower income status were 26% more likely to become non-adherent
Very important to study the effect of Poverty on medical adherence because low-income adults usually have less disposable income, making affording of high cholesterol drugs such as statins difficult.
A study in 2003 of diabetic adults aged 65+ found that approximately
Economic Factors
affordability is a major issue when it comes to medical adherence
High poverty individuals & the uninsured have less disposable income
- less disposable income to use on on drugs (costs)
The reason we decided to also look at neighborhood poverty level as…. area-based social
There may be lower levels of healthcare utilizations by individuals who live impoverished neighborhoods due to the characteristics of the communities in which they live in
This is very important to study since the U.S is highly segregated by income…
(controlling for individual level poverty, and they still found this finding
As you can see, the neighborhoods with very high poverty (which are outlined in red) also have the highest prevelance of heart disease which shows how geographic region can really impact your health
Health
-literature has shown social support plays a big role in whether or not a patient takes their medication as prescribed
- Can also come from spouse
Social
- - understanding your physician’s directions is important because if a patient goes back home and realizes they don’t understand their physicians instructions, they’re less likely to take medication as prescriber
Transition To summarize, the pieces that we’re looking at fall together as follows:
To summarize, Here I have a diagram of our causual pathway
Key independent variables
Outcome
1) - most previous studies focus on specific factors such as income or certain type of health insurance such as medicare, but we ….
2) Most studies regarding adherence to cholesterol medication focus mostly on adults over the age of 65… however
- we did this because cardiovascular disease begins to increase substantially at the age of 30 (Jolly, 2010)
3) Conflicting results.. Some say poverty doesn’t affect medical adherence much while some say poverty and medical adherence are strongly correlated (not as important as side effects)
recent so it updates past research
TRANSITION: As such, our current study…
TRANSITIOn: In order to address our questions, we use data from the…
an Annually conducted (since 2002)*..
The survey also includes information about social and economic status of respondents such as education level, marital status, poverty level and neighborhood of residence.
Design Survey data were collected through a computer-assisted telephone interview (CATI) system which includes both landline and cell phones
Stratifies data using 34 United Hospital Fund's (UHF) neighborhood in NYC, from all 5 boroughs
SubAnalytical sample
Cooperation Rate (88.9%) is defined as the number of those who participated in the survey, divided by the number of individuals in the sample who were contacted and identified as eligible.The Response Rate is a more conservative measure and is defined as the number of individuals who participated in the survey, divided by the number of individuals in the sample who were contacted and identified as eligible, as well as those never contacted and those with unknown elgibility.
In order to reach our analytical among adults who responded yes to being diagnosed with high cholesterol.. “….” the survey asked
We specifically decided look at people who were told by health professionals they need to tke medication as not everyone who is diagnosed with high cholesterol are recommended to take medication but perhaps were recommended to make lifestyle changes such as diet and exercise
Sample size with our inclusion criteria 1859
once we restricted our sample to completed cases only… our final ssample size is 1,744 of nyc adults diagnosed with high cholesterol who were aslo told by a health professional they need to take medication for their high cholesterol
Medical adherence is our outcome variable
Our dependent variable, a binary variable that represents medical adherence
¼ of adults with high cholesterol do not take their medication
Our first Key IV is individual level poverty
The survey originally broke down the individual poverty level into quintiles however we decided to recode it into a dichotomous variable
with <200% represented as the low income
>200% represented as higher income individuals more affluent people
*we decided to look at 200% instead of 100% to account for the high cost of living in nyc**
*our 2nd key indepdent variable is neighborhood pl which was measured….
broken down into quartiles
neighborhoods with 0 -<10% below the fpl are neighborhoods with low poverty
and neighborhoods with 30 - <100% of the population below of the fpl are neighborhoods with high poverty
Neighborhood level poverty was measured by the percent of population living below 100% FPL, * again broken down by neighborhood quartiles.
Increasing order of neighborhood poverty
Majority of NYC adults with high cholesterol are living in neighborhoods with 10-30% of the population living below 100% of the FPL
TRANSITION: We also controlled for a variety of factors that we know to be related to medical adherence, such as...
Highlighted categories we hypothesized to be related to medical non-adherence,
X % of adults with high cholesterol are uninsured, use the ED as their usual source of care, 8.9% did not get medical care when needed, 21.5% are diagnosed with depression and 3.5% are diagnosed with sother mental illnesses
Usual source of care
original 7 categories for ex, private doctors, community health center
but we chose to focus on ED as literature shows people who use the ED as usual source of care are less likely to adhere to medication
Didn’t get care
whether responded received medical care when they needed it, in the past 12 months
Depression (diagnosed)
Other mental illnesses
such as bipolar disorder and schizophrenia
TRANSITION: We also controlled for a variety of factors that we know to be related to medical adherence, such as...
*here for our demographic factors, I highlighted the modal group instead,
Large proportion of nyc adults with high cholesterol are white nh, between 45-64 years & males
To understand the relationship between adherence to high cholesterol medication and poverty, we used chi square tests to test for significant associations between our ivs and dvs
Strong predcitors
**This particular chart is showing you the estimated odds predicting medical non-adherence by both individual and neighborhood poverty level.
Reference category
*the odds of not adhering to high cholesterol medication was 27% higher for individuals below 200% of the FPL (or low income individuals) than those at or above
*we found individuals in very high poverty neighborhoods actually had (7.6% & 25.4)- lower odds of not taking medication compared to individuals in low poverty neighborhoods
Medium poverty neighborhood, High poverty neighborhood, Very highpoverty neighborhoods
**This particular chart is showing you the estimated odds predicting medical non-adherence by insurance status.
- Noninsured adults have 2.387 times the odds of being non adherent than…
Interestly enough we also found that adults with public insurance (such as medicare and medicaid) have lower odds of being non adherent than individuals with provate insurance
TRANSITION:
On my horizontal axis I have those who said yes to our 4 health factors....didn’tgetcare..depression..other mental illnesses..
As you can see, adults who use the ED or urgent care as a usual source care and have been diagnosed with depression have higher odds of being non-adherent
** contrary to expectations.. Those with some college have greater odds of non-adherence compared to college graduate
Adults who are not married also greater odds than adults who are married
***The final results for our logistic regression models in each for each racial category
Predictors with higher odds (compared to reference group) are represented with an upward arrow sign, while predictors with lower odds are represented with a downward arrow sign,
*to our surprise, Hispanics have lower odds of being non-adherent than white non-Hispanics
***The final results for our logistic regression models in each for each racial category
*all age grops have greater odds than
Consistent to our hypothesis..High individual level poverty… or individuals with low incomes…
We had hypothesized that neighborhoods struck with high poverty were more likely to not adhere to their high cholesterol medication
But Contrary to our expectation we found that..neighborhoods with lower levels…
This is concerning that poor individuals are not taking lifesaving medication. It is important to study vulnerable groups who are not taking their hc medication in order to reduce health inequities that are attributed to wealth
All of our IV were found to be statistically significant, however, some Ivs went in the opposite direction of our hypothesis
The rest of our V were also found to be statistically significant,
well suited dataset to address our research question
Because our survey collected data on New York City, the largest city in the country, it allowed us to examine neighborhood level factors in a highly diverse population.
2)There are only a few validated surveys at the city level in the country/world
Most data sources are national sources… doesn’t allow to explore community level factors in medical adherence
LIMITATIONS
-self reported data rather than clinicial data such as pill refill counts to measure medical adhernce, which can introduce response bias
We only had a binary measure of medical adherence
More comprehensive questions would be better such as asking, “if not why” allowing us to examine possible reasons why some people don’t adhere
may not take bc expensive or bc of side effects
We used a cross sec which doesn’t allow us to draw any casual relaitonships
Had a longitudinal study would have allowed us to follow respondents oand see how their medical adherence changed over time
- Not a nationally representative sample which possibly hinders us from generalizing our results to the country as a whole
As previously shown, odds of being non-adherent are higher for the uninsured and lower for adults on Medicaid and medicare than those with
23 million Americans will lose coverage if the ACA is repealed and this study is Powerful evidence of what could happen if we increase the number of the people of uninsured
2) Continue the expansion of medical homes is important for medical adherence
as results show, adults who use the ER have greater odds to be nonadherent than adults with a usual source of care
- ED visits don’t allow for that personal & extensive health check up that health clinics provider
- more one time visits…therefore no follow ups
4) Health professionals & pharmacys can have more extensive follow ups to ensure medical adherence of their patients
again this is more feasible if more low-income adults have access to a primary care provider or a usual source of care rather than using the ED
4) lastly, we saw that Poverty independently matters for medical adherence
a topic out of our scope is how to address income inequities…
important to address thi country’s wealth distribution
*here I have the 2013 FPL threshold table
The 100% FPL for a single person household is 11,940$
2 person household.. Etc…
*While we know education level predicts one’s income level, the primary reasons we decided to look at educational attainment is to see if health literacy and critical thinking affect medical adherence. However, our results could be a representation of multicollinearity since educational level and individual poverty are closely correlated.
Will use interactions to have a better measure of NPL… using 34 zip codes
Sample size of adults with hc below 45 could be a possible reason for skewed results
Looking at language can perhaps clear up Hispanic results
Would control for primary language
Spanish speakers
2014 NYC vital statistics
Since 2005, the age-adjusted death rate has decreased across all categories of neighborhood poverty with a 15.2% decrease for the very high poverty areas and a 28.0% decrease for the low poverty areas. • The age-adjusted all-cause death rate was 1.7 times greater in areas with very high poverty compared to areas with low poverty in 2014 as compared to 1.5 times in 2005.
The 2 maps show the boroughs of New York City. The first map shows the percentage of residents living in poverty, and the second shows the number of diabetes deaths per 100,000 population. Comparison of the 2 maps indicates that areas of greater poverty tend to have higher rates of diabetes deaths.
***results for our logistic regression models for our social factors.
All predictors were found to be strong predictors of medical non-adherence
Predictors with higher odds (compared to reference group) are represented with an upward arrow sign, while predictors with lower odds are represented with a downward arrow sign,
** contrary to expectations.. Those with some college have greater odds of non-adherence compared to college graduate
Adults who are not married also greater odds than adults who are married
however, some Ivs went in the opposite direction of our hypothesis