# Presentation: 748258

## Building Capacity to Use Data

- Hilary K. Wall, MPH
- Heart Disease & Stroke Prevention & Control Program
- MA Department of Public Health
- September 20, 2007

## Learning Objectives

- Participants should be able to:
- Explain common data terms
- List statewide and national surveillance datasets
- Help partners build their capacity for using data

**Notes:**
Show state HDSP program managers ways in which they can build partners’ capacities for using data

## “Facts do not cease to exist because they are ignored.”

- Aldous Huxley, author of “Brave New World”

- - Aldous Huxley, author of “Brave New World”

**Notes:**
Surveillance data will continue to be collected whether or not it is utilized by BOH...Might as well put it to good use.

(Aldous Huxley wrote “Brave New World”)

## Data Terms 101

**Notes:**
Epi 101

## Data Types

- Quantitative – numerical data; expressible in terms of a quantity
- Qualitative – non-numerical data
- Focus groups
- Key informant interviews
- Written documents (e.g. meeting minutes)
- Direct observation (e.g. photographs)

## Age Adjustment

- Statistical procedure used to minimize the effect of age on population differences
- Based on 2000 US Census population
- “crude” numbers and rates are NOT age-adjusted
- Used to compare apples to apples
- Example – town where most people are older versus town where most people are young – need to age adjust the populations in order to determine which has the higher rate of disease

## 2006 Massachusetts Stroke Statistics by Gender

**Notes:**
Crude counts and crude rate – looks like stroke effects women more
AA – takes into account that women live longer than men

Smaller amount of deaths in men but there is a smaller population of men who can be affected. 

More women, across the age span, die from stroke but when you take into account that women live longer than men, the death rate for stroke is actually higher in men than women.  (larger pool of women than men)

## Prevalence

- The proportion of individuals who have an attribute or disease in a given population at a designated point in time
- Example –  The prevalence of diabetes in MA is 6%* means that 6% of the MA population has diabetes
- *age-adjusted statistic

**Notes:**
Common statistic
Screening effect – movements in screening may make it look like an increase in disease prevalence when more cases are just being found.

## Incidence Rate

- The number of new events that occur in a given population over a designated period of time*
- Example – The MA incidence rate of lung cancer in 2002 was 72** per 100,000 means that there were 72 _new_ cases of lung cancer for every 100,000 MA residents in 2002
- * usually 1 year
- ** age-adjusted statistic

**Notes:**
Mostly pertinent to “reportable” diseases – communicable diseases, cancer registries
Not very applicable to chronic diseases

## Hospitalization Rate

- The number of new hospitalizations for a designated diagnosis code (ICD-9) that occur in a given population over a designated period of time
- Example – The MA inpatient hospitalization rate for coronary heart disease in 2002 was 602* per 100,000 = there were 602 hospitalizations for CHD for every 100,000 MA residents in 2002
- *age-adjusted statistic

**Notes:**
ICD = international classification of diseases
Common statistic

## Mortality Rate

- aka death rate
- The number of people in a given population that dies from the same cause over a designated period of time
- Example – the 2003 mortality rate of stroke was 50* deaths per 100,000 MA residents = there were 50 deaths due to stroke for every 100,000 MA residents in 2003
- *age-adjusted statistic

**Notes:**
Common statistic

## Morbidity

- Measures of illness
- (WHO) General term that can encompass
- Persons who are ill
- Illnesses that these persons experienced
- Duration of these illnesses
- Example – the morbidity of heart disease and stroke can include acute events, hypertension, high cholesterol, heart attack and stroke related disability, hospitalizations, and quality of life

**Notes:**
Treatment adverse effects

## Data Element

- A named unit of data
- or
- A named identifier of each component in a database
- Examples – “race”, “gender”, “documented dysphagia screening”, “NPO”, “acute ischemic stroke diagnosis”

**Notes:**
NPO – abbreviation for the Latin version of “nothing by mouth”

Dysphagia = difficulty swallowing

## Performance Measure

- Created from two or more data elements
- Indicator of quality of care

- All acute ischemic stroke patients

- 80%

- =

- 60 + 20

- 100

- *

- 100

## Relevant Data Sources

## Slide 16

**Notes:**
Frequently in the form of surveys

## U.S. Census

- Conducted every 10 years
- City/town fact sheet:  http://factfinder.census.gov/home/saff/main.html?_lang=en
- Profile includes:
- Gender, race, age breakdowns
- Social (disability status), economic (income and employment status), and housing characteristics (year built, heating fuel type)

**Notes:**
Mailed survey to all households w/ in-person follow-up

## Slide 18

**Notes:**
Enter city/town and state – one town at a time or state as a whole

## Slide 19

**Notes:**
If you click on “show more” link, you get additional information (see next slide for example)

## Slide 20

## Mortality Data

- Causes of death listed on a death certificate
- Primary and all secondary causes of death, based on ICD-10 codes
- By city or town
- Subjective data but good for death estimates
- Includes deaths from chronic diseases, infectious diseases, injuries, etc.

**Notes:**
Certain conditions won’t be represented in mortality data very much – e.g. asthma, diabetes

Underreporting of co-morbidities (e.g. diabetes)

## Behavioral Risk Factor Surveillance System (BRFSS)

- Random-digit-dial telephone survey  with adults age 18 and older
- Health characteristics/behaviors and risk factors:
- Heart disease and stroke prevalence and risk factor screening
- Diabetes and related complications prevalence and disease screening
- Nutrition and physical activity habits
- Tobacco use and asthma prevalence

**Notes:**
This is the primary disease and risk factor prevalence data source
Injury information including proportion of falls, handgun ownership, and seatbelt utilization
Established in 1984 by the Centers for Disease Control and Prevention (CDC), the Behavioral Risk Factor Surveillance System (BRFSS) is a state-based system of health surveys that collects information on health risk behaviors, preventive health practices, and health care access primarily related to chronic disease and injury. For many states, the BRFSS is the only available source of timely, accurate data on health-related behaviors. 
Currently, data are collected monthly in all 50 states, the District of Columbia, Puerto Rico, the U.S. Virgin Islands, and Guam. More than 350,000 adults are interviewed each year, making the BRFSS the largest telephone health survey in the world.

## BRFSS (cont’d)

- Limited city/town-level data – may be over-sampled in large metropolitan areas
- No cellular phone numbers
- No transient populations without an address
- Lengthy survey (~30 minutes)
- Population dislike of “phone solicitation”

**Notes:**
During the day, working population is not at home (though night calls are made as well)
Transient populations – homeless, students, etc.
Not all questions are asked every year – non-continuous data
CDC combines common data across states for national estimates

## Hospital Discharge Data

- Inpatient stays –long-term treatment of acute events, surgery, and recovery
- Emergency Department (ED) visits –short-term treatment of acute events
- Outpatient observation stays –evaluation for hospital admission

**Notes:**
Mandatory administrative dataset from hospitals. Based on billing codes.

certain conditions will have all three involved (chest pain), others only one or two (asthma – ED and possibly OO)

## Hospital Discharge Data (cont’d)

- City/Town specific
- Hospital-specific
- Based on billing codes (ICD-9)
- Primary and secondary causes including chronic conditions, injuries
- Underreporting of certain diagnoses

**Notes:**
(diabetes, CVD)
Based on hospital charges

## Youth Risk Behavior Surveillance System (YRBSS)

- Biannual survey of grades 9-12 (randomly selected public and private schools)
- Conducted by departments of health and education
- Health behaviors including:
- Tobacco use
- Alcohol consumption and illicit drug use
- Sexual behaviors and STDs
- Dietary behaviors and physical activity patterns
- Those contributing to violence and unintentional injury

**Notes:**
The national survey, conducted by CDC, provides data representative of high school students in public and private schools in the United States. The state and local surveys, conducted by departments of health and education, provide data representative of public high school students in each state or local school district. 
Not available by school or community (aggregate only)
Self-reported, voluntary responses
Behaviors that contribute to:
Unintentional Injury ViolenceViolence on School Property
Prevalence of:
Alcohol UseCigarette UseDietary Behaviors and Weight Control PracticesMarijuana, Cocaine, and Other Illegal Drug UseOverweightPhysical ActivitySexual BehaviorsSuicide Ideation and AttemptsTobacco Use

## Other Pertinent Data

- National Health and Nutrition Examination Survey (NHANES)
- Combines interviews and physical exams
- Stroke Registries
- e.g. Paul Coverdell National Acute Stroke Registry, ASA Get With the Guidelines – Stroke
- National Emergency Medical Services Information System (NEMSIS)

**Notes:**
NHANES - If a health problem is suspected based on findings from the survey protocol, participants are informed and referred to their local health care provider or health department clinic for evaluation and management. 

NEMSIS – 49 states (not NY) and DC - the NEMSIS project was developed to help states collect more standardized elements and eventually submit the data to a national EMS database. 425 data elements (required and optional)

## Other Pertinent Data (cont’d)

- Cancer Registries
- Capture all newly diagnosed cases of cancer (incidence)
- Programmatic data
- Policy inventories
- Materials distribution
- Quality improvement

**Notes:**
Cancer
Captures all newly diagnosed cases of cancer (incidence) from all MA acute care hospitals
Based on physician reporting
City/town specific data (for 5 or more cases)
Underreporting in border towns or cancers not diagnosed in hospitals 
All states are funded through 
National Program of Cancer Registries (NPCR - CDC)  - or- 
Surveillance, Epidemiology, and End Results (SEER - NCI) - cancer incidence and survival data from population-based cancer registries covering approximately 26 percent of the US population 
QI – health disparities collaboratives 

## Data Are Your Friends

## Data Uses for HDSP Programs

- Needs assessment (aka the “burden” document)
- Identifying disparate populations
- By race/ethnicity, gender, disability status, geography, SES, arrival mode to hospital...
- Funding justification
- Requests for Response (RFRs)

**Notes:**
RFR - (identifying needs) – responding and posting 

## Data Uses for HDSP Programs (cont’d)

- Convincing advocates/decision makers to raise HD&S importance
- Reports to stakeholders
- EVALUATION

**Notes:**
First bullet – which may lead to action or policy

## Data Uses for Community Partners

- Needs assessment and decision-making
- Grant writing
- Staff justification
- Stakeholder awareness
- Helps fulfill the 10 essential public health services

**Notes:**
Partners – can include BOH, municipalities, planning boards, YMCAs, CHCs, etc.

1. Monitor health status to identify and solve community health problems. 
2. Diagnose and investigate health problems and health hazards in the community. 
3. Inform, educate, and empower people about health issues. 
4. Mobilize community partnerships and action to identify and solve health problems. 
5. Develop policies and plans that support individual and community health efforts. 
6. Enforce laws and regulations that protect health and ensure safety. 
7. Link people to needed personal health services and assure the provision of health care when otherwise unavailable. 
8. Assure competent public and personal health care workforce. 
9. Evaluate effectiveness, accessibility, and quality of personal and population-based health services. 
10. Research for new insights and innovative solutions to health problems. 

## Examples

- Local health profiles
- Community comparisons
- Supporting municipal decisions
- Sidewalk and athletic field justification
- Elder and disability issues
- Sub-division control approval process
- Emergency preparedness

**Notes:**
Sidewalks/athletic fields
Decrease pedestrian accidents
Increase children walking to school
Increase overall level of physical activity throughout life
Elder/disability
Salting, snow removal justification
Flu clinics
Universal design
Sub-division control
To provide rationale for why parks, sidewalks should be included; omission of cul de sacs (by means of a local BOH regulation)
Emergency preparedness
Emergency Response Plans – special needs, elders, pets
“The need to deal with sheltering special medical need populations was and is a major gap that needs plugging.” 

## More Examples

- Describing target/outreach population
- Racial/ethnic populations
- Elders
- Disabled
- Specific health issue summaries
- Heart attack
- Stroke
- Risk factors
- Identifying potential partners

## Data Uses for Advocates

- Telling the heart disease and stroke story
- Legislative briefs
- State budget allocation request justification
- Providing testimony  for proposed legislation

## Group Discussion

- Examples of other state/national data sets?
- Examples of data uses/users?

**Notes:**
Start interactive discussion – examples audience might have for when they’ve used data for grants, etc. OR examples of projects they have w/ brainstorming for surveillance data (from epi and audience)

Ex:  Farmer’s markets – nutrition data, hypertension, cholesterol, diabetes – USE EXAMPLES FROM THE MAHB JOURNAL OF LOCAL PUBLIC HEALTH

## “In God we trust; all others bring data.”

- W. Edwards Deming – statistician and quality improvement pioneer (PDSA cycle)

- - W. Edwards Deming – statistician and quality improvement pioneer (PDSA cycle)

**Notes:**
Whenever we make statements about a problem or make programmatic decisions, we should always remember to use data for support.

## Contact Info

- Hilary Wall
- hilary.wall@state.ma.us
- 617-624-5411