TECHNICAL NOTES
Indicator Definitions and
Measurement Criteria
o
Poverty
○
Reported Chlamydia Infections
○
Treated Chlamydia Infections
○
Child Immunizations
Reported to the Child Profile Immunization Registry
·
Prevention and Health Promotion
○
Years of Healthy Life Expected at Age 20
○
Adult Fruit/Vegetable
Consumption
○
Adults with Poor Mental
Health
○
Hospitalizations for Falls in Older Adults
○
On-site Sewage System Corrections
○
First Trimester Prenatal Care
○
Childhood Unintentional Injury
Hospitalizations
○
Adults with Unmet Medical Need
○
Adults with Personal
Health Care Provider
○
Adult Preventive Cancer
Screening ─ Breast
○
Adult Preventive Cancer Screening ─ Cervical
○
Adult Preventive Cancer Screening ─ Colorectal
○
Adults with Health Insurance
○
Children with Health
Insurance
Data
Analysis
Data
Sources
·
State and Local Data Sources
○
Behavioral Risk Factor Surveillance System (BRFSS)
○
Child Profile Immunization Registry
○
Fetal Death Certificate System
○
Hospital Discharge Data System
○ Public Health Issues
Management System - Sexually Transmitted Disease
○ Small Area Income and Poverty
Estimates (SAIPE)
○ Washington Tracking Network –
Air Monitoring Data
National
Data Sources
·
Behavioral Risk Factor Surveillance System
·
Births
·
Deaths
·
Sexually Transmitted Diseases
·
Small Area Income and Poverty Estimates (SAIPE)
·
Youth Risk Behavior Surveillance System (YRBS)
Sexually Transmitted Disease
INDICATOR
DEFINITIONS AND MEASUREMENT CRITERIA
The indicators are listed by category, in the order in which they appear
on the website.
Definition
Population living at or below 100% of the U.S.
Federal Poverty Level
Unit of measure
Crude percent
Years of reporting
·
Baseline (Update 1): posted 2011
o
State and local data: 2008
o
National data: 2008
·
Update 2: posted 2011
o
State and local data: 2009
o
National data: 2009
Sources
·
State and local
data: Small Area Income and Poverty Estimates
(SAIPE), U.S.
Census Bureau, accessed 8/12/2011.
·
National data: Small Area Income and Poverty Estimates (SAIPE), U.S. Census
Bureau, accessed 10/12/2011
Rationale for inclusion
People
living in poverty are more likely to have poorer health status and die at
younger ages than people with more financial resources. The health impacts of
poverty can begin before birth and build up over time. Those living in poverty
are more likely than others to have difficulty accessing health care for
prevention and early treatment of disease, to face stresses that can alter
immune function, to find it difficult to buy and prepare nutritious food and to
be physically active, to be exposed to relatively high levels of toxins in the
environment, and to be victims of violence. There is a stepwise relationship
between economic resources and health. While those with fewest resources have
the shortest life expectancies, those in middle ranges of economic resources
have shorter life expectancies than those with the most economic resources.
·
Baseline (Update 1): none
·
Update 2: none
Definition
Cases of Chlamydia diagnosed and reported for
females ages 15–24
Unit of measure
Age-specific rate per 100,000 females ages 15–24
Years of reporting
·
Baseline: posted 2007; revised 2012 using population estimates adjusted to the
2010 U.S. Census
o
State and local data: cases diagnosed during
2004–2006 and reported to the Washington State Department of Health (DOH) as of
4/12/07
o
National data: 2005
·
Update 1: posted 2009; revised 2012 using population estimates adjusted to the
2010 U.S. Census
o
State and local data: cases diagnosed during
2007–2008 and reported to DOH as of 3/9/09
o
National data: 2007
·
Update
2: posted 2012
o
State and local data: cases diagnosed during
2009–2010 and reported to DOH as of 3/2010
o
National data: 2009
Sources
·
State and local data: Compiled using Community Health Assessment Tool (CHAT) January 2012
o
WA-DOH, Public Health Issues Management System
(PHIMS), Sexually Transmitted Disease
o
Washington State Office of Financial Management, Forecasting Division, single year intercensal
estimates 2001–2009, December, 2011
·
National data: CDC Division of Sexually Transmitted Disease Prevention
Rationale for inclusion
Chlamydia
infection is the most commonly reported sexually transmitted infection in
Washington State. Undetected and untreated chlamydia
is a major cause of reproductive health problems for women of childbearing age.
Females ages 15–24 have the highest Chlamydia rates. State and national
screening recommendations target females 15–24.
·
Baseline
o
Suppressed for Columbia (small numbers), Garfield
(small numbers), Lincoln (small numbers), Wahkiakum (small numbers)
·
Update 1
o
Suppressed for Columbia (small numbers), Garfield
(small numbers), Lincoln (small numbers), Wahkiakum (small numbers)
·
Update 2
o
Suppressed for Columbia (small numbers), Garfield (small
numbers), San Juan (small numbers), Wahkiakum (small numbers)
Definition
Cases of Chlamydia treated and reported for females
ages 15–24
Unit of measure
Crude percent
Years of reporting
·
Baseline: posted 2007
o
State and local data: cases diagnosed during
2004–2006 and reported to the Washington State Department of Health (DOH) as of
4/12/2007
·
Update 1: posted 2009
o
State and local data: cases diagnosed during
2007–2008 and reported to DOH as of 3/9/2009
·
Update 2: posted 2011
o
State and local data: cases diagnosed during
2009–2010 and reported to DOH as of 3/2011
Sources
·
State and local data: WA-DOH, Public Health Issues Management System (PHIMS), Sexually
Transmitted Disease
·
National data: Not available
Rationale for inclusion
Chlamydia
infection is the most commonly reported sexually transmitted infection in
Washington State. Undetected and untreated chlamydia is a major cause of
reproductive health problems for women of childbearing age. Females ages 15–24 have the highest Chlamydia rates. State and national
screening recommendations target females 15–24.
·
Baseline
o
Suppressed for Columbia (small numbers), Garfield
(small numbers), Lincoln (small numbers), Wahkiakum (small numbers)
·
Update 1
o
Suppressed for Columbia (small numbers), Garfield
(small numbers), Lincoln (small numbers), Wahkiakum (small numbers)
·
Update 2
o
Suppressed for Columbia (small numbers), Grant
(small numbers), Skagit (small numbers), Wahkiakum (small numbers)
Definition
Adults who respond “yes” to the question, “During
the past 12 months have you had a flu shot?”
Unit of measure
Weighted, age-adjusted percent
Years of posting and reporting
·
Baseline: posted 2007; revised 2011 to reflect change in definition
o
State and local data: 2004–2006
o
National data: 2004–2006
·
Update 1: posted 2009; revised 2011 to reflect change in definition
o
State and local data: 2007–2008
o
National data: 2007
·
Update 2: posted 2011
o
State and local data: 2009–2010
o
National data are for 2010
Sources
·
State and local data: WA-DOH, Behavioral Risk Factor Surveillance System
·
National data: CDC, Behavioral Risk Factor Surveillance System
Rationale for inclusion
In
Washington from 2000–2009, an average of 37 people died each year from
influenza and about 550 were hospitalized. As elsewhere, Washington was
particularly hard hit in 2009 with 91 deaths and 1,648 hospitalizations. While
risks of serious complications are highest in children and those over 65 years
old, for 2000–2009 combined about two-thirds of influenza hospitalizations in
Washington occurred in people younger than 65. Vaccination is effective in
preventing influenza. It is 90% effective in people younger than 65.
Vaccination of people ages 6 months and older helps to protect children younger
than 6 months who are too young to be vaccinated; older people for whom the
vaccine is less effective; and people at high risk of complications who might
not be vaccinated, such as pregnant women and people with certain chronic
medical conditions. Annual vaccination is needed since immunity from the
vaccine is estimated to be less than one year.
Previous Public Health Indicator cycles presented
information on adults ages 65 and older. For the 2011 posting, this indicator
was changed to include all adults (ages 18 and older). This change is
consistent with the Center for Disease Control and Prevention’s 2010
recommendations of annual influenza vaccination for persons
ages 6 months or older. Data from previous KHI releases were updated to reflect
this change.
·
Baseline: None
·
Update 1: None
·
Update 2: None
Definition
Percent of children ages 19–35 months registered in
the Child Profile Immunization Registry with complete vaccination records on
file in the Registry. A complete vaccination record includes 4-DTP, 3-Polio,
1-MMR, 3-Hib, 3-HepB, 1-Varicella and 4-PCV.
Unit of measure
Crude percent
Years of posting and reporting
·
Baseline (Update
1): posted 2009
o
State and local data: 2008
·
Update 2: posted 2011
o
State and local data: 2010
Sources
·
State and local data: WA-DOH, Child Profile Immunization Registry
·
National data: Not available
Rationale for inclusion
Childhood immunizations have provided one of the
greatest improvements in public health by controlling serious conditions such
as measles, polio, diphtheria, and tetanus. Caution is needed when interpreting
this indicator because vaccination data in the Child Profile Immunization
Registry are not complete. The data are currently useful for tracking
participation in the Child Profile system, but not for assessing the percentage
of children who have received all necessary vaccines. (See Child Profile Immunization
Registry.)
·
Baseline (Update 1)
o
Suppressed for Garfield (small numbers)
·
Update 2
o
Suppressed for
Garfield (small numbers)
PREVENTION AND HEALTH
PROMOTION
Definition
Additional years a 20 year-old is expected to live
in good, very good, or excellent health. “Years of
healthy life” is calculated by adjusting life expectancy derived from death
certificate data with health status measured by the question, “Would you say
your health in general is excellent, very good, good, fair, or poor?” Information
on the method used to compute years of healthy life expectancy is available at:
http://www.doh.wa.gov/phip/products/phi/doc/tools/ledescription.pdf
Unit of measure
Number of years
Years of reporting
·
Baseline: posted 2007; revised 2012 using population estimates adjusted to the
2010 U.S. Census
o
State and local data: 2003–2005
o
National data: 2003
·
Update 1: posted 2009; revised 2012 using population estimates adjusted to the
2010 U.S. Census
o
State and local data: 2006–2007
o
National data: 2005
·
Update 2: posted 2012
o
State and local data: 2008–2009
o
National data: 2007
Sources
·
State and local data
o
WA-DOH, Behavioral Risk Factor Surveillance System
o
WA-DOH, Death Certificate System
o
Washington State Office of Financial Management, Forecasting Division, single year intercensal
estimates 2001–2009, December, 2011
·
National data:
o
CDC, Behavioral Risk
Factor Surveillance System
o
U.S. life expectancy tables from annual CDC National
Center for Health Statistics National Vital Statistics Reports.
Rationale for inclusion
Public health aims to improve health status and
extend life by preventing and controlling disease. This indicator reflects the
value of extending the years of healthy of life, not just delaying death.
Missing (10% or more) and suppressed
data
·
Baseline: None
·
Update 1: None
·
Update 2: None
Definition
Adults who respond “yes” to the question, “Have you smoked at least 100 cigarettes in your entire
life?” AND answer, “Some days or every day” to the question, ”Do you now smoke
cigarettes every day, some days, or not at all?”
Unit of measure
Weighted, age-adjusted percent (age groups used for
age-adjustment: 18–24, 25–34, 35–44, 45–64, 65+)
Years of reporting
·
Baseline: posted 2007; revised 2011 for minor corrections (See NOTE)
o
State and local data: 2004–2006
o
National data: 2004–2006
·
Update 1: posted 2009; revised 2011 for minor corrections (See NOTE)
o
State and local data: 2007–2008
o
National data: 2007
·
Update 2: posted 2011
o
State and local data: 2009–2010
o
National data: 2010
NOTE: Stata coding for the original baseline and cycle 1 releases resulted in
inclusion of some records with age coded to missing or refused. For this
indicator, the error did not change the state rate or the rates for most
counties. Smaller counties might see small discrepancies between the original
postings and the current data.
Sources
·
State and local data: WA-DOH, Behavioral Risk Factor Surveillance System
·
National data: CDC, Behavioral Risk Factor Surveillance System
Rationale for inclusion
Tobacco use is one of the main leading
causes of preventable of disease and death in Washington and the United States.
Cigarette smoking is the most common type of tobacco use. Other forms of
tobacco use and exposure to second-hand tobacco smoke also have detrimental
effects on health.
·
Baseline: None
·
Update 1: None
·
Update 2: None
Definition
Adults ages 18 and older who report-
·
Moderate physical activity for at least 30 minutes
a day on five or more days a week in response to the questions, “In a usual
week, do you do moderate activities for at least 10 minutes at a time, such as
brisk walking, bicycling, vacuuming, gardening, or anything else that causes
small increases in breathing or heart rate?” If yes, “How many days per week do
you do these moderate activities for at least 10 minutes at a time?” and “On
days when you do moderate activities for at least 10 minutes at a time, how
much total time per day do you spend doing these activities?” OR
·
Vigorous physical activity for at least 20 minutes
a day on three or more days a week in response to the questions, “In a usual
week, do you do vigorous activities for at least 10 minutes at a time, such as
running, aerobics, heavy yard work, or anything else that causes large
increases in breathing or heart rate?” If yes, “How many days per week do you
do these vigorous activities for at least 10 minutes at a time?” and “On days
when you do vigorous activities for at least 10 minutes at a time, how much
total time per day do you spend doing these activities?” OR
·
“Mostly walking” or “mostly heavy labor or
physically demanding work” in response to the question, “When you are at work,
which of the following best describes what you do?”
Unit of measure
Weighted, age-adjusted percent (age groups used for
age-adjustment: 18–24, 25–34, 35–44, 45–64, 65+)
Years of reporting (Data are collected
biannually in odd-numbered years.)
·
Baseline: posted 2007; revised 2011 for minor corrections (See NOTE)
o
State and local data: 2003 and 2005
o
National data: 2003 and 2005
·
Update 1: posted 2009; revised 2011 for minor corrections (See NOTE)
o
State and local data: 2007
o
National data: 2007
·
Update 2: posted 2011
o
State and local data: 2009
o
National data: 2009
NOTE: Stata coding for the original baseline and cycle 1
releases resulted in inclusion of some records with age coded to missing or
refused. For this indicator, the error did not change the state rate or rates
for most counties. Smaller counties might see small discrepancies between the
original postings and the current data.
Sources
·
State and local data: WA-DOH, Behavioral Risk Factor Surveillance System
·
National data: CDC, Behavioral Risk Factor Surveillance System
Rationale for inclusion
Physical activity improves life expectancy,
functional independence, quality of life, and reduces the risk for developing
or dying from chronic conditions such as heart disease, diabetes and high blood
pressure. Unlike data presented at the National Behavioral Risk Factor
Surveillance website, this indicator includes work-related physical activity.
The U.S. Department of Health and Human Services 2008 Physical Activity
Guideline for Americans states “Some people (such as postal carriers or carpenters on
construction sites) may get enough physical activity on the job to meet the
Guidelines.” (http://www.health.gov/paguidelines/guidelines/chapter1.aspx;
accessed August 26, 2011.)
·
Baseline: None
·
Update 1: None
·
Update 2: None
Definition
Adults ages 18 and older who have a body mass index
(BMI) of 30 kg/m2 or more based on their answers to the following
BRFSS questions: "About how much do you weigh without shoes?" and
"About how tall are you without shoes?" Body mass is calculated by
dividing weight in kilograms by height in meters squared.
Unit of measure
Weighted, age-adjusted percent (age groups used for
age-adjustment: 18–24, 25–34, 35–44, 45–64, 65+)
Years of reporting
·
Baseline: posted 2007; revised 2011 to reflect change in
definition
o
State and local data: 2004–2006
o
National data: 2004–2006
·
Update 1: posted 2009; revised 2011 to reflect change in
definition
o
State and local data: 2007–2008
o
National data: 2007
·
Update 2: posted 2011
o
State and local data: 2009–2010
o
National data: 2010
Sources
·
State and local data: WA-DOH, Behavioral Risk Factor Surveillance System
·
National data: CDC, Behavioral Risk Factor Surveillance System
Rationale for inclusion
Unhealthy weight resulting from an imbalance
between caloric intake and energy expenditure is a leading cause of premature,
preventable mortality. Previous Public Health Indicator cycles presented
information on overweight and obese adults (body mass index [BMI] ≥25
kg/m2). For the 2011 posting, this indicator was changed to include
obesity only, because negative health impacts have been more consistently
associated with BMIs ≥30 kg/m2 than with BMIs in the 25 to
29.9 kg/m2 range. Data from previous Public Health Indicator
releases were updated to reflect this change.
·
Baseline
o
Missing data
for Adams (10%–19%)
·
Update 1
o
None
·
Update 2
o
Missing data for Yakima (10%–19%)
Definition
Adults ages 18 and older who
report that they eat fruits or vegetables five or more times per day. This indicator is a composite of six questions that ask how many times
the respondent drinks fruit juice and eats fruit, potatoes, carrots, green
salad, and other vegetables. The respondent can answer as the number of times
each day, week, month, or year.
Unit of measure
Weighted, age-adjusted percent (age groups used for
age-adjustment: 18–24, 25–34, 35–44, 45–64, 65+)
Years of reporting (Data are collected
biannually in odd-numbered years.)
·
Baseline: posted 2007
o
State and local data: 2003 and 2005
o
National data: 2003 and 2005
·
Update 1: posted 2009
o
State and local data: 2007
o
National data: 2007
·
Update 2: posted 2011
o
State and local data: 2009
o
National data: 2009
Sources
·
State and local data: WA-DOH, Behavioral Risk Factor Surveillance System
·
National data: CDC, Behavioral Risk Factor Surveillance System
Rationale for inclusion
A
nutritious diet, including sufficient consumption of fruits and vegetables, can
reduce major risk factors for chronic disease such as obesity, high blood
pressure, and high blood cholesterol. Sufficient intakes of fruits and
vegetables can also reduce risk for some types of cancer. The 2010 Dietary Guidelines for
Americans recommends from 4–13 daily servings of fruits and vegetables
depending on caloric need. The Behavioral Risk Factor Surveillance System metric—eating
fruits and vegetables at least 5 times daily—serves as an indicator of
sufficient fruit and vegetable intake. This metric
approximates recommendations in earlier versions of Dietary Guidelines for Americans that included eating at least five
servings of fruits and vegetables daily.
·
Baseline: None
·
Update 1: None
·
Update 2: None
Definition
Adults ages 18 and older who
report binge drinking in the past 30 days. Binge
drinking is defined as five or more drinks for men and four or more drinks for
women on one occasion.
Unit of measure
Weighted, age-adjusted percent (age groups used for
age-adjustment: 18–24, 25–34, 35–44, 45–64, 65+)
Years of reporting
·
Baseline: posted 2007; revised 2011 for minor corrections (See NOTE)
o
State and local data: 2006 only. Data on binge
drinking are collected annually, but data collected prior to 2006 used a
definition of binge drinking that is not comparable to the definition adopted
in 2006.
o
National data: 2006
·
Update 1: posted 2009; revised 2011 for minor corrections (See NOTE)
o
State and local data: 2007–2008
o
National data: 2007
·
Update 2: posted 2011
o
State and local data: 2009–2010
o
National data: 2010
NOTE: Stata coding for the original baseline and cycle 1 releases resulted in
inclusion of some records with age coded to missing or refused. For this indicator,
the error did not change the state rate or rates for most counties. Smaller
counties might see small discrepancies between the original postings and the
current data.
Sources
·
State and local data: WA-DOH, Behavioral Risk
Factor Surveillance System
·
National data: CDC, Behavioral Risk Factor Surveillance System
Rationale for inclusion
Binge drinking defined as 5 drinks for men and 4
drinks for women on one occasion approximates a .08 blood alcohol level. This
level of drinking is related to increased risk of morbidity and mortality due
to trauma, such as alcohol-related motor vehicle deaths. Other health effects
(such as liver disease and some cancers) are due to long-term excessive
drinking. In Washington in 2008―2010, about one-quarter of binge drinkers
also reported heavy drinking (≥2 drinks/day for men; ≥1 drink/day
for women) and are at increased risk for both immediate and long-term morbidity
and mortality; about three-quarters of heavy drinkers also reported binge
drinking.
·
Baseline: None
·
Update 1: None
·
Update 2: None
Definition
Adults ages 18 and older who
answer “yes” in response to the question, “Have you even been told by a doctor
that you have diabetes?” This definition does not
include people who have been told that they are pre-diabetic or borderline
diabetic and women who experienced diabetes only during pregnancy.
Unit of measure
Weighted, age-adjusted percent (age groups used for
age-adjustment: 18–24, 25–34, 35–44, 45–64, 65+)
Years of reporting
·
Baseline: posted 2007; ; revised 2011 for minor corrections (See NOTE)
o
State and local data: 2004–2006
o
National data: 2004–2006
·
Update 1: posted 2009; revised 2011 for minor corrections (See NOTE)
o
State and local data: 2007–2008
o
National data: 2007
·
Update 2: posted 2011
o
State and local data: 2009–2010
o
National data: 2010
NOTE: Stata coding for the original baseline and cycle 1 releases resulted in
inclusion of some records with age coded to missing or refused. For this
indicator, the error did not change the state rate or rates for most counties.
Smaller counties might see small discrepancies between the original postings
and the current data.
Sources
·
State and local data: WA-DOH, Behavioral Risk Factor Surveillance System
·
National data: CDC, Behavioral Risk Factor Surveillance System
Rationale for inclusion
Diabetes is one of the top 10 causes of death. Most
adult onset diabetes can be prevented through healthy ways of living, such as
maintaining a healthy weight, being physically active, and eating a healthy
diet.
·
Baseline: None
·
Update 1: None
·
Update 2: None
Definition
Adults ages 18 and older who answer “14
or more days” in response to the question, “Now thinking about your mental
health, which includes stress, depression, and problems with emotions, for how
many days during the past 30 days was your mental health not good?”
Unit of measure
Weighted, age-adjusted percent (age groups used for
age-adjustment: 18–24, 25–34, 35–44, 45–64, 65+)
Years of reporting
·
Baseline: posted 2007; revised 2011 for minor corrections (See NOTE)
o
State and local data: 2004–2006
o
National data: 2004–2006
·
Update 1: posted 2009; revised 2011 for minor corrections (See NOTE)
o
State and local data: 2007–2008
o
National data: 2007
·
Update 2: posted 2011
o
State and local data: 2009–2010
o
National data: 2010
NOTE: Stata coding for the original baseline and cycle 1 releases resulted in
inclusion of some records with age coded to missing or refused. For this
indicator, the error did not change the state rate or rates for most counties.
Smaller counties might see small discrepancies between the original postings
and the current data.
Sources
·
State and local data: WA-DOH, Behavioral Risk Factor Surveillance System
·
National data: CDC, Behavioral Risk Factor Surveillance System
Rationale for inclusion
Poor mental health is a major source of distress,
disability, and social burden. The Public Health Indicator has yielded similar
results to more complex measures of recent mental health (CDC. Self-Reported
Frequent Mental Distress among Adults ─ United States, 1993–1996. MMWR 1998; 47:326-331).
·
Baseline
o
None
·
Update 1
o
Suppressed for Jefferson (small numbers)
·
Update 2
o
Suppressed for Columbia (small numbers), Wahkiakum
(small numbers)
Definition
Hospitalizations
of Washington residents ages 65 and older whose hospital discharge record in the
Comprehensive Hospital Abstract Recording System (CHARS) or in the Oregon State
Inpatient Dataset (OR-SID) contains a diagnosis of ICD9 E880–E886 or E888 as
the first diagnosis. (Note: Federal military and Veterans Administration
(VA) hospitals no longer provide data to WA-DOH. Data from 2005–2007 for
Veterans Administration hospitals, Madigan Army Hospital, and Naval Hospital
Oak Harbor suggests that not including hospitalizations from these facilities
would not substantively affect rates for any counties except, perhaps, Pierce
County. In Pierce County about 6% of hospitalizations for falls among older
adults occurred at one or more of these facilities. Data for 2003–2005 from
Naval Hospital Bremerton indicates that omitting hospitalization data from this
facility would not substantively affect rates for this indicator.)
Unit of measure
Age-specific rate for Washington residents age 65
or older
Years of reporting
·
Baseline: posted 2012
o
State and local data: 2003–2005
·
Update 1: posted 2012
o
State and local data: 2006–2007
·
Update 2: posted 2012
o
State and local data: 2008–2009
Sources
·
State and local data: Compiled using Community Health Assessment Tool (CHAT) January 2012
o
WA-DOH, Comprehensive Hospitalization Abstract
Reporting System (CHARS)
o
Agency
for Healthcare Research and Quality, Healthcare Cost and Utilization Project
State Inpatient Databases (Oregon) (OR-SID)
o
Washington State Office of Financial Management, Forecasting Division, single year intercensal
estimates 2001–2009, December, 2011
·
National data: Not available
Rationale for inclusion
Falls among older adults are the leading cause of
injury-related hospitalizations in Washington. One of every three people age 65
and older living in the community falls each year. Fall-related injuries cause
significant mortality, morbidity, disability, loss of independence, and early
admission to nursing homes. Most falls are preventable.
Missing (10% or more) and suppressed
data
·
Baseline
o
Suppressed for Garfield (small numbers, missing
records due to hospitalization in Idaho); Asotin (missing records due to
hospitalizations in Idaho)
·
Update 1
o
Suppressed for Garfield (small numbers, missing
records due to hospitalization in Idaho); Asotin (missing records due to
hospitalizations in Idaho)
·
Update 2
o
Suppressed for Garfield (small numbers, missing
records due to hospitalization in Idaho); Asotin (missing records due to
hospitalizations in Idaho)
Definition
Permanent food service establishments that received a
routine inspection and were found to have fewer than 36 critical violation
points.
(Follow-up inspections and temporary food service establishments are excluded).
Unit of measure
Crude percent (of total number of inspections)
Years of reporting
·
Baseline (Update
1): posted 2009
o
State and local data: 2008
·
Update 2: posted 2011
o
State and local data: 2010
Sources
·
State and local data: WA-DOH, Office of Public Health Systems Development data collected for
the Washington State Public Health Improvement Partnership (PHIP) Activities
and Services Inventory
·
National data: Not available
Rationale for inclusion
Inspection of food service establishments has been
shown to reduce the risk factors associated with food-borne illness. Critical
violations involve important food safety items such as proper heating and
cooling, cross contamination, hand washing and proper food storage. Food
service establishments with a high level of violations pose the highest risk
for causing food-borne outbreaks. Caution is needed when interpreting this
indicator because inspection practices, staff training and workloads can
influence inspection scores, and these factors vary between health
jurisdictions. A higher percent of violations may be a result of new training,
standardization or increased focus on identification and documentation of
critical violations. More information is available at http://www.doh.wa.gov/ehp/food/default.htm
·
Baseline (update 1)
o
Missing for Pacific (10%–19%), Skamania (20%–30), Wahkiakum
(10%–19%)
o
Suppressed for Columbia (small numbers), Island
(small numbers)
·
Update 2
o
None
Definition
On-site sewage system failures for which corrective action was initiated
within two weeks.
Unit of measure
Crude percent
Years of reporting
·
Baseline (Update 1): posted 2009
o
State and local data: 2008
·
Update 2: posted 2011
o
State and local data: 2010
Sources
·
State and local data: WA-DOH, Office of Public Health Systems Development data collected for
the Washington State Public Health Improvement Partnership (PHIP) Activities
and Services Inventory
·
National
data: Not available
Rationale for inclusion
On-site sewage systems (OSS), commonly called
septic systems, treat and dispose of sewage on the site where it is created. It
is important to correct OSS failures when first detected to prevent surface and
ground water contamination and risk to public health. For a variety of reasons,
OSS repairs sometimes take a long time to complete. The indicator measures
percent of documented failing on-site systems for which the repair process is
started promptly. The on-site sewage system permits and inspections are handled
by the local health jurisdiction and not by the State Department of Health.
·
Baseline (Update 1)
o
Missing for Kittitas (10%–19%), Pacific (10%–19%), Skamania
(10%–19%), Wahkiakum (20%–30%)
o
Suppressed for Columbia (>30% missing), Garfield
(small numbers), Island (>30 missing), Klickitat (small numbers), Lincoln
(small numbers) Walla Walla (small numbers)
·
Update 2
o
Missing for Adams (>30%), Asotin (>30%),
Cowlitz (>30%), Garfield (>30%)
o
Suppressed for Klickitat (small numbers), Lincoln
(small numbers), Whitman (small numbers)
Definition
Days meeting the Washington State Department of Ecology 24-hour average
healthy air goal of ≤20ug/m3 for particulate matter 2.5
microns in diameter or less (PM2.5)
Unit of measure
Crude percent
Years of reporting
·
Baseline (Update 1): posted 2011
o
Local data: 2009
·
Update 2: posted 2011
o
Local data: 2010
Sources
·
Local data: WA-DOH Washington Tracking Network, compiled from data received from
Washington State Department of Ecology
·
State and National data: Not available
Rationale for inclusion
Studies
show serious health effects from short term and long term exposure to PM2.5
(particulate matter of 2.5 microns or less). Those at risk from breathing PM2.5
are people with heart and lung disease, diabetes, and infants and children. The
Department of Ecology has established a daily healthy air goal of 20ug/m3.
However, some people can experience health effects below this level.
·
Baseline (Update 1)
o
Missing for Garfield, Island, Klickitat, Lewis, Lincoln,
Pacific, San Juan, Skamania, Wahkiakum due to no air monitors in these counties
·
Update 2
o
Missing for Garfield, Island, Klickitat, Lincoln,
Pacific, San Juan, Skamania, Wahkiakum due to no air monitors in these counties
• » » » » » » »
» » » » »
Definition
Women giving
birth who received prenatal care starting in the first trimester of pregnancy. The month prenatal care began is calculated from the reported
menses date and the date of the first prenatal care visit.
Unit of measure
Crude percent of all live births
Years of reporting
·
Baseline: posted 2007
o
State and local data: 2003–2005
o
National data: 2004
·
Update1: posted 2009
o
State and local data: 2006–2007
o
National data: Not available for 2005 due to
changes in the U.S. Certificate of Birth and lack of uniform adoption of this
certificate across states; see www.cdc.gov/nchs/data/nvsr/nvsr56/nvsr56_06.pdf for more information.
·
Update 2: posted 2011
o
State and local data: 2008–2009
o
National data: 2008
Sources
·
State and local data: WA-DOH, Birth Certificate System; data compiled using Community Health
Assessment Tool (CHAT) July 2011
·
National data: CDC, National Vital Statistics Reports
Rationale for inclusion
Early and continuous prenatal care is important for
preventing adverse birth outcomes and improving the health of mothers.
·
Baseline
o
Missing data for Washington State (20%–30%), Grays Harbor (10%–19%), Lewis (10%–19%),
Lincoln (10%–19%), Mason (10%–19%), Yakima (10–19%), Seattle-King
(20%–30%), Snohomish (20%–30%), Tacoma-Pierce (20%–30%),
Thurston (20%–30%), Yakima (10%–19%)
·
Update 1
o
Missing data for Washington State (10%–19%),
Columbia (10%–19%), Seattle-King (20%–30%), Snohomish (10%–19%), Tacoma-Pierce (20%–30%), Thurston
(10%–19%)
o
Suppressed for Asotin (>30% missing), Garfield
(small numbers)
·
Update 2
o
Missing data for Asotin (10–19%), Garfield
(10–19%), Seattle-King (10–19%), Snohomish (10–19%)
Definition
Women giving birth who report that they
smoked cigarettes anytime during the first, second, or third trimester of
pregnancy
Unit of measure
Crude percent of all live births
Years of reporting
·
Baseline: posted 2007
o
State and local data: 2003–2005
o
National data: 2004
·
Update 1: posted 2009
o
State and local data: 2006–2007
o
National data: Not available for 2005 due to
changes in the U.S. Certificate of Birth and lack of uniform adoption of this
certificate across states; see www.cdc.gov/nchs/data/nvsr/nvsr56/nvsr56_06.pdf for more information.
·
Update 2: posted 2011
o
State and local data: 2008–2009
o
National data: 2008
Sources
·
State and local data: WA-DOH, Birth Certificate System; data compiled using Community Health
Assessment Tool (CHAT) July 2011
·
National data: CDC, National Vital Statistics Reports
Rationale
for inclusion
Tobacco smoking during pregnancy is the most important
preventable cause of low birth weight. Smoking is also associated with
spontaneous abortion, impaired fertility, preterm delivery, and sudden infant
death syndrome (SIDS). This indicator uses data from the birth certificate
because this is the only source of data for most counties. Compared to the Pregnancy Risk Assessment Monitoring System (PRAMS), the birth
certificate underestimates smoking early in pregnancy. In 2007–2009, 12% of
women in Washington reported smoking in the three months before pregnancy and
8% reported smoking in the third trimester on the birth certificate. PRAMS data
for the same years showed 21% of women reporting smoking in the three months
before pregnancy and 10% in the third trimester.
·
Baseline
o
Missing data for State (10%–19%) Clark (10%–19%), Wahkiakum (10%–19%),
Pacific (20%–30%), Klickitat (20%–30%), Skamania (20%–30%)
o
Suppressed for Garfield (small numbers)
·
Update 1
o
Missing data for Wahkiakum (10%–19%)
o
Suppressed for Garfield (small numbers), Klickitat
(>30% missing values), Pacific (>30% missing values), Skamania (>30%
missing values)
·
Update 2
o
Missing data for Island (20%–30%)
o
Suppressed for Garfield (small numbers)
Definition
Pregnancies among teens ages 15–17. The mother’s age is
computed from the mother’s date of birth and the date of birth, fetal death, or
abortion.
Unit of measure
Age-specific rate per 1,000 teens ages 15–17
Years of reporting
·
Baseline: posted 2007; revised 2012 to reflect change in definition
from birth rate to pregnancy rate
o
State and local data: 2003–2005
·
Update 1: posted 2009; revised 2012 to reflect change in definition
from birth rate to pregnancy rate
o
State and local data: 2006–2007
·
Update 2: posted 2012
o
State and local data: 2008–2009
Sources
·
State and local data: Compiled using Community Health Assessment Tool (CHAT) January 2012
o
WA-DOH, Birth Certificate System
o
WA-DOH, Abortion Registry
o
WA-DOH, Fetal Death Certificate System
o
Washington State Office of Financial Management, Forecasting Division, single year intercensal
estimates 2001–2009, December, 2011
·
National data: Not available
Rationale
for inclusion
Teen
pregnancy rates are used to evaluate teen pregnancy prevention efforts and
access to services across Washington. About 60% of teen pregnancies result in a
live birth. Teen mothers are less likely to complete their education. Children
born to teen mothers are more likely to live in poverty and to suffer adverse
birth outcomes than children born to older women.
Missing (10% or more) and suppressed
data
· Baseline
o Suppressed for Columbia (small numbers), Garfield (small numbers), Lincoln (small numbers), San Juan (small numbers), Skamania (small numbers), Wahkiakum (small numbers)
·
Update 1
o
Suppressed for Columbia (small numbers), Garfield
(small numbers), Lincoln (small numbers), San Juan (small numbers), Skamania
(small numbers), Wahkiakum (small numbers), Whitman (small numbers)
·
Update 2:
o
Suppressed for Columbia (small numbers), Garfield
(small numbers), Lincoln (small numbers), San Juan (small numbers), Skamania
(small numbers), Wahkiakum (small numbers), Whitman (small numbers)
Definition
Live born singleton infants with a
reported birth weight of less than 2,500 grams
Unit of measure
Crude percent of all singleton live births
Years of reporting
·
Baseline: posted 2007
o
State and local data: 2003–2005
o
National data: 2004
·
Update 1: posted 2009
o
State and local data: 2006–2007
o
National data: 2005
·
Update 2: posted 2011
o
State and local data: 2008–2009
o
National data: 2009
Sources
·
State and local data: WA-DOH, Birth Certificate System; data compiled using Community Health
Assessment Tool (CHAT) July 2011
·
National data: CDC, National Vital Statistics Reports
Rationale
for inclusion
Low birth weight is a major contributor to infant
morbidity and mortality. Low birth weight infants include infants who grow
normally but are born too early (preterm) and infants with inadequate growth.
Preterm infants are at risk for respiratory, gastrointestinal, immunologic, and
neurological problems. Newborns with inadequate fetal growth are prone to birth
asphyxia, hypoglycemia, temperature instability, infection, and circulatory
problems. This indicator is restricted to singleton births, excluding twins and
other multiple births. Multiple births tend to be born much smaller than
singletons. Limiting analysis to singleton births highlights factors related to
low birth weight separate from factors related to multiple birth.
·
Baseline
o
Suppressed for Columbia (small numbers), Garfield
(small numbers), Wahkiakum (small numbers)
·
Update 1
o
Suppressed for Columbia (small numbers), Garfield
(small numbers), San Juan (small numbers), Skamania (small numbers), Wahkiakum
(small numbers)
·
Update 2
o
Suppressed for Columbia (small numbers), Garfield
(small numbers), Klickitat (small numbers), Lincoln (small numbers), San Juan
(small numbers), Skamania (small numbers), Wahkiakum (small numbers)
Definition
10th grade students who answer “5,” “6,” or “7” in response to the question, “In the past 7 days,
on how many days were you physically active for a total of at least 60 minutes
per day? (Add up all the time you spent in any kind of physical activity that
increases your heart rate or makes you breathe hard some of the time.)”
Unit of measure
Crude percent
Years of reporting
·
Baseline: posted 2007
o
State and local data: 2006
o
National data: 2005
·
Update 1: posted 2009
o
State and local data: 2008
o
National data: 2007
·
Update 2: posted 2011
o
State and local data: 2010
o
National data: 2009
Sources
·
State and local data: WA-DOH, Healthy Youth Survey
·
National data: CDC, Youth Risk Behavior Surveillance System
Rationale for inclusion
U.S. Dietary Guidelines for Americans recommend
that children and adolescents participate in at least 60 minutes of
moderate-intensity physical activity most days of the week and preferably every
day. Young people who make exercise part of their daily routine will likely
continue this behavior into adulthood. Some immediate effects of physical
activity include building and maintaining healthy bones and lean muscles,
controlling weight, reducing feelings of depression and anxiety, and promoting
psychological well-being. Physical activity can also lower high blood pressure
and cholesterol levels in youth.
Local Public Health Indicators based on Healthy
Youth Survey use 10th grade data. For some indicators, reporting by 10th grade
students is more reliable than reporting by 8th grade students. Students in
grade 10 represent the high school population better than those in grade 12,
because fewer have dropped out of high school.
·
Baseline
o
Suppressed for Clallam (response rate <40%),
Grant (response rate <40%), Northeast Tri-County (response rate <40%)
·
Update 1
o
Suppressed for Garfield (small numbers), Wahkiakum
(small numbers), Clallam (response rate <40%)
·
Update 2
o
Suppressed for Clallam (response rate <40%)
Definition
10th grade students who answer one or more to the
question, “During the past 30 days, on how many days did you smoke cigarettes?”
Unit of measure
Crude percent
Years of reporting
·
Baseline: posted 2007
o
State and local data: 2006
o
National data: 2005
·
Update 1: posted 2009
o
State and local data: 2008
o
National data: 2007
·
Update 2: posted 2011
o
State and local data: 2010
o
National data: 2009
Sources
·
State and local data: WA-DOH, Healthy Youth Survey
·
National data: CDC, Youth Risk Behavior Surveillance System
Rationale for inclusion
Tobacco use is an unhealthy practice that may begin
in childhood. Tobacco use is an important cause of some cancers, cardiovascular
disease, and other serious health problems. Local Public Health Indicators
based on Healthy Youth Survey use 10th grade data. For some indicators,
reporting by 10th grade students is more reliable than reporting by 8th grade
students. Students in grade 10 represent the high school population better than
those in grade 12, because fewer have dropped out of high school.
·
Baseline
o
Suppressed for Clallam (response rate <40%),
Columbia (small numbers), Garfield (small numbers), Grant (response rate
<40%), Northeast Tri-County (response rate <40%), Wahkiakum (small
numbers)
·
Update 1
o
Suppressed for Clallam (response rate <40%),
Columbia (small numbers), Garfield (small numbers), San Juan (small numbers),
Wahkiakum (small numbers)
·
Update 2
o
Suppressed for Clallam (response rate <40%),
Columbia (small numbers), Garfield (small numbers), and Wahkiakum (small
numbers)
Definition
Teens overweight and obesity is computed from responses to "How
tall are you without your shoes on?" and "How much do you weigh
without your shoes on?" It includes overweight and obese 10th grade
students, who are in the top 15% for body mass index by age and gender based on
growth charts developed by the Centers for Disease Control and Prevention
(2000).
Unit of measure
Crude percent
Years of reporting
·
Baseline: posted 2007
o
State and local data: 2006
o
National data: 2005
·
Update 1: posted 2009
o
State and local data: 2008
o
National data: 2007
·
Update 2: posted 2011
o
State and local data: 2010
o
National data: 2009
Sources
·
State and local data: WA-DOH, Healthy Youth Survey
·
National data: CDC, Youth Risk Behavior Surveillance System
Rationale for inclusion
Unhealthy weight resulting from an imbalance
between caloric intake and energy expenditure is a leading cause of premature,
preventable mortality. Diet and weight patterns established in youth often
continue into adulthood. Overweight and obese adolescents are at increased risk
of obesity as adults. The earlier the age at which an
individual becomes obesity the higher the risk of premature death.
Local Public Health Indicators based on Healthy
Youth Survey use 10th grade data. For some indicators, reporting by 10th grade
students is more reliable than reporting by 8th grade students. Students in
grade 10 represent the high school population better than those in grade 12,
because fewer have dropped out of high school.
·
Baseline
o
Missing for Benton-Franklin (10%–19%),
Lewis (10%–19%), Okanogan (10%–19%), Skamania (10%–19%)
o
Suppressed for Clallam (response rate
<40%), Columbia (small numbers), Garfield (small numbers), Grant (response
rate <40%), Northeast Tri-County (response rate <40%), Wahkiakum (small
numbers)
·
Update 1
o
Missing for Adams (10%–19%), Cowlitz
(10%–19%), Mason (10%–19%), Pacific (10%–19%), Skagit (10%–19%) Yakima
(10%–19%)
o
Suppressed for Clallam (response rate
<40%), Wahkiakum (small numbers)
·
Update 2
o
Suppressed for Clallam (response rate <40%),
Columbia (small numbers), Garfield (small numbers), San Juan (small
numbers), Skamania (small numbers),
Wahkiakum (small numbers)
Definition
10th grade students who answer one or more to the
question, “During the past 30 days, on how many days did you drink a glass, can
or bottle of alcohol (beer, wine, wine coolers, hard liquor)?”
Unit of measure
Percent
Years of reporting
·
Baseline: posted 2007
o
State and local data: 2006
o
National data: 2005
·
Update 1: posted 2009
o
State and local data: 2008
o
National data: 2007
·
Update 2: posted 2011
o
State and local data: 2010
o
National data: 2009
Sources
·
State and local data: WA-DOH, Healthy Youth Survey
·
National data: CDC, Youth Risk Behavior Surveillance System
Rationale
for inclusion
The earlier a person starts drinking, the higher
that person’s chances of developing an alcohol use disorder at some time in
life. The four leading causes of death among 15–20 year-olds are vehicle
crashes, homicides, suicides, and other unintentional injuries; alcohol is a
factor in many of these deaths. Local Public Health Indicators based on Healthy
Youth Survey use 10th grade data. For some indicators, reporting by 10th grade
students is more reliable than reporting by 8th grade students. Students in
grade 10 represent the high school population better than those in grade 12,
because fewer have dropped out of high school.
·
Baseline
o
Suppressed for Clallam (response rate <40%), Garfield (small
numbers), Grant (response rate <40%), and
Northeast Tri (response rate <40%)
o
.
·
Update 1
o
Suppressed for Clallam (response rate <40%), Garfield (small
numbers)
·
Update 2
o
Suppressed for Clallam (response rate <40%),
Wahkiakum (small numbers)
Definition
10th grade students who answer “yes” to the
question, “During the past 12 months, did you ever feel so sad or hopeless
almost every day for two weeks or more in a row that you stopped doing some
usual activities?”
Unit of measure
Crude percent
Years of reporting
·
Baseline: Posted 2011
o
State and local data: 2006
o
National data: 2005
·
Update 1: Posted 2011
o
State and local data: 2008
o
National data: 2007
·
Update 2: Posted 2011
o
State and local data: 2010
o
National data: 2009
Sources
·
State and local data: WA-DOH, Healthy Youth Survey
·
National data: CDC, Youth Risk Behavior Surveillance System
Rationale
for inclusion
Youth
who reported feeling sad and hopeless are more likely than others to engage in
high risk behaviors, such as drinking alcoholic beverages, abusing prescription
pain killers, and carrying weapons. They are also more likely to report
considering suicide, being abused by an adult, and having a low quality of
life. This measure should not be interpreted as reflecting depression because
the rates of depression in youth are much lower than the percentages who report
feeling sad and hopeless. Local Public Health
Indicators based on Healthy Youth Survey use 10th grade data. For some
indicators, reporting by 10th grade students is more reliable than reporting by
8th grade students. Students in grade 10 represent the high school population
better than those in grade 12, because fewer have dropped out of high school.
·
Baseline
o
Suppressed for Clallam (response rate <40%),
Columbia (small numbers), Garfield (small numbers), Grant (response rate <40%), Northeast
Tri-County (response rate <40%), Wahkiakum (small numbers),
·
Update 1
o
Suppressed for Clallam (response rate <40%),
Columbia (small numbers), Garfield (small numbers), Wahkiakum (small numbers)
·
Update 2
o
Suppressed for Clallam (response rate <40%),
Columbia (small numbers), Wahkiakum (small numbers)
Definition
Unintentional injury hospitalizations for
Washington residents ages 0–17 whose hospital discharge record in the Comprehensive
Hospital Abstract Recording System (CHARS) or in the Oregon State Inpatient
Dataset (OR-SID) contains a diagnosis with ICD-9-CM
external cause of injury codes E800–E869 or E880–E929.
Unit of measure
Age-specific rate per 100,000 population ages 0–17
Years of reporting
·
Baseline: posted 2007; revised 2012 using population estimates adjusted to the
2010 U.S. Census and omitting federal hospitals for consistency across postings
(See NOTE.)
o
State and local data: 2003–2005
·
Updated 1: posted 2009; revised 2012 using population estimates adjusted to the
2010 U.S. Census and omitting federal hospitals for consistency across postings
(See NOTE.)
o
State and local data:2006–2007.
·
Update 2: posted 2012
o
State and local data: 2008–2009
NOTE: Federal military and Veterans
Administration hospitals no longer provide data to WA-DOH. Children of
active military can be hospitalized in military hospitals. Data from 2003–2005
showed that about 7% of children with unintentional injury hospitalizations in
Kitsap County were hospitalized in Naval Hospital Bremerton. Data from
2005–2007 indicated that about 8% of unintentional childhood injury
hospitalizations in Pierce and 10% in Thurston Counties were in Madigan Army
Hospital. Proportions of these hospitalizations for all other counties were
less than 1% with most counties having no hospitalizations at this facility.
Data from 2005–2007 also show no counties being substantively impacted by
hospitalization in Naval Hospital Oak Harbor.
Sources
· State and local data: Compiled using Community Health Assessment Tool (CHAT) January 2012
· WA-DOH, Comprehensive Hospital Abstract Reporting System
· Agency for Healthcare Research and Quality, Healthcare Cost and Utilization Project State Inpatient Databases (Oregon) (OR-SID)
· Washington State Office of Financial Management, Forecasting Division, single year intercensal estimates 2001–2009, December, 2011
·
National data: Not available.
Rationale for inclusion
Unintentional injury is a leading cause of
hospitalization and mortality among children ages 0–17. This indicator includes
in-patient hospitalizations from all causes of unintentional injury, including
motor vehicle crashes, falls, poisoning, drowning, firearms, and others causes.
Unintentional injury can often be prevented. If rates of unintentional injury
are high or increasing, public health and other stakeholders need additional
data about the specific causes and risk groups to plan prevention activities.
·
Baseline
o
Suppressed for Asotin (small numbers, missing
records due to hospitalizations in Idaho), Columbia (small numbers), Garfield
(small numbers, missing records due to hospitalizations in Idaho), San Juan (small
numbers), Skamania (small numbers), Wahkiakum (small numbers)
·
Update 1
o
Suppressed for Asotin (small numbers, missing
records due to hospitalizations in Idaho), Columbia (small numbers), Garfield
(small numbers, missing records due to hospitalizations in Idaho), San Juan
(small numbers), Wahkiakum (small numbers)
·
Update 2
o
Suppressed for Asotin (small numbers, missing
records due to hospitalizations in Idaho), Columbia (small numbers), Garfield
(small numbers, missing records due to hospitalizations in Idaho), Skamania
(small numbers), Wahkiakum (small numbers)
Definition
Adults who respond “yes” to
the question, “Was there a time in the past 12 months
when you needed to see a doctor but could not because of the cost?”
Unit of measure
Weighted, age-adjusted percent (age groups used for
age-adjustment: 18–24, 25–34, 35–44, 45–64, 65+)
Years of reporting
·
Baseline: posted 2007; revised 2011 for minor corrections (See NOTE)
o
State and local data: 2004–2006
o
National data: 2004–2006
·
Update 1: posted 2009; revised 2011 for minor corrections (See NOTE)
o
State and local data: 2007–2008
o
National data: 2007
·
Update 2: posted 2011
o
State and local data: 2009–2010
o
National data: 2010
NOTE: Stata coding for the original baseline and cycle 1 releases resulted
in inclusion of some records with age coded to missing or refused. For this
indicator, the error did not substantively affect the state rate or rates for
most counties. Smaller counties might see small discrepancies between the
original postings and the current data.
Sources
·
State and local data: WA-DOH, Behavioral Risk Factor Surveillance System
·
National data: CDC, Behavioral Risk Factor Surveillance System
Rationale for inclusion
Inability to cover costs of health care
may result in health concerns not being addressed in a timely or comprehensive
manner. Many health conditions have less serious consequences for long term
health when treated in a timely manner.
·
Baseline: None
·
Update 1: None
·
Update 2: None
Definition
Adults who respond “yes, only one” or “more than
one” to the question, “Do you have one person (or
more than one person) you think of as your personal doctor or health care
provider?”
Unit of measure
Weighted, age-adjusted percent (age groups used for
age-adjustment: 18–24, 25–34, 35–44, 45–64, 65+)
Years of reporting
·
Baseline: posted 2007; revised 2011 for minor corrections (See NOTE)
o
State and local data: 2004–2006
o
National data: 2004–2006
·
Update 1: posted 2009; revised 2011 for minor corrections (See NOTE)
o
State and local data: 2007–2008
o
National data: 2007
·
Update 2: posted 2011
o
State and local data: 2009–2010
o
National data: 2009
NOTE: Stata coding for the original baseline and cycle 1 releases resulted in
inclusion of some records with age coded to missing or refused. For this
indicator, the error did not change the state rate or rates for most counties.
Smaller counties might see small discrepancies between the original postings
and the current data.
Sources
·
State and local data: WA-DOH, Behavioral Risk Factor Surveillance System
·
National data: CDC, Behavioral Risk Factor Surveillance System
Rationale for inclusion
Having a personal doctor or health care provider
establishes the link to primary health care services that support prevention,
early detection and treatment of disease.
·
Baseline: None
·
Update 1: None
·
Update 2: None
Definition
Adults ages 18 and older who respond “within the
past year” to the question, “How long has it been since you last visited a
dentist or a dental clinic for any reason?”
Unit of measure
Weighted, age-adjusted percent (age groups used for
age-adjustment: 18–24, 25–34, 35–44, 45–64, 65+)
Years of reporting
Dental care data are collected biannually in
even-numbered years.
·
Baseline: posted 2007; revised 2011 for minor corrections (See NOTE)
o
State and local data: 2004 and 2006
o
National data: 2004 and 2006
·
Update 1: posted 2009; revised 2011 for minor corrections (See NOTE)
o
State and local data: 2008
o
National data: 2008
·
Update 2: posted 2011
o
State and local data: 2010
o
National data: 2010
NOTE: Stata coding for the original baseline and cycle 1 releases resulted in
inclusion of some records with age coded to missing or refused. For this
indicator, the error did not substantively change the state rate or rates for
most counties. Smaller counties might see small discrepancies between the
original postings and the current data.
Sources
·
State and local data: WA-DOH, Behavioral Risk Factor Surveillance System
·
National data: CDC, Behavioral Risk Factor Surveillance System
Rationale for inclusion
Preventive dental care can reduce the development
of disease and facilitate early diagnosis and treatment. Annual dentist visits
indicate routine dental care and preventive behavior.
·
Baseline: None
·
Update 1: None
·
Update 2: None
Definition
Women ages 50 and older who respond “yes” to the
question, “Have you ever had a mammogram?” AND respond “within the past year”
or “within the past two years” to the question, “How long has it been since you
had your last mammogram?”
Unit of measure
Weighted, age-adjusted percent (age groups used for
age adjustment: 50–64, 65+)
Years of reporting
Cancer screening data are collected biannually in
even-numbered years.
·
Baseline: posted 2007
o
State and local data: 2004 and 2006
o
National data: 2004 and 2006
·
Update 1: posted 2009
o
State and local data: 2008
o
National data: 2008
·
Update 2: posted 2011
o
State and local data: 2010
o
National data: 2010
Sources
·
State and local data: WA-DOH, Behavioral Risk Factor Surveillance System
·
National data: CDC, Behavioral Risk Factor Surveillance System
Rationale for inclusion
Breast cancer is a significant cause of death for
women. Breast cancer screening allows early detection and improved survival for
this disease. Recent evidence suggests that mammography screening beginning at
age 40 saves lives. This Public Health Indicator was developed before there was
good evidence for the benefit of mammography for women ages 40–49. The Public
Health Indicator was not changed for this cycle, because the U.S. Preventive
Services Task Force and the Centers for Disease Control and Prevention
continues to recommend universal screening mammograms for women beginning at
age 50. For younger women, they recommend individualized decisions. Changes in
the age range and frequency of mammography will be considered for the next
cycle.
·
Baseline: None
·
Update 1: None
·
Update 2: None
Definition
Women ages 21 and older, with and without a uterine
cervix, who respond “yes” to the question, “Have you ever had a Pap test?” AND
respond “within the past year,” “within the past two years,” or “within the
past three years” to the question, “How long has it been since you had your
last Pap test?”
Unit of measure
Weighted, age-adjusted percent (age groups used for
age-adjustment: (21–34, 35–44, 45–65, 66+)
Years of reporting
Cancer screening data are collected biannually in
even-numbered years.
·
Baseline: posted 2007; revised 2011 to reflect change in
definition
o
State and local data: 2004 and 2006
o
National data: 2004 and 2006
·
Update 1: posted 2009; revised 2011 to reflect change in
definition
o
State and local data: 2008
o
National data: 2008
·
Update 2: posted 2011
o
State and local data: 2010
o
National data: 2010
Sources
·
State and local data: WA-DOH, Behavioral Risk Factor Surveillance System
·
National data: CDC, Behavioral Risk Factor Surveillance System
Rationale for inclusion
Cervical cancer screening allows for early detection and improved survival for this disease. The U.S. Preventive Service Task Force (USPSTF) recommends routine cervical cancer screening within three years of becoming sexually active or at age 21, whichever occurs first, and screening at least every three years. The USPSTF recommends against routinely screening women who have had total hysterectomies for benign disease or women older than 65 who have had adequate recent screening with normal results and are not at high risk for cervical cancer. The Public Health Indicator, however, includes these women.
·
Baseline: None
·
Update 1: None
·
Update 2: None
Definition
Adults ages 50 and older who report that they had a
blood stool test in the past year, a sigmoidoscopy in
the past five years, or a colonoscopy in the past 10 years. This is a
calculated variable based on responses to the following five colorectal
screening questions: “Have you ever had
a blood stool test using a home kit?”; “How long has it been since you had your
last blood stool test using a home kit?”; “Have you ever had a sigmoidoscopy or a colonoscopy?”; “How long has it been
since you had your last sigmoidoscopy or
colonoscopy?”; and “Which test (sigmoidoscopy or
colonoscopy) have you had most recently?”
Unit of measure
Weighted, age-adjusted percent (age groups used for
age-adjustment: 50–64, 65+)
Years of reporting
Cancer screening data are collected biannually in
even-numbered years.
·
Baseline: posted 2007; revised 2011 for minor corrections (See NOTE)
o
State and local data: 2004 and 2006
o
National data: Not available
·
Update 1: posted 2009; revised 2011 for minor corrections (See NOTE)
o
State and local data: 2008
o
National data: 2008
·
Update 2: posted 2011
o
State and local data: 2010
o
National data: 2010
NOTE: Stata coding for the original baseline and cycle 1 releases resulted in
inclusion of some records with age coded to missing or refused. For this
indicator, the error did not substantively change the state rate or rates for
most counties. Smaller counties might see small discrepancies between the
original postings and the current data.
Sources
·
State and local data: WA-DOH, Behavioral Risk Factor Surveillance System
·
National data: CDC, Behavioral Risk Factor Surveillance System (2008 only)
Rationale for inclusion
Colorectal cancer is the second leading cause of
cancer death in Washington. Screening allows for prevention, early detection,
and improved survival for this disease. The American Cancer Society, the
Centers for Disease Control and Prevention, and the Washington Comprehensive
Cancer Control Partnership recommend that people ages 50 and older talk to
their doctors about which screening tests are best for them and when to begin
regular screening. Recommended screening options include a fecal occult blood
test or fecal immunochemical test yearly; a sigmoidoscopy
every five years; a combination of yearly fecal occult blood test and sigmoidoscopy every five years; double-contrast barium
enema every five years; OR colonoscopy every 10 years.
·
Baseline: None
·
Update 1: None
·
Update 2: None
Definition
Adults ages 18–64 who respond “yes” to the
question. “Do you have any kind of health care coverage including health
insurance, prepaid plans such as HMOs, or government plans such as Medicare?”
Unit of measure
Weighted, age-adjusted percent (age groups used for
age-adjustment: 18–24, 25–34, 35–44, 45–64)
Years of reporting
·
Baseline: posted 2007
o
State and local data: 2004–2006
o
National data: 2004–2006
·
Update 1: posted 2009
o
State and local data: 2007–2008
o
National data: 2007–2008
·
Update 2: posted 2011
o
State and local data: 2009–2010
o
National data: 2010
Sources
·
State and local data: WA-DOH, Behavioral Risk Factor Surveillance System
·
National data: CDC, Behavioral Risk Factor Surveillance System
Rationale for Inclusion
People with health insurance are more likely to
receive preventive health care services and see a health care provider for
early diagnosis and treatment of disease. Preventive care and early detection
and treatment of disease helps people live longer, healthier lives. This
indicator includes adults ages 18–64 because most
people ages 65 and older have Medicare.
·
Baseline: None
·
Update 1: None
·
Update 2: None
Definition
Adults that answer “yes” to the following question
about one randomly selected child in their household “Does this child currently
have some health care plan?”
Unit of measure
Weighted percent
Years of reporting
·
Baseline (Update 1): posted 2009
o
State and local data: 2008
·
Update 2: posted 2011
o
State and local data: 2009–2010
Sources
·
State and local data: WA-DOH, Behavioral Risk Factor Surveillance System, modified to include
child weights (see description of the Behavioral Risk Factor Surveillance
System, Child Weights Section, for additional information.)
·
National data: Not available
Rationale for inclusion
Children with health insurance are more likely to
have access to primary care and a variety of preventive health care services.
·
Baseline (Update 1): None
·
Update 2: None
DATA ANALYSIS
Crude percent reflects the number of events
per 100 of the population. It is calculated by dividing the number of events by
the total population who could experience the event and multiplying by 100. All
events are equally weighted. Crude percent is a simple, useful measure for many
local public health indicators such as low birth weight and maternal cigarette
smoking. Crude percent is also used for the environmental indicators as the
number of times an event met or exceeded a threshold per 100 times the event
was measured. Healthy Youth Survey data are also reported as crude percents
(the number providing a particular response divided by the number answering the
question and multiplied by 100).
A weighted percent is a percentage that is based on
data that have been weighted by the inverse of the sampling probabilities and
adjusted to be similar to the state population by age and sex. Weighting is
done for survey data from the Behavioral Risk Factor Surveillance System so
that sample data are representative of the total population of interest.
Age-adjustment
is used to allow comparisons of the frequency with which an age-related health
event occurs between populations that may vary in age. An age-adjusted percent or rate is based on
data that have been weighted to a standard population. Except for the child
health insurance indicator, data from the Behavioral Risk Factor Surveillance
System are age-adjusted as well as weighted to facilitate local, state, and
national comparisons. Healthy People 2010
goals for health indicators generally use age-adjusted data.
Age-adjusted rates in this website are
adjusted to the 2000 U.S. standard population, using age groups as shown in the
following table. The specific grouping used depends on the age group
targeted by the Indicator, such as mammography screening for women ages 50 and
older.
Readers should be aware that an age-adjusted percent has no
absolute meaning; it is an artificial number based on a reference population
and is useful only for comparing with other percents calculated in the same
manner.
|
|
Age groups used to age-adjust BRFSS data for local health indicators |
|
||||
|
|
Age Grouping |
|
||||
|
Ages 18+ |
Ages 18-64 |
Ages 21+ |
Ages 50+ |
Ages 65+ |
||
|
18–24 |
18–24 |
21-34 |
50–64 |
65–74 |
||
|
25–34 |
25–34 |
35-44 |
65+ |
75+ |
||
|
35–44 |
35–44 |
45-65 |
|
|
||
|
45–64 |
45–64 |
66+ |
|
|
||
|
65+ |
|
|
|
|
||
Age-specific rate
An age-specific rate is a number of events in a specified age group and time period
divided by the total number of people in that age group and time period. This
figure is multiplied by a constant such as 1,000 or 100,000 to produce a number
that is easy to read and compare and thus, the rate is reported as “per 1,000”
or “per 100,000.” Age-specific rates are used with population-based datasets to
measure age-dependent health indicators, such as the pregnancy rate for teens
ages 15–17.
The Public Health Indictors website uses the most
current data available at time of release for each indicator. To the extent
possible, multiple years of data are combined to increase sample size and
improve stability of the estimates. Multiple years of data are not always
available because the website is updated every two years and some indicators,
such as breast, cervical, and colorectal cancer screening, are not collected
every year.
Confidence intervals provide a measure of how much a rate,
percent, or other point estimate might vary due to random factors or chance.
They do not account for several other sources of uncertainty, including missing
or incomplete data, bias resulting from non-response to a survey, or poor data
collection.
Confidence intervals are reported for all indicators. With
sample data, such as BRFSS and HYS, confidence intervals are used to account
for the difference between a sample from a population and the population
itself. With total population data, such as births, confidence intervals help
account for uncertainty that arises from random variation that occurs when we
divide a continuous phenomenon such as time into discreet periods for analysis.
The Local Public Health Indicators website reports 95%
confidence intervals for all indicators. A 95% confidence interval captures the
true value of the point estimate in 95 out of 100 cases. In the data tables
included in the Data by Jurisdiction section of the website, 95% confidence
intervals are reported as numbers, representing the lower and upper limits of
the interval. In the bar charts found in the Data by Indicator section,
confidence intervals are shown as horizontal lines with small vertical lines at
the lower and upper limits of the interval. Narrow
confidence intervals indicate greater certainty that the calculated rate is a
reliable approximation of the true rate. Conversely, wide confidence intervals
signal greater potential variability and less certainty that the calculated
rate is a good estimate of the true rate.
The methods used to calculate confidence intervals for
population data are consistent with the Guidelines for Using Confidence
Intervals for Public Health Assessment (http://www.doh.wa.gov/Data/Guidelines/ConfIntguide.htm). For indicators that use survey data, confidence intervals
are calculated in Stata 11.0 using tabulate commands for weighted survey data.
For indicators that use data from total population data such as the Birth
Certificate System, Sexually Transmitted Disease Registry, Child Profile Immunization
Registry, and environmental health databases, binomial confidence intervals are
computed using the Score method. For indicators that use hospital discharge
data, confidence intervals are calculated through a modified method that
adjusts for duplicated, non-independent events.
Except for the air pollution indicator, the Local Public
Health Indicators website reports whether local health jurisdictions are
statistically significantly better, worse, or similar to the state as a whole
for each indicator. The website also compares indicators for a given
jurisdiction across years and reports whether the two time periods are
statistically significantly different. Both types of comparisons are determined
by performing a Z test. A p-value less than .05 is considered statistically
significant. If an indicator measure is 0% or 100% (example: 100% of inspected
food establishments scored less than 36 critical violation points), the test
for difference is modified to account for the absence of a standard error. In
these instances, the p-value equals the state average to the power of the
sample size for the health jurisdiction. For the poverty indicator, see Small Area Income and Poverty Estimates Caveats for
detail.
Two health jurisdictions can have the same rate for an
indicator, with one jurisdiction marked as statistically different from the
state (“better” or “worse”) and the other as statistically “similar” to the state.
Rates based on small numbers have wide confidence intervals; rates based on
large numbers have more narrow confidence intervals. If the confidence
intervals for health jurisdiction and state rates overlap, this might mean that
the two rates are statistically similar; if the confidence intervals do not
overlap, they are always statistically different. Local estimates are not
independent from the state average, because the state average contains data
from each local health jurisdiction. But this lack of independence does not
make a big enough difference to substantively change results, except possibly
for Public Health - Seattle & King County.
Local and state indicator data are
suppressed if:
·
The
amount of missing data exceeds 30% or 25% for Air Quality. (See Missing
Data.)
·
The
relative standard error (RSE) is greater than 30%. The
RSE is a measure of an estimate's reliability. The RSE of an estimate is
obtained by dividing the standard error of the estimate (SE(r)) by the estimate
itself (r). This quantity is expressed as a percent of the estimate and is
calculated as follows: RSE=100 x (SE(r)/r). The National Center for
Health Statistics considers a rate unreliable if it has an RSE of greater than
30%.
·
There
are fewer than five records per year. (For On-site Sewage, a record is the
number of failures.) (See Small
Numbers.)
·
For
indicators using the Healthy Youth Survey, the response rate was less than 40%.
Missing data result either when records do not include all
of the information required (missing values) or when records that should be
included in a dataset are not (missing records). For population-based data, missing
records are cases of disease, hospitalizations, births or deaths that are not
included in the dataset. For survey data, missing records are those of people
who do not respond to the survey, commonly referred to as non-response rate
when presented as a percent of the total sampling frame. Rates estimated from
datasets with a large amount of missing data can result in bias, such that the
estimated rates do not reflect the true situation. Bias occurs only when the
data are not missing completely at random and bias is most likely to occur when
the amount of missing data is relatively large.
This website reports the percent of missing values for local
and state indicators. Estimates are flagged if 10% or more of the records
needed for a specific indicator have missing values: a single dagger (†)
identifies 10–19% and a double dagger (††) identifies 20–30% of records with
missing values. Data are suppressed if more than 30% of records are missing
values. Most Local Public Health Indicators have low levels of missing values.
Presenting and interpreting statistics when there are a
small number of events or few survey respondents present several challenges.
Statistics developed when there are few events or the population in which the
events occurred is relatively small presents a risk of breaching
confidentiality. Interpreting data based on few survey respondents or a small
number of events irrespective of the size of the population can be difficult,
because random fluctuation can be relatively large when the number of events is
small. As the amount of potential random fluctuation increases, the predictive
value of a statistic generally decreases. For example, with large annual
fluctuation, knowing a rate for one year might not allow us to reliably
anticipate the rate for another year. This instability makes it difficult to
use rates based on small numbers for program planning or assessment.
DATA SOURCES
Except for Poverty, indicators state
and health jurisdiction data for the Local Public Health Indicators come from
databases maintained by the Washington State Department of Health (WA-DOH).
Staff from the WA-DOH’s Non-Infectious Conditions Epidemiology Office
calculated the indicators. Other WA-DOH units assisted with quality assurance
processes and Stata programming. Several indicators were produced using the
Community Health Assessment Tool (CHAT). A description of each database as it relates to the Local Public Health
Indicators and links for additional information follows. Accurate
interpretation of data depends on understanding the strengths and limitations
of the data systems.
Purpose
The
Abortion Reporting System collects information on induced abortions that can be
used to help address issues related to family planning, maternal and child
health, and access to care.
Coverage
In-state
health care providers and facilities that perform induced abortions are
required to report these to the WA-DOH per WAC 246-490-100 Additionally, reports from other states and Canada are regularly exchanged.
Except for Oregon, the numbers from other localities are small. In 2008, the
number of reported abortions was about 0.1% lower than the number of abortions
reported in an independent study conducted by the Guttmacher
Institute (Jones RK, Kooistra K. Abortion Incidence
and Access to Services in the United States, 2008. Perspectives on Sexual and Reproductive Health, 2011,
43(1):41–50).
Reporting system
For each induced abortion performed
on Washington State residents, the attending physician, hospital, or medical
facility must complete a reporting form with specified non-identified
information about the patient, the procedure performed, and the medical
complications and submit the form to the Washington State Department of Health.
Forms are submitted electronically and as paper forms.
Data quality procedures
To
improve data quality, abortion providers are queried if the information
obtained on their reporting forms is incomplete, inconsistent, or falls outside
expected ranges. Tables are sent back to each provider annually for a review of
the completeness and accuracy of information reported for their facility.
·
The age and county of residence fields
are more than 90% complete and so inconsistencies in reporting are not expected
to substantively affect the Local Public Health Indicators teen pregnancy
indicator that uses data from the Abortion Registry.
http://www.doh.wa.gov/ehsphl/chs/chs-data/abortion/abormain.htm
Behavioral Risk Factor Surveillance
System (BRFSS)
Purpose
BRFSS provides information on health risk behaviors, preventive
practices, health care use and access, and prevalence of selected diseases.
Coverage
For years of data included in the Local Public Health Indicators,
Washington BRFSS included a random sample of English- and Spanish-speaking
residents ages 18 and older living in households with landline telephones. In
2010, there were 19,628 respondents. The average statewide CASRO (Council of American
Survey Research Organizations) response rate for 2004-2010 was 47%.
Reporting system
BRFSS is a random-digit-dialed telephone survey. The Washington
State Department of Health (Department) contracts with a survey research firm to contact
potential participants and administer the survey following protocols
established by the Centers for Disease Control and Prevention (CDC). The
questionnaire includes core questions used by all states and questions on
topics of specific interest to Washington. Annual data are generally available
six to eight months after the close of the calendar year. Some data for the
Local Public Health Indicators are collected annually; others are collected
every other year.
Data quality procedures
The survey research contractor uses
several procedures to improve response rates, such as advance letters to
households. Interviewers use computer-assisted interview software to minimize
errors. CDC or the survey research contractor
tests all questions to assure that respondents can understand them and answer.
Interviewers receive professional training, and supervisors and project
directors regularly monitor calls to maintain quality standards.
Caveats
·
We do not know whether people who
respond to BRFSS are different from those who do not respond on factors
measured by the survey. CDC has concluded that “BRFSS
data show minimum bias that does not appear to be associated with response
rate.” (Mokdad A.
"The Future of The
Behavioral Risk Factor Surveillance System [BRFSS] in a Changing Environment.” Paper presented at the annual meeting of the
American Association for Public Opinion Research, Sheraton Music City,
Nashville, TN, Aug 16, 2003, available at http://www.allacademic.com/meta/p116188_index.html
·
A growing
percentage of adults do not have landlines or use cell phones for most
telephone calls. Estimates from the National Center for Health Statistics for
July 2009–June 2010 indicate >25% of Washington adults live in households
with wireless telephones only. (National Health Statistics Report, No. 39,
April 20, 2011) In 2008, Washington BRFSS included adults living in
wireless-only households. An unpublished Washington State Department of Health
study compared cell phone and landline respondents on selected indicators
including six that are also Local Public Health Indicators. Cell phone
respondents were more likely to report smoking, binge drinking, not having a
regular health care provider and having cost barriers to health care; they were
less likely to report obesity and diabetes. Most of these differences were due
to demographic differences between wireless only and landline respondents.
Differences remained, however, for binge drinking and not having a regular
health care provider even after accounting for sex, age, race/ethnicity, income
and education. We do not know whether these differences affect some counties
more than others. The Department is working to add a cellular-only component to
the 2012 BRFSS.
·
The Washington
BRFSS does not represent people who do not speak English or Spanish, or people
who live in institutions or other group settings, such as dormitories, group
homes, hospitals, in-patient drug treatment facilities, jails, or prisons.
·
BRFSS data are
self-reported. The BRFSS might underestimate behaviors that others might not
find acceptable (such as smoking) and overestimate more positive behaviors
(such as physical activity). As long as over- and underreporting are consistent
across time and county, misreporting would not affect comparisons over time or
among counties.
·
High unknowns in
some fields (such as the month prenatal care began) may make patterns and
trends difficult to interpret.
·
Differences
between counties might reflect incomplete extraction of information from
medical records by some hospitals.
·
Smoking during pregnancy
on the birth certificate is underestimated compared to smoking during pregnancy
reported on the Pregnancy Risk Assessment Monitoring System (See Maternal Smoking).
http://www.doh.wa.gov/EHSPHL/CHS/CHS-Data/birth/bir_main.htm
Child
Profile Immunization Registry
The
registry uses several procedures to improve the quality of data including
·
An automated algorithm to de-duplicate
demographic records, combined with a manual procedure to resolve duplicates
that cannot be resolved by the algorithm.
·
A user-initiated process to report
duplicate records which staff then manually resolve, merging records when
needed.
·
A process to identify children who have
died, using the state’s Early Notification of Childhood Death bulletin board
and death records from the Department, and flagging these records as
“inactive-deceased”.
·
A mailing system to identify children under
age six who have moved out-of-state.
·
Vaccination data in the registry are not
complete and underestimate the percent of children who have received the
complete vaccination series.
○
Registry data are currently useful for
tracking participation in Child Profile system but not for assessing the
percentage of children who have received all necessary vaccines, statewide or
at the local level.
○
Compared to the 2008 NIS data,
local/regional vaccine coverage rates reported by the Child Profile
Immunization Registry were significantly lower (p <
.01) than NIS estimates for 26 of 35 health jurisdictions.
·
The number of demographic records for
children in the Child Profile Immunization Registry is larger than population
estimates published by the Office of Financial Management (OFM). Factors that
account for this include:
o The
registry including children who have moved out of state.
o Duplicate
records not being identified.
o OFM
population counts undercounting migrant families.
http://www.doh.wa.gov/cfh/childprofile/
Purpose
The death certificate establishes legal benefits and can be used
to monitor causes of death and changes in causes over time.
Coverage
The Washington Death Certificate System includes approximately 99%
of deaths for Washington residents including residents who die elsewhere.
Reporting system
Death certificates are currently
submitted both electronically and in paper format. Less than 30% of all death
certificates are electronic. Funeral directors gather demographic information
including age and legal residence of the decedent; the attending physician or
the coroner/medical examiner reports the immediate and contributing causes of
death. The paper certificate is filed with the local health jurisdiction, which
retains it for about 60 days for local issuance purposes, then files it with
WA-DOH. Electronic death records are
submitted to DOH by the county registrar and uploaded to the WA-DOH death
certificate system on a daily basis. WA-DOH follows the guidelines of the
National Center for Health Statistics to code data collected on the death
certificate.
Data quality procedures
WA-DOH provides instruction manuals
to physicians, coroners, and medical examiners, as well as local health
jurisdictions and others involved in completing and managing death
certificates. The Local Public Health Indicators use only the numbers of
deaths, legal residence and age at death from the Death Certificate System.
County of residence data are verified by using geocoding
software that identifies county based on street address. Age at death is
verified with internal computer-assisted consistency checks using date of birth
and date of death.
·
None relevant to
LPHI
http://www.doh.wa.gov/ehsphl/chs/chs-data/FetDeath/fd_main.htm
Fetal Death Certificate System
Purpose
The Fetal Death Certificate System provides
a death record for proper disposition of human remains. It also provides
information such as pregnancy history, prenatal care, and causes of death.
Coverage
Fetal deaths are required to be
reported to the state only for gestational ages of 20 weeks or more (RCW 70.58.160).
Thus, early fetal deaths (commonly called ‘miscarriages’) are not included in
the total. A fetal death certificate must be completed and filed before a
Burial Transit permit is used and before final disposition for every fetus 20
or more weeks gestation, therefore WA-DOH estimates at
least 90% completeness except for events that occur near 20 weeks. (See Caveats.)
Reporting system
For fetal deaths that occur in
Washington State, a Certificate of Fetal Death is completed by the hospital or
birth attendant and initially filed with the local health jurisdiction. The
certificates are then sent to the Department of Health within 60 days of the
fetal death.
Data quality procedures
WA-DOH queries hospitals and birth
attendants if the information on their reporting forms are incomplete or
inconsistent. WA-DOH also conducts computer assisted data edits to check for
consistency and validity and certifiers are queried if the information is
incomplete or inconsistent.
·
Fetal
deaths are required to be reported to the state only for gestational ages of 20
weeks or more. Thus, early fetal deaths (commonly called ‘miscarriages’) which
are much more common than late fetal deaths are not included in the total. Thus, pregnancy rates are likely higher than reported.
·
Reporters may
differ in classifying an event as a fetal death for events happening near 20
weeks of gestation. However, since the number of fetal deaths to mothers ages 15-17
is low (generally less than 10 each year) compared to the numbers of abortions and
births, these inconsistencies likely do not substantively affect teen pregnancy
rates reported in Local Public Health Indicators.
http://www.doh.wa.gov/ehsphl/chs/chs-data/FetDeath/fd_main.htm
Food Safety and On-site Sewage
(Septic)
Purpose
Data
on food safety inspections and on-site sewage assist health jurisdictions to
identify the need for action to reduce health risks associated with unsafe food
handling practices and on-site sewage system failures.
Coverage
All
permanent food service establishments operating under permit from health
departments as required by Washington administrative codes and local
regulations; all on-site sewage systems regulated by local health jurisdictions
in Washington (systems with design flows less than 3,500 gallons per day). The
completeness of reporting, however, varies by jurisdiction.
Reporting System
All
35 local health jurisdictions maintain environmental health data systems and
submit the food and on-site sewage data to DOH as part of the annual PHIP
Public Health Activities and Services (PHAS) inventory. DOH uploads all data
submitted by the LHJs directly into the Activities and Services data system.
Environmental Health Directors review their data before they enter it into the
Activities and Services survey and are sent a link to check their data before
the PHAS website is published. Summary reports are also reviewed annually at
the Environmental Health Directors meetings.
Data quality procedures
In
addition to the reviews by LHJs and Environmental Health Directors discussed
under “Reporting System,” Trainings, field evaluations and standardized
protocols are used to improve accuracy of conducting inspections and reporting.
Caveats
·
Food
inspection results may vary between jurisdictions due to training (e.g. FDA
course completion), resources, time allocated per inspection, program
evaluation, and the inspector’s experience and training.
·
Length
of time for initiating septic system repairs may vary between jurisdictions due
to special projects such shoreline surveys to find and correct failures, the
number of housing presale inspections, climate (some repairs are seasonal but
the correction can be initiated), resources, workloads, and training.
·
Self-reported
information is not verified through other means, although some questions have
been validated through special studies done elsewhere.
Hospital Discharge Data System
Indicators
using Washington’s hospital discharge data system combine data on Washington
residents hospitalized in Washington (Comprehensive Hospital Abstract Reporting
System—CHARS) or Oregon (Healthcare Cost and
Utilization Project [HCUP] State Inpatient Databases [Oregon]—OR-SID).
Purpose
CHARS and OR-SID were developed to monitor
hospitalization costs and utilization. These datasets can be used to examine
trends in causes of hospitalization, create hospital-specific case-mix indices,
characterize access to and quality of health care, and monitor morbidity from
selected health conditions.
Coverage
·
CHARS
includes inpatient and observation (beginning in 2008) stays for patients treated
in all state-licensed acute care hospitals in Washington regardless of patient
residence. It does not include federal hospitals (e.g,
Veterans Administration and military hospitals) or private alcoholism
hospitals, and no-fee hospitals. CHARS does not
include Washington State psychiatric hospitals, but does include community
psychiatric hospitals and psychiatric hospitalizations in acute care hospitals.
·
OR-SID includes inpatient and observation
(beginning in 2008) stays for patients treated in community hospitals defined as "nonfederal, short-term, general and other
specialty hospitals, excluding hospital units of institutions..." and
including "academic medical centers and specialty hospitals….
Non-community hospitals include federal hospitals …, long-term hospitals,
psychiatric hospitals, alcohol/chemical dependency treatment facilities and
hospitals units within institutions such as prisons." (http://www.hcup-us.ahrq.gov/db/state/siddist/Introduction_to_SID.pdf
accessed February 2012) For 2004–2009 OR-SID included all but one identified
community hospital; for 2003, OR-SID was missing two community hospitals. It is
not known how these might affect hospitalization records for Washington
residents.
Both datasets contain 98%–100% of hospitalization records
from reporting hospitals. External cause of injury codes needed for the LPHI
are approximately 95% complete in CHARS (2.5% of these are imputed) and 95% complete in OR-SID (3.5% of these are imputed). LHPI indicators use information from inpatient stays
only.
Reporting System
·
For CHARS, hospitals summarize
information from the uniform billing form, code diagnoses and procedures, and
submit the information to the state by electronic file transfer.
·
WA-DOH obtains OR-SID directly from
the Healthcare Cost and Utilization Project (HCUP) sponsored by the Agency for
Healthcare Research and Quality. Information on the HCUP is available at http://www.hcup-us.ahrq.gov/overview.jsp; information on SID is available at http://www.hcup-us.ahrq.gov/db/state/siddbdocumentation.jsp.
Both datasets code reasons for hospitalization to ICD-9-CM
codes. The reason in the first diagnosis field is considered to be the
principal reason the patient was admitted to the hospital. Both datasets also
contain external cause of injury ICD-9-CM codes that are used for both LHPI
indicators.
Data quality procedures
The Washington State Department of Health edits
CHARS data for accuracy and completeness through computerized system program checks. Information on
OR-SID quality control procedures is available at http://www.hcup-us.ahrq.gov/db/quality.pdf.
·
The unit of observation is the hospitalization
episode, not the individual.
·
Changes in hospitalization practices or
coding conventions might affect trends over time.
·
Residence is
based on five-digit ZIP codes. For LPHI, ZIP codes have been assigned to
counties based on U.S. Postal Service conventions that assign ZIP codes to
counties based on the physical location of the post office. When ZIP codes
cross county borders, some hospitalizations are assigned to the wrong county.
http://www.doh.wa.gov/EHSPHL/hospdata/default.htm
http://www.hcup-us.ahrq.gov/db/state/siddbdocumentation.jsp
Purpose
The U.S. Constitution
mandates a count of people living in the United States (the U.S. Decennial
Census) every 10 years to determine how many seats each state will have in the
U.S. House of Representatives. The U.S. Decennial Census is also used for
political redistricting, distribution of federal and state funds, and other
governmental needs. The Office of Financial Management develops intercensal and postcensal
estimates to provide population counts for non-census years. State and local
governments, non-governmental organizations and individuals use population
counts for diverse purposes.
Coverage
The U.S. Decennial
Census attempts to count everyone living in the United States on April 1 of the
census year. In March 2001, the U.S. Census Monitoring Board reported that approximately
98.5% of people living in Washington in April 2000 had been counted in the 2000
census. Data on completeness of the Washington count for 2010 could not be
found. As of February 2012, there were no challenges to the 2010 census count
in Washington.
Reporting system
For information on the U.S. Census, see http://2010.census.gov/2010census/. Population counts used in developing rates for this version of the Local Public Health Indicators used Office of Financial Management’s counts for single years between the 2000 and 2010 U.S. Censuses. OFM developed these estimates using information from the decennial censuses, annual data on the number of births and deaths in Washington, and a variety of other data, such as housing starts, to estimate migration into and out of Washington.
Data quality
procedures
Contact Office of Financial Management (http://www.ofm.wa.gov/pop/contact.asp) for information on estimation procedures.
Caveats
In general, the larger the population, the greater the
accuracy of an estimate. Thus, counts might be more accurate for larger counties and
age groups compared to smaller counties and age groups. However, given that
counts used in the current version of the LPHIs use intercensal
estimates interpolated between two known endpoints, inaccuracies are unlikely
to substantively affect the Local Public Health Indicators. Data from previous
versions was updated to reflect changes to population counts as a result of the
2010 U.S. Census.
More information
http://www.ofm.wa.gov/pop/default.asp
Public Health Issues Management
System – Sexually Transmitted Diseases (PHIMS-STD)
Purpose
PHIMS-STD supports surveillance
activities for legally reportable cases of sexually transmitted diseases and
enables public health agencies to act quickly to treat and prevent the spread
of disease.
Coverage
PHIMS-STD includes incident cases of
laboratory confirmed Chlamydial infection, gonorrhea,
and syphilis reported by diagnosis date. Genital herpes may be reported without
laboratory confirmation. Cases are not unique persons diagnosed with disease
(e.g., a person may have more than one infection within a given year).
Completeness of reporting is high for persons seeking and receiving care for
STDs, reproductive health services or other care in both public and private
care settings.
Reporting system
Public and private health care
providers complete confidential case reports, which are submitted to local
health jurisdictions. Laboratories providing diagnostic or screening services
are also required to report positive test results to the local health
jurisdiction where the person lives. Local health jurisdiction staff members
enter the case information into the statewide PHIMS-STD web-based electronic
system.
Data quality procedures
Training for users new to PHIMS-STD is
available through SmartPH. Monthly data quality checks are performed for
crucial data elements with lists of cases needing review distributed to state
field staff. Case reports are also geocoded,
providing assurance that cases are attributed to the correct jurisdiction for
official reporting purposes
Caveats
·
Clinically diagnosed cases of STD may
be underreported through public health surveillance systems.
·
Laboratory confirmed cases
underestimate the burden of disease because not all cases of Chlamydia are
diagnosed and not all diagnosed cases are laboratory confirmed.
·
Depending upon diagnosing practices,
completeness of reporting may vary by source of health care, particularly
private versus publicly funded sources of care.
More information
http://www.doh.wa.gov/cfh/STD/default.htm
Small Area Income and Poverty Estimates (SAIPE)
Purpose
The U.S. Census
Bureau's Small Area Income and Poverty Estimates (SAIPE) provide estimates of
income and poverty for the administration of federal programs and allocation of
federal funds to local jurisdictions. State and local programs use the income
and poverty estimates for distributing funds and managing programs.
Coverage
SAIPE provides
estimates for all counties.
Reporting system
The SAIPE program's
combines survey data (such as the American Community Survey, ACS) with
population estimates (such as the U.S. Census) and administrative records (such
as tax returns and Supplemental Nutrition Assistance Program recipients) to
model single year estimates of poverty and income. For areas with populations
less than 65,000, estimates are based on 3-year or 5-year accumulations of ACS
data.
Data quality procedures
The estimates for
the counties of a given state are controlled to sum to the independently
derived state estimate (which in turn has been controlled to sum to the ACS
national estimate).
·
Comparing across jurisdictions. SAIPE technical
documentation cautions about comparing between jurisdictions, stating, “All SAIPE model-based
estimates are correlated because they depend on the same regression
coefficients. Also estimates for individual states are controlled to add up to
the national ACS estimate, and counties within each state are controlled to add
up to the state-level estimate. These controls create additional correlation.
Therefore, to make comparisons between two or more states or counties, it is
not sufficient to take the variances (implied by the confidence intervals) for
the two different places and apply the usual estimates-difference hypothesis
testing.” Because the amount of correlation between local health jurisdictions
and between a given local health jurisdiction and the state varies, standard
errors needed for estimate-differences hypothesis testing vary depending on the
specific difference examined. To compare a given local health jurisdiction to
the state and the other 34 local health jurisdictions would require 35 separate
formulas. The chart comparing each local health jurisdiction to the state does
not adjust variances for the correlation, resulting in potential failure to
find statistically significant differences between a county and the state. This
same caution applies when using confidence intervals to compare two local
health jurisdictions: the variance implied by the confidence intervals on the
chart is larger (i.e. wider confidence intervals) than would be the case if
accounting for the correlation.
·
Comparing across two time periods: SAIPE estimates are
correlated across years and cautions similar to those described for comparing
between jurisdictions apply.
More information
http://www.census.gov/did/www/saipe/about/faq.html
http://www.census.gov/did/www/saipe/methods/cautions.html
Washington
Tracking Network (WTN) – Air Monitoring Data
Purpose
WTN integrates
hazard, exposure, and health outcome information for use by public health
advocates, professionals, and researchers.
Coverage
In 2009 71% percent
of LHJs, had at least one active monitor that functioned for at least 75% of
days in all four quarters of the year; for 2010, 74% met these criteria. https://fortress.wa.gov/ecy/enviwa/Default.ltr.aspx; click “no” when prompted.
Reporting System
The Washington
State Department of Ecology (Ecology) and its partners monitor PM2.5
pollution levels around Washington State using the Federal Reference Method
(FRM) http://www.ecy.wa.gov/biblio/99205.html and continuous
monitors http://www.ecy.wa.gov/biblio/0002007.html and http://www.ecy.wa.gov/biblio/0102001.html. Ecology provides these data to WTN.
Data quality procedures
Ecology is the
EPA’s designated Primary Quality Assurance Organization for Washington State. Ecology
conducts a comprehensive quality assurance process for all FRM and continuous
monitor data. See http://www.ecy.wa.gov/biblio/99201.html and PDFs
referenced in previous section for details.
Caveats
·
For
counties with more than one monitor, the calculation used the monitor with the
highest average daily value.
·
Not
everyone living in the county may be exposed to unhealthy levels of PM on days
that a local health jurisdiction does not meet Ecology’s healthy air goal for
PM2.5. Factors such as weather, geography, population distribution,
and the placement of monitors determine how completely the monitors in a given
jurisdiction reflect the air quality breathed by residents.
More
information
https://fortress.wa.gov/doh/wtn/WTNPortal/
Behavioral Risk Factor
Surveillance System
CDC Wonder,
Underlying causes of death 1999–2008, http://wonder.cdc.gov/ucd-icd10.html
CDC Wonder,
Sexually transmitted disease morbidity data 1996–2009, http://wonder.cdc.gov/std-v2009-race-age.html
Small Area Income and Poverty Estimates (SAIPE)
Information
on and data from the U.S. Census Bureau on Small Area Income and Poverty
Estimates can be found at http://www.census.gov/did/www/saipe/index.html.
CDC
National Center for Health Statistics National Vital Statistics Reports. Data for 2007 can
be found at http://www.cdc.gov/nchs/data/nvsr/nvsr58/nvsr58_19.pdf.
Youth Risk Behavior Surveillance
System (YRBS)