Upload
others
View
0
Download
0
Embed Size (px)
Citation preview
FHOP Orientation for MCAH Program Consultants
AGENDA
9:45 Sign in 10: 00 Introductions and Logistics 10:15 Who is FHOP and what do we do? - Gerry Oliva 10:45 Navigating the FHOP website
Public Health Data CA MCAH Resources Planning Tools- Planning Guide Software – EpiBC and Epi HOSP Training
11:30 Practice time: Can you find it? 12:00 Lunch 1:00 Orientation to the Databooks 1:15 Understanding and Using the Indicator Rate Tables - Gerry Oliva 1:45 Practice looking at rate tables for counties and finding data templates 2:15 How Data Quality Issues Impact the Validity of County Level Indicator
Rates - Linda Remy 2:45 Break 3:00 Do we have a Trend? - Linda Remy 3:35 Practice looking at trend graphs 3:50 Where we go from here? 4:00 Evaluations
The Family Health The Family Health Outcomes Project: Outcomes Project:
Orientation Orientation
Gerry Oliva M.D., MPHFamily Health Outcomes Project
September 25, 2007
FHOP MissionFHOP Mission
To improve the health of children and their families and communities by supporting the development and implementation of comprehensive community planning, data-driven policies, evidence-based interventions, and effective evaluation strategies.
FHOP BackgroundFHOP Background
Located at the University of California, San Francisco, Department of Family and Community MedicineAffiliated with the Institute for Health Policy StudiesMCAH project funded by State, Federal, and Foundation grants
FHOP ApproachFHOP Approach
CollaborationLiterature ReviewConsulting ExpertsCommunity Advisory Groups
FHOP Areas of ActivityFHOP Areas of Activity
Web siteAutomated Resources and ToolsTrainingResearch and DevelopmentAnalytic MethodsEvaluationTechnical Assistance
FHOP Activities:FHOP Activities:Improving Data AccessibilityImproving Data Accessibility
Work with state to develop standardized reportsDisseminate data reports (e.g. annual birth and death data)Disseminate electronic data (e.g. hospital discharge file)Data resources library on FHOP website
Data Sources on the WebData Sources on the Web
FHOP Website: www.ucsf.edu/fhop
• Data tables and spreadsheets
• Links to other sites with relevant data CDPH Center for Health Statistics
CDPH Communicable Disease Branch
UCLA/California Health Interview Survey
CDPH EPIC Center- Injury Data
CADSS Foster Care Data
• Data and links organized by topic area
Areas of Activity:Areas of Activity:Automated Resources Automated Resources
EXCEL Templates – Calculate Rates and confidence intervals – Calculate Risk statistics
Analysis – EpiInfo Based software – EpiBC– EpiHOSP
#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!011.0%169,2742003-2005
#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!010.5%156,563200-2002
#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!010.5%156,1231997-1999
#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!010.3%163,5391994-1996
Upper 95% C.L.
Lower 95% C.L.Ratio
Upper 95% C.L.
Lower 95% C.L.PercentEventsPercentEvents3 Year Aggregates
County/State ComparisonCountyCalifornia
#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!11.2%59,2252005
#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!11.0%55,7382004
#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!10.8%54,3112003
#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!10.5%52,0672002
#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!10.4%51,9742001
#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!10.5%52,5222000
#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!10.6%51,8071999
#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!10.6%52,4411998
#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!10.4%51,8751997
#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!10.3%53,0221996
#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!10.3%54,4531995
#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!10.3%56,0641994
Upper 95% C.L.
Lower 95% C.L.Ratio
Upper 95% C.L.
Lower 95% C.L.PercentEventsPercentEventsYear
County/State ComparisonCountyCalifornia
FHOP Data Templates
FHOP Local FHOP Local DatabooksDatabooksEXCEL Spreadsheets that display County-level data over 12 years for different indicators
Display the comparison of rates between county data, the State and Healthy People 2010 Objectives
Contain a data quality tab to alert counties to missing or unlikely values and how they may affect accuracy of rate calculations
Perform trend tests- “are things getting better or worse?” “how does progress in my county compare to the state?”
FHOP Automated Resources:FHOP Automated Resources:EpiBCEpiBC
Based on public domain EpiInfo 2005Easy-to-use windows versionAnalyzes local birth certificate data and generates reportsGenerates graphs and mapsBuilt-in tutorials, references, documentationUpdated version for 2005
FHOP Activities:FHOP Activities:Research and DevelopmentResearch and Development
Develop public health indicators
Develop methods and guidelines and analytic tools
Perform literature reviews for risk and protective factors and evidence-based practices
Study trends in hospitalizations for children and youth for specific diagnoses
Perform research and evaluation of innovative intervention strategies for high risk women
PerinatalPerinatal and Child Health and Child Health SurveySurvey
Developed in conjunction with the MCAH Action Rural Caucus committeeContains a core survey along with 5 optional modulesStand-alone Adolescent health surveyData entry and analysis template in EpiINFO
Developing an Effective Developing an Effective Planning Process: A Guide for Planning Process: A Guide for
Local MCAH ProgramsLocal MCAH ProgramsNew edition published in 2003Reviews the traditional health planning cycle with a focus on MCAH activitiesProvides tools to facilitate planning during each phase of the processProvides tools to simplify data analysis
Analytic Statistical Guidelines Analytic Statistical Guidelines 20022002
Data StandardsData Standards
A 2003 set of guidelines for the collection, coding and reporting of race and ethnicity on all data sets maintained by the CADHSGoal is to be able to compare data by uniformly defined categories that are consistent with Federal Office of Management and Budget census categories so that standard rates can be calculated
Race/Ethnicity GuidelinesRace/Ethnicity Guidelines
A set of guidelines for the collection, coding and reporting of race and ethnicity on all data sets maintained by the CADHSDeveloped through a cooperative process with the Center for Health Statistics and the Prevention DivisionNew revision for 2002
FHOP Areas of Activity: FHOP Areas of Activity: TrainingTraining
Topics and schedule developed in collaboration with state MCAH staff and consultation with CCLDMCAHFormat focuses on interactive skill developmentAim to target trainings toward current MCAH needs
FHOP Areas of Activity:FHOP Areas of Activity:Technical AssistanceTechnical Assistance
FHOP staff available for assistance by phone 5 days a week (415-476-5283)– Data related questions– Assistance in use of automated toolsGuidance in planning process
FHOP StaffFHOP StaffGeraldine Oliva MD, MPH, DirectorLinda Remy, PhD, Assoc. Director, ResearchJudith Belfiori, MA, MPH, Planning and Evaluation Jennifer Rienks, PhD, Research Associate Gosia Pellarin, Administrator and Web masterJaime Sanchez, Administrative Assistant
Looking at Indicator Rates: Is There a Difference?
Gerry Oliva M.D., MPH
September 25th, 2007
FHOP Orientation for MCAH Program Consultants, Sacramento, CA
Family Health Outcomes Project, UCSF
Member of the Board of Supervisors Listening to County Presentation on Indicator Rates
How Do We Use Rates in the Analysis of a Public Health Problem
• To determine a benchmark or starting point before addressing an area of concern
• To see how we’re doing compared to a national, state or regional rate
• To compare ourselves with a standard such as the HP 2010
• To determine which population(s) are at highest risk
How Do We Use Rates in the Analysis of a Public Health Problem
• To determine which risk or causal factor(s) are most strongly associated with a condition or outcome of interest
• To determine which factor(s) contributes most to that outcome or condition
Purpose of this Presentation
• To review the basics of risk analysis– confidence intervals– risk analysis– the use of relative risk/ risk ratios in determining the
strength of the association with the identified problem
– the use of attributable risk and population attributable risk in quantifying the impact of an outcome or condition
• Demonstrate how the FHOP webstie can assist in this process– databooks– small numbers guidelines– data templates
Comparing RisksMost public health reports compare Most public health reports compare percents or rates of occurrence of percents or rates of occurrence of conditions, problems or outcomes among a conditions, problems or outcomes among a number of groupsnumber of groups
These differences are used to identify and These differences are used to identify and quantify disparities in health by age, quantify disparities in health by age, geography, race/ethnicity or social classgeography, race/ethnicity or social class
In order to be meaningful, statistical tests In order to be meaningful, statistical tests need to be applied to determine the need to be applied to determine the likelihood that observed differences are not likelihood that observed differences are not due to chancedue to chance
Normal Distribution –Can the Result Be Due to Chance?
95% CI 95% CI
Using Confidence Intervals (CI)
• A confidence interval is the range of values within which the “true” value is likely to fall
• Ninety-five percent is the most commonly used CI
• A 95% CI indicates that there is a 95% chance that the “true” value of the estimate (rate) is included in the interval
• All risk calculations should be accompanied by a CI
Effects of Sample Size on the Confidence Interval
• The smaller the sample, the larger the confidence interval and the harder it is to establish if observed differences are statistically significance
• The larger the sample, the smaller the confidence interval
95% Confidence Interval
Confidence Interval Width & Number of Events
4.00%
5.00%
6.00%
7.00%
8.00%6,
000
LBW
and
100,
000
Tota
lBi
rths
600
LBW
and
10,0
00 T
otal
Birth
s
60 L
BW a
nd1,
000
Tota
lBi
rths
Low
Birt
hwei
ght P
erce
nt
Upper 95% CI
LBW Percent
Lower 95% CI
The percent of live births less than 37 weeks gestation
9.7 8.9 9.3 1,919 11.3 11.2 11.2 59,225 2005
9.6 8.8 9.2 1,880 11.1 10.9 11.0 55,738 2004
10.2 9.4 9.7 2,050 10.9 10.7 10.8 54,311 2003
10.5 9.7 10.1 2,110 10.6 10.4 10.5 52,067 2002
10.6 9.8 10.2 2,169 10.5 10.4 10.4 51,974 2001
10.2 9.4 9.8 2,096 10.5 10.4 10.5 52,522 2000
10.7 9.9 10.3 2,052 10.7 10.5 10.6 51,807 1999
10.6 9.7 10.1 2,035 10.7 10.5 10.6 52,441 1998
10.6 9.7 10.1 2,034 10.5 10.3 10.4 51,875 1997
10.3 9.4 9.8 1,965 10.4 10.2 10.3 53,022 1996
10.7 9.9 10.3 2,095 10.4 10.2 10.3 54,453 1995
10.7 9.9 10.3 2,142 10.4 10.2 10.3 56,064 1994
Upper C.L. Lower C.L. Rate Births Upper C.L. Lower C.L. Rate Births Year
LocalCalifornia
#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!011.0%169,2742003-2005
#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!010.5%156,563200-2002
#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!010.5%156,1231997-1999
#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!010.3%163,5391994-1996
Upper 95% C.L.
Lower 95% C.L.Ratio
Upper 95% C.L.
Lower 95% C.L.PercentEventsPercentEvents3 Year Aggregates
County/State ComparisonCountyCalifornia
#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!11.2%59,2252005
#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!11.0%55,7382004
#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!10.8%54,3112003
#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!10.5%52,0672002
#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!10.4%51,9742001
#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!10.5%52,5222000
#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!10.6%51,8071999
#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!10.6%52,4411998
#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!10.4%51,8751997
#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!10.3%53,0221996
#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!10.3%54,4531995
#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!#DIV/0!10.3%56,0641994
Upper 95% C.L.
Lower 95% C.L.Ratio
Upper 95% C.L.
Lower 95% C.L.PercentEventsPercentEventsYear
County/State ComparisonCountyCalifornia
FHOP Data Templates
Risk Difference (RD)
• Definition: the differences in rates of outcomes for subgroups of the population ( e.g. race or income groups)
• Calculation: Rate in one group minus the rate in the comparison group
Risk Difference Calculation
If the infant mortality rate is 14 per 1000 in African- American infants and 7 per 1000 in White infants the risk difference is:
14 per 1000 minus 7 per 1000 = 7 per thousand
This means that African American infants have a 7 per thousand more or twice the risk of dying than white infants
Limitations of Rate Comparisons
• The numbers may be too small to ever get a stable rate with which to compare differences using CIs
• A rate difference may be significant but the actual increase in risk may be minimal
• A rate difference may be significant but the contribution of a small group to the total number of cases in terms of actual numbers may be minimal in a jurisdiction
Risky Analysis
What are the chances of this man getting hit compared to another who is not a data analyst?
What Do We Mean by Risk?
The probability of a disease-free individual developing a given disease over a specified period
or in public health
The probability of occurrence of a negative outcome in a population in a specified time
and
The probability of occurrence of this outcome in one group compared to another group
and
The proportion or number of adverse events that can be attributed to each group of affected individuals
Organizing Data for Risk Analysis : The 2x2 Table
a b
c d
Yes
Group A
a + c b + d
a+b (n1)
c+d (n2)
Group B
NoPoor Outcome
Calculating Risk
• Risk in exposed = a / a+b
• Risk in non exposed = c / c+d
• Risk can vary from zero to one (or in the case of a protective factor from minus 1 to one)
Organizing Data for Risk Analysis - Example
Infant Death Yes No Births(n)
Group A 240 (a) 19,760 (b) 20,000 (a+b)
Group B 160 (c) 19,840 (d) 20,000 (c+d)
Total 400 39,600 40,000(a+c) (b+d)
Risk Analysis Questions
• What is the risk of infant death in each group?
• How does the risk in Group A compare to the risk in Group B?
Calculating Risk - Example
Group A 240 ÷ 20,000 = .012
Group B 160 ÷ 20,000 = .008
÷ =Infant Deaths in group
Total Births in group
Risk of Death in group
Reporting Risk Results
• These results can be expressed as rates:
– the risk of infant death for Group A infants is 12 deaths per 1000 births
– for Group B infants, the risk of infant death is 8 deaths per 1000 births
• However, in a risk analysis, risks are usually reported using measures that compare the risks among two or more groups.
Relative Risk/Risk Ratio (RR)
• Definition: the relative risk is the calculated ratio of the incidence rate of an outcome in two groups of people, one with the risk factor of interest and the other without that risk factor
• Calculation: a / a+b (n1) divided by c/ c+d (n2)
• Measurement: the RR can vary from zero to infinity
Relative Risk Questions
• How do the infant death rates of Group A babies compare to those in Group B?
OR• How much more likely is it that
Group A babies will die compared to babies in Group B?
Relative Risk (RR) for Infant Death in Group A Infants
Risk RRGP A .012 (a/a+b)
= 1.5 GP B .008 (c/c+d)
The risk of infant death in Group A babies is 1.5 times than that in Group B babies
Just how many cases does this increased risk account for?
• Definition: The portion of the incidence of an outcome among people exposed to a risk factor that can be attributed to the exposure to the risk factor
• Calculation: The incidence rate among exposed minus (-) the incidence rate among non-exposed
Attributable Risk
The Attributable Risk is a Measure of Excess Risk
Attributable Risk Calculation (AR)
Risk for GP A babies = a/a+b = .012
Risk for GP B = c/c+d = .008
AR = .012 - .008 = .004
Group A accounts for an extra 4 deaths per 1000 births. A program to target this group could reduce the infant mortality rate by up to 4 per 1000
Attributable Risk Percent (AR%)
• Definition - The percent of deaths (cases) among Group A members that can be attributed to being part of Group A (or other risk exposure)
• Calculation:
(AR GP A –AR not GP A) / AR GP A x 100
= (.012 - .008) / .012
= .333 x 100
= 33.3%
33% of the deaths in the counties infants can be attributed to being part of the GP A population
Population Attributable Risk: Definition
Population attributable risk (PAR) is the proportion of cases with a negative outcome that can be attributed to belonging to a particular risk group or having exposure to a negative condition
or conversely
the reduction in the proportion of cases that could be achieved if the outcomes in the group of interest are improved
Population Attributable Risk (PAR) Calculation
PAR = p0 ( the proportion of the total population with the outcome of interest minus p2 ( the proportion of those in Group B [or those without exposure] with that outcome)
= p0 - p2 o = (p1-p2) x n1/N = AR x n1/N= .004 x 20,000/40,000= .004 x .5= .002
An excess of 2 deaths per thousand infants can be attributed to the Group A population
Population Attributable Risk Percent (PAR%)
Definition: % of cases with a negative outcome in the total population that can be attributed to a group or a factor of interest
Calculation:
PAR% = PAR divided by the overall risk times 100
= .002 / .010 x 100
= 20%
Being part of Group A is associated with 20% of infant deaths in total population
ATTRIBUTABLE RISK CALCULATIONS Population: Describe the Study Population. Exposure: Describe the Exposure. Note that membership in a group (race, ethnicity, age) can be an exposure. Outcome: Describe the disease. Note that "disease" refers to the outcome associated with the exposure. ENTER THE FOLLOWING NUMBERS: <--- The total population, including the exposed and the non-exposed. <--- The number exposed. <--- The total number (exposed and non-exposed) with the disease.
<--- The number of the exposed with the disease.
Disease
Yes No Total
Yes 0 0 0 Exposure No 0 0 0
Total 0 0 0 #DIV/0! Proportion of the total population with the exposure. #DIV/0! Risk of disease for the total population. #DIV/0! Risk of disease for the exposed population.
#DIV/0! Risk of disease for the non-exposed population.
#DIV/0! Risk Ratio = Relative Risk
The risk for the exposed divided by the risk for the non-exposed. The risk for the exposed is: #DIV/0! times the risk for the non-exposed.
#DIV/0! Attributable Risk Among the Exposed = Risk Difference = Excess Risk The risk for the exposed minus the risk for the non-exposed. The risk for the exposed is: #DIV/0! greater than the risk for the non-exposed.
#DIV/0! Upper limit of 95% confidence interval.
#DIV/0! Lower limit of 95% confidence interval.
#DIV/0! Relative Confidence Interval (at 95% level) #DIV/0! Number of cases attributable to the exposure.
#DIV/0! Upper limit of 95% confidence interval.
#DIV/0! Lower limit of 95% confidence interval.
Take Home
1. Risk differences supplemented by confidence intervals are essential to understanding and quantifying health disparities
2. Relative risk (RR), a measure of the strength of the association or causal link between a risk factor and an outcome is an important adjunct in determining a focus for an intervention
Take Home (cont.)
• We recommend Population Attributable Risk Percent (PAR %) which quantifies the contribution of a population group at risk or a risk factor to the outcome and can thus help direct interventions.
• FHOP tools are available to make these analysis simple !!!!!
So Relax and Have Fun !!!
1
Impact of Data Quality on Birth Indicators 1989-2005: Preterm Delivery
Sacramento CASeptember 25 2007
2
Linda Remy, MSW PhDGerry Oliva, MD MPH
Ted Clay, MS
Family Health Outcomes ProjectDepartment of Family and Community Medicine
University of California, San Francisco
3
California Data Quality
• The National Center for Health Statistics (NCHS) calculates data quality for required birth certificate variables.
• In 2004, California did not meet NCHS standards on mother’s Hispanic origin, education, and month and year of last menstrual period.
• The Center for Health Statistics (CHS) began a targeted training program for birth clerks in hospitals providing poor quality data.
• Significant improvements are reported to have resulted from this approach.
4
FHOP’s Data Quality Reports
• Given the importance of certain variables for calculating health indicators, FHOP has been exploring the impact of improbable data (missing, out of range) on the calculation of health indicators.
• We do routine quality checks for perinatal indicators counties monitor for Title V.
• Among others, these include preterm birth, first trimester entry into prenatal care, adequacy of prenatal care (APNCU), low and very low birthweight, each by mother’s ethnicity.
5
Utility of Data Quality Reports
• Data quality reports help counties: – assess the potential impact of data errors on
the accuracy of indicators – give health department staff information to
work with providers and hospitals to improve data quality.
• For rural counties, a few missing or unlikely values can result in misleading conclusions about the quality and adequacy of prenatal care or the effectiveness of outreach.
• Using the statewide average to gauge problems may not be helpful for a state as large and diverse as California.
6
Purpose
• Describe birth certificate data quality variation in between 1989 and 2005 for gestational age (GA).
• Examine preterm birth rate differences using GA as reported and as cleaned.
• Show how data quality impacts assessment of race/ethnic disparities.
7
Improbable ValuesGestational Age (GA)
Improbable Gestational Age (Weeks)Year Missing Lt 18 Gt 47 Total Pct
1989 17,484 306 6,397 24,187 4.21990 17,009 273 5,718 23,000 3.81991 16,768 292 5,387 22,447 3.71992 17,018 150 4,950 22,118 3.7
… … … … … …2002 30,124 286 3,378 33,788 6.42003 34,093 204 2,853 37,150 6.92004 33,482 262 2,756 36,500 6.72005 18,537 324 3,408 22,269 4.1
8
Between 1989 and 2005
• Improbable GA (missing, less than 18 weeks, more than 47 weeks) ranged from a low of 3.7% (1992) to 6.9% (2003).
• Of records with improbable GA, 72% were due to missing data in 1992, compared with 92% in 2004.
• In 2005, after CHS training began for selected hospitals, the statewide number of records with improbable GA dropped 64% compared with 2004, and number of cases fully missing gestational age dropped 80%.
9
Change in Improbable GA
-
2
4
6
8
1992 2003 2005
10
• The 1992 county range for improbable GA was 0.0%-13%, median 3.2%. The 2003 range was 0.0%-17.7%, median 5.3%.
• In 1992, most counties with data quality problems were rural. In 2003, more counties had data quality problems and most were larger.
• In 1992, only 5 counties with 5,000 or more births had more than 5% of records with improbable data. Of 20 counties with 5,000 or more births in 2003, 13 had improbable GA above 5%.
Improbable GA distributed unevenly
11
• To understand the impact of data quality on preterm birthrates reported in FHOP databooks, we imputed GA when it was missing or out of range by using a section of Kotelchuck’s algorithm to calculate adequacy of prenatal care.
• This allows us to estimate “true” preterm birthrates if data were of higher quality.
• The Federal government uses a similar algorithm to calculate state-level statistics for their reports.
Imputing Gestational Age
12
California Preterm Birth Rates Reported and Imputed 1989-2005
-
3
6
9
12
15
18
21
24
27
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Observed Imputed HP2010
13
Local Preterm Birth Rates Reported and Imputed 1989-2005
-
3
6
9
12
15
18
21
24
27
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Observed Imputed HP2010
14
Local Asian Preterm Birth Rates Reported and Imputed 1989-2005
-
3
6
9
12
15
18
21
24
27
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Observed Imputed HP2010
15
Local Black Preterm Birth Rates Reported and Imputed 1989-2005
-
3
6
9
12
15
18
21
24
27
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Observed Imputed HP2010
16
Local Hispanic Preterm Birth Rates Reported and Imputed 1989-2005
-
3
6
9
12
15
18
21
24
27
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Observed Imputed HP2010
17
Local White Preterm Birth Rates Reported and Imputed 1989-2005
-
3
6
9
12
15
18
21
24
27
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Observed Imputed HP2010
18
Implications
• Local jurisdictions must carefully review their data quality reports for a given indicator, both to understand the impact of quality data on results in a given year or trends over time.
• FHOP has prepared a new spreadsheet presenting annual preterm birth data quality. It is available on the website.
• Local jurisdictions are advised to consult that spreadsheet before reporting their preterm birth rates.
19
Conclusions
• Data quality issues result in significant under-estimates of California’s true performance with respect to achieving Healthy People 2010 objectives.
• The shift of poor quality data from smaller to more populous counties has a greater impact on the accuracy of state rates.
• Before concluding that population-based rates are changing, it is important to evaluate and understand the impact of data quality.
20
For Further Information:
• Linda Remy, MSW, PhD– Email: [email protected]
• Gerry Oliva, MD, MPH– Email: [email protected]
• Mail: Family Health Outcomes Project– 3333 California Street, #365– San Francisco, CA 94118
• Website: www.ucsf.edu/fhop
21
Improbable ValuesBirthweight
Improbable Birthweight (Grams)Year Births Missing Lt 250 Gt 4999 Total Pct
1992 600,838 104 57 1,325 1,486 0.25 1993 584,483 85 82 1,281 1,448 0.25 1994 567,034 78 64 1,135 1,277 0.23
…2001 527,371 5 70 965 1,040 0.20 2002 529,241 7 77 929 1,013 0.19 2003 540,827 13 82 944 1,039 0.19
22
Between 1992 and 2003:
• The number of births to California residents dropped 11.1% from 600,838 to 540,827.
• Improbable BWT (missing, less than 250 grams and more than 4999 grams) dropped from 1,486 to 1,039 births.
• Most of the decrease was associated with BWT greater than 4999 grams.
• In 1992, improbable BWT represented 0.25% of records. In 2003, this was 0.19%.
23
Improbable birthweightdistributed unevenly
• In 1992, county-level improbable BWT values ranged from 0.0%-2.4%, median 0.27%.
• The 2003 range was 0.0%-0.9%, median 0.2%. The median was little changed, reflecting that jurisdictions tended to improve data quality on this measure with time.
• Several reabstraction studies have found BWT is one of the most reliably coded variables. Unlikely values are not a significant factor in calculating low birthweight rates.
24
Local Asian Preterm Birth Rates Reported and Cleaned 1992 and 2003
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
Reported 1992 Cleaned 1992 Reported 2003 Cleaned 2003
25
African-American Preterm Birth RateState and Local 1992 and 2003
-
3
6
9
12
15
18
21
St-1992 Loc-1992 St-2003 Loc-2003
1
Do We Have a Trend?
Sacramento CASep 25 2007
Linda Remy, MSW, PhDGeraldine Oliva, MD, MPH
2
How is Trend Analysis Different from Comparison of Rates?
• When comparing rates we are looking at two points and their confidence intervals at specific times
• Rates may differ due to random fluctuation rather than a true difference
• Trend analysis uses many data points and controls for random variation using various statistical techniques
3
To Eyeball or Not to Eyeball: That is the Question
YES! NO!
4
Why Trend Analysis?• Allows us to look at rates over the time
period of 1994-2005 and see if upward or downward trends on key indicators are significant (as opposed to the inaccurate eyeballing approach)
• Useful for identifying and monitoring trends in disparities among racial/ethnic groups
• Allows for comparisons with statewide trends on key indicators for different race/ethnic groups
5
Why Trend Analysis?
• Useful for identifying whether a problem affects people across groups or disproportionately impacts subgroups
• Allows local health jurisdictions to track their progress toward reaching Healthy People 2010 Goals
6
What Do Trend Charts and Tables Tell Us?
• If there are significant upward or downward trends in rates over time in the local jurisdiction
• If there are significant upward or downward trends in rates over time at the state level
• If the local trend and the state trend are significantly different
7
What Do Trend Charts and Tables Tell Us?
• If the local or state trend is linear or curvilinear. When state or local trend is curvilinear, we cannot test if the curvilinear trend is significantly different from a linear trend
• Were local average rates at period start - say 1994-1996 --significantly different from average rates at the state level
• Were average rates at period end - say 2003-2005 - at the local level significantly different from average rates at the state level
8
Definition of Terms for Regression Analysis of Trends
• Slope – the average rate of change over time where:
zero = flat
(+) = increasing trend
(-) = downward trend
• Intercept – the rate at the start of the study period - where the line intercepts the vertical (x) axis
9
Slope: Change in Rate per Population
0
20
40
60
80
100
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
COUNTY: y = 70.3 - 0.75x
STATE: y = 82.8 - 1.5x
10
Change in the Intercept
0
2
4
6
8
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
COUNTY: y = 7.5 - 0.13x
STATE: y = 5.9 + 0.04x
11
Statistical Terms for Trend Regression Analysis
• P-value - likelihood that model results are statistically significant
• Standard Error - the confidence interval around the slope and intercept
• Both are used for significance of indicator trend and differences among comparison groups
12
Factors Impacting Analysis
• Sample size and the number of time periods being examined
• Presence of outliers• Availability of numerators and
denominators• Confounding - changes over time in
factors related to the indicator of interest
13
Small Numbers and Data Points
• For large sample sizes at the federal and state level simple linear plots describing changes are treated as error free
• For the usual county, city or district, the problem of unstable rates due to small numbers requires tests of statistical significance and other statistical techniques
• For the impact of a particular program or policy on individuals, small numbers problems apply
14
Sample Size Issues
• Minimum number for stable rate may require statistical techniques such as aggregating or averaging years
• Many years of data may be needed to obtain at least 3 usable data points for a trend analysis
• One size does not fit all• Approach differs between one jurisdiction
and another and between one indicator and another. This can make comparisons difficult
15
1. No trend, no difference in rates
• Neither state nor local had a statistically significant trend.
• Local was not different from state.
• At period start and period end, Local rate was not significantly different from State rate.
• Neither State nor local rates met the HP2010 goal
0
5
10
15
20
25
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005California Local HP 2010 Goal
Trend Regression ResultsIntercept Slope
Level Date Range Est. Std. Err Est. Std. Err P-Value Sig?State 1994-2005 15.72 0.11 0.00 0.02 0.857 NoLocal 1994-2005 13.47 1.79 0.27 0.28 0.370 No Different? 0.343 No State Avg 1994-1996 15.77 0.11 Local Avg vs State 15.34 1.89 0.821 NoState Avg 2003-2005 15.82 0.13 Local Avg vs State 16.48 1.94 0.734 No
16
2. No trend, Local below State at period start and end
• Neither Local nor State had statistically significant trends.
• Local trend not significantly different from State trend.
• At period start and end, Local rate was lower than State rate.
• Neither State nor Local rates met the HP2010 goal.
0
3
6
9
12
15
18
21
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005California Local HP 2010 Goal
Trend Regression Results
Intercept SlopeLevel Date Range Est. Std. Err Est. Std. Err P-Value Sig?State 1994-2005 15.72 0.11 0.00 0.02 0.857 NoLocal 1994-2005 13.54 0.56 -0.15 0.09 0.142 No Different? 0.126 No State Avg 1994-1996 15.77 0.11 Local Avg vs State 13.61 0.87 0.014 YesState Avg 2003-2005 15.82 0.13 Local Avg vs State 11.95 1.00 0.000 Yes
17
3. No trend, Local same as State at start, above at end
• Neither Local nor State had statistically significant trends.
• Local trend not significantly different from State trend.
• At period start Local rate was not significantly different from State rate.
• At period end, Local rate was significantly higher than State rate
• Neither State nor Local rates met the HP2010 goal.
0
3
6
9
12
15
18
21
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005California Local HP 2010 Goal
Trend Regression ResultsIntercept Slope
Level Date Range Est. Std. Err Est. Std. Err P-Value Sig?State 1994-2005 9.94 0.12 0.02 0.02 0.231 NoLocal 1994-2005 10.49 0.37 0.08 0.06 0.185 No Different? 0.337 No State Avg 1994-1996 10.04 0.07 Local Avg vs State 10.56 0.48 0.280 NoState Avg 2003-2005 10.27 0.07 Local Avg vs State 11.27 0.47 0.034 Yes
18
4. Local trend, local trend different, local rates different
• State trend was flat while Local had a statistically significant downward trend.
• Local trend significantly different from State trend.
• At period start and end, Local was significantly higher than State.
• Neither State nor Local rates met the HP2010 goal but Local trend moving toward the goal.
0
5
10
15
20
25
1994 1996 1998 2000 2002 2004California Local HP 2010 Goal
Trend Regression Results
Intercept SlopeLevel Date Range Est. Std. Err Est. Std. Err P-Value Sig?State 1994-2005 9.94 0.12 0.02 0.02 0.231 NoLocal 1994-2005 19.03 0.90 -0.53 0.13 0.003 Yes Different? 0.000 Yes State Avg 1994-1996 10.04 0.07 Local Avg vs State 19.35 1.10 0.000 YesState Avg 2003-2005 10.27 0.07 Local Avg vs State 14.12 1.05 0.000 Yes
19
5. Upward trend, Local lower start and end
• Both Local and State had statistically significant upward trends.
• Trends cannot be compared because State trend was curvilinear.
• At period start and end, Local rate significantly lower than State rate.
• State and local trends are moving away from the HP2010 goal
0
2
4
6
8
10
12
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
California Local HP 2010 Goal
Trend Regression ResultsIntercept Slope
Level Date Range Est. Std. Err Est. Std. Err P-Value Sig?State 1994-2002 9.00 0.04 0.08 0.01 0.000 Yes 2002-2005 7.39 0.49 0.28 0.05 0.001 YesLocal 1994-2005 8.14 0.08 0.13 0.01 0.000 Yes State Avg 1994-1996 9.06 0.04 Local Avg vs State 8.28 0.08 0.000 YesState Avg 2003-2005 10.18 0.04 Local Avg vs State 9.53 0.09 0.000 Yes
20
6. Upward trend, Local lower start, same at end
• Both Local and State had statistically significant upward trends.
• Trends cannot be compared because State trend was curvilinear.
• At period start, Local rate significantly lower than State rate.
• At period end, Local rate was not significantly different from State rate.
• State and local trends moving away from HP2010 Goal.
0
2
4
6
8
10
12
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005California Local HP 2010 Goal
Trend Regression ResultsIntercept Slope
Level Date Range Est. Std. Err Est. Std. Err P-Value Sig?State 1994-2002 9.00 0.04 0.08 0.01 0.000 Yes 2002-2005 7.39 0.49 0.28 0.05 0.001 YesLocal 1994-2005 8.17 0.18 0.19 0.03 0.000 Yes State Avg 1994-1996 9.06 0.04 Local Avg vs State 8.43 0.14 0.000 YesState Avg 2003-2005 10.18 0.04 Local Avg vs State 10.24 0.17 0.760 No
21
7. Upward trend State, Lower trend Local, Local start same, Local different end
• State had statistically significant upward trends.
• Local had statistically significant downward trend.
• Trends cannot be compared because State trend was curvilinear.
• At period start, Local rate not significantly different from State rate. At period end, Local rate significantly lower than State rate.
• Neither State nor Local rates meet the HP2010 goal, but Local trend moving toward the goal.
0
2
4
6
8
10
12
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005California Local HP 2010 Goal
Trend Regression ResultsIntercept Slope
Level Date Range Est. Std. Err Est. Std. Err P-Value Sig?State 1994-2002 10.31 0.06 0.03 0.01 0.060 No 2002-2005 8.75 0.64 0.22 0.06 0.010 YesLocal 1994-2005 10.36 0.15 -0.08 0.02 0.006 Yes State Avg 1994-1996 10.30 0.02 Local Avg vs State 10.14 0.12 0.192 NoState Avg 2003-2005 11.00 0.03 Local Avg vs State 9.42 0.12 0.000 Yes
22
8. No trend, Local rate higher than State, then same
• Neither state nor local had a statistically significant trend
• Local trend not different from state.
• At period start, Local rate was significantly higher than State rate.
• At period end, Local rate was not significantly different from State rate.
• Neither State nor local rates meet the HP2010 goal.
0
5
10
15
20
1994 1996 1998 2000 2002 2004California Local HP 2010 Goal
Trend Regression Results
Intercept SlopeLevel Date Range Est. Std. Err Est. Std. Err P-Value Sig?State 1994-2005 15.72 0.11 0.00 0.02 0.857 NoLocal 1994-2005 16.67 0.29 -0.06 0.04 0.201 No Different? 0.226 No State Avg 1994-1996 15.77 0.11 Local Avg vs State 16.66 0.34 0.012 YesState Avg 2003-2005 15.82 0.13 Local Avg vs State 16.30 0.34 0.184 No
23
9. No Trend, Local trend different from State, Local rate higher then same
• Both State and Local trends were statistically non-significant but moved in different directions.
• As a result, Local trend is significantly different from State trend.
• At period start, Local rate was higher than State rate.
• At period end, Local rate was same as State rate.
• Neither Local nor State rates meet HP2010 Goal.
24
Exercise Time!
25
Trend Regression ResultsIntercept Slope
Level Date Range Est. Std. Err Est. Std. Err P-Value Sig?State 1994-2005 15.72 0.11 0.00 0.02 0.857 NoLocal 1994-2005 13.75 0.44 0.09 0.07 0.203 No Different? 0.173 No State Avg 1994-1996 15.77 0.11 Local Avg vs State 13.93 0.42 0.000 YesState Avg 2003-2005 15.82 0.13 Local Avg vs State 15.22 0.44 0.183 No
African-American
02468
1012141618
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Ca lifornia Loc a l HP 2 0 10 Goa l
26
Trend Regression ResultsIntercept Slope
Level Date Range Est. Std. Err Est. Std. Err P-Value Sig?State 1994-2002 9.00 0.04 0.08 0.01 0.000 Yes 2002-2005 7.39 0.49 0.28 0.05 0.001 YesLocal 1994-2005 8.29 0.21 0.11 0.03 0.006 Yes State Avg 1994-1996 9.06 0.04 Local Avg vs State 8.32 0.16 0.000 YesState Avg 2003-2005 10.18 0.04 Local Avg vs State 9.39 0.18 0.000 Yes
White
0
2
4
6
8
10
12
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Ca lifornia Loc a l HP 2 0 10 Goa l
27
Trend Regression ResultsIntercept Slope
Level Date Range Est. Std. Err Est. Std. Err P-Value Sig?State 1994-2002 9.00 0.04 0.08 0.01 0.000 Yes 2002-2005 7.39 0.49 0.28 0.05 0.001 YesLocal 1994-2000 8.25 0.09 0.16 0.03 0.006 Yes 2000-2003 9.65 1.39 -0.08 0.19 0.703 No 2003-2005 4.70 1.98 0.47 0.19 0.065 No State Avg 1994-1996 9.06 0.04 Local Avg vs State 8.35 0.13 0.000 YesState Avg 2003-2005 10.18 0.04 Local Avg vs State 9.43 0.14 0.000 Yes
White
0
2
4
6
8
10
12
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Ca lifornia Loc a l HP 2 0 10 Goa l
28
For Further Information:
• Linda Remy, MSW, PhD– Email: [email protected]
• Gerry Oliva, MD, MPH– Email: [email protected]
• Mail: Family Health Outcomes Project– 3333 California Street, #365– San Francisco, CA 94118
• Website: www.ucsf.edu/fhop