Overview of the Prevention Agenda Dashboard
- The New York State Prevention Agenda Dashboard is an interactive visual presentation of the most current tracking indicator data to track progress of the New York State’s Health Improvement Plan at state and county levels. It serves as a key source for monitoring progress that communities around the state have made regarding meeting the Prevention Agenda objectives.
- Prevention Agenda State Dashboard
- The state dashboard homepage provides a quick view of the most currently available data and the 2024 objectives for nearly one hundred tracking indicators (n=99). On this page, indicators are grouped by priority area and the most current data are compared to the previous data period to assess the annual progress for each indicator. From here, historical (trend) data for the tracking indicators are easily accessed. We have also enhanced the state level dashboard for 47 indicators, with drill-down data and visualizations by major socio-demographic characteristics such as age group, race and ethnicity, sex, region, health insurance status, level of education, etc., where available. These visualizations and data can be accessed from the state dashboard link above.
- Prevention Agenda County Dashboard
- The county dashboard homepage includes the most current data available for 70 tracking indicators, again grouped by priority area. Each county in the state has its own dashboard homepage. County maps and graphs and comparison across counties are available. Data at sub-county level, including ZIP Code, School District, and Minor Civil Division/Community District, are available for 6 indicators. These visualizations and data can be accessed from the county dashboard link above.
Prevention Agenda 2019-2024 Data Export Links
Technical Notes
-
- Definition of Indicators
-
Dynamic Prevention Agenda Filters
Select priority area(s) and/or indicator(s) from the dropdown lists and click “Apply Filters” to filter the table. Click “Reset” to return the table to its original view.
- a Vital Records (Vital Statistics):
- Vital Event Registration:
New York State consists of two registration areas, New York City and New York State Exclusive of New York City (also referred
to as Rest of State). New York City (NYC) includes the five counties of Bronx, Kings (Brooklyn), New York (Manhattan), Queens and
Richmond (Staten Island); the remaining 57 counties comprise New York State Exclusive of New York City. The Bureau of Vital
Records, New York State Department of Health (NYSDOH), processes data from live birth, death, fetal death and marriage
certificates recorded in New York State Exclusive of New York City. Through a cooperative agreement, the New York State
Department of Health receives data on live births, deaths, fetal deaths and marriages recorded in New York City from the
New York City Department of Health and Mental Hygiene (NYCDOHMH). The New York State Department of Health also receives data,
from other states and Canada, on live births and deaths recorded outside of New York State to residents of New York State.
Vital Event indicators for NYC geographical areas reported by NYSDOH and NYCDOHMH may be different since the former may
include all NYC residents' events regardless of where they occurred and the latter reports only events to NYC residents that
occurred in NYC. The indicators may also differ due to timing and/or completeness of data.
The counts of births and deaths may be influenced by specific reporting issues each year. The specific issues are reported
in the NYSDOH Annual Vital Statistics Tables, in the Report Measures section of the Technical Notes.
All the vital statistics presented in this report are based on the county/borough of residence.
- b Statewide Planning and Research Cooperative System (SPARCS):
- Information about hospitalizations is collected through the hospital inpatient discharge data system. Each hospitalization receives an ICD-10-CM code
at discharge that indicates the primary reason for the hospitalization. There are also up to 24 other diagnosis codes recorded to further describe
the hospitalization. Statistics presented in these tables are based on the primary diagnosis unless otherwise noted. This data system does not include
information about events that did not result in a hospitalization, such as cases that were only treated in a hospital emergency room. The SPARCS data
do not include visits/discharges by people who sought care from hospitals outside of New York State, which may lower numbers and rates for some counties,
especially those which border other states. Numbers and rates are based on the number of hospitalizations that occurred and not the number of
individuals who were hospitalized. SPARCS measures provided are generated based on patient residence county at time of discharge.
- c Behavioral Risk Factor Surveillance System (BRFSS):
- The BRFSS is an annual statewide random telephone and cellular surveillance survey designed by the Centers for Disease Control
and Prevention (CDC). The survey is conducted in all 50 states and US territories. BRFSS monitors modifiable risk behaviors and
other factors contributing to the leading causes of morbidity and mortality in the population.
County level data. The Expanded BRFSS (EBRFSS) augments the annual CDC Behavioral Risk Factor Surveillance System Survey (BRFSS).
The goal of the Expanded BRFSS is to collect county-specific data on preventative health practices, risk factors, injuries and preventable
chronic and infectious diseases. County level data are currently available for 2002-03, 2008-09, 2013-14, 2016 and 2018.
The New York State BRFSS website has further information.
- Methodology and Limitations
-
Index
- Types of Estimates
- Population Estimates
- Unstable Estimates
- Direction of Indicator Estimates
- Grouping County Estimates into Three Categories
- Comparing the Prevention Agenda Estimates with the Prevention Agenda Objectives
- Assessing the Indicator Performance
- Data Filters
- Sub-county Data
- Data Suppression for Confidentiality
- Data Limitations
- Revisions
- References
Types of Estimates
- Percentage/age-adjusted percentage: Percentages are calculated per 100 population
(e.g. the percentage of infants exclusively breastfed in the hospital represents the number of infants that were
fed exclusively with breast milk among 100 live infants born in the hospital).
The percentages were age-adjusted to the U.S. 2000 standard population using appropriate age distributions.
1 Age-adjustment is a process that is performed to allow communities with different age
structures to be compared.2
- Weighted percentage/age-adjusted weighted percentage: Weighted percentages were
generated for survey data (e.g., Behavioral Risk Factor Surveillance System, Youth Tobacco Survey, Youth Risk
Behavior Surveillance System, National Immunization Survey) which ensures that the data are as representative of New York's
population as possible. Weighted estimates are shown as a percentage (%) and corresponding 95% confidence
intervals (CI) are presented when available.
The weighted percentages were age-adjusted to the U.S. 2000 standard population using appropriate age distributions.
1 Age-adjustment is a process that is performed to allow communities with different age
structures to be compared.2
- Rate/age-adjusted rate: A rate is a measure of the frequency with which an event
occurs in a defined population over a specified period of time. Rates used for the Prevention Agenda tracking
indicators are per 1,000, 10,000 or 100,000 population.
The rates were age-adjusted to the U.S. 2000 standard population using appropriate age distributions.
1 Age-adjustment is a process that is performed to allow communities with different age
structures to be compared.2
- Ratio: A ratio is the relative magnitude of two quantities or a comparison of
any two values. The ratios that are included in the Prevention Agenda are calculated by dividing the percentage or
rate of one racial ethnic group (i.e., Black non-Hispanic or Hispanic) by the percentage or rate for the white
non-Hispanic group.
- Rate/percentage difference: The rate/percentage difference is the absolute difference between two rates/percentages.
Among the Prevention Agenda tracking indicators, the rate/percentage difference is used as a measure when comparing the percentage of
premature deaths among (a) the non-Hispanic Black population versus the non-Hispanic White population and (b) the
Hispanic population versus the non-Hispanic White population.
Population Estimates
Population estimates are developed by the US Census Bureau.
Estimates for 2020 and earlier are from Bridged Race Categories files,
developed by the Census Bureau for the National Center for Health Statistics. The 2018 population estimates are used to calculate rates for 2019 and 2020.
Estimates for 2021 and later are from Special Tabulations from the US Census Population and Housing Unit Estimates Program.
See this document for information about why different estimates were used, the differences in these estimates, and why 2018 estimates were used to calculate rates for 2019 and 2020.
Unstable Estimates
Multiple years of data were combined to generate more stable estimates when the number of events for an indicator
was small (i.e., rare conditions).
The relative standard error (RSE) is a tool for assessing reliability of an estimate. A large RSE is produced when
estimates are calculated based on a small number of cases.2 Estimates with large
RSEs are considered less reliable than estimates with small RSEs. The
National Center for Health Statistics recommends that estimates with RSEs greater than 30% should be considered
unreliable/unstable.3
The RSE is calculated by dividing the standard error of the estimate by the estimate itself, then multiplying that
result by 100. The RSE is expressed as a percent of the estimate.
For the Prevention Agenda dashboard, an asterisk (*) or plus (+) symbol is used to indicate that a percentage, rate,
or ratio is unreliable/unstable. This usually occurs when there are less than 10 events in the numerator (RSE is greater
than 30%).
Direction of Indicator Estimates
Prevention Agenda tracking indicators fall into two categories with regard to the desirable direction of their estimates.
Sometimes lower estimates are better (e.g., the percentage of premature deaths before age 65 years, or the age-adjusted
rate of potentially preventable hospitalizations among adults) and in other cases higher estimates are better (e.g., the percentage of the
population
with health insurance, or the percentage of infants exclusively breastfed in the hospital).
The desirable direction of the Prevention Agenda tracking indicator is important to note because the county bar chart, map,
concern level and visual distribution use color categories that are based on the direction of the Prevention Agenda tracking indicator. The assessment of indicator performance
is also based on the direction of the Prevention Agenda tracking indicator.
Grouping County Estimates into Three Categories for the County Visual Distributions, County Maps, and County Bar Charts
Color Categories Defined
For each Prevention Agenda tracking indicator, county visual distributions, maps and bar charts are generated when there are enough counties with
data different from each other so that county visual distributions, maps and charts can show meaningful differences among the counties. In particular,
county visual distributions, maps and charts are not generated if 46 or more counties have rates that are equal to 0 or are missing,
or if more than half the counties have the same rate. County visual distributions, maps and charts are generated all other times.
Tables are generated for all indicators in all counties, regardless of rate values.
When county visual distributions, maps and charts are generated, county estimates are grouped into three categories: light green, blue-green, and dark blue.
These categories are displayed consistently in the county visual distribution, the bar chart, and the New York State map for each tracking indicator.
The three colors represent the quartile distribution of estimates for the counties ordered from those doing the
best to those doing the worst.
For Prevention Agenda tracking indicators where lower estimates are better (e.g., percentage of
premature deaths before age 65 years or the age-adjusted rate of potentially preventable hospitalizations among adults):
- The LIGHT GREEN category includes counties that are performing the best (i.e., 50% of counties with the
lowest
estimates; those in quartile 1 and quartile 2) and is the most favorable category or least concern for a county's estimate to be in.
- The DARK BLUE category includes counties that are performing the worst (i.e., 25% of counties with the
highest estimates; those in quartile 4) and is the least favorable or high concern category for a county's
estimate to be in.
- The BLUE-GREEN category includes counties that are performing in the middle (i.e., 25% of counties or those in quartile 3).
For Prevention Agenda tracking indicators where higher estimates are better (e.g., the percentage of
the population with health insurance or the percentage of infants exclusively breastfed in the hospital):
- The LIGHT GREEN category includes counties that are performing the best (i.e., 50% of counties with the
highest estimates; those in quartile 3 and quartile 4) and is the most favorable or least concern category for a
county's estimate to be in.
- The DARK BLUE category includes counties that are performing the worst (i.e., 25% of counties with the
lowest estimates; those in quartile 1) and is the least favorable or high concern category for a county's
estimate to be in.
- The BLUE-GREEN category includes counties that are performing in the middle (i.e., 25% of counties or
those with estimates in quartile 2).
Length of Color Categories in the County Visual Distribution
The length of each color in the county visual distribution represents the minimum and maximum values or cut-off points
for the three categories. If the area is very big, this indicates that the range of county estimates is large;
while a small area indicates a small range of county estimates.
For example, the county visual distribution for the asthma emergency department visit rate per 10,000 for those aged 0-17 years in Albany County in 2020 shows
a very large dark blue area which ranges from 26.8 to 110.2; while the light green area ranges from 0.0-<18.1 and has
a much narrower width; similarly, the blue-green area has a narrow range of estimates from 18.1-<26.8.
Color Switch in County Visual Distribution Based on Direction of Indicator Estimates
For Prevention Agenda tracking indicators where lower estimates are better (e.g., potentially preventable hospitalizations among adults, age-adjusted rate per 10,000),
the dark blue category is displayed on the right side of the county visual distribution.
For Prevention Agenda tracking indicators where higher estimates are better (e.g., the percentage
of women with a preventive medical visit in the past year, aged 18-44 years), the dark blue category is displayed on the left side
of the county visual distribution.
Comparing the Prevention Agenda Estimates with the Prevention Agenda Objectives
A green color in bar charts or for a number displayed in a data table indicates that the current value for
the Prevention Agenda tracking indicator met the Prevention Agenda 2024 Objective. A red color
in bar charts or for a number displayed in a data table indicates that the current value for the Prevention Agenda
tracking indicator did not meet the Prevention Agenda 2024 Objective.
Assessing the Indicator Performance
Three different methods were used to assess indicator performance.
- Conduct one-sided z-test to assess the change (decrease/increase or improve/worsen) in estimates between the two most
recent time periods. The p-value for rejecting the null hypothesis is less than or equal to 0.05 and the critical
value for the one-sided test (p-value at 0.05) is 1.645. 4
- A comparison of confidence intervals of estimates for the two most recent time periods was performed
where this method was more appropriate. 4 A confidence interval is a range around an estimate
that conveys how precise the estimate is. Differences between estimates are considered “statistically significant” when the estimates
being compared do not have overlapping confidence intervals. For the purposes of this dashboard, in cases where the confidence intervals
overlap, the difference is interpreted as not statistically significant at the 95% confidence level.
For survey related indicators, estimates and the two-sided 95% confidence intervals were obtained and used.
In some instances for count data (e.g., births, deaths, hospitalizations, and emergency department visits),
we calculated the one-sided 95% confidence intervals for the estimates and used them for comparison to evaluate
the indicator performance.5
NOTE: This method is an approximation of a statistical
test and may result in a more conservative finding. In some cases, an appropriate statistical test would indicate a statistically
significant difference even though the confidence intervals overlap and falsely imply no significant difference.
When two confidence intervals do not overlap, though, a comparable statistical test would always indicate a statistically
significant difference.6
- Simple comparison was conducted where the two estimates were directly compared to each other
based on their magnitude. This was performed when there was not a sufficient amount of data to conduct significance
testing; or if confidence intervals could not be calculated; or if there is some overlap of the two time intervals being
compared (e.g., 2014-2016 and 2015-2017 maternal mortality indicators).
The categories for the Indicator Performance
are as follows:
- Significantly improved
- Significantly worsened
- No significant change
- Improved^
- Worsened^
- No change^
- Baseline data
The "^" sign indicates that the performance was determined using simple comparison and not statistical tests.
The most recent data available during the planning phase were used as a baseline to set the Prevention Agenda 2024 Objective/Target.
“Indicator Performance” is not assessed if there are no available data points that are more recent than the baseline data.
Therefore, the “Indicator Performance” is labeled as "Baseline data" until updated data are available.
See Table 1 below for statistical significance techniques used for each type of data source to assess
the indicator performance.
Use caution when interpreting significance. For more common conditions (i.e., high incidence rates), there is a
higher likelihood that a relatively small change could be detected as statistically significant. Conversely,
for rare conditions, the likelihood of detecting a statistically significant change is low even for reasonable changes.
Data Filters
Several data filters are available on state and county views to quickly select indicators based on commonly desired criteria.
Multiple filters can be selected simultaneously.
- State data filters: two data filters are available for state level indicators
- Filter on meeting 2024 objective target: This filter displays indicators, where the most recent state level data meeting
or not meeting the Prevention Agenda 2024 objectives.
- Filter on indicator performance over time: The performance status for each indicator is generated by comparing state level data
for the two most recent time periods. This filter displays indicators based on indicator performance categories selected.
- County data filters: three data filters are available for county level indicators
- Filter on meeting 2024 objective target: This filter displays indicators, where the most recent specific county data meeting
or not meeting the Prevention Agenda 2024 objectives.
- Filter on indicator performance over time: The performance status for each indicator is generated by comparing estimates
for the two most recent time periods for a specific county. This filter displays indicators based on indicator performance categories selected.
- Filter on county’s position of concern level: This filter displays indicators based on how a county is doing in comparison to other counties in a quartile distribution.
Sub-county Data
To better serve the needs for more local level data, we have assessed the availability of sub-county level data
for the existing county level indicators. Depending on the availability of the information from the data sources,
sub-county level data are presented in one of the following three geographic levels: ZIP Code, school district,
or minor civil division (MCD)/community district (CD).
- ZIP Code: For indicators generated from SPARCS (inpatient and outpatient)
data, ZIP Code refers to resident address ZIP Code. If a ZIP Code crosses county borders, the ZIP Code
is assigned to the county which has the largest proportion of the population of that ZIP Code.
ZIP Code level population estimates used for calculating rates were obtained by the
New York State Department of Health (NYSDOH) from the Claritas Corporation.
- School District: Student weight data at the school district level
(outside of New York City) came from the Student Weight Status Category Reporting System (SWSCR).
A school district boundary file was generated by combining two US Census shape files – a unified school district file and
a secondary school district file.
- Minor Civil Division (MCD)/Community District (CD): Birth and death
certificate data for counties outside of New York City (NYC) are presented by MCD. Data for NYC boroughs
are presented by CD. There are 59 CDs within the five NYC boroughs.
Note: A small
percent of records did not have an assigned MCD or CD, but the records had other geographic identifiers.
For NYC records with missing CD, ZIP Code information was used to look up and assign the CD location
utilizing NYC Department of Planning geographic data linking ZIP Codes to CDs. This linked file contains
a small number of ZIP Codes, which were completely contained within a single CD. Records with those ZIP
codes were assigned to those CDs. For records of residents from the rest of the state (ROS),
outside of NYC, with a missing MCD, the New York State Gazetteer code* was used to look up the MCD code.
DOH staff linked the Gazetteer codes to MCDs, and the linkage was used to assign records outside of
NYC to an MCD.
*The New York State Gazetteer was prepared by the New York State Department of Health
(see:
health.data.ny.gov/Health/New-York-State-Gazetteer/cpcx-4uew)
Based on further assessment of the stability of the estimates and the impact of data suppression,
the following six indicators were selected for incorporating into the current PA tracking dashboard.
- ZIP Code level:
- Potentially preventable hospitalizations among adults, age-adjusted rate per 10,000
- Asthma emergency department visits, rate per 10,000, aged 0-17 years
- School district level:
- Percentage of children and adolescents with obesity (NYS excluding NYC)
- MCD/CD level:
- Percentage of premature deaths (before age 65 years)
- Percentage of births that are preterm
- Percentage of infants who are exclusively breastfed in the hospital among all infants
Data Suppression for Confidentiality
Results are not shown (i.e., suppressed) when issues of confidentiality exist. Suppression rules vary depending
on the data source and the indicator.
Table 1. Summary of data suppression and statistical evaluation significance for the Prevention Agenda Indicators
by data source
Data Sources
Suppression Criteria
Statistical Significance Techniques
Sample Surveys
Pregnancy Risk Assessment Monitoring System
Denominator <30
95% CI comparison
BRFSS and Expanded BRFSS
Numerator <6 or Denominator <50
95% CI comparison
US Census
90% CI comparison
National Survey on Drug Use and Health
95% CI comparison
Youth Risk Behavior Surveillance System
Denominator <100
95% CI comparison
Youth Tobacco Survey
95% CI comparison
Population Count Data
Death
Single Year: Denominator population <50;
Three-Year Combined: Denominator population <30
Rate/percentage: one sided chi-square test with p-value <0.05
Rate difference: one sided 95% CI comparison
Birth
Single Year: Denominator total Births <50
One sided chi-square test with p-value <0.05
Sexually Transmitted Infection (STI) Surveillance
One sided chi-square test with p-value <0.05
HIV Surveillance
Numerator 1-2 cases
County level (rate): one sided 95% CI comparison;
State level (rate): one sided chi-square test with p-value <0.05
SPARCS
Numerator between 1 - 5 cases
Rate/percentage: one sided chi-square test with p-value <0.05;
Ratio/Rate difference: one sided 95% CI comparison
Prescription Monitoring Program (PMP) Registry
Numerator between 1 - 5 cases
One sided chi-square test with p-value <0.05
CI: Confidence Interval
BRFSS: Behavioral Risk Factor Surveillance System
SPARCS: Statewide Planning and Research Cooperative System
Data Limitations
Table 2. Summary of Limitations for Data Presented on the Prevention Agenda Dashboard
Indicator
Data Source
Limitations
Sample Surveys
6 - Percentage of children and adolescents with obesity (NYS excluding NYC)
Student Weight Status Category Reporting System (SWSCRS)
Because of restrictions in reporting due to the Family Educational Rights and Privacy Act (FERPA), parents'/guardians' ability to
request that their child's weight status data be excluded from reporting, and other sources of missing data, not all students
have data in the data system. The percent of students with reported data varies from county to county. Therefore these estimates
do not necessarily represent all school aged-children attending school in that county. School districts report weight status data
separately for students in elementary school, middle/high school, and the school district as a whole, so that the counts of students
represented in the district totals for a county will not necessarily equal the counts of students in the elementary and middle/high
totals for that county.
Population Count Data
2 - Potentially preventable hospitalizations among adults, age-adjusted rate per 10,000
SPARCS
At the ZIP Code level, very unusual distributions in population denominator and/or numerator
(possibly due to multiple hospitalizations per individual) may result in extreme age adjusted rates,
therefore, these estimates are suppressed or to be interpreted with caution.
78 - Newly diagnosed HIV cases, rate per 100,000 population
HIV Surveillance
The discrepancy in totals is due to the exclusion of prisoner cases for counties outside NYC, but not for NYC
OR for the NYS total.
Note: The 2018 population estimates are also used to calculate rates for 2019 and 2020.
Revisions
References
- Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy People
Statistical Notes, no. 20. Hyattsville, Maryland: National Center for Health Statistics. January 2001.
(see: www.cdc.gov/nchs/data/statnt/statnt20.pdf)
- About Age Adjusted Rates, 95% Confidence Intervals and Unstable Rates
(see: www.health.ny.gov/statistics/cancer/registry/age.htm)
- Klein RJ, Proctor SE, Boudreault MA, Turczyn KM. Healthy People 2010 criteria for data suppression.
Statistical Notes, no 24. Hyattsville, Maryland: National Center for Health Statistics. June 2002.
(see: www.cdc.gov/nchs/data/statnt/statnt24.pdf)
- Statistical Significance (see:
www.health.ny.gov/statistics/chac/chai/docs/statistical_significance.pdf)
- One-sided 95% confidence interval
(see:
http://www.graphpad.com/guides/prism/6/statistics/index.htm?one_sided_confidence_intervals.htm)
- Guidelines for using confidence intervals for public health assessment, Washington State
Department of Health, (see:
www.doh.wa.gov/Portals/1/Documents/1500/ConfIntGuide.pdf)
Select priority area(s) and/or indicator(s) from the dropdown lists and click “Apply Filters” to filter the table. Click “Reset” to return the table to its original view.
- a Vital Records (Vital Statistics):
- Vital Event Registration:
New York State consists of two registration areas, New York City and New York State Exclusive of New York City (also referred to as Rest of State). New York City (NYC) includes the five counties of Bronx, Kings (Brooklyn), New York (Manhattan), Queens and Richmond (Staten Island); the remaining 57 counties comprise New York State Exclusive of New York City. The Bureau of Vital Records, New York State Department of Health (NYSDOH), processes data from live birth, death, fetal death and marriage certificates recorded in New York State Exclusive of New York City. Through a cooperative agreement, the New York State Department of Health receives data on live births, deaths, fetal deaths and marriages recorded in New York City from the New York City Department of Health and Mental Hygiene (NYCDOHMH). The New York State Department of Health also receives data, from other states and Canada, on live births and deaths recorded outside of New York State to residents of New York State.
Vital Event indicators for NYC geographical areas reported by NYSDOH and NYCDOHMH may be different since the former may include all NYC residents' events regardless of where they occurred and the latter reports only events to NYC residents that occurred in NYC. The indicators may also differ due to timing and/or completeness of data.
The counts of births and deaths may be influenced by specific reporting issues each year. The specific issues are reported in the NYSDOH Annual Vital Statistics Tables, in the Report Measures section of the Technical Notes.
All the vital statistics presented in this report are based on the county/borough of residence.
- b Statewide Planning and Research Cooperative System (SPARCS):
- Information about hospitalizations is collected through the hospital inpatient discharge data system. Each hospitalization receives an ICD-10-CM code at discharge that indicates the primary reason for the hospitalization. There are also up to 24 other diagnosis codes recorded to further describe the hospitalization. Statistics presented in these tables are based on the primary diagnosis unless otherwise noted. This data system does not include information about events that did not result in a hospitalization, such as cases that were only treated in a hospital emergency room. The SPARCS data do not include visits/discharges by people who sought care from hospitals outside of New York State, which may lower numbers and rates for some counties, especially those which border other states. Numbers and rates are based on the number of hospitalizations that occurred and not the number of individuals who were hospitalized. SPARCS measures provided are generated based on patient residence county at time of discharge.
- c Behavioral Risk Factor Surveillance System (BRFSS):
- The BRFSS is an annual statewide random telephone and cellular surveillance survey designed by the Centers for Disease Control
and Prevention (CDC). The survey is conducted in all 50 states and US territories. BRFSS monitors modifiable risk behaviors and
other factors contributing to the leading causes of morbidity and mortality in the population.
County level data. The Expanded BRFSS (EBRFSS) augments the annual CDC Behavioral Risk Factor Surveillance System Survey (BRFSS). The goal of the Expanded BRFSS is to collect county-specific data on preventative health practices, risk factors, injuries and preventable chronic and infectious diseases. County level data are currently available for 2002-03, 2008-09, 2013-14, 2016 and 2018.
The New York State BRFSS website has further information.
Index
- Types of Estimates
- Population Estimates
- Unstable Estimates
- Direction of Indicator Estimates
- Grouping County Estimates into Three Categories
- Comparing the Prevention Agenda Estimates with the Prevention Agenda Objectives
- Assessing the Indicator Performance
- Data Filters
- Sub-county Data
- Data Suppression for Confidentiality
- Data Limitations
- Revisions
- References
Types of Estimates
- Percentage/age-adjusted percentage: Percentages are calculated per 100 population
(e.g. the percentage of infants exclusively breastfed in the hospital represents the number of infants that were
fed exclusively with breast milk among 100 live infants born in the hospital).
The percentages were age-adjusted to the U.S. 2000 standard population using appropriate age distributions. 1 Age-adjustment is a process that is performed to allow communities with different age structures to be compared.2 - Weighted percentage/age-adjusted weighted percentage: Weighted percentages were
generated for survey data (e.g., Behavioral Risk Factor Surveillance System, Youth Tobacco Survey, Youth Risk
Behavior Surveillance System, National Immunization Survey) which ensures that the data are as representative of New York's
population as possible. Weighted estimates are shown as a percentage (%) and corresponding 95% confidence
intervals (CI) are presented when available.
The weighted percentages were age-adjusted to the U.S. 2000 standard population using appropriate age distributions. 1 Age-adjustment is a process that is performed to allow communities with different age structures to be compared.2 - Rate/age-adjusted rate: A rate is a measure of the frequency with which an event
occurs in a defined population over a specified period of time. Rates used for the Prevention Agenda tracking
indicators are per 1,000, 10,000 or 100,000 population.
The rates were age-adjusted to the U.S. 2000 standard population using appropriate age distributions. 1 Age-adjustment is a process that is performed to allow communities with different age structures to be compared.2 - Ratio: A ratio is the relative magnitude of two quantities or a comparison of any two values. The ratios that are included in the Prevention Agenda are calculated by dividing the percentage or rate of one racial ethnic group (i.e., Black non-Hispanic or Hispanic) by the percentage or rate for the white non-Hispanic group.
- Rate/percentage difference: The rate/percentage difference is the absolute difference between two rates/percentages. Among the Prevention Agenda tracking indicators, the rate/percentage difference is used as a measure when comparing the percentage of premature deaths among (a) the non-Hispanic Black population versus the non-Hispanic White population and (b) the Hispanic population versus the non-Hispanic White population.
Population Estimates
Population estimates are developed by the US Census Bureau.
Estimates for 2020 and earlier are from Bridged Race Categories files, developed by the Census Bureau for the National Center for Health Statistics. The 2018 population estimates are used to calculate rates for 2019 and 2020.
Estimates for 2021 and later are from Special Tabulations from the US Census Population and Housing Unit Estimates Program.
See this document for information about why different estimates were used, the differences in these estimates, and why 2018 estimates were used to calculate rates for 2019 and 2020.
Unstable Estimates
Multiple years of data were combined to generate more stable estimates when the number of events for an indicator
was small (i.e., rare conditions).
The relative standard error (RSE) is a tool for assessing reliability of an estimate. A large RSE is produced when
estimates are calculated based on a small number of cases.2 Estimates with large
RSEs are considered less reliable than estimates with small RSEs. The
National Center for Health Statistics recommends that estimates with RSEs greater than 30% should be considered
unreliable/unstable.3
The RSE is calculated by dividing the standard error of the estimate by the estimate itself, then multiplying that result by 100. The RSE is expressed as a percent of the estimate.
For the Prevention Agenda dashboard, an asterisk (*) or plus (+) symbol is used to indicate that a percentage, rate, or ratio is unreliable/unstable. This usually occurs when there are less than 10 events in the numerator (RSE is greater than 30%).
Direction of Indicator Estimates
Prevention Agenda tracking indicators fall into two categories with regard to the desirable direction of their estimates. Sometimes lower estimates are better (e.g., the percentage of premature deaths before age 65 years, or the age-adjusted rate of potentially preventable hospitalizations among adults) and in other cases higher estimates are better (e.g., the percentage of the population with health insurance, or the percentage of infants exclusively breastfed in the hospital).
The desirable direction of the Prevention Agenda tracking indicator is important to note because the county bar chart, map, concern level and visual distribution use color categories that are based on the direction of the Prevention Agenda tracking indicator. The assessment of indicator performance is also based on the direction of the Prevention Agenda tracking indicator.
Grouping County Estimates into Three Categories for the County Visual Distributions, County Maps, and County Bar Charts
Color Categories Defined
For each Prevention Agenda tracking indicator, county visual distributions, maps and bar charts are generated when there are enough counties with data different from each other so that county visual distributions, maps and charts can show meaningful differences among the counties. In particular, county visual distributions, maps and charts are not generated if 46 or more counties have rates that are equal to 0 or are missing, or if more than half the counties have the same rate. County visual distributions, maps and charts are generated all other times. Tables are generated for all indicators in all counties, regardless of rate values.
When county visual distributions, maps and charts are generated, county estimates are grouped into three categories: light green, blue-green, and dark blue. These categories are displayed consistently in the county visual distribution, the bar chart, and the New York State map for each tracking indicator.
The three colors represent the quartile distribution of estimates for the counties ordered from those doing the best to those doing the worst.
For Prevention Agenda tracking indicators where lower estimates are better (e.g., percentage of premature deaths before age 65 years or the age-adjusted rate of potentially preventable hospitalizations among adults):
- The LIGHT GREEN category includes counties that are performing the best (i.e., 50% of counties with the lowest estimates; those in quartile 1 and quartile 2) and is the most favorable category or least concern for a county's estimate to be in.
- The DARK BLUE category includes counties that are performing the worst (i.e., 25% of counties with the highest estimates; those in quartile 4) and is the least favorable or high concern category for a county's estimate to be in.
- The BLUE-GREEN category includes counties that are performing in the middle (i.e., 25% of counties or those in quartile 3).
For Prevention Agenda tracking indicators where higher estimates are better (e.g., the percentage of the population with health insurance or the percentage of infants exclusively breastfed in the hospital):
- The LIGHT GREEN category includes counties that are performing the best (i.e., 50% of counties with the highest estimates; those in quartile 3 and quartile 4) and is the most favorable or least concern category for a county's estimate to be in.
- The DARK BLUE category includes counties that are performing the worst (i.e., 25% of counties with the lowest estimates; those in quartile 1) and is the least favorable or high concern category for a county's estimate to be in.
- The BLUE-GREEN category includes counties that are performing in the middle (i.e., 25% of counties or those with estimates in quartile 2).
Length of Color Categories in the County Visual Distribution
The length of each color in the county visual distribution represents the minimum and maximum values or cut-off points for the three categories. If the area is very big, this indicates that the range of county estimates is large; while a small area indicates a small range of county estimates.
For example, the county visual distribution for the asthma emergency department visit rate per 10,000 for those aged 0-17 years in Albany County in 2020 shows
a very large dark blue area which ranges from 26.8 to 110.2; while the light green area ranges from 0.0-<18.1 and has
a much narrower width; similarly, the blue-green area has a narrow range of estimates from 18.1-<26.8.
Color Switch in County Visual Distribution Based on Direction of Indicator Estimates
For Prevention Agenda tracking indicators where lower estimates are better (e.g., potentially preventable hospitalizations among adults, age-adjusted rate per 10,000),
the dark blue category is displayed on the right side of the county visual distribution.
For Prevention Agenda tracking indicators where higher estimates are better (e.g., the percentage
of women with a preventive medical visit in the past year, aged 18-44 years), the dark blue category is displayed on the left side
of the county visual distribution.
Comparing the Prevention Agenda Estimates with the Prevention Agenda Objectives
A green color in bar charts or for a number displayed in a data table indicates that the current value for the Prevention Agenda tracking indicator met the Prevention Agenda 2024 Objective. A red color in bar charts or for a number displayed in a data table indicates that the current value for the Prevention Agenda tracking indicator did not meet the Prevention Agenda 2024 Objective.
Assessing the Indicator Performance
Three different methods were used to assess indicator performance.
- Conduct one-sided z-test to assess the change (decrease/increase or improve/worsen) in estimates between the two most recent time periods. The p-value for rejecting the null hypothesis is less than or equal to 0.05 and the critical value for the one-sided test (p-value at 0.05) is 1.645. 4
- A comparison of confidence intervals of estimates for the two most recent time periods was performed
where this method was more appropriate. 4 A confidence interval is a range around an estimate
that conveys how precise the estimate is. Differences between estimates are considered “statistically significant” when the estimates
being compared do not have overlapping confidence intervals. For the purposes of this dashboard, in cases where the confidence intervals
overlap, the difference is interpreted as not statistically significant at the 95% confidence level.
For survey related indicators, estimates and the two-sided 95% confidence intervals were obtained and used.
In some instances for count data (e.g., births, deaths, hospitalizations, and emergency department visits),
we calculated the one-sided 95% confidence intervals for the estimates and used them for comparison to evaluate
the indicator performance.5
NOTE: This method is an approximation of a statistical test and may result in a more conservative finding. In some cases, an appropriate statistical test would indicate a statistically significant difference even though the confidence intervals overlap and falsely imply no significant difference. When two confidence intervals do not overlap, though, a comparable statistical test would always indicate a statistically significant difference.6 - Simple comparison was conducted where the two estimates were directly compared to each other
based on their magnitude. This was performed when there was not a sufficient amount of data to conduct significance
testing; or if confidence intervals could not be calculated; or if there is some overlap of the two time intervals being
compared (e.g., 2014-2016 and 2015-2017 maternal mortality indicators).
The categories for the Indicator Performance are as follows:- Significantly improved
- Significantly worsened
- No significant change
- Improved^
- Worsened^
- No change^
- Baseline data
The "^" sign indicates that the performance was determined using simple comparison and not statistical tests.
The most recent data available during the planning phase were used as a baseline to set the Prevention Agenda 2024 Objective/Target. “Indicator Performance” is not assessed if there are no available data points that are more recent than the baseline data. Therefore, the “Indicator Performance” is labeled as "Baseline data" until updated data are available.
See Table 1 below for statistical significance techniques used for each type of data source to assess the indicator performance.
Use caution when interpreting significance. For more common conditions (i.e., high incidence rates), there is a higher likelihood that a relatively small change could be detected as statistically significant. Conversely, for rare conditions, the likelihood of detecting a statistically significant change is low even for reasonable changes.
Data Filters
Several data filters are available on state and county views to quickly select indicators based on commonly desired criteria. Multiple filters can be selected simultaneously.
- State data filters: two data filters are available for state level indicators
- Filter on meeting 2024 objective target: This filter displays indicators, where the most recent state level data meeting or not meeting the Prevention Agenda 2024 objectives.
- Filter on indicator performance over time: The performance status for each indicator is generated by comparing state level data for the two most recent time periods. This filter displays indicators based on indicator performance categories selected.
- County data filters: three data filters are available for county level indicators
- Filter on meeting 2024 objective target: This filter displays indicators, where the most recent specific county data meeting or not meeting the Prevention Agenda 2024 objectives.
- Filter on indicator performance over time: The performance status for each indicator is generated by comparing estimates for the two most recent time periods for a specific county. This filter displays indicators based on indicator performance categories selected.
- Filter on county’s position of concern level: This filter displays indicators based on how a county is doing in comparison to other counties in a quartile distribution.
Sub-county Data
To better serve the needs for more local level data, we have assessed the availability of sub-county level data for the existing county level indicators. Depending on the availability of the information from the data sources, sub-county level data are presented in one of the following three geographic levels: ZIP Code, school district, or minor civil division (MCD)/community district (CD).
- ZIP Code: For indicators generated from SPARCS (inpatient and outpatient) data, ZIP Code refers to resident address ZIP Code. If a ZIP Code crosses county borders, the ZIP Code is assigned to the county which has the largest proportion of the population of that ZIP Code. ZIP Code level population estimates used for calculating rates were obtained by the New York State Department of Health (NYSDOH) from the Claritas Corporation.
- School District: Student weight data at the school district level (outside of New York City) came from the Student Weight Status Category Reporting System (SWSCR). A school district boundary file was generated by combining two US Census shape files – a unified school district file and a secondary school district file.
- Minor Civil Division (MCD)/Community District (CD): Birth and death
certificate data for counties outside of New York City (NYC) are presented by MCD. Data for NYC boroughs
are presented by CD. There are 59 CDs within the five NYC boroughs.
Note: A small percent of records did not have an assigned MCD or CD, but the records had other geographic identifiers. For NYC records with missing CD, ZIP Code information was used to look up and assign the CD location utilizing NYC Department of Planning geographic data linking ZIP Codes to CDs. This linked file contains a small number of ZIP Codes, which were completely contained within a single CD. Records with those ZIP codes were assigned to those CDs. For records of residents from the rest of the state (ROS), outside of NYC, with a missing MCD, the New York State Gazetteer code* was used to look up the MCD code. DOH staff linked the Gazetteer codes to MCDs, and the linkage was used to assign records outside of NYC to an MCD.
*The New York State Gazetteer was prepared by the New York State Department of Health (see: health.data.ny.gov/Health/New-York-State-Gazetteer/cpcx-4uew)
Based on further assessment of the stability of the estimates and the impact of data suppression, the following six indicators were selected for incorporating into the current PA tracking dashboard.
- ZIP Code level:
- Potentially preventable hospitalizations among adults, age-adjusted rate per 10,000
- Asthma emergency department visits, rate per 10,000, aged 0-17 years
- School district level:
- Percentage of children and adolescents with obesity (NYS excluding NYC)
- MCD/CD level:
- Percentage of premature deaths (before age 65 years)
- Percentage of births that are preterm
- Percentage of infants who are exclusively breastfed in the hospital among all infants
Data Suppression for Confidentiality
Results are not shown (i.e., suppressed) when issues of confidentiality exist. Suppression rules vary depending on the data source and the indicator.
Table 1. Summary of data suppression and statistical evaluation significance for the Prevention Agenda Indicators by data source
| Data Sources | Suppression Criteria | Statistical Significance Techniques |
|---|---|---|
| Sample Surveys | ||
| Pregnancy Risk Assessment Monitoring System | Denominator <30 | 95% CI comparison |
| BRFSS and Expanded BRFSS | Numerator <6 or Denominator <50 | 95% CI comparison |
| US Census | 90% CI comparison | |
| National Survey on Drug Use and Health | 95% CI comparison | |
| Youth Risk Behavior Surveillance System | Denominator <100 | 95% CI comparison |
| Youth Tobacco Survey | 95% CI comparison | |
| Population Count Data | ||
| Death | Single Year: Denominator population <50; Three-Year Combined: Denominator population <30 |
Rate/percentage: one sided chi-square test with p-value <0.05 Rate difference: one sided 95% CI comparison |
| Birth | Single Year: Denominator total Births <50 | One sided chi-square test with p-value <0.05 |
| Sexually Transmitted Infection (STI) Surveillance | One sided chi-square test with p-value <0.05 | |
| HIV Surveillance | Numerator 1-2 cases | County level (rate): one sided 95% CI comparison; State level (rate): one sided chi-square test with p-value <0.05 |
| SPARCS | Numerator between 1 - 5 cases | Rate/percentage: one sided chi-square test with p-value <0.05; Ratio/Rate difference: one sided 95% CI comparison |
| Prescription Monitoring Program (PMP) Registry | Numerator between 1 - 5 cases | One sided chi-square test with p-value <0.05 |
CI: Confidence Interval
BRFSS: Behavioral Risk Factor Surveillance System
SPARCS: Statewide Planning and Research Cooperative System
Data Limitations
Table 2. Summary of Limitations for Data Presented on the Prevention Agenda Dashboard
| Indicator | Data Source | Limitations |
|---|---|---|
| Sample Surveys | ||
| 6 - Percentage of children and adolescents with obesity (NYS excluding NYC) | Student Weight Status Category Reporting System (SWSCRS) | Because of restrictions in reporting due to the Family Educational Rights and Privacy Act (FERPA), parents'/guardians' ability to request that their child's weight status data be excluded from reporting, and other sources of missing data, not all students have data in the data system. The percent of students with reported data varies from county to county. Therefore these estimates do not necessarily represent all school aged-children attending school in that county. School districts report weight status data separately for students in elementary school, middle/high school, and the school district as a whole, so that the counts of students represented in the district totals for a county will not necessarily equal the counts of students in the elementary and middle/high totals for that county. |
| Population Count Data | ||
| 2 - Potentially preventable hospitalizations among adults, age-adjusted rate per 10,000 | SPARCS | At the ZIP Code level, very unusual distributions in population denominator and/or numerator (possibly due to multiple hospitalizations per individual) may result in extreme age adjusted rates, therefore, these estimates are suppressed or to be interpreted with caution. |
| 78 - Newly diagnosed HIV cases, rate per 100,000 population | HIV Surveillance | The discrepancy in totals is due to the exclusion of prisoner cases for counties outside NYC, but not for NYC OR for the NYS total. |
Note: The 2018 population estimates are also used to calculate rates for 2019 and 2020.
Revisions
References
- Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy People Statistical Notes, no. 20. Hyattsville, Maryland: National Center for Health Statistics. January 2001. (see: www.cdc.gov/nchs/data/statnt/statnt20.pdf)
- About Age Adjusted Rates, 95% Confidence Intervals and Unstable Rates (see: www.health.ny.gov/statistics/cancer/registry/age.htm)
- Klein RJ, Proctor SE, Boudreault MA, Turczyn KM. Healthy People 2010 criteria for data suppression. Statistical Notes, no 24. Hyattsville, Maryland: National Center for Health Statistics. June 2002. (see: www.cdc.gov/nchs/data/statnt/statnt24.pdf)
- Statistical Significance (see: www.health.ny.gov/statistics/chac/chai/docs/statistical_significance.pdf)
- One-sided 95% confidence interval (see: http://www.graphpad.com/guides/prism/6/statistics/index.htm?one_sided_confidence_intervals.htm)
- Guidelines for using confidence intervals for public health assessment, Washington State Department of Health, (see: www.doh.wa.gov/Portals/1/Documents/1500/ConfIntGuide.pdf)
User's Guide
Other Dashboards and Useful Links
- Asthma in New York
- Communicable Disease Annual Reports
- Community Health Indicator Reports (CHIRS)
- County Health Indicators by Race/Ethnicity (CHIRE)
- County/ZIP Code Perinatal Data Profile
- Environmental Public Health Tracker
- Expanded Behavioral Risk Factor Surveillance System
- Leading Causes of Death
- Maternal and Child Health (MCH) Dashboard
- Minority Health Data and Statistics
- New York State Cancer Registry
- Opioid-related Data in New York State
- Pregnancy Risk Assessment Monitoring System (PRAMS) Dashboard
- Sexually Transmitted Diseases Data and Statistics
- Vital Statistics
Contact Us
If you have questions about the reports, please contact:
Public Health Information Group at: prevention@health.ny.gov