• printer-friendly Data Resource Guide

On this page:

• How to Use the NFA Data Resource Guide
• Extrapolating Data To A Service Area Or Target Population
• Section 1: Core Barriers
• Section 2: Other Barriers
• Section 3: Core Disparities
• Section 4: Other Disparities
• Glossary of Terms

How to Use the NFA Data Resource Guide

This Need for Assistance (NFA) Data Resource Guide is provided to assist potential new access point (NAP) applicants in completing the NFA Worksheet as part of a future NAP application. As before, the NFA Worksheet focuses on both barriers and disparities. It is to be used in conjunction with the revised Need for Assistance (NFA) Worksheet. This NFA Data Resource Guide presents data sources for each of the barriers and disparity indicators/factors included in the revised NFA Worksheet.

Efforts were made to find and reference county–level data or sub-county level data wherever possible (such as data by county subdivisions, census tracts or zip codes). For indicators not available at county or sub-county levels, some States may have defined multi-county areas or other sub-State regional health areas. For each indicator used, applicants may use the available data for the county, county subdivision(s) or other sub-State area that includes their service area. Alternatively, applicants may utilize extrapolation techniques to estimate the correct value in the service area from the data available at higher levels. Where county-level or other local data is not available, State-level data that is broken down by categories such as race, ethnicity gender, and age may be used for extrapolation to applicants’ service areas.

Where possible, for each barrier and disparity indicator/factor, the data sources that are specific to persons experiencing homelessness and migratory/seasonal farmworker populations have also been identified. For these populations, extrapolation from national or regional data sources (or contiguous States) may be necessary rather than from State or county data.

For each section of the NFA Worksheet, applicants will be encouraged to utilize one of the sources identified in this NFA Data Resource Guide in developing responses. To maintain comparability, all applicants will be required to utilize the data source provided in the NFA Data Resource Guide for the barrier (b): Percent of Population at or below 200 Percent of Poverty and barrier (g): 12-Month Average Unemployment Rate.

While the data sources specified in the NFA Data Resource Guide are preferred, applicants may use an alternate data source to provide a response for any other barrier or disparity on the NFA Worksheet only under the following guidelines:

(a) the response must be expressed in the same units of analysis identified for that specific barrier or disparity (e.g., cannot use mortality ratio to provide a response to age-adjusted death rate);
(b) alternate data must be from a reliable and independent source, such as a State or local government agency, professional body, foundation, or other well-known organization using recognized, scientifically accepted data collection and/or analysis methods;
(c) for applicants requesting funding to serve the general population (under section 330(e)), alternate data should be used only if the alternate data are more specific to the proposed service area (i.e., are available at the appropriate sub-county levels) and/or are more current; and/or
(d) for applicants requesting funding to serve the homeless (under section 330 (h)), migrant/seasonal farmworkers (under section 330(g)) or residents of public housing (under section 330(i)), alternate data should be used only if the alternate data are more specific to the population to be served, and/or are more current.

All responses must be based on data for the proposed service area or target population, as appropriate, per the following criteria:

(a) Applicants that will be requesting funds to serve the general population (under section 330(e) only) must provide responses that reflect the total population within the defined service area for the application. When the service area is a sub-county area (made up of groups of census tracts, other county divisions or zip codes), but data for the particular barrier or disparity indicator are not available at sub-county levels, applicants may use an extrapolation technique to appropriately modify the available county-level or other level data to reflect the service area population. See below in this NFA Data Resource Guide for further information regarding extrapolation techniques.

(b) Applicants that will be requesting funds to serve ONLY a homeless population (under section 330 (h)), a migrant/seasonal farmworkers population (under section 330(g)) or residents of public housing (under section 330(i)), or any combination of these special populations, may use an extrapolation technique to appropriately modify available data for these special populations to reflect their specific population(s) within the proposed service area. See below in this NFA Data Resource Guide for further information regarding extrapolation techniques.

(c) Applicants that will be requesting funds to serve a homeless population (under section 330 (h)), a migrant/seasonal farmworker population (under section 330(g)) or residents of public housing (under section 330(i)) in combination with the general population of a service area (under section 330(e)), must present responses that reflect the total population to be served. In calculating the response, applicants may use extrapolation techniques to appropriately modify available data to reflect the homeless, migrant/seasonal farmworker or public housing population within the service area (as in (b) above), then combine this with data the general population within the defined the service area. As above, where sub-county data are not available, applicants may use an extrapolation technique to modify available county-level or other level data to reflect the service area population. See below in this NFA Data Resource Guide for further information regarding extrapolation techniques.

EXTRAPOLATING DATA TO A SERVICE AREA OR TARGET POPULATION

In some cases, it may be difficult to find data specific to the proposed service area or target population at the appropriate level to effectively describe the need in the proposed service area or target population. However, key demographic information about the service area or target population may permit an extrapolation of data from a more aggregate level, such as the State or county, to the proposed service area or target population.

Instead of using more aggregate level data that may not reflect the real experience of your population, you can use the experience of one population (the “standard” population) to project the data for the target population. This approach, which can be employed in a number of situations, involves using the proportional make-up of the target population (by race/ethnicity, age or income level, for example) and the actual experience (percent or rate of disease occurring) in the “standard” population to determine what the target population would be expected to experience for that disease or outcome if they had the same experience as the standard population.

The following three examples are provided to assist applicants in performing extrapolation techniques to better represent the need in the service area or amongst the target population when data is not available at that level. All of the examples provided below are hypothetical using simulated numbers. Types of data needed to perform these types of calculations generally include the number of people in target population (by race/ethnicity as necessary) and the rate for a particular disease or outcome for the closest available level of population (county, State, national).

For the purpose of the following examples, the population data for ethnic/racial make-up of the target population is simulated to represent data available through the U.S. Census and the disease or outcome rates for population groups that could be expected at the State level from a State Department of Health. This data is simulated and does not represent any specific community or area.


Step 1: Calculate what percent of the total target population each ethnic/racial group represents.

(A/12,500)
.520 (52.0%)
.280 (28.0%)
.200 (20.0%)
1.00 (100.0%)


Step 2: Using State Department of Health data, determine the prevalence of infant mortality (IM) for each ethnic/racial group at the State level and the total IM prevalence for the State:

Percentage of IM By Sub-Group for the State
.112 (11.2%)
.096 (9.6%)
.071 (7.1%)
.084 (8.4%)


Step 3: Calculate the projected IM data for the target population by multiplying the percent of people in each sub-group in the target population by the prevalence/percent of IM in each of the same groups experienced at the State level:

Example 1: How to project the infant mortality (IM) rate for a target population whose racial/ethnic make-up is different from the population for which that data is available.

(B*C)

.05824

.02688

.01420

.09932

Step 4: To determine the difference in the State rate for IM versus the rate expected to be experienced by the target population, multiply both the State IM rate and the target population IM rate by 100:

State Infant Mortality Rate: .084 X 100 = 8.4%
Expected Target Population Infant Mortality Rate: .09932 X 100 = 9.9%

CONCLUSION: This example shows that there is a disparity in the target population. That is, there is an excess/more infant mortality in the target population (9.9%) than in the State population (8.4%).


If specific data for the target population is not available, some implications regarding your target population may be useful in identifying proxy data that is available. For example, for a target population of migrant and seasonal farmworkers (MSFWs), an applicant might use statewide or national data for the ethnic group that makes up the majority of the MSFW target population (e.g., Latino, Mexican, Cambodian). In such a case, an applicant could use data on that ethnic group from available statewide data as a proxy for the experience for a disease or outcome for the target population by assuming that the ethnic group at a State level had the same experience as that ethnic group at the local level. Some examples of implications for a MSFW target population may be:

MSFWs in the target population are generally Latino (or other ethnic group); therefore, issues common among Latinos at the State level are anticipated to be the same for the MSFW target population.
MSFWs are generally low income; therefore, low income underserved issues at the State level are anticipated to be the same for the MSFW target population.
Migrant workers increase the area population “XX” amount; therefore, area health indicators are worse, reflecting the additional “ABC” migrant needs.

Agricultural work has “XYZ” environmental and occupational issues; therefore, MSFWs who work in agriculture would be expected to experience “XYZ” environmental and occupational issues.

Example 2: How to identify proxy data for a target population consisting of a “special population” (migrant and seasonal farmworkers) for which specific data is not available.

Similar implications can be made for homeless populations in that one could “imply” from their characteristics (low income, ethnic/racial group, etc.) their similarities to other groups and utilize data sets that reflect the experiences of the other groups to determine what the homeless population would be expected to experience for a disease or outcome if they had the same experience as the group at the reported data level.

In using this method, data can be “borrowed” from one data set (county, State, national) to represent the experience that would be expected for the target population. The proxy must be similar to the target population based on the implications used to identify the target population. An example of this type of proxy measure might be the following:

Step 1: If no specific data is available for the target population, identify any assumptions/implications that can be made to represent the target population (e.g., the target population is MSFWS and MSFWs in the area are predominantly Latinos).

Step 2: Using State Department of Health data, determine the prevalence of infant mortality (IM) for each ethnic/racial group at the State level and the total IM prevalence for the State:

By Sub-Group for the State
.084 (8.4%)
.116 (11.6%)
.063 (6.3%)
.075 (7.5%)


Step 3: To determine the difference in the State rate for IM versus the rate experienced by Latinos in the States (through implication representing the target population of MSFWs):

State Infant Mortality Rate: .075 X 100 = 7.5%
Expected Infant Mortality Rate for MSFWs: .116 X 100 = 11.6%

CONCLUSION: This example shows that there is a disparity in the target population of MSFWs when compared to the State rate – that is, there is an excess/more infant mortality in the target population of MSFWs represented by the rate for all Latinos in the State (11.6%) than in the total State population for all races (7.5%).



This type of extrapolation technique can be accomplished in two ways. First, if “raw” data is available (i.e., the number of asthma cases from each county), the total number of people and the total number of outcomes/cases from each county can be combined to form a denominator (total population for the combined area) and numerator (total number of events) for this calculation.

Example 3: How to project the asthma rate for a target population that consists of two different areas such as two adjacent counties.

(B/A)
.0047

To determine the combined rate of asthma per 100,000 for the target population that consists of both County A and County B, multiply the total population rate of .0047 by 100,000 to get a combined asthma rate of 470/100,000 population. This should be compared to the rate for the State or other benchmark to determine if a disparity exists.

The second method of calculating the asthma rate is similar to that utilized in Example 1 above by calculating projected rates based on known population and State data if the “raw” data for asthma is not available for the specific counties.

Step 1: Calculate what percent of the total target population each county represents.

(A/19,968)
.630 (63.0%)
.370 (37.0%)
1.00 (100.0%)

Step 2: Determine the prevalence of asthma for each county at the State level and the total asthma prevalence for the State using State Department of Health data:

By County for the State
.0057 (.57%)
.0033 (.33%)
.0029 (.29%)


Step 3: Calculate the projected asthma data for the target population (County A and County B) by multiplying the percent of the target population represented by population from each county by the prevalence/percent of asthma experienced by each county at the State level:

(B*C)
.0036
.0012
.0048


To make this into a rate of asthma per 100,000 for the target population multiply the total target population rate of .0048 by 100,000 = 480/100,000.

Step 4: To determine the difference in the State rate for asthma versus the rate expected to be experienced by the target population (County A and County B), multiply both the State asthma rate and the asthma rate for the target population by 100,000:

State Asthma Rate: .0029 X 100,000 = 290/100,000
Expected Target Population Asthma Rate: .0048 X 100,000 = 480/100,000

CONCLUSION: This example shows that there is a disparity in the target population of County A and County B rate for asthma when compared to the State rate – that is, there is an excess/more asthma in the target population (480/100,000) than in the State population (290/100,000).

SECTION 1: CORE BARRIERS

(a) Population to Primary Care Physician Ratio – MANDATORY

Data Sources:

• Health Professional Shortage Areas (HPSA) Database
• BPHC UDS, Table 5

(b) Percent of Population at or Below 200 Percent of Poverty – MANDATORY

• U.S. Department of Labor. Findings from the National Agricultural Workers Survey (NAWS).

• Urban Institute National Survey of Homeless Assistance Providers and Clients

• Census Bureau

• U.S. Department of Labor. Findings from the National Agricultural Workers Survey (NAWS) 2001-2002. March 2005

• Urban Institute National Survey of Homeless Assistance Providers and Clients

NOTE: Applicants must use the Census Bureau data source provided below for response, except for MHC and/or HCH applicants who may instead use the population-specific Census data .

SECTION 2: OTHER BARRIERS

1. Methodology: Using a web based mapping service, calculate the distance/travel time from the zip code of the nearest provider accepting new Medicaid and/or uninsured patients to the zip code of the proposed service area or target population.

Distance (miles) OR travel time (minutes) to nearest primary care provider accepting new Medicaid patients and/or uninsured patients – OPTIONAL

Percent of Population Linguistically Isolated (Percent of people 5 years and over who spoke a language other than English at home) – OPTIONAL. Select link for: ‘American Factfinder’

U.S. Department of Labor. Findings from the National Agricultural Workers Survey (NAWS) 2001-2002. March 2005:
See Census data for service area.

Age Adjusted death Rate – OPTIONAL
NCHS, NVSS CDC WONDER (by service area)

12-Month Average Unemployment Rate – OPTIONAL

Bureau of Labor Statistics: Trends in Monthly Unemployment Statistics:
Homeless Specific Data Source:
National Coalition for the Homeless Information Clearinghouse

NOTE: Applicants must use the Labor Statistics data sources provided below for response, except for MHC and/or HCH applicants who may instead use the population specific source cited below.
Bureau of Labor Statistics


Data Source(s):

• National data at HUD
• Local data at HUD
• Urban Institute National Survey of Homeless Assistance Providers and Clients
  

SECTION 3: CORE DISPARITIES

1. Diabetes, Obesity

Indicator(s):
1(a) Diabetes Short-term Complication Hospital Admission Rate (PQI 1)
1(b) Diabetes Long-term Complication Hospital Admission Rate (PQI 3)
1(c) Uncontrolled Diabetes Hospital Admission Rate (PQI 14)

Data Sources:

• CDC: National Diabetes Surveillance System
• CDC Wonder
• Agency for Healthcare Research and Quality (AHRQ) Prevention Quality Indicators
• Center for Chronic Disease Prevention and Health Promotion
• Bi-National Health Survey
• 2004 BRFSS
• Villarejo D. et al. Suffering in Silence: A Report on the Health of California’s Agricultural Workers
• CDC NCHS NVSS Mortality File
  
  

2. Cardiovascular Disease

Indicator(s):
2(a) Hypertension Hospital Admission Rate (PQI 7)
2(b) Congestive Heart Failure Hospital Admission Rate (PQI 8)
2(c) Angina without Procedure Hospital Admission Rate (PQI 13)
AHRQ Prevention Quality Indicators
2(d) Mortality from Diseases of the Heart
2. CDC NCHS NVSS Mortality File
2(e) Proportion of Adults reporting diagnosis of high blood pressure

Data Sources:

• CDC Wonder
• 2003 BRFSS: Category: ‘Hypertension Awareness’
  

3. Asthma, Respiratory Disease
Villarejo D. et al. Suffering in Silence: A Report on the Health of California’s Agricultural Workers:
Burt, M.R., Aron, L.Y., Douglas, T., Valente, J., Lee, E., Iwen, B. (1999) Homelessness: Programs and the People They Serve. Washington, DC: Interagency Council on the Homeless

Indicators:
3(a) Adult Asthma Hospital Admission Rate (PQI 15)
3(b) Pediatric Asthma Hospital Admission Rate (PQI 4)
3(c) Chronic Obstructive Pulmonary Disease Hospital Admission Rate (PQI 5)
3(d) Bacterial Pneumonia Hospital Admission Rate (PQI 11)
3(e) Three Year Average Pneumonia Death Rate
3(f) Adult Current Asthma Prevalence
3(g) Adult Ever Told Had Asthma

Data Sources:

• CDC Wonder
• AHRQ Prevention Quality Indicators
• CDC NCHS NVSS Mortality File
• 2004 BRFSS: Category: ‘Asthma’
• Bi-National Health Survey
• CDC NCHS NVSS
  

4. Prenatal and Perinatal Health
4(a) Low Birth Weight Rate, 5 year average
HRSA Geospatial Data Warehouse Select link for ‘County Profile’ Birth and Infant Mortality Statistic
Gelberg et al. Severity of homelessness and adverse birth outcomes. Health Psychology 19(6), pp. 524-534, 2000
4(b) Infant Mortality Rate, 5 year average
HRSA Geospatial Data Warehouse: Select link for ‘County Profile’ Birth and Infant Mortality Statistic
4(c) Births to Teenage Mothers (15-19)
4(d) First Trimester entry into Prenatal care
Select link for ‘Births: Final Data for 2003’ – Table 34
Larson, Kim. “Maternal Care Coordination for Migrant Farmworker Women”: Journal of Rural Health, 1992

Data Sources:

• HRSA Geospatial Data Warehouse
• National Healthcare Quality Report
• CDC NCHS NVSS (Births)
• CDC NCHS NVSS Mortality File
  

5. Mental Health/ Substance Abuse/ Behavioral Health
5(a) Depression Prevalence
Riolo et al. Prevalence of Depression by Race/Ethnicity: Findings from the National Health and Nutrition Examination Survey III. AmJPh;95(5):998
Hovey & Magana. Acculturative Stress, Anxiety and Depression among Migrant Immigrant Farmworkers in the Midwest. J Immigrant Health; 2:119-131; 2000

5(b) Suicide death rate. National Healthcare Quality Report
Data Source: 2002 – 2003 National Survey on Drug Use and Health

5(c) Youth Suicide attempts requiring medical attention
- Youth Risk Behavior Surveillance - page 47-8 of the report

5(d) Adults with Mental disorders not receiving treatment
- SAMHSA Nat’l Survey on Drug Use and Health

5(e) Any Illicit Drug Use in the Past Month
- Bi-National Health Survey

5(f) Heavy alcohol use 12 and over

Data Sources:

• National Survey on Drug Abuse and Health
• Bi-National Health Survey
• Koegel, Paul, et al. "The Causes of Homelessness," in Homelessness in America, 1996, Oryx Press.

5(g) Homeless with severe mental illness
- Urban Institute National Survey of Homeless Assistance Providers and Clients

SECTION 4: OTHER DISPARITIES

6. Oral Health - % without dental visit in last year
Select Variable ‘Utilization, Expenditure and SOP Variable’ – ‘Dental Visits’
U.S. Department of Labor. Findings from the National Agricultural Workers Survey (NAWS) 2001-2002. March 2005

7. HIV Infection Prevalence
CDC HIV/AIDS Surveillance System

CDC: HIV Infection, Syphilis, and TB Screening Among Migrant Farmworkers – Florida; MMWR: 41(39);723-725:
Urban Institute National Survey of Homeless Assistance Providers and Clients

8. Percent of children not receiving recommended immunizations 4-3-1-3-3 (4 DTaP, 3 polio, 1 MMR, 3 Hib, 3 hepatitis B).
CDC/NCHS National Immunization Survey: Table 04

Data Source(s):
Census Bureau:

9. Percent Elderly (65 and older)
Census Bureau Quickfacts
Bi-National Health Survey
Urban Institute National Survey of Homeless Assistance Providers and Clients

10. Cancer Screening – No Pap test in past 3 years; women 18+
BRFSS: Category: ‘Women’s Health’

11. Cancer Screening – No Mammogram in past 3 years; women 40+
BRFSS: Category: ‘Women’s Health’

12. Cancer Screening – No FOBT within the past 2 years; adults 50+
BRFSS: Category: ‘Colorectal Cancer Screening’

13. Unintentional Injury (Accidents) Deaths
CDC NCHS NVSS Mortality File

Bureau of Labor Statistics; Workplace Injuries and Illness
See CDC Data for service area of homeless population

GLOSSARY OF TERMS

Age-Adjusted Death Rate: The rates of almost all causes of disease, injury, and death vary by age. Age adjustment is a technique for "removing" the effects of age from crude rates so as to allow meaningful comparisons across populations with different underlying age structures. Age-adjusted rates are calculated by applying the age-specific rates of various populations to a single standard population. Source: CDC.gov

Asthma:

Adult Asthma Hospital Admission Rate: Admissions for adult asthma per 100,000 population. Discharges with ICD-9-CM principal diagnosis codes for asthma. Age 18 years and older. Exclude patients transferring from another institution, MDC 14 (pregnancy, childbirth, and puerperium), or MDC 15 (newborns and neonates). Source: AHRQ.gov

Adult Current Asthma Prevalence: Adults who have been told they currently have asthma. Source: CDC.gov

Pediatric Asthma Hospital Admission Rate: Admissions for pediatric asthma per 100,000 population. Discharges with ICD-9-CM principal diagnosis codes for asthma. Age less than 18 years old. Exclude patients transferring from another institution, MDC 14 (pregnancy, childbirth, and puerperium), or MDC 15 (newborns and neonates). Source: AHRQ.gov

Birth Rate: Calculated by dividing the number of live births in a population in a year by the midyear resident population. Birth rates are expressed as the number of live births per 1,000 population. The rate may be restricted to births to women of specific age, race, marital status, or geographic location (specific rate, e.g., Births to Teenage Mothers ages 15-19), or it may be related to the entire population (crude rate). Source: CDC.gov

Cardiovascular Disease:

Angina without Procedure Hospital Admission Rate: Admissions for angina (without procedures) per 100,000 population. Discharges with ICD-9-CM principal diagnosis codes for angina. Age 18 years and older. Exclude discharges with a procedure code for cardiac procedure, patients transferring from another institution, MDC 14 (pregnancy, childbirth, and puerperium), or MDC 15 (newborns and neonates). Source: AHRQ.gov

Congestive Heart Failure Hospital Admission Rate: Admissions for CHF per 100,000 population. Discharges with ICD-9-CM principal diagnosis codes for CHF. Age 18 years and older. Exclude patients discharged with specified cardiac procedure codes in any field, patients transferring from another institution, MDC 14 (pregnancy, childbirth, and puerperium), or MDC 15 (newborns and neonates). Source: AHRQ.gov

Mortality from Diseases of the Heart: Deaths with ICD-9-CM principal diagnosis codes: I00–I09, I11, I13, I20-151. Source: CDC.gov

Centroid: A centroid is a point that approximates the center of an area. Centroids are often assigned by the firm or organization providing data and may not exactly identify the geographic center nor the population-weighted center of an area. If you are generating your own estimates of distance using a geographic information system (GIS), use the geographic center of the area provided it lies within the boundary of that area. If not, use the closest point on the boundary of the area.

Dental Visit: This refers to care by or visits to any type of dental care provider, including general dentists, dental hygienists, dental technicians, dental surgeons, orthodontists, endodontists, and periodontists. Source: AHRQ.gov

Depression Prevalence: Prevalence based on criteria from Diagnostic and Statistical Manual of Mental Disorders, Revised Third Edition (DSM-III-R). The 2 outcomes were (1) dysthymic disorder: at least 2 years of dysphoric mood ("[have you] ... felt depressed or sad almost all the time, even if you felt OK sometimes?") plus 2 other symptoms of depression, and (2) major depressive disorder: at least 2 weeks of depressed mood ("[have you] ... felt sad, blue, depressed, or ... lost all interest and pleasure in things that you usually cared about or enjoyed?") plus 4 other symptoms. Source: AJPH.org

Diabetes:

Diabetes Prevalence: Diabetes mellitus is a group of diseases characterized by high levels of blood glucose resulting from defects in insulin production, insulin action, or both. Prevalence was calculated based on the total number of people with diabetes (both diagnosed and undiagnosed). Source: CDC.gov

Diabetes Short-term Complication Hospital Admission Rate: Admissions for diabetes with short-term complications* (excluding obstetric admissions and transfers from other institutions) per 100,000 population, age 18 years and older * Ketoacidosis, hyperosmolarity, or coma. Source: AHRQ.gov

Diabetes Long-term Complication Hospital Admission Rate: Admissions for diabetes with long-term complications* (excluding obstetric admissions and transfers from other institutions) per 100,000 population, age 18 years and older * Renal, eye, neurological, circulatory, or other unspecified complications. Source: AHRQ.gov

Rate of Lower-extremity Amputation Among Patients with Diabetes: Lower extremity amputations among patients with diabetes (excluding trauma, obstetric admissions, and transfers from other institutions) per 100,000 population, age 18 years and older. Source: AHRQ.gov

Uncontrolled Diabetes Hospital Admission Rate: Admissions for uncontrolled diabetes without complication* (excluding obstetric and neonatal admissions and transfers from other institutions) per 100,000 population, age 18 years and older * Without short-term (ketoacidosis, hyperosmolarity, coma) or long-term (renal, eye, neurological, circulatory, other unspecified) complications. Source: AHRQ.gov

Elderly: Population age 65 and older. Source: census.gov

FOBT: stands for “fecal occult blood test.” The FOBT checks for hidden blood in three consecutive stool samples, and is a screening mechanism for colorectal cancer. Source: CDC.gov

Heavy Alcohol Use: Five or more drinks on the same occasion on at least 5 different days in the past 30 days. Source:SAMHSA.gov .

HIV Infection Prevalence Rate Seroprevalence: The percentage of all persons infected with HIV (not AIDS) adjusted for age.

Homeless: A homeless individual means an individual who lacks housing (without regard to whether the individual is a member of a family), including an individual whose primary residence is a supervised public or private facility that provides temporary accommodations and an individual who is a resident in transitional housing.

Hypertension Hospital Admission Rate: Admissions for hypertension per 100,000 population. Discharges with ICD-9-CM principal diagnosis codes for hypertension. Age 18 years and older. Exclude discharges with specified cardiac procedure codes in any field, patients transferring from another institution, MDC 14 (pregnancy, childbirth, and puerperium), or MDC 15 (newborns and neonates). Source: AHRQ.gov

Illicit Drug Use (Any): The National Survey on Drug Use and Health (NSDUH) obtains information on nine different categories of illicit drug use: marijuana, cocaine, heroin, hallucinogens, inhalants, and non-medical use of prescription-type pain relievers, tranquilizers, stimulants, and sedatives. Over-the-counter drugs and legitimate uses of prescription drugs are not included. Estimates of "any illicit drug use" reported from NSDUH reflect use of any of the nine substance categories listed above. Source: SAMHSA.gov

Infant Mortality Rate: is based on period files calculated by dividing the number of infant deaths during a calendar year by the number of live births reported in the same year. It is expressed as the number of infant deaths per 1,000 live births. Source: CDC.gov

Linguistically Isolated: Percent of people 5 years and over who spoke a language other than English at home. Source: census.gov

Low Birth Weight: Birth weight less than 2,500 grams or 5 pounds 8 ounces. Source: CDC.gov

Mammogram: An x-ray image of the breast used to detect irregularities in breast tissue and is a screening mechanism for breast cancer. Source: CDC.gov

Migratory and Seasonal Agricultural Workers: Migratory agricultural worker means an individual whose principal employment is in agriculture, who has so been employed within the last 24 months, and who establishes for the purposes of such employment a temporary abode. Seasonal agricultural worker means an individual whose principal employment is in agriculture on a seasonal basis and who is not a migratory agricultural worker.

Obesity: Obesity is defined as a Body Mass Index (BMI) for adults of 30 or greater, the adult definition is 20 years and older. Source: Tracking Healthy People 2010 Guideline 19-1, page B19-5.

Pap Test: A Pap test (also known as a Papanicolaou smear or Pap smear) is a microscopic examination of cells scraped from the cervix that is used to detect cancerous or precancerous conditions of the cervix or other medical conditions. Source: CDC.gov

Perinatal: Pertaining to the period immediately before and after birth. The perinatal period is generally defined as starting at the 28th week of gestation and ending 1 week (7 days) after birth. Source: CDC.gov

Prenatal Care: Prenatal care is medical care provided to a pregnant woman to prevent complications and decrease the incidence of maternal and prenatal mortality. Information on when pregnancy care began is recorded on the birth certificate. Source: CDC.gov

Primary Care Physician FTE: The number of full-time- equivalent (FTE) non-Federal practitioners available to provide patient care to the area or population group. "Non-Federal" means practitioners who are not Federal employees and are not obligated-service members of the National Health Service Corps. It would include non-obligated-service hires of Federal grantees. "Practitioner" means allopathic (M.D.) or osteopathic (D.O.) primary medical care physicians. "Patient care" for primary care physicians includes seeing patients in the office, on hospital rounds and in other settings, and activities such as interpreting laboratory tests and X-rays and consulting with other physicians. Source: HRSA.gov

Poverty: Following the Office of Management and Budget's (OMB) Statistical Policy Directive 14, the Census Bureau uses a set of money income thresholds that vary by family size and composition to determine who is in poverty. Source: census.gov

Public Housing: Based on section 330 of the Public Health Service Act, public housing is defined as having the same meaning as the same term in 1437a (b)(1) of the PHS Act (42 USC 1437):
"The term 'public housing' means low-income housing, and all necessary appurtenances thereto, assisted under this chapter other than under section 1437f of this title. The term “public housing” includes dwelling units in a mixed finance project that are assisted by a public housing agency with capital or operating assistance. When used in reference to public housing, the term “low-income housing project” or “project” means
(A) housing developed, acquired, or assisted by a public housing agency under this chapter, and
(B) the improvement of any such housing."

Respiratory Disease:

Bacterial Pneumonia Admission Rate: Bacterial pneumonia is a relatively common acute condition, treatable for the most part with antibiotics. If left untreated in susceptible individuals – such as the elderly – pneumonia can lead to death. Admissions for bacterial pneumonia per 100,000 population. Discharges with ICD-9-CM principal diagnosis code for bacterial pneumonia. Exclude patients with sickle cell anemia or HB-S disease, patients transferring from another institution, MDC 14 (pregnancy, childbirth, and puerperium), or MDC 15 (newborns and neonates). Source: AHRQ.gov

Chronic Obstructive Pulmonary Disease Admission Rate: Chronic obstructive pulmonary disease (COPD) comprises three primary diseases that cause respiratory dysfunction – asthma, emphysema, and chronic bronchitis – each with distinct etiologies, treatments, and outcomes. This indicator examines emphysema and bronchitis; asthma is discussed separately for children and adults. Admissions for COPD per 100,000 population. Discharges with ICD-9-CM principal diagnosis codes for COPD. Age 18 years and older. Exclude patients transferring from another institution, MDC 14 (pregnancy, childbirth, and puerperium), or MDC 15 (newborns and neonates). Source: AHRQ.gov

Three Year Average Pneumonia Death Rate: Deaths with ICD-10-CM principal diagnosis codes: J12–J18. Source: CDC.gov

Serious Mental Illness: Having at some time during the past 12 months a diagnosable mental, behavioral, or emotional disorder that met the criteria in the 4th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) and resulted in functional impairment that substantially interfered with or limited one or more major life activities. Source: SAMHSA.gov

Suicide Death Rate: Death from intentional self harm. Deaths with ICD-9-CM principal diagnosis codes: *U03, X60–X84, Y87.0. Source: CDC.gov

Unemployed Persons: Included are all persons who had no employment during the reference week, were available for work, except for temporary illness, and had made specific efforts to find employment some time during the 4-weekperiod ending with the reference week. Persons who were waiting to be recalled to a job from which they had been laid off need not have been looking for work to be classified as unemployed. Source: BLS.gov

Unemployment Rate: The ratio of unemployed to the civilian labor force expressed as a percent [i.e., 100 times (unemployed/labor force)]. Source: BLS.gov

Unintentional Injury (Accidents): Deaths with ICD-9-CM principal diagnosis codes: V01–X59, Y85–Y86. Source: CDC.gov
Uninsured: People are considered uninsured if they were not covered by any type of health insurance for the entire year. Source: census.gov

Youth Suicide Attempts requiring medical attention: This is a rate based on response to the question on the Youth Risk Behavior Surveillance Survey, "If you attempted suicide during the past 12 months, did any attempt result in an injury, poisoning, or overdose that had to be treated by a doctor or nurse?" The denominator is adolescents in grades 9-12. Source: CDC.gov