| OMB No: 0906-XXXX | |||||||||||
| Expiration Date: XX/XX/XXXX | |||||||||||
| MIECHV Needs Assessment Data Summary [STATE NAME] Data Summary Contents Table 1. Simplified Method Overview Table 2. Description of Indicators Table 3. Descriptive Statistics Table 4. Raw Indicators Table 5. Standardized Indicators Table 6. At-Risk Domains Table 7. At-Risk Counties Table 8. Example Formulas |
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| Public Burden Statement: An agency may not conduct or sponsor, and a person is not required to respond to, a collection of information unless it displays a currently valid OMB control number. The OMB control number for this project is 0906-XXXX. Public reporting burden for this collection of information is estimated to average 120 hours per response, including time for reviewing instructions, searching existing data sources, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to HRSA Reports Clearance Officer, 5600 Fishers Lane, Room 14N39, Rockville, Maryland, 20857. | |||||||||||
| OMB No: 0906-XXXX | ||||||||||||||
| Simplified Method Overview Indicators were selected in collaboration with HRSA/MCHB to match as closely as possible the statutorily-defined1 criteria for identifying target communities for home visiting programs. We considered issues such as data availability and reliability of indicators at the county level when selecting the final indicator list. After selecting indicators, we grouped them according to five domains (Socioeconomic Status, Adverse Perinatal Outcomes, Substance Use Disorder, Crime, and Child Maltreatment). The algorithm for identifying at-risk counties is as follows: 1. Obtain raw, county-level data for each indicator from the listed data source as defined in Tab 2. Description of Indicators. 2. Compute mean of counties and standard deviation (SD) for each indicator as well as other descriptive statistics (number of missing, range, etc.) (Tab 3. Descriptive Statistics). 3. Standardize indicator values (compute z-score) for each county so that all indicators have a mean of 0 and a SD of 1. Z-score = (county value - mean)/SD. (Tab 5. Standardized Indicators). 4. Using the resulting z-scores for each county, calculate the proportion of indicators within each domain for which that county’s z-score was greater than 1, that is, the proportion of indicators for which a given county is in the ‘worst’ 16% of all counties in the state (16% is the percentage of values greater than 1 SD above the mean in the standard normal distribution). If at least half of the indicators within a domain have z-scores greater or equal to 1 SD higher than the mean, then a county is considered at-risk on that domain. The total number of domains at-risk (out of 5) is summed to capture the counties at highest risk across domains. Counties with 2 or more at-risk domains is identified as at-risk. (Tab 6. At-Risk Domains). 1Not included are indicators for infant mortality and domestic violence. Infant mortality was excluded from the Adverse Perinatal Outcomes domain because the level of suppression at the county level for 5-year aggregate data was too high for meaningful inclusion (all but 13 states have >50% of counties with suppressed data). Preterm and low birth weight births together are the second largest cause of infant mortality. Given that the other two indicators in the domain are direct precursors of infant mortality, we evaluated the extent to which similar counties were identified when infant mortality rate was included or excluded (among counties with non-suppressed data). The level of suppression for preterm birth and low birthweight was also substantial for individual year data. Thus, we compiled 3-yr and 5-yr aggregated data to obtain reliable estimates for smaller counties. Domestic violence was excluded because there are no national sources available with county-level data for domestic violence. |
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| Domain | Indicator | Indicator Definition | Alignment with statute definition of at-risk communities | Year | Source | Source Link | Source Notes | Next Update | OMB No: 0906-XXXX | |
| Socioeconomic Status (SES) | Poverty | % population living below %100 FPL | Poverty | 2016 | Census Small Area Income and Poverty Estimates | https://www.census.gov/data/datasets/2016/demo/saipe/2016-state-and-county.html | 2017 data available in 2019 | Expiration Date: XX/XX/XXXX | ||
| Unemployment | Unemployed percent of the civilian labor force | Unemployment | 2016 | Bureau of Labor Statistics | https://www.bls.gov/lau/#cntyaa | 2017 data available in 2019 | ||||
| HS Dropout | % of 16-19 year olds not enrolled in school with no high school diploma | High school dropouts | 2016 | American Community Survey | https://factfinder.census.gov | 1 year estimates used for counties with populations >65,000; 5 year estimate used for counties with populations <65,000 | 2017 data available in 2019 | |||
| % of 16-19 year olds not enrolled in school with no high school diploma | 2012-2016 | |||||||||
| % of 16-19 year olds not enrolled in school with no high school diploma | 2012-2016 OR 2016 | |||||||||
| Income Inequality | Gini Coefficient - 1 Yr Estimate | N/A | 2016 | American Community Survey | https://factfinder.census.gov | 1 year estimates used for counties with populations >65,000; 5 year estimate used for counties with populations <65,000 | 2017 data available in 2019 | |||
| Gini Coefficient - 5 Yr Estimate | 2012-2016 | |||||||||
| Gini Coefficient - 1 Yr or 5 Yr Estimate | 2012-2016 OR 2016 | |||||||||
| Adverse Perinatal Outcomes | Preterm Birth | % live births <37 weeks | Premature birth, low-birth weight infants, and infant mortality, including infant death due to neglect or other indicators of at-risk prenatal, maternal, newborn, or child health | 2012-2016 | NVSS - Raw Natality File | File received by HRSA | Births <10 were suppressed; the mean of counties was inputted for counties with missing data | 2017 data available in 2019 | ||
| Low Birth Weight | % live births <2500 g | Premature birth, low-birth weight infants, and infant mortality, including infant death due to neglect or other indicators of at-risk prenatal, maternal, newborn, or child health | 2012-2016 | NVSS - Raw Natality File | File received by HRSA | Births <10 were suppressed; the mean of counties was inputted for counties with missing data | 2017 data available in 2019 | |||
| Substance Use Disorder | Alcohol | Prevalence rate: Binge alcohol use in past month | Substance abuse | 2012-2014 | SAMHSA - National Survey of Drug Use and Health | https://www.samhsa.gov/data/population-data-nsduh/reports?tab=38 | County estimates are inputted using the estimate for the Substance Abuse Treatment Planning Region in which they belong. Nonmedical use of pain relievers refer to any form of prescription pain relievers that were not prescribed for the person or that the person took only for the experience or feeling they caused. |
2014-2016 available mid-2018; limited set only | ||
| Marijuana | Prevalence rate: Marijuana use in past month | 2012-2014 | ||||||||
| Illicit Drugs | Prevalence rate: Use of illicit drugs, excluding Marijuana, in past month | 2012-2014 | ||||||||
| Pain Relievers | Prevalence rate: Nonmedical use of pain medication in past year | 2012-2014 | ||||||||
| Crime | Crime Reports | # reported crimes/1000 residents | Crime | 2014 | Institute for Social Research - National Archive of Criminal Justice Data | https://www.icpsr.umich.edu/icpsrweb/NACJD/series/57 | Used county population count from ICPSR - NACJD, not PEP | Unknown | ||
| Juvenile Arrests | # crime arrests ages 0-17/100,000 juveniles aged 0-17, 2014 | 2014 | Institute for Social Research - National Archive of Criminal Justice Data | https://www.icpsr.umich.edu/icpsrweb/NACJD/series/57 | Used county population of 0-17 year olds from PEP | Unknown | ||||
| Juvenile Arrests | # crime arrests ages 0-17/100,000 juveniles aged 0-17, 2015 | 2015 | Institute for Social Research - National Archive of Criminal Justice Data | https://www.icpsr.umich.edu/icpsrweb/NACJD/studies/36794 | Used county population of 0-17 year olds from PEP | Unknown | ||||
| Child Maltreatment | Child Maltreatment | Rate of maltreatment victims aged <1-17 per 1,000 child (aged <1-17) residents | Child maltreatment | 2016 | ACF | File received by HRSA | 2017 data available in 2019 | |||
| Domain | Indicator | Indicator Definition | Year | Missing (n) | Missing (%) | Mean of counties | SD | Median | Interquartile Range | Min | Max | Other Notes | State Estimate | OMB No: 0906-XXXX | |
| Socioeconomic Status | Poverty | % population living below %100 FPL | 2016 | Expiration Date: XX/XX/XXXX | |||||||||||
| Unemployment | Unemployed percent of the civilian labor force | 2016 | |||||||||||||
| HS Dropout | % of 16-19 year olds not enrolled in school with no high school diploma - 1 Yr Estimate | 2016 | |||||||||||||
| % of 16-19 year olds not enrolled in school with no high school diploma - 5 Yr Estimate | 2012-2016 | ||||||||||||||
| % of 16-19 year olds not enrolled in school with no high school diploma - 1 Yr or 5 Yr Estimate | 2012-2016 OR 2016 | ||||||||||||||
| Income Inequality | Gini Coefficient - 1 Yr Estimate | 2016 | |||||||||||||
| Gini Coefficient - 5 Yr Estimate | 2012-2016 | ||||||||||||||
| Gini Coefficient - 1 Yr or 5 Yr Estimate | 2012-2016 OR 2016 | ||||||||||||||
| Adverse Perinatal Outcomes | Preterm Birth | % live births <37 weeks | 2012-2016 | ||||||||||||
| Low Birth Weight | % live births <2500 g | 2012-2016 | |||||||||||||
| Substance Use Disorder | Alcohol | Prevalence rate: Binge alcohol use in past month | 2012-2014 | ||||||||||||
| Marijuana | Prevalence rate: Marijuana use in past month | 2012-2014 | |||||||||||||
| Illicit Drugs | Prevalence rate: Use of illicit drugs, excluding Marijuana, in past month | 2012-2014 | |||||||||||||
| Pain Relievers | Prevalence rate: Nonmedical use of pain medication in past year | 2012-2014 | |||||||||||||
| Crime | Crime Reports | # reported crimes/1000 residents | 2014 | ||||||||||||
| Juvenile Arrests | # crime arrests ages 0-17/100,000 juveniles aged 0-17, 2014 | 2014 | |||||||||||||
| Juvenile Arrests | # crime arrests ages 0-17/100,000 juveniles aged 0-17, 2015 | 2015 | |||||||||||||
| Child Maltreatment | Child Maltreatment | Rate of maltreatment victims aged <1-17 per 1,000 child (aged <1-17) residents | 2016 | ||||||||||||
| County | Poverty | Unemployment | HS dropout | HS dropout 1 Yr | HS dropout 5 Yr | Income Inequality | Income Inequality 1 Yr | Income Inequality 5 Yr | Low Birth Weight | Preterm Birth | Alcohol | Marijuana | Illicit Drugs | Pain Relievers | Crime Reports | Juvenile Arrests (2014) | Juvenile Arrests (2015) | Child Maltreatment | OMB No: 0906-XXXX | |
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| County | Poverty | Unemployment | HS dropout | HS dropout 1 Yr | HS dropout 5 Yr | Income Inequality | Income Inequality 1 Yr | Income Inequality 5 Yr | Low Birth Weight | Preterm Birth | Alcohol | Marijuana | Illicit Drugs | Pain Relievers | Crime Reports | Juvenile Arrests (2014) | Juvenile Arrests (2015) | Child Maltreatment | OMB No: 0906-XXXX | |
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| County | 2016 Population | SES | Adverse Perinatal Outcomes | Substance Use Disorder | Crime | Child Maltreatment | Number of At-Risk Domains | OMB No: 0906-XXXX | |
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| Geographic Location | Standardized Indicator Values | Standardized Indicator Value ≥1 | Proportion of Standardized Indicator Values ≥1 | Proportion of High Standardized Indicator Values ≥0.5 | Number of At-Risk Domains | OMB No: 0906-XXXX | |||||
| County | Low Birth Weight | Preterm Birth | Low Birth Weight | Preterm Birth | Adverse Perinatal Outcomes | Adverse Perinatal Outcomes | At-Risk Domains | Expiration Date: XX/XX/XXXX | |||
| [Insert County or Geography Name] | #DIV/0! | #DIV/0! | #DIV/0! | #DIV/0! | #DIV/0! | #DIV/0! | #DIV/0! | ||||
| These formulas can be used to standardize (ie calculate z-scores) for each of the cleaned, raw indicator values. The EXCEL formula is '=STANDARDIZE(value, mean, SD). The mean and standard deviation should be calculated based on the raw values for all counties/geographic locations. | This formula returns a value of 1 if the standardized indicator value is ≥1 and returns a value of 0 if the standardized indicator value is <1. | This formula calculations the proportion of standardized indicators with values ≥1 within a domain. If new indicators are added to a domain, they should be added to this formula. | This formula returns a value of 1 if the proportion of standardized indicators with values ≥1 is 0.5 or more and returns a 0 if the proportion is <0.5. A value of 1 denotes the domain is considered at-risk. | This formula sums the number of at-risk domains. Counties or geographic locations with 2 or more at-risk domains may be considered at-risk. | |||||||
| File Type | application/vnd.openxmlformats-officedocument.spreadsheetml.sheet |
| File Modified | 0000-00-00 |
| File Created | 0000-00-00 |