Federally Available Data (FAD) Resource Document

Federally Available Data Resource Document 08/12/2016

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FEDERALLY AVAILABLE DATA (FAD) RESOURCE

DOCUMENT

This document provides detailed data notes, FAD availability, stratifier information, the complete FAD excel file, and SAS code as available for each

National Outcome Measure and National Performance Measure. It is designed to support a deeper understanding of the new measures and FAD,

issue any clarifications, enable states to make comparisons to U.S. and other state data, and to provide statistical code for states to examine their own

indicator data on a timelier or more granular basis than available federally. It is a living document that will be updated as new data notes or

clarifications become available.

Release Version August 12, 2016


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Table of Contents

Document Version History ............................................................................................................................ 5

Figure 1: National Outcome Measures (NOMs)............................................................................................ 7

NOM 1 - Percent of pregnant women who receive prenatal care beginning in the first trimester ........... 12

NOM 2 - Rate of severe maternal morbidity per 10,000 delivery hospitalizations .................................... 14

NOM 3 - Maternal mortality rate per 100,000 live births .......................................................................... 26

NOM 4.1 - Percent of low birth weight deliveries (


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NOM 16.3 - Adolescent suicide rate ages 15 through 19 per 100,000 ....................................................... 75

NOM 17.1 - Percent of children with special health care needs ................................................................ 77

NOM 17.2 - Percent of children with special health care needs (CSHCN) receiving care in a well-functioning system ...................................................................................................................................... 80

NOM 17.3 - Percent of children diagnosed with an autism spectrum disorder ......................................... 90

NOM 17.4 - Percent of children diagnosed with Attention Deficit Disorder/Attention Deficit Hyperactivity Disorder (ADD/ADHD) ................................................................................................................................. 93

NOM 18 - Percent of children with a mental/behavioral condition who receive treatment or counseling .................................................................................................................................................................... 96

NOM 19 - Percent of children in excellent or very good health ................................................................. 99

NOM 20 - Percent of children and adolescents who are overweight or obese (BMI at or above the 85th percentile) ................................................................................................................................................. 102

NOM 21 - Percent of children without health insurance.......................................................................... 106

NOM 22.1 - Percent of children, ages 19 through 35 months, who have received the 4:3:1:3(4):3:1:4 series of routine vaccinations ................................................................................................................... 109

NOM 22.2 - Percent of children 6 months through 17 years who are vaccinated annually against seasonal influenza .................................................................................................................................................... 111

NOM 22.3 - Percent of adolescents, ages 13 through 17, who have received at least one dose of the HPV vaccine ...................................................................................................................................................... 113

NOM 22.4 - Percent of adolescents, ages 13 through 17, who have received at least one dose of the Tdap vaccine ............................................................................................................................................. 115

NOM 22.5 - Percent of adolescents, ages 13 through 17, who have received at least one dose of the meningococcal conjugate vaccine ............................................................................................................ 117

Figure 2: National Performance Measures (NPMs) .................................................................................. 119

NPM 1 - Percent of women with a past year preventive medical visit ..................................................... 120

NPM 2 - Percent of cesarean deliveries among low-risk first births......................................................... 123

NPM 3 - Percent of very low birth weight (VLBW) infants born in a hospital with a Level III+ Neonatal Intensive Care Unit (NICU) ........................................................................................................................ 125

NPM 4 - A) Percent of infants who are ever breastfed and B) Percent of infants breastfed exclusively through 6 months ..................................................................................................................................... 127

NPM 5 - Percent of infants placed to sleep on their backs ....................................................................... 130

NPM 6 - Percent of children, ages 10 through 71 months, receiving a developmental screening using a parent-completed screening tool ............................................................................................................. 133

NPM 7 - Rate of hospitalization for non-fatal injury per 100,000 children ages 0 through 9 and adolescents 10 through 19 ....................................................................................................................... 136


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NPM 8 - Percent of children ages 6 through 11 and adolescents ages 12 through 17 who are physically active at least 60 minutes per day ............................................................................................................ 138

NPM 9 - Percent of adolescents, ages 12 through 17, who are bullied or who bully others ................... 143

NPM 10 - Percent of adolescents, ages 12 through 17, with a preventive medical visit in the past year 147

NPM 11 - Percent of children with and without special health care needs having a medical home ....... 150

NPM 12 - Percent of adolescents with and without special health care needs who received services necessary to make transitions to adult health care .................................................................................. 156

NPM 13 - A) Percent of women who had a dental visit during pregnancy and B) Percent of children, ages 1 through 17, who had a preventive dental visit in the past year ............................................................ 160

NPM 14 - A) Percent of women who smoke during pregnancy and B) Percent of children who live in households where someone smokes ........................................................................................................ 165

NPM 15 - Percent of children ages 0 through 17 who are adequately insured ....................................... 170


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Document Version History

Version #

Implemented By

Revision Date

Modifications

1.0 Ashley Hirai 06/30/2016 Initial 2016 Release with complete FAD excel file (provided as attachment)

2.0 Ashley Hirai 08/12/2016 1) NPM-7 Injury Hospitalization denominator data were corrected to rely on U.S. Census Bureau population estimates instead of Nielsen estimates originally provided for 2017 Block Grant applications

2) Form 11 Other State Data are provided as attachment

Attachments There are two excel files that can be accessed by clicking on the paper clip (attachment) icon on the left hand panel of this document.

1) All FAD Data File This excel file contains all Federally Available Data (FAD) for National Performance and Outcome Measures for all states/jurisdictions and the U.S. Data are generally available by year (to monitor trends and set objectives) and by various demographic stratifiers (to identify and monitor disparities and target programmatic efforts accordingly). Standard errors (SEs) and 95% confidence intervals (CIs) are provided to facilitate analytic comparisons both within and across states/jurisdictions. Numerators and denominators (weighted if from surveys) are also provided to quantify and communicate impact and numbers affected. Data can be filtered (e.g. by measure, data source, data year, state, or stratifier of interest) and sorted (e.g., highest to lowest estimate rankings) to facilitate comparisons. Data notes and alerts for each measure are provided within separate worksheets of the file; available definitions and notes on the stratifiers are contained within the measure details of this PDF.

2) Form 11 Data This excel file contains Other State Data that may be helpful for states/jurisdictions to monitor and review. It will be pre-populated for next years annual application. Specific data elements are noted below. Standard errors (SEs) and 95% confidence intervals (CIs) are provided to facilitate analytic comparisons both within and across states/jurisdictions. Numerators and denominators (weighted if from surveys) are also provided to quantify and communicate impact and numbers affected. Data can be filtered (e.g. by measure, data source, data year, state, or stratifier of interest) and sorted (e.g., highest to lowest estimate rankings) to facilitate comparisons. Data notes are provided within a separate worksheet of the file. Please see the Block Grant guidance for more information on this form. #1A/B: Infant mortality rate and low birth weight rate by race/ethnicity (already provided as NOM 9.1 and 4.1) #2: Infant mortality rate and low birth weight rate by county #3: State MCH Workforce (counts and rate per 100,000 population)

Obstetricians Family practitioners Certified family nurse practitioners not available Certified nurse midwives Pediatricians
http://mchb.hrsa.gov/sites/default/files/mchb/MaternalChildHealthInitiatives/TitleV/blockgrantguidance.pdf


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Certified pediatric nurse practitioners not available


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Figure 1: National Outcome Measures (NOMs) linked to National Performance Measures (NPMs)

NPM #

National Performance Measure (NPM) National Outcome Measures Associated with National Performance Measure

1 Well-woman visit (Percent of women with a past year preventive medical visit)

Severe maternal morbidity per 10,000 delivery hospitalizations

Maternal mortality rate per 100,000 live births*

Low birth weight rate (%)

Very low birth weight rate (%)*

Moderately low birth weight rate (%)*

Preterm birth rate (%)

Early preterm birth rate (%)*

Late preterm birth rate (%)*

Early term birth rate (%)*

Infant mortality per 1,000 live births

Perinatal mortality per 1,000 live births plus fetal deaths

Neonatal mortality per 1,000 live births

Postneonatal mortality rate per 1,000 live births*

Preterm-related mortality per 100,000 live births

2 Low risk cesarean deliveries (Percent of cesarean deliveries among low-risk first births)


Maternal mortality rate per 100,000 live births

3 Perinatal regionalization (Percent of very low birth weight (VLBW) infants born in a hospital with a Level III+ Neonatal Intensive Care Unit (NICU))





4 Breastfeeding (A. Percent of infants who are ever breastfed and B. Percent of infants breastfed exclusively through 6 months)

Infant mortality rate per 1,000 live births*


Sleep-related SUID per 100,000 live births

5 Safe sleep (Percent of infants placed to sleep on their backs)


Postneonatal mortality per 1,000 live births


6 Developmental screening (Percent of children, ages 10 through 71 months, receiving a developmental screening using a parent-completed screening tool)

Percent of children in excellent or very good health

Percent of children meeting the criteria developed for school readiness

7 Child Injury (Rate of hospitalization for non-fatal injury per 100,000 children ages 0 through 9 and adolescents ages 10 through 19

Child mortality ages 1 through 9 per 100,000

Adolescent mortality ages 10 through 19 per 100,000

Adolescent motor vehicle mortality ages 15 through 19 per 100,000*

Adolescent suicide ages 15 through 19 per 100,000*


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NPM #


8 Physical activity (Percent of children ages 6 through 11 and adolescents ages 12 through 17 who are physically active at least 60 minutes per day)

Percent of children in excellent or very good health*

Percent of children and adolescents who are overweight or obese (BMI at or above the 85th percentile)

9 Bullying (Percent of adolescents, 12 through 17, who are bullied or who bully others)


Adolescent suicide ages 15 through 19 per 100,000

10 Adolescent well-visit (Percent of adolescents, ages 12 through 17, with a preventive medical visit in the past year)


Percent of children ages 6 months through 17 years who are vaccinated annually against seasonal influenza

Percent of adolescents, ages 13 through 17, who have received at least one dose of the HPV vaccine

Percent of adolescents, ages 13 through 17, who have received at least one dose of the Tdap vaccine

Percent of adolescents, ages 13 through 17, who have received at least one dose of the meningococcal conjugate vaccine


Adolescent motor vehicle mortality ages 15 through 19 per 100,000*

Adolescent suicide ages 15 through 19 per 100,000

Percent of children with mental/behavioral health condition who receive treatment or counseling

Percent of adolescents who are overweight or obese (BMI at or above the 85th percentile)*

Severe maternal morbidity per 10,000 delivery hospitalizations*


Low birth weight rate (%)*



Preterm birth rate (%)*




Infant mortality per 1,000 live births*

Perinatal mortality per 1,000 live births plus fetal deaths*

Neonatal mortality per 1,000 live births*


Preterm-related mortality per 100,000 live births*


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NPM #


11 Medical home (Percent of children with and without special health care needs having a medical home)

Percent of children with special health care needs (CSHCN) receiving care in a well-functioning system


Percent of children ages 19 through 35 months, who have received the 4:3:1:3(4):3:1 :4 combined series of routine vaccinations*

Percent of children, ages 6 months through 17 years, who are vaccinated annually against seasonal influenza*

Percent of adolescents, ages 13 through 17, who have received at least one dose of the HPV vaccine*

Percent of adolescents, ages 13 through 17, who have received at least one dose of the Tdap vaccine*

Percent of adolescents, ages 13 through 17, who have received at least one dose of the meningococcal conjugate vaccine*

12 Transition (Percent of adolescents with and without special health care needs who received services necessary to make transitions to adult health care)

Percent of children with special health care needs (CSHCN) receiving care in a well-functioning system


13 Oral health (A. Percent of women who had a dental visit during pregnancy and B. Percent of children, ages 1 through 17, who had a preventive dental visit in the past year)


Percent of children ages 1 through 17 who have decayed teeth or cavities in the past 12 months

14 Smoking during Pregnancy and Household Smoking (A. Percent of women who smoke during pregnancy and B. Percent of children who live in households where someone smokes)



Low birth weight rate (%)



Preterm birth rate (%)








Post neonatal mortality per 1,000 live births




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NPM #


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Adequate insurance coverage (Percent of children ages 0 through 17 who are adequately insured)

Percent of children without health insurance

Systems of care for children with special health care needs (Percent of children and youth with special health care needs (CYSHCN) receiving care in a well-functioning system)

*Additional NOMs that may be related but are not part of the OMB-approved guidance or TVIS


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Figure 2: National Outcome Measures (NOMs) NOM # National Outcome Measure 1 Percent of pregnant women who receive prenatal care beginning in the first trimester 2 Rate of severe maternal morbidity per 10,000 delivery hospitalizations

3 Maternal mortality rate per 100,000 live births 4.1 Percent of low birth weight deliveries (


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NOM 1 - Percent of pregnant women who receive prenatal care beginning in the first trimester

GOAL To ensure early entrance into prenatal care to enhance pregnancy outcomes.

DEFINITION Numerator: Number of live births with reported first prenatal visit during the first trimester (before 13 weeks gestation) in the calendar year Denominator: Number of live births Units: 100 Text: Percent

HEALTHY PEOPLE 2020 OBJECTIVE Related to Maternal, Infant, and Child Health (MICH) 10.1. Increase the proportion of pregnant women who receive prenatal care beginning in the first trimester. (Baseline: 70.8 % of females delivering a live birth received prenatal care beginning in the first trimester in 2007, Target: 77.9%)

DATA SOURCES and DATA ISSUES National Vital Statistics System (NVSS) Birth File

SIGNIFICANCE Early identification of maternal disease and risks for complications of pregnancy or birth are the primary reason for first trimester entry into prenatal care. This can help ensure that women with complex problems and women with chronic illness or other risks are seen by specialists. Early high-quality prenatal care is critical to improving pregnancy outcomes.

FAD Availability by Year Year Data Not Available 2014 CT, NJ, RI, AS, FM, MH, PW, VI 2013 AL, AZ, AR, CT, HI, ME, NJ, RI, WV, AS, FM, MH, PW, VI 2012 AL, AK, AZ, AR, CT, HI, ME, MS, NJ, RI, VA, WV, AS, FM, MH, PW, VI 2011 AL, AK, AZ, AR, CT, HI, ME, MS, MN, MS, NJ, RI, VA, WV, AS, FM, GU, MH, PW, VI 2010 AL, AK, AZ, AR, CT, HI, LA, ME, MA, MN, MS, NJ, NC, RI, VA, WV, WI, AS, FM, GU, MH,

PW, VI 2009 AL, AK, AZ, AR, CT, DC, HI, IL, LA, ME, MD, MA, MN, MS, MO, NV, NJ, NC, OK, RI, VA,

WV, WI, AS, FM, GU, MH, MP, PW, VI


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Data Notes Prenatal care utilization was modified in the 2003 revision of the U.S. Standard Certificate of Live Birth and is only available for the states/jurisdictions that had implemented the 2003 revision as of January 1 of the data year. Overall U.S. estimates by year are not comparable due to the addition of states over time that have implemented the 2003 revision. Trends within a state after the 2003 revision are comparable. Urban/rural residence is not available for territories. For more information about the birth file, please see the User's Guide located at http://www.cdc.gov/nchs/data_access/VitalStatsOnline.htm

Available Stratifiers and Notes Stratifier Subcategory Special Notes Maternal Age


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NOM 2 - Rate of severe maternal morbidity per 10,000 delivery hospitalizations

GOAL To reduce life-threatening maternal illness and complications.

DEFINITION Numerator: Number of deliveries hospitalizations with an indication of severe morbidity from ICD-10 diagnosis or procedure codes (e.g. heart or kidney failure, stroke, embolism, hemorrhage). Denominator: Number of delivery hospitalizations Units: 10,000 Text: Rate

HEALTHY PEOPLE 2020 OBJECTIVE Related to Maternal, Infant, and Child Health (MICH) 5. Reduce the rate of maternal mortality. (Baseline:12.7 maternal deaths per 100,000 live births in 2007, Target: 11.4 maternal deaths per 100,000 live births) Related to Maternal, Infant, and Child Health (MICH) 6. Reduce maternal illness and complications due to pregnancy (complications during hospitalized labor and delivery) . (Baseline: 31.1%, Target: 28%)

DATA SOURCES and DATA ISSUES State Inpatient Database (SID)

SIGNIFICANCE Severe maternal morbidity is more than 100 times as common as pregnancy-related mortalitytranslating to about 52,000 women affected annuallyand it is estimated to have increased by 75 percent over the past decade. Rises in chronic conditions, including obesity, diabetes, hypertension, and cardiovascular disease, are likely to have contributed to this increase (Callaghan et al, 2012). Minority women and particularly non-Hispanic black women have higher rates of severe maternal morbidity. Non-Hispanic Black, Hispanic, Asian/Pacific Islander, and American Indian/Alaska Native women had 2.1, 1.3, 1.2, and 1.7 times, respectively, higher rates of severe morbidity compared with non-Hispanic white women (Creanga et al, 2014).

FAD Availability by Year Year Data Not Available 2013 AK, AL, DE, ID, ME, MS, NH, AS, FM, GU, MH, MP, PW, PR, VI 2012 AL, DC, DE, ID, MS, NH, AS, FM, GU, MH, MP, PW, PR, VI 2011 AL, DC, DE, ID, NH, AS, FM, GU, MH, MP, PW, PR, VI 2010 AL, DC, DE, ID, ND, NH, AS, FM, GU, MH, MP, PW, PR, VI 2009 AK, AL, DC, DE, ID, MS, ND, AS, FM, GU, MH, MP, PW, PR, VI 2008 AK, AL, DC, DE, ID, MS, MT, ND, NM, AS, FM, GU, MH, MP, PW, PR, VI


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Data Notes This measure follows the CDC-developed definition of severe maternal morbidity identified from hospital discharge procedure and diagnosis codes that indicate a potentially life-threatening condition or maternal complication (Callaghan et al, 2012). Specific ICD-9-CM diagnosis and procedure codes are available at http://www.cdc.gov/reproductivehealth/MaternalInfantHealth/SevereMaternalMorbidity.html In 2009, the code for acute renal failure changed from 584.8 to 277.88. In 2010, a new code was added for major puerperal sepsis (670.2). With the exception of hospitalizations with in-hospital mortality, transfer, or severe complications identified by procedure codes (e.g., hysterectomy, blood transfusion, ventilation), cases of severe maternal morbidity identified by diagnostic codes were reclassified as hospitalizations without severe maternal morbidity if they had an implausibly short length of stay (


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Stratifier Subcategory Special Notes Residence Small/Medium Metro

Non-Metro Codes.

SAS Code /******************************************************************* Title: Macro for Estimating Severe Maternal Morbidity for Delivery and Postpartum Hospitalizations - With and Without Severity Re-calculation from Callaghan, 2012 This macro produces: a. 25 condition specification (Callaghan et al 2012) for delivery hospitalizations (with and without severity re-calc) Modified from code written by Dr. Elena Kuklina (CDC) and Renata Howland (NYC DOH). Generalized by Kristin Rankin, UIC ([email protected]) Date created: 12/3/14 Last updated: 4/27/15 Description of Severity Re-calculation: Based on Callaghan (2012), this program recalculates the presence of an diagnosis-related (but not procedure-related) SMM based on 3 criteria: death, transfer, and length of stay. This is to make the estimate of SMM more conservative and exclude morbidities that may be "rule-out" diagnoses based on implausibly short length of stay (except in the case of transfers or in-hospital deaths). Instructions: After initial review of the dataset to ensure that diagosis code, procedure code, and DRG variables are character variables with the correct number of digits (diagnosis = 1-5, procedure=2-4), input the variable names for all diagnoses and procedures available in your hospital discharge dataset into the %LET statements below (see example provided). Also add the dataset name (dsn) and variable for DRG code, with DRG type (see bottom of code document for information on DRG type). Do not make any other changes to the macro. For severity re-calculation variables Died, TransferIn and TransferOut, you will first need to create numeric, dichotomous variables from the Patient Status and Admission Source variables such that: TransferIn = 1 where Admission Source/Admit Point of Origin = Transfer from another hospital TransferOut = 1 where Patient Status is transferred to any other healthcare facility Died = 1 where Patient Status indicates that the patient died in the hospital ********************************************************************/ %let dsn= ; /*Dataset name */ %let DiagCodes= ; /*List of primary and all secondary ICD-9CM diagnosis codes available - please input these in order with primary and secondary first*/ %let ProcCodes= ; /*List of primary and all secondary ICD-9CM procedure codes available*/ %let DRG= ; /*Variable name for DRG code*/


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%let DRGtype= ; /*Type of DRG code - MS or APR - see bottom of this file for description of APR vs MS delivery DRGs*/ %let patient_age= ; /*Variable name for patient age - assumed numeric*/ %let patient_sex= ; /*Variable name for patient age - assumed character (M/F)*/ /*Statements below are for performing the severity re-calculation for the delivery hospitalizations*/ %let LOS= ;/*Input variable name for length of stay variable - assumed numeric in macro*/ %let died= ;/*Input variable name for newly-created dichotomous variable where 1 = in-hospital mortality*/ %let TransferIn= ;/*Input variable name for newly-created dichotomous variable where 1 = admission source from another hospital*/ %let TransferOut= ;/*Input variable name for newly-created dichotomous variable where 1 = patient status is transferred to another healthcare facility*/ /*Example: %let dsn=ilhdd.ilhdd2013b; %let DiagCodes=Diag_Code_1-Diag_Code_9 Diag_Code_A Diag_Code_B Diag_Code_C Diag_Code_D Diag_Code_E Diag_Code_F; %let ProcCodes=Proc_Code_1-Proc_Code_9; %let DRG=DRG; %let DRGtype=MS; %let patient_age=patient_age; %let patient_sex=sex; %let LOS=LOS; %let died=died; %let TransferIn=TransferIn; %let TransferOut=TransferOut; */ **Data check to ensure that Diagnosis and Procedure codes are character variables; proc contents data=&dsn varnum; /*Varnum option displays variables in order they were created*/ run; /*Data Checks for numbers of digits in all code fields proc freq data=&dsn; tables &DiagCodes &ProcCodes; run;*/ /*Create dataset with Delivery Hospitalizations only and SMM indicator, plus subtypes*/ data DeliveryHosps (drop=exclude1-exclude2 deliveryHosp delivery_flg1-delivery_flg3 cesareanDiag cesareanProc cesareanDRG priorCesareanDiag i Total_SMM i); set &dsn; /*Restrict dataset by age and gender (female)*/ where (8 le &patient_age le 65) and (&patient_sex='F' or &patient_sex='f'); DRGtype="&DRGtype"; /*Set up arrays for diagnoses, procedure and DRG variables*/ array diagvar {*} &DiagCodes; array procvar {*} &ProcCodes; array drgvar {*} &DRG;


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*Delivery hospitalizations; delivery_flg1=0; cesareanDiag=0; priorCesareanDiag=0; do i=1 to dim(diagvar); if diagvar{i} in:('V27', '650') then delivery_flg1=1; /*Delivery diagnoses*/ if diagvar{i} in ('66970','66971') then cesareanDiag=1; /*cesarean diagnosis code - for severity re-calc*/ if diagvar{i} in:('65420', '65421', '65423') then priorCesareanDiag=1; /*Prior cesarean - for severity re-calc*/ end; delivery_flg2=0; cesareanDRG=0; if DRGtype="MS" then do; do i=1 to dim(drgvar); if drgvar{i} in:('765', '766', '767', '768', '774', '775') then delivery_flg2=1; /*Delivery DRGs*/ if drgvar{i} in:('765', '766') then cesareanDRG=1; /*cesarean deliveries - for severity re-calc*/ end; end; if DRGtype="APR" then do; do i=1 to dim(drgvar); if drgvar{i} in:('370', '371', '372', '373', '374', '375') then delivery_flg2=1; /*Delivery DRGs*/ if drgvar{i} in:('370', '371') then cesareanDRG=1; /*cesarean deliveries - for severity re-calc */ end; end; delivery_flg3=0; /*Delivery Procedures*/ cesareanProc=0; do i=1 to dim(procvar); if procvar{i} in:('720', '721', '7221', '7229', '7231', '7239', '724', '726', '7251', '7252', '7253', '7254' '7271', '7279', '728', '729', '7322', '7359', '736', '740', '741', '742', '744', '7499') then delivery_flg3=1; if procvar{i} in:('740', '741', '742', '744', '7499') then cesareanProc=1; end; /*Restrict dataset to delivery hospitalizations*/ if delivery_flg1=1 or delivery_flg2 = 1 or delivery_flg3 = 1 then deliveryHosp=1; else delete; /*Delivery Type - for severity re-calc*/ if priorCesareanDiag=1 and (cesareanProc=1 or CesareanDRG=1 or CesareanDiag=1) then DeliveryType=1;/*repeat cesarean*/ else if (cesareanProc=1 or CesareanDRG=1 or CesareanDiag=1) then DeliveryType=2;/*primary cesarean*/ else DeliveryType=3; /*vaginal*/ *Flag cases for exclusion; exclude1=0; do i=1 to dim(diagvar);


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if diagvar{i} in :('630', '631', '632', '633', '634', '635', '636', '637', '638', '639') then exclude1=1; end; *Ectopic or molar pregnancy and pregnancy with abortive outcome; exclude2=0; do i=1 to dim(procvar); if procvar{i} in:('6901', '6951', '7491', '750') then exclude2=1; *Abortion procedure codes; end; if exclude1=1 or exclude2=1 then delete; /*************************** *Morbidities**************** ****************************/ *DIAGNOSIS CODES; label Renal_flag = "Acute renal failure"; Renal_flag = 0; do i=1 to dim(diagvar);; if diagvar{i} in :('584', '27788', '6693') /*Code 584.8 was changed to 277.88 in 2009, so both are included here per Callaghan email in 2015*/ then Renal_flag =1; end; label Cardiac_flag = "Cardiac arrest"; Cardiac_flag = 0; do i=1 to dim(diagvar); if diagvar{i} in :('42741', '42742', '4275') then Cardiac_flag =1; end; label Heart_flag = "Heart failure during procedure or surgery"; Heart_flag = 0; do i=1 to dim(diagvar); if diagvar{i} in :('6694', '9971') then Heart_flag =1; end; label Shock_flag = "Shock"; Shock_flag = 0; do i=1 to dim(diagvar); if diagvar{i} in :('6691', '9980', '9950', '9954','7855') then Shock_flag =1; end; label Sepsis_flag = "Sepsis"; Sepsis_flag = 0; do i=1 to dim(diagvar); if diagvar{i} in :('038', '99591', '99592', '6702') /*670.2 added in 2010 - per Callaghan email in 2015*/ then Sepsis_flag =1; end; label Coagulation_flag = "Disseminated intravascular coagulation"; Coagulation_flag = 0; do i=1 to dim(diagvar); if diagvar{i} in :('2866', '2869', '6663')


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then Coagulation_flag =1; end; label Embolism_flag = "Amniotic fluid embolism"; Embolism_flag = 0; do i=1 to dim(diagvar); if diagvar{i} in :('6731') then Embolism_flag =1; end; label Throm_flag = "Thrombotic embolism"; Throm_flag = 0; do i=1 to dim(diagvar); if diagvar{i} in :('4151', '6730', '6732', '6733', '6738') then Throm_flag =1; end; label Cerebro_flag = "Puerperal cerebrovascular disorders"; Cerebro_flag = 0; do i=1 to dim(diagvar); if diagvar{i} in :('430', '431', '432', '433', '434', '436', '437', '6715', '6740', '9972', '9992') then Cerebro_flag =1; end; label Anesthesia_flag = "Severe anesthesia complications"; Anesthesia_flag = 0; do i=1 to dim(diagvar); if diagvar{i} in :('6680','6681','6682') then Anesthesia_flag =1; end; label Pulmonary_flag = "Pulmonary edema"; Pulmonary_flag = 0; do i=1 to dim(diagvar); if diagvar{i} in :('4281', '5184') then Pulmonary_flag =1; end; label Resp_flag = "Adult respiratory distress syndrome"; Resp_flag = 0; do i=1 to dim(diagvar); if diagvar{i} in :('5185','51881', '51882', '51884', '7991') then Resp_flag =1; end; label MI_flag = "Acute myocardial infarction"; MI_flag = 0; do i=1 to dim(diagvar); if diagvar{i} in :('410') then MI_flag =1; end; label Eclampsia_flag = "Eclampsia"; Eclampsia_flag = 0; do i=1 to dim(diagvar); if diagvar{i} in :('6426') then Eclampsia_flag =1;


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end; label Sicklecell_flag = "Sickle Cell Anemia with Crisis"; Sicklecell_flag = 0; do i=1 to dim(diagvar); if diagvar{i} in :('28262', '28264', '28269') then Sicklecell_flag =1; end; label BrainInj_flag = "Intracranial Injuries"; BrainInj_flag = 0; do i=1 to dim(diagvar); if diagvar{i} in :('800', '801', '803', '804', '851', '852', '853', '854' ) then BrainInj_flag =1; end; label BodyInj_flag = "Internal injuries of the thorax, abdomen, and pelvis"; BodyInj_flag = 0; do i=1 to dim(diagvar); if diagvar{i} in :('860', '861', '862', '863', '864', '865', '866', '867', '868', '869' ) then BodyInj_flag =1; end; label Aneurysm_flag = "Aneurysm"; Aneurysm_flag = 0; do i=1 to dim(diagvar); if diagvar{i} in :('441') then Aneurysm_flag =1; end; *PROCEDURE CODES; label Transfusion_flag = "Blood Transfusion"; Transfusion_flag = 0; do i=1 to dim(procvar); if procvar{i} in :('9900', '9901', '9902', '9903', '9904', '9905', '9906', '9907', '9908', '9909') then Transfusion_flag =1; end; label Hysterectomy_flag = "Hysterectomy"; Hysterectomy_flag = 0; do i=1 to dim(procvar); if procvar{i} in :('683', '684', '685', '686', '687', '688', '689') then Hysterectomy_flag =1; end; label Ventilation_flag = "Ventilation"; Ventilation_flag = 0; do i=1 to dim(procvar); if procvar{i} in :('9390', '9601', '9602', '9603', '9604', '9605', '967') then Ventilation_flag =1; end; label Operations_flag = "Operations on the heart and pericardium"; Operations_flag = 0; do i=1 to dim(procvar);


22

if procvar{i} in :('35', '36', '37', '39') then Operations_flag =1; end; label Cardiomon_flag = "Cardio monitoring"; Cardiomon_flag = 0; do i=1 to dim(procvar); if procvar{i} in :('896') then Cardiomon_flag =1; end; label Tracheo_flag = "Temporary Tracheostomy"; Tracheo_flag = 0; do i=1 to dim(procvar); if procvar{i} in :('311') then Tracheo_flag =1; end; label Conversion_flag = "Conversion of cardiac rhythm"; Conversion_flag = 0; do i=1 to dim(procvar); if procvar{i} in :('996') then Conversion_flag =1; end; /***************************************************************************************************************** CALLAGHAN ET AL OVERALL SMM Variables - Count and Dichotomous *****************************************************************************************************************/ *CALLAGHAN 25 (WITH TRANSFUSION); *Create a count variable for total morbidites; array SMM_array {*} MI_flag Aneurysm_flag Cardiac_flag Heart_flag Conversion_flag Operations_flag Cardiomon_flag Coagulation_flag Transfusion_flag Hysterectomy_flag Tracheo_flag Ventilation_flag Resp_flag Pulmonary_flag BodyInj_flag BrainInj_flag Renal_flag Shock_flag Cerebro_flag Throm_flag Eclampsia_flag Sepsis_flag Embolism_flag Anesthesia_flag Sicklecell_flag ; Total_SMM=0; do i = 1 to dim(SMM_array); Total_SMM = sum(of SMM_array{*}); end; *Create overall SMM flag; if Total_SMM >=1 then SMM25 = 1; else SMM25 = 0; label smm25='Any Severe Maternal Morbidity - Callaghan (2012) - 25 conditions - without severity re-calculation' run;


23

/*Calculating 90th percentile of length of stay by delivery type for severity re-calculation*/ proc means data=DeliveryHosps p90 noprint; class deliveryType; var &LOS; output out=LOS90 p90=LOS90; run; data tempDeliveries; set DeliveryHosps; run; proc sort data=tempDeliveries; by deliveryType; run; data DeliveryHosps; merge tempDeliveries LOS90 (keep=deliverytype LOS90 where=(deliverytype ne .)); by deliverytype; run; /*Delete temporary datasets*/ proc datasets noprint; delete tempDeliveries LOS90; run; quit; /*Apply severity re-calculation to the delivery hosptialization dataset - For severity re-calculation, new variables are created with the _recalc suffix instead of the _flag suffix to re-assign SMMs that have implausibly low lengths of stay for the delivery type, and were not transfers or in-hospital deaths*/ data DeliveryHosps (drop=Total_SMM_recalc Total_SMMproc Total_SMMdiag_recalc Total_SMM_recalc_notrans LOS90 hosptype i); set DeliveryHosps; array SMMdiag {*} MI_flag Aneurysm_flag Cardiac_flag Heart_flag Coagulation_flag Resp_flag Pulmonary_flag BodyInj_flag BrainInj_flag Renal_flag Shock_flag Cerebro_flag Throm_flag Eclampsia_flag Sepsis_flag Embolism_flag Anesthesia_flag Sicklecell_flag ; array SMMproc {*} Conversion_flag Operations_flag Cardiomon_flag Transfusion_flag Hysterectomy_flag Tracheo_flag Ventilation_flag; array SMMre {*} MI_recalc Aneurysm_recalc Cardiac_recalc Heart_recalc Coagulation_recalc Resp_recalc Pulmonary_recalc BodyInj_recalc BrainInj_recalc Renal_recalc Shock_recalc Cerebro_recalc Throm_recalc Eclampsia_recalc Sepsis_recalc Embolism_recalc Anesthesia_recalc Sicklecell_recalc; do i=1 to dim(SMMdiag); *SMMre{i}=.; if SMMdiag{i}=1 and (0 < &LOS < LOS90) and &TransferIn ne 1 and &TransferOut ne 1 and &Died ne 1 then SMMre{i}=0; else SMMre{i}=SMMdiag{i};


24

end; Total_SMM_recalc=0; do i = 1 to dim(SMMdiag); Total_SMMdiag_recalc = sum(of SMMre{*}); end; do i = 1 to dim(SMMproc); Total_SMMproc = sum(of SMMproc{*}); end; Total_SMM_recalc=sum (Total_SMMdiag_recalc, Total_SMMproc); *Create overall SMM flag; if Total_SMM_recalc >=1 then SMM25_recalc = 1; else SMM25_recalc = 0; run; title 'Frequencies/%s of SMMs for Delivery Hospitalizations- Callaghan 25 Condition - '; title2 "Summary Measure and Individual Conditions (w/ and w/o severity re-calculation)"; proc freq data=DeliveryHosps; tables smm25 MI_flag Aneurysm_flag Cardiac_flag Heart_flag Conversion_flag Operations_flag Cardiomon_flag Coagulation_flag Transfusion_flag Hysterectomy_flag Tracheo_flag Ventilation_flag Resp_flag Pulmonary_flag BodyInj_flag BrainInj_flag Renal_flag Shock_flag Cerebro_flag Throm_flag Eclampsia_flag Sepsis_flag Embolism_flag Anesthesia_flag Sicklecell_flag; /*With severity recalculation*/ tables smm25_Recalc MI_recalc Aneurysm_recalc Cardiac_recalc Heart_recalc Coagulation_recalc Resp_recalc Pulmonary_recalc BodyInj_recalc BrainInj_recalc Renal_recalc Shock_recalc Cerebro_recalc Throm_recalc Eclampsia_recalc Sepsis_recalc Embolism_recalc Anesthesia_recalc Sicklecell_recalc ; run; /*Check DRG codes are consistent with year of data, conversion below: APR-DRG -- MS-DRG 370 -- 765 Cesarean section w CC/MCC 371 -- 766 Cesarean section w/o CC/MCC 372 -- 774 Vaginal delivery w complicating diagnoses 373 -- 775 Vaginal delivery w/o complicating diagnoses 374 -- 767 Vaginal delivery w sterilization &/or D&C 375 -- 768 Vaginal delivery w O.R. proc except steril &/or D&C 376 -- 776 Postpartum & post abortion diagnoses w/o O.R. procedure 377 -- 769 Postpartum & post abortion diagnoses w O.R. procedure 378 -- 777 Ectopic pregnancy 379 -- 778 Threatened abortion 380 -- 779 Abortion w/o D&C 381 -- 770 Abortion w D&C, aspiration curettage or hysterotomy 382 -- 780 False labor 383 -- 781 Other antepartum diagnoses w medical complications 384 -- 782 Other antepartum diagnoses w/o medical complications;


25

/*April 2011, Hospitalizations Related to Childbirth, 2008: http://www.hcup-us.ahrq.gov/reports/statbriefs/sb110.jsp*/


26

NOM 3 - Maternal mortality rate per 100,000 live births

GOAL To reduce the maternal mortality rate.

DEFINITION Numerator: Number of deaths related to or aggravated by pregnancy and occurring within 42 days of the end of a pregnancy Denominator: Number of live births Units: 100,000 Text: Rate

HEALTHY PEOPLE 2020 OBJECTIVE Identical to Maternal, Infant, and Child Health (MICH) 5. Reduce the rate of maternal mortality. (Baseline:12.7 maternal deaths per 100,000 live births in 2007, Target: 11.4 maternal deaths per 100,000 live births) Related to Maternal, Infant, and Child Health (MICH) 6. Reduce maternal illness and complications due to pregnancy (complications during hospitalized labor and delivery) . (Baseline: 31.1%, Target: 28%)

DATA SOURCES and DATA ISSUES National Vital Statistics System (NVSS) - Mortality and Birth Files

SIGNIFICANCE Maternal deaths related to childbirth in the U.S. are nearly at the highest rate in a quarter century, and the U.S. has seen a rise in maternal mortality over the past decade. The rate of death for mothers for every 100,000 live births was18.5 in the U.S. in 2013, a total of almost 800 deaths, showing a rise in pregnancy-related deaths in the U.S. since at least 1987, when the mortality rate was 7.2 per 100,000 births. There are also significant racial disparities with Black women being three times as likely White women to experience maternal death.

FAD Availability by Year Year Data Not Available 2010-2014 AL, AK, CO, HI, IA, KY, LA, MA, ME, MN, MS, NC, PA, TN, VA, WI, WV, AS, GU, FM,

MH, MP, PW, PR, VI 2009-2013 AL, AK, AZ, CO, HI, IA, KY, LA, MA, ME, MN, MO, MS, NC, PA, TN, VA, WI, WV, AS,

GU, FM, MH, MP, PW, PR, VI 2008-2012 AL, AK, AZ, CO, HI, IA, KY, LA, MA, ME, MN, MO, MS, NC, PA, TN, VA, VT, WI, WV, AS,

GU, FM, MH, MP, PW, PR, VI 2007-2011 AL, AK, AR, AZ, CO, GA, HI, IL, IN, IA, KY, LA, MA, ME, MN, MO, MS, NV, NC, ND, PA,

TN, VA, VT, WI, WV, AS, GU, FM, MH, MP, PW, PR, VI 2006-2010 AL, AK, AR, AZ, CO, DE, GA, HI, IL, IN, IA, KY, LA, MA, ME, MN, MO, MS, NV, NC, ND,

OH, PA, TN, VA, VT, WI, WV, AS, GU, FM, MH, MP, PW, PR, VI 2005-2009 AL, AK, AR, AZ, CO, DE, DC, GA, HI, IL, IN, IA, KY, LA, MA, ME, MN, MO, MS, NV, NM,

NC, ND, OH, OR, PA, RI, TN, TX, VA, VT, WI, WV, AS, GU, FM, MH, MP, PW, PR, VI


27

Data Notes Ascertainment of maternal deaths was modified by a pregnancy checkbox in the 2003 revision of the U.S. Standard Certificate of Death and is only reported for states/jurisdictions that had implemented the 2003 revision or had a comparable checkbox item as of January 1 of the first year of five-year maternal mortality rates. Five-year estimates are necessary for many states due to the small number of maternal deaths. Overall U.S. estimates by year are not comparable due to the addition of states over time that have implemented the 2003 revision. Trends within a state after the 2003 revision are comparable; however, changes are mitigated with five-year data where each estimate shares 80% (4/5) of the data with the next estimate. Standard statistical tests that assume independence should not be used when comparing overlapping 5-year estimates; significance will be exaggerated without accounting for dependence. For more information about the mortality file, please see the User's Guide located at http://www.cdc.gov/nchs/data_access/VitalStatsOnline.htm


28

NOM 4.1 - Percent of low birth weight deliveries (

29


30

NOM 4.2 - Percent of very low birth weight deliveries (

31

year. Urban/rural residence is not available for territories. For more information about the birth file, please see the User's Guide located at http://www.cdc.gov/nchs/data_access/VitalStatsOnline.htm


32

NOM 4.3 - Percent of moderately low birth weight deliveries (1,500-2,499 grams)

GOAL To reduce the proportion of low birth weight deliveries

DEFINITION Numerator: Number of live births weighing between 1,500-2,499 grams Denominator: Number of live births Units: 100 Text: Percent

HEALTHY PEOPLE 2020 OBJECTIVE Related to Maternal, Infant, and Child Health (MICH) Objective 8.1: Reduce low birth weight (LBW). (Baseline: 8.2% in 2007, Target 7.8%)


SIGNIFICANCE The general category of low birth weight infants includes pre-term infants and infants with intrauterine growth retardation. Many risk factors have been identified for low birth weight babies including: both young and old maternal age, poverty, late prenatal care, smoking, substance abuse, and multiple births. Advanced maternal age and in vitro fertilization has increased the number of multiple births. Multiple births often result in shortened gestation and low or very low birth weight infants. In 2010, 68% of all infant deaths occurred to the 8.2% of low birth weight infants and over half (53%) of all infant deaths occurred to the 1.5% of very low birth weight infants. Infants born to non-Hispanic Black women have the highest rates of low birth weight, particularly very low birth weight. In 2012, 13.2 percent of non-Hispanic Black infants were born low birthweight and 2.9 percent were born at very low birth weight--these rates are 1.9 and 2.6 times the rates for infants born to non-Hispanic Whites women (7.0 and 1.1 percent, respectively). Infants born to Puerto Rican women also have elevated rates of low and very low birth weight (9.4 and 1.8, respectively).

FAD Availability by Year Year Data Not Available 2014 FM, MH, PW, VI 2013 FM, MH, PW, VI 2012 FM, MH, PW 2011 FM, MH, PW 2010 FM, MH, PW 2009 FM, MH, PW

Data Notes Follows NCHS birth weight edits to replace as unknown if outside of 227-8165 grams or grossly incompatible with both the obstetric estimate and LMP-based estimate of gestational age. Stratifiers that were modified or newly added on the 2003 revision (i.e., maternal education, delivery payment source, prenatal WIC participation) are

33

only reportable for the states/jurisdictions that had implemented the 2003 revision as of January 1 of the data year. Urban/rural residence is not available for territories. For more information about the birth file, please see the User's Guide located at http://www.cdc.gov/nchs/data_access/VitalStatsOnline.htm


34

NOM 5.1 - Percent of preterm births (

35

Data Notes Based on obstetric/clinical estimate of gestation, following NCHS edits to replace as unknown if outside of 17-47 weeks. Stratifiers that were modified or newly added on the 2003 revision (i.e., maternal education, delivery payment source, prenatal WIC participation) are only reportable for the states/jurisdictions that had implemented the 2003 revision as of January 1 of the data year. Urban/rural residence is not available for territories. For more information about the birth file, please see the User's Guide located at http://www.cdc.gov/nchs/data_access/VitalStatsOnline.htm


36

NOM 5.2 - Percent of early preterm births (

37

Year Data Not Available 2010 AS, FM, MH, PW 2009 AS, FM, MH, PW



38

if 17

39

NOM 5.3 - Percent of late preterm births (34-36 weeks gestation)

GOAL To reduce the proportion of all preterm, early term, and early elective deliveries.

DEFINITION Numerator: Number of live births between 34 and 36 weeks of completed gestation Denominator: Number of live births Units: 100 Text: Percent

HEALTHY PEOPLE 2020 OBJECTIVE Identical to MICH Objective 9.2: Reduce late preterm or births at 34-36 weeks' gestation. (Baseline: 9.0% in 2007, Target 8.1%)


SIGNIFICANCE Babies born preterm, before 37 completed weeks of gestation, are at increased risk of immediate life-threatening health problems, as well as long-term complications and developmental delays. Among preterm infants, complications that can occur during the newborn period include respiratory distress, jaundice, anemia, and infection, while long-term complications can include learning and behavioral problems, cerebral palsy, lung problems, and vision and hearing loss. As a result of these risks, preterm birth is a leading cause of infant death and childhood disability. Although the risk of complications is greatest among those babies who are born the earliest, even those babies born late preterm (34 to 36 weeks gestation) and "early term" (37, 38 weeks' gestation) are more likely than full-term babies to experience morbidity and mortality. Infants born to non-Hispanic Black women have the highest rates of preterm birth, particularly early preterm birth. In 2012, 16.5 percent of non-Hispanic Black infants were born preterm and 5.9 percent were born early preterm--these rates are 1.6 and 2.0 times the rates for infants born to non-Hispanic Whites women (10.3 and 2.9 percent, respectively). Infants born to Puerto Rican, Cuban, and American Indian/Alaska Native mothers also had elevated rates of preterm and early preterm birth. Non-medically indicated early term births (37,38 weeks) present avoidable risks of neonatal morbidity and costly NICU admission (Clark et al, 2009; Tita et al, 2009). Early elective delivery prior to 39 weeks is an endorsed perinatal quality measure by the Joint Commission, National Quality Forum, ACOG/NCQA, Leapfrog Group, and CMS/CHIPRA.

FAD Availability by Year Year Data Not Available 2014 AS, FM, MH, PW, VI 2013 AS, FM, MH, PW, VI 2012 AS, FM, MH, PW 2011 AS, FM, MH, PW 2010 AS, FM, MH, PW 2009 AS, FM, MH, PW

40



41

NOM 6 - Percent of early term births (37, 38 weeks gestation)


DEFINITION Numerator: Number of live births born at 37,38 weeks of completed gestation Denominator: Number of live births Units: 100 Text: Percent

HEALTHY PEOPLE 2020 OBJECTIVE None


SIGNIFICANCE Babies born preterm, before 37 completed weeks of gestation, are at increased risk of immediate life-threatening health problems, as well as long-term complications and developmental delays. Among preterm infants, complications that can occur during the newborn period include respiratory distress, jaundice, anemia, and infection, while long-term complications can include learning and behavioral problems, cerebral palsy, lung problems, and vision and hearing loss. As a result of these risks, preterm birth is a leading cause of infant death and childhood disability. Although the risk of complications is greatest among those babies who are born the earliest, even those babies born late preterm (34 to 36 weeks gestation) and "early term" (37, 38 weeks' gestation) are more likely than full-term babies to experience morbidity and mortality. Infants born to non-Hispanic Black women have the highest rates of preterm birth, particularly early preterm birth. In 2012, 16.5 percent of non-Hispanic Black infants were born preterm and 5.9 percent were born early preterm--these rates are 1.6 and 2.0 times the rates for infants born to non-Hispanic Whites women (10.3 and 2.9 percent, respectively). Infants born to Puerto Rican, Cuban, and American Indian/Alaska Native mothers also had elevated rates of preterm and early preterm birth. Non-medically indicated early term births (37,38 weeks) present avoidable risks of neonatal morbidity and costly NICU admission (Clark et al, 2009; Tita et al, 2009). Early elective delivery prior to 39 weeks is an endorsed perinatal quality measure by the Joint Commission, National Quality Forum, ACOG/NCQA, Leapfrog Group, and CMS/CHIPRA.

FAD Availability by Year Year Data Not Available 2014 AS, FM, MH, PW, VI 2013 AS, FM, MH, PW, VI 2012 AS, FM, MH, PW 2011 AS, FM, MH, PW 2010 AS, FM, MH, PW 2009 AS, FM, MH, PW

42



43

NOM 7 - Percent of non-medically indicated early elective deliveries


DEFINITION Numerator: Number of inductions or cesareans without labor or spontaneous rupture of membranes among deliveries at 37, 38 weeks' gestation without conditions possibly justifying elective delivery

44

Data Notes Indicator data reflect all births in Medicare-certified hospitals (virtually all U.S. hospitals excluding critical access hospitals). Standard errors, numerators, and denominators are not available; hospital births are often sampled and are not weighted when forming total estimates for states and the US overall. Indicator data are available for download with hospital-specific detail at https://data.medicare.gov/data/hospital-compare (PC_01 within Timely and Effective Care)

Available Stratifiers and Notes No stratifiers available

SAS Code Not available
https://data.medicare.gov/data/hospital-compare

45

NOM 8 - Perinatal mortality rate per 1,000 live births plus fetal deaths

GOAL To reduce the rate of perinatal deaths.

DEFINITION Numerator: Number of fetal deaths 28 weeks or more gestation plus early neonatal deaths occurring under 7 days Denominator: Number of live births plus fetal deaths Units: 1,000 Text: Rate

HEALTHY PEOPLE 2020 OBJECTIVE Related to Maternal, Infant, and Child Health (MICH) Objective 1.2: Reduce the rate of fetal and infant deaths during the perinatal period (28 weeks of gestation to 7 days after birth). (Baseline: 6.6 fetal and infant deaths per 1,000 live births and fetal deaths occurred during the perinatal period, 28 weeks gestation to 7 days after birth, in 2005; Target: 5.9 perinatal deaths per 1,000 live births and fetal deaths)

DATA SOURCES and DATA ISSUES National Vital Statistics System (NVSS) Fetal Death and Period Linked Birth/Infant Death Files

SIGNIFICANCE Perinatal mortality is a reflection of the health of the pregnant woman and newborn and reflects the pregnancy environment and early newborn care. Perinatal mortality is particularly high for non-Hispanic Black women. In 2006, the rate for non-Hispanic black women (11.76) was the highest among the racial and ethnic groups, and was more than twice the rate for non-Hispanic white women.

FAD Availability by Year Year Data Not Available 2013 AS, FM, MH, MP, PW, VI 2012 AS, FM, MH, MP, PW, VI 2011 AS, FM, MH, MP, PW 2010 AS, FM, MH, MP, PW 2009 AS, FM, MH, MP, PW

Data Notes This measure uses the traditional LMP-based estimate of gestation to determine fetal deaths, live births, infant deaths at 28+ weeks gestation. Fetal deaths with missing or not stated gestational age that were presumed to be 20+ weeks were proportionally distributed to

46


47

NOM 9.1 - Infant mortality rate per 1,000 live births

GOAL To reduce the rate of infant death.

DEFINITION Numerator: Number of deaths to infants from birth through 364 days of age Denominator: Number of live births Units: 1,000 Text: Rate

HEALTHY PEOPLE 2020 OBJECTIVE Identical to Maternal, Infant, and Child Health (MICH) Objective 1.3: Reduce the rate of all infant deaths (within 1 year). (Baseline: 6.7 infant deaths per 1,000 live births within the first year of life in 2006, Target: 6.0 infant deaths per 1,000 live births)

DATA SOURCES and DATA ISSUES National Vital Statistics System (NVSS) Period Linked Birth/Infant Death File

SIGNIFICANCE The U.S. infant mortality rate has substantially declined over the last century. Based on preliminary data for 2011, 23,910 infants died before age one year, representing an infant mortality rate of 6.05 deaths per 1,000 live births, which is the lowest infant mortality rate recorded in the U.S. However, significant disparities continue to persist in U.S. infant deaths between racial groups, especially for Blacks and American Indians and Alaskan Natives. The non-Hispanic Black infant mortality rate (12.2 deaths per 1,000 live births in 2010) is nearly two and half times the rate among non-Hispanic Whites and Hispanics. (Child Health USA 2013: Department of Health and Human Services, HRSA). The infant mortality rate in American Indians and Alaskan Natives is more than one and a half times the rate of non-Hispanic Whites. Infant mortality continues to be an extremely complex health issue with many medical, social, and economic determinants, including race/ethnicity, maternal age, education, smoking and health status.

FAD Availability by Year Year Data Not Available 2013 FM, MH, PW, VI 2012 FM, MH, PW 2011 FM, MH, PW 2010 FM, MH, PW 2009 FM, MH, PW

Data Notes Infant deaths are weighted to account for deaths that were unable to be linked to a birth certificate in a given state or territory. Estimates by stratifiers are calculated with three-year data to improve precision and reportability. Stratifiers that were modified or newly added on the 2003 revision (i.e., maternal education, delivery payment source, prenatal WIC participation) are only reportable for the states/jurisdictions that had implemented the 2003 revision as of January 1 of the data year. Urban/rural residence is not available for territories. Unlinked data are used for American Samoa and the Northern Marianas Islands since linked data are unavailable; therefore no stratifiers are available as race/ethnicity data were highly incomplete in the mortality file. For more information about the linked birth/infant death file, please see the User's Guide located at http://www.cdc.gov/nchs/data_access/VitalStatsOnline.htm
http://www.cdc.gov/nchs/data_access/VitalStatsOnline.htm

48


49

NOM 9.2 - Neonatal mortality rate per 1,000 live births

GOAL To reduce the rate of neonatal deaths.

DEFINITION Numerator: Number of deaths to infants under 28 days Denominator: Number of live births Units: 1,000 Text: Rate

HEALTHY PEOPLE 2020 OBJECTIVE Identical to Maternal, Infant, and Child Health (MICH) Objective 1.4: Reduce the rate of neonatal deaths (within the first 28 days of life). (Baseline: 4.5 neonatal deaths per 1,000 live births occurred within the first 28 days of life in 2006, Target: 4.1 neonatal deaths per 1,000 live births)

DATA SOURCES and DATA ISSUES National Vital Statistics System (NVSS) - Period Linked Birth/Infant Death File

SIGNIFICANCE The preliminary U.S. neonatal infant mortality rate was 4.06 deaths per 1,000 live births in 2011, accounting for two-thirds of all infant deaths. Neonatal mortality is related to gestational age, low birth weight, congenital malformations and health problems originating in the perinatal period, such as infections or birth trauma. A significant disparity exists in neonatal deaths between racial groups, especially for infants born to Black women. Non-Hispanic black women had the highest neonatal mortality rate in 2010 at 7.45, 2.2 times that for non-Hispanic white women (3.35). Neonatal mortality rates were also higher for Puerto Rican (4.82), AIAN (4.28), and Mexican women (3.53) than for non-Hispanic white women.



50


51

NOM 9.3 - Post neonatal mortality rate per 1,000 live births

GOAL To reduce the rate of post-neonatal deaths.

DEFINITION Numerator: Number of deaths to infants 28 through 364 days of age Denominator: Number of live births Units: 1,000 Text: Rate

HEALTHY PEOPLE 2020 OBJECTIVE Identical to Maternal, Infant, and Child Health (MICH) Objective 1.5: Reduce the rate of post-neonatal deaths (between 28 days and 1 year). (Baseline: 2.2 post-neonatal deaths per 1,000 live births occurred between 28 days and 1 year of life in 2006, Target: 2.0 post-neonatal deaths per 1,000 live births)

DATA SOURCES and DATA ISSUES National Vital Statistics System (NVSS) - Period Linked Birth/Infant Death File

SIGNIFICANCE Postneonatal mortality is generally related to Sudden Unexpected Infant Death (SUID)/Sudden Infant Death Syndrome (SIDS), unintentional injuries and congenital malformations. In 2011, the preliminary U.S. postneonatal mortality rate was 2.01 deaths per 1,000 live births. Similar to overall infant mortality, infants of non-Hispanic black (4.01) and AIAN (4.00) women had the highest postneonatal mortality rates of any groupmore than twice those for non-Hispanic white women (1.82) in 2010. The postneonatal mortality rate was also higher for Puerto Rican women (2.28) than for non-Hispanic white women.



52

Available Stratifiers and Notes Stratifier Subcategory Special Notes Maternal Age =28 THEN POSTNEONATAL=1; * age at death 28-364 days;

53

NOM 9.4 - Preterm-related mortality rate per 100,000 live births

GOAL To reduce the rate of preterm-related death.

DEFINITION Numerator: Number of deaths due to preterm-related causes. Causes are defined as preterm-related if 75% or more of infants whose deaths were attributed to that cause were born at at less than 37 weeks of gestation, and the cause of death was a direct consequence of preterm birth based on a clinical evaluation and review of the literature. This includes low birth weight, several maternal complications, respiratory distress, bacterial sepsis, etc. To be included as a preterm-related death, the infant must have been born preterm (

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Year Data Not Available 2012 AS, FM, MH, MP, PW 2011 AS, FM, MH, MP, PW 2010 AS, FM, MH, MP, PW 2009 AS, FM, MH, MP, PW

Data Notes Follows the CDC definition of preterm-related cause if 75% or more of infants whose deaths were attributed to a cause were born at less than 37 weeks of gestation, and the cause of death was a direct consequence of preterm birth based on a clinical evaluation and review of the literature. Preterm-related causes of death are further restricted to preterm infants when determining preterm-related deaths. Gestational age was based on the obstetric/clinical estimate. This measure provides a conservative estimate of the preterm contribution as indirect causes are not included and many non-specific causes of death (e.g. other perinatal conditions) have a high percentage of deaths to preterm infants but lack etiologic specificity. Infant deaths are weighted to account for deaths that were unable to be linked to a birth certificate in a given state or territory. Estimates by stratifiers are calculated with three-year data to improve precision and reportability. Stratifiers that were modified or newly added on the 2003 revision (i.e., maternal education, delivery payment source, prenatal WIC participation) are only reportable for the states/jurisdictions that had implemented the 2003 revision as of January 1 of the data year. Urban/rural residence is not available for territories. For more information about the linked birth/infant death file, please see the User's Guide located at http://www.cdc.gov/nchs/data_access/VitalStatsOnline.htm Callaghan WD, MacDorman MF, Rasmussen SA, Qin C, Lackritz EM, et al. The contribution of preterm birth to infant mortality rates in the United States. Pediatrics 118(4):156673. 2006. Mathews TJ, MacDorman MF. Infant mortality statistics from the 2010 period linked birth/infant death data set. National vital statistics reports; vol 62 no 8. Hyattsville, MD: National Center for Health Statistics. 2013.


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Stratifier Subcategory Special Notes Plurality Singleton

Multiple Birth Includes imputed plurality

Race/ethnicity Non-Hispanic White Non-Hispanic Black Hispanic Non-Hispanic American Indian/Alaska Native Non-Hispanic Asian/Pacific Islander

Refers to maternal race/ethnicity. Includes imputed race and multiple race bridged to single race.

Urban-Rural Residence

Large Central Metro Large Fringe Metro Small/Medium Metro Non-Metro

Based on 2013 NCHS Urban-Rural Classification Scheme for Counties. Not available for territories.

WIC Participation Yes No

From the 2003 revision to the U.S. Certificate of Live Birth. Refers to prenatal WIC participation.

SAS Code IF RESTATUS NE 4; *restrict to resident births; if (substr(ICD,1,3) in ('P22','P36','P77') or substr(ICD,1,4) in ('K550', 'P000', 'P010', 'P011', 'P015', 'P020', 'P021', 'P027', 'P102', 'P280', 'P281') or 'P070'

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NOM 9.5 - Sleep-related Sudden Unexpected Infant Death (SUID) rate per 100,000 live births

GOAL To reduce the rate sleep-related SUID

DEFINITION Numerator: Number of sleep-related SUID deaths to infants Denominator: Number of live births Units: 100,000 Text: Rate

HEALTHY PEOPLE 2020 OBJECTIVE Identical to Maternal, Infant, and Child Health (MICH) Objective 1.9: Reduce the rate of infant deaths from sudden unexpected infant deaths (includes SIDS, Unknown Cause, Accidental Suffocation, and Strangulation in Bed). (Baseline: .93 per 1,000 live births in 2006, Target: .84 infant deaths per 1,000 live births)

DATA SOURCES and DATA ISSUES National Vital Statistics System (NVSS) Period Linked Birth/Infant Death File

SIGNIFICANCE Sleep-related SUIDs are the leading cause of death in infants from one month up to one year (postneonatal deaths) and the third leading cause of all infant deaths. In 2010, there were a total of 3,610 or 0.9 sudden unexpected infant deaths (SUID) per 1,000 live births, accounting for 43 percent of postneonatal deaths and 15 percent of all infant deaths. SUID rates vary greatly by race and ethnicity. In 2010, SUID rates were highest for infants born to American Indian/Alaska Native and non-Hispanic Black mothers (1.82 and 1.77 per 1,000, respectively); these rates were more than twice the rate among infants born to non-Hispanic Whites (0.87 per 1,000).


Data Notes Infant deaths are weighted to account for deaths that were unable to be linked to a birth certificate in a given state or territory. Estimates by stratifiers are calculated with three-year data to improve precision and reportability. Stratifiers that were modified or newly added on the 2003 revision (i.e., maternal education, delivery payment source, prenatal WIC participation) are only reportable for the states/jurisdictions that had implemented the 2003 revision as of January 1 of the data year. Urban/rural residence is not available for territories. Unlinked data are used for American Samoa and the Northern Marianas Islands since linked data are unavailable; therefore no stratifiers are available as race/ethnicity data were highly incomplete in the mortality file. For more information

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about the linked birth/infant death file, please see the User's Guide located at http://www.cdc.gov/nchs/data_access/VitalStatsOnline.htm


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NOM 10 - The percent of infants born with fetal alcohol exposure in the last 3 months of pregnancy

GOAL To reduce the percent of infants born with fetal alcohol exposure

DEFINITION Numerator: Number of women who report drinking alcohol in the last 3 months of pregnancy Denominator: Number of live births Units: 100 Text: Percent

HEALTHY PEOPLE 2020 OBJECTIVE Related to Maternal, Infant, and Child Health (MICH) 2.11. Increase abstinence from alcohol among pregnant women. (Baseline: 89.4 percent of pregnant females ages15 to 44 years reported abstaining from alcohol in the past 30 days in 200708, Target: 98.3%) Related to Maternal, Infant, and Child Health (MICH) 25. Reduce the occurrence of fetal alcohol syndrome. (Baseline: 3.6 cases of fetal alcohol syndrome per 10,000 live births in 2006 were suspected or confirmed among children born in 200104, Target: Not Applicable)

DATA SOURCES and DATA ISSUES Pregnancy Risk Assessment Monitoring System (PRAMS)

SIGNIFICANCE Fetal alcohol spectrum disorders (FASDs), which result in life-long physical and cognitive or behavioral problems, are caused by drinking during pregnancy. Fetal alcohol syndrome (FAS) represents the severe end of FASDs, and is characterized by abnormal facial features (e.g., smooth ridge between nose and upper lip), lower than average height or weight, and central nervous system problems that create deficits in learning, memory, attention, communication, vision, and/or hearing. FASDs are preventable through abstinence from alcohol among pregnant women. Early diagnosis and intervention programs are critical to improve developmental outcomes for children with FAS.

FAD Availability by Year Year Data Not Available 2013 AL*, AZ, CA, CT*, DE^, DC, FL*, HI^, IL^, IN, ID, KS, KY, LA*, ME^, MI^, MN^, MS*, MT,

NC*, NJ^, ND, NV, NYC^, OH*, OR^, PA^, SC^, SD, TX^, VA^, WV^, AS, FM, GU, MH, MP, PR, PW, VI

2012 AL*, AZ, CA, CT, DC, FL*, IA, IN, ID, KS, KY, LA*, MS*, MT, NC*, ND, NH, NV, NY*, SC*, SD, TX*, VA*, WV^, AS, FM, GU, MH, MP, PR, PW, VI

2011 AL*, AK*, AZ, CA, CT, DC, FL*, IA, IL*, IN, ID, KS, KY, LA*, MS*, MT, NC*, ND, NH, NV, OH*, SC*, SD, TN*, TX*, VA*, AS, FM, GU, MH, MP, PR, PW, VI

2010 AL*, AZ, CA, CT, DC, FL*, IA, IN, ID, KS, KY, LA*, MS*, MT, NC*, ND, NH, NM*, NV, SC*, SD, TN*, VA*, WI*, AS, FM, GU, MH, MP, PR, PW, VI

2009 AL*, AZ, CA, CT, DC, FL*, IA, IN, ID, KS, KY, LA*, MT, NC*, ND, NH, NM*, NV, NY*, NYC*, SC*, SD, VA*, AS, FM, GU, MH, MP, PR, PW, VI

2008 AL*, AZ, CA, CT, DC, FL*, IA, IN, ID, KS, KY, LA*, MO*, MT, ND, NH, NM*, NV, NYC*, SC*, SD, TX*, VA*, AS, FM, GU, MH, MP, PR, PW, VI

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Year Data Not Available 2007 AL*, AZ, CA, CT, DC, FL*, IA, IN, ID, KS, KY, LA*, MS*, MT, ND, NH, NM*, NV, SD, TN*,

TX*, VA*, AS, FM, GU, MH, MP, PR, PW, VI *PRAMS data are available to be reported by the state/jurisdiction; did not meet response rate threshold required for reporting by CDC PRAMS but MCHB does not use a response rate criteria for TVIS as long as data are appropriately weighted to account for non-response bias. ^Data may be available from CDC PRAMS but have not been weighted as of 3/4/2016.

Data Notes Per CDC PRAMS policy, only states/jurisdictions that met the 60% response rate threshold are included in U.S. estimates (n=28 in 2012; n=20 in 2013). Prior year estimates include states/jurisdictions with a 65% response threshold. Overall U.S. estimates by year may not be comparable due to the different states/jurisdictions included in any given year. For NY, 2008 and 2013 estimates do not include NYC while 2012 estimates only include NYC. For 2013, 14 states/jurisdictions did not yet have weighted data as of 3/4/2016. The stratifiers of maternal race/ethnicity and education include states using both the 1989 and 2003 revisions of the U.S. Standard Certificate of Live Birth. The lack of detail in the 1989 revision generally results in an overestimate of single race mothers (multiple race is not ascertained) and an overestimate of maternal educational attainment. Health insurance at delivery is only available for states using the 2003 revision to the birth certificate. The estimates, numerators, and denominators presented are weighted to account for the probability of selection, non-response, and non-coverage. Standard errors account for the complex survey design. For more information on the PRAMS methodology, visit http://www.cdc.gov/prams/


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Stratifier Subcategory Special Notes WIC Participation Yes

No From the PRAMS questionnaire. Refers to prenatal WIC participation

SAS Code durpreg_drnk = max(drk53l_a,drk63l_a);

Data Alert The health insurance stratifier changed from PRAMS to birth certificate documentation of insurance at delivery between FAD provided in 2015 and 2016. Due to the lack of comparability, 2011 insurance data were removed from the FAD data file.

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NOM 11 - The rate of infants born with neonatal abstinence syndrome per 1,000 delivery hospitalizations

GOAL To reduce the rate of infants born with drug dependency.

DEFINITION Numerator: Number of infants born with neonatal abstinence syndrome Denominator: Number of delivery hospitalizations Units: 1,000 Text: Rate

HEALTHY PEOPLE 2020 OBJECTIVE Related to Maternal, Infant, and Child Health Objective 11.4. Increase abstinence from illicit drugs among pregnant women. (Baseline: 94.8 percent of pregnant females ages 15 to 44 years reported abstaining from illicit drugs in the past 30 days in 200708; Target 100%)

DATA SOURCES and DATA ISSUES HCUP - State Inpatient Database (SID)

SIGNIFICANCE Neonatal drug dependency or withdrawal symptoms, known as neonatal abstinence syndrome (NAS), occur from maternal use of opiates such as heroin, methadone, and prescription pain medications. Symptoms of NAS include fever, diarrhea, irritability, trembling, and increased muscle tone. Along with a rise in prescription drug abuse, the incidence of NAS nearly tripled over the past decade with substantial increases in health care costs (Patrick et al, 2012). Prevention strategies exist along the continuum from preconception, prenatal, postpartum, and infant/childhood stages to help avert substance-exposed pregnancies and improve outcomes for infants born with NAS (ASTHO, 2014; SAMHSA, 2009).

FAD Availability by Year Year Data Not Available 2013 AK, AL, DE, ID, ME, MS, NH, AS, FM, GU, MH, MP, PW, PR, VI 2012 AL, DC, DE, ID, MS, NH, AS, FM, GU, MH, MP, PW, PR, VI 2011 AL, DC, DE, ID, NH, AS, FM, GU, MH, MP, PW, PR, VI 2010 AL, DC, DE, ID, ND, NH, AS, FM, GU, MH, MP, PW, PR, VI 2009 AK, AL, DC, DE, ID, MS, ND, AS, FM, GU, MH, MP, PW, PR, VI 2008 AK, AL, DC, DE, ID, MS, MT, ND, NM, AS, FM, GU, MH, MP, PW, PR, VI

Data Notes Cases of neonatal abstinence syndrome were identified by ICD-9-CM diagnosis codes 779.5 (drug withdrawal syndrome in newborn) and/or 760.72 (noxious influences affecting fetus or newborn via placenta or breast milk, narcotics). The use of multiple codes may significantly increase previous estimates of neonatal abstinence syndrome. Delivery hospitalizations were identified by diagnosis codes for an outcome of delivery, diagnosis-related group delivery codes, and procedure codes for selected delivery-related procedures (Kuklina et al, 2008). State-level estimates include inpatient stays for state residents treated in their home state and state residents

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treated in other states that provide data to the Healthcare Cost and Utilization Project (HCUP). For information on the HCUP Partner organizations, please visit https://www.hcup-us.ahrq.gov/partners.jsp This analysis is limited to community hospitals, which are defined as short-term, non-Federal hospitals. Community hospitals include obstetrics and gynecology, otolaryngology, orthopedic, cancer, pediatric, public, and academic medical hospitals. Excluded are long-term care facilities such as rehabilitation, psychiatric, and alcoholism and chemical dependency hospitals. U.S. estimates are calculated using the available State data and are not nationally weighted; therefore, U.S. estimates may not be comparable across years due to the different states included in any given year. In addition, certain states did not provide reliable race and/or ethnicity data and are excluded from totals by race/ethnicity. For more information about the HCUP State Inpatient Databases (SID), please visit https://www.hcup-us.ahrq.gov/sidoverview.jsp Kuklina EV, Whiteman MK, Hillis SD, Jamieson DJ, Meikle SF, Posner SF, et al. An enhanced method for identifying obstetric deliveries: implications for estimating maternal morbidity. Matern Child Health J 2008;12:46977.

Available Stratifiers and Notes Stratifier Subcategory Special Notes Health Insurance Private

Medicaid Other Public Uninsured

Refers to expected primary payor. Medicaid may include CHIP and Medicare. Other Public includes military insurance, Indian Health Service, and other federal, state, or local government payment source.

Race/Ethnicity Non-Hispanic White Non-Hispanic Black Hispanic Non-Hispanic American Indian/Alaska Native Non-Hispanic Asian/Pacific Islander Other

Other includes other and multiple race.

Urban-Rural Residence

Large Metro Small/Medium Metro Non-Metro

Based on 2003 Urban Influence Codes.

SAS Code IF COMMUNITY_NONREHAB =1; * restrict to non-federal, non-rehab facilities; ARRAY DX (&MAXDX) DX1-DX&MAXDX; * diagnosis codes; ABSTISYNDR=0; *NUMERATOR; IF DX1 NE ' ' THEN DO; DO I=1 TO &MAXDX; IF (DX(I)='7795' OR DX(I)='76072') THEN ABSTISYNDR=1; END; END; DROP I; array x (&MAXDX) DX1-DX&MAXDX; * diagnosis codes; array z (&MAXPR) PR1-PR&MAXPR; * procedure codes; do i=1 to &MAXDX; IF x[i]=:'V27' THEN DELIVERY_V27=1; IF x[i]=:'650' THEN DELIVERY_650=1;/*NORMAL DELIVERY*/
https://www.hcup-us.ahrq.gov/partners.jsphttps://www.hcup-us.ahrq.gov/sidoverview.jsp

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/*EXCLUDE ABORTIONS, ECTOPIC, HYDATIDIFORM MOLE*/ if x[i]='630' OR x[i]='631' OR x[i]='632' OR x[i]=:'633' OR x[i]=:'634' OR x[i]=:'635' OR x[i]=:'636' OR x[i]=:'637' OR x[i]=:'638' OR x[i]=:'639' then ABORT_DX=1; END; do i=1 to &MAXPR; IF z[i] in ('720', '721', '7221', '7229', '7231', '7239', '724', '7251', '7252', '7253', '7254', '726', '7271', '7279', '728','729', '7322','7359','736', '740', '741', '742', '744', '7499') then DELIVERY_PR=1; if z[i] in ('6901','6951','7491','750') THEN ABORT_PR=1; END; DROP I; IF DRG=765 OR DRG=766 OR DRG=767 OR DRG=768 OR DRG=774 OR DRG=775 THEN DELIVERY_DRG=1; ELSE DELIVERY_DRG=2; IF ABORT_DX=1 OR ABORT_PR=1 THEN ABORT=1; ELSE ABORT=2; IF (DELIVERY_V27=1 OR DELIVERY_650=1 OR DELIVERY_DRG=1 OR DELIVERY_PR=1) AND ABORT=2 THEN DELIVERY=1; ELSE DELIVERY=0; *DENOMINATOR; *PATIENT STATE RESIDENCE; LENGTH PSTATE $2; PSTCOC=PUT(PSTCO2,Z5.); IF SUBSTR(PSTCOC,1,2)='98' THEN DELETE; PSTATE=FIPSTATE(SUBSTR(PSTCOC,1,2)); LABEL ABSTISYNDR="INFANT WITH ABSTINENCE SYNDROME" PSTATE="PATIENT RESIDENCE STATE"; RUN;

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NOM 12 - Percent of eligible newborns screened for heritable disorders with on time physician notification for out of range screens who are followed up in a timely manner. (DEVELOPMENTAL)

GOAL To increase the percent of eligible newborns screened for heritable disorders with on-time physician notification for out of range screens and timely follow up.

DEFINITION Numerator: Number of eligible newborns screened for heritable disorders with on time physician notification for out of range screens who are followed up in a timely manner. UNDER DEVELOPMENT. Denominator: Number of live eligible births Units: 100 Text: Percent

HEALTHY PEOPLE 2020 OBJECTIVE Identical to Maternal, Infant, and Child Health (MICH) Objective 32: Increase appropriate newborn blood-spot screening and follow-up testing (Baseline: 98.3% of screen-positive children received follow-up testing within the recommended time period in 200306, Target: 100%).

DATA SOURCES and DATA ISSUES The American Public Health Laboratories data set

SIGNIFICANCE Newborn screening detects thousands of babies each year with potentially devastating, but treatable disorders. The benefits of newborn screening depend upon timely collection of the newborn blood-spots or administration of a point-of-care test (pulse oximeter for critical congenital heart disease), receipt of the newborn blood spot at the laboratory, testing of the newborn blood spot, and reporting out of all results. Timely detection prevents death, mental retardation, and other significant health complications. 1) The number of eligible infants for screening differs by state so the denominator should reflect the individual state protocol. This will typically be the number of live births minus those w

Documents

Federally Available Data (FAD) Resource Document