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Pregnancy Episode Grouper: Development, Validation, and Applications. Mark C. Hornbrook, PhD AcademyHealth Annual Research Meeting Washington, DC June 9, 2008 . Reproductive Health Division, CDC Cynthia J. Berg, MD, MPH F. Carol Bruce, RN, MPHD William M. Callaghan, MD, MPH

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pregnancy episode grouper development validation and applications

Pregnancy Episode Grouper: Development, Validation, and Applications

Mark C. Hornbrook, PhD

AcademyHealth Annual Research Meeting

Washington, DC

June 9, 2008

research team
Reproductive Health Division, CDC

Cynthia J. Berg, MD, MPH

F. Carol Bruce, RN, MPHD

William M. Callaghan, MD, MPH

Susan Y. Chu, PhD

Patricia M. Dietz, DrPH

The Center for Health Research, KPNW

Mark C. Hornbrook, PhD

Donald J. Bachman, MS

Rachel Gold, PhD, MPH

Maureen C. O’Keeffe Rosetti, MS

Kimberly Vesco, MD

Selvi B. Williams, MD, MPH

Evelyn P. Whitlock, MD, MPH

Research Team
funding
Contract # CDC 200-2001-00074, Task # MC2-02, “Extent of Maternal Morbidity in a Managed Care Setting,” from the Centers for Disease Control and Prevention

America’s Health Insurance Plans administered this contract

Contract # CDC 200-2006-17832, “Extent of Maternal Morbidity in a Managed Care Setting”

Funding
maternal health
Maternal Health
  • Over 6 million pregnancies in the US annually
  • Previously, hospitalizations used as proxy for morbidity
  • Today, we use a more comprehensive assessment of the incidence and prevalence of maternal morbidity
    • Changes in medical practice have led to more outpatient treatment for pregnancy complications
    • Medical informatics now frequently include computerized clinical and laboratory/pathology information systems
objectives
Objectives
  • Develop a pregnancy episode grouper algorithm using HMO electronic data warehouse
      • Identify all pregnancies occurring in HMO members during the study period
      • Identify each pregnancy’s outcome
      • Identify maternal morbidities occurring within pregnancy episodes
      • Estimate the prevalence of maternal morbidity in the study population
  • Develop research and quality improvement applications
research setting
Research Setting
  • Kaiser Permanente Northwest (KPNW), a non­profit, prepaid group practice HMO in the Pacific Northwest, with 475,000 members
  • Includes commercial, individual, Washington State Basic Health Plan, Medicare, and Medicaid enrollees
  • Demographically representative of the local community
  • Automated ambulatory medical record system linked to administrative, encounter, financial, and clinical management information systems
over 2 3 of pregnancies ended in live birth and almost 1 3 in spontaneous or induced abortion
Over 2/3 of pregnancies ended in live birthand almost 1/3 in spontaneous or induced abortion

Live births create inpatient delivery records, birth certificates, and health plan enrollment records

episodes
Episodes
  • Fundamental unit of measure for health care phenomena
  • Conceptual taxonomy
      • Health problem/illness episodes
        • Patient’s perspective on lived experience of health problem and related treatment
      • Disease episodes
        • Model of the natural course of a disease or health problem
      • Care Episodes
        • Clusters of utilization linked to a specific therapeutic problem/goal
  • Pregnancy
      • Quintessential episode—well-defined beginning and ending points and natural course
episode definition
Episode Definition
  • Pregnancy = Interval between estimated date of LMP and eight weeks after delivery/pregnancy termination
  • Other potential specifications
    • Entire pregnancy episode may/may not have occurred within the observation period
    • Women had to be enrolled on outcome date or enrolled at any time
methods
Methods
  • Diagnostic, treatment, laboratory, pharmacy, imaging, home health, and other databases searched for codes that could indicate pregnancy
  • Complex hierarchical decision rules to determine if a pregnancy occurred and, if so, the outcome and the date it began and ended
electronic data sources
Hospital discharge abstracts

Same-day surgery records

Ambulatory encounter abstracts or electronic medical records

Emergency department visits

Pharmacy dispensings

Outside professional & facility claims and referrals

Imaging procedures

Laboratory test results

Home health visits

Birth certificates

Electronic Data Sources
pregnancy end date and outcome
Pregnancy End Date and Outcome
  • Retrospective, omniscient logic
    • Start at the end of the pregnancy because the data are most reliable, then work on the episodes with less data
  • Diagnostic and procedure codes and selected claims data, and their associated dates, indicate the outcome of pregnancy and when it ended
ectopic pregnancies
Ectopic Pregnancies
  • Medical termination
    • Rx = Methotrexate
    • Repeat pregnancy tests until hormone levels drop to pre­pregnancy levels
  • Surgical termination
    • Surgical procedure for removal of embryo
    • Repeat pregnancy tests until hormone levels drop to pre­pregnancy levels
spontaneous losses
Spontaneous Losses
  • Positive pregnancy test or diagnosis
  • Prenatal care encounters stop
  • No delivery/termination procedure
  • Many undetected if woman is not trying to get pregnant
elective losses
Elective Losses
  • Positive pregnancy test or diagnosis
  • Therapeutic abortion procedure
    • Surgical
    • Medical
  • No evidence of delivery within expected episode window
births
Births
  • Live births
    • Delivery codes
    • Infant hospital discharge
    • Birth certificates
    • Addition of infant to family health plan contract
  • Stillbirths
    • Look at delivery codes, especially delivery complications
    • No birth certificate or infant utilization data available
overlapping episodes
Overlapping Episodes
  • Overlapping pregnancy episodes are medically impossible
  • Grouper algorithm has hierarchical logic to resolve implausible episode patterns
    • Select the most likely scenario and ignore the competing data
algorithm validation methods
Algorithm Validation: Methods
  • Gold Standard = blinded medical records abstractors (MRAs) using actual electronic and hard-copy medical and billing records
  • Stratified sampling to obtain representation of all types of pregnancy outcomes
pregnancies missed by algorithm n 24
Pregnancies Missed by Algorithm (N= 24)

n = 511 women, 702 pregnancies

definition maternal morbidity
Definition:Maternal Morbidity
  • Any condition during a pregnancy episode that adversely affected women’s physical or psychological health
  • Condition are unique to, or exacerbated by, pregnancy
  • Used ICD-9-CM codes to classify morbidity into forty-six major categories
  • Clinically experienced authors reviewed all ICD-9-CM codes and developed a list of 46 major maternal morbidity disease classes
results
Results
  • Type of morbidity varied by pregnancy outcome
    • UTI common with all outcomes
    • Mental health conditions common with all outcomes, especially stillbirth
    • Anemia common with live/stillbirth
    • Infections common with stillbirth
article
Article

Am J Psych 2007;164:1515-1520

percent of women diagnosed with depression who received treatment before during or after pregnancy
Percent of Women Diagnosed with Depression who Received Treatment Before, During, or After Pregnancy

% of

Women

maternal depression
Depression before, during, or after pregnancy was common (15.4%) among women enrolled in KPNW

Depression diagnosis did not vary substantially before (8.7%), during (6.9%), or after (10.4%) pregnancy, but the clinical specialty of where women were diagnosed did

About 50% of women with depression before pregnancy relapsed during the postpartum period

About 50% of women diagnosed with depression did not have any prior history during the study period

Over 90% of women with diagnosed depression received treatment

Anti-depressant use was common during pregnancy

Depressed women were more likely than non-depressed women to receive Medicaid, to be unmarried, to have 3 or more children, to be white, and to have smoked during pregnancy

Maternal Depression
pregnancy and obesity
Pregnancy and Obesity
  • Increasing maternal BMI is associated with greater utilization of health care, especially for pregnancies associated with more extreme obesity (BMI >35.0)
  • Almost all of this increase in utilization was related to the increased rates of cesarean delivery, gestational diabetes, diabetes mellitus, and hypertensive disorders among obese pregnant women
diabetes screening
Diabetes Screening
  • All pregnant women who receive prenatal care are screened for diabetes mellitus (DM)
  • DM first diagnosed in pregnancy is coded as Gestational Diabetes Mellitus (GDM)
  • All women with GDM should receive post-partum blood glucose screening
  • GDM increases risk of obesity in offspring
slide38
Percent of Pregnancies with Confirmed Gestational Diabetes (GDM):1999-2006 Kaiser Permanente Northwest
slide39
Percent of Clinician Orders and Percent of Completed Postpartum Glucose Tests among Confirmed Gestational Diabetes-affected Pregnancies
gdm intervention
GDM Intervention
  • Adherence to GDM screening guideline varies widely by medical office within HMO
  • Intervention
    • Provider reminders to order FBS test
    • Patient reminders to obtain FBS test
    • Track noncompliant women and escalate reminders to patients and physicians
limitations of pregnancy grouper
Missing or erroneous input data

Coding errors

Problems in rolling up billing records

Pregnancies with little or no prenatal care

Use of multiple healthcare systems

Inconsistent pregnancy indicators

Multiple providers: differing documentation styles

Complex pregnancies with high utilization

Close early losses

Ectopic pregnancies and trophoblastic disease are inherently difficult to define

Limitations of Pregnancy Grouper
conclusions
Conclusions
  • Algorithm error rates are nearly identical to those for the MRAs (the gold standard)
  • Algorithm can be applied to very large datasets at low marginal cost and much below the costs of manual chart abstraction
  • Pregnancy-specific algorithm supports much more refined and, therefore, clinically meaningful episode classification
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