# Presentation: 471471

## Prevalence & Risk of Pregnancy-associated Injury Hospitalizations:A Population-based Approach

- Harold (Hank) Weiss PhD, MS, MPH
- Center for Injury Research and Control (CIRCL), Pittsburgh, PA
- Dr. Bruce Lawrence & Dr. Ted Miller, PIRE

**Notes:**
The title of my talk today is Pregnancy-associated Assault Hospitalizations. []

## Acknowledgments

**Acknowledgments**

- Supported by a Grant from the National Institute of Justice
- Based on methods pioneered by Greenblatt, Dannenberg & Johnson
- Karen Zuri, HDD Data Coordinator

**Notes:**
I would like to acknowledge the support of the National Institute of Justice, the cooperation of Dr.s Lawrence and Miller with data editing and cost modeling and the use of methods pioneered by  Greenblatt, Dannenberg and Johnson []

## Objectives

- Estimate the incidence of pregnancy- associated injury hospitalization in order to better understand the causes of serious (hospitalized) pregnancy-associated injury and thus fetal threats
- Explore if pregnancy increases or decreases injury risks (controlling for age, race, and severity)

## Population Coverage:19 State Hospital Discharge Data (HDD)

- Population Coverage:19 State Hospital Discharge Data (HDD)

-  19 states
** ****36 *****million*** women (15-49)
** ****52%** of US age/sex group
** ****1.9 million** live births, 1997

## 19 State HDDPatient Coverage

- 19 State HDDPatient Coverage

** ****~2,000 hospitals...**
** ****~13 million discharges (all age, sex & causes)...**
** ****1.2 million ****injuries ****(all age, sex)...**
** ****175,000 injuries, women ages 15-49**
** ****E-Coding Completeness = ~90% **

**Notes:**
The 19 states shown here, representing the hospitalization experience of more than half the U.S. population. We collected data from
 ~2,000 hospitals...
~13 million inpatient discharges (all age, sex & causes)...
1.2 million injuries (all age, sex)...
Ending up with 175,000 injuries, women ages 15-49

Each dataset was cleaned and pooled separately. Since most states provide no straightforward way to distinguish between initial and follow-up visits some duplication may have occurred. A computer program (ICD-Map) was applied to the diagnoses fields to calculate Abbreviated Injury Scores by major body regions. Cost models were provided by Dr. Ted Miller. 

We selected injury admissions using detailed algorithms that examined both diagnoses and E-codes for validity and maximized E-code assignments no matter where the E-code may have been placed in the record. Most states E-coded more than 90% of injury records, but a few E-coded less than 70%.  Some states thoroughly cleaned their data, yielding datasets with no invalid diagnoses or E-codes, while others proved difficult to clean and correct.  In the end, []

## Advantages of HDD 

- Intrinsic importance of severe injuries
- Large, population-based coverage available
- Standardized data across states (UB-92)
- Captures cases missed by phone surveys
- Unlike ED visits, patient has longer time to relate underlying cause and gets more documentation

**Notes:**
Intrinsic importance of severe injuries
Population-based
Large populations, necessary for rare events
Standardized data across states (UB-92), rich in clinical parameters
Gives direct way to estimate medical costs

## MethodsCo-existent Injury & Pregnancy Dx(Greenblatt  et. al. approach)

-  **Pregnancy-association:**
-  Complications of pregnancy/childbirth(630-669.9 & 760-779.9)
-  “V” codes(normal pregnancy (V22), high-risk (V23), etc.)
-  **Injury:**
-  Injury dx in first 3 fields or injury E-code

**Notes:**
One drawback to using hospital discharge data is the lack of a specific field that indicates pregnancy status at the time of the visit. To get around this, diagnosis fields (9) can be searched for co-existent injury and pregnancy codes.

[]A case was defined as pregnancy-associated if it had one or more diagnosis codes listed as complications of pregnancy and childbirth, mostly ICD-9-CM: 630-669.9 & 760-779.9 or a “V” code for normal pregnancy (V22), high-risk pregnancy (V23), postpartum care (V24), outcome of delivery (V27) or antenatal screening (V28)[] 

[]A case was defined as injury related if the record indicated either an injury related diagnosis or an injury E-code.

## Rate Calculations

- Rates calculated / 100,000 person-years
- Pregnant population - denominators derived from live births adjusted for nine-month gestation and difficulty detecting pregnancy during first 2 months
*e.g. [1,984,049 live births x ****7****/12 =1,157,362 person years]*
- All injured women - denominator from population
-  *e.g. [36 million women 15-49 x 1 year = 36 million person years]*
- Rate ratios constructed between rates for pregnant-injured women / all injured women aged 15 to 49

**Notes:**
The best and so far only attempt to try and get around the denominator limitations was pioneered by Greenblatt, Dannenberg, and Johnson, published in 1997. They examined Maryland hospital discharge data for the years 1979 through 1990 using an ICD-9 based pregnancy screen combined with injury case selection and rate comparisons. They developed methods to compare rates that takes into account differences in the time period of exposure and measurement brought about by one group being pregnant only part of the year and estimating the number of pregnant women in a population by using live births.

Their method relies on calculating rates per 100,000 person years at risk:

For the pregnant group - rate denominators were derived from annual state live birth estimates adjusted downward to account for: 
nine-month gestation (not 1 year), and the difficulty of detecting pregnancy during first 2 months.
                        [1,984,049 live births x 7/12 =1,157,362 person years]

For the referent group of all injured women, the denominator was derived from the 1995 population estimates:
[36 million women age 15-44 x 1 year =36 million person years]

[]Comparisons, rate ratios, were constructed between rates for pregnant injured women divided by all women (aged 15 to 44) and confidence intervals  computed.[]

## Results

## Case Selection Process(from 13 million discharges)

- Case Selection Process(from 13 million discharges)

**Notes:**
For the pregnancy-associated assault study we further refined the cases to try and include only acute injury cases and only women who were residents of the state (since birth data used in denominators was resident-based).

We ended up with 5,330 E-coded pregnancy-associated cases among 137,339 injured women 15-49 (4%)
and 746 E-coded pregnancy-associated assault cases among 7,418 assaults  (10%)

## Proportion of Pregnancy-associated Hospitalized Injuries for Females, All causes, by Age

*19 States*

**Notes:**
The first table for all hospitalized female injuries ages 15-49 shows the percentage pregnant in each single year of age. In the peak years 19-26, pregnancy-associated cases make up about 9-10% of all injured women.

## Selected Comparisons, Ages 15-49 by Pregnancy-association19 State HDD, 1997.

| Characteristic | Pregnancy associated | All women
aged 15-49 |
| --- | --- | --- |
| Number | 5,498 (4.0%) | 137,887 |
| Rate (per 100,000 person years) | 476	 | 383 |
| Average age | 26	 | 32 |
| % white | 56	 | 73 |
| Average length of stay (days) | 3.1	 | 3.9 |
| Mean injury severity score (ISS) | 2.7	 | 3.6 |
| Drug or alcohol involvement (%) | 19.9	 | 39.6 |
| Median charge per visit | $4,206  | $5,872 |

## Leading Mechanisms of Injury by Pregnancy Status, Women 15-49

- _Pregnancy-associated_
- Transportation - 32%
- Falls - 22%
- Poisonings - 17%
- Struck by - 8%

- _All Women 15-49_
- Poisonings - 34%
- Transportation - 22%
- Falls - 18%
- Struck by - 4%

**Notes:**
From a percentage of cases standpoint, the leading cause of injury hospitalization among all women was poisonings (33%), but this percentage[] dropped to 16% among the pregnant cases.

[]Transportation related causes moved from number 2 at 26% among all cases, to number 1 at 34% among the pregnancy-associated cases.

[]Falls also jumped from 17% to 26%, while []struck by mechanisms increased considerably, but maintained its ranking.[]

## Hospitalized InjuryProportion by Intent – 19 States

## Rate & Rate RatiosPregnant vs. Women 15-49: Age

**Notes:**
The next graph is the first that shows the rate ratio between pregnant women and all women in the same group. Any rate ratio over one, if significant, suggests an increased risk of hospitalization during pregnancy. What we we see here, without any adjustments yet, is that the rate is increased significantly for falls, MVT, decreased significantly for poisonings, and greatly increased for struck by injuries.

## Rate Ratios: Mechanism

**Notes:**
The next graph is the first that shows the rate ratio between pregnant women and all women in the same group. Any rate ratio over one, if significant, suggests an increased risk of hospitalization during pregnancy. What we we see here, without any adjustments yet, is that the rate is increased significantly for falls, MVT, decreased significantly for poisonings, and greatly increased for struck by injuries.

## Rate Ratios: Intent

**Notes:**
But when we examine the rate ratio the assaults begin to stand out with a rate ratio of over 3.
So lets focus just on the assaults now.

## Rate Ratios: Severity

## Pregnancy Associated Rate versus Rate Ratio

## Rates & Rate Ratios, ISS ≥ 4Pregnant vs. Women 15-49: Age, Race

- White

- Non-White

## Assaults, Rate & Rate RatioAge and Race, ISS ≥ 4

- White

- Non-white

**Notes:**
This slide shows the dual axis graph of rates and rate ratio's like the preceding but it separates for comparison nonwhites and whites.
As one might guess, you can see the clear racial disparity in the underlying rate of pregnancy-associated assaults in the two populations, the rate is higher in non-whites for all age groups by a factor of 4 or more. But this is because the underlying assault rate is so much higher in the non-white population. 
Interestingly, the rate ratio pattern for pregnancy-associated cases follows a more or less similar age related course, very high in the young moms,  descending to where it is barely above 1 in the older moms. Therefore while the rate of assaults is much lower in whites, the differential of the rate comparisons between pregnant and nonpregnant women is high among both white and non-white young women.
 []

## Limitations

**Limitations**

- Quality and completeness of coding not quantified
- Fails to identify early pregnancy and post-partum risks
- No info on fetus

**Notes:**
The limitations of this study include the following:
[]Quality and completeness of coding not quantified
[]Fails to identify early pregnancy risks
[]Fails to elucidate post-partum risks

## Identifying Pregnancies from Diagnosis Codes

**Pros**
- Does not require patient identifiers
- Quick and easy to do and replicate
**Cons**
- Ascertainment not 100% (some pregnant women will not have a pregnancy-related diagnosis)
- Not useful for looking at fetal/child outcomes

## Conclusions & Summary

- Demonstrates feasibility of characterizing and tracking the most serious threats to fetus (*a good first start*)
- First multi-state study to describe pregnancy-associated hospitalization in a mostly complete E-coded population
- Pregnancy lowers hospital admission threshold for most injuries (pregnancy as a “sensitive” population)
- Once adjusted for severity, pregnancy is not related to increase in overall injury risk
- Highlights the motor vehicle problem
- Showed a moderate increase in rate ratio for assaults against young women (after severity adjustment)

**Notes:**
[]First study to describe pregnancy-associated hospitalization in a mostly complete statewide E-coded population
[]First to describe significant increase in rate ratio for assaults after adjusting for severity, but only among younger women

## Injury Prevention

- Questions?

**Injury Prevention**
**Begins at Conception!**

## Contact Information

**Associate Professor**
**Phone: (412) 648-9290**
**Email: **_[hw@injurycontrol.com](mailto:hw@injurycontrol.com)_**University of Pittsburgh**
**Center for Injury Research and Control (CIRCL)**
**Links:**
- _[http://](http://www.circl.pitt.edu/home/seminars.htm)__[www.circl.pitt.edu](http://www.circl.pitt.edu/home/seminars.htm)__[/home/](http://www.circl.pitt.edu/home/seminars.htm)_