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Patterns of obstetric infection rates in a large sample of US hospitals

  • Sarah L. Goff
    Correspondence
    Reprints: Sarah L. Goff, MD, Baystate Medical Center, Pediatrics, 3300 Main St., Suite 1D, Springfield, MA 01199
    Affiliations
    Department of Medicine, Baystate Medical Center, Springfield, and Tufts University School of Medicine, Boston, MA

    Center for Quality of Care Research, Baystate Medical Center, Springfield, and Tufts University School of Medicine, Boston, MA

    Clinical and Translational Science Institute at Tufts University School of Medicine, Boston, MA
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  • Penelope S. Pekow
    Affiliations
    Center for Quality of Care Research, Baystate Medical Center, Springfield, and Tufts University School of Medicine, Boston, MA
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  • Jill Avrunin
    Affiliations
    Center for Quality of Care Research, Baystate Medical Center, Springfield, and Tufts University School of Medicine, Boston, MA
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  • Tara Lagu
    Affiliations
    Department of Medicine, Baystate Medical Center, Springfield, and Tufts University School of Medicine, Boston, MA

    Center for Quality of Care Research, Baystate Medical Center, Springfield, and Tufts University School of Medicine, Boston, MA
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  • Glenn Markenson
    Affiliations
    Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, Baystate Medical Center, Springfield, and Tufts University School of Medicine, Boston, MA
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  • Peter K. Lindenauer
    Affiliations
    Department of Medicine, Baystate Medical Center, Springfield, and Tufts University School of Medicine, Boston, MA

    Center for Quality of Care Research, Baystate Medical Center, Springfield, and Tufts University School of Medicine, Boston, MA

    Clinical and Translational Science Institute at Tufts University School of Medicine, Boston, MA
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Published:February 07, 2013DOI:https://doi.org/10.1016/j.ajog.2013.02.001

      Objective

      Maternal infection is a common complication of childbirth, yet little is known about the extent to which infection rates vary among hospitals. We estimated hospital-level risk-adjusted maternal infection rates (RAIR) in a large sample of US hospitals and explored associations between RAIR and select hospital features.

      Study Design

      This retrospective cohort study included hospitals in the Perspective database with >100 deliveries over 2 years. Using a composite measure of infection, we estimated and compared RAIR across hospitals using hierarchical generalized linear models. We then estimated the amount of variation in RAIR attributable to hospital features.

      Results

      Of the 1,001,189 deliveries at 355 hospitals, 4.1% were complicated by infection. Patients aged 15-19 years were 50% more likely to experience infection than those aged 25-29 years. Rupture of membranes >24 hours (odds ratio [OR], 3.0; 95% confidence interval [CI], 3.24−3.5), unengaged fetal head (OR, 3.11; 95% CI, 2.97−3.27), and blood loss anemia (OR, 2.42; 95% CI, 2.34−2.49) had the highest OR among comorbidities commonly found in patients with infection. RAIR ranged from 1.0−14.4% (median, 4.0%; interquartile range, 2.8−5.7%). Hospital features such as geographic region, teaching status, urban setting, and higher number of obstetric beds were associated with higher infection rates, accounting for 14.8% of the variation observed.

      Conclusion

      Obstetric RAIR vary among hospitals, suggesting an opportunity to improve obstetric quality of care. Hospital features such as region, number of obstetric beds, and teaching status account for only a small portion of the observed variation in infection rates.

      Key words

      Childbirth is the most common reason for hospital admission in the United States, with >4,000,000 admissions for labor and delivery occurring annually.
      • Martin J.A.
      • Hamilton B.E.
      • Ventura S.J.
      • Osteman M.J.K.
      • Wilson T.C.
      • Matthews T.J.
      National Vital Statistics Report: Births, Final Data 2010. FastStats−births and natality.
      Although most births are uncomplicated, a small but significant number of women experience complications such as infection, trauma, and hemorrhage during childbirth.
      • Berg C.J.
      • Mackay A.P.
      • Qin C.
      • Callaghan W.M.
      Overview of maternal morbidity during hospitalization for labor and delivery in the United States: 1993-1997 and 2001-2005.
      • Srinivas S.K.
      • Epstein A.J.
      • Nicholson S.
      • Herrin J.
      • Asch D.A.
      Improvements in US maternal obstetrical outcomes from 1992 to 2006.
      Reducing obstetric complications has emerged as a national priority in the US, as reflected in goals established by Healthy People 20204 and the Centers for Medicare and Medicaid Services' Partnership for Patients.
      Centers for Medicare and Medicaid Services. CMMI
      Partnership for patients: a common commitment.
      For Editors' Commentary, see Contents
      See related editorial, page 427
      Maternal infection is one of the most common perinatal complications, affecting nearly 6% of deliveries,
      • Berg C.J.
      • Mackay A.P.
      • Qin C.
      • Callaghan W.M.
      Overview of maternal morbidity during hospitalization for labor and delivery in the United States: 1993-1997 and 2001-2005.
      and many of these infections may be preventable. Several small studies and reviews have described clinical practices that can increase the risk of infection, primarily related to cesarean deliveries.
      • Conroy K.
      • Koenig A.F.
      • Yu Y.-H.
      • Courtney A.
      • Lee H.J.
      • Norwitz E.R.
      Infectious morbidity after cesarean delivery: 10 strategies to reduce risk.
      • Olsen M.A.
      • Butler A.M.
      • Willers D.M.
      • Gross G.A.
      • Devkota P.
      • Fraser V.J.
      Risk factors for endometritis after low transverse cesarean delivery.
      • Dutta S.
      • Reddy R.
      • Sheikh S.
      • Kalra J.
      • Ray P.
      • Narang A.
      Intrapartum antibiotics and risk factors for early onset sepsis.
      • Ghuman M.
      • Rohlandt D.
      • Joshy G.
      • Lawrenson R.
      Post-cesarean section surgical site infection: rate and risk factors.
      • Leth R.A.
      • Møller J.K.
      • Thomsen R.W.
      • Uldbjerg N.
      • Nørgaard M.
      Risk of selected postpartum infections after cesarean section compared with vaginal birth: a five-year cohort study of 32,468 women.
      Some larger epidemiologic studies have estimated overall regional and national obstetric infection rates
      • Berg C.J.
      • Mackay A.P.
      • Qin C.
      • Callaghan W.M.
      Overview of maternal morbidity during hospitalization for labor and delivery in the United States: 1993-1997 and 2001-2005.
      • Srinivas S.K.
      • Epstein A.J.
      • Nicholson S.
      • Herrin J.
      • Asch D.A.
      Improvements in US maternal obstetrical outcomes from 1992 to 2006.
      • Lyndon A.
      • Lee H.C.
      • Gilbert W.M.
      • Gould J.B.
      • Lee K.A.
      Maternal morbidity during childbirth hospitalization in California.
      and still others have explored the associations between complications and factors such as an obstetrician's residency training site.
      • Asch D.A.
      • Nicholson S.
      • Srinivas S.
      • Herrin J.
      • Epstein A.J.
      Evaluating obstetrical residency programs using patient outcomes.
      However, little is known about the extent to which obstetric infection rates vary across hospitals or what impact structural and organizational features of a hospital may have on these rates.
      To support the national goal of improving maternal outcomes following childbirth, we used hierarchical generalized linear modeling to estimate risk-adjusted maternal infection rates (RAIR) in a large sample of US hospitals. We then examined whether hospital features, such as the number of hospital beds, teaching status, geographic region, volume of deliveries, and level of implementation of electronic health records (EHR), were associated with higher rates of infection.

      Materials and Methods

      Study sample and data source

      We conducted a cross-sectional study using Perspective, a voluntary, fee-supported database developed by Premier Inc (Charlotte, NC) that enables participating hospitals to analyze care quality and costs at their institution and to compare their performance to other institutions within the database. The database is comprised of a structurally and geographically diverse set of approximately 450 US hospitals that together account for approximately 20% of all annual hospital admissions in the United States. In addition to information derived from standard hospital discharge files (ie, Uniform Billing form-04) Perspective contains a date-stamped log of all items (eg, medications, laboratory, diagnostic tests) and therapeutic services billed to the patient or their insurer.
      Women were included in the study if they were discharged from Jan. 1, 2008, through Dec. 31, 2009, and had an International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) principal or secondary diagnosis or procedure code for a vaginal delivery (650, 640.0x-676.9x [x = 1 or 2], or 73.59) or cesarean delivery (763.4, 669.71, 74.x [x = 0-2, 4], or 74.99). We excluded discharges for ectopic and molar pregnancies and for pregnancies ending in spontaneous or elective abortion because we were interested in exploring intrapartum/peripartum infections. We also excluded patients who were transferred from or to another institution, because we did not have information about the clinical course or treatments prior to admission or subsequent outcomes, and women age <15 or >44 years because 15-44 years is a common age range for childbearing. In addition we excluded hospitals that recorded <100 deliveries over the 2-year study period to provide stable estimates of infection rates, and because these institutions do not routinely provide obstetric care. Permission to conduct the study was obtained from the institutional review board at Baystate Medical Center in Springfield, MA.

      Obstetric infection

      A delivery was considered complicated by infection if the patient received ≥1 diagnoses consistent with infection using a broad set of ICD-9-CM codes that have been used in earlier studies of infections associated with childbirth
      • Berg C.J.
      • Mackay A.P.
      • Qin C.
      • Callaghan W.M.
      Overview of maternal morbidity during hospitalization for labor and delivery in the United States: 1993-1997 and 2001-2005.
      • Asch D.A.
      • Nicholson S.
      • Srinivas S.
      • Herrin J.
      • Epstein A.J.
      Evaluating obstetrical residency programs using patient outcomes.
      (Appendix; Supplementary Table 1). We excluded ICD-9-CM infection codes with a fifth digit of 3, which indicates an antepartum condition, because we were most interested in risk-adjusted infection rates occurring in the intrapartum/peripartum period as well as the association between these risk-adjusted infection rates and hospital features. We organized infection codes into groups of related diagnoses for descriptive purposes (Table 1). Each infection code was counted toward the overall frequency of each type of infection. When calculating hospital-level infection rates, a patient was considered to either have experienced or not experienced an infectious complication regardless of the number of infection codes associated with a single delivery.
      TABLE 1Frequency of maternal infections by category of infection
      Infectionn (%)
      Any infection below40,605 (4.1)
       Puerperal infection20,519 (2.1)
       Maternal pyrexia16,067 (1.6)
       Surgical site infection3523 (0.4)
       Infection of genitourinary  tract1964 (0.2)
       Sepsis1319 (0.1)
       Other maternal infection1456 (0.2)
      Goff. Patterns of obstetric infection rates. Am J Obstet Gynecol 2013.

      Patient characteristics

      We recorded patient demographics (age, gender, race/ethnicity, marital status, and insurance status) and conditions that might confer elevated risk for obstetric infection. We used 2 complementary methods to identify maternal comorbidities and pregnancy-specific conditions that could influence a patient's risk of infection. The presence of any of 29 comorbidities was computed using Elixhauser Comorbidity Software, version 3.1, developed by the Agency for Healthcare Research and Quality.
      • Elixhauser A.
      • Steiner C.
      • Harris D.R.
      • Coffey R.M.
      Comorbidity measures for use with administrative data.
      In addition, we identified the presence of a set of pregnancy-specific conditions that may confer higher risk for infection.
      • Gregory K.D.
      • Korst L.M.
      • Gornbein J.A.
      • Platt L.D.
      Using administrative data to identify indications for elective primary cesarean delivery.
      These conditions were originally developed to predict risk for cesarean delivery, but have also been used for risk adjustment for infection rates in obstetric patients.
      • Asch D.A.
      • Nicholson S.
      • Srinivas S.
      • Herrin J.
      • Epstein A.J.
      Evaluating obstetrical residency programs using patient outcomes.
      For conditions that appeared in both sets, such as hypertension and substance abuse, we created combined indicators for patients identified by either method. Gestational diabetes and diabetes existing prior to pregnancy were assessed separately because they confer different risk for infection.
      • Piper J.M.
      • Georgiou S.
      • Xenakis E.M.
      • Langer O.
      Group B streptococcus infection rate unchanged by gestational diabetes.
      A total of 41 maternal comorbidities and pregnancy-specific conditions were evaluated for inclusion in risk-adjustment modeling (Table 2).
      TABLE 2Characteristics of patients included in study
      Demographics
      Variables retained in model for P < .05;
      Overall (N = 1,001,189)Infection present (N = 40,605)P value
      P value for χ2 test of association with any infection present vs not present;
      n%n(%)
      Age, y< .0001
       15-1994,738(9.5)6161(15.2)
       20-24236,439(23.6)10,892(26.8)
       25-29280,433(28.0)10,835(26.7)
       30-34232,606(23.2)8038(19.8)
       35-44156,973(15.7)4679(11.5)
      Marital status< .0001
       Married497,959(49.7)17,283(42.6)
       Single363,647(36.3)18,557(45.7)
       Other/unknown139,583(13.9)4765(11.7)
      Race/ethnicity< .0001
       White500,170(50.0)17,434(42.9)
       Black153,258(15.3)7963(19.6)
       Hispanic127,105(12.7)5323(13.1)
       Other220,656(22.0)9885(24.3)
      Insurance< .0001
       Managed care419,879(41.9)16,145(39.8)
       Medicaid417,643(41.7)17,895(44.1)
       Medicare6957(0.7)283(0.7)
       Commercial−indemnity80,777(8.1)3002(7.4)
       Self-pay26,820(2.7)979(2.4)
       Other49,113(4.9)2301(5.7)
      Elixhauser comorbidities
       Deficiency anemias71,578(7.2)5528(13.6)< .0001
       Blood loss anemia
      Variables retained in model for P < .05;
      70,964(7.1)7146(17.6)< .0001
       Valvular disease
      Variables retained in model for P < .05;
      3941(0.4)176(0.4).191
       Other neurological disorders
      Variables retained in model for P < .05;
      3771(0.4)224(0.6)< .0001
       Rheumatoid arthritis/CVD
      Variables retained in model for P < .05;
      1660(0.2)112(0.3)< .0001
       Paralysis
      Variables forced into model.
      212(<0.1)16(<0.1).010
       Cancer: lymphoma, metastatic, or solid tumor
      Variables retained in model for P < .05;
      188(<0.1)17(<0.1).001
       Peripheral vascular disease
      Variables forced into model.
      56(<0.1)5(<0.1).064
      Pregnancy risk factors
       Prior cesarean
      Variables retained in model for P < .05;
      182,821(18.3)4149(10.2)< .0001
       Advanced maternal age156,973(15.7)4679(11.5)< .0001
       Preterm gestation
      Variables retained in model for P < .05;
      75,730(7.6)5027(12.4)< .0001
       Fetal malpresentation68,696(6.9)3175(7.8)< .0001
       Maternal soft-tissue disorder
      Variables retained in model for P < .05;
      36,724(3.7)2196(5.4)< .0001
       Macrosomia
      Variables retained in model for P < .05;
      31,931(3.2)1246(3.1).158
       Oligohydramnios
      Variables retained in model for P < .05;
      31,213(3.1)1250(3.1).662
       Intrauterine growth restriction
      Variables retained in model for P < .05;
      25,629(2.6)778(1.9)< .0001
       Isoimmunization26,145(2.6)981(2.4).012
       Herpes
      Variables retained in model for P < .05;
      21,818(2.2)1096(2.7)< .0001
       AP bleed/placental abruption
      Variables retained in model for P < .05;
      18,657(1.9)1167(2.9)< .0001
       Unengaged fetal head
      Variables retained in model for P < .05;
      18,446(1.8)2204(5.4)< .0001
       Multiple gestation
      Variables retained in model for P < .05;
      18,446(1.8)909(2.2)< .0001
       Rupture of membranes >24 h
      Variables retained in model for P < .05;
      11,820(1.2)2066(5.1)< .0001
       Polyhydramnios
      Variables retained in model for P < .05;
      9442(0.9)374(0.9).639
       Uterine scar unrelated to cesarean2082(0.2)72(0.2).166
       Congenital fetal anomaly1371(0.1)65(0.2).198
       Maternal pulmonary embolism215(<0.1)49(0.12)< .0001
       Maternal hypotension or obstetric shock
      Variables retained in model for P < .05;
      186(<0.1)65(0.2)< .0001
       Cerebral hemorrhage
      Variables retained in model for P < .05;
      60(<0.1)19(0.1)< .0001
       Gestational diabetes56,182(5.6)2163(5.3).011
       Premature rupture of membranes
      Variables retained in model for P < .05;
      40,963(4.1)2940(7.2)< .0001
      Combined risk factors
       Severe hypertension: eclampsia, preeclampsia
      Variables retained in model for P < .05;
      14,092(1.4)900(2.2)< .0001
       Other types of hypertension
      Variables retained in model for P < .05;
      81,223(8.1)4191(10.3)< .0001
       Mental disorder39,802(4.0)1933(4.8)< .0001
       Obesity37,928(3.8)2087(5.1)< .0001
       Chronic pulmonary condition
      Variables retained in model for P < .05;
      32,761(3.3)1777(4.4)< .0001
       Thyroid condition23,361(2.3)922(2.3).393
       Abuse of any substance
      Variables forced into model.
      12,332(1.2)618(1.5)< .0001
       Preexisting DM
      Variables forced into model.
      9247(0.9)447(1.1).0002
       CHF and other heart disease
      Variables retained in model for P < .05;
      7375(0.7)580(1.4)< .0001
       Renal condition
      Variables retained in model for P < .05;
      2210(0.2)192(0.5)< .0001
       Liver condition
      Variables forced into model.
      1743(0.2)101(0.2).0002
      AP, antepartum; CHF, congestive heart failure; CVD, collagen vascular disease; DM, diabetes mellitus.
      Goff. Patterns of obstetric infection rates. Am J Obstet Gynecol 2013.
      a P value for χ2 test of association with any infection present vs not present;
      b Variables retained in model for P < .05;
      c Variables forced into model.

      Structural and organizational hospital features

      Using data from the American Hospital Association (AHA) annual survey and Premier Inc, we noted each hospital's geographic location, number of hospital beds, number of obstetric beds, number of deliveries in the 2-year period, whether the hospital was located in an urban or rural setting, teaching status, and whether a hospital reported full implementation of EHR. Four questions on the AHA Annual Survey (2008)
      • Bernheim S.
      • Lin Z.
      • Bhat K.
      • et al.
      2012 Measures maintenance technical report: acute myocardial infarction, heart failure, and pneumonia 30-day risk-standardized readmission measures. Yale New Haven Health Services Corp/Center for Outcomes Research and Evaluation.
      were used to define a hospital's level of implementation. The questions encompassed EHR use related to patient-level health information, results management, order entry management, and decision support. A hospital was categorized as having a fully implemented EHR if all 4 domains were reported as “fully implemented.”

      Statistical analysis

      We evaluated the association of patient demographics, maternal comorbidities, pregnancy-related conditions, and structural and organizational hospital features with the presence of “any infection” using χ2 statistics. We used this composite measure of infection to assess hospital infection rates because it allowed for inclusion of rare diagnoses while reducing the risk that variation in coding practices across hospitals would result in biased rate estimates. Using a model-building strategy that retained factors with P < .05, or those that were theoretically important to obstetric infections, we employed hierarchical generalized linear modeling to model the log odds of experiencing infection related to childbirth adjusting for patient demographics, maternal comorbidities, and pregnancy-specific conditions that could increase risk of infection, while including a random hospital effect. Conditions, such as diabetes existing prior to pregnancy, which did not meet the significance criterion for inclusion in the model but were clinically important were forced into the model. Selected interaction terms were evaluated. From the final model, we calculated hospital-specific RAIR as the ratio of predicted (using hospital random effect) to expected (using average hospital effect) events multiplied by the overall unadjusted infection rate, a form of indirect standardization that is used in hospital outcomes measurement initiatives sponsored by the Centers for Medicare and Medicaid Services.
      • Bernheim S.
      • Lin Z.
      • Bhat K.
      • et al.
      2012 Measures maintenance technical report: acute myocardial infarction, heart failure, and pneumonia 30-day risk-standardized readmission measures. Yale New Haven Health Services Corp/Center for Outcomes Research and Evaluation.
      Our primary model included all deliveries, and we stratified by vaginal or cesarean delivery in a secondary analysis.
      We then evaluated the bivariate associations of structural and organizational hospital features with RAIR using analysis of variance and t tests. Lastly, we modeled RAIR across hospitals as a function of structural and organizational hospital features and estimated the proportion of variation in RAIR attributable to hospital features.

      Results

      Study sample

      From the initial sample of 1,038,555 deliveries at 424 hospitals, 3913 were excluded due to presence of an ICD-9-CM code for ectopic or molar pregnancy or spontaneous or induced abortion, 29,888 due to transfer into or out of the hospital or unknown discharge status, 3140 for maternal age <15 or >44 years, and 425 because the delivery occurred at a hospital (n = 69) with <100 deliveries during the 2-year study period. Our final sample included 1,001,189 deliveries at 355 hospitals (Figure 1).
      Figure thumbnail gr1
      FIGURE 1Flow diagram depicting exclusions and final sample size
      Goff. Patterns of obstetric infection rates. Am J Obstet Gynecol 2013.
      The majority of women (75%) were between ages 20-34 years, 50% were married, 25% were black or Hispanic, and 42% had a public form of health insurance such as Medicaid (Table 2). Cesarean deliveries accounted for 39% of the deliveries included in the study. The most commonly identified maternal comorbidities and pregnancy-specific conditions included cesarean delivery during a previous pregnancy (18.3%), advanced maternal age (≥35 years) (15.7%), hypertension (8.1%), and preterm delivery (7.6%) (Table 2). Maternal mortality was 0.01% and median length of stay was 2 days (interquartile range [IQR], 2−3) for vaginal deliveries and 3 days (IQR, 3−4) for cesarean deliveries.
      Of the deliveries included in the study, 40,605 (4.1%) were complicated by infection. Puerperal infections were the most common, affecting 2.1% of deliveries, followed by maternal pyrexia (1.6%) and surgical site infections (0.4%). Genitourinary tract infections (0.2%) and sepsis (0.1%) were relatively uncommon (Table 1). Of the deliveries complicated by infection, maternal mortality was 0.06% and median length of stay was 3 days (IQR, 2−3) for vaginal deliveries and 4 days (IQR, 3−5) for cesarean deliveries.
      Among the hospitals, 28% were teaching hospitals, 77% were in an urban setting, 43% were in the south region, and 28% had >30 obstetric beds. Relatively few hospitals (19%) reported complete implementation of EHR (Table 3).
      TABLE 3Association between hospital features and risk-adjusted maternal infection rates
      CharacteristicnMean RAIR95% CIP value
      LLUL
      Region.0003
       South1544.03.74.4
       Midwest834.54.15.0
       West715.34.75.9
       Northeast475.34.46.2
      No. of deliveries in 2 y.0002
       100-999934.03.64.4
       1000-2149844.23.74.7
       2150-4099874.74.25.2
       ≥4100915.44.95.9
      No. of obstetric beds< .0001
       <151033.83.54.2
       15-291294.54.14.9
       ≥301015.44.95.9
       Unknown224.63.55.7
      No. of hospital beds.0004
       <2001164.13.74.4
       200-3991314.54.04.8
       ≥4001085.34.85.8
      Teaching status< .0001
       Nonteaching2564.34.04.6
       Teaching995.44.95.9
      Setting.01
       Urban2754.84.55.0
       Rural804.03.54.5
      Electronic health record.67
       Not complete implementation2894.64.34.8
       Complete implementation664.74.15.3
      CI, confidence interval; LL, lower limit; RAIR, risk-adjusted maternal infection rates; UL, upper limit.
      Goff. Patterns of obstetric infection rates. Am J Obstet Gynecol 2013.

      Hierarchical models

      Patient demographics, maternal comorbidities, and pregnancy-specific conditions considered in modeling RAIR are shown in Table 2. Adjusted odds ratios (OR) from the final main effects model for RAIR are shown in Supplementary Table 2. Age was strongly associated with risk for infection; when compared to patients ages 25-29 years, patients ages 15-19 years had 59% higher risk for infection, and patients ages 35-44 years had 29% lower risk. Maternal comorbidities and pregnancy-specific conditions that were most strongly associated with infection (OR, >2.0) and occurred in >5% of deliveries with infection included rupture of membranes >24 hours (OR, 3.0; 95% confidence interval [CI], 3.24−3.5), blood loss anemia (OR, 2.54; 95% CI, 2.47−2.62), and unengaged fetal head (OR, 3.11; 95% CI, 2.97−3.27). Although other risk factors such as cerebral hemorrhage were strongly associated with infection, they occurred infrequently. Interactions of the patient demographic variables (eg, age, race, insurance, and marital status) with comorbid and pregnancy-specific conditions were included in a final model used to estimate RAIR.
      Separate models for vaginal and cesarean deliveries gave results similar in magnitude and direction for most risk factors (Supplementary Table 2).

      Hospital risk-adjusted infection rates

      Unadjusted hospital infection rates ranged from 0.0−12.3% (median, 3.2%; IQR, 2.0−4.6%). However, after adjusting for differences in patient case mix, the RAIR ranged from 1.0−14.4% (median, 4.0%; IQR, 2.8−5.7%) (Figure 2). Women delivering at hospitals at the 75th percentile of infection rates had a 2-fold risk of experiencing an infection as compared to women delivering at a hospital at the 25th percentile.
      Figure thumbnail gr2
      FIGURE 2Distributions of unadjusted and risk-adjusted hospital infection rates
      Distribution of A, unadjusted and B, risk-adjusted hospital-level composite infection rates.
      Goff. Patterns of obstetric infection rates. Am J Obstet Gynecol 2013.
      Secondary analysis of RAIR for cesarean deliveries revealed infection rates ranging from 1.5−18.4% (median, 5.4%; IQR, 3.9−7.7%). Vaginal delivery infection rates ranged from 0.05−13.3% (median, 3.1%; IQR, 2.1−4.6%) (Supplementary Table 2). RAIR for vaginal and cesarean deliveries were strongly correlated (Spearman r = 0.69, P < .0001).

      Association between structural and organizational hospital features and risk-adjusted infection rates

      RAIR was associated with a number of hospital features (Table 3). Hospitals in the west region had the highest mean RAIR (5.3; 95% CI, 4.7−5.9) while those in the south region had the lowest mean RAIR (4.0; 95% CI, 3.7−4.4). Larger hospitals (≥400 beds) had higher rates (5.3; 95% CI, 4.8−5.8) than smaller hospitals (<200 beds) (4.1; 95% CI, 3.7−4.4). Higher RAIR was also observed in hospitals with a greater number of obstetric beds (≥30) (5.4; 95% CI, 4.9−5.9) when compared to hospitals with fewer obstetric beds (<15) (3.8; 95% CI, 3.5−4.2) (Table 3). Teaching hospitals had higher RAIR (5.4; 95% CI, 4.9−5.9) compared to nonteaching hospitals (4.3; 95% CI, 4.0−4.6). In a multiple regression model, structural and organizational hospital features explained approximately 14.8% of the observed variation in risk-adjusted infection rates (Table 4).
      TABLE 4Risk for higher hospital risk-adjusted infection rate attributable to structural and organizational hospital features (n = 355)
      VariableEstimate
      Interpretation: adjusting for other factors in model, hospitals in west region have risk-adjusted maternal infection rates averaging 1.51% higher than those in south region; hospitals with <15 obstetric beds have rates averaging 1.39% lower than hospitals with ≥30 obstetric beds; teaching hospitals have rates averaging 0.83% higher than nonteaching hospitals.
      SEP value
      Region
       West1.510.32< .0001
       Northeast0.850.39.0280
       Midwest0.520.31.0936
       South (reference)0
      No. of obstetric beds
       Unknown−0.760.54.1593
       <15−1.390.33< .0001
       15-29−0.710.31.0206
       ≥30 (reference)0
      Teaching status
       Teaching hospital0.830.28.0036
       Nonteaching hospital (reference)0
      Goff. Patterns of obstetric infection rates. Am J Obstet Gynecol 2013.
      a Interpretation: adjusting for other factors in model, hospitals in west region have risk-adjusted maternal infection rates averaging 1.51% higher than those in south region; hospitals with <15 obstetric beds have rates averaging 1.39% lower than hospitals with ≥30 obstetric beds; teaching hospitals have rates averaging 0.83% higher than nonteaching hospitals.

      Comment

      In this study of >1 million deliveries at 355 hospitals across the United States, approximately 4.1% of women experienced an infection during hospital admission for childbirth. We observed substantial variation in hospital infection rates that persisted even after adjustment for differences in patient case mix across institutions, with hospitals at the 75th percentile having RAIR twice that of hospitals at the 25th percentile. Although several structural and organizational hospital features were associated with higher RAIR, together these features explained only a small fraction of the observed variation in infection rates, consistent with other estimates of the impact such factors have on patient outcomes.
      • Brand C.A.
      • Barker A.L.
      • Morello R.T.
      • et al.
      A review of hospital characteristics associated with improved performance.
      Our study provides important additions to the literature on obstetric infection rates by including a large sample of US hospitals, estimating hospital-specific RAIR, and examining variation in these rates across hospitals. In a study by Berg et al,
      • Berg C.J.
      • Mackay A.P.
      • Qin C.
      • Callaghan W.M.
      Overview of maternal morbidity during hospitalization for labor and delivery in the United States: 1993-1997 and 2001-2005.
      obstetric infection rates were compared for 2 time periods, 1993 through 1997 and 2001 through 2005, using the National Hospital Discharge Survey, but this study did not report hospital-specific rates. Srinivas et al
      • Srinivas S.K.
      • Epstein A.J.
      • Nicholson S.
      • Herrin J.
      • Asch D.A.
      Improvements in US maternal obstetrical outcomes from 1992 to 2006.
      also explored trends in maternal complication rates, but used 2 state databases for the primary analysis and did not explore variation at the hospital level. Gregory et al
      • Gregory K.D.
      • Fridman M.
      • Shah S.
      • Korst L.M.
      Global measures of quality- and patient safety-related childbirth outcomes: should we monitor adverse or ideal rates?.
      developed a method to measure uncomplicated or “ideal” deliveries. However, this unique approach does not allow for comparison of rates of undesirable outcomes such as infection.
      Our study also contributes to the literature on how structural and organizational hospital characteristics may impact obstetric infection rates. Although this area has been studied for other conditions, such as acute myocardial infarction,
      • Bradley E.H.
      • Curry L.A.
      • Spatz E.S.
      • et al.
      Hospital strategies for reducing risk-standardized mortality rates in acute myocardial infarction.
      there is a paucity of research in this area for obstetrics. A recent study by Kyser et al
      • Kyser K.L.
      • Lu X.
      • Santillan D.A.
      • et al.
      The association between hospital obstetrical volume and maternal postpartum complications.
      demonstrated an association between the volume of deliveries and lower composite complication rates. In that study, hospitals with approximately 100-1600 deliveries annually had the lowest unadjusted infection rates. This is similar to our findings of lower RAIR being associated with lower number of deliveries over 2 years (100-2149). Studies in disciplines other than obstetrics have compared care quality and patient outcomes at teaching hospitals and nonteaching hospitals. These studies have generally shown lower mortality and better overall patient outcomes in the teaching hospitals.
      • Ayanian J.Z.
      • Weissman J.S.
      Teaching hospitals and quality of care: a review of the literature.
      In contrast, we found that teaching hospitals had higher infection rates. Although health care−associated infections are a common measure of quality and patient safety,
      • Welsh C.A.
      • Flanagan M.E.
      • Hoke S.C.
      • Doebbeling B.N.
      • Herwaldt L.
      Reducing health care-associated infections (HAIs): lessons learned from a national collaborative of regional HAI programs.
      • Fakih M.G.
      • Greene M.T.
      • Kennedy E.H.
      • et al.
      Introducing a population-based outcome measure to evaluate the effect of interventions to reduce catheter-associated urinary tract infection.
      infection has not been a commonly selected outcome when comparing teaching and nonteaching hospitals, limiting comparison to other studies. It is possible that differences in the gestational age of patients delivered at teaching vs nonteaching hospitals might serve as an unmeasured confounder, however gestational age was not available in the data used for this study.
      This study had a number of strengths. First, we included a large number of patients drawn from a diverse group of hospitals, enhancing the generalizability of our findings. Second, we used a composite measure of infection rather than individual infection codes to reduce the risk that the variation in the rates we observed could be explained by differences in the coding practices at individual hospitals. Third, while “present on admission” codes
      Centers for Medicare and Medicaid Services
      Hospital-acquired conditions (present on admission indicator).
      were not commonly used during the time period studied, we limited diagnoses to those most likely to occur during admission for childbirth by using a subset of fifth-digit codes. This made the infections identified more likely to be associated with factors related to hospital practices at the time of the delivery, but may have caused us to miss some pertinent infections. Fourth, we adjusted for a large number of maternal comorbidities and pregnancy-specific conditions found to be associated with the risk of maternal infection, thereby reducing the chance that variation in RAIR across hospitals reflected differences in patient case mix. Additionally, the use of multilevel regression modeling accounted for the natural clustering of patients within hospitals. Cesarean delivery is a known risk factor for postpartum endometritis and rates of cesarean deliveries vary across hospitals. By including both modes of delivery in the primary analysis, our hospital-level infection risk estimates were not influenced by local preferences for cesarean deliveries and offer a more patient-centric view of the risk of infection associated with the choice of hospital.
      The first potential limitations of this study are the accuracy of ICD-9-CM coding and inability to capture outpatient codes. These are inherent limitations of all studies using administrative data. Prior validation studies of obstetric ICD-9-CM codes demonstrate variation in estimates of sensitivity and specificity of codes for a number of conditions, with codes for surgical procedures generally being the most reliable.
      • Yasmeen S.
      • Romano P.S.
      • Schembri M.E.
      • Keyzer J.M.
      • Gilbert W.M.
      Accuracy of obstetric diagnoses and procedures in hospital discharge data.
      • Romano P.S.
      • Yasmeen S.
      • Schembri M.E.
      • Keyzer J.M.
      • Gilbert W.M.
      Coding of perineal lacerations and other complications of obstetric care in hospital discharge data.
      • Goff S.L.
      • Pekow P.S.
      • Markenson G.
      • Knee A.
      • Chasan-Taber L.
      • Lindenauer P.K.
      Validity of using ICD-9-CM codes to identify selected categories of obstetric complications, procedures and co-morbidities.
      • Korst L.M.
      • Gregory K.D.
      • Gornbein J.A.
      Elective primary cesarean delivery: accuracy of administrative data.
      Other studies have found ICD-9-CM coding highly reliable for diagnoses such as diabetes and hospital-based procedures.
      • Chen G.
      • Khan N.
      • Walker R.
      • Quan H.
      Validating ICD coding algorithms for diabetes mellitus from administrative data.
      • Andrade S.E.
      • Moore Simas T.A.
      • Boudreau D.
      • et al.
      Validation of algorithms to ascertain clinical conditions and medical procedures used during pregnancy.
      Although we attempted to mitigate some of the potential limitations of coding accuracy by using a composite of multiple diagnosis codes, some of our study's findings may be partly explained by differences in documentation and coding across institutions. For example, our surgical site infection rate was lower than rates found in other studies.
      • Conroy K.
      • Koenig A.F.
      • Yu Y.-H.
      • Courtney A.
      • Lee H.J.
      • Norwitz E.R.
      Infectious morbidity after cesarean delivery: 10 strategies to reduce risk.
      This may be due in part to the lack of a specific ICD-9-CM code to identify obstetric surgical site infections or ability to capture only infections identified during the admission. Although the code for maternal pyrexia is associated with infection, this code may also be used for fever from a noninfectious source, such as blood transfusion. A final example of limitations due to coding accuracy relates to obesity codes, which have been found to have high specificity but low sensitivity in obstetrics.
      • Goff S.L.
      • Pekow P.S.
      • Markenson G.
      • Knee A.
      • Chasan-Taber L.
      • Lindenauer P.K.
      Validity of using ICD-9-CM codes to identify selected categories of obstetric complications, procedures and co-morbidities.
      • Andrade S.E.
      • Moore Simas T.A.
      • Boudreau D.
      • et al.
      Validation of algorithms to ascertain clinical conditions and medical procedures used during pregnancy.
      The second potential study limitation was our focus on infections that became apparent during the index hospitalization; overall infection rates are undoubtedly higher than we estimated. Third, although we attempted to adjust for a large number of maternal comorbidities and pregnancy-specific factors that could influence the risk of infection, some of the variation in infection rates may be related to limitations on our ability to fully account for the patient-level risks. Fourth, some of the unexpected findings related to the comorbidity rates may be explained by unmeasured factors, such as preterm births excluded due to transfers of preterm deliveries. Finally, the overrepresentation of southern hospitals in the perspective dataset means interpretation of geographic differences in RAIR must be made with care. This geographic oversampling may explain why the rate of cesarean deliveries found in this sample was higher than national averages.
      The Henry J. Kaiser Family Foundation
      Kaiser state health facts.
      • Menacker F.
      • Hamilton B.E.
      Centers for Disease Control and Prevention
      Recent trends in cesarean delivery in the U.S. National Center for Health Statistics. Data Brief no. 35, March 2010.
      In conclusion, we found that risk-adjusted infection rates following childbirth vary considerably across hospitals, and that key structural and organizational hospital features explain only a modest amount of this variation. To support large-scale efforts to improve the quality of obstetric care, additional research is needed to identify organizational factors and clinical strategies that enable some hospitals to achieve lower infection rates.

      Appendix

      SUPPLEMENTARY TABLE 1International Classification of Diseases, Ninth Revision, Clinical Modification infection codes
      InfectionICD-9-CM codeDescription
      Puerperal infection658.40Infection of amniotic cavity, unspecified as to episode of care or not applicable
      658.41Infection of amniotic cavity, delivered, with or without mention of antepartum condition
      670.00Major puerperal infection, unspecified as to episode of care or not applicable
      670.02Major puerperal infection, delivered, with mention of postpartum complication
      670.04Major puerperal infection, postpartum condition or complication
      670.10Puerperal endometritis, unspecified as to episode of care or not applicable
      670.12Puerperal endometritis, delivered, with mention of postpartum complication
      670.14Puerperal endometritis, postpartum condition or complication
      670.20Puerperal sepsis, unspecified as to episode of care or not applicable
      670.22Puerperal sepsis, delivered, with mention of postpartum complication
      670.24Puerperal sepsis, postpartum condition or complication
      670.30Puerperal septic thrombophlebitis, unspecified as to episode of care or not applicable
      670.32Puerperal septic thrombophlebitis, delivered, with mention of postpartum complication
      670.34Puerperal septic thrombophlebitis, postpartum condition or complication
      670.80Other major puerperal infection, unspecified as to episode of care or not applicable
      670.82Other major puerperal infection, delivered, with mention of postpartum complication
      670.84Other major puerperal infection, postpartum condition or complication
      Maternal pyrexia659.20Maternal pyrexia during labor, unspecified, unspecified as to episode of care or not applicable
      659.21Maternal pyrexia during labor, unspecified, delivered, with or without mention of antepartum condition
      672.00Pyrexia of unknown origin during the puerperium, unspecified as to episode of care or not applicable
      672.02Pyrexia of unknown origin during the puerperium, delivered, with mention of postpartum complication
      672.04Pyrexia of unknown origin during the puerperium, postpartum condition or complication
      Sepsis038.0Streptococcal septicemia
      038.1Staphylococcal septicemia
      038.10Staphylococcal septicemia, unspecified
      038.11Methicillin susceptible Staphylococcus aureus septicemia
      038.12Methicillin resistant Staphylococcus aureus septicemia
      038.19Other Staphylococcal septicemia
      038.2Pneumococcal septicemia
      038.3Septicemia due to anaerobes
      038.4Septicemia due to other gram-negative organisms
      038.40Gram-negative septicemia NOS
      038.41H influenzae septicemia
      038.42E coli septicemia
      038.43Pseudomonas septicemia
      038.44Serratia septicemia
      038.49Gram-negative septicemia NEC
      038.8Other specified septicemias
      038.9Unspecified septicemia
      659.30Generalized infection during labor, unspecified as to episode of care or not applicable
      659.31Generalized infection during labor, delivered, with or without mention of antepartum condition
      785.52Septic shock
      790.7Bacteremia
      995.90Systemic inflammatory response syndrome (SIRS), unspecified
      995.91SIRS without organ dysfunction
      995.92SIRS with organ dysfunction
      Infection of genitourinary tract590.1Acute pyelonephritis
      590.2Renal and perinephric abscess
      590.3Pyeloureteritis cystica
      590.80Pyelonephritis, unspecified
      590.9Infection of kidney, unspecified
      590.10Acute pyelonephritis without lesion of renal medullary necrosis
      590.11Acute pyelonephritis with lesion of renal medullary necrosis
      646.62Infections of genitourinary tract in pregnancy, delivered, with mention of postpartum complication
      646.64Infections of genitourinary tract in pregnancy, postpartum condition or complication
      486Pneumonia, organism unspecified
      With:
      Other maternal infection648.90Other current conditions classifiable elsewhere of mother, unspecified as to episode of care or not applicable
      648.91Other current conditions classifiable elsewhere of mother, delivered, with or without mention of antepartum condition
      648.92Other current conditions classifiable elsewhere of mother, delivered, with mention of postpartum complication
      648.94Other current conditions classifiable elsewhere of mother, postpartum condition or complication
      682.2Cellulitis and abscess of trunk
      682.5Cellulitis and abscess of buttock
      996.62Infection and inflammatory reaction due to other vascular device, implant, and graft
      999.31Infection due to central venous catheter
      999.39Infection following other infusion, injection, transfusion, or vaccination
      ICD-9-CM, International Classification of Diseases, Ninth Revision, Clinical Modification.
      Goff. Patterns of obstetric infection rates. Am J Obstet Gynecol 2013.
      SUPPLEMENTARY TABLE 2Odds ratios with 95% confidence intervals from main effects risk-adjustment models
      DemographicAll deliveriesCesarean deliveriesVaginal deliveries
      OR95% CIOR95% CIOR95% CI
      Age group, y
       15-191.59(1.54−1.65)1.42(1.34−1.49)1.79(1.70−1.87)
       20-241.19(1.16−1.23)1.15(1.10−1.20)1.25(1.20−1.31)
       25-29
       30-340.87(0.84−0.90)0.88(0.84−0.92)0.85(0.81−0.89)
       35-440.71(0.69−0.74)0.80(0.76−0.84)0.59(0.55−0.62)
      Race
       White
       Black1.13(1.09−1.17)1.33(1.27−1.39)0.98(0.93−1.03)
       Hispanic1.23(1.18−1.28)1.34(1.27−1.42)1.21(1.15−1.28)
       Other1.25(1.21−1.29)1.31(1.26−1.38)1.22(1.16−1.28)
      Marital status
       Single1.17(1.14−1.20)1.09(1.05−1.13)1.20(1.15−1.24)
       Married
       Other/unknown1.06(1.01−1.12)1.09(1.02−1.17)0.99(0.91−1.07)
      Insurance
       Medicare1.00(0.88−1.13)1.17(0.99−1.37)0.83(0.68−1.02)
       Medicaid0.89(0.87−0.92)1.08(1.04−1.12)0.81(0.78−0.85)
       Managed care
       Commercial−indemnity1.02(0.97−1.07)1.03(0.97−1.10)1.01(0.94−1.08)
       Self-pay0.90(0.84−0.96)1.12(1.01−1.24)0.85(0.77−0.94)
       Other1.02(0.97−1.08)1.06(0.98−1.14)1.02(0.95−1.10)
      Elixhauser comorbidities
       Blood loss anemia2.54(2.47−2.62)2.12(2.03−2.20)2.53(2.42−2.65)
       Valvular disease0.60(0.50−0.71)0.56(0.44−0.71)0.72(0.53−0.97)
       Rheumatoid arthritis/CVD1.45(1.19−1.77)1.21(0.93−1.58)1.73(1.27−2.34)
       Other neurological disorders1.18(1.02−1.36)1.07(0.89−1.89)1.21(0.97−1.52)
       Peripheral vascular disease1.48(0.54−4.03)1.09(0.33−3.54)1.51(0.19−11.97)
       Paralysis1.11(0.64−1.91)0.62(0.28−1.39)2.00(0.95−4.19)
       Cancer: lymphoma, metastatic, or solid tumor2.03(1.21−3.39)1.77(0.95−3.31)1.84(0.72−4.70)
      Pregnancy risk factors
       Prior cesarean0.57(0.55−0.59)0.29(0.28−0.30)0.97(0.88−1.07)
       Pulmonary embolism4.58(3.21−6.53)5.04(3.38−7.53)2.78(1.26−6.14)
       Unengaged fetal head3.11(2.97−3.27)1.83(1.74−1.92)1.57(1.20−2.06)
       Maternal soft-tissue disorder1.42(1.35−1.49)1.07(1.01−1.13)1.54(1.42−1.68)
       Preterm gestation1.29(1.24−1.33)1.11(1.06−1.17)1.41(1.34−1.48)
       Cerebral hemorrhage4.41(2.42−8.01)4.25(2.05−8.79)4.58(1.46−14.41)
       Intrauterine growth restriction0.62(0.57−0.67)0.62(0.56−0.68)0.53(0.47−0.59)
       Ruptured membranes >24 h3.77(3.58−3.97)3.88(3.61−4.18)3.24(3.00−3.50)
       Maternal hypotension or obstetric shock6.34(4.55−8.85)7.14(4.85−10.52)4.04(1.96−8.34)
       Oligohydramnios0.94(0.88−0.99)0.82(0.76−0.89)0.88(0.80−0.97)
       AP bleed/placental abruption1.16(1.09−1.23)0.77(0.72−0.84)1.40(1.25−1.57)
       Herpes1.07(1.01−1.15)0.89(0.81−0.97)1.12(1.02−1.24)
       PROM1.59(1.53−1.66)1.61(1.52−1.71)1.55(1.46−1.64)
      Combined factors
       Severe hypertension: eclampsia, preeclampsia1.22(1.14−1.31)0.87(0.80−0.94)1.07(0.91−1.25)
       Other types of hypertension1.21(1.17−1.25)0.94(0.90−0.98)1.25(1.18−1.32)
       Liver condition1.22(0.99−1.50)1.24(0.94−1.64)1.22(0.88−1.68)
       CHF and other heart disease2.12(1.91−2.36)2.03(1.78−2.32)1.90(1.58−2.29)
       Substance abuse0.95(0.87−1.03)1.03(0.92−1.17)0.90(0.80−1.02)
       Renal condition1.64(1.41−1.92)1.61(1.32−1.96)1.60(1.25−2.05)
       Diabetes, preexisting1.10(0.99−1.50)0.84(0.75−0.95)1.28(1.06−1.54)
       Lung disease1.07(1.02−1.13)1.08(1.01−1.16)0.98(0.91−1.07)
      AP, antepartum; CHF, congestive heart failure; CI, confidence interval; CVD, collagen vascular disease; OR, odds ratio; PROM, premature rupture of membranes.
      Goff. Patterns of obstetric infection rates. Am J Obstet Gynecol 2013.

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