Are pregnancy outcomes associated with risk factor reporting in routinely collected perinatal data?

Abstract

Aim:To assess reporting characteristics of commonly dichotomised pregnancy outcomes (e.g. preterm/term birth); and to investigate whether behaviours (e.g. smoking), medical conditions (e.g. diabetes) or interventions (e.g. induction) were reported differently by pregnancy outcomes. Methods: Further analysis of a previous validation study was undertaken, in which 1680 perinatal records were compared with data extracted from medical records. Continuous and polytomous variables were dichotomised, and risk factor reporting was assessed within the dichotomised outcome groups. Agreement, kappa, sensitivity and positive predictive value calculations were undertaken. Results: Gestational age, birthweight, Apgar scores, perineal trauma, regional analgesia and baby discharge status (live birth/ stillbirth) were reported with high accuracy and reliability when dichotomised (kappa values 0.95-1.00, sensitivities 94.7-100.0%). Although not statistically significant, there were trends for hypertension, infant resuscitation and instrumental birth to be more accurately reported among births with adverse outcomes. In contrast, smoking ascertainment tended to be poorer among preterm births and when babies were ,2500 g. Conclusion: Dichotomising variables collected as continuous or polytomous variables in birth data results in accurate and well ascertained data items. There is no evidence of systematic differential reporting of risk factors.Population level data are well suited to studies evaluating health care. With the risk of sampling bias removed, estimation of incidence and prevalence rates can be made, allowing for description of the total burden of a particular disease or outcome, analysis of risk factors and trends, as well as identification of health inequalities and estimation of health costs. 1,2 Accurate conclusions from such analyses rely on high quality data that truly represent the population experience. Assessment of data quality (completeness and accuracy) is typically undertaken by a validation study, in which data from a sample of records from the population dataset are compared to a highly reliable and accurate source of data ('the gold standard') for the corresponding records. The accuracy and reliability of individual data items are typically reported. 3,4 The variables in perinatal population data can be continuous (e.g. gestational age), nominal (e.g. mode of delivery) and ordinal (e.g. first, second, third or fourth degree perineal tears), with validation of such variables typically reporting percent agreement and kappa statistics. These types of variables are frequently dichotomised in analyses (e.g. preterm birth, caesarean section, or third-fourth degree tears), 5,6 but little assessment has been undertaken into the accuracy and reliability of their dichotomised form.Differential reporting in population health data occurs when a variable is reported with different accuracy and reliability amongst different strata of another variable. This can introduce systematic bias, leading to under or over...

Comparison of recording of hepatitis B infection in the NSW Perinatal Data Collection with linked hepatitis B notifications

“…A 1998 validation study showed high levels of accuracy and reliability compared with medical records for dichotomised perinatal variables such as preterm birth, low and high birthweight, Apgar scores, perineal trauma, regional analgesia and stillbirth (Kappa 0.95-1.00, sensitivities 94.7-100.0%). 11 The lower estimate of HBV prevalence from the PDC compared with prevalence using linked NCIMS records is consistent with previous analyses of PDC data for rarer events. 12 The level of agreement was similar for private and public hospitals, in contrast to a study of PDC versus hospital records of postpartum haemorrhage requiring transfusion, which found poorer agreement for private hospitals.…”

“…However, we hypothesise that, as the data collection practice becomes more ingrained in the health system, sensitivity will improve, as it did for the perineal trauma 12 and postpartum haemorrhage data 13 , and to the level of accuracy and reliability of other dichotomised variables suggested by previous validation studies. 11 If reporting of maternal HBV status in the PDC data becomes more reliable, it could be used to determine HBV prevalence across different AHSs and population groups, as well as to monitor prevalence changes over time, as has been done in previous studies using linked data. 6,7 Variations in accuracy across AHSs, and important sociodemographic factors such as country of birth also require further analysis over time to determine if there are consistent factors that affect the accuracy of reporting.…”

Critérios pragmáticos da definição de near miss neonatal: um estudo comparativo

“…In addition to completeness, the information is available and easily obtainable. Accuracy and concordance of pregnancy outcomes (including birth weight, gestational age, and Apgar score of dichotomized medical record data) are generally considered as good and unbiased 1 . Brazilian studies have shown that birth weight 21 and fifth-minute Apgar score showed good to excellent reliability 5 , 21 .…”

Obstetric anal sphincter injury rates among primiparous women with different modes of vaginal delivery

“…The present study highlights that, if improved detection is responsible for the apparent increase in OASIS incidence, such detection is not occurring among vacuum deliveries because there was no evidence of an increasing trend in this group. Whether differential reporting has occurred, with higher rates of third‐ and fourth‐degree tears being reported among groups with known risk factors (such as instrumental deliveries), is also not known; however, a local validation study provides some reassurance that this is not the case [25]. Comparisons of sensitivities and PPVs for perineal trauma between non‐instrumental and instrumental deliveries were reported as not being statistically different (sensitivity 96.6 vs 97.9, PPV 96.0 vs 98.6, respectively) in the validation study [25].…”