Where have the hospital delivered babies gone?

Factors associated with hospital births not recoded in the NSW Admitted Patient Data collection

The Faculty of Health Sciences

Mary Lam | Dr

Contents

› Background

› Methods

- Record Linkage

› Results

› Discussions and Conclusions

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Background

› Useful source for secondary data analysis (e.g. Chen et al. 2010, Lain et al. 2009, Roberts et al. 2008, Lu et al. 2007)

- Trends

- Epidemiology

- Service utilisation

› Data Quality

- Non-inclusion of valid cases

- A potential source of bias for subsequent analysis (Ford et al. 2006)

› Validation studies to understand dataset and its quality

- Record linkage of similar datasets

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Routinely collected hospital admission data

Aims and Methods

› Examine factors that contribute to hospital birth records not being recorded in the APDC dataset

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Aims:

› Datasets

› NSW Admitted Patients Data Collection (APDC)

› NSW Midwives Data Collection (MDC)

› NSW Registry of Births, Deaths and Marriages (RBDM)

Methods:

Methods

› Conducted by the NSW Centre for Health Record Linkage (CHeReL)

› Births recorded in the APDC and MDC datasets for the calendar year 2005 were used for analysis

› Births registered in the NSW RBDM for the same period were used as validation

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Record Linkage:

› Bivariate analysis:

› χ2 Analysis

› Logistic regression analyses:

› Backward model reduction

Statistical Analysis:

Record Linkage at CHeReL

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Step 1:

Source: Guide to Health Record Linkage Service, http://www.cherel.org.au/HowCHeReLworks.pdf

Record Linkage at CHeReL

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Step 2:

Source: Guide to Health Record Linkage Service, http://www.cherel.org.au/HowCHeReLworks.pdf

Record Linkage at CHeReL

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Step 3:

Source: Guide to Health Record Linkage Service, http://www.cherel.org.au/HowCHeReLworks.pdf

Record Linkage at CHeReL

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Step 4:

Source: Guide to Health Record Linkage Service, http://www.cherel.org.au/HowCHeReLworks.pdf

Record Linkage at CHeReL

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Step 5:

Source: Guide to Health Record Linkage Service, http://www.cherel.org.au/HowCHeReLworks.pdf

Results

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APDC and MDC

record counts

MatchedUnmatched

Results

Variables Sig Variables Sig

Hospital CharacteristicsLevel of maternal hospital Yes

Separation StatusMotherBabies

YesYes

Mother CharacteristicsMother ageMaternal diabetesMaternal hypertensionGestational diabetesPre-eclampsia

YesYesNoYesNo

Babies CharacteristicsSexPluralParity

NoYesYes

Labour, Presentation, DeliveryLabourPresentationDelivery

YesYesYes

Baby’s ConditionsGestation ageBirth weight

YesYes

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Bi-variates Analyses – Matched and Unmatched MDC records

Results

Variables Likelihood of unmatched

Hospital CharacteristicsLevel of maternal hospitalAs compare to Level 1-3 hospitals, Levels 4-6 As compare to Level 1-3 hospitals, Private Hospitals

70% reduction30% reduction

Separation StatusBabyAs compare to babies who were discharged, babies that were stillborn/neonatal death, or transfer Increased odd

Baby’s ConditionsGestation ageAs compare to babies of normal gestation age, babies who were born less ten 37 weeks 100% Increase

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Logistic Regression – Final model (α = 0.001)

Results

› Level of hospital

- When compare to Levels 1-3 hospitals (local, small isolated, small metropolitan hospitals)

- Levels 4-6 hospitals (large metropolitan, tertiary hospitals) – 70% reduction of odd in baby records not included in APDC

- Private hospital – 30 % reduction of odd in baby records not included in APDC

› Baby separation status and gestational age

- Records from babies who were stillborn, died had an increased odd to be not included in APDC

- Records from babies who had lower than normal gestation age had an increased odd to be not included in APDC

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Discussions

› Significant factors associated with missing APDC records

- Levels 1- 3 hospitals more likely to have missed APDC records

- Lack of resources?

- Gestational age - Babies of less than 37 weeks - more likely to be not included

- Likely not to survived? - Thus no recode in APDC?

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Conclusions

› MDC is better in recording birth

- More records captured

- Less duplication

- For Hospital births less likely to be affected by:

- Hospital level

- Survival status of the baby

› APDC – overall quality is good

- Able to provide information on

- Procedure used

- Co-morbidities

- Longitudinal aspect of hospitalisation

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Chen JS, Roberts CL, Ford JB, Taylor LK, Simpson JM (2010), ‘Cross-sectional reporting of previous Casarean birth was validated using longitudinal linked data’, Journal of clinical Epidemiology, vol.63, pp.672-678.

Ford JB, Roberts C, Taylor LK (2006), ‘Characteristics of unmatched maternal and baby records in linked birth records and hospital discharge data’, Paediatric and Perinatal Epidemiology, vol.20, pp.329-337.

 Lain SJ, Algert CS, Tasevski V, Morris JM, Roberts CL (2009), ‘Record linkage to obtain birth outcomes for the evaluation of screening biomarkers in pregnancy: a feasibility study’, BMC Medical Research Metholology, vol. 9:48.

 

Lu TH, Walker SM, Anderson RN, McKenzieK, Bjorkenstam C, Hou WH (2007), ‘The proportion of injury deaths with unspecified external cause codes-- a comparison of Australia, Sweden, Taiwan and the United States’, Injury Prevention, vol.13, pp.276-281.

 

Roberts CL, Bell JC, Ford JB, Hadfield RM, Algert CS, Morris JM (2008), ‘The accuracy of reporting of the hypertensive disorders of pregnancy in population health data’, Hypertension in Pregnancy, vol 27, pp.285-297.

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References:

Thank you

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