Efecto sobre la expresión del receptor para leptina

Risk factors of interest for this study recorded in HES included birth weight, gestational age and sex, maternal age, postcode and IMD score. I also developed an indicator of congenital anomalies using diagnoses recorded in longitudinal inpatient admission records in HES and causes of death recorded in ONS mortality data (described in Section 3.4.3.2).

3.4.3.1 Improving the completeness of risk factor variables using mother-baby linkage in HES

As outlined in Section 3.3.1.3 above, variables recorded in the “baby tail” are kept in two separate records in HES – a birth record for each baby and a delivery record for the mother.¹³² Maternal delivery records are often more complete than birth records.¹²⁹ Therefore, completeness of recordings of birth weight, gestational age, maternal age, IMD scores and postcodes can be improved by replacing missing values in the baby record with complete recordings from the maternal delivery record. In this chapter, I refer to this process as “enhancing” the data, resulting in “enhanced” birth weight, gestational age, maternal age, IMD scores and postcodes.

Methods for linking mothers and babies were developed by Dr Katie Harron and are described in detail in Section C.3 of Appendix C. In brief, maternal delivery episodes and babies’ birth episodes can be linked in the de-identified HES database as much of the information recorded in the two episodes overlap (such as variables describing maternal characteristics, pregnancy, delivery and birth outcomes which are recorded in the “baby tail” or residency details).¹⁵⁴ Harron et al.¹⁵⁴ developed methods for

identifying births and deliveries in the HES dataset and linking mothers with their

babies using deterministic and probabilistic methods. Deterministic linkage, such as the algorithm for linking HES inpatient admissions with ONS mortality data, requires an

exact or approximate agreement between a set of identifiers such as date of birth, postcode or sex to make a match (conflicting information is not permitted).¹⁵⁴ Probabilistic linkage methods allow calculating the likelihood of a match, given the agreement or disagreement in the set of observed identifiers amongst all possible pairs. The pair with the highest likelihood is identified as a match.

Harron et al.’s¹⁵⁴ linkage algorithm was replicated by my UCL colleague Dr Linda Wijlaars in the HES extract available for this study.

3.4.3.2 Cleaning birth characteristics variables

3.4.3.2.1 Birth weight and gestational age

Some hospitals in HES are known to report gestational age in days rather than in weeks in their maternity systems. The last digit consequently gets truncated by the HES cleaning algorithm; for example 280 days (40 weeks) would be recorded as 28 weeks.¹⁵⁵ Such errors lead to misclassification of term births as preterm, resulting in a bimodal distribution of birth weight at lower gestations (Figure 3.2), and biasing

downward the estimates of child mortality in high risk babies born at early gestations as term babies have a much lower risk of death.

Figure 3.2 – Distribution of birth weight by week of gestation before removing

implausible combinations of birth weight and gestational age in HES -ONS birth cohort

HES=Hospital Episode Statistics; ONS=Office for National Statistics.

To minimise the impact of these recording errors, I changed values of birth weight and gestational age to missing if the recorded birth weight fell outside +/-4 standard deviations (SD) of mean birth weight for each gestational age. To obtain birth weight centiles, I used LMSgrowth, a Microsoft Excel add-in with growth references for children in the UK, developed by Pan and Cole.¹⁵⁶ For preterm babies, I used birth weight centiles based on the UK WHO preterm reference, which was extrapolated to 22 weeks; for term babies born from 37 to 42 weeks I used UK WHO term reference.

Data on 43-45 weeks was unavailable. Investigating birth weight curves from Australia and USA revealed that mean birth weight and centiles do not increase further after 42

weeks, so I used the values for 42 weeks as cut-offs for higher gestations.^157–159 The growth references are sex-specific. For a small number of records with missing recording for sex, I used values of birth weight centiles which were overlapping

between boys and girls. That is, I used -4SD values for boys (as they were higher than for girls) and +4SD values for girls (as they were lower than for boys).

3.4.3.2.2 Sex

Where missing, information about sex of the baby was completed using longitudinal hospital admissions records and the ONS mortality records (where available) by taking the mode of recorded sexes across records.

3.4.3.2.3 Congenital anomalies

Presence of congenital anomalies may not be immediately obvious at birth, as it could take time for some of the anomalies to manifest and be diagnosed. Therefore, I indicated children as having a congenital anomaly if they had a relevant ICD-10 code recorded as any diagnosis within first two years of life, or as any cause of death recorded in the ONS mortality data. I used ICD-10 codes for congenital anomalies taken from a chronic condition code list developed by Hardelid et al. which identifies children that require medical follow-up for more than 12 months in 50% or more of cases.⁵⁸I used only codes beginning with “Q”, from Chapter 17 of ICD-10 “Congenital malformations, deformations and chromosomal abnormalities” included in the Hardelid et al.’s code list.²⁴

3.4.3.3 Socio-economic factors

3.4.3.3.1 Maternal age

Maternal age was enhanced through mother-baby linkage using mother’s age at admission for delivery.

3.4.3.3.2 IMD score and postcode

In the financial years 2007/8 - 2012/13, the patient postcode and all variables derived from the patient’s postcode (including the IMD score) were missing from all birth

episodes where the episode type was specified as birth.¹⁶⁰ In my cohort, this accounted for 85% of all singleton live births in 2007/8-2012/13. This was the result of an

extraction error while processing HES extracts by NHS Digital.¹⁶⁰ It is possible that birth episodes before 2007/8 were also affected; however, issues with the quality of HES data identified by NHS Digital during data processing were not documented prior to 2007/8.

Maternal delivery records have near 100% completeness of postcode, thus enhancing the data through mother-baby linkage was crucial for obtaining information about the only measure of socio-economic status in HES – the IMD score. For babies’ HESIDs which did not link to a maternity record, I copied the earliest recording of postcode and IMD scores from longitudinal hospital admissions in infancy to the birth record. This helped to maximise the completeness of recording the available information on postcode.

I then calculated quintiles of IMD scores amongst all pregnant women in a given calendar year, in order to derive a comparable indicator of SES to that available in the Swedish cohort (described in Chapter 4). To match inclusion criteria to the Swedish birth cohort (also described in Chapter 4) I used the enhanced information on postcode to exclude non-English residents from the birth cohort.

3.4.3.4 Cohort validation

I first evaluated rates of missing data for each risk factor of interest among live births and among deaths (by age at death). I compared mortality rates in infancy based on all births in the HES-ONS birth cohort (“whole cohort”), and in the “complete case” cohort, defined as cohort of births with complete information on all birth characteristics (birth weight, gestational age, sex) and socio-economic factors (maternal age and IMD score), with rates reported for England and Wales, published by the ONS (and freely available on the ONS website).42,44,161,162

I then validated the distribution of birth weight, gestational age and maternal age in live births in the whole and complete case cohorts against national statistics from ONS for singleton live births in England and Wales.^34,42,161 Finally, I compared mortality rates by age at death and categories of birth weight, gestational age and maternal age in the complete case HES-ONS birth cohort, with rates reported by ONS for England and Wales.^34,42,161