Using Administrative Data to Count and Classify Households with Local Applications

Harper, Gill; Mayhew, Les

doi:10.1007/s12061-015-9162-2

Cited by 5 publications

(6 citation statements)

References 15 publications

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“…Research including HCM is predominantly based on surveys of relatively small numbers, or other estimated household measures [ 39 ]; our methods estimate household composition using individual-level data for a whole population. Current household composition metrics at government level are provided through detailed Census and survey results, an expensive and time consuming task, utilising the longitudinal data in the SAIL Databank we show HCM methods can describe household composition over a wide time period in an efficient manner.…”

Section: Discussionmentioning

confidence: 99%

Deriving household composition using population-scale electronic health record data—A reproducible methodology

et al. 2021

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Background Physical housing and household composition have an important role in the lives of individuals and drive health and social outcomes, and inequalities. Most methods to understand housing composition are based on survey or census data, and there is currently no reproducible methodology for creating population-level household composition measures using linked administrative data. Methods Using existing, and more recent enhancements to the address-data linkage methods in the SAIL Databank using Residential Anonymised Linking Fields we linked individuals to properties using the anonymised Welsh Demographic Service data in the SAIL Databank. We defined households, household size, and household composition measures based on adult to child relationships, and age differences between residents to create relative age measures. Results Two relative age-based algorithms were developed and returned similar results when applied to population and household-level data, describing household composition for 3.1 million individuals within 1.2 million households in Wales. Developed methods describe binary, and count level generational household composition measures. Conclusions Improved residential anonymised linkage field methods in SAIL have led to improved property-level data linkage, allowing the design and application of household composition measures that assign individuals to shared residences and allow the description of household composition across Wales. The reproducible methods create longitudinal, household-level composition measures at a population-level using linked administrative data. Such measures are important to help understand more detail about an individual’s home and area environment and how that may affect the health and wellbeing of the individual, other residents, and potentially into the wider community.

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Section: Discussionmentioning

confidence: 99%

Deriving household composition using population-scale electronic health record data—A reproducible methodology

et al. 2021

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“…An alternative forecasting approach is the household membership method , used when information about the household representative is missing but when administrative or census microdata are available. For example, Harper and Mayhew () and NISRA (, ) adopt a method that merges the household formation and typology steps. It is assumed that each dwelling address contains one household.…”

Section: Review Of Household Projection Methodsmentioning

confidence: 99%

“…For example, Harper and Mayhew (2016) and NISRA (2015aNISRA ( , 2015b adopt a method that merges the household formation and typology steps. It is assumed that each dwelling address contains one household.…”

Section: The Household Membership Methodsmentioning

confidence: 99%

Household Forecasts for the Planning of Long‐Term Domestic Water Demand: Application to London and the Thames Valley

Rees

Clark

Nawaz

2020

Population Space and Place

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Methods for forecasting households in London and the Thames Valley were developed for input to forecasts of domestic water consumption. Households were forecast by ethnicity, size and property type. South Asian‐headed households consumed more water (per capita) than average. Forecast populations for 60 Local Authorities were extracted from a UK‐wide forecast and aggregated to six Water Resource Zones (WRZs). Household populations by age, sex and ethnicity were multiplied by trended headship rates to forecast households. Households were classified by size and property type using census microdata. Water demand was generated using modelled consumption rates, based on policy interventions. Between 2011 and 2101, the region will experience 85% growth in populations and 113% in households. The household growth will vary across WRZs between 54% and 126%. Water demand in London and the Thames Valley is forecast to grow by 90%, 69% and 46% under status quo, moderate and extreme conservation scenarios. The future growth in water demand under all scenarios poses a huge challenge for the region, already under water stress.

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“…We categorised household composition using a modified Harper and Mayhew method 27 by counting the number of household members in three age brackets: 0-17 years (children), 18-64 years (working-aged adults) and 65 or older (older adults) and grouping into: working-aged adults with children; a single workingaged adult with children; at least one working-aged and one older adult with children (three-generation household), or at least one older adult with children (skippedgeneration household).…”

Section: Open Accessmentioning

confidence: 99%

Is obesity more likely among children sharing a household with an older child with obesity? Cross-sectional study of linked National Child Measurement Programme data and electronic health records

Firman,

Wilk,

Marszalek

et al. 2024

bmjpo

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Background/objectivesWe identified household members from electronic health records linked to National Child Measurement Programme (NCMP) data to estimate the likelihood of obesity among children living with an older child with obesity.MethodsWe included 126 829 NCMP participants in four London boroughs and assigned households from encrypted Unique Property Reference Numbers for 115 466 (91.0%). We categorised the ethnic-adjusted body mass index of the youngest and oldest household children (underweight/healthy weight <91st, ≥91st overweight <98th, obesity ≥98th centile) and estimated adjusted ORs and 95% CIs of obesity in the youngest child by the oldest child’s weight status, adjusting for number of household children (2, 3 or ≥4), youngest child’s sex, ethnicity and school year of NCMP participation.ResultsWe identified 19 702 households shared by two or more NCMP participants (% male; median age, range (years)—youngest children: 51.2%; 5.2, 4.1–11.8; oldest children: 50.6%; 10.6, 4.1–11.8). One-third of youngest children with obesity shared a household with another child with obesity (33.2%; 95% CI: 31.2, 35.2), compared with 9.2% (8.8, 9.7) of youngest children with a healthy weight. Youngest children living with an older child considered overweight (OR: 2.33; 95% CI: 2.06, 2.64) or obese (4.59; 4.10, 5.14) were more likely to be living with obesity.ConclusionsIdentifying children sharing households by linking primary care and school records provides novel insights into the shared weight status of children sharing a household. Qualitative research is needed to understand how food practices vary by household characteristics to increase understanding of how the home environment influences childhood obesity.

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Using Administrative Data to Count and Classify Households with Local Applications

Cited by 5 publications

References 15 publications

Deriving household composition using population-scale electronic health record data—A reproducible methodology

Deriving household composition using population-scale electronic health record data—A reproducible methodology

Household Forecasts for the Planning of Long‐Term Domestic Water Demand: Application to London and the Thames Valley

Is obesity more likely among children sharing a household with an older child with obesity? Cross-sectional study of linked National Child Measurement Programme data and electronic health records

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