Child Respiratory Illness

Respiratory illness is a major childhood condition. Although there are numerous studies of general respiratory epidemiology few data exist describing the burden of respiratory tract-related illness in general practice. Children present with acute respiratory infections and high prevalence rates are noted for asthma and otitis media.

Improved understanding of childhood respiratory illness presentation could enable more systematic approaches to care and resource allocation, and a context for exploring important social and ethnic variations in hospitalisation rates.

This work used data from electronic medical record systems to identify general practice presentation related to common childhood respiratory tract conditions and their complications. It was undertaken jointly with the Wellington School of Medicine, Patients First and Compass Health.

Data were collected directly from electronic medical record systems using software. The data set comprised records from consultations generated during both standard office hours and out-of-hours practice. Data were extracted from the EMR for all child-GP consultations at consenting practices during the study period (n=687 136)

These data have demonstrated a clear and consistent pattern in general practice utilisation for children with respiratory tract-related illness. Results of this type can assist with general practice workforce planning, and inform debate about current presentation and triage models seen in primary care. The study also highlighted the burden of respiratory disease carried by the youngest members of society and reinforces calls to focus prevention and health promotion campaigns on early stages of the maternal and child health continuum.

The methodology used can be applied to provide similar estimates of respiratory and other conditions and workload across an entire population at all ages. The use of NLP software in this way also provides a tool for health service planning in primary care which would have increasing application across a wide range of countries.

Our Role
DataCraft staff provided support to the research team, including:

• data collection
• database development
• development of the natural language processing algorithms
• training, validation and testing
• data analysis
• authorship of the paper

DataCraft’s Chief Scientist, Jayden MacRae was an author on the paper.