calculate_age_if_missing

Internal function to calculate age from date of birth if not present

internal

Facilitates probabilistic record linkage between infectious disease
surveillance datasets (notifiable disease registers, outbreak line-lists),
vaccination registries, and hospitalization records using methods based on
Fellegi and Sunter (1969) <doi:10.1080/01621459.1969.10501049> and
Sayers et al. (2016) <doi:10.1093/ije/dyv322>. The package provides
core functions for data preparation, linkage, and analysis: clean_the_nest()
standardizes variable names and formats across heterogeneous datasets;
murmuration() performs machine learning-based record linkage using blocking
variables and similarity metrics; molting() deidentifies datasets for secure
sharing; homing() re-identifies previously deidentified datasets; plumage()
identifies and categorizes comorbidities; and preening() creates analysis-ready
variables including age categories and temporal groupings. Designed for
epidemiological research linking acute and post-acute disease outcomes to
vaccination status and healthcare utilization. Supports multiple linkage
scenarios including case-to-vaccination, case-to-hospitalization, and
event-based vaccination status determination (e.g., outbreak attendees, flight
passengers, exposure site visitors).

Nicolas Smoll

starling

Link Infectious Disease Cases to Vaccination and Hospitalization
Records

calculate_age_if_missing function

<dl><dt>data</dt>
<dd>A data frame</dd>
<dt>reference_date</dt>
<dd>Character. Column name to use as reference date.
If NULL, uses first available from standard date columns.</dd></dl>

Arguments

Calculate Age if Missing — calculate_age_if_missing

<dl>

<dt>data</dt>
<dd>A data frame</dd>


<dt>reference_date</dt>
<dd>Character. Column name to use as reference date.
If NULL, uses first available from standard date columns.</dd>

</dl>

calculate_age_if_missing: Calculate Age if Missing

Description

Usage

Value

Arguments