apportion_phc_date: Apportion cases on basis of prior healthcare interactions by...

In PublicHealthEngland/hcaidcs: R functions for the mandatory surveillance of bacteraemia and C. difficile infection

Apportion cases on basis of prior healthcare interactions by date of most recent discharge

Description

The mandatory surveillance launched a new apportioning algorithm on 01/04/2019. This new apportioning algorithm takes into account the date of the most recent discharge from the reporting trust.

Usage

apportion_phc_date(
  data_collection,
  patient_location,
  patient_category,
  adm_date,
  spec_date,
  adm_3_mo,
  date_discharge,
  date_record_created
)

Arguments

data_collection	String giving "CDI" or any other value - CDI has a different PTE rule
patient_location	The patient's location at time of sample
patient_category	The patient's category at time of sample
adm_date	Date of admission
spec_date	Date of specimen
adm_3_mo	Was the patient an inpatient at the trust in previous three months. String yes, no or don't know
date_discharge	date the patient was most recently discharged from the reporting trust
date_record_created	date the record was entered onto the system

Details

Depending on the data collection under surveillance (CDI or bacteraemia), cases are then apportioned to one of the following groups:

• Healthcare onset - healthcare associated

• Community onset - healthcare associated

• Community onset - indeterminate association (CDI only)

• Community onset - community associated

• Community onset - missing

• Community onset - unknown

Value

A string variable giving the apportioning type; one of hoha, coha, coia or coca, all_blank if all of the prior healthcare exposures were NA, unknown_3_mo if prior health care in past three months was "Don't know" or NA if date record created < 01/04/2017

Examples

phc_dat <- data.frame(stringsAsFactors=FALSE,
 id = c(1, 2, 3, 4, 5),
 organism = rep("CDI", 5),
 patient_location = c("NHS acute trust", "NHS acute trust",
                      "NHS acute trust", "NHS acute trust",
                      "NHS acute trust"),
 patient_category = c("Inpatient", "Inpatient", "Inpatient",
                      "Inpatient", "Inpatient"),
 admission_date = lubridate::dmy(c("01/06/2019", "01/06/2019",
                                   "01/06/2019", "01/06/2019",
                                   "01/06/2019")),
 specimen_date = lubridate::dmy(c("01/06/2019", "01/06/2019",
                                  "01/06/2019", "01/06/2019",
                                  "01/06/2019")),
 patient_admitted_at_three_months = c("yes", "yes", "yes", "yes", "no"),
 date_most_recent_discharge = lubridate::dmy(c("31/05/2019", "21/05/2019",
                                               "03/05/2019", "08/03/2019", NA)),
 date_record_created = lubridate::dmy(c("15/06/2019", "15/06/2019",
                                        "15/06/2019", "15/06/2019",
                                        "15/06/2019"))
 )

phc_dat$days_diff <- phc_dat$specimen_date - phc_dat$date_most_recent_discharge
phc_dat$apportion_phc_date <- apportion_phc_date(
    data_collection = phc_dat$organism,
    patient_location = phc_dat$patient_location,
    patient_category = phc_dat$patient_category,
    adm_date = phc_dat$admission_date,
    spec_date = phc_dat$specimen_date,
    adm_3_mo = phc_dat$patient_admitted_at_three_months,
    date_discharge = phc_dat$date_most_recent_discharge,
    date_record_created = phc_dat$date_record_created
    )
# view the results
phc_dat[, c("days_diff", "apportion_phc_date")]

PublicHealthEngland/hcaidcs documentation built on Jan. 19, 2024, 8:38 a.m.