This can be considered as a conservative approach, as a gender payment gap remains in many countries in Europe, including Germany [46].
χ tests were applied to examine the relationship between delegation to dental hygiene-therapy students and patient socio-demographic characteristics; this included patient ethnicity, age, gender, payment status, smoking status, and quintile of deprivation.
The MDS instrument provides individual level data on the following: background information, such as age, gender, payment source; patient status such as cognitive patterns, physical functioning; and the care provided.
These include patient age, gender, payment status (fee-waivers or service payers), district of residence, service type (general out-patient, Family Medicine Specialist Clinic (FMSC), staff clinics) and appointment type (new or subsequent visits) All these data were routinely entered by clinic staffs when patients attended for consultation, apart from service type.
Covariates that were tested for potential confounding in model estimates of stroke and rehabilitation payments included age, gender, race, Charlson morbidity score, and number of days alive in the year after index date.
XponentTM includes both new and refilled prescriptions issued daily from each of these dispensaries, with data aggregated for stratification by geographic region, patient age, patient gender, patient copayment, and method of payment, including cash payment.
The interesting variables included patient's general information (name, gender, age, payment method), discharge diagnosis, length of hospital stay, and expenditure (total expenditure and out-of-pocket payment).
The interesting variables included the patients' general information (name, gender, age, payment method), discharge diagnosis, length of hospital stay, and expenditure (total expenditure and out-of-pocket payment).
Covariates in the model included age and gender, insurance (payment), facility volume, treatment complications, hospital length of stay (HLOS), and injury severity.
These data included age, gender, ethnicity, payment for prescriptions and occupation.
