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As Wagstaff [60] suggests for binary health variables, we normalize the CI by dividing it through by the reciprocal of the mean of the variable in question (1-μ): W C I=frac{2}{ nmu left 1-mu right)}{displeft 1-musum_{i=1}^n}{y}_i{right1.
Logistic regression was used to analyse the binary health outcomes.
The binary health state indicates whether the respondent reported a limiting long term illness.
Based on the linear additive relationship between the binary health outcome variable h i, C can be expressed as: (2) The decomposition equation has two components.
For binary health outcomes (e.g. use or non-use of a health service), the feasible bounds of the concentration index narrow as the prevalence rate rises.
The first is the 'explained' component, in which β k is the coefficient of each determinant calculated using generalized linear models with a binomial distribution and identity link on the binary health outcome, is the mean of each determinant, µ is the mean of the binary health outcome and ck is the concentration index for each determinant.
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Descriptive, multivariable and mediation analyses were conducted separately for the three binary health-seeking behaviour outcomes available for each illness in Stata SE13.
Logistic regression analyses were used for the binary health-related outcomes, that is smoking status, self-checking blood glucose level and knowledge of type of diabetes.
Associations between the binary main health outcomes and instrument groups were estimated using logistic regression with robust standard error adjusting for gender, age, number of playing years on the main instrument, and using orchestra of employment as the cluster variable.
We use thirty binary variables for health dummies, each representing a specific health disorder or disease mentioned above, taking the value 1 if an individual is suffering from the disease and 0 otherwise.
Second, instead of using a binary definition for health facilities (providing basic EmOC or not providing basic EmOC), we graded each health facility on a scale from 0 to 6 based on the number of signal functions that they could perform.
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