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2) Socioeconomic status (enabling variables).
After adjusting for enabling variables (model 2), the regression coefficients did not change much, and for some types of care showed an increase, possibly indicating a suppressor effect of one or more of the enabling variables.
Enabling variables play a larger role in predicting the utilization of health services, with two statistically significant variables.
Of the enabling variables, travel time and household income were statistically significant in distinguishing health service users from non-users.
Environmental variables include health care delivery system characteristics, the external environment (e.g. the economic climate) and community-level enabling variables.
Several enabling variables were significantly and strongly associated with utilization for Black African and Coloured participants (Table 3).
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One such actuator is the electromechanical valve actuator, recently receiving attention for enabling variable valve timing in internal combustion engines.
Our individual-level predisposing variables were age and sex, while our individual-level enabling variable was relative income ranking (in deciles).
One other enabling variable, stigma consciousness, was significantly associated with treatment utilization for Coloured participants only.
Unlike the linear model which contains only pre-disposing and health care resource variables, the log-linear model contains two need for health care variables (DPT3COVER and OPDTOTAL), one enabling variable (URBANISATION) and two health care resources variables (HFGOVTOT and HCIITOT) and hence can be considered a more balanced model.
The log linear model for total PHC pharmaceutical expenditure comprising of two need for health care variables (DPT3COVER and OPDTOTAL), one enabling variable (URBANISATION) and two health care resources variables (HFGOVTOT and HCIITOT) is a balanced model and has a high explanatory power.
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