A Path Analysis Model Examining Factors Affecting the Caregiving Burden Experienced by the Family Caregivers of Drug Addicts in Egypt

Abstract

Background: Although the caregiving burden experienced by the family caregivers of drug addicts is receiving increased attention, there is still a need to study the possible predictors of the care burden, especially with the increasing numbers of addicts in Egypt and the important role of family caregivers in the support and treatment of addicts. Study Design: A cross-sectional study. Methods: This study was conducted at Minia Hospital for Mental Health and Addiction Treatment, Egypt. Data was collected during interviews with addicts and their family caregivers. The caregiver burden was assessed using the Family Burden Interview Schedule (FBIS). The path analysis was used to assess the interrelationships between the burden and characteristics of addicts and caregivers. Results: Based on the results, 96.7% of addicts were males, and their mean age was 28.8 ± 8.1 years, while their caregivers aged 39.7 ± 10.4 years and included 58.7% males. The caregivers reported a severe burden of care which was predicted by the addict’s drug-related problems (B = 0.25, P = 0.0003), financial hardship (B = 0.46, P < 0.0001), and the caregiver’s occupation (B = -0.16, P = 0.017). Financial hardship had an indirect association with the burden of care (B = 0.06, P = 0.041) mediated through drug-related problems score, which was predicted by the severity of dependence, admission for treatment, and the level of social support. Conclusion: The burden of caring for addicts depends on patient-related problems, as well as caregivers’ situations and income. Strategies to provide social support, financial aid, and problem-solving skills should be provided to the addicts and their caregivers as a part of treatment programs to help reduce the caregiving burden.

A Hybrid of Random Forests and Generalized Path Analysis: A Causal Modeling of Crashes in 52,524 Suburban Areas

“…The indices, including chi-square test/degree of freedom values ( ) below five, Tucker-Lewis index (TLI), and comparative fit index (CFI) values over 0.90 were used for model goodness of fit. 21 , 22 The root means square error of approximation (RMSEA) was the next measure of goodness-of-fit, with values below 0.05 being considered a good fit and values up to.08 representing acceptable errors in the population. 20 For an inadequate model, the model modification includes adjusting an identified and estimated model through modification indices provided by the model.…”