Psychosocial and Socioeconomic Patterns of Post-Rehabilitation Reintegration Among Former Substance Users in China: An Exploratory Multiple Correspondence Analysis of Administrative Case Records

Background: Relapse risks and social reintegration after substance rehabilitation are not only influenced by the individual’s social context, but also by social, family, and socioeconomic circumstances. However, there is limited quantitative research that has studied the co-occurrence of these factors among persons who have been rehabilitated from substance dependence in the Chinese context. Methods: Administrative case narratives from the Legal Services Casebook Database of the Ministry of Justice of China from 2017 to 2021 were analysed. Of the 227 drug rehabilitation-related cases initially identified, 92 contained available and codable information for all variables required for Multiple Correspondence Analysis. Narrative data were coded and then re-categorised into indicators of age, gender, educational level, family relationships, social relationships, financial status, employment status, and recorded relapse history. MCA was not used to estimate the causal effects or prevalence of these categories across the nation, but rather as a method for exploring patterns of association. Results: For the retained complete-case subsample, characteristics of lower educational attainment, unstable employment, financial difficulty, poor family relationships, and poor social relationships tended to be positioned close together in the MCA space. This suggests a pattern of co-occurring psychosocial and socioeconomic disadvantage within the analysed records. Relapse history made a weak contribution to the main MCA dimensions, indicating that recorded relapse alone did not organise the principal structure of association in the data. Conclusions: The findings offer exploratory evidence that reintegration-related disadvantage in these records was configured across relational and socioeconomic domains. Caution should be used when interpreting the results, as they are from a selected, small, complete-case subsample and from variables recoded from administrative narratives. Future research should utilise larger-scale, longitudinal, and mixed-methods datasets to conduct validation studies, to investigate whether these findings are consistent with the remaining data, and to examine their association with long-term reintegration outcomes.