Strengths and limitations
Our study has several strengths. Using CROSS-TRACKS’s wide range of data is a major strength. This enables detailed descriptions of the study population and a wide variety of sub-group analyses.The heterogeneous population of general medical patients reflects a real-world population in regional hospital settings. This is also a strength, as most regional hospitals have general medical wards with a wide variety of medical conditions. This underlines the external validity. Compared to other similar studies, the sample size is rather large, which gives the study high power. Furthermore, the long study period prevented seasonal variation among the outcome measures, contributing to more valid results. Detection bias is not present in this study, as outcomes were identified through the CROSS-TRACKS register and assessed in all patients. Moreover, this study reports on DAOH and DAOHGP, which are upcoming outcomes within this research field. This adds a new perspective on the impact of a TCI.Nevertheless, the study also has several limitations. First, the absence of frailty status and, thus, frailty sub-group analysis make it difficult to assess the intervention effect in this group. However, it would be impossible to screen the participants in the control group with a frailty tool, as they were identified through a register. However, we performed several sub-group analyses with variables included in different frailty tools (e.g. CCI, cohabiting and home healthcare prior to admission). Our sub-group analysis did not find any difference in the effect among the sub-groups.Second, potential selection bias may threaten the internal validity, as the allocation was conducted according to the municipality of residence. In addition, the allocation process was untraditional, as one group was identified during hospitalisation and the other through a data register, thus preventing it from being a contemporary control group. This concern and possible attrition bias were handled in the analysis. The data were analysed based on the allocated intervention rather than the received intervention. Third, we conducted a difference-in-difference analysis to address the potential effect on readmission over a long study period. This showed no effect of time on readmissions in the intervention group. Fourth, performance bias may be present, as the healthcare services offered and provided were heterogeneous across the four municipalities. Fifth, the intervention fidelity was not calculated, thus making it difficult to assess whether the lack of intervention impact was affected by implementation failure. Hence, as with many similar studies [Lastly, we adjusted for covariates that were statistically significantly different between the groups in the statistical models. However, we did not have access to data on functional disability, socioeconomic status, prior falls and method of referral, which are known risk factors. This may have had a small impact on the outcome estimates.