Worldwide, polypharmacy and medication appropriateness-related outcomes (MARO) are growing public health concerns associated with potentially inappropriate prescribing, adverse health effects, and avoidable costs to health systems. Continuity of care (COC) is a cornerstone of high-quality care that has been shown to improve patient-relevant outcomes. However, the relationship between COC and polypharmacy/MARO has not been systematically explored. Objective

We performed a systematic literature search in PubMed, Embase, and CINAHL. Quantitative observational studies investigating the associations between COC and polypharmacy and/or COC and MARO by applying multivariate regression analysis techniques were eligible. Qualitative or experimental studies were not included. Information on the definition and operationalization of COC, polypharmacy, and MARO and reported associations was extracted. COC measures were assigned to the relational, informational, or management dimension of COC and further classified as objective standard, objective non-standard, or subjective. Risk of bias was assessed by using the NIH Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. Results where f is a nonlinear function implemented as a multilayer perceptron, and h i contains the substructure information from different receptive fields centering at i-th atom. Nicolet A, Al-Gobari M, Perraudin C, Wagner J, Peytremann-Bridevaux I, Marti J. Association between continuity of care (COC), healthcare use and costs: what can we learn from claims data? A rapid review. BMC Health Serv Res. 2022;22:658. https://doi.org/10.1186/s12913-022-07953-z. Menec VH, Sirski M, Attawar D, Katz A. Does continuity of care with a family physician reduce hospitalizations among older adults? J Health Serv Res Policy. 2006;11:196–201. https://doi.org/10.1258/135581906778476562.

Bachrach LL. Continuity of care for chronic mental patients: a conceptual analysis. Am J Psychiatry. 1981;138:1449–56. https://doi.org/10.1176/ajp.138.11.1449. Green JL, Hawley JN, Rask KJ. Is the number of prescribing physicians an independent risk factor for adverse drug events in an elderly outpatient population? Am J Geriatr Pharmacother. 2007;5:31–9. https://doi.org/10.1016/j.amjopharm.2007.03.004.

Saultz JW. Defining and measuring interpersonal continuity of care. Ann Fam Med. 2003;1:134–43. https://doi.org/10.1370/afm.23. Introduction Complex or co-existing diseases are commonly treated using drug combinations by taking advantage of the synergistic effects caused by drug–drug interactions (DDIs). 1 However, unexpected DDIs also increase the risk of triggering adverse side effects or even serious toxicity. 2 With the increasing need for multi-drug treatments, the identification of unexpected DDIs becomes increasingly crucial. Traditionally, the detection of DDIs is performed through extensive biological or pharmacological assays. However, this process is time-consuming and labor-intensive, because a great number of combinations of drugs should be considered for experiments. As a result, computational methods can be used as a low-cost, yet effective alternative to predict potential DDIs by identifying patterns from known DDIs. Non-linear mixed effect modelling quantitatively estimates the magnitude of unexplained variability in the population of interest. Identification of factors contributing to this variability and exploration of their relationship to exposure is an important component of the population pharmacokinetic approach and can be used to support dosing recommendations. Gnjidic D, Tinetti M, Allore HG. Assessing medication burden and polypharmacy: finding the perfect measure. Expert Rev Clin Pharmacol. 2017;10:345–7. https://doi.org/10.1080/17512433.2017.1301206.Regarding the operationalization of PIM, different versions of the Beers criteria [ 71] were applied [ 46, 47, 64]. Other instruments were used, such as the Japanese STOPP-J list [ 59], the Norwegian General Practice (NORGEP) criteria, which are based on the Beers criteria [ 56], the German PRISCUS list [ 49], and the STOPP/START criteria [ 52]. PIM was always analyzed by using a binary (yes vs no) variable. Concerning DDI, the outcome variable was dichotomized (yes vs no) in all but one included study, which treated DDI as a continuous variable [ 45]. PIDC, as used by Tamblyn et al. [ 61], is a combination of PIM and DDI, identified by an expert review. Duplicated medications were used as outcomes by Cheng and Chen [ 43] and Chu et al. [ 44]. ADE were defined as either the presence of an ADE-specific code [ 58] or as a binary (yes vs no) outcome self-reported by the study participants [ 63]. One study [ 60] measured unnecessary drug use based on the Medication Appropriateness Index [ 72]. Finally, overdose as an outcome was defined as the occurrence of one or more medical claims containing a diagnosis code for opioid or benzodiazepine poisoning on a person-day of opioid-benzodiazepine overlap [ 51] (Table S2, see ESM). 3.3 Association Between COC and Polypharmacy Kerse N, Buetow S, Mainous AG, Young G, Coster G, Arroll B. Physician-patient relationship and medication compliance: a primary care investigation. Ann Fam Med. 2004;2:455–61. https://doi.org/10.1370/afm.139. Fig. 8 Quantitative analysis of the SSIM. (a) The distributions of predictive probability for SA-DDI and SA-DDI_GMP in the DrugBank dataset. (b) The training and testing losses for SA-DDI and SA-DDI_GMP in the DrugBank dataset.