## 2 Literature Review  
There are more than 30 indices for measuring COC, which can be classified according to five categories: duration, density, dispersion, sequence, and subjective-based [26; 27]. Among the non-subjective indices, the most frequently used are the Usual Provider Continuity (UPC) index (also called most frequent provider continuity (MFPC)), which measures the density of appointments with the main provider [28]; the Herfindahl Index (HI) and the Bice-Boxerman Continuity of Care Index (COCI), which both measure dispersion among different providers over a certain period of time [29; 30]; and the Sequential Continuity (SECON) index, which uses the sequences of appointments to evaluate the continuity of the provider over consecutive visits [31]. UPC, measured as the percentage of the attention performed by the most frequent provider, is the most frequently used metric for density. One study used this metric to study newly diagnosed cardiovascular patients with conditions such as hypertension, diabetes, and hypercholesterolemia, with the goal of determining their impact on mortality, costs, and health outcomes. Participants with UPC below the median were compared to those with values above the median, using multivariable-adjusted hazard ratios, concluding that lower indices of COC were associated with higher mortality, more frequent cardiovascular events, and higher healthcare costs [32]. A study used both UPC and COCI to compare COC between primary health practices that were using new appointment scheduling methods and those using the existing methods, finding no COC differences between both groups [26]. Another study in general practice with 22 health conditions found that higher COC was associated with fewer admissions for ambulatory care-sensitive conditions, concluding that implementing strategies that improve COC may reduce secondary care costs [25]. These three studies segmented patients according to their COC levels, and conducted statistical analyses to identify differences in outcomes for each group. However, this metric only calculates the density for one provider, without considering other providers that the patient may have seen. One of the most frequently used metrics for dispersion is COCI. One study of patients with hypertension calculated COCI and analyzed differences in differences (DID), to compare clinical outcomes between patients with high and low COC, finding that a long-term physician-patient relationship may improve their health-related quality of life [33]. Several studies have used two or more metrics to evaluate COC, considering density, dispersion, and sequence [11; 34; 35; 36; 37; 38]. For instance, a study on patients with multiple chronic conditions, including diabetes, used UPC, COCI, and SECON to propose an integrated COC index (ICOC) using principal component analysis. This study analyzed COC at the physician and medical facility levels, and found that a higher COC was related to lower emergency room use and lower hospitalization rates, also finding that the combined COC index was more stable than each metric considered separately [35]. Another study used UPC, COCI, and SECON to analyze the relationship between COC and emergency service use, finding a negative relationship between COC and use of emergency services [36]. Regarding patients with diabetes, low levels of COC have been found to be associated with poor HbA1c control [17] and a higher risk of end-stage renal disease and hospitalization [13], while high levels of COC were associated with good LDL control [17], lower costs (considering diabetes-related hospitalization and emergency visits) [39] and lower odds of being admitted to the hospital [40]. Some studies have found no association between COC and HbA1c, lipid, or eye exam frequency [41]. Only one of the reviewed studies considers a second discipline of healthcare professionals beyond physicians, comparing the COC of physicians with the COC of a nurse-physician team [26].