Open access Original research Do hospitals have a higher mortality rate on weekend admissions? An observational study to analyse weekend effect on urgent admissions to hospitals in Catalonia Franco Amigo,1 Albert Dalmau-­Bueno,2 Anna García-­Altés ‍ ‍2,3,4 To cite: Amigo F, Dalmau-­ ABSTRACT Bueno A, García-­Altés A. Do Strengths and limitations of this study Background ‘Weekend effect’ is a term used to describe hospitals have a higher mortality the increased mortality associated with weekend rate on weekend admissions? ►► We found no other studies that analyse the weekend emergency admissions to hospital, in contrast with An observational study to effect in Catalonia; this has not been studied any- admission on weekdays. The objective of the present study analyse weekend effect on where else in Spain. urgent admissions to hospitals is to determine whether the weekend effect is present in ►► The primary outcome (death) was measured at 30 in Catalonia. BMJ Open hospitals in Catalonia. days, whether or not the patient was at the hospital. 2021;11:e047836. doi:10.1136/ Methods We analysed all urgent admissions in Catalonia ►► The model is adjusted with information from the pa- bmjopen-2020-047836 in 2018, for a group of pathologies. Two groups were tients’ medical history. defined (those admitted on a weekday and those admitted ►► Prepublication history for ►► The results cannot be extrapolated to all diagnoses. on a weekend). We obtained mortality at 3, 7, 15 and this paper is available online. ►► The intrinsic risk of the admission episode could not To view these files, please visit 30 days, and applied a proportions test to both groups. be captured by the variables included in the model. the journal online (http://​dx.​doi.​ Additionally, we used Cox’s regression for mortality at 30 org/​10.​1136/​bmjopen-​2020-​ days, using the admission on a weekend as the exposition, 047836). adjusting by socioeconomic and clinical variables. We used the hospital discharge database and the Central Registry and in different health systems. Surprisingly, Received 09 December 2020 of the Insured Population. despite this large number of studies, there is Accepted 26 October 2021 Results 72 427 admissions for the selected pathologies still debate about whether the weekend effect during 2018 were found. No statistically significant exists and, if so, what causes it.3–6 In 2015, differences in mortality at 30 days (p=0.524) or at 15 the results of an investigation that included days (p=0.119) according to the day of admission were almost 15 million hospital admissions were observed. However, significant differences were found in published7; it was found that the HR of dying mortality at 7 days (p=0.025) and at 3 days (p=0.002). at 30 days was 1.10 for Saturday and 1.15 for The hazard rate associated with the weekend was 1.13 Sunday, when compared with a weekday. In (95% CI: 1.04 to 1.23). By contrast, the adjusted HR of the 2017, a meta-­analysis that included 97 studies weekend interaction with time was 0.99 (95% CI: 0.99 to 1.00). in English was published, covering a total © Author(s) (or their Conclusions There is a weekend effect, but it is not of 51 million admissions in different coun- employer(s)) 2021. Re-­use constant in time. This could suggest the existence of tries. This study showed that mortality at the permitted under CC BY-­NC. No dysfunctions in the quality of care during the weekend. weekend was 17% higher than during the commercial re-­use. See rights and permissions. Published by week.6 The latest meta-­analysis, published in BMJ. 2019, includes more than 640 million admis- 1 IMIM, Barcelona, Spain INTRODUCTION sions, and concludes that the weekend effect 2 Agència de Qualitat i Avaluació Since the late 1970s, a considerable number for urgent admissions is 11%.8 However, there Sanitàries de Catalunya, of studies have sought to explain the exis- are also some studies that indicate that part Barcelona, Spain 3 tence of the so-­called ‘weekend effect’.1 This of this effect is a statistical artefact,9 and that Fundacio Institut de Recerca Hospital de la Santa Creu i Sant effect is the phenomenon where patients therefore it is not possible to affirm that these Pau, Barcelona, Spain admitted to the hospital at weekends appar- deaths are preventable or attributable to a 4 CIBER de Epidemiología y Salud ently have a higher mortality rate than poorer quality of care at the weekend.10–12 Pública (CIBERESP), Barcelona, patients admitted on weekdays and, there- One possible cause, which has been Spain fore, the hospital quality could be worse on addressed by various studies, is that at the Correspondence to weekends compared with weekdays.2 weekend there is less staffing, which would Dr Anna García-­Altés; To date, many studies have explored the cause a poorer quality of care.11 13 Another ​agarciaaltes@​gencat.​cat weekend effect in various patient populations hypothesis is that the doctors who attend at Amigo F, et al. BMJ Open 2021;11:e047836. doi:10.1136/bmjopen-2020-047836 1 Open access the weekend have less experience, and that this would Saturday or Sunday, plus any public holiday, according to explain the differences in mortality. However, a study that the calendar of public holidays of Catalonia. adjusted for the level of experience of physicians on the Next, a bivariate analysis of mortality in admitted day of admission showed that the differences between patients at 3, 7, 15 and 30 days was performed globally weekends and weekdays remained significant.14 and according to the different independent variables, Some studies have also linked the weekend effect with depending on whether the admissions were on a weekend the way in which patients arrive at hospitals.15 It has or a weekday. The results were accompanied by their been observed that, at the weekend, a greater number of respective 95% CI and the p value of the proportions patients arrive by ambulance, and when adjusting for this test, allowing us to compare whether there were differ- variable, the risk of mortality associated with the weekend ences in the proportion of deaths between weekdays and is reduced by half. However, in this study they did not weekends. adjust for the severity of the patient and, therefore, the use Finally, Cox regression models were performed, in which of an ambulance could be a confounding factor.15 Finally, the dependent variable was death (yes/no) at time t, in a study published in 2016 showed that the difference in days from the date of admission (t0), where the exposure mortality was associated with patterns of poorer quality of variable was weekday (yes/no). It was verified whether the care, some of which coincided with the weekend, while proportional hazards assumption was fulfilled and, since others occurred at different times of the week.16 it was not fulfilled, the interaction with time was included. Thus, there is no consensus regarding the existence The confounding variables included in the model were of the weekend effect, its size or the possible causes that sex, age, diagnosis, hospital level, origin, income level, could explain it. For this reason, the present study aims Adjusted Morbidity Group (AMG) and previous contact to determine whether there are differences in hospital with the emergency department in the last 24 hours. mortality within 30 days in urgent admissions in hospitals Additionally, the models were stratified according to sex, of the public healthcare network of Catalonia (SISCAT), diagnosis, hospital level and income level, adjusting for for different pathologies, among those admitted to a the rest of the variables. For all models performed, HRs hospital at the weekend or during holidays compared with were calculated for the exposure variable and the rest of those admitted on a weekday. Furthermore, it is analysed adjustment variables, with their corresponding 95% CI whether there are differences in this effect according to and the associated p value. gender, diagnosis, income level, and hospital level. The income level variable was obtained from the registry of the insured population, specifically from the pharmacy copayment information, and the population was classified into four categories: (1) exhausted unem- METHODS ployment subsidy and others; (2) income lower than This study was a population-­based, observational, retro- €18 000; (3) income between €18 000 and €100 000; spective cohort study. All urgent hospital admissions to (4) income greater than €100 000. The variable weekday SISCAT hospitals during 2018 for a group of pathologies was generated from the date of admission, and Saturdays, were analysed. To do this, all patients were followed for 30 Sundays, and holidays were considered weekend. days after admission. The variable hospital level was categorised following The study population was made up of the entire popu- the CMBD-­ HA: high-­ tech public hospital; mono- lation of Catalonia, according to the Central Registry of graphic high-­ tech public hospital; high-­ resolution the Insured Population by CatSalut (RCA) of 2018, who public hospital; reference public hospital; regional had an urgent hospital admission to a SISCAT centre, and public hospital; and isolated public hospital. They are whose main diagnosis was one of the following patholo- ordered from greatest capacity and complexity to least. gies17: ST-­segment elevation myocardial infarction, non-­ Therefore, the high-­tech public hospital corresponds ST-­segment elevation myocardial infarction, congestive to those of a higher level, that have subspecialties and heart failure, ictus, gastrointestinal bleeding, hip frac- new technologies. They are able to solve problems that a ture and pneumonia. We considered any non-­ elective referral or high-­resolution hospital cannot. The mono- admission as urgent. Two databases from 2018 were used: graphic high-­tech hospital is like a high-­tech hospital, the hospital discharge database (CMBD-­ HA, Catalan but it focuses only on one specialty. The high-­resolution acronym)18 and the RCA.19 public hospital and the reference public hospital are First, a descriptive analysis of hospital admissions was very similar in that they are intended to solve practically carried out according to different independent vari- all health problems, but they are less specialised than ables: sex, diagnosis, age, income level, hospital level, the high-­tech ones. The high-­resolution hospital is a morbidity group 7 days before admission, origin and hospital with some subspecialties. The regional public previous contact with the emergency department (in hospital responds to the usual requirements of the the primary care centre, Medical Emergencies Service population, but does not treat pathologies that require (MES) and hospital) in the 24 hours prior to admission. a significant degree of specialisation. Finally, an isolated The results were stratified according to the time of admis- public hospital is similar to a regional public hospital, sion: weekday or weekend. We defined weekend as any but it is geographically isolated. 2 Amigo F, et al. BMJ Open 2021;11:e047836. doi:10.1136/bmjopen-2020-047836 Open access The AMG is a risk tool which classifies each individual Differences in mortality between urgent admissions on into a health status and a complexity level group, using weekdays and weekends administrative data.20 21 To construct the AMG score, The 3-­day mortality of those admitted on a weekday was comorbidity and complexity information was gathered 3.27% (95% CI: 3.12% to 3.42%), while that of those automatically from the Catalan Health Surveillance admitted on a weekend was 3.74% (95% CI: 3.48% System database, for present and previous years. For each to 4.01%) with a p value of 0.002. The differences in admission, we constructed the AMG score using data mortality at 7 days were also statistically significant (p from 7 days before the said admission. For this analysis we value=0.025), being 5.42% on a weekday (95% CI: 5.23% only used the comorbidity data of the AMG because the to 5.61%) and on a weekend, 5.85% (95% CI: 5.52% to complexity data did not provide additional information. 6.17%). In terms of the diagnosis, it was observed that Finally, for the variable contact with emergency depart- the 3-­day mortality due to heart failure on weekends ments, the CMBD-­HA was used to check if the patient had was higher (4.15%; 95% CI: 3.61% to 4.70%) than that been admitted to another centre in the previous 24 hours. observed on weekdays (3.10%; 95% CI: 2.83% to 3.38%) Stata V.14.2 statistical software was used for all analyses. with a p value <0.001. Likewise, the 7-­day mortality due to heart failure was higher in weekends admissions (6.39%; Patient and public involvement 95% CI: 5.72% to 7.06%) than for weekdays admissions There was no involvement of patients or the public in this (5.50%; 95% CI: 5.14% to 5.86%) (p value=0.018) (see study. table 2). Mortality after 15 days of those admitted on a weekday was 7.96% (95% CI: 7.72% to 8.19%) and on a weekend RESULTS it was 8.31% (95% CI: 7.92% to 8.69%). However, Description of hospital admissions these differences were not statistically significant (p During 2018, there were a total of 72 427 admissions for value=0.119). The same was true for mortality at 30 days the selected pathologies: 19 957 (27.55%) on weekends (p value=0.524), being 10.81% for those admitted on and 52 470 (72.45%) on weekdays. Of these admissions, weekdays (95% CI: 10.55% to 11.08%), while for those 51.37% were men (37 204) and 48.63% were women (35 admitted on a weekend it was 10.98% (95% CI: 10.54% 222). Overall, 28.41% of admissions were due to heart to 11.41%). In regard to the pathologies that moti- failure, 22.07% to pneumonia, 16.53% to stroke, 12.90% vated the admission, no statistically significant differ- to hip fracture, 8.89% to gastrointestinal bleeding, 6.12% ences were observed in mortality at 15 and 30 days (see to ST-­segment elevation myocardial infarction and 5.09% table 2). to non-­ ST-­ segment elevation myocardial infarction. In terms of age, 47.46% of admitted patients were over 80 years old and 29.97% were between 66 and 80 years old. Cox regression model In regard to income level, 74.30% had an income of The results of the Cox model (table 3) showed that the less than €18 000. In terms of the degree of morbidity, adjusted HR when admitted on a weekend was 1.13 (95% 62.61% had pathologies affecting four or more systems, CI: 1.04 to 1.22). By contrast, the adjusted HR of the and 19.87% had an active neoplasia (see table 1). weekend interaction with time was 0.99 (95% CI: 0.99 to Furthermore, 29.40% of admissions occurred in refer- 1.00). ence public hospitals, 27.59% in high-­technology public The adjusted HR in women was 0.87 (95% CI: 0.83 to hospitals, 22.15% in district public hospitals and 18.57% 0.92). Regarding income level, the adjusted HR in those in high-­resolution public hospitals. In addition, it was with an income between €18 000 and €100 000 was 0.79 found that 50.20% of the admissions came from a home (95% CI: 0.69 to 0.91) in comparison to the mortality of or nursing home and 38.28% from a unit or service of the those with exhausted unemployment benefit. same hospital. In regard to previous contact, 3.87% had In terms of diagnosis, taking hip fracture as the refer- attended the emergency room of a primary care centre in ence category, all adjusted HRs were significant: the the 24 hours before their admission, 64.21% to the MES adjusted HR for gastrointestinal bleeding mortality was and 92.22% to the emergency room of a hospital (see 1.38 (95% CI: 1.22 to 1.55), that of ST-­segment elevation table 1). myocardial infarction was 2.77 (95% CI: 2.44 to 3.15), Regarding the differences in the characteristics of the non-­ST-­segment elevation myocardial infarction was 1.70 patients who were admitted on a weekday compared with (95% CI: 1.48 to 1.97), for heart failure it was 1.96 (95% those admitted on a weekend, we observed that there CI: 1.80 to 2.13), for stroke it was 3.53 (95% CI: 3.23 to were only significant differences in age (p value <0.001) 3.85), and for pneumonia it was 2.01 (95% CI: 1.84 to and diagnosis (p value<0.001). For age, the proportion of 2.20). people under 40 was higher during weekends. Regarding In both the AMG variable and the hospital level vari- the diagnosis, the greatest differences were observed in able, neither category had a significant adjusted HR. The heart failure (29.49% weekday vs 25.57% weekend) and same was true for the adjusted HR for attending a primary in hip fracture (12.43% vs 14.13%). care emergency room or a hospital emergency room. Amigo F, et al. BMJ Open 2021;11:e047836. doi:10.1136/bmjopen-2020-047836 3 Open access Table 1 Number of urgent hospital admissions according to sociodemographic, clinical and health resource characteristics, broken down by weekday or weekend Number of hospital admissions Total Weekday Weekend χ2 N % N % N % P value Total 72 427 52 470 72.45 19 957 27.55 Sex 0.169  Man 37 204 51.37 27 035 51.52 10 169 50.95  Woman 35 222 48.63 25 434 48.47 9788 49.05 Age <0.001  <5 years 1105 1.53 780 1.49 325 1.63  5–17 years 561 0.77 380 0.72 181 0.91  18–39 years 1385 1.91 957 1.82 428 2.14  40–65 years 13 293 18.35 9593 18.28 3700 18.54  66–80 years 21 709 29.97 15 927 30.35 5782 28.97  >80 years 34 374 47.46 24 833 47.33 9541 47.81 Income level 0.312  Exhausted unemployment subsidy, RMI, RAI, 3123 4.31 2272 4.33 851 4.26 PNC and others  Income less than €18 000 53 811 74.30 38 937 74.21 14 874 74.54  Income between €18 000 and €100 000 15 198 20.99 11 060 21.08 4138 20.74  Income greater than €100 000 291 0.40 199 0.38 92 0.46 Diagnosis <0.001  Hip fracture 9341 12.90 6522 12.43 2819 14.13  Gastrointestinal bleeding 6436 8.89 4656 8.87 1780 8.92  ST-­elevation myocardial infarction 4430 6.12 3131 5.97 1299 6.51  Non-­ST-­elevation myocardial infarction 3686 5.09 2704 5.15 982 4.92  Heart failure 20 578 28.41 15 474 29.49 5104 25.57  Stroke 11 972 16.53 8497 16.19 3475 17.41  Pneumonia 15 984 22.07 11 486 21.89 4498 22.54 AMG 0.950  Healthy patient 1115 1.55 805 1.55 310 1.57  Acute disorder 1039 1.44 736 1.41 303 1.53  Chronic pathologies affecting 1 system 2340 3.25 1693 3.25 647 3.27  Chronic pathologies affecting 2–3 systems 8044 11.19 5825 11.18 2219 11.20  Chronic pathologies affecting >3 systems 45 020 62.61 32 657 62.68 12 363 62.42  Births and pregnancies 66 0.09 48 0.09 18 0.09  Active neoplasia 14 286 19.87 10 339 19.84 3947 19.93 Hospital level 0.277  High-­tech public hospital 19 984 27.59 14 549 27.73 5435 27.23  Monographic high-­tech public hospital 436 0.60 308 0.59 128 0.64  High-­resolution public hospital 13 450 18.57 9801 18.68 3649 18.28  Reference public hospital 21 292 29.40 15 370 29.29 5922 29.67  Regional public hospital 16 041 22.15 11 576 22.06 4465 22.37  Isolated public hospital 1224 1.69 866 1.65 358 1.79 Origin 0.348  Home or nursing home 36 352 50.20 26 340 50.20 10 012 50.17  Primary care 3259 4.50 2402 4.58 857 4.29  Other hospital 5141 7.10 3699 7.05 1442 7.23  Unit or service of the same hospital 27 669 38.21 20 024 38.17 7645 38.31 Continued 4 Amigo F, et al. BMJ Open 2021;11:e047836. doi:10.1136/bmjopen-2020-047836 Open access Table 1 Continued Number of hospital admissions Total Weekday Weekend χ2 N % N % N % P value Primary care emergency in the last 24 hours 0.408  Yes 2801 3.87 2010 3.83 791 3.96  No 69 626 96.13 50 460 96.17 19 166 96.04 MES in the last 24 hours 0.591  Yes 46 504 64.21 33 721 64.27 12 783 64.05  No 25 923 35.79 18 749 35.73 7174 35.95 Hospital emergency in the last 24 hours 0.652  Yes 66 789 92.22 48 371 92.19 18 418 92.29  No 5638 7.78 4099 7.81 1539 7.71 AMG, Adjusted Morbidity Group; MES, Medical Emergencies Service; PNC, unremarkable pension; RAI, active insertion income; RMI, minimum insertion income. Weekend effect according to the stratification of variables of there were only significant differences in age, with those interest who were admitted on a weekday being slightly younger, When comparing whether the weekend effect was different and in the distribution according to admission diagnoses. according to gender, an adjusted HR was obtained in men Among the main results, it can be highlighted that no for admissions on weekends of 1.12 (95% CI: 1.00 to 1.26) significant differences were found between weekdays compared with on weekdays; in women it was 1.14 (95% and weekends in regard to mortality rates at 30 days or CI: 1.01 to 1.27) (see table 4). 15 days. However, differences were found in 7-­day and The adjusted HR in the weekend group was 0.97 for 3-­day mortality. It was also found that the adjusted HR of people with exhausted unemployment benefit (95% CI: mortality associated with being admitted on a weekend 0.62 to 1.53) and for those with an income less than €18 was statistically significant, having a risk of dying 13% 000, it was 1.17 (95% CI: 1.07 to 1.28). higher than for people admitted on a weekday. Further- In terms of diagnosis, it was observed that the adjusted more, it was observed that the assumption of proportional HR in the weekend group for people with heart failure was hazards over time was not fulfilled for this variable, and 1.22 (95% CI: 1.05 to 1.42) in comparison to the weekday the HR of the interaction between the weekend variable group, for those with ST-­segment elevation myocardial and time was 0.99 (p value=0.003) and this indicated that infarction it was 1.32 (95% CI: 0.96 to 1.82), for stroke for each day of stay the risk of mortality associated with it was 1.12 (95% CI: 0.97 to 1.30), for pneumonia it was the weekend effect decreased by 1%. 1.02 (95% CI: 0.85 to 1.23), for hip fracture it was 0.99 These results are consistent with each other, since in (95% CI: 0.72 to 1.37), and for non-­ST-­segment elevation both cases a significant difference in mortality between myocardial infarction it was 0.95 (95% CI: 0.60 to 1.49). admissions on weekdays and weekends was seen during Finally, when comparing the weekend effect by hospital the first days of stay, but that it reduced over time. The level, the adjusted HR of the weekend group in compar- order of magnitude of these results is in alignment with ison to the weekday group for those admitted to a high-­ what is reported in the literature, since the last published tech public hospital was 1.04 (95% CI: 0.89 to 1.21), for meta-­analysis found that the weekend effect for urgent those admitted to a high-­resolution public hospital it was admissions was 11% (95% CI: 6% to 16%).8 1.04 (95% CI: 0.86 to 1.26), for a public referral hospital When analysing whether this effect was different it was 1.20 (95% CI: 1.04 to 1.39), for a regional public according to sex, it was found that in the men’s model hospital it was 1.23 (95% CI: 1.03 to 1.47), and for an the effect was 12% and in women 14%. However, only in isolated public hospital it was 1.05 (95% CI: 0.60 to 1.86). women it was statistically significant. Although in this case the differences were small, this finding is consistent with DISCUSSION the multiple studies that have described the existence of In the first place, it was observed that the study population a gender bias in healthcare.22–24 was an ageing population, since almost 50% were over 80 When comparing according to income level, it was years old. In addition, they had significant morbidity, as observed that there was only a statistically significant effect 62% of them had chronic diseases affecting more than in the group who earned less than €18 000 per year, with three organ systems. When comparing the characteristics a weekend effect of 17%. This was likely due to the fact of people who were admitted on a weekday versus those that it was the biggest population group: almost 75% of who were admitted on a weekend, it was observed that hospital admissions were of people with this income level. Amigo F, et al. BMJ Open 2021;11:e047836. doi:10.1136/bmjopen-2020-047836 5 6 Table 2 Number and percentage of deaths at 3, 7, 15 and 30 days after urgent hospital admission according to sociodemographic, clinical and health resource characteristics, broken down according to weekday or weekends 3 Days 7 Days 15 Days 30 Days Weekday Weekend Weekday Weekend Weekday Weekend Weekday Weekend Mortality Mortality Mortality Mortality Mortality Mortality Mortality Mortality Open access N (95% CI) N (95% CI) P value N (95% CI) N (95% CI) P value N (95% CI) N (95% CI) P value N (95% CI) N (95% CI) P value Total 1717 3.27 (3.12 747 3.74 (3.48 0.002 2844 5.42 (5.23 1167 5.85 (5.52 0.025 4174 7.96 (7.72 1658 8.31 (7.92 0.119 5674 10.81 2191 10.98 0.524 to 3.42) to 4.01) to 5.61) to 6.17) to 8.19) to 8.69) (10.55 to (10.54 to 11.08) 11.41) Sex  Man 818 3.03 (2.82 347 3.41 (3.06 0.056 1365 5.05 (4.79 533 5.24 (4.81 0.452 2007 7.42 (7.11 782 7.69 (7.17 0.385 2724 10.08 (9.72 1024 10.07 (9.48 0.986 to 3.23) to 3.77) to 5.31) to 5.67) to 7.74) to 8.21) to 10.43) to 10.65)  Woman 899 3.53 (3.31 400 4.09 (3.69 0.014 1479 5.82 (5.53 634 6.48 (5.99 0.019 2166 8.52 (8.17 876 8.95 (8.38 0.194 2949 11.59 1167 11.92 0.391 to 3.76) to 4.48) to 6.10) to 6.96) to 8.86) to 9.52) (11.20 to (11.28 to 11.99) 12.56) Age  <5 years 0 Not 1 Not 0 Not 1 Not 0 No aplica 1 No aplica 1 No aplica 1 No aplica applicable applicable applicable applicable  5–17 years 0 Not 0 Not 0 Not 0 Not 0 No aplica 0 No aplica 0 No aplica 1 No aplica applicable applicable applicable applicable  18–39 years 9 0.94 (0.33 6 1.40 (0.29 0.443 10 1.04 (0.40 8 1.87 (0.59 0.211 10 1.04 (0.40 8 1.87 (0.59 0.211 17 1.78 (0.94 9 2.10 (0.74 0.679 to 1.55) to 2.52) to 1.69) to 3.15) to 1.69) to 3.15) to 2.61) to 3.46)  40–65 years 151 1.57 (1.32 67 1.81 (1.38 0.335 216 2.25 (1.95 97 2.62 (2.11 0.207 292 3.04 (2.70 131 3.54 (2.95 0.144 376 3.92 (3.53 176 4.76 (4.07 0.030 to 1.82) to 2.24) to 2.55) to 3.14) to 3.39) to 4.14) to 4.31) to 5.44)  66–80 years 363 2.28 (2.05 157 2.72 (2.30 0.063 567 3.56 (3.27 241 4.17 (3.65 0.036 847 5.32 (4.97 347 6.00 (5.39 0.051 1189 7.47 (7.06 459 7.94 (7.24 0.245 to 2.51) to 3.13) to 3.85) to 4.68) to 5.67) to 6.61) to 7.87) to 8.64)  >80 year 1194 4.81 (4.54 516 5.41 (4.95 0.022 2051 8.26 (7.92 820 8.59 (8.03 0.314 3025 12.18 1171 12.27 0.816 4091 16.47 1545 16.19 0.529 to 5.07) to 5.86) to 8.60) to 9.16) (11.77 to (11.61 to (16.01 to (15.45 to 12.59) 12.93) 16.94) 16.93) Income level  Exhausted 66 2.90 (2.21 22 2.59 (1.52 0.631 99 4.36 (3.52 38 4.47 (3.08 0.896 136 5.99 (5.01 50 5.88 (4.30 0.908 183 8.05 (6.94 68 7.99 (6.17 0.953 unemployment to 3.60) to 3.65) to 5.20) to 5.85) to 6.96) to 7.46) to 9.17) to 9.81) subsidy, RMI, RAI, PNC and others*  Income less than 1333 3.42 (3.24 602 4.05 (3.73 0.001 2225 5.71 (5.48 940 6.32 (5.93 0.008 3300 8.48 (8.20 1333 8.96 (8.50 0.072 4517 11.60 1754 11.79 0.536 €18 000 to 3.60) to 4.36) to 5.94) to 6.71) to 8.75) to 9.42) (11.28 to (11.27 to 11.92) 12.31)  Income between 313 2.83 (2.52 121 2.92 (2.41 0.757 512 4.63 (4.24 184 4.45 (3.82 0.632 725 6.56 (6.09 270 6.52 (5.77 0.946 959 8.67 (8.15 364 8.80 (7.93 0.807 €18 000 and €100 to 3.14) to 3.44) to 5.02) to 5.07) to 7.02) to 7.28) to 9.20) to 9.66) 000  Income greater 5 2.51 (0.34 2 2.17 (−0.81 0.861 8 4.02 (1.29 5 5.43 (0.80 0.587 13 6.53 (3.10 5 5.43 (0.80 0.718 15 7.54 (3.87 5 5.43 (0.80 0.510 than €100 000 to 4.69) to 5.15) to 6.75) to 10.07) to 9.97) to 10.07) to 11.21) to 10.07) Diagnosis  Hip fracture 64 0.98 (0.74 27 0.96 (0.60 0.915 158 2.42 (2.05 70 2.48 (1.91 0.862 306 4.69 (4.18 124 4.40 (3.64 0.535 498 7.64 (6.99 197 6.99 (6.05 0.274 to 1.22) to 1.32) to 2.80) to 3.06) to 5.21) to 5.16) to 8.28) to 7.93) Continued Amigo F, et al. BMJ Open 2021;11:e047836. doi:10.1136/bmjopen-2020-047836 Table 2 Continued 3 Days 7 Days 15 Days 30 Days Weekday Weekend Weekday Weekend Weekday Weekend Weekday Weekend Mortality Mortality Mortality Mortality Mortality Mortality Mortality Mortality N (95% CI) N (95% CI) P value N (95% CI) N (95% CI) P value N (95% CI) N (95% CI) P value N (95% CI) N (95% CI) P value  Gastrointestinal 100 2.15 (1.73 38 2.13 (1.46 0.974 151 3.24 (2.73 63 3.54 (2.68 0.553 218 4.68 (4.08 102 5.73 (4.65 0.084 304 6.53 (5.82 141 7.92 (6.67 0.049 bleeding to 2.56) to 2.81) to 3.75) to 4.40) to 5.29) to 6.81) to 7.24) to 9.18)  ST-­elevation 126 4.02 (3.34 64 4.93 (3.75 0.177 172 5.49 (4.70 78 6.00 (4.71 0.502 230 7.35 (6.43 98 7.54 (6.11 0.818 270 8.62 (7.64 112 8.62 (7.10 0.999 myocardial to 4.71) to 6.10) to 6.29) to 7.30) to 8.26) to 8.98) to 9.61) to 10.15) infarction  Non-­ST-­elevation 53 1.96 (1.44 21 2.14 (1.23 0.733 96 3.55 (2.85 37 3.77 (2.58 0.754 145 5.36 (4.51 54 5.50 (4.07 0.871 191 7.06 (6.10 73 7.43 (5.79 0.700 myocardial to 2.48) to 3.04) to 4.25) to 4.96) to 6.21) to 6.92) to 8.03) to 9.07) infarction  Heart failure 480 3.10 (2.83 212 4.15 (3.61 <0.001 851 5.50 (5.14 326 6.39 (5.72 0.018 1336 8.63 (8.19 482 9.44 (8.64 0.077 1933 12.49 686 13.44 0.078 to 3.38) to 4.70) to 5.86) to 7.06) to 9.08) to 10.25) (11.97 to (12.50 to 13.01) 14.38)  Stroke 550 6.47 (5.95 258 7.42 (6.55 0.060 852 10.03 (9.39 385 11.08 0.086 1135 13.36 493 14.19 0.229 1381 16.25 590 16.98 0.331 to 7.00) to 8.30) to 10.67) (10.04 to (12.63 to (13.03 to (15.47 to (15.73 to 12.12) 14.08) 15.35) 17.04) 18.23)  Pneumonia 344 2.99 (2.68 127 2.82 (2.34 0.564 564 4.91 (4.52 208 4.62 (4.01 0.448 804 7.00 (6.53 305 6.78 (6.05 0.624 1097 9.55 (9.01 392 8.71 (7.89 0.102 to 3.31) to 3.31) to 5.31) to 5.24) to 7.47) to 7.52) to 10.09) to 9.54) Amigo F, et al. BMJ Open 2021;11:e047836. doi:10.1136/bmjopen-2020-047836 AMG  Healthy patient 32 3.98 (2.63 13 4.19 (1.96 0.868 47 5.84 (4.22 18 5.81 (3.20 0.984 68 8.45 (6.53 26 8.39 (5.30 0.974 91 11.30 (9.12 34 10.97 (7.49 0.873 to 5.32) to 6.42) to 7.46) to 8.41) to 10.37) to 11.47) to 13.49) to 14.45)  Acute disorder 13 1.77 (0.81 10 3.30 (1.29 0.127 23 3.13 (1.87 21 6.93 (4.07 0.006 45 6.11 (4.38 28 9.24 (5.98 0.073 62 8.42 (6.42 33 10.89 (7.38 0.210 to 2.72) to 5.31) to 4.38) to 9.79) to 7.85) to 12.50) to 10.43) to 14.40)  Chronic 49 2.89 (2.10 23 3.55 (2.13 0.408 80 4.73 (3.71 39 6.03 (4.19 0.200 122 7.21 (5.97 51 7.88 (5.81 0.576 169 9.98 (8.55 73 11.28 (8.84 0.355 pathologies to 3.69) to 4.98) to 5.74) to 7.86) to 8.44) to 9.96) to 11.41) to 13.72) affecting 1 system  Chronic 188 3.23 (2.77 82 3.70 (2.91 0.298 309 5.30 (4.73 123 5.54 (4.59 0.672 455 7.81 (7.12 176 7.93 (6.81 0.858 614 10.54 (9.75 228 10.27 (9.01 0.728 pathologies to 3.68) to 4.48) to 5.88) to 6.50) to 8.50) to 9.06) to 11.33) to 11.54) affecting 2–3 systems  Chronic 1121 3.43 (3.24 472 3.82 (3.48 0.048 1836 5.62 (5.37 719 5.82 (5.40 0.428 2659 8.14 (7.85 1020 8.25 (7.77 0.708 3612 11.06 1368 11.07 0.988 pathologies to 3.63) to 4.16) to 5.87) to 6.23) to 8.44) to 8.74) (10.72 to (10.51 to affecting >3 11.40) 11.62) systems  Births and 1 2.08 (0 to 1 5.56 (0 to 0.464 4 8.33 (0.51 1 5.56 (−5.03 0.704 7 14.58 (4.60 1 5.56 (0 to 0.317 8 16.67 (6.12 1 5.56 (0 to 0.241 pregnancies 6.12) 16.14) to 16.15) to 16.14) to 24.57) 16.14) to 27.21) 16.14)  Active neoplasia 305 2.95 (2.62 144 3.65 (3.06 0.032 531 5.14 (4.71 241 6.11 (5.36 0.022 793 7.67 (7.16 348 8.82 (7.93 0.024 1077 10.42 (9.83 442 11.20 0.175 to 3.28) to 4.23) to 5.56) to 6.85) to 8.18) to 9.70) to 11.01) (10.21 to 12.18) Hospital level  High-­tech public 485 3.33 (3.04 187 3.44 (2.96 0.709 817 5.62 (5.24 304 5.59 (4.98 0.952 1191 8.19 (7.74 442 8.13 (7.41 0.902 1634 11.23 592 10.89 0.498 hospital to 3.63) to 3.93) to 5.99) to 6.20) to 8.63) to 8.86) (10.72 to (10.06 to 11.74) 11.72) Continued Open access 7 8 Table 2 Continued 3 Days 7 Days 15 Days 30 Days Weekday Weekend Weekday Weekend Weekday Weekend Weekday Weekend Mortality Mortality Mortality Mortality Mortality Mortality Mortality Mortality N (95% CI) N (95% CI) P value N (95% CI) N (95% CI) P value N (95% CI) N (95% CI) P value N (95% CI) N (95% CI) P value Open access  Monographic 8 2.60 (0.82 3 2.34 (0 to 0.878 14 4.55 (2.22 7 5.47 (1.53 0.682 22 7.14 (4.27 9 7.03 (2.60 0.967 26 8.44 (5.34 10 7.81 (3.16 0.828 high-­tech public to 4.37) 4.96) to 6.87) to 9.41) to 10.02) to 11.46) to 11.55) to 12.46) hospital  High-­resolution 341 3.48 (3.12 123 3.37 (2.79 0.759 523 5.34 (4.89 212 5.81 (5.05 0.283 770 7.86 (7.32 296 8.11 (7.23 0.626 1044 10.65 401 10.99 (9.97 0.574 public hospital to 3.84) to 3.96) to 5.78) to 6.57) to 8.39) to 9.00) (10.04 to to 12.00) 11.26)  Reference public 506 3.29 (3.01 257 4.34 (3.82 <0.001 846 5.50 (5.14 372 6.28 (5.66 0.029 1215 7.91 (7.48 514 8.68 (7.96 0.064 1616 10.51 673 11.36 0.073 hospital to 3.57) to 4.86) to 5.86) to 6.90) to 8.33) to 9.40) (10.03 to (10.56 to 11.00) 12.17)  Regional public 349 3.01 (2.70 163 3.65 (3.10 0.040 591 5.11 (4.70 247 5.53 (4.86 0.277 899 7.77 (7.28 362 8.11 (7.31 0.471 1256 10.85 467 10.46 (9.56 0.474 hospital to 3.33) to 4.20) to 5.51) to 6.20) to 8.25) to 8.91) (10.28 to to 11.36) 11.42)  Isolated public 28 3.23 (2.06 14 3.91 (1.90 0.554 53 6.12 (4.52 25 6.98 (4.34 0.574 77 8.89 (7.00 35 9.78 (6.70 0.625 98 11.32 (9.21 48 13.41 (9.88 0.304 hospital to 4.41) to 5.92) to 7.72) to 9.62) to 10.79) to 12.85) to 13.43) to 16.94) Origin  Home or nursing 856 3.25 (3.04 357 3.57 (3.20 0.134 1380 5.24 (4.97 564 5.63 (5.18 0.136 2060 7.82 (7.50 823 8.22 (7.68 0.208 2847 10.81 1090 10.89 0.830 home to 3.46) to 3.93) to 5.51) to 6.08) to 8.15) to 8.76) (10.43 to (10.28 to 11.18) 11.50)  Primary care 93 3.87 (3.10 33 3.85 (2.56 0.978 157 6.54 (5.55 50 5.83 (4.27 0.469 208 8.66 (7.53 62 7.23 (5.50 0.194 288 11.99 94 10.97 (8.88 0.425 to 4.64) to 5.14) to 7.52) to 7.40) to 9.78) to 8.97) (10.69 to to 13.06) 13.29)  Other hospital 132 3.57 (2.97 55 3.81 (2.83 0.673 215 5.81 (5.06 92 6.38 (5.12 0.440 303 8.19 (7.31 127 8.81 (7.34 0.474 402 10.87 (9.86 155 10.75 (9.15 0.902 to 4.17) to 4.80) to 6.57) to 7.64) to 9.08) to 10.27) to 11.87) to 12.35)  Unit or service of 636 3.18 (2.93 302 3.95 (3.51 0.001 1092 5.45 (5.14 461 6.03 (5.50 0.062 1603 8.01 (7.63 646 8.45 (7.83 0.226 2136 10.67 852 11.14 0.253 the same hospital to 3.42) to 4.39) to 5.77) to 6.56) to 8.38) to 9.07) (10.24 to (10.44 to 11.09) 11.85) Primary care emergency in the last 24 hours  Yes 72 3.58 (2.77 35 4.42 (2.99 0.295 103 5.12 (4.16 52 6.57 (4.85 0.131 149 7.41 (6.27 76 9.61 (7.55 0.054 203 10.10 (8.78 102 12.90 0.033 to 4.39) to 5.86) to 6.09) to 8.30) to 8.56) to 11.66) to 11.42) (10.56 to 15.23)  No 1645 3.26 (3.11 712 3.71 (3.45 0.003 2741 5.43 (5.23 1115 5.82 (5.49 0.047 4025 7.98 (7.74 1582 8.25 (7.86 0.229 5471 10.84 2089 10.90 0.828 to 3.41) to 3.98) to 5.63) to 6.15) to 8.21) to 8.64) (10.57 to (10.46 to 11.11) 11.34) MES in the last 24 hours  Yes 1088 3.23 (3.04 468 3.66 (3.34 0.020 1816 5.39 (5.14 738 5.77 (5.37 0.101 2658 7.88 (7.59 1069 8.36 (7.88 0.089 3587 10.64 1405 10.99 0.271 to 3.42) to 3.99) to 5.63) to 6.18) to 8.17) to 8.84) (10.31 to (10.45 to 10.97) 11.53)  No 629 3.35 (3.10 279 3.89 (3.44 0.036 1028 5.48 (5.16 429 5.98 (5.43 0.120 1516 8.09 (7.70 589 8.21 (7.57 0.743 2087 11.13 786 10.96 0.688 to 3.61) to 4.34) to 5.81) to 6.53) to 8.48) to 8.85) (10.68 to (10.23 to 11.58) 11.68) Amigo F, et al. BMJ Open 2021;11:e047836. doi:10.1136/bmjopen-2020-047836 Continued Open access Table 3 Adjusted Cox survival model 30 days after P value 0.599 11.18 (9.60 0.635 emergency hospital admission according to day of admission, sociodemographic, clinical and health resource characteristics Mortality (95% CI) (10.51 to to 12.75) 11.41) 10.96 HR* 95% CI P value Weekend Weekday 2019 10.73 (9.79 172  Yes 1 N  No 1.13 1.04 to 1.22 0.002 Mortality (95% CI) (10.54 to to 11.68) Interaction 11.10) 10.82  Time and weekend 0.99 0.99 to 1.00 0.003 Weekday 30 Days Sex 5234 440 N  Man 1 P value  Woman 0.87 0.83 to 0.92 <0.001 0.166 0.412  Age 1.06 1.06 to 1.06 <0.001 Income level 8.30 (7.90 8.45 (7.06 Mortality (95% CI) to 8.69) to 9.84)  Exhausted unemployment 1 subsidy. RMI. RAI. PNC and Weekend others 1528  Income less than €18 000 0.91 0.80 to 1.03 0.138 130 AMG, Adjusted Morbidity Group; MES, Medical Emergencies Service; PNC, unremarkable pension; RAI, active insertion income; RMI, minimum insertion income. N  Income between €18 000 0.79 0.69 to 0.91 0.001 7.97 (7.73 7.78 (6.96 Mortality (95% CI) and €100 000 to 8.21) to 8.60)  Income greater than €100 0.66 0.42 to 1.04 0.075 Weekday 15 Days 000 3855 Diagnosis 319 N  Hip fracture 1 P value  Gastrointestinal bleeding 1.38 1.22 to 1.55 <0.001 0.058 0.128  ST-­elevation myocardial 2.77 2.44 to 3.15 <0.001 infarction 5.79 (5.46 6.50 (5.27 Mortality (95% CI) to 6.13) to 7.73)  Non-­ST-­elevation myocardial 1.70 1.48 to 1.97 <0.001 infarction Weekend  Heart failure 1.96 1.80 to 2.13 <0.001 1067 100  Stroke 3.53 3.23 to 3.85 <0.001 N 5.42 (5.22 5.44 (4.75  Pneumonia 2.01 1.84 to 2.20 <0.001 Mortality (95% CI) to 5.62) to 6.13) AMG Weekday  Healthy patient 1 7 Days 2621  Acute disorder 0.79 0.60 to 1.02 0.076 223 P value N  Chronic pathologies 0.90 0.73 to 1.12 0.363 affecting 1 system 0.006 0.093  Chronic pathologies 0.93 0.77 to 1.13 0.463 affecting 2–3 systems 685 3.72 (3.45 4.03 (3.05 Mortality (95% CI) to 3.99) to 5.01)  Chronic pathologies 0.98 0.82 to 1.18 0.862 Weekend affecting >3 systems  Births and pregnancies 1.24 0.63 to 2.44 0.532 62 N  Active neoplasia 0.96 0.79 to 1.16 0.656 *P value corresponds to a test of proportions. 3.29 (3.13 3.12 (2.59 Mortality (95% CI) Hospital level to 3.44) to 3.66)  High-­tech public hospital 1 Weekday  Monographic high-­tech 0.78 0.56 to 1.09 0.146 3 Days 1589 public hospital 128 Continued N  High-­resolution public 0.94 0.88 to 1.01 0.118 Hospital emergency hospital in the last 24 hours  Reference public hospital 0.99 0.93 to 1.05 0.665  Regional public hospital 0.96 0.90 to 1.02 0.268 Table 2  Isolated public hospital 1.10 0.93 to 1.30 0.248  Yes  No Continued Amigo F, et al. BMJ Open 2021;11:e047836. doi:10.1136/bmjopen-2020-047836 9 Open access Table 3 Continued Table 4 Adjusted Cox survival models 30 days after HR* 95% CI P value emergency hospital admission according to admission day stratified by sociodemographic, clinical and health resource Origin characteristics  Home or nursing home 1 HR* 95% CI P value  Primary care 1.08 0.97 to 1.20 0.183 Sex†  Other hospital 1.00 0.90 to 1.10 0.959 Man  Unit or service of the same 0.97 0.92 to 1.03 0.277  Weekday 1 hospital  Weekend 1.12 1.00 to 1.26 0.052 Primary care emergency in the last 24 hours Woman  Yes 1  Weekday 1  Not 0.99 0.88 to 1.11 0.858  Weekend 1.14 1.01 to 1.27 0.022 MES in the last 24 hours Income level‡  Yes 1 Exhausted unemployment subsidy, RMI, RAI, PNC and  No 0.94 0.90 to 0.99 0.021 others* Hospital emergency in the last  Weekday 1 24 hours  Weekend 0.97 0.62 to 1.53 0.907  Yes 1 Income less than €18 000  No 1.04 0.95 to 1.13 0.431  Weekday 1 *Hazard ratios.  Weekend 1.17 1.07 to 1.28 0.001 MES, Medical Emergencies Service; PNC, unremarkable pension; RAI, active insertion income; RMI, minimum insertion income. Income between €18 000 and €100 000  Weekday 1 Therefore, there may not be sufficient statistical power to  Weekend 0.98 0.80 to 1.19 0.827 identify the effect in the models of other income levels. Income greater than €100 000 In regard to diagnosis, there was a weekend effect of  Weekday 1 22% for people who were admitted for heart failure,  Weekend 2.20 0.36 to 13.58 0.395 and this effect was statistically significant. This result was Diagnosis§ unexpected, since in the literature the weekend effect for Hip fracture heart failure is usually between 1% and 10%, and in many  Weekday 1 cases no significant difference has been found.13 25–27  Weekend 0.99 0.72 to 1.37 0.958 On the other hand, a weekend effect of 32% was found in the group who were admitted for ST-­segment Gastrointestinal bleeding elevation myocardial infarction, but this was not statisti-  Weekday 1 cally significant. This is a worrying finding, even more so  Weekend 1.10 0.79 to 1.55 0.566 considering that in Catalonia there is a rapid care code ST-­elevation myocardial for this pathology that seeks to guarantee the quality of infarction care that the patient receives and a maximum speed of  Weekday 1 care.28 We believe that it is important to investigate this  Weekend 1.32 0.96 to 1.82 0.088 pathology in more detail, since in this study it accounted Non-­ST-­elevation myocardial for 6% of admissions and it may be that, in an analysis infarction with more years included, there would be sufficient statis-  Weekday 1 tical power to detect statistically significant differences for  Weekend 0.95 0.60 to 1.49 0.812 this pathology. Heart failure Finally, when comparing the weekend effect by hospital  Weekday 1 level, it can be seen that the adjusted HR for weekend was significant for the reference public hospitals and  Weekend 1.22 1.05 to 1.42 0.008 the regional public hospitals, with 20% and 22% higher Stroke mortality due to the weekend effect, respectively. There-  Weekday 1 fore, it was observed that the weekend effect was stronger  Weekend 1.12 0.97 to 1.30 0.125 in smaller hospitals. This could be due to the fact that Pneumonia it is the small hospitals that have the greatest difficulty  Weekday 1 in covering shifts and services during the weekend, compared with larger hospitals.29 Continued 10 Amigo F, et al. BMJ Open 2021;11:e047836. doi:10.1136/bmjopen-2020-047836 Open access admission and allows control of the case mix. This is very Table 4 Continued important because there are studies that indicate that HR* 95% CI P value the weekend effect could be due to the fact that patients  Weekend 1.02 0.85 to 1.23 0.812 admitted to hospital on weekends are sicker than those Hospital level¶ admitted on weekdays.30 High-­tech public hospital We can conclude that there was a weekend effect, but it was not constant over time: on the first day, the risk of  Weekday 1 dying was 13% higher in those patients who were admitted  Weekend 1.04 0.89 to 1.21 0.638 on a weekend, and decreased by 1% for each additional Monographic high-­tech public day of stay. In addition, there were differences according hospital to sex, hospital level, diagnosis and income level, since a  Weekday 1 greater effect was found in women; in public reference  Weekend 1.59 0.48 to 5.29 0.448 hospitals and regional public hospitals; in heart failure High-­resolution public hospital patients; and in those who earned less than €18 000 per  Weekday 1 year.  Weekend 1.04 0.86 to 1.26 0.651 This may suggest that there were quality of care problems Reference public hospital at the weekend. It is necessary to expand the investigation to a greater number of pathologies and carry out studies that  Weekday 1 delve deeper into the factors that produce this effect.  Weekend 1.20 1.04 to 1.39 0.012 Regional public hospital Contributors FA reviewed the literature. FA, AD-­B and AG-­A conceived and designed the study. AD-­B and AG-­A collected the data. FA cleaned and analysed the  Weekday 1 data and drafted the initial version of the manuscript. All authors reviewed the initial  Weekend 1.23 1.03 to 1.47 0.022 draft, made critical contributions to the interpretation of the data and approved Isolated public hospital the manuscript. The corresponding author attests that all listed authors meet the authorship criteria and that no others meeting the criteria have been omitted. AG-­A  Weekday 1 is responsible for the overall content as guarantor.  Weekend 1.05 0.60 to 1.86 0.858 Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-­for-­profit sectors. *Hazard ratios. †Adjusted age, diagnosis, hospital level, origin, income level, Adjusted Competing interests None declared. Morbidity Group, previous contact with the emergency department in Patient and public involvement Patients and/or the public were not involved in the last 24 hours. the design, or conduct, or reporting or dissemination plans of this research. ‡Adjusted age, diagnosis, sex, hospital level, origin, Adjusted Morbidity Group, previous contact with the emergency department in Patient consent for publication Not applicable. the last 24 hours. §Adjusted age, sex, hospital level, origin, income level, Adjusted Provenance and peer review Not commissioned; externally peer reviewed. Morbidity Group, previous contact with the emergency department in Data availability statement Data are available upon reasonable request. the last 24 hours. A governmental structure called PADRIS exists within the Catalan healthcare ¶Adjusted age, diagnosis, sex, origin, income level, Adjusted administration. PADRIS is an analytical data programme for research and innovation Morbidity Group, previous contact with the emergency department in in health. The purpose of the structure is to make data available to scientific the last 24 hours. communities to promote research, innovation and evaluation in health, with the aim of reusing and exchanging the data generated by the health system in accordance with the legal and regulatory framework. The programme PADRIS ensures that all data made available to researchers is fully anonymised. As workers within the In regard to the possible limitations of the study, it Catalan healthcare administration, we are provided data by PADRIS, and therefore should be mentioned that the income level variable has are never given access to identifiable information. Thus, the study did not involve very wide ranges in some of its categories; there may any data collection, requiring neither human participants nor patient consent. For be a lot of heterogeneity within groups and, therefore, that reason, and due to the use of existing anonymised data for research, the study was exempt from institutional review committee approval. It is the standard way of there may have been some effect in the different groups proceeding in the healthcare administration to systematically check the quality of of income that could not be analysed. This limitation is the healthcare providers in our context. due to the origin of the data, since such disaggregation Open access This is an open access article distributed in accordance with the according to income was the minimum possible. Further- Creative Commons Attribution Non Commercial (CC BY-­NC 4.0) license, which more, it should be noted that the results cannot be extrap- permits others to distribute, remix, adapt, build upon this work non-­commercially, olated to all hospital admissions, since a list of specific and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use pathologies was selected, covering those that should be is non-­commercial. See: http://​creativecommons.​org/​licenses/​by-​nc/​4.​0/. most affected if there was a weekend effect.17 In regard to strengths, a population database was used ORCID iD Anna García-­Altés http://​orcid.​org/​0000-​0003-​3889-​5375 and, therefore, it was possible to have access to all urgent hospital admissions recorded in Catalonia in 2018 for these pathologies, and both intrahospital and extrahos- pital mortality was monitored. In addition, the adjusted REFERENCES 1 Clarke J, Davidson MM, Downham APS. Newcastle survey of morbidity group was included in the model, which was an deaths in early childhood 1974/76, with special reference to sudden excellent indicator of the patient’s health status prior to unexpected deaths. Arch Dis Child 1977;52:828–35. Amigo F, et al. BMJ Open 2021;11:e047836. doi:10.1136/bmjopen-2020-047836 11 Open access 2 Bray BD, Steventon A. What have we learnt after 15 years of 17 AHRQ Quality Indicators, Farquhar M, Hughes RG, ed. Patient safety research into the 'weekend effect'? BMJ Qual Saf 2017;26:607–10. and quality: an evidence-­based Handbook for nurses. Rockville, MD: 3 Lilford RJ, Chen Y-­F. The ubiquitous weekend effect: moving Agency for Healthcare Research and Quality, 2011. past proving it exists to clarifying what causes it. BMJ Qual Saf 18 Conjunt mínim b sic de dades (CMBD). Barcelona (Spain): CatSalut. 2015;24:480–2. Servei Catal de la Salut, 2020. Available: https://​catsalut.​gencat.​ 4 Godlee F. What to do about the “weekend effect”. BMJ cat/​ca/​proveidors-​professionals/​registres-​catalegs/​registres/​cmbd/ 2015;2015:h4840. [Accessed 9 Nov 2020]. 5 Zhou Y, Li W, Herath C, et al. Off-­hour admission and mortality risk 19 Registre central de població del CatSalut. Barcelona (Spain): for 28 specific diseases: a systematic review and meta-­analysis of CatSalut. Servei Catal de la Salut, 2020. Available: https://​catsalut.​ 251 cohorts. J Am Heart Assoc 2016;5:e003102. gencat.​cat/​ca/​proveidors-​professionals/​registres-​catalegs/​registres/​ 6 Pauls LA, Johnson-­Paben R, McGready J, et al. The weekend central-​poblacio/ [Accessed 9 Nov 2020]. effect in hospitalized patients: a meta-­analysis. J Hosp Med 20 Monterde D, Vela E, Clèries M, et al. [Adjusted morbidity groups: a 2017;12:760–6. new multiple morbidity measurement of use in primary care. Aten 7 Freemantle N, Ray D, McNulty D, et al. Increased mortality Primaria 2016;48:674–82. associated with weekend hospital admission: a case for expanded 21 Dueñas-­Espín I, Vela E, Pauws S, et al. Proposals for enhanced seven day services? BMJ 2015;351:h4596. health risk assessment and stratification in an integrated care 8 Chen Y-­F, Armoiry X, Higenbottam C, et al. Magnitude and modifiers scenario. BMJ Open 2016;6:e010301. of the weekend effect in hospital admissions: a systematic review 22 Ruiz-­Cantero MT, Verdú-Delgado M. Sesgo de género en El esfuerzo and meta-­analysis. BMJ Open 2019;9:e025764. terapéutico. Gac Sanit 2004;18:118–25. 9 McKee M. The weekend effect: now you see it, now you don’t. BMJ 23 Rohlfs I, García MdelM, Gavaldà L, et al. Género Y cardiopatía 2016;i:i2750. isquémica. Gac Sanit 2004;18:55–64. 10 McKee M. Is the UK government right that seven day working in 24 Monteagudo-­Piqueras O, Sarría-­Santamera A. Diferencias entre hospitals would save 6000 lives a year? BMJ 2015;351:h4723. varones Y mujeres respecto a la mortalidad hospitalaria Y La 11 Aldridge C, Bion J, Boyal A, et al. Weekend specialist intensity and utilización de procedimientos en El infarto agudo de miocardio. admission mortality in acute Hospital trusts in England: a cross-­ Gaceta Sanitaria 2006;20:77–8. sectional study. Lancet 2016;388:178–86. 25 Aylin P, Yunus A, Bottle A, et al. Weekend mortality for emergency 12 Meacock R, Sutton M. Elevated mortality among weekend hospital admissions. A large, multicentre study. Qual Saf Health Care admissions is not associated with adoption of seven day clinical 2010;19:213–7. standards. Emerg Med J 2018;35:108–13. 26 Cram P, Hillis SL, Barnett M, et al. Effects of weekend admission 13 Bell CM, Redelmeier DA. Mortality among patients admitted to and hospital teaching status on in-­hospital mortality. Am J Med hospitals on weekends as compared with weekdays. N Engl J Med 2004;117:151–7. 2001;345:663–8. 27 Roberts SE, Thorne K, Akbari A, et al. Weekend emergency 14 Ruiz M, Bottle A, Aylin PP. Exploring the impact of consultants' admissions and mortality in England and Wales. Lancet experience on hospital mortality by day of the week: a 2015;385:385. retrospective analysis of hospital episode statistics. BMJ Qual 28 Observatori del Sistema de Salut de Catalunya. Central de Resultats. Saf 2016;25:337–44. Àmbit d’emergències mèdiques. Dades 2018. Barcelona (Spain): 15 Anselmi L, Meacock R, Kristensen SR, et al. Arrival by Agència de Qualitat i Avaluació Sanit ries de Catalunya. Departament ambulance explains variation in mortality by time of admission: de Salut. Generalitat de Catalunya 2019. retrospective study of admissions to hospital following 29 Observatori del Sistema de Salut de Catalunya. Central de Resultats. emergency department attendance in England. BMJ Qual Saf Àmbit hospitalari. Dades 2018. Barcelona (Spain): Agència de 2017;26:613–21. Qualitat I Avaluació Sanit ries de Catalunya. Departament de Salut. 16 Bray BD, Cloud GC, James MA, et al. Weekly variation in health-­ Generalitat de Catalunya 2019. care quality by day and time of admission: a nationwide, registry-­ 30 Sun J, Girling AJ, Aldridge C, et al. Sicker patients account for the based, prospective cohort study of acute stroke care. Lancet weekend mortality effect among adult emergency admissions to a 2016;388:170–7. large Hospital trust. BMJ Qual Saf 2019;28:223–30. 12 Amigo F, et al. BMJ Open 2021;11:e047836. doi:10.1136/bmjopen-2020-047836