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Background: An updated time-trend analysis of anti-dementia drugs (ADDs) is lacking. The aim of this study is to assess the incident rate (IR) of ADD in individuals with dementia using real-world data. Setting: Primary care data (country/database) from the UK/CPRD-GOLD (2007–20), Spain/SIDIAP (2010–20) and the Netherlands/IPCI (2008–20), standardised to a common data model. Methods: Cohort study. Participants: dementia patients ≥40 years old with ≥1 year of previous data. Follow-up: until the end of the study period, transfer out of the catchment area, death or incident prescription of rivastigmine, galantamine, donepezil or memantine. Other variables: age/sex, type of dementia, comorbidities. Statistics: overall and yearly age/sex IR, with 95% confidence interval, per 100,000 person-years (IR per 105 PY (95%CI)). Results: We identified a total of (incident anti-dementia users/dementia patients) 41,024/110,642 in UK/CPRD-GOLD, 51,667/134,927 in Spain/SIDIAP and 2,088/17,559 in the Netherlands/IPCI. In the UK, IR (per 105 PY (95%CI)) of ADD decreased from 2007 (30,829 (28,891–32,862)) to 2010 (17,793 (17,083–18,524)), then increased up to 2019 (31,601 (30,483 to 32,749)) and decrease in 2020 (24,067 (23,021–25,148)). In Spain, IR (per 105 PY (95%CI)) of ADD decreased by 72% from 2010 (51,003 (49,199–52,855)) to 2020 (14,571 (14,109–15,043)). In the Netherlands, IR (per 105 PY (95%CI)) of ADD decreased by 77% from 2009 (21,151 (14,967–29,031)) to 2020 (4763 (4176–5409)). Subjects aged ≥65–79 years and men (in the UK and the Netherlands) initiated more frequently an ADD. Conclusions: Treatment of dementia remains highly heterogeneous. Further consensus in the pharmacological management of patients living with dementia is urgently needed.

Purpose: Real-world data (RWD) offers a valuable resource for generating population-level disease epidemiology metrics. We aimed to develop a well-tested and user-friendly R package to compute incidence rates and prevalence in data mapped to the observational medical outcomes partnership (OMOP) common data model (CDM). Materials and Methods: We created IncidencePrevalence, an R package to support the analysis of population-level incidence rates and point- and period-prevalence in OMOP-formatted data. On top of unit testing, we assessed the face validity of the package. To do so, we calculated incidence rates of COVID-19 using RWD from Spain (SIDIAP) and the United Kingdom (CPRD Aurum), and replicated two previously published studies using data from the Netherlands (IPCI) and the United Kingdom (CPRD Gold). We compared the obtained results to those previously published, and measured execution times by running a benchmark analysis across databases. Results: IncidencePrevalence achieved high agreement to previously published data in CPRD Gold and IPCI, and showed good performance across databases. For COVID-19, incidence calculated by the package was similar to public data after the first-wave of the pandemic. Conclusion: For data mapped to the OMOP CDM, the IncidencePrevalence R package can support descriptive epidemiological research. It enables reliable estimation of incidence and prevalence from large real-world data sets. It represents a simple, but extendable, analytical framework to generate estimates in a reproducible and timely manner.

Purpose: The primary aim of this work was to convert the Information System for Research in Primary Care (SIDIAP) from Catalonia, Spain, to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). Our second aim was to provide a descriptive analysis of COVID-19-related outcomes among the general population. Patients and Methods: We mapped patient-level data from SIDIAP to the OMOP CDM and we performed more than 3,400 data quality checks to assess its readiness for research. We established a general population cohort as of the 1st March 2020 and identified outpatient COVID-19 diagnoses or tested positive for, hospitalised with, admitted to intensive care units (ICU) with, died with, or vaccinated against COVID-19 up to 30th June 2022. Results: After verifying the high quality of the transformed dataset, we included 5,870,274 individuals in the general population cohort. Of those, 604,472 had either an outpatient COVID-19 diagnosis or positive test result, 58,991 had a hospitalisation, 5,642 had an ICU admission, and 11,233 died with COVID-19. A total of 4,584,515 received a COVID-19 vaccine. People who were hospitalised or died were more commonly older, male, and with more comorbidities. Those admitted to ICU with COVID-19 were generally younger and more often male than those hospitalised and those who died. Conclusion: We successfully transformed SIDIAP to the OMOP CDM. From this dataset, a general population cohort of 5.9 million individuals was identified and their COVID-19-related outcomes over time were described. The transformed SIDIAP database is a valuable resource that can enable distributed network research in COVID-19 and beyond.

[Background] Non-alcoholic fatty liver disease (NAFLD) is one of the most important liver comorbidities in people living with HIV (PLWH). Factors that could lead to a higher prevalence of NAFLD or ease the onset of fibrosis are unclear., [Methods] Cohort study of the Spanish HIV Research Network, which comprehends 46 hospitals and more than 15,000 PLWH. Primary objectives were to assess NAFLD prevalence and liver fibrosis according to hepatic steatosis index (HSI) and NAFLD fibrosis score, respectively. Factors associated with both were analysed., [Results] A total of 4798 PLWH were included of whom 1461 (30.5%) showed an HSI>36; these patients had higher risk for significant fibrosis (OR 1.91; 95%CI 1.11–3.28). Factors associated with NAFLD were body mass index (OR 2.05; 95%CI 1.94–2.16) and diabetes (OR 4.68; 95%CI 2.17–10.08), while exposure to integrase strand transfer inhibitors showed a lower risk (OR 0.78; 95%CI 0.62–0.97). In patients with HSI>36, being female (OR 7.33; 95%CI 1.34–40), age (OR 1.22; 95%CI 1.11–1.34), body mass index (OR 1.35; 95%CI 1.18–1.54) and exposure to thymidine analogues (OR 75.4, 95%CI 6.9–823.5) were associated with a higher risk of significant fibrosis. However, exposure to non-nucleoside reverse transcriptase inhibitors (OR 0.12, 95%CI 0.02–0.89) and time of exposure to protease inhibitors (OR 0.97, 95%CI 0.95–1) showed a lower risk., [Conclusion] NAFLD prevalence was high in our cohort. Patients exposed to INSTI showed a lower risk of NAFLD. In patients with hepatic steatosis, exposure to thymidine analogues had 75-fold more risk of significant fibrosis while exposure to NNRTIs reduced this risk.

Objective: To study the association between covid-19 vaccines, SARS-CoV-2 infection, and risk of immune mediated neurological events. Design: Population based historical rate comparison study and self-controlled case series analysis. Setting: Primary care records from the United Kingdom, and primary care records from Spain linked to hospital data. Participants: 8 330 497 people who received at least one dose of covid-19 vaccines ChAdOx1 nCoV-19, BNT162b2, mRNA-1273, or Ad.26.COV2.S between the rollout of the vaccination campaigns and end of data availability (UK: 9 May 2021; Spain: 30 June 2021). The study sample also comprised a cohort of 735 870 unvaccinated individuals with a first positive reverse transcription polymerase chain reaction test result for SARS-CoV-2 from 1 September 2020, and 14 330 080 participants from the general population. Main outcome measures: Outcomes were incidence of Bell's palsy, encephalomyelitis, Guillain-Barré syndrome, and transverse myelitis. Incidence rates were estimated in the 21 days after the first vaccine dose, 90 days after a positive test result for SARS-CoV-2, and between 2017 and 2019 for background rates in the general population cohort. Indirectly standardised incidence ratios were estimated. Adjusted incidence rate ratios were estimated from the self-controlled case series. Results: The study included 4 376 535 people who received ChAdOx1 nCoV-19, 3 588 318 who received BNT162b2, 244 913 who received mRNA-1273, and 120 731 who received Ad26.CoV.2; 735 870 people with SARS-CoV-2 infection; and 14 330 080 people from the general population. Overall, post-vaccine rates were consistent with expected (background) rates for Bell's palsy, encephalomyelitis, and Guillain-Barré syndrome. Self-controlled case series was conducted only for Bell's palsy, given limited statistical power, but with no safety signal seen for those vaccinated. Rates were, however, higher than expected after SARS-CoV-2 infection. For example, in the d

Evidence on the impact of the COVID-19 vaccine rollout on socioeconomic COVID-19–related inequalities is scarce. We analyzed associations between socioeconomic deprivation index (SDI) and COVID-19 vaccination, infection, and hospitalization before and after vaccine rollout in Catalonia, Spain. We conducted a population-based cohort study during September 2020–June 2021 that comprised 2,297,146 adults >40 years of age. We estimated odds ratio of nonvaccination and hazard ratios (HRs) of infection and hospitalization by SDI quintile relative to the least deprived quintile, Q1. Six months after rollout, vaccination coverage differed by SDI quintile in working-age (40–64 years) persons: 81% for Q1, 71% for Q5. Before rollout, we found a pattern of increased HR of infection and hospitalization with deprivation among working-age and retirement-age (>65 years) persons. After rollout, infection inequalities decreased in both age groups, whereas hospitalization inequalities decreased among retirement-age persons. Our findings suggest that mass vaccination reduced socioeconomic COVID-19–related inequalities.

Objectives: The majority of available scores to assess mortality risk of coronavirus disease 2019 (COVID-19) patients in the emergency department have high risk of bias. Therefore, this cohort aimed to develop and validate a score at hospital admission for predicting in-hospital mortality in COVID-19 patients and to compare this score with other existing ones. Methods: Consecutive patients (≥ 18 years) with confirmed COVID-19 admitted to the participating hospitals were included. Logistic regression analysis was performed to develop a prediction model for in-hospital mortality, based on the 3978 patients admitted between March–July, 2020. The model was validated in the 1054 patients admitted during August–September, as well as in an external cohort of 474 Spanish patients. Results: Median (25–75th percentile) age of the model-derivation cohort was 60 (48–72) years, and in-hospital mortality was 20.3%. The validation cohorts had similar age distribution and in-hospital mortality. Seven significant variables were included in the risk score: age, blood urea nitrogen, number of comorbidities, C-reactive protein, SpO2/FiO2 ratio, platelet count, and heart rate. The model had high discriminatory value (AUROC 0.844, 95% CI 0.829–0.859), which was confirmed in the Brazilian (0.859 [95% CI 0.833–0.885]) and Spanish (0.894 [95% CI 0.870–0.919]) validation cohorts, and displayed better discrimination ability than other existing scores. It is implemented in a freely available online risk calculator (https://abc2sph.com/). Conclusions: An easy-to-use rapid scoring system based on characteristics of COVID-19 patients commonly available at hospital presentation was designed and validated for early stratification of in-hospital mortality risk of patients with COVID-19.

Leishmaniasis is considered a neglected disease that causes thousands of deaths annually in some tropical and subtropical countries. There are various techniques to diagnose leishmaniasis of which manual microscopy is considered to be the gold standard. There is a need for the development of automatic techniques that are able to detect parasites in a robust and unsupervised manner. In this paper we present a procedure for automatizing the detection process based on a deep learning approach. We train a U-net model that successfully segments leismania parasites and classifies them into promastigotes, amastigotes and adhered parasites., Peer Reviewed, Postprint (published version)