Social and ideological determinants of COVID-19 vaccination status in Spain

Objectives This study analyzed the association between social and ideological determinants with COVID-19 vaccine accessibility and hesitancy in the Spanish adult population. Study design Repeated cross-sectional study. Methods The data analyzed are based on monthly surveys conducted by the Centre for Sociological Research between May 2021 and February 2022. Individuals were classified according to their COVID-19 vaccination status into (1) Vaccinated (reference group);(2) Willing to vaccinate but not vaccinated, proxy of lack of vaccine accessibility;(3) Hesitant, proxy of vaccine hesitancy. Independent variables included social (educational attainment, gender) and ideological determinants (voting in the last elections, importance attached to the health vs the economic impact of the pandemic, and political self-placement). We estimated Odds Ratio (OR) and 95% Confidence Interval (CI) conducting one age-adjusted multinomial logistic regression model for each determinant and then stratified them by gender. Results Both social and ideological determinants had a weak association with the lack of vaccine accessibility. Individuals with medium educational attainment had higher odds of vaccine hesitancy (OR=1.44, CI 1.08-1.93) compared to those with high educational attainment. People self-identified as conservative (OR=2.90;CI 2.02-4.15), those that prioritized the economic impact (OR=3.80;CI 2.62-5.49), and voted for parties opposed to the Government (OR=2.00;CI 1.54-2.60) showed higher vaccine hesitancy. The stratified analysis showed a similar pattern for both men and women. Conclusions Considering the determinants of vaccine uptake and hesitancy could help to design strategies that increase immunization at the population level and minimize health inequities.

[What have we missed because of COVID-19? Missed diagnoses and delayed follow-ups. SESPAS Report 2022]. / ¿Qué hemos dejado de atender por la COVID-19? Diagnósticos perdidos y seguimientos demorados. Informe SESPAS 2022.

The COVID-19 pandemic and the associated public health emergency have affected patients and health services in non-COVID-19 pathologies. Several studies have shown its dissociation from health services, with a decrease in emergency department visits, in hospital admissions for non-COVID-19 pathologies, as well as in the reported weekly incidence of acute illnesses and new diagnoses in primary care. In parallel, the pandemic has had direct and indirect effects on people with chronic diseases; the difficulties in accessing health services, the interruption of care, the saturation of the system itself and its reorientation towards non-face-to-face formats has reduced the capacity to prevent or control chronic diseases. All this has also had an impact on the different areas of people's lives, creating new social and economic difficulties, or aggravating those that existed before the pandemic. All these circumstances have changed with each epidemic wave. We present a review of the most relevant studies that have been analyzing this problem and incorporate as a case study the results of a retrospective observational study carried out in Primary Care in the Madrid Health Service, which provides health coverage to a population of more than 6 million people, and whose objective was to analyze the loss of new diagnoses in the most prevalent pathologies such as common mental health problems, cardiovascular and cerebrovascular diseases, type 2 diabetes, chronic obstructive pulmonary disease, and breast and colon tumors, in the first and second waves. Annual incidence rates with their confidence interval were calculated for each pathology and the monthly frequency of new codes recorded between 1/01/2020 and 12/31/2020 was compared with the monthly mean of observed counts for the same months between 2016 and 2019. The annual incidence rate for all processes studied decreased in 2020 except for anxiety disorders. Regarding the recovery of lost diagnoses, heart failure is the only diagnosis showing an above-average recovery after the first wave. To return to pre-pandemic levels of diagnosis and follow-up of non-COVID-19 pathology, the healthcare system must reorganize and contemplate specific actions for the groups at highest risk.

Temporal trends in within-city inequities in COVID-19 incidence rate by area-level deprivation in Madrid, Spain.

Patterns of exposure and policies aiming at reducing physical contact might have changed the social distribution of COVID-19 incidence over the course of the pandemic. Thus, we studied the temporal trends in the association between area-level deprivation and COVID-19 incidence rate by Basic Health Zone (minimum administration division for health service provision) in Madrid, Spain, from March 2020 to September 2021. We found an overall association between deprivation and COVID-19 incidence. This association varied over time; areas with higher deprivation showed higher COVID-19 incidence rates from July to November 2020 and August-September 2021, while, by contrast, higher deprivation areas showed lower COVID-19 incidence rates in December 2020 and July 2021.

Selective perimeter lockdowns in Madrid: a way to bend the COVID-19 curve?

Lockdowns have been widespread used to limit social interaction and bend the epidemic curve. However, their intensity and geographical delimitation have been variable across different countries. Madrid (Spain) implemented perimeter lockdowns in September with the purpose of bending the COVID-19 curve. In this article, we compared, using join point regressions, the evolution of COVID-19 cases in those areas where this intervention was implemented and those where it was not. According to our analysis, the decrease in the epidemic curve started before the impact of the perimeter lockdown could be reflected.

Characteristics, complications and outcomes among 1549 patients hospitalised with COVID-19 in a secondary hospital in Madrid, Spain: a retrospective case series study.

OBJECTIVES: To describe demographic, clinical, radiological and laboratory characteristics, as well as outcomes, of patients admitted for COVID-19 in a secondary hospital. DESIGN AND SETTING: Retrospective case series of sequentially hospitalised patients with confirmed SARS-CoV-2, at Infanta Leonor University Hospital (ILUH) in Madrid, Spain. PARTICIPANTS: All patients attended at ILUH testing positive to reverse transcriptase-PCR on nasopharyngeal swabs and diagnosed with COVID-19 between 1 March 2020 and 28 May 2020. RESULTS: A total of 1549 COVID-19 cases were included (median age 69 years (IQR 55.0-81.0), 57.5% men). 78.2% had at least one underlying comorbidity, the most frequent was hypertension (55.8%). Most frequent symptoms at presentation were fever (75.3%), cough (65.7%) and dyspnoea (58.1%). 81 (5.8%) patients were admitted to the intensive care unit (ICU) (median age 62 years (IQR 51-71); 74.1% men; median length of stay 9 days (IQR 5-19)) 82.7% of them needed invasive ventilation support. 1393 patients had an outcome at the end of the study period (case fatality ratio: 21.2% (296/1393)). The independent factors associated with fatality (OR; 95% CI): age (1.07; 1.06 to 1.09), male sex (2.86; 1.85 to 4.50), neurological disease (1.93; 1.19 to 3.13), chronic kidney disease (2.83; 1.40 to 5.71) and neoplasia (4.29; 2.40 to 7.67). The percentage of hospital beds occupied with COVID-19 almost doubled (702/361), with the number of patients in ICU quadrupling its capacity (32/8). Median length of stay was 9 days (IQR 6-14). CONCLUSIONS: This study provides clinical characteristics, complications and outcomes of patients with COVID-19 admitted to a European secondary hospital. Fatal outcomes were similar to those reported by hospitals with a higher level of complexity.

The PANDEMYC Score. An Easily Applicable and Interpretable Model for Predicting Mortality Associated With COVID-19.

This study aimed to build an easily applicable prognostic model based on routine clinical, radiological, and laboratory data available at admission, to predict mortality in coronavirus 19 disease (COVID-19) hospitalized patients. METHODS: We retrospectively collected clinical information from 1968 patients admitted to a hospital. We built a predictive score based on a logistic regression model in which explicative variables were discretized using classification trees that facilitated the identification of the optimal sections in order to predict inpatient mortality in patients admitted with COVID-19. These sections were translated into a score indicating the probability of a patient's death, thus making the results easy to interpret. RESULTS: Median age was 67 years, 1104 patients (56.4%) were male, and 325 (16.5%) died during hospitalization. Our final model identified nine key features: age, oxygen saturation, smoking, serum creatinine, lymphocytes, hemoglobin, platelets, C-reactive protein, and sodium at admission. The discrimination of the model was excellent in the training, validation, and test samples (AUC: 0.865, 0.808, and 0.883, respectively). We constructed a prognostic scale to determine the probability of death associated with each score. CONCLUSIONS: We designed an easily applicable predictive model for early identification of patients at high risk of death due to COVID-19 during hospitalization.

The PANDEMYC Score. An Easily Applicable and Interpretable Model for Predicting Mortality Associated With COVID-19

This study aimed to build an easily applicable prognostic model based on routine clinical, radiological, and laboratory data available at admission, to predict mortality in coronavirus 19 disease (COVID-19) hospitalized patients. Methods: We retrospectively collected clinical information from 1968 patients admitted to a hospital. We built a predictive score based on a logistic regression model in which explicative variables were discretized using classification trees that facilitated the identification of the optimal sections in order to predict inpatient mortality in patients admitted with COVID-19. These sections were translated into a score indicating the probability of a patient"s death, thus making the results easy to interpret. Results. Median age was 67 years, 1104 patients (56.4%) were male, and 325 (16.5%) died during hospitalization. Our final model identified nine key features: age, oxygen saturation, smoking, serum creatinine, lymphocytes, hemoglobin, platelets, C-reactive protein, and sodium at admission. The discrimination of the model was excellent in the training, validation, and test samples (AUC: 0.865, 0.808, and 0.883, respectively). We constructed a prognostic scale to determine the probability of death associated with each score. Conclusions: We designed an easily applicable predictive model for early identification of patients at high risk of death due to COVID-19 during hospitalization.