Gender health care inequalities in health crisis: when uncertainty can lead to inequality.

BACKGROUND: In health crisis, inequalities in access to and use of health care services become more evident. The objective of this study is to analyse the existence and evolution of gender inequalities in access to and use of healthcare services in the context of the COVID-19 health crisis. METHODS: Retrospective cohort study using data from all individuals with a confirmed COVID-19 infection from March 2020 to March 2022 in Aragón (Spain) (390,099 cases). Health care access and use was analysed by gender for the different pandemic waves. Univariate and multivariate analyses were conducted to evaluate the effect of sex in health care. Blinder-Oaxaca decomposition methods were performed to explain gender gaps observed. RESULTS: The health care received throughout the COVID-19 pandemic differed between men and women. Women were admitted to hospital and intensive care units less frequently than men and their stays were shorter. Differences observed between men and women narrowed throughout the pandemic, but persisted even after adjusting for age, socioeconomic status, morbidity burden or the patient's place of residence. Differences in sociodemographic characteristics and morbidity burden could explain partially the gender inequalities found, mainly in the later phases of the pandemic, but not in the earlier waves. CONCLUSIONS: There were gender inequalities in access to and use of health services during the COVID-19 pandemic. Inequalities were greater in the first waves of the pandemic, but did not disappear. Analysis of health crises must take into account an intersectional gender perspective to ensure equitable health care.

A novel application of interrupted time series analysis to identify the impact of a primary health care reform on intersectional inequities in avoidable hospitalizations in the adult Swedish population.

Primary health care (PHC) systems are a crucial instrument for achieving equitable population health, but there is little evidence of how PHC reforms impact equities in population health. In 2010, Sweden implemented a reform that promoted marketization and privatization of PHC. The present study uses a novel integration of intersectionality-informed and evaluative epidemiological analytical frameworks to disentangle the impact of the 2010 Swedish PHC reform on intersectional inequities in avoidable hospitalizations. The study population comprised the total Swedish population aged 18-85 years across 2001-2017, in total 129 million annual observations, for whom register data on sociodemographics and hospitalizations due to ambulatory care sensitive conditions were retrieved. Multilevel Analysis of Individual Heterogeneity and Discriminatory Analyses (MAIHDA) were run for the pre-reform (2001-2009) and post-reform (2010-2017) periods to provide a mapping of inequities. In addition, random effects estimates reflecting the discriminatory accuracy of intersectional strata were extracted from a series MAIHDAs run per year 2001-2017. The estimates were re-analyzed by Interrupted Time Series Analysis (ITSA), in order to identify the impact of the reform on measures of intersectional inequity in avoidable hospitalizations. The results point to a complex reconfiguration of social inequities following the reform. While the post-reform period showed a reduction in overall rates of avoidable hospitalizations and in age disparities, socioeconomic inequities in avoidable hospitalizations, as well as the importance of interactions between complex social positions, both increased. Socioeconomically disadvantaged groups born in the Nordic countries seem to have benefited the least from the reform. The study supports a greater attention to the potentially complex consequences that health reforms can have on inequities in health and health care, which may not be immediate apparent in conventional evaluations of either population-average outcomes, or by simple evaluations of equity impacts. Methodological approaches for evaluation of complex inequity impacts need further development.

Exploring sex variations in the incidence of cardiovascular events: a counterfactual decomposition analysis.

BACKGROUND: Some cardiovascular risk factors (CVRFs) that occur differently in men and women can be addressed to reduce the risk of suffering a major adverse cardiovascular event (MACE). Furthermore, the development of MACE is highly influenced by social determinants of health. Counterfactual decomposition analysis is a new methodology that has the potential to be used to disentangle the role of different factors in health inequalities. This study aimed to assess sex differences in the incidence of MACE and to estimate how much of the difference could be attributed to the prevalence of diabetes, hypertension, hypercholesterolaemia and socioeconomic status (SES). METHODS: Descriptive and counterfactual analyses were conducted in a population of 278 515 people with CVRFs. The contribution of the causal factors was estimated by comparing the observed risk ratio with the causal factor distribution that would have been observed if men had been set to have the same factor distribution as women. The study period was between 2018 and 2021. RESULTS: The most prevalent CVRF was hypercholesterolaemia, which was similar in both sexes, while diabetes was more prevalent in men. The incidence of MACE was higher in men than in women. The main causal mediating factors that contributed to the sex differences were diabetes and SES, the latter with an offsetting effect. CONCLUSIONS: This result suggests that to reduce the MACE gap between sexes, diabetes prevention programmes targeting men and more gender-equal salary policies should be implemented.

Influence of cardiovascular risk factors and treatment exposure on cardiovascular event incidence: Assessment using machine learning algorithms.

Assessment of the influence of cardiovascular risk factors (CVRF) on cardiovascular event (CVE) using machine learning algorithms offers some advantages over preexisting scoring systems, and better enables personalized medicine approaches to cardiovascular prevention. Using data from four different sources, we evaluated the outcomes of three machine learning algorithms for CVE prediction using different combinations of predictive variables and analysed the influence of different CVRF-related variables on CVE prediction when included in these algorithms. A cohort study based on a male cohort of workers applying populational data was conducted. The population of the study consisted of 3746 males. For descriptive analyses, mean and standard deviation were used for quantitative variables, and percentages for categorical ones. Machine learning algorithms used were XGBoost, Random Forest and Naïve Bayes (NB). They were applied to two groups of variables: i) age, physical status, Hypercholesterolemia (HC), Hypertension, and Diabetes Mellitus (DM) and ii) these variables plus treatment exposure, based on the adherence to the treatment for DM, hypertension and HC. All methods point out to the age as the most influential variable in the incidence of a CVE. When considering treatment exposure, it was more influential than any other CVRF, which changed its influence depending on the model and algorithm applied. According to the performance of the algorithms, the most accurate was Random Forest when treatment exposure was considered (F1 score 0.84), followed by XGBoost. Adherence to treatment showed to be an important variable in the risk of having a CVE. These algorithms could be applied to create models for every population, and they can be used in primary care to manage interventions personalized for every subject.

Enfermedad musculoesquelética en población trabajadora: perfil de los afectados y manejo farmacológico / Musculoesqueletic diseases in working population: subjects' profile and pharmacological management

El presente estudio describe las enfermedades musculoesqueléticas (EME) de una cohorte de trabajadores de tipo manual, así como el uso de los fármacos indicados para el control del dolor. Estudio observacional retrospectivo llevado a cabo en el ámbito del Aragon Workers' Health Study (AWHS). Se ha analizado la prevalencia de las diferentes EME, la tasa de utilización de fármacos empleados en el tratamiento del dolor y el número de dosis diarias definidas (DDD) consumidas. El 15,4% de los trabajadores estudiados fueron diagnosticados de, al menos, una EME. De ellos, el 54,1% tenía sobrepeso y el 74,0% eran mayores de 55 años. La tasa de utilización de los antiinflamatorios no esteroideos (AINE) fue del 69,5%, y de los analgésicos no opiáceos, del 29,9%. Los datos presentados ponen de manifiesto la utilización elevada y, en algunos casos, continuada que existe de tratamientos analgésicos y antiinflamatorios. (AU)

The present study describes the musculoskeletal diseases (MSD) of a cohort of manual workers, as well as the use of drugs indicated for pain control. Retrospective observational study carried out within the framework of the Aragon Workers' Health Study (AWHS). The prevalence of the different EMEs, the rate of use of drugs used in the treatment of pain and the number of defined daily doses (DDD) consumed have been analysed. 15.4% of the workers studied were diagnosed with at least one EME. Of them, 54.1% were overweight and 74.0% were older than 55 years. The utilization rate of non-steroidal anti-inflammatory drugs (NSAIDs) was 69.5%, and of non-opioid analgesics, 29.9%. The data presented show the high use and, in some cases, the continuous use of analgesic and anti-inflammatory treatments. (AU)

Patterns of statin adherence in primary cardiovascular disease prevention during the pandemic.

Background: Study of medication adherence patterns can help identify patients who would benefit from effective interventions to improve adherence. Objectives: To identify and compare groups of statin users based on their adherence patterns before and during the COVID-19 pandemic, to characterize the profile of users in each group, and to analyze predictors of distinct adherence patterns. Methods: Participants of the CARhES (CArdiovascular Risk factors for HEalth Services research) cohort, comprising individuals aged >16 years, residing in Aragón (Spain), with hypertension, diabetes mellitus and/or dyslipidemia, took part in this observational longitudinal study. Individuals who began statin therapy during January-June 2019 were selected and followed up until June 2021. Those with a cardiovascular event before or during follow-up were excluded. Data were obtained from healthcare system data sources. Statin treatment adherence during the implementation phase was estimated bimonthly using the Continuous Medication Availability (CMA9) function in the AdhereR package. Group-based trajectory models were developed to group statin users according to their adherence pattern during July 2019-June 2021. Group characteristics were compared and predictors of each adherence pattern were analyzed using multinomial logistic regression. Results: Of 15,332 new statin users, 30.8% had a mean CMA9 ≥80% for the entire study period. Four distinct adherence patterns were identified: high adherence (37.2% of the study population); poor adherence (35.6%); occasional use (14.9%); and gradual decline (12.3%). The latter two groups included users who showed a change in adherence (increase or decrease) during the pandemic emergence. Users with suboptimal adherence were likely to be younger, not pensioners, not institutionalized, with low morbidity burden and a low number of comorbidities. Female sex and switching between statins of different intensity increased the likelihood of belonging to the occasional use group, in which improved adherence coincided with the pandemic. Conclusion: We identified four distinct adherence patterns in a population of new statin users; two of them modified their adherence during the pandemic. Characterization of these groups could enable more effective distribution of resources in future similar crisis and the routine implementation of patient-centered interventions to improve medication adherence.

Understanding the COVID-19 Pandemic in Nursing Homes (Aragón, Spain): Sociodemographic and Clinical Factors Associated With Hospitalization and Mortality.

Old people residing in nursing homes have been a vulnerable group to the coronavirus disease 2019 (COVID-19) pandemic, with high rates of infection and death. Our objective was to describe the profile of institutionalized patients with a confirmed COVID-19 infection and the socioeconomic and morbidity factors associated with hospitalization and death. We conducted a retrospective cohort study including data from subjects aged 65 years or older residing in a nursing home with a confirmed COVID-19 infection from March 2020 to March 2021 (4,632 individuals) in Aragón (Spain). We analyzed their sociodemographic and clinical profiles and factors related to hospitalization and mortality at 7, 30, and 90 days of COVID-19 diagnosis using logistic regression analyses. We found that the risk of hospitalization and mortality varied according to sociodemographic and morbidity profile. There were inequalities in hospitalization by socioeconomic status and gender. Patients with low contributory pensions and women had a lower risk of hospitalization. Diabetes mellitus, heart failure, and chronic kidney disease were associated with a higher risk of hospitalization. On the contrary, people with dementia showed the highest risk of mortality with no hospitalization. Patient-specific factors must be considered to develop equitable and effective measures in nursing homes to be prepared for future health threats.

Effectiveness of Statins for Primary Prevention of Cardiovascular Disease in Low- and Medium-Risk Males: A Causal Inference Approach with Observational Data.

In this study, we analyzed the effectiveness of statin therapy for the primary prevention of cardiovascular disease (CVD) in low- and medium-risk patients. Using observational data, we estimated effectiveness by emulating a hypothetical randomized clinical trial comparing statin initiators with statin non-initiators. Two approaches were used to adjust for potential confounding factors: matching and inverse probability weighting in marginal structural models. The estimates of effectiveness were obtained by intention-to-treat and per-protocol analysis. The intention-to-treat analysis revealed an absolute risk reduction of 7.2 (95% confidence interval (CI95%), -6.6-21.0) events per 1000 subjects treated for 5 years in the matched design, and 2.2 (CI95%, -3.9-8.2) in the marginal structural model. The per-protocol analysis revealed an absolute risk reduction of 16.7 (CI95%, -3.0-36) events per 1000 subjects treated for 5 years in the matched design and 5.8 (CI95%, 0.3-11.4) in the marginal structural model. The indication for statin treatment for primary prevention in individuals with low and medium cardiovascular risk appears to be inefficient, but improves with better adherence and in subjectvs with higher risk.

Evolution of Cardiovascular Risk Factors in a Worker Cohort: A Cluster Analysis.

The identification of the cardiovascular risk factor (CVRF) profile of individual patients is key to the prevention of cardiovascular disease (CVD), and the development of personalized preventive approaches. Using data from annual medical examinations in a cohort of workers, the aim of the study was to characterize the evolution of CVRFs and the CVD risk score (SCORE) over three time points between 2009 and 2017. For descriptive analyses, mean, standard deviation, and quartile values were used for quantitative variables, and percentages for categorical ones. Cluster analysis was performed using the Kml3D package in R software. This algorithm, which creates distinct groups based on similarities in the evolution of variables of interest measured at different time points, divided the cohort into 2 clusters. Cluster 1 comprised younger workers with lower mean body mass index, waist circumference, blood glucose values, and SCORE, and higher mean HDL cholesterol values. Cluster 2 had the opposite characteristics. In conclusion, it was found that, over time, subjects in cluster 1 showed a higher improvement in CVRF control and a lower increase in their SCORE, compared with cluster 2. The identification of subjects included in these profiles could facilitate the development of better personalized medical approaches to CVD preventive measures.