Effect of Health Literacy on Decision Delay in Patients With Acute Myocardial Infarction

Identifying Health Literacy Strengths and Needs Among Jordanian Acute Myocardial Infarction Patients

Acute myocardial infarction is a significant health issue, particularly in Jordan where ischemic heart disease is the leading cause of death. Effective management of acute myocardial infarction is essential to mitigate its consequences. Although health literacy is crucial for the successful management of acute myocardial infarction, research about the strengths and needs of health literacy among acute myocardial infarction patients is still limited. This study was conducted to identify the health literacy strengths and needs of Jordanian acute myocardial infarction patients using cluster analysis. A cross-sectional design was used to conduct this study in a sample of acute myocardial infarction patients in Jordan (N = 114). A demographics questionnaire and the Health Literacy Questionnaire were used to collect the data. Data analysis was performed using hierarchical cluster analysis using Ward's method. Seven distinct clusters of acute myocardial infarction patients were identified, each characterized by unique health literacy profiles and sociodemographic characteristics. Cluster 7 had the highest health literacy scores across all nine Health Literacy Questionnaire scales. Sociodemographic factors such as age, education level, and gender influenced health literacy levels, with female, younger, more educated patients exhibiting higher health literacy. Through identifying the specific strengths and needs, this research provides a foundation for developing targeted health literacy interventions for acute myocardial infarction patients. Improving health literacy among acute myocardial infarction patients can enhance their ability to manage their health and potentially reduce the complications associated with acute myocardial infarction.

Number of comorbidities and the risk of delay in seeking treatment for coronary heart disease: a longitudinal study in Bogor City, Indonesia

BACKGROUND

The aim of this study was to investigate the relationship between the number of patient comorbidities and the delays in seeking treatment for coronary heart disease (CHD).

METHODS

This longitudinal study utilized secondary data from the Non-Communicable Disease Risk Factor (NCDRF) cohort study conducted in Bogor City. Individuals who participated in the NCDRF cohort study and were diagnosed with CHD within the 6-year study period met the inclusion criteria. Respondents who were not continuously monitored up to the 6th year were excluded. The final sample included data from respondents with CHD who participated in the NCDRF cohort study and were monitored for the full 6-year duration. The final logistic regression analysis was conducted on data collected from 812 participants.

RESULTS

Among the participants with CHD, 702 out of 812 exhibited a delay in seeking treatment. The risk of a delay in seeking treatment was significantly higher among individuals without comorbidities, with an odds ratio (OR) of 3.5 (95% confidence interval [CI], 1.735-7.036; p<0.001). Among those with a single comorbidity, the risk of delay in seeking treatment was still notable (OR, 2.6; 95% CI, 1.259-5.418; p=0.010) when compared to those with 2 or more comorbidities. These odds were adjusted for age, sex, education level, and health insurance status.

CONCLUSION

The proportion of patients with CHD who delayed seeking treatment was high, particularly among individuals with no comorbidities. Low levels of comorbidity also appeared to correlate with a greater tendency to delay in seeking treatment.

Predictive models for delay in medical decision-making among older patients with acute ischemic stroke: a comparative study using logistic regression analysis and lightGBM algorithm

OBJECTIVE

To explore the factors affecting delayed medical decision-making in older patients with acute ischemic stroke (AIS) using logistic regression analysis and the Light Gradient Boosting Machine (LightGBM) algorithm, and compare the two predictive models.

METHODS

A cross-sectional study was conducted among 309 older patients aged ≥ 60 who underwent AIS. Demographic characteristics, stroke onset characteristics, previous stroke knowledge level, health literacy, and social network were recorded. These data were separately inputted into logistic regression analysis and the LightGBM algorithm to build the predictive models for delay in medical decision-making among older patients with AIS. Five parameters of Accuracy, Recall, F1 Score, AUC and Precision were compared between the two models.

RESULTS

The medical decision-making delay rate in older patients with AIS was 74.76%. The factors affecting medical decision-making delay, identified through logistic regression and LightGBM algorithm, were as follows: stroke severity, stroke recognition, previous stroke knowledge, health literacy, social network (common factors), mode of onset (logistic regression model only), and reaction from others (LightGBM algorithm only). The LightGBM model demonstrated the more superior performance, achieving the higher AUC of 0.909.

CONCLUSIONS

This study used advanced LightGBM algorithm to enable early identification of delay in medical decision-making groups in the older patients with AIS. The identified influencing factors can provide critical insights for the development of early prevention and intervention strategies to reduce delay in medical decisions-making among older patients with AIS and promote patients' health. The LightGBM algorithm is the optimal model for predicting the delay in medical decision-making among older patients with AIS.

Disparities in Emergency Medical Services Care Delivery in the United States: A Scoping Review

BACKGROUND

Emergency medical services (EMS) often serve as the first medical contact for ill or injured patients, representing a critical access point to the health care delivery continuum. While a growing body of literature suggests inequities in care within hospitals and emergency departments, limited research has comprehensively explored disparities related to patient demographic characteristics in prehospital care.

OBJECTIVE

We aimed to summarize the existing literature on disparities in prehospital care delivery for patients identifying as members of an underrepresented race, ethnicity, sex, gender, or sexual orientation group.

METHODS

We conducted a scoping review of peer-reviewed and non-peer-reviewed (gray) literature. We searched PubMed, CINAHL, Web of Science, Proquest Dissertations, Scopus, Google, and professional websites for studies set in the U.S. between 1960 and 2021. Each abstract and full-text article was screened by two reviewers. Studies written in English that addressed the underrepresented groups of interest and investigated EMS-related encounters were included. Studies were excluded if a disparity was noted incidentally but was not a stated objective or discussed. Data extraction was conducted using a standardized electronic form. Results were summarized qualitatively using an inductive approach.

RESULTS

One hundred forty-five full-text articles from the peer-reviewed literature and two articles from the gray literature met inclusion criteria: 25 studies investigated sex/gender, 61 studies investigated race/ethnicity, and 58 studies investigated both. One study investigated sexual orientation. The most common health conditions evaluated were out-of-hospital cardiac arrest (n = 50), acute coronary syndrome (n = 36), and stroke (n = 31). The phases of EMS care investigated included access (n = 55), pre-arrival care (n = 46), diagnosis/treatment (n = 42), and response/transport (n = 40), with several studies covering multiple phases. Disparities were identified related to all phases of EMS care for underrepresented groups, including symptom recognition, pain management, and stroke identification. The gray literature identified public perceptions of EMS clinicians' cultural competency and the ability to appropriately care for transgender patients in the prehospital setting.

CONCLUSIONS

Existing research highlights health disparities in EMS care delivery throughout multiple health outcomes and phases of EMS care. Future research is needed to identify structured mechanisms to eliminate disparities, address clinician bias, and provide high-quality equitable care for all patient populations.