Online journal of public health informatics最新文献

Background: There is a growing gap between the supply of surgeons and the demand for orthopedic services in the United States.

Objective: We analyzed publicly available online data to assess the correlation between the supply of orthopedic surgeons and patient demand across the United States. The geographic trends of this gap were assessed by using the relative demand index (RDI) to guide precision public health interventions such as resource allocation, residency program expansion, and workforce planning to specific regions.

Methods: The data used were from the US Census Bureau, Association of American Medical Colleges (AAMC) through their 2024 Electronic Residency Application Service (ERAS) directory, AAMC State Physician Workforce Data Report, and Google Trends. We calculated the normalized relative search volume (RSV) and the RDI and compared them to the densities of orthopedic surgeons across the United States. We examined the disparities with the Spearman rank correlation coefficient.

Results: The supply of orthopedic surgeons varied greatly across the United States, with a significantly higher demand for them in southern states (P=.02). The orthopedic surgeon concentration, normalized to the highest density, was the highest in Alaska (n=100), the District of Columbia (n=96), and Wyoming (n=72); and the lowest in Texas (n=0), Arkansas (n=6), and Oklahoma (n=64). The highest RDI values were observed in Utah (n=97), Florida (n=88), and Texas (n=83), while the lowest were observed in Alaska (n=0), the District of Columbia (n=5), and New Hampshire (n=7). The 7 states of Alaska, Maine, South Dakota, Wyoming, Montana, Delaware, and Idaho lacked orthopedic surgery residencies. In 2023, New York (n=19), Michigan (n=17), Ohio (n=17), Pennsylvania (n=16), and California (n=16) had the most residency programs. Demand and supply, represented by the RDI and orthopedic surgeon concentration, respectively, were strongly correlated negatively (ρ=-0.791, P<.001). States that were in the top quartile of residency programs (≥4 residency programs) exhibited a high demand for orthopedic surgeons (ρ=.6035, P=.02).

Conclusions: This study showed that regional disparities in access to orthopedic care can be addressed by increasing orthopedic residencies. The study highlights the novel application of the RDI to mapping the regional need for orthopedics, and this map allows for better targeted resource allocation to expand orthopedic surgery training.

Background: Perinatal depression remains a substantial public health challenge, often overlooked or incorrectly diagnosed in numerous low-income nations.

Objective: The goal of this study was to establish statistical baselines for the prevalence of perinatal depression in Kampala and understand its relationship with key demographic variables.

Methods: We employed an Android-based implementation of the Edinburgh Postnatal Depression Scale (EPDS) to survey 12,913 women recruited from 7 government health facilities located in Kampala, Uganda. We used the standard EPDS cutoff, which classifies women with total scores above 13 as possibly depressed and those below 13 as not depressed. The χ2 test of independence was used to determine the most influential categorical variables. We further analyzed the most influential categorical variable using odds ratios. For continuous variables such as age and the weeks of gestation, we performed a simple correlation analysis.

Results: We found that 21.5% (2783/12,913, 95% CI 20.8%-22.3%) were possibly depressed. Respondents' relationship category was found to be the most influential variable (χ21=806.9, P<.001; Cramer's V=0.25), indicating a small effect size. Among quantitative variables, we found a weak negative correlation between respondents' age and the total EPDS score (r=-0.11, P<.001). Similarly, a weak negative correlation was also observed between the total EPDS score and the number of previous children of the respondent (r=-0.07, P<.001). Moreover, a weak positive correlation was noted between weeks of gestation and the total EPDS score (r=0.02, P=.05).

Conclusions: This study shows that demographic factors such as spousal employment category, age, and relationship status have an influence on the respondents' EPDS scores. These variables may serve as proxies for latent factors such as financial stability and emotional support.

Unlabelled: Microbial diversity is vast, with bacteria playing a crucial role in human health. However, occurrence records (location, date, observer, and host interaction of human-associated bacteria) remain scarce. This lack of information hinders our understanding of human-microbe relationships and disease prevention. In this study, we show that existing solutions such as France's Système d'Information sur le Patrimoine Naturel framework, can be used to efficiently collect and manage occurrence data on human-associated bacteria. This user-friendly system allows medical personnel to easily share and access data on bacterial pathogens. By adopting similar national infrastructures and treating human-associated bacteria as biodiversity data, we can significantly improve public health management and research, and our understanding of the One Health concept, which emphasizes the interconnectedness of human, animal, and environmental health.

Background: Smoking is a modifiable risk factor for SARS-CoV-2 infection. Evidence of smoking behavior during the pandemic is ambiguous. Most investigations report an increase in smoking. In this context, Google Trends data monitor real-time public information-seeking behavior and are therefore useful to characterize smoking-related interest over the trajectory of the pandemic.

Objective: This study aimed to use Google Trends data to evaluate the effect of the pandemic on public interest in smoking-related topics with a focus on lockdowns, vaccination campaigns, and incidence.

Methods: The weekly relative search volume was retrieved from Google Trends for England, Germany, Italy, and Spain from December 31, 2017, to April 18, 2021. Data were collected for keywords concerning consumption, cessation, and treatment. The relative search volume before and during the pandemic was compared, and general trends were evaluated using the Wilcoxon rank-sum test. Short-term changes and hereby temporal clusters linked to lockdowns or vaccination campaigns were addressed by the flexible spatial scan statistics proposed by Takahashi and colleagues. Subsequently, the numbers of clusters after the onset of the pandemic were compared by chi-square test.

Results: Country-wise minor differences were observed while 3 overarching trends prevailed. First, regarding cessation, the statistical comparison revealed a significant decline in interest for 58% (7/12) of related keywords, and fewer clusters were present during the pandemic. Second, concerning consumption, significantly reduced relative search volume was observed for 58% (7/12) of keywords, while treatment-related keywords exhibited heterogeneous trends. Third, substantial clusters of increased interest were sparsely linked to lockdowns, vaccination campaigns, or incidence.

Conclusions: This study reports a substantial decline in overall relative search volume and clusters for cessation interest. These results underline the importance of intensifying cessation aid during times of crisis. Lockdowns, vaccination, and incidence had less impact on information-seeking behavior. Other public measures that positively affect smoking behavior remain to be determined.

Background: On average, people in the United States visit a doctor 4 times a year, and many of them have chronic illnesses. Because of the increased use of technology, people frequently rely on the internet to access health information and statistics. People use health care information to make better-educated decisions for themselves and others. Health care dashboards should provide pertinent and easily understood data, such as information on timely cancer screenings, so the public can make better-informed decisions. In order to enhance health outcomes, effective dashboards should provide precise data in an accessible and easily digestible manner.

Objective: This study identifies the top 15 attributes of a health care dashboard. The objective of this research is to enhance health care dashboards to benefit the public by making better health care information available for more informed decisions by the public and to improve population-level health care outcomes.

Methods: The authors conducted a survey of health care dashboards with 218 individuals identifying the best practices to consider when creating a public health care dashboard. The data collection was conducted from June 2023 to August 2023. The analyses performed were descriptive statistics, frequencies, and a comparison to a prior study.

Results: From May 2023 to June 2023, we collected 3259 responses in multiple different states around the United States from 218 people aged 18 years or older. The features ranking in descending order of importance are as follows: (1) easy navigation, (2) historical data, (3) simplicity of design, (4) high usability, (5) use of clear descriptions, (6) consistency of data, (7) use of diverse chart types, (8) compliance with the Americans with Disabilities Act, (9) incorporated user feedback, (10) mobile compatibility, (11) comparison data with other entities, (12) storytelling, (13) predictive analytics with artificial intelligence, (14) adjustable thresholds, and (15) charts with tabulated data.

Conclusions: Future studies can extend the research to other types of dashboards such as bioinformatics, financial, and managerial dashboards as well as confirm these top 15 best practices for medical dashboards with further evidentiary support. The medical informatics community may benefit from standardization to improve efficiency and effectiveness as dashboards can communicate vital information to patients worldwide on critically prominent issues. Furthermore, health care professionals should use these best practices to help increase population health care outcomes by informing health care consumers to make better decisions with better data.

Unlabelled: This paper introduces population digital health (PDH)-the use of digital health information sourced from health internet of things (IoT) and wearable devices for population health modeling-as an emerging research domain that offers an integrated approach for continuous monitoring and profiling of diseases and health conditions at multiple spatial resolutions. PDH combines health data sourced from health IoT devices, machine learning, and ubiquitous computing or networking infrastructure to increase the scale, coverage, equity, and cost-effectiveness of population health. This contrasts with the traditional population health approach, which relies on data from structured clinical records (eg, electronic health records) or health surveys. We present the overall PDH approach and highlight its key research challenges, provide solutions to key research challenges, and demonstrate the potential of PDH through three case studies that address (1) data inadequacy, (2) inaccuracy of the health IoT devices' sensor measurements, and (3) the spatiotemporal sparsity in the available digital health information. Finally, we discuss the conditions, prerequisites, and barriers for adopting PDH drawing on from real-world examples from different geographic regions.

Background: Digital health is a new health field initiative. Health professionals require security in digital places because cybercriminals target health care professionals. Therefore, millions of medical records have been breached for money. Regarding digital security, there is a gap in studies in limited-resource countries. Therefore, surveying health professionals' attitudes toward digital health data security has a significant purpose for interventions.

Objective: This study aimed to assess the attitudes of health professionals toward digital health data security and their associated factors in a resource-limited country.

Methods: A cross-sectional study was conducted to measure health professionals' attitudes toward digital health data security. The sample size was calculated using a single population. A pretest was conducted to measure consistency. Binary logistic regression was used to identify associated factors. For multivariable logistic analysis, a P value ≤.20 was selected using Stata software (version 16; StataCorp LP).

Results: Of the total sample, 95% (402/423) of health professionals participated in the study. Of all participants, 63.2% (254/402) were male, and the mean age of the respondents was 34.5 (SD 5.87) years. The proportion of health professionals who had a favorable attitude toward digital health data security at specialized teaching hospitals was 60.9% (95% CI 56.0%-65.6%). Educational status (adjusted odds ratio [AOR] 3.292, 95% CI 1.16-9.34), basic computer skills (AOR 1.807, 95% CI 1.11-2.938), knowledge (AOR 3.238, 95% CI 2.0-5.218), and perceived usefulness (AOR 1.965, 95% CI 1.063-3.632) were factors associated with attitudes toward digital health data security.

Conclusions: This study aimed to assess health professionals' attitudes toward digital health data security. Interventions on educational status, basic computer skills, knowledge, and perceived usefulness are important for improving health professionals' attitudes. Improving the attitudes of health professionals related to digital data security is necessary for digitalization in the health care arena.