Online Journal of Public Health Informatics

One Health is a collaborative approach that can be used to evaluate and enhance the sectors of human health, animal health and environmental health and emphasize their sectoral interconnectedness. Empirical evaluation of the one health performance of a country in the form of an index provides direction for actionable interventions such as targeted funding; prioritized resource allocation; rigorous data management and evidence based-policy decisions, amid other efforts such as public engagement and awareness on disease management; environmental degradation and preparedness towards disease outbreaks and thereby strengthening global health security. Thus, developing a One Health Index (OHI) Calculator for India is a significant step towards evidence based one health governance in the context of low-and middle-income countries.

The outbreak of COVID-19 in 2019 led governments worldwide to introduce various public health measures, which included restrictions on travel and public gatherings, effectively reducing the spread of the virus and associated mortality rates. In Japan, non-legally binding restrictions on outings effectively curbed infections as in other countries. However, the restrictions impacted lifestyles, including with regard to reduced physical activity, increased frailty, and overeating issues, beyond the effect of preventing the spread of infection. Outing behavior during the pandemic was influenced by various factors such as personality, age, and cultural norms and differed by activity type.

In response to the increasing incidence and prevalence of hypertension, Ethiopia has been piloting hypertension control at the primary healthcare level in selected sentinel sites. However, no evaluation has been conducted and its success and failures has not been ascertained.

During the COVID-19 pandemic in 2020, hospitals encountered numerous challenges that compounded their difficulties. Some of these challenges directly impacted patient care, such as the need to expand capacities, adjust services, and use new knowledge to save lives in an ever-evolving situation. In addition, hospitals faced regulatory challenges.

Although many studies have used smartphone apps to examine alcohol consumption, none have clearly delineated long-term (>1 year) consumption among the general population.

Clinical risk prediction models integrated into digitized health care informatics systems hold promise for personalized primary prevention and care, a core goal of precision health. Fairness metrics are important tools for evaluating potential disparities across sensitive features, such as sex and race or ethnicity, in the field of prediction modeling. However, fairness metric usage in clinical risk prediction models remains infrequent, sporadic, and rarely empirically evaluated.

Currently, the methods used to collect dietary intake data in Ireland are inflexible to the needs of certain populations, who are poorly represented in nutrition and health data as a result. As the Irish population is becoming increasingly diverse, there is an urgent need to understand the habitual food intake and diet quality of multiple population subgroups, including different nationalities and ethnic minorities, in Ireland. Foodbook24 is an existing web-based 24-hour dietary recall tool, which has previously been validated for use within the general Irish adult population. Because of its design, Foodbook24 can facilitate the improved inclusion of dietary intake assessment in Ireland.

There is a growing gap between surgeon availability and demand for orthopedic services in the United States. This study analyzes the geographic trends of this gap with a Relative Demand Index to guide precision public health interventions such as resource allocation, residency program expansion, and workforce planning to specific regions.

Perinatal depression remains a significant public health challenge often overlooked or incorrectly diagnosed in numerous developing nations.

Applying nowcasting methods to partially accrued reportable disease data can help policymakers interpret recent epidemic trends despite data lags and quickly identify and remediate health inequities. During the 2022 mpox outbreak in New York City (NYC), we applied Nowcasting by Bayesian Smoothing (NobBS) to estimate recent cases, citywide and stratified by race or ethnicity (Black/African American, Hispanic/Latino, and White). However, in real time, it was unclear if estimates were accurate.

Microbial diversity is vast, with bacteria playing a crucial role in human health. However, occurrence records (location, date, observer, relation to host) of bacteria associated with humans remain scarce. This lack of information hinders our understanding of human-microbe relationships and disease prevention. Here, we show that existing solutions, such as France's Système d'Information sur le Patrimoine Naturel (SINP) 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 implementing similar national infrastructures and considering 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.

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.