Lower Fatality Research Articles

How user language affects conflict fatality estimates in ChatGPT

OpenAI’s ChatGPT language model has gained popularity as a powerful tool for problem-solving and information retrieval. However, concerns arise about the reproduction of biases present in the language-specific training data. In this study, we address this issue in the context of the Israeli–Palestinian and Turkish–Kurdish conflicts. Using GPT-3.5, we employed an automated query procedure to inquire about casualties in specific airstrikes, in both Hebrew and Arabic for the former conflict and Turkish and Kurdish for the latter. Our analysis reveals that GPT-3.5 provides 34 ± 11% lower fatality estimates when queried in the language of the attacker than in the language of the targeted group. Evasive answers denying the existence of such attacks further increase the discrepancy. A simplified analysis on the current GPT-4 model shows the same trends. To explain the origin of the bias, we conducted a systematic media content analysis of Arabic news sources. The media analysis suggests that the large-language model fails to link specific attacks to the corresponding fatality numbers reported in the Arabic news. Due to its reliance on co-occurring words, the large-language model may provide death tolls from different attacks with greater news impact or cumulative death counts that are prevalent in the training data. Given that large-language models may shape information dissemination in the future, the language bias identified in our study has the potential to amplify existing biases along linguistic dyads and contribute to information bubbles.

Epidemiology of human metapneumovirus among children with severe or very severe pneumonia in high pneumonia burden settings: the PERCH study experience

ObjectivesAfter respiratory syncytial virus (RSV), human metapneumovirus (hMPV) was the second-ranked pathogen attributed to severe pneumonia in the PERCH study. We sought to characterize hMPV-positive cases in high burden settings, which have limited data, by comparing to RSV-positive and other cases. MethodsChildren aged 1-59 months hospitalized with suspected severe pneumonia and age/season-matched community controls in seven African and Asian countries had nasopharyngeal/oropharyngeal swabs tested by multiplex PCR for 32 respiratory pathogens, among other clinical and lab assessments at admission. Odds ratios adjusted for age and site (aOR) were calculated using logistic regression. Etiologic probability was estimated using Bayesian nested partial latent class analysis. Latent class analysis identified syndromic constellations of clinical characteristics. ResultsHMPV was detected more frequently among cases (267/3887, 6.9%) than controls (115/4976, 2.3%), among cases with pneumonia chest X-ray findings (8.5%) than without (5.5%), and among controls with respiratory tract illness (3.8%) than without (1.8%; all p≤0.001). HMPV-positive cases were negatively associated with the detection of other viruses (aOR=0.18), especially RSV (aOR=0.11; all p<0.0001), and positively associated with the detection of bacteria (aORs 1.77, p=0.03). No single clinical syndrome distinguished hMPV-positive from other cases. Among hMPV-positive cases, 65.2% were aged <1 year and 27.5% had pneumonia danger signs; positive predictive value was 74.5%; mortality was 3.9%, similar to RSV-positive (2.4%) and lower than other cases (9.6%). ConclusionsHMPV-associated severe pediatric pneumonia in high burden settings was predominantly in young infants and clinically indistinguishable from RSV. HMPV-positives had low case fatality, similar to that in RSV-positives.

Whooping cough – a recurrent epidemic

Abstract Background Whooping cough (WC), or Pertussis, is a vaccine-preventable and highly contagious infectious disease. Although it has a relatively low fatality rate, approximately 13% of reported cases develop secondary bacterial pneumonia. This was because of several recurrent epidemics across Europe in the last decades, with the latest beginning in mid-2023 and still ongoing. The Central Region of Portugal is currently being affected, and this study aims to describe the current epidemic at the regional level. Methods Data from epidemiological inquiries of confirmed and probable cases of WC pertaining to the Central Region of Portugal from January 2020 till April 2024 was gathered from the mandatory report national epidemiological surveillance system (SINAVE). Both a temporal comparison of number of cases and a statistical space-time scan with permutation were conducted (software SaTScan v10.) Results A comparison by month of reported cases shows an increase in cases from September 2023 until December 2023 followed by an exponential increase from January 2024 until April 2024 denoting an ongoing epidemic. In the statistical space-time analysis of 107 reported cases between September 2023 and April 2024, 8 nonsignificant clusters were identified with different locations involved in non-overlapping periods. Conclusions The finding of several unrelated clusters, even though nonsignificant, during a short time period indicates that Pertussis transmission is ongoing at the local level probably through asymptomatic or barely symptomatic cases that act as reservoirs. Clinicians must hold a high clinical suspicion for a timely diagnosis, treatment and report and health authorities must reinforce control measures to limit the spread of this disease. Key messages • Pertussis remains a disease of significant concern recurrent outbreaks across Europe show that public health local authorities have an important role in timely management of cases and outbreaks. • Local level transmission and intervention may play a pivotal role in the epidemiologic dynamic of Pertussis.

Using the Health Belief Model to Understand Why Making Oral HIV Self-Testing Available to Truck Drivers in Kenya Had Little Impact on Six-Month Testing.

Research has found that offering HIV self-testing (HIVST) to truckers in Kenya increased testing rates at baseline but not over 6-month follow-up. We explored possible explanations based on the Health Belief Model by assessing HIV risk perception, self-efficacy, and fatalism as possible effect modifiers of the impact of offering HIVST (intervention n = 150) versus standard of care (SOC n = 155) on 6-month testing on the multiplicative and additive scales using log binomial and linear binomial regression and stratifying on significant modifiers. We found significant interaction between the intervention and fatalism on both the multiplicative (p = 0.020) and additive (p = 0.020) scales. In the stratified models, the HIVST intervention was associated with higher HIV testing among participants with low fatalism but lower testing among those with high fatalism (risk ratio [RR] = 1.30, p = 0.065 versus RR = 0.74, p = 0.072; risk difference [RD] per 100 = 14.00, p = 0.080 versus RD=-14.69, p = 0.086). Truckers in Kenya are described as being highly fatalistic, feeling lack of control over their lives and health. We found that fatalistic views negated the potential benefit of offering HIVST to truckers. For HIVST to have an impact among truckers, psychosocial interventions may be needed that address fatalistic views.

An overview of SARS-CoV-2 viral proteins with relevance to improved diagnostic and therapeutic platforms

In the past 25 years, the world has witnessed outbreaks of illnesses in humans from three different coronaviruses. Both the SARS-CoV outbreak of 2003 and the MERS-CoV outbreak of 2013 resulted in overall low fatalities in part due to inefficient human-to-human spread of each virus. In contrast, SARS-CoV-2, which emerged in 2019, was highly efficient at human-to-human spread and caused a global pandemic resulting in millions of casualties. Zoonotic transmission of viruses, including the three coronaviruses, poses an ongoing threat that cannot be ignored. In this review, we have focused on the diagnostics and therapeutics fronts using SARS-CoV-2 as a model. Specifically, we have selected proteins associated with the virus particles as targets and discussed various platform technologies. These insights hold the potential to inform the development of more effective therapeutics and vaccines not only for SARS-CoV-2 but also for future viral pandemics, thus contributing to global health on a broader scale.

Pattern dynamics in a predator–prey model with Smith growth function and prey refuge in predator poisoned environment

In this article, we investigate temporal as well as spatiotemporal dynamics of a predator–prey system where prey grows according to Smith growth function and prey refuges in predator poisoned environment. Self as well as cross diffusion are taken into consideration to describe spatial moment of the species which make the system more realistic. This study aims to investigate the role of prey refuge, toxin-carrying carcass and the impact of cross-diffusion on the spatial distribution of species densities and associated patterns. The existence of equilibria and their stability conditions for non-spatial system are derived. We discuss the occurrence of Hopf bifurcation and detect the stability of limit cycle by computing first Lyapunov number. Temporal dynamics of the system is studied by varying the significant parameters. Study reveals that both prey refuge and fatality rate of predator due to toxin-carrying carcass, initially makes the system stable for low refuge and fatality rate but predator population goes to extinction as those effects increase. Turing instability conditions are listed and different instability regions so formed are discussed. In Turing region, pattern selection with the help of amplitude equation via weakly nonlinear analysis are performed which also numerically supported. Intensity of refuge and fatality rate due to toxin carrying carcass result paradox with real ecological world due to the presence of cross diffusion. Various kind of patterns such as mixture of stripe and spot, spot, irregular chaotic patterns emerge due to Hopf and Turing instability. Particularly, spatiotemporal chaos is a controversial topic because of its significant consequences for population dynamics. The results of this work are expected to contribute to a better understanding the level of prey refuge, use of toxin-carrying carcass considering movement in population and effect of cross-diffusion.

COVID-19-infected cases in Thailand during the Omicron wave using the capture-recapture method

Thailand is among many countries, severely affected by the COVID-19 pandemic. In December 2021, the Thai Ministry of Public Health announced the discovery of the first imported case of the Omicron variant, and the local transmission of the Omicron variant was confirmed. The Omicron variant spread rapidly but was less dangerous than the Delta. Some patients experienced mild or no symptoms and self-treated without requiring medical assistance. Lower fatality rates during the Omicron outbreak were reported compared with previous variants. Information concerning hidden COVID-19-infected patients is beneficial and will lead to a better understanding of the disease outbreak mechanism. This study proposed the lower bound estimator under the capture-recapture (CR) method to estimate the true value of COVID-19 infections in Thailand during the Omicron wave. The Chao lower bound estimator for the Poisson mixture model was created to take into account the fact that populations can be different based on how cases and deaths have been spread out over time. The approximate variance of the estimator was constructed for a confidence interval of 95%, focusing on the ratio of total estimated infected cases to observed cases in March 2022. The average ratio was 2.99 (95% CI: 2.96 to 3.01), suggesting that for every 100 observed patients, at least 199 infected patients were underreported.

Risk Stratification Of Signs Of Possible Serious Bacterial Infection For Prediction Of Mortality Among Young Infants

Objective: Possible serious bacterial infection (PSBI) is one of the leading causes of infant mortality. Based on WHO multi-centric study, signs of PSBI are categorized into: signs of clinical severe infection (CSI) and signs of critical illness (CI). Assessment of signs of PSBI helps to divide infants into risk groups for appropriate management and prognostication. This study aimed to assess risk stratification of signs of PSBI among young infants (0- 59 days of age) by correlating signs of PSBI with mortality. Methods: In this hospital-based prospective cohort study, 220 young infants with signs of PSBI were recruited. Infants were categorized into two: those with signs of CSI and those with signs of CI. Correlation of each sign of PSBI was done with mortality. Time to death was assessed by using the Kaplan Meier survival analysis. Results: 220 infants were analyzed for outcomes. Signs of CSI including fever, fast breathing, poor feeding, and movement only on stimulation had a low fatality rate; however low body temperature and severe chest pain was associated with high mortality of 20% and 9%, respectively. CI signs – convulsion, no movement at all, and no feeding at all were associated with high death rates of 14%, 63.7%, and 47.4% respectively. The majority of deaths were occurred within 48 hours of presentation. Conclusions: Mortality was high in infants with low body temperature, chest pain, or any sign of CI. Risk stratification based on clinical signs could help prioritize infants in need for urgent intervention and thus decrease infant mortality.

A novel deterioration prediction system for mild COVID-19 patients in Korea: a retrospective study

The ongoing coronavirus disease 2019 (COVID-19) pandemic presents serious public health threats. Omicron, the current most prevalent strain of COVID-19, has a low fatality rate and very high transmissibility, so the number of patients with mild symptoms of COVID-19 is rapidly increasing. This change of pandemic challenges medical systems worldwide in many aspects, including sharp increases in demands for hospital infrastructure, critical shortages in medical equipment, and medical staff. Predicting deterioration in mild patients could alleviate these problems. A novel scoring system was proposed for predicting the deterioration of patients whose condition may worsen rapidly and those who all still mild or asymptomatic. Retrospective cohorts of 954 and 2,035 patients that quarantined in the Residential Treatment Center were assembled for derivation and external validation of mild COVID-19, respectively. Deterioration was defined as transfer to a local hospital due to worsening condition of the patients during the 2-week isolation period. A total of 15 variables: sex, age, seven pre-existing conditions (diabetes, hypertension, cardiovascular disease, respiratory disease, liver disease, kidney disease, and organ transplant), and five vital signs (systolic blood pressure (SBP), diastolic blood pressure (DBP), heart rate (HR), body temperature, and oxygen saturation (SpO2)) were collected. A scoring system was developed using seven variables (age, pulse rate, SpO2, SBP, DBP, temperature, and hypertension) with significant differences between the transfer and not transfer groups in logistic regression. The proposed system was compared with existing scoring systems that assess the severity of patient conditions. The performance of the proposed scoring system to predict deterioration in patients with mild COVID-19 showed an area under the receiver operating characteristic (AUC) of 0.868. This is a statistically significant improvement compared to the performance of the previous patient condition assessment scoring systems. During external validation, the proposed system showed the best and most robust predictive performance (AUC = 0.768; accuracy = 0.899). In conclusion, we proposed a novel scoring system for predicting patients with mild COVID-19 who will experience deterioration which could predict the deterioration of the patient’s condition early with high predictive performance. Furthermore, because the scoring system does not require special calculations, it can be easily measured to predict the deterioration of a patients’ condition. This system can be used as effective tool for early detection of deterioration in mild COVID-19 patients.

Impact of “day 90” CD4+ T cells on clinical outcomes in children with relapsed/refractory acute myeloid leukemia after allogeneic hematopoietic stem cell transplantation

BackgroundThe severity of complications after hematopoietic stem cell transplantation (HSCT) is dictated by the degree of immune reconstitution. However, the connection between immune reconstitution and the prognosis of pediatric patients undergoing allogeneic hematopoietic stem cell transplantation (allo-HSCT) remains unclear. Therefore, the aim of this study was to evaluate the impact of lymphocyte subsets in children diagnosed with refractory or relapsed acute myeloid leukemia (R/R-AML) after allo-HSCT. MethodsWe retrospectively investigated the prognosis and lymphocyte subsets at d 90 (D90) post-allo-HSCT in 130 children diagnosed with R/R-AML between September 2019 and October 2022 at the Children's Hospital of Soochow University. Lymphocyte subgroups were assessed by flow cytometric analysis on D90 and compared among human leukocyte antigen (HLA)-matched sibling donor HSCT (MSD) (n = 14), haploidentical donor HSCT (n = 94), and HLA-matched unrelated donor HSCT (n = 22) groups. The associations between the counts and frequencies of lymphocyte subgroups and prognosis were assessed. ResultsIn the MSD group, CD4+ T cell frequency and count were the highest (P < 0.001). Among the examined lymphocyte subsets, a lower proportion of CD4+ T cells (<14.535 %) at D90 correlated with a higher risk of cytomegalovirus infection (P = 0.002). A higher CD4+ T cell count (>121.39/μL) at D90 after HSCT was the single predictor of a lower fatality risk across all lymphocyte subgroups (univariate: P = 0.038 cut-off: 121.39/μL; multivariate: P = 0.036). No association with relapse was observed. ConclusionsCD4+ T cell count may be used to identify pediatric patients with R/R-AML with a greater mortality risk early after HSCT.

A small opening in the storm clouds: overview of the registry-based study on the management of thyroid storm.

Due to the high mortality rate of TS, effective guidance for its diagnosis and treatment is essential. The diagnostic criteria introduced by the JTA in 2012, along with the Burch-Wartofsky Point Scale, constitute valuable tools for the diagnosis of TS. In 2016, Guidelines on the management of TS were produced by the JTA and the JES. Recently, a prospective multicenter register-based study compared the prognosis and outcome of 110 new-onset TS patients with the results of previous comparable studies and evaluated the efficacy of the Guidelines. The study revealed higher APACHE II scores and significant correlations between lower BMI, post-resuscitation shock, and fever with outcomes and, overall, improved TS prognosis. Most patients in the study received methimazole and potassium iodide, the timely administration of which was linked to lower fatality rates. Adherence to treatment guidelines correlates with lower mortality rates, emphasizing the importance of experienced multidisciplinary teams in ICU settings and the necessity for periodic review of the guidelines to enhance therapeutic approaches and reduce mortality.

Cannabis policy bundles and traffic fatalities in the American States over time.

Since 1996, 38 US states have legalized access to cannabis (medical and/or adult-use recreational). We aimed to estimate the effect of three dimensions of state cannabis policy design - pharmaceutical, permissive and fiscal - on levels of overall, alcohol-impaired, occupant, light truck and pedestrian fatality rates. Observational study of US states' overall, alcohol-impaired, occupant, light truck and pedestrian fatalities between 1994 and 2020. The unit of analysis was at the state level, consisting of 50 states and 27 years of time series data, resulting in a total of 1350 state-year observations. Fatality rates associated with alcohol-impaired, pedestrian, total occupant, passenger car and light truck fatality rates were obtained from the Fatality Analysis Reporting System of the National Highway Traffic Safety Administration and normalized per 10 billion vehicle miles traveled. State cannabis policies are measured in three bundles (scales): pharmaceutical, permissive and fiscal. The pharmaceutical bundle was associated with increases in all fatality rates [β = 0.145; 95% confidence interval (CI) = 0.116-0.173; P < 0.000]. The permissive bundle was associated with lower overall fatality rates (β = -0.319; 95% CI = -0.361 to -0.277; P < 0.000). The fiscal bundle was generally associated with higher fatality rates (β = 0.062; 95% CI = 0.043-0.081; P < 0.000), occupant (β = 0.070; 95% CI = 0.042-0.098; P < 0.000), light trucks (β = 0.049; 95% CI = 0.026-0.072; P < 0.000). US state cannabis regulations influence traffic safety. Greater permissiveness in US state cannabis regulations does not appear to correlate with traffic fatality rate increases, but greater medicalization and fiscal operation does.

Pharmacy Naloxone Standing Order and Community Opioid Fatality Rates Over Time

Despite the proliferation of pharmacy standing-order naloxone dispensing across many US states before the change to over-the-counter status, few policy analyses have evaluated the implementation of pharmacy naloxone standing orders in addressing opioid overdose fatality among communities. To determine whether the implementation of pharmacy standing-order naloxone was associated with lower opioid fatality rates compared with communities without pharmacies with standing-order naloxone. This retrospective multisite study was conducted with an interrupted time series analysis across 351 municipalities in Massachusetts over 24 quarters (from January 1, 2013, through December 31, 2018). Standing-order naloxone dispensing data were collected from 2 sources for all major chain pharmacies and many independent pharmacies, covering 70% of retail pharmacies in Massachusetts. Municipalities had various standing-order naloxone implementation inceptions during the study period. Data were analyzed from December 2021 to November 2023. The main exposure was measured by the first quarter with standing-order naloxone dispensation as the actual implementation inception. The primary study outcome was municipal opioid fatality rate per 100 000 population obtained from the Massachusetts Registry of Vital Records and Statistics. The median (IQR) population size across 351 municipalities was 10 314 (3635 to 21 781) people, with mean (SD) proportion of female individuals was 51.1% (2.8 percentage points). Pharmacies from 214 municipalities (60.9%) reported dispensing standing-order naloxone over the study period. At the baseline of the first quarter of 2013, municipalities that eventually had standing-order naloxone had greater quarterly opioid fatality rates compared with those that never implemented standing-order naloxone (3.51 vs 1.03 deaths per 100 000 population; P < .001). After adjusting for municipal-level sociodemographic and opioid prevention factors, there was significant slope decrease of opioid fatality rates (annualized rate ratio, 0.84; 95% CI, 0.78-0.91; P < .001) following standing-order naloxone dispensing, compared with the municipalities that did not implement standing-order naloxone. There were no significant level changes of opioid fatality rates in the adjusted models. Sensitivity analyses yielded similar and significant findings. These findings suggest that community pharmacy dispensing of naloxone with standing orders was associated with a relative, gradual, and significant decrease in opioid fatality rates compared with communities that did not implement the standing-order naloxone program. These findings support the expansion of naloxone access, including over-the-counter naloxone as part of a multifaceted approach to address opioid overdose.

The COVID-19 pandemic as a modifier of DGBI symptom severity: A systematic review and meta-analysis.

This SRMA reviewed and assessedthe changes in the severity of disorders of gut-brain interaction (DGBI) symptoms during the COVID-19 pandemic, and evaluated factors associated with symptom severity changes. Electronic databases were searched until February 2024, for articles reporting on changes in symptom severity in DGBI patients during the COVID-19 pandemic. The proportion of DGBI patients who reported a change in their symptom severity were pooled using a random-effects model, and subgroup analyses were conducted to assess the effect of socio-cultural modifiers on symptom severity in DGBI. Twelve studies including 3610 DGBI patients found that 31.4% (95% CI, 15.9-52.5) of DGBI patients experienced symptom deterioration, while 24.3% (95% CI, 10.2-47.5) experienced improvement. Countries with high gross domestic product (GDP) had a 43.5% (95% CI, 16.3-75.2) likelihood of symptom deterioration, compared to 9.2% (95% CI, 1.4-42.2) in lower GDP countries. Similarly, countries with low COVID fatality rates had a 60.1% (95% CI, 19.7-90.3) likelihood of symptom deterioration, compared to 18.3% (95% CI, 7.8-36.9) in higher fatality rate countries. Countries with lenient COVID policies had a 58.4% (95% CI, 14.1-92.3) likelihood of symptom deterioration, compared to 19% (95% CI, 8.2-38.1) in countries with stricter policies. Patients in high vaccine hesitancy countries had a 51.4% (95% CI, 19.5-82.2) likelihood of symptom deterioration, compared to 10.6% (95% CI, 2.7-33.4) in low vaccine hesitancy countries. This meta-analysis reveals that a significantly higher proportion of DGBI patients experienced deterioration of symptoms during the COVID-19 pandemic. Various sociocultural, economic and environmental factors potentially modify the effects of the COVID-19 pandemic on DGBI.

Impact of infectious diseases on wild bovidae populations in Thailand: insights from population modelling and disease dynamics.

The wildlife and livestock interface is vital for wildlife conservation and habitat management. Infectious diseases maintained by domestic species may impact threatened species such as Asian bovids, as they share natural resources and habitats. To predict the population impact of infectious diseases with different traits, we used stochastic mathematical models to simulate the population dynamics over 100 years for 100 times in a model gaur (Bos gaurus) population with and without disease. We simulated repeated introductions from a reservoir, such as domestic cattle. We selected six bovine infectious diseases; anthrax, bovine tuberculosis, haemorrhagic septicaemia, lumpy skin disease, foot and mouth disease and brucellosis, all of which have caused outbreaks in wildlife populations. From a starting population of 300, the disease-free population increased by an average of 228% over 100 years. Brucellosis with frequency-dependent transmission showed the highest average population declines (-97%), with population extinction occurring 16% of the time. Foot and mouth disease with frequency-dependent transmission showed the lowest impact, with an average population increase of 200%. Overall, acute infections with very high or low fatality had the lowest impact, whereas chronic infections produced the greatest population decline. These results may help disease management and surveillance strategies support wildlife conservation.

Human development and disaster mortality: evidence from India

Natural disasters present substantial dangers to both human life and physical infrastructure. Although the human development index (HDI) is recognized for its pivotal role in mitigating mortality resulting from natural disasters, the exact extent of its impact on fatalities remains unclear. In this study, we investigate the relationship between HDI and fatalities resulting from floods and cyclones using panel data for 19 states of India spanning from 1983 to 2011. Employing Fixed Effects Poisson and Negative Binomial estimates, we establish a causal-effect relationship between HDI and disaster-related fatalities. Additionally, we utilize the Instrumental Variable Poisson (IV) model to address the endogeneity between HDI and fatalities. Our empirical findings indicate that states with higher HDI levels experience lower fatalities due to natural disasters. Furthermore, our results underscore the importance of critical policy discussions regarding the role of inequality-adjusted HDI, government responsiveness, and human capital development in disaster risk reduction strategies.

Effective health systems facing pandemic crisis: lessons from COVID-19 in Europe for next emergencies

PurposeThe investigation goal is the analysis of the relation between healthcare expenditures and other resources, and COVID-19 fatality rates among European countries to design effective health policies for crisis management.Design/methodology/approachResearch methodology is based on descriptive statistics and various parametric methods, also including a linear model of regression to analyze basic relationships of variables under study.FindingsResults show that a lower COVID-19 fatality rate is associated with higher levels of health expenditure (% GDP), of healthcare expenditure per capita, health expenditure in preventive care (% GDP), hospitals per million inhabitants, physicians, nurses, hospital beds and curative acute care beds per 1,000 inhabitants. Regression analysis shows that a 1% increase in healthcare expenditures per capita of countries, it reduces the level of COVID-19 fatality rate by 0.74%. In fact, many countries in Eastern Europe with low healthcare expenditures per capita in 2019 (e.g., Bulgaria, Romania, Hungary, Poland, Latvia, Slovakia, Lithuania, etc.), they have experienced high COVID-19 fatality rates. Instead, a lot of countries in Western Europe, with high healthcare expenditures per capita, such as Germany, Denmark, Austria, and the Netherlands, they had resilient health systems to face pandemic crisis and lower COVID-19 fatality rates.Practical implicationsThese findings suggest strategies of systematic and continuous investments in healthcare, medical technologies, and ICT infrastructures to support effective health policy of crisis management in countries to face future pandemic crisis and other emergencies in society.Originality/valueThe explanation of critical role of high health expenditure (% GDP) and healthcare expenditure per capita to support robust health systems that bolster the resilience in nations to face health emergencies and worldwide crises.

The Dynamics of COVID-19 in Hiroshima Prefecture Compared to Japan and Its Association With Meteorological Factors: A Comparative Analysis.

Introduction Despite the implementation of countermeasures and mass vaccination programs, the COVID-19 pandemic incidence was a vital public health concern. This study aimed to explore the dynamics of COVID-19 cases and assess the association of COVID-19 pandemic epidemiological data with meteorological factors in Hiroshima Prefecture compared to Japan. Methods We analyzed COVID-19 pandemic data in Japan's Hiroshima Prefecture from January 16, 2020, to May 9, 2023. Meteorological factors were examined at different time frames, and Spearman correlation coefficients were calculated for COVID-19 variables and variants based on GISAID whole genome analysis. Results Hiroshima Prefecture reported 816,788 COVID-19 cases and 1,371 fatalities, with a city-to-rural case ratio of 0.97:1. Infection rates were 17.42% for Japan and 15.83% for Hiroshima. Gender-wise, the ratio was 99:1, and the 30-39 age group in Hiroshima had the highest cases (15.5%). Among all meteorological factors, daily and 14-day average wind speed showed a weak correlation with incidence (-0.1954, P < 0.01; 0.3669 P < 0.01), fatalities (-0.1148, P < 0.01; -0.2232 P < 0.01), and incidence rate (-0.2042, P < 0.01; -0.3751, P < 0.01), respectively. Clade GRA was most frequent (39.7%), and among 61 variants, B.1.1.7, AY.29, and BA.1.1.2 were predominant. Precipitation was associated significantly with the Alpha variant (0.3373, P<0.01), while the Delta variant (0.2934, <0.05) weakly correlated with humidity. Conclusion COVID-19 pandemic trends in Hiroshima Prefecture paralleled Japan's, yet with lower incidence and fatalities compared to most prefectures. Significant associations were found between meteorological factors and COVID-19 metrics, including incidence, fatalities, incidence rate, and mutations in Hiroshima.

Cost-effectiveness of One Health intervention to reduce risk of human exposure and infection with non-typhoidal salmonellosis (NTS) in Nigeria

BackgroundNon-typhoidal Salmonella infection (NTS) is an important foodborne zoonosis with underappreciated health and economic burdens, and low case fatality. It has global prevalence, with more burdens in under-resourced countries with poor health infrastructures. Using a cohort study, we determined the cost-effectiveness of NTS in humans in Nigeria for the year 2020. MethodsUsing a customized Excel-based cost-effectiveness analysis tool, structured (One Health) and unstructured (episodic intervention against NTS) in Nigeria were evaluated. Input data on the disease burdens, costs surveillance, response and control of NTS were obtained from validated sources and the public health system. ResultsThe non-complicated and complicated cases were 309,444 (95%) and 16,287 (5%) respectively, and the overall programme cost was US$ 31,375,434.38. The current non-systematic episodic intervention costed US$ 14,913,480.36, indicating an additional US$ 16,461,954 to introduce the proposed intervention. The intervention will avert 4036.98 NTS DALYs in a single year. The non-complicated NTS case was US$ 60/person with significant rise in complicated cases. The cumulative costs of NTS with and without complications far outweighed the program cost for One Health intervention with an incremental cost-effectiveness ratio (ICER) of -US$ 221.30). ConclusionsUtilising structured One Health intervention is cost-effective against NTS in Nigeria, it carries additional mitigative benefits for other diseases and is less costly and more effective, indicative of a superior health system approach. Identified limitations must be improved to optimize benefits associated and facilitate policy discussions and resource allocation.

Characteristics and outcomes of a hospitalized cohort with reduced mortality from COVID-19, White Mountain apache tribal lands, April 1 – July 31, 2020

BackgroundWidespread transmission of COVID-19 continues to threaten public health, particularly of rural, American Indian communities. Although COVID-19 risk factors for severe disease and clinical characteristics are well described in the general population, there has been little shared on hospitalized American Indian populations.MethodsIn this observational study, we performed chart extractions on all persons hospitalized with COVID-19 from April 1 through July 31, 2020 among an exclusively American Indian population living on or near Tribal lands in eastern Arizona. We provide descriptive statistics for the cohort stratified by presentation, comparing those who self-presented or were referred by an outreach program. Exploratory analyses were performed to identify risk factors for morbidity and mortality.ResultsDuring the observation period, 2262 persons were diagnosed with COVID-19 and 490 (22%) were hospitalized. Hospitalized persons had a median age of 54 years; 92% had at least one comorbidity, 72% had greater than one comorbidity, and 60% had a BMI of > 30. Most persons required supplemental oxygen (83%), but the majority (62%) only required nasal cannula and only 11% were intubated. The case fatality rates were 1.7% for the population, 7.1% among hospitalizations, and 9.3% among hospitalized patients 50 years and older. All rates that are significantly lower than those reported nationally during the same period.ConclusionsWe observed a cohort of American Indian patients hospitalized secondary to COVID-19 with greater number of comorbidities compared to the general population but with lower mortality rates. We posit that the primary driver of mortality reduction for this population and the hospitalized cohort was a community-based referral program that led to disproportionately lower fatality rates among the oldest persons.