Public Health Resource Allocation Research Articles

Excess Fatal Overdoses in the United States During the COVID-19 Pandemic by Geography and Substance Type: March 2020-August 2021.

Objectives. To assess heterogeneity in pandemic-period excess fatal overdoses in the United States, by location (state, county) and substance type. Methods. We used seasonal autoregressive integrated moving average (SARIMA) models to estimate counterfactual death counts in the scenario that no pandemic had occurred. Such estimates were subtracted from actual death counts to assess the magnitude of pandemic-period excess mortality between March 2020 and August 2021. Results. Nationwide, we estimated 25 668 (95% prediction interval [PI] = 2811, 48 524) excess overdose deaths. Specifically, 17 of 47 states and 197 of 592 counties analyzed had statistically significant excess overdose-related mortality. West Virginia, Louisiana, Tennessee, Kentucky, and New Mexico had the highest rates (20-37 per 100 000). Nationally, there were 5.7 (95% PI = 1.0, 10.4), 3.1 (95% PI = 2.1, 4.2), and 1.4 (95% PI = 0.5, 2.4) excess deaths per 100 000 involving synthetic opioids, psychostimulants, and alcohol, respectively. Conclusions. The steep increase in overdose-related mortality affected primarily the southern and western United States. We identified synthetic opioids and psychostimulants as the main contributors. Public Health Implications. Characterizing overdose-related excess mortality across locations and substance types is critical for optimal allocation of public health resources. (Am J Public Health. 2024;114(6):599-609. https://doi.org/10.2105/AJPH.2024.307618).

Disease burden of Parkinson's disease in China and its provinces from 1990 to 2021: findings from the global burden of disease study 2021

Parkinson's disease (PD) has become a public health concern with global ageing. However, comprehensive assessments of the temporal and geographical trend of PD disease burden in China remain insufficient. This study aimed to examine the burden of PD by age, gender, and geographical region in China during 1990-2021. Using data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021, we analysed the incidence, prevalence, mortality, and DALY burden of PD in 33 Chinese provinces/regions. We compared the national figure with the global average and the corresponding estimates from the G20 countries. The estimated annual percentage change (EAPC) was used to quantify the temporal trends of PD burden during 1990-2021. We further assessed the PD burden by age and gender during 1990-2021. We used a decomposition analysis to investigate the changes in the number of new cases, patients, and deaths of PD during 1990-2021. In 2021, China recorded the highest age-standardised incidence and prevalence of PD among the G20 countries, at 24.3 per 100,000 and 245.7 per 100,000, respectively, figures that were much higher than the global average. During 1990-2021, the age-standardised incidence of PD in China increased by 89.7%, and the age-standardised prevalence by 167.8%, both marking the largest increases among the G20 countries. In contrast, the age-standardised mortality for PD has significantly decreased since 1990, whereas the age-standardised DALY rate for PD has remained relatively unchanged since 1990. The PD burden gradually increased with age, especially in the elderly population aged ≥65 years. During 1990-2021, the burden in males consistently surpassed that in females, with the gender difference widening over time. The increase in new cases and patients of PD was primarily driven by changes in age-specific rates, while the rise in PD-related deaths was largely attributable to population ageing. The disease burden of PD varied considerably across the Chinese provinces. In 2021, age-standardised incidence and prevalence of PD were generally higher in China's southeastern coastal regions than in the western regions, and age-standardised DALY rates were higher in the northern regions than in other regions. The disease burden of PD in China has consistently risen over the past three decades, particularly among elderly men. The increasing causative factors and population aging highlight the need for enhancing public health intervention and resource allocation, especially in etiological research, early diagnosis, preventive strategies, and region-specific management for PD. Ministry of Science and Technology of the People's Republic of China (2022YFC2304900, 2022YFC2505100); National Key R&D Program of China (2022YFC2505100, 2022YFC2505103, 2018YFC1315300); Outstanding Young Scholars Support Program (grant number: 3111500001); Epidemiology modeling and risk assessment (grant number: 20200344), and Xi'an Jiaotong University Young Scholar Support Grant (grant number: YX6J004).

Trends in SARS-CoV-2 cycle threshold (Ct) values from nucleic acid testing predict the trajectory of COVID-19 waves

Forecasting COVID-19 waves helps with public health planning and resource allocation. Cycle threshold (Ct) values obtained from positive SARS-CoV-2 nucleic acid amplification test (NAAT) results offer limited value for individual patient management, but real-time analysis of temporal trends of aggregated Ct values may provide helpful information to predict the trajectories of COVID-19 waves in the community. Ct value trends on 59,609 SARS-CoV-2 NAAT-positive results from 574,403 tests using a single testing assay system, between September 2021 and January 2023, were examined to monitor the trend of the proportion of positive NAAT with lower Ct values (≤28) in relation to changing COVID-19 case numbers over time. We applied regression with autoregressive integrated moving average errors modelling approach to study the relation between Ct values and case counts. We also developed an insight product to monitor the temporal trends with Ct values obtained from SARS-CoV-2 NAAT-positive results. In this study, the proportion of lower Ct values preceded by a range of 7-32 days the rising population COVID-19 testing rate reflecting onset of a COVID-19 wave. Monitoring population Ct values may assist in predicting increased disease activity.

Territorial patterns of COVID-19 in Iran

Motives: Spatial analysis has become an essential tool in understanding the underlying factors that contribute to the distribution of viral pandemics, diseases, injuries, and mortality patterns. By visualizing geographical data in spatial maps, researchers can identify local distribution patterns and potential drivers behind these patterns. In health and medical sciences, there has been a growing recognition that spatial analysis and mapping techniques are helpful in addressing various challenges related to the allocation of healthcare resource in both urban and rural areas. Aim: The objective of this study was to analyze the spatial distribution pattern of the COVID-19 pandemic and the Index of Proximity Distribution (IPD) across 31 provinces of Iran between February 2019 and February 2023. A two-stage sampling method combining convenience and cluster sampling was used to examine COVID-19 distribution patterns in 31 provinces of Iran between 22 February 2020 and 22 February 2023. COVID-19 and IPD data were collected as part of this panel study. Data were analyzed using t-tests, chi-square tests, and analysis of variance (ANOVA) in SPSS version 28 (α = 0.05). Subsequently, daily COVID-19 infection data for each province in the analyzed period were processed in ArcGIS software, and the spatial distribution pattern of the pandemic in Iran were visualized by point density analysis. Standard distance and standard deviation ellipse techniques were employed to assess the density or dispersion of infected individuals and to determine the spatial distribution pattern of COVID-19 in Iran. A spatial autocorrelation (Moran’s I) analysis was conducted to identify the spatial distribution pattern of COVID-19 in Iran. Additionally, distance-based spatial autocorrelation was used to examine the prevalence of COVID-19 infection across Iranian provinces. In a grouping analysis, 31 Iranian provinces were classified into five groups based on the number of COVID-19 cases, and spatial statistics were used to examine the prevalence of COVID-19 within each group. A hot spot analysis and a standard distance (SD) analysis were conducted to explore spatial correlations in the number of individuals affected by COVID-19 in each province. Results: Based on the Moran index, a random spatial pattern with a Z-Score of 1.485 was identified in March 2019, whereas a clustered distribution of COVID-19 with a Z-Score of 3.039 was determined in February 2023. The distance-based spatial autocorrelation analysis revealed a positive value of the Moran index (0.136627) at a distance of 383.3 kilometers from Tehran, which points to positive spatial autocorrelation and a higher number of COVID-19 cases in nearby regions. Conversely, the Moran index assumed a negative value of 0.040246 at a distance of 726.6 kilometers from Tehran, which suggests that the number of pandemic cases decreased over distance from Tehran. Moreover, based on the results of the hot spot analysis, Tehran province was identified as a hot cluster with a higher prevalence of COVID-19 cases in that region. In contrast, Bushehr province was classified as a cold cluster with a lower prevalence of COVID-19 cases in comparison with the surrounding regions. These findings provide valuable insights into the spatial distribution and clustering of COVID-19 cases in Iran. The shift from a random spatial pattern in 2019 to clustered distribution in 2023 indicates that the pandemic spread rate increased over time. The positive spatial autocorrelation near Tehran highlights the role of proximity and population movement in the transmission of the virus. Furthermore, the identification of hot spots and cold spots in a country can inform targeted interventions and resource allocation to effectively manage and control the pandemic. Overall, this study demonstrates the value of spatial analysis in identifying the spatial distribution patterns and the dynamics of the COVID-19 pandemic in Iran. The integration of spatial analysis techniques with epidemiological data contributes to a better understanding of spatial-temporal patterns, facilitates effective public health responses and resource allocation strategies. These findings contribute to the growing body of knowledge on the spatial epidemiology of COVID-19 and can aid in informing future preparedness and response efforts in Iran and other regions that face similar challenges.

EPIDEMIOLOGICAL STUDY OF TYPHOID FEVER PREVALENCE AMONG SUSPECTED CASES IN RAWALPINDI AND ISLAMABAD: IMPLICATIONS FOR PUBLIC HEALTH INTERVENTION

Typhoid fever remains a significant public health concern in Pakistan, particularly in urban areas such as Rawalpindi and Islamabad. Understanding the prevalence of this disease is crucial for informing public health interventions and resource allocation. This study aims to determine the prevalence of typhoid fever among patients suspected of the disease in District Rawalpindi and Islamabad. A cross-sectional study was conducted over three months, from November 2011 to January 2012. A random sample of 500 patients suspected of typhoid fever was collected from various regional hospitals and clinical laboratories. Samples were tested using the Widal test or Typhi dot, and data on patient demographics and test results were recorded. Descriptive statistics, including frequencies and percentages, were used to summarize the data. Prevalence rates were calculated, and subgroup analyses were conducted to explore variations in prevalence among different age groups. Of the 500 samples collected, 95 tested positive for typhoid fever, yielding an overall prevalence rate of 19%. Subgroup analysis revealed that 31.5% of positive cases were in adults, while 68.5% were in children under the age of 15 years. Among children, the prevalence rates varied across different age groups, with 13.8%, 55.3%, and 30.7% of positive cases reported in 1-4 years, 5-12 years, and 12-15 years, respectively. Further analysis estimated the total prevalence of typhoid fever in Rawalpindi and Islamabad, revealing a prevalence rate of 685 per 10,000 individuals among children. This study highlights the significant burden of typhoid fever in Rawalpindi and Islamabad, particularly among children. The findings underscore the importance of targeted interventions to control the spread of the disease, including vaccination campaigns and improved sanitation measures. Continued surveillance and monitoring are essential for tracking trends in typhoid prevalence and guiding regional public health strategies.

Forecasted Dementia Prevalence in Portugal (2020-2080).

Dementia is a global public health challenge, and its impact on Portugal is yet unclear. This study forecasts dementia prevalence in Portugal until 2080. Using the Gonçalves-Pereira et al (2021) method, we estimated dementia cases among older adults (≥65 years) in the community. Applying age-sex specific prevalence rates of the Gonçalves-Pereira study to population projections for Portugal between 2020-2080, based on the 10/66 Dementia Research Group criteria (10/66 DRG) and the Diagnostic and Statistical Manual of Mental Disorders IV criteria (DSM-IV), to Portugal's population projections (2020-2080) under various growth scenarios (low, medium, and high). We anticipate a more than 2-fold increase in dementia prevalence from 2020 to 2080, both for 10/66 DRG [2.1%-5.0%] and DSM-IV [.8%-2.0%]. By 2080, those aged ≥80 years are projected to constitute 75.0% (vs 59.0% in 2020) of all dementia cases, particularly affecting women. Addressing dementia growth in Portugal calls for a comprehensive global response, while country-level estimates facilitate informed public health planning, policy-making, and resource allocation.

Lung Cancer Prevalence in Virginia: A Spatial Zipcode-Level Analysis via INLA.

Examining lung cancer (LC) cases in Virginia (VA) is essential due to its significant public health implications. By studying demographic, environmental, and socioeconomic variables, this paper aims to provide insights into the underlying drivers of LC prevalence in the state adjusted for spatial associations at the zipcode level. We model the available VA zipcode-level LC counts via (spatial) Poisson and negative binomial regression models, taking into account missing covariate data, zipcode-level spatial association and allow for overdispersion. Under latent Gaussian Markov Random Field (GMRF) assumptions, our Bayesian hierarchical model powered by Integrated Nested Laplace Approximation (INLA) considers simultaneous (spatial) imputation of all missing covariates through elegant prediction. The spatial random effect across zip codes follows a Conditional Autoregressive (CAR) prior. Zip codes with elevated smoking indices demonstrated a corresponding increase in LC counts, underscoring the well-established connection between smoking and LC. Additionally, we observed a notable correlation between higher Social Deprivation Index (SDI) scores and increased LC counts, aligning with the prevalent pattern of heightened LC prevalence in regions characterized by lower income and education levels. On the demographic level, our findings indicated higher LC counts in zip codes with larger White and Black populations (with Whites having higher prevalence than Blacks), lower counts in zip codes with higher Hispanic populations (compared to non-Hispanics), and higher prevalence among women compared to men. Furthermore, zip codes with a larger population of elderly people (age ≥ 65 years) exhibited higher LC prevalence, consistent with established national patterns. This comprehensive analysis contributes to our understanding of the complex interplay of demographic and socioeconomic factors influencing LC disparities in VA at the zip code level, providing valuable information for targeted public health interventions and resource allocation. Implementation code is available at GitHub.

The importance of meropenem resistance, rather than imipenem resistance, in defining carbapenem-resistant Enterobacterales for public health surveillance: an analysis of national population-based surveillance

BackgroundIn Japan, carbapenem-resistant Enterobacterales (CRE) infections were incorporated into the National Epidemiological Surveillance of Infectious Diseases (NESID) in 2014, necessitating mandatory reporting of all CRE infections cases. Subsequently, pathogen surveillance was initiated in 2017, which involved the collection and analysis of CRE isolates from reported cases to assess carbapenemase gene possession. In this surveillance, CRE is defined as (i) minimum inhibitory concentration (MIC) of meropenem ≥2 mg/L (MEPM criteria) or (ii) MIC of imipenem ≥2 mg/L and MIC of cefmetazole ≥64 mg/L (IPM criteria). This study examined whether the current definition of CRE surveillance captures cases with a clinical and public health burden.MethodsCRE isolates from reported cases were collected from the public health laboratories of local governments, which are responsible for pathogen surveillance. Antimicrobial susceptibility tests were conducted on these isolates to assess compliance with the NESID CRE definition. The NESID data between April 2017 and March 2018 were obtained and analyzed using antimicrobial susceptibility test results.ResultsIn total, 1681 CRE cases were identified during the study period, and pathogen surveillance data were available for 740 (44.0%) cases. Klebsiella aerogenes and Enterobacter cloacae complex were the dominant species, followed by Klebsiella pneumoniae and Escherichia coli. The rate of carbapenemase gene positivity was 26.5% (196/740), and 93.4% (183/196) of these isolates were of the IMP type. Meanwhile, 315 isolates were subjected to antimicrobial susceptibility testing. Among them, 169 (53.7%) fulfilled only the IPM criteria (IPM criteria-only group) which were susceptible to meropenem, while 146 (46.3%) fulfilled the MEPM criteria (MEPM criteria group). The IPM criteria-only group and MEPM criteria group significantly differed in terms of carbapenemase gene positivity (0% vs. 67.8%), multidrug resistance rates (1.2% vs. 65.8%), and mortality rates (1.8% vs 6.9%).ConclusionThe identification of CRE cases based solely on imipenem resistance has had a limited impact on clinical management. Emphasizing resistance to meropenem is crucial in defining CRE, which pose both clinical and public health burden. This emphasis will enable the efficient allocation of limited health and public health resources and preservation of newly developed antimicrobials.

Trends in mortality due to tracheal, bronchial, and lung cancer across the BRICS: An age-period-cohort analysis based on the Global Burden of Disease Study 1990-2019.

Tracheal, bronchus, and lung cancer (TBL) is a major cause of mortality and top contributor to productivity loss in large emerging economies such as the BRICS (Brazil, Russia, India, China, and South Africa). We examined the time trends of TBL mortality across the BRICS to better understand the disease burden in these countries and inform public health and healthcare resource allocation. TBL mortality-related data between 1990 and 2019 were obtained from the Global Burden of Disease Study 2019 and analyzed using age-period-cohort models. Net drift (local drift) was used to describe the expected age-adjusted TBL mortality rate over time overall (each age group); the longitudinal age curve was used to reflect the age effect; the period rate ratios (RRs) were used to reflect the period effect; and the cohort RR was used to reflect the cohort effect. In 2019, there were 958.3 thousand TBL deaths across the BRICS, representing 46.9% of the global TBL deaths. From 1990 to 2019, the age-standardized mortality rate (ASMR) of TBL decreased in Russia, Brazil, and South Africa while increased in China and India, with the largest reduction reported in Russia (-29.6%) and the largest increase in China (+22.4%). India showed an overall increase (+15.7%) in TBL mortality but the mortality risk decreased among individuals born after 1990 (men) and 1995 (women). Although South Africa and Brazil experienced an overall decline in TBL mortality, their recent birth cohorts, such as Brazilian individuals born after 1985 (men) and 1980 (women), and South African men born after 1995, had an increasing TBL mortality risk. China has experienced an overall increase in TBL mortality, with the mortality risk rising among individuals born after 1995 for both men and women. Russia, which had the highest TBL mortality among the BRICS countries in 1990, has demonstrated significant improvement over the past three decades. Over the past 30years, the BRICS accounted for an increasing proportion of global TBL mortality. TBL mortality increased in older women in all the BRICS countries except Russia. Among the recent birth cohort, the risk of TBL mortality increased in Brazil, China, and South Africa. More effective efforts are needed in the BRICS to reduce the burden of TBL and help achieve the United Nation's Sustainable Development Goals.

Abstract WP256: Evaluating the State-by-State Burden of Stroke and the Effectiveness of Stroke Prevention Efforts in the United States: A Cross-Sectional Study

Background: Assessing the economic impact of stroke and the potential for prevention through policy and programs raises questions about progress and underscores the importance of state-level data for targeted public health resource allocation. In this study, the National Inpatient Sample Database was utilized to examine the proportion of hospital admissions attributed to stroke between two distinct time intervals: 2008-2010 and 2016-2020. Methods: A retrospective cross-sectional study was performed using nationwide inpatient sample and ischemic and hemorrhagic stroke was identified using MSD-RGC codes of 62, 64-71, and total admissions associated with any medical conditions were extracted. The average percentages of AIS, ICH, other unspecified stroke types, and the total stroke admissions for each year were compared for each state. Univariate analysis was made using chi-square and Cochran Armitage trend test. P<0.05 was considered as statistical significant. Results: By state, there is a nationwide trend of decreasing percentage of AIS stroke admission and increasing percentage of ICH type of stroke among all four regions in the United States. These may account for the total increase in stroke burden and hospital utilization. Among all states, comparing the two periods of time, the greatest reduction rate of AIS was noticed in Maryland (0.78% vs. 0.35%, P< 0.0001). The greatest increment in ICH was noticed in South Carolina (1.41 vs 1.84%, P< 0.0001), and in total stroke was noticed in Washington state (1.76% vs 2.28%, increased by 0.51%, P < 0.0001; Figure 1). Conclusions: The findings of our study indicate a significant decrease in the proportion of admissions for AIS across the entire country, accompanied by a corresponding rise in admissions for ICH. Further investigation is warranted to ascertain the underlying causes that have led to the rise in the proportion of ICH cases necessitating hospitaizations.

Artificial intelligence in parasitic disease control: A paradigm shift in health care.

Parasitic diseases, including malaria, leishmaniasis, and trypanosomiasis, continue to plague populations worldwide, particularly in resource-limited settings and disproportionately affecting vulnerable populations. It has limited the use of conventional health-care delivery and disease control approaches and necessitated exploring innovative strategies. In this direction, artificial intelligence (AI) has emerged as a transformative tool with immense promise in parasitic disease control, offering the potential for enhanced diagnostics, precision drug discovery, predictive modeling, and personalized treatment. Predictive AI algorithms have assisted in understanding parasite transmission patterns and outbreaks by analyzing vast amounts of epidemiological data, environmental factors, and population demographics. This has strengthened public health interventions, resource allocation, and outbreak preparedness strategies, enabling proactive measures to mitigate disease spread. In diagnostics, AI-enabled accurate and rapid identification of parasites by analyzing microscopic images. This capability is particularly valuable in remote regions with limited access to diagnostic facilities. AI-driven computational methods have also assisted in drug discovery for parasitic diseases by identifying novel drug targets and predicting the efficacy and safety of potential drug candidates. This approach has streamlined drug development, leading to more effective and targeted therapies. This article reviews these current developments and their transformative impacts on the health-care sector. It also assessed the hurdles that require attention before these transformations can be realized in real-life scenarios.

Fluoridated food on preventing dental caries in Latin American and Caribbean children and adolescents: a systematic review

Dental caries is a significant global public health challenge, particularly among children and adolescents. It affects individuals' quality of life and can lead to various adverse outcomes, including pain, infection, and impaired school performance. Fluoride has been widely recognized as a key component in oral health improvement programs due to its ability to prevent tooth demineralization and promote remineralization. This systematic review aimed to evaluate the existing literature on the effectiveness of fluoridated food interventions in preventing dental caries in Latin American and Caribbean children and adolescents. The study focuses on this specific region, which faces unique challenges such as limited access to dental care services, socioeconomic inequalities, and cultural practices that may influence oral hygiene and dietary habits. The review follows the Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA) guidelines. A comprehensive search was conducted in multiple databases, including EMBASE, MEDLINE, Scielo, and Web of Science. The search strategy utilized relevant MeSH terms and Boolean phrases related to fluoridated food interventions and dental caries in children aged 12 years and younger. After screening the articles based on predetermined inclusion criteria, no study met the eligibility criteria for inclusion in the systematic review. Overall, the systematic review highlights the absence of high-quality available evidence specifically focused on the effectiveness of fluoridated food interventions on children and adolescents of the Latin American and the Caribbean, underscoring the importance of conducting further studies to inform evidence-based decisions regarding public health policies, resource allocation, and oral health promotion strategies in this population.

Transmissibility and severity of COVID-19 in a humanitarian setting: First few X investigation of cases and contacts in Juba, South Sudan, 2020.

The first few 'X' (FFX) studies provide evidence to guide public health decision-making and resource allocation. The adapted WHO Unity FFX protocol for COVID-19 was implemented to gain an understanding of the clinical, epidemiological, virological and household transmission dynamics of the first cases of COVID-19 infection detected in Juba, South Sudan. Laboratory-confirmed COVID-19 cases were identified through the national surveillance system, and an initial visit was conducted with eligible cases to identify all close contacts. Consenting cases and close contacts were enrolled between June 2020 and December 2020. Demographic, clinical information and biological samples were taken at enrollment and 14-21 days post-enrollment for all participants. Twenty-nine primary cases and 82 contacts were included in the analyses. Most primary cases (n= 23/29, 79.3%) and contacts (n= 61/82, 74.4%) were male. Many primary cases (n= 18/29, 62.1%) and contacts (n= 51/82, 62.2%) were seropositive for SARS-CoV-2 at baseline. The secondary attack rate among susceptible contacts was 12.9% (4/31; 95% CI: 4.9%-29.7%). All secondary cases and most (72%) primary cases were asymptomatic. Reported symptoms included coughing (n= 6/29, 20.7%), fever or history of fever (n= 4/29, 13.8%), headache (n= 3/29, 10.3%) and shortness of breath (n= 3/29, 10.3%). Of 38 cases, two were hospitalised (5.3%) and one died (2.6%). These findings were used to develop the South Sudanese Ministry of Health surveillance and contract tracing protocols, informing local COVID-19 case definitions, follow-up protocols and data management systems. This investigation demonstrates that rapid FFX implementation is critical in understanding the emerging disease and informing response priorities.

CKD is the Major Cause of Death in Uddanam: A Population-Representative Study Using Smart Verbal Autopsy

IntroductionUddanam is an agricultural area with a high burden of chronic kidney disease of unknown aetiology (CKDu). Despite reports of many deaths due to CKD in the lay press, the exact contribution of CKD to deaths remains uncertain as most deaths occur outside medical care. MethodsWe used SmartVA automated verbal autopsy (VA) tool to ascertain the cause-specific mortality fractions amongst a 2419 subject-strong general population cohort in Uddanam between 2018-2022. VA interviews were conducted twice with the family members of the deceased. ResultsA total of 133 deaths were recorded, giving a crude death rate of 5.5%, 10 times higher than that recorded in national surveys. CKD was responsible for 45% of all deaths, followed by ischemic heart disease (15%) and respiratory disease (6%). ConclusionThis study confirms CKD as the leading cause of mortality in this high CKD burden area and provides crucial data for public health decision-making and resource allocation.

Trends in medical care utilization in patients with cancer: An analysis of real-world data in a tertiary hospital in Korea, 2014-2019.

Rising costs of cancer treatments challenge even areas with universal health coverage. There's a need to assess current medical care utilization trends among patients with cancer to guide public health policy, resource allocation, and set informed healthcare goals. We analyzed the latest trends in medical care utilization by cancer patients in four areas-drugs, radiation therapy (RT), surgery, and diagnostic procedures-using clinical databases extracted from electronic medical records of a tertiary hospital in Korea between 2014 and 2019. Compound adjusted growth rates (CAGR) were computed to capture the annual growth over the study period. A total of 74,285 cancer patients were identified, with 40.3% (29,962), 14.2% (10,577), 31.1% (23,066), and 92.6% (68,849) of patients having received at least one anticancer agent, RT, surgery, and diagnostic procedure, respectively, over the period. We observed a 1.7-fold increase in the use of targeted · immune-oncology agents (from 6.8% to 11.6%) and a 21-fold increase (from 3.0% in 2014 to 65.7%) in intensity-modulated RT (IMRT) use over the period. In contrast, we observed a continuous decrease in the proportion of patients who underwent surgical treatment from 12.2% in 2014 to 10.9% in 2019. This decrease was particularly noticeable in patients with colon cancer (from 28.5% to 24.2%) and liver cancer (from 4.1% to 2.9%). From 2014 to 2019, there was a significant rise in the use of targeted· immune-oncology agents and IMRT, alongside a decline in surgeries. While targeted · immune-oncology agents and IMRT may offer promising outcomes, their financial impact and potential for overuse necessitate careful oversight and long-term cost-effectiveness studies.

Using indoor temperature in heat health warning systems: Deployment in community housing in Canada

Abstract Background Heat waves are a major global public health concern and present a significant challenge to society, especially to underserved populations and those aged 50+, due to their increasing frequency and intensity. They also add significant burdens to the healthcare system's resources, and it has been shown that most deaths occur indoors. Still, our warning systems are based on outdoor temperature measurements. We developed a real-time indoor temperature alert ecosystem to capture indoor temperatures that may aid in assessing and responding during a heat wave. Methods We placed ecobee thermostats into homes with community housing partners and local health authorities (N = 70), recorded indoor temperature data in near real-time, and administered three surveys during the study period. We investigated: (i) indoor temperatures trends during the heatwave season of 2022 in Vancouver and Ottawa, Canada; (ii) behaviours of participants during the study; (iii) housing characteristics; (iv) delivery of time-sensitive temperature alerts to enable check-ins, and (v) perceptions to heat risk and methods of communication. Results Initial results show different observations that include: a) unsafe indoor temperatures were reached and persisted (>26 °C and 31 °C) despite a milder heat wave season (in British Columbia); b) gaps in understanding of best practices to stay safe exist; c) home characteristics increase risk of overheating; d) coordinating check-in can protect individual health; and e) ensure individuals are protected during extreme heat events. Results suggest that indoor temperature provides vital information to inform heat health response plans. Conclusions The smart thermostat technology can be retrofitted into homes in Canada as it is commercially available. This study presents a scalable solution for monitoring indoor temperatures; it demonstrates they can be used to monitor indoor temperature exposure conditions to prevent deaths during extreme heat events. Key messages • This study presents a scalable solution for monitoring indoor temperatures, demonstrating how smart thermostats can monitor indoor temperature conditions to prevent deaths during extreme heat events. • Indoor temperatures pose significant risks to health; this study seeks to understand indoor temperature exposures to guide public health resource allocation during extreme heat events.

Seroprevalence of Respiratory Syncytial Virus among Rural Population: Insights into Infection Rates and Immune Status: Pilot Study

Respiratory Syncytial Virus (RSV) is a significant cause of respiratory tract infections, particularly affecting infants, young children, and immunocompromised individuals, with a substantial global burden of morbidity and mortality. This study aimed to assess the seroprevalence of RSV among a rural population in Karad Taluka, India, an area where RSV data is scarce due to its predominantly urban focus. Ethical approval was obtained, and written informed consent was collected from participants' parents or legal guardians Blood samples were collected from individuals aged 16 months to 5 years residing in rural areas, and IgM RSV-specific antibodies were detected to identify recent infections using SERION enzyme-linked immunosorbent assay (ELISA). Additionally, demographic information and clinical history were collected through a structured questionnaire. The data were entered into an electronic database, and seroprevalence was calculated based on the proportion of individuals with positive serological results. The study revealed that by the age of 3 years, 81.0% of children exhibited IgM seropositivity to RSV, with a cumulative seroprevalence of 72% in the entire study population. The seroprevalence varied with age, demonstrating an ongoing RSV activity within the community, as evidenced by the high IgM positivity. However, as children grew older, a decline in IgM positivity was observed, possibly due to protective antibodies developed from previous exposures or a longer time elapsed between RSV exposure and sample collection in older children. These findings underscore the vulnerability of young infants to RSV and the need for early protection in India. It highlights the importance of considering timing and age-appropriate administration of RSV vaccines in vaccination strategies, as well as the potential limitations of maternal immunization in protecting preterm infants. Understanding RSV seroprevalence in rural areas is crucial for guiding public health interventions and resource allocation to mitigate the impact of this virus on vulnerable populations, particularly young children who are at the highest risk.

A Comparative Study on Deep Learning Models for COVID-19 Forecast.

The COVID-19 pandemic has led to a global health crisis with significant morbidity, mortality, and socioeconomic disruptions. Understanding and predicting the dynamics of COVID-19 are crucial for public health interventions, resource allocation, and policy decisions. By developing accurate models, informed public health strategies can be devised, resource allocation can be optimized, and virus transmission can be reduced. Various mathematical and computational models have been developed to estimate transmission dynamics and forecast the pandemic's trajectories. However, the evolving nature of COVID-19 demands innovative approaches to enhance prediction accuracy. The machine learning technique, particularly the deep neural networks (DNNs), offers promising solutions by leveraging diverse data sources to improve prevalence predictions. In this study, three typical DNNs, including the Long Short-Term Memory (LSTM) network, Physics-informed Neural Network (PINN), and Deep Operator Network (DeepONet), are employed to model and forecast COVID-19 spread. The training and testing data used in this work are the global COVID-19 cases in the year of 2021 from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. A seven-day moving average as well as the normalization techniques are employed to stabilize the training of deep learning models. We systematically investigate the effect of the number of training data on the predicted accuracy as well as the capability of long-term forecast in each model. Based on the relative L2 errors between the predictions from deep learning models and the reference solutions, the DeepONet, which is capable of learning hidden physics given the training data, outperforms the other two approaches in all test cases, making it a reliable tool for accurate forecasting the dynamics of COVID-19.

The heterogeneous relationship between public health expenditures and household medical expenditures: evidence from relative poverty households in China.

Exploring the heterogeneous relationship between public health expenditures and household medical expenditures from the perspective of relative poverty has positive significance for improving the ability of relative poverty households to cope with the risk of large rigid expenditures and optimizing the public health resource allocation. This paper uses the China Family Panel Studies (CFPS) from 2016 to 2020 to identify relative poverty standard from the perspective of medical needs, analyzes the impact of public health expenditures on medical expenditures of different household types, and evaluates the effect of public health expenditures on ensuring the medical needs of relative poverty households. The panel threshold regression result shows that 19.36% of the provinces per capita disposable where the household is located is the identification standard of relative poverty households. Public health expenditures have a crowding-in effect on household medical expenditures and have a stronger impact on relative poverty households, an effect that is also confirmed by two-stage least squares regression. In addition, ensuring household medical needs through public health expenditures focuses on the level of basic medical needs, and the role of household healthcare expenditures that reflects high-level medical needs is not obvious. In the future, the government should establish a monitoring mechanism for relative poverty households, ensure the basic medical needs of relative poverty households, and clarify the heterogeneity among different types of households, provide targeted public health services.

Trends in the Diagnosed Prevalence and Incidence of Major Eye Diseases in Medicare Part B Fee-for-Service Beneficiaries 68 Years of Age or Older

To study contemporary trends in the diagnosed prevalence and incidence of age-related eye diseases among Medicare Fee-for-Service (FFS) beneficiaries. Analysis of Medicare administrative claims data. Medicare FFS beneficiaries 68 years of age and older from 2005 through 2020 who were enrolled continuously in both Part A and Part B for 3 years, including the index year and a 2-year lookback period. Annual cross-sectional diagnosed prevalence and incidence rates were calculated. Age standardization was performed using the direct standardization method to account for changes in the age structure of the study population. Rates stratified by demographics (age, sex, race, and ethnicity) also were calculated. Annual prevalence and incidence of diagnosed age-related macular degeneration (AMD), diabetic retinopathy (DR) (among those with diabetes), and glaucoma. At baseline, in 2005, 60% of included beneficiaries were female, 20% were 85 years of age or older, 86% were non-Hispanic White, and one-quarter had a diagnosis of diabetes. From 2005 through 2019, the prevalence of a diagnosis of any of the conditions studied increased from 16.4% (n = 3 628 996) to 17.9% (n= 3 731 281). Diagnosed incidence decreased over this period from 4.9% (n = 954 878) in 2005 to 4.2% in 2019 (n=757 696). The diagnosed prevalence of AMD increased from 6.8% (n= 1 504 770) to 9.4% (n= 1 965 176); the diagnosed prevalence of any DR among those with diabetes decreased from 9.3% (n= 504 135) to 9.0% (n=532 859), although the diagnosed prevalence of vision-threatening DR increased from 2.0% to 3.4%; and the diagnosed prevalence of any diagnosed glaucoma decreased from 8.8% (n= 1 951 141) to 8.1% (n= 1 692 837). In 2020, the diagnosed prevalence and incidence of all diagnoses decreased. During the study period, we detected demographic differences in the prevalence and incidence of diagnosis of each condition. This study presents updated data on the prevalence and incidence of diagnosed major chronic, age-related eye diseases among Medicare FFS beneficiaries. Compared with older epidemiologic estimates, we found that the diagnosed prevalence of each condition studied was higher in more recent years. These findings may inform public health and policy planning and resource allocation to address the eye health of an increasingly older United States population. The author(s) have no proprietary or commercial interest in any materials discussed in this article.