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Hand, foot, and mouth disease (HFMD) is an acute infectious illness primarily caused by enteroviruses. The present study aimed to describe the epidemiological characteristics of hospitalized HFMD patients in a hospital in Henan Province (Zhengzhou, China), and to predict the future epidemiological parameters. In this study, we conducted a retrospective analysis of general demographic and clinical data on hospitalized children who were diagnosed with HFMD from 2014 to 2023. We used wavelet analysis to determine the periodicity of the disease. We also conducted an analysis of the impact of the COVID-19 epidemic on the detection ratio of severe illness. Additionally, we employed a Seasonal Difference Autoregressive Moving Average (SARIMA) model to forecast characteristics of future newly hospitalized HFMD children. A total of 19 487 HFMD cases were included in the dataset. Among these cases, 1515 (7.8%) were classified as severe. The peak incidence of HFMD typically fell between May and July, exhibiting pronounced seasonality. The emergence of COVID-19 pandemic changed the ratio of severe illness. In addition, the best-fitted seasonal ARIMA model was identified as (2,0,2)(1,0,1)12. The incidence of severe cases decreased significantly following the introduction of the vaccine to the market (χ2 = 109.9, p < 0.05). The number of hospitalized HFMD cases in Henan Province exhibited a seasonal and declining trend from 2014 to 2023. Non-pharmacological interventions implemented during the COVID-19 pandemic have led to a reduction in the incidence of severe illness.

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AIM: To investigate the impact of public health emergencies on the prevalence of suicidal ideation among healthcare workers (HCWs) and medical students. METHODS: The prevalence of suicidal ideation among HCWs and medical students was searched for analysis. The platforms included PubMed, medRVix, bioRvix, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Web of Science. Interrupted time-series analysis was employed to determine whether the COVID-19 pandemic influenced the prevalence and trends of suicidal ideation. To account for autocorrelation and heteroskedasticity, Newey-West standard errors were utilized with a lag of order one. RESULTS: Seventy studies with 145,641 HCWs and medical students from 30 countries were included in the final analysis, with 30 studies before COVID-19 and 40 studies during the pandemic. Before the pandemic outbreak (April 2020), the monthly increasing rate was 0.063 % (95 % CI: -0.009 %, 0.135 %, z = 1.73, P = 0.084). The tendency of suicidal ideation prevalence increased by 1.116 % (95%CI: 0.888 %, 1.344 %, z = 9.60, P < 0.001). In other words, the calculated monthly growth rate of suicidal ideation after the pandemic outbreak is 1.179 % (95%CI: 0.968 %, 1.391 %, z = 10.93, P < 0.001) per month. The overall growing trend of prevalence of suicidal ideation during the pandemic is 1.896 % per month in America; 1.590 % in Europe; 0.443 % (95%CI: 0.213 %, 0.673 %, z = 3.77, P < 0.001) in Asia; 1.055 % in HCWs; and 0.645 % in medical students. CONCLUSION: This study highlights that the COVID-19 pandemic can significantly impact the prevalence of suicidal ideation among HCWs and medical students, and the prevalence showed an upward trend.

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PURPOSE: Since late 2017, the use of ulipristal acetate 5 mg (UPA; Proprietary name: Esmya) has been under review in the European Union, due to an emerging hepatic risk. In February 2018 and in July 2018, the Spanish Agency of Medicines and Medical Devices and the marketing authorization holder put two risk minimization measures (RMM) in place, in order to inform about new safety information and to mitigate this risk. This study aims to assess RMM effectiveness in Spain, by performing an interrupted time-series (ITS) analyses, between 2014 and 2019. METHOD: Two quasi-experimental ITS analyses to examine the use of UPA before and after the RMM release were performed: (a) an ecological study using aggregated data from a drug consumption database; and (b) a study using primary healthcare data gathered from electronic clinical records. RESULTS: Regulatory interventions were associated with an immediate and significant decrease level of DID (the number of DDD dispensed per 100 000 inhabitants and day) and incidence. The DID was 70% less than expected 12 months after the interventions. This value was 59% for the incidence. However, a change in the slope was not observed and the use started rising again in the last segment of the study period. CONCLUSION: Despite RMM had an immediate strong impact on UPA use, the last segment upward trend in the long-term might have been affected by the lack of comparable therapeutic alternatives. Further studies should be performed to confirm the increase trend observed and analyze subsequent measures and additional data.

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Combining medical IoT and artificial intelligence technology is an effective approach to achieve the intelligence of medical equipment. This integration can address issues such as low image quality caused by fluctuations in power quality and potential equipment damage, and this study proposes a predictive model, ISSA-TCN-BiLSTM, based on a bi-directional long short-term memory network (BiLSTM). Firstly, power quality data and other data from MRI and CT equipment within a 6-month period are collected using current fingerprint technology. The key factors affecting the active power of medical equipment are explored using the Pearson coefficient method. Subsequently, a Temporal Convolutional Network (TCN) is employed to conduct multi-layer convolution operations on the input temporal feature sequences, enabling the learning of global temporal feature information while minimizing the interference of redundant data. Additionally, bidirectional long short-term memory (BiLSTM) is integrated to model the intermediate active power features, facilitating accurate prediction of medical equipment power quality. Additionally, an improved Sparrow Search Algorithm (ISSA) is utilized for hyperparameter optimization of the TCN-BiLSTM model, enabling optimization of the active power of different medical equipment. Experimental results demonstrate that the ISSA-TCN-BiLSTM model outperforms other comparative models in terms of RMSE, MSE, and R2, with values of 0.1143, 0.1157, 0.0873, 0.0817, 0.95, and 0.96, respectively, for MRI and CT equipment. This model exhibits both prediction speed and accuracy in power prediction for medical equipment, providing valuable guidance for equipment maintenance and diagnostic efficiency enhancement.

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This study investigates the incidence of Class B respiratory infectious diseases (RIDs) in China under the Coronavirus disease 2019 (COVID-19) epidemic and examines variations post-epidemic, following the relaxation of non-pharmaceutical interventions (NPIs). Two-stage evaluation was used in our study. In the first stage evaluation, we established counterfactual models for the pre-COVID-19 period to estimate expected incidences of Class B RIDs without the onset of the epidemic. In the second stage evaluation, we constructed seasonal autoregressive integrated moving average intervention (SARIMA-Intervention) models to evaluate the impact on the Class B RIDs after NPIs aimed at COVID-19 pandemic were relaxed. The counterfactual model in the first stage evaluation suggested average annual increases of 10.015%, 78.019%, 70.439%, and 67.799% for tuberculosis, scarlet fever, measles, and pertussis respectively, had the epidemic not occurred. In the second stage evaluation, the total relative reduction in 2023 of tuberculosis, scarlet fever, measles and pertussis were - 35.209%, - 59.184%, - 4.481%, and - 9.943% respectively. The actual incidence declined significantly in the first stage evaluation. However, the results of the second stage evaluation indicated that a rebound occurred in four Class B RIDs after the relaxation of NPIs; all of these showed a negative total relative reduction rate.

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The association of short-term ambient air pollution exposure with osteoarthritis (OA) outpatient visits has been unclear and no study has assessed the modifying roles of district-level characteristics in the association between ambient air pollution exposure and OA outpatient visits. We investigated the cumulative associations of ambient air pollution exposure with daily OA outpatient visits and vulnerable factors influencing the associations using data from 16 districts of Beijing, China during 2013-2019. A total of 18,351,795 OA outpatient visits were included in the analyses. An increase of 10 µg/m3 in fine particulate matter (PM2.5), inhalable particulate matter (PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), maximum 8-hour moving-average ozone (8 h-O3), and 0.1 mg/m3 in carbon monoxide (CO) at representative lag days were associated with significant increases of 0.31 %, 0.06 %, 0.77 %, 0.87 %, 0.30 %, and 0.48 % in daily OA outpatient visits, respectively. Considerable OA outpatient visits were attributable to short-term ambient air pollution exposure. In addition, low temperature and high humidity aggravated ambient air pollution associated OA outpatient visits. District-level characteristics, such as population density, green coverage rate, and urbanization rate modified the risk of OA outpatient visits associated with air pollution exposure. These findings highlight the significance of controlling ambient air pollution during the urbanization process, which is useful in policy formation and implementation.

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Breast cancer (BC) is the most common neoplasm, and its global burden has become one of the most important factors jeopardizing the health of the world population, especially women. The aim of this study was to analyze mortality trends and the spatial distribution of BC in women in the capital and state of Sergipe, aiming to contribute to the implementation and improvement of strategies for the prevention and health promotion of women with BC. Trends were calculated using the Joinpoint Regression Program 5.0.2. Spatial analyses were performed using the empirical Bayesian model, thematic maps were created using QGIS 3.10.7 and Moran's I indices were calculated using TerraView 4.2.2. Between 1996 and 2022, 1384 and 3128 BC deaths were recorded in the capital and state of Sergipe, respectively. The mortality trend increased in the age groups of 45-75+ for the state of Sergipe, while in the capital, we observed stability in all age groups. The highest AAPC was 4.6213, with a 95 % confidence interval (2.16; 7.14). Univariate global Moran's I analysis indicated spatial autocorrelation during the study period. A direct relationship was found between mortality rates and the more economically developed regions.

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During infectious disease outbreaks, estimates for the instantaneous reproduction number, R(t), are essential for understanding transmission dynamics. This study develops and analyzes new methodology to improve estimation of R(t) when observed case counts are subject to reporting patterns and available serial interval estimates are subject to uncertainty and non-representativeness. Specifically, we developed a Bayesian time-since-infection model with layers to adjust for reporting measurement error, integrate multiple candidate serial interval estimates, and estimate transmission with an autoregressive time-series model incorporating factors relevant to transmission. Additionally, we provide practical tools to identify reporting patterns and determine when to smooth case counts for more usable R(t) estimates. We evaluated model performance relative to widely adopted methodology by simulating outbreak data, finding improved R(t) estimation with the proposed methodology. We also used 2020 COVID-19 data to analyze transmission trends and predictors, identifying strong day-of-week and social distancing effects that subsequently reduced estimate volatility. In addition to new approaches for addressing serial interval uncertainty and incorporating transmission predictor information, this study provides an alternative approach for addressing case-reporting patterns without delaying detection or smoothing over relevant transmission signals. These tools and findings may be used or built upon for current and future outbreaks.

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BACKGROUND: Malaria remains an important public health problem, particularly in sub-Saharan Africa. In Rwanda, where malaria ranks among the leading causes of mortality and morbidity, disease transmission is influenced by climatic factors. However, there is a paucity of studies investigating the link between climate change and malaria dynamics, which hinders the development of effective national malaria response strategies. Addressing this critical gap, this study analyses how climatic factors influence malaria transmission across Rwanda, thereby informing tailored interventions and enhancing disease management frameworks. METHODS: The study analysed the potential impact of temperature and cumulative rainfall on malaria incidence in Rwanda from 2012 to 2021 using meteorological data from the Rwanda Meteorological Agency and malaria case records from the Rwanda Health Management and Information System. The analysis was performed in two stages. First, district-specific generalized linear models with a quasi-Poisson distribution were applied, which were enhanced by distributed lag non-linear models to explore non-linear and lagged effects. Second, random effects multivariate meta-analysis was employed to pool the estimates and to refine them through best linear unbiased predictions. RESULTS: A 1-month lag with specific temperature and rainfall thresholds influenced malaria incidence across Rwanda. Average temperature of 18.5 °C was associated with higher malaria risk, while temperature above 23.9 °C reduced the risk. Rainfall demonstrated a dual effect on malaria risk: conditions of low (below 73 mm per month) and high (above 223 mm per month) precipitation correlated with lower risk, while moderate rainfall (87 to 223 mm per month) correlated with higher risk. Seasonal patterns showed increased malaria risk during the major rainy season, while the short dry season presented lower risk. CONCLUSION: The study underscores the influence of temperature and rainfall on malaria transmission in Rwanda and calls for tailored interventions that are specific to location and season. The findings are crucial for informing policy that enhance preparedness and contribute to malaria elimination efforts. Future research should explore additional ecological and socioeconomic factors and their differential contribution to malaria transmission.

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PurposeThis study aims to analyze trends in youth suicide rates and the odds of suicide comparing different age groups from 2000 to 2022 in Brazil. MethodsWe conducted a temporal trend study on suicide mortality in Brazil from 2000 to 2022, using data from the Mortality Information System and population projections from the Brazilian Institute of Geography and Statistics (IBGE). We calculates mortality rates for the following age groups: adolescents (10-19 years), young adults (20-29 years), youth (10-29 years), adults including youth (20 years and older), and adults excluding youth (30 years and older). We applied Joinpoint regression to identify changes in trends and calculated the Mortality Odds Ratio (MOR) to compare suicide mortality between youth and adults. ResultsBetween 2000 and 2022, suicide mortality significantly increased among adolescents, with a higher average annual percentage change compared to young adults. The Mortality Odds Ratio (MOR) for youth was 5.05 in 2000, rising to 8.56 in 2022. For adolescents, the MOR increased from 3.27 to 8.86 over the same period, indicating that in 2022, the suicide risk was 21% higher among adolescents than young adults, highlighting a significant reversal in the context of the Covid-19 pandemic. DiscussionThese findings underscore the urgent need for national mental health policies targeted at adolescents, particularly those in high vulnerability. The results of this study provide an evidence base for policymakers and public health officials to develop and implement interventions aimed at curbing the rising trend of youth suicide in Brazil.

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Outcrops play an important role in groundwater recharge. Understanding groundwater origins, dynamics and its correlation with different water sources is essential for effective water resources management and planning in terms of quantity and quality. In the case of the Guarani Aquifer System (GAS) outcrop areas are particularly vulnerable to groundwater pollution due to direct recharge processes. This study focuses on the Alto Jacaré-Pepira sub-basin, a watershed near Brotas, a city in the central region of the state of São Paulo, Brazil, where groundwater is vital for supporting tourism, agriculture, urban water supply, creeks, river and wetlands. The area has a humid tropical climate with periods of both intense rainfall and drought, and the rivers remain perennial throughout the year. Therefore, the aim of this study is to investigate the interconnections between a spring and its potential sources of contribution, namely rain and groundwater, in order to elucidate the relationships between the different water sources. To achieve this, on-site monitoring of groundwater depth, rainfall amount, and stable isotope ratios (deuterium (2H) and oxygen-18 (18O)) from rain, spring discharge, and a monitoring well was carried out from 2013 to 2021. The results indicate that the mean and standard deviations for δ18O in rainwater exhibit higher variability, resulting in -4.49 ± 3.18 ‰ VSMOW, while δ18O values from the well show minor variations, similar to those of the spring, recording -7.25 ± 0.32 ‰ and -6.94 ± 0.28 ‰ VSMOW, respectively. The mixing model's outcomes reveal seasonal variations in water sources contribution and indicate that groundwater accounts for approximately 80 % of spring discharge throughout the year. Incorporating stable isotopes into hydrological monitoring provides valuable data for complementing watershed analysis. The values obtained support the significance of the aquifer as a primary source, thereby offering critical insights into stream dynamics of the region.

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Objectives: To assess the effects of the transparent online open procurement arrangement on the prices, volumes, and costs of medicines in Ningxia, China. Methods: Data were extracted from the Ningxia pharmaceutical procurement platform, covering 16 months of purchase orders (December 2019 to March 2021) prior to the implementation of the transparent online open procurement policy and 20 months of purchase orders after the implementation of the policy (April 2021 to November 2022). Interrupted time series (ITS) analysis was performed to evaluate the effects of the transparent online open procurement policy on the prices, volumes, and total costs of the purchase orders. Results: After implementation of the transparent online open procurement policy, the average price of purchased medicines showed a declining trend by 0.012 Yuan per month, while the total volume of purchase orders declined at a rate by 1.741 million per month measured by the smallest formulation units and the total costs of the purchase orders decreased at a rate by 5.525 million Yuan per month. Conclusion: The transparent online open procurement policy resulted in reduced prices, lowered volumes, and lowered total costs of purchased orders of medicines.

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INTRODUCTION: This study aimed to assess the impact of the implementation of legally sanctioned supervised consumption sites (SCS) in the Canadian province of Ontario on opioid-related deaths, emergency department (ED) visits and hospitalisations at the public health unit (PHU) level. METHODS: Monthly rates per 100,000 population of opioid-related deaths, ED visits and hospitalisations for PHUs in Ontario between December 2013 and March 2022 were collected. Aggregated and individual analyses of PHUs with one or more SCS were conducted, with PHUs that instituted an SCS being matched to control units that did not. Autoregressive integrated moving average models were used to estimate the impact of SCS implementation on opioid-related deaths, ED visits and hospitalisations. RESULTS: Twenty-one legally sanctioned SCS were implemented across nine PHUs in Ontario during the study period. Interrupted time series analyses showed no statistically significant changes in opioid-related death rates in aggregated analyses of intervention PHUs (increase of 0.02 deaths/100,000 population/month; p = 0.27). Control PHUs saw a significant increase of 0.38 deaths/100,000 population/month; p < 0.001. No statistically significant changes were observed in the rates of opioid-related ED visits in intervention PHUs (decrease of 0.61 visits/100,000 population/month; p = 0.39) or controls (increase of 0.403 visits; p = 0.76). No statistically significant changes to the rates of opioid-related hospitalisations were observed in intervention PHUs (0 hospitalisations/100,000 population/month; p = 0.98) or controls (decrease of 0.05 hospitalisations; p = 0.95). DISCUSSION AND CONCLUSIONS: This study did not find significant mortality or morbidity effects associated with SCS availability at the population level in Ontario. In the context of a highly toxic drug supply, additional interventions will be required to reduce opioid-related harms.

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This paper presents a novel approach to the phase space reconstruction technique, fractional-order phase space reconstruction (FOSS), which generalizes the traditional integer-order derivative-based method. By leveraging fractional derivatives, FOSS offers a novel perspective for understanding complex time series, revealing unique properties not captured by conventional methods. We further develop the multi-span transition entropy component method (MTECM-FOSS), an advanced complexity measurement technique that builds upon FOSS. MTECM-FOSS decomposes complexity into intra-sample and inter-sample components, providing a more comprehensive understanding of the dynamics in multivariate data. In simulated data, we observe that lower fractional orders can effectively filter out random noise. Time series with diverse long- and short-term memory patterns exhibit distinct extremities at different fractional orders. In practical applications, MTECM-FOSS exhibits competitive or superior classification performance compared to state-of-the-art algorithms when using fewer features, indicating its potential for engineering tasks.

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BACKGROUND: Federal deregulation of opioid agonist therapies are an attractive policy option to improve access to opioid use disorder care and achieve widespread beneficial impacts on growing opioid-related harms. There have been few evaluations of such policy interventions and understanding effects can help policy planning across jurisdictions. METHODS: Using health administrative data from eight of ten Canadian provinces, this study evaluated the impacts of Health Canada's decision in May 2018 to rescind the requirement for Canadian health professionals to obtain an exemption from the Canadian Drugs and Substance Act to prescribe methadone for opioid use disorder. Over the study period of June 2017 to May 2019, we used descriptive statistics to capture overall trends in the number of agonist therapy prescribers across provinces and we used interrupted time series analysis to determine the effect of this decision on the trajectories of the agonist therapy prescribing workforces. RESULTS: There were important baseline differences in the numbers of agonist therapy prescribers. The province with the highest concentration of prescribers had 7.5 more prescribers per 100,000 residents compared to the province with the lowest. All provinces showed encouraging growth in the number of prescribers through the study period, though the fastest growing province grew 4.5 times more than the slowest. Interrupted time series analyses demonstrated a range of effects of the federal policy intervention on the provinces, from clearly positive changes to possibly negative effects. CONCLUSIONS: Federal drug regulation policy change interacted in complex ways with provincial health professional regulation and healthcare delivery, kaleidoscoping the effects of federal policy intervention. For Canada and other health systems such as the US, federal policy must account for significant subnational variation in OUD epidemiology and drug regulation to maximize intended beneficial effects and mitigate the risks of negative effects.

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BACKGROUND: Outreach clinics were part of efforts to maximise uptake in COVID-19 vaccination. METHODS: We used controlled interrupted time series, matching on age, sex, deprivation and vaccination eligibility date, to determine the effect of outreach clinics on time to first COVID-19 vaccine, using a population-based electronic health record database of 914,478 people, from December 2020 to December 2021; people living within 1 mile of each outreach clinics were exposed. RESULTS: 50% of 288,473 exposed citizens were white British, and 71% were aged 0-49 years. There was no evidence for an overall statistically significant increase in cumulative percentage vaccinated due to the outreach clinic at 6 weeks, with an overall pooled effect estimate of -0.07% (95% CI: -1.15%, 1.02%). The pooled estimate for increased cumulative vaccine uptake varied slightly depending on how the analysis was stratified; by ethnic group it was - 0.12% (95% CI: -0.90%, 0.66%); by age group it was - 0.06% (95% CI: -0.41%, 0.28%); and by deprivation it was 0.03% (95% CI: -0.74%, 0.79%). CONCLUSIONS: Living within a mile of an outreach clinic was not associated with higher vaccine uptake. Evaluation of future outreach clinics should consider the relative importance of travel amongst other barriers to accessing vaccines.

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PURPOSE: Due to overdiagnosis, the incidence of thyroid cancer (TC) has increased in high-income countries, including Italy. Efforts have been made to address this issue since the mid-2010s, but more information is needed about how TC incidence has changed. We aim to examine the trend in TC incidence in the Lazio Region (central Italy) and assess the impact of the 2014 Italian Consensus for the Classification and Reporting of Thyroid Cytology (ICCRTC) to identify potential changes in TC diagnosis. METHODS: To identify TC cases, we conducted a population-based study (period 2007-2019) using the data from the Lazio Region Cancer Registry (5.8 million residents). We calculated the annual age-standardized incidence rates of TC for both sexes and analyzed the impact of ICCRTC on monthly incidence rates using segmented linear regression applied to interrupted time-series (ITS). RESULTS: Throughout the 13 years, there was a significant decline in TC annual incidence rates, more pronounced in females. Our results are consistent with reports from outside Europe (United States and South Korea). Following ICCRTC implementation in 2014, a step-change reduction in both sexes was revealed. CONCLUSIONS: Our study indicates a significant decrease in the incidence of TC, particularly among females. The ITS analysis highlights the possible role of ICCRTC in reducing overdiagnosis. As the Lazio Region reflects the Italian population in terms of various demographic, health, and lifestyle indicators, our findings can be applicable at the national level.

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The increasing use of time-series analyses in exploring the relationship between daily ambient temperature and mortality has expanded our understanding of the potential health impacts of climate change. However, it raises significant concerns about the risk of overinterpretation and misattribution of statistical findings. This review examines the methodological assumptions and interpretation pitfalls prevalent in current research on ambient temperature-mortality associations. Extremely elevated ambient temperatures are well-known to elicit physiological stress and increase mortality risk; however, there is no physiological evidence for lethality risk within normal ambient temperature ranges. Despite this, many studies attribute mortality risks across the entire ambient temperature-mortality curve, including normal range ambient temperatures, thus oversimplifying complex underlying physiological processes. Overinterpretation may lead to inaccurate assessments and misguided public health policies. We caution against the tendency to extrapolate results from extreme heat conditions to milder, more typical summer ambient temperature ranges. We advocate for an interdisciplinary approach that combines physiological, clinical, and epidemiological perspectives, with a strong emphasis on the role of behavioral thermoregulation and socio-economic factors to link normal range ambient temperatures with mortality. We recommend analyses centered on excess mortality during defined heatwave periods, and to incorporate heat stress biomarkers to substantiate causal claims for temperatures below heatwaves threshold. A careful approach to interpreting ambient temperature-mortality associations is crucial for formulating evidence-based public health policies.

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Introduction: Both high- and low-income countries reported increased antibiotic consumption among COVID-19 patients during the first months of the pandemic. To date, however, no studies have examined changes in antibiotic consumption during the COVID-19 pandemic within humanitarian emergency contexts. Method: Data was collected by Médecins Sans Frontières (MSF) for the years 2018-2021 across the following humanitarian settings: Afghanistan (Lashkar Gah), Bangladesh (Kutupalong), the Democratic Republic of Congo (Mweso and Baraka), and South Sudan (Bentiu). Inpatient and outpatient antibiotic consumption was calculated as Daily Defined Dose (DDD) per 1000 inhabitants per day, as per the World Health Organisation's (WHO) Collaborating Centre for Drug Statistics Methodology. Interrupted time series (ITS) analysis, using an autoregressive integrated moving average (ARIMA) model was used to analyse retrospective monthly antibiotic consumption. The impact of COVID-19 pandemic was evaluated as total antibiotic consumption and according to WHO Access, Watch, Reserve (AWaRe) group classifications within each humanitarian setting. Results: The COVID-19 pandemic had no statistically significant impact on total antibiotic consumption in South Sudan (Bentiu) and Bangladesh (Kutupalong). Similarly, the pandemic had no impact on total antibiotic consumption in DR Congo (Baraka), despite an initial 0.27% (estimate=.274, p-value=0.006) increase in March 2020 driven by Access group antibiotics. Meanwhile, total antibiotic consumption in DR Congo (Mweso) and Afghanistan (Lashkar Gah) declined by 0.74% (estimate = -.744, p = 0.003) and 0.26% (estimate = -.26, p < 0.001), respectively with the COVID-19 pandemic. Conclusion: Further studies are required to investigate what may have contributed to these results.

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This study investigates the forecasting of cardiovascular mortality trends in Greece's elderly population. Utilizing mortality data from 2001 to 2020, we employ two forecasting models: the Autoregressive Integrated Moving Average (ARIMA) and Facebook's Prophet model. Our study evaluates the efficacy of these models in predicting cardiovascular mortality trends over 2020-2030. The ARIMA model showcased predictive accuracy for the general and male population within the 65-79 age group, whereas the Prophet model provided better forecasts for females in the same age bracket. Our findings emphasize the need for adaptive forecasting tools that accommodate demographic-specific characteristics and highlight the role of advanced statistical methods in health policy planning.

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