Frontiers in epidemiology最新文献

Malaria control efforts in Kenya face persistent challenges due to fragmented health information systems, despite notable digital innovations. This mini review evaluates implementations in western Kenya, contrasting successes like Siaya County's Electronic Community Health Information System (eCHIS), developed through collaborations between the Ministry of Health, local agencies, and frontline health workers, which reduces reporting delays through real-time mobile data collection, with ongoing struggles including paper-based records in health facilities and unreliable rural internet. We document how analytical methods, when properly supported, can transform surveillance. Methods such as spatiotemporal models using climate and case data can improve outbreak predictions, while machine learning techniques can optimize insecticide-treated bed net distributions by pinpointing high-risk households. However, these analytical tools remain underutilized due to data fragmentation and limited technical capacity. Key implementation challenges emerged, including device charging difficulties for community health workers, inconsistent data standards between systems, and privacy concerns under Kenya's new Digital Health Act that policymakers are currently addressing through revised guidelines. Key recommendations from this review include the expansion of digital health platforms with co-design input from end-users, improved data quality through standardized reporting mechanisms enforced by county health leadership, and the incorporation of predictive modeling to identify high-risk areas and optimize intervention timing. Investing in robust health information infrastructure will not only strengthen malaria control efforts in Kenya but also serve as a model for other malaria-endemic regions. Digital tools show tremendous potential when paired with sustained training, community engagement, and realistic maintenance solutions supported by public-private partnerships.

Background: Two critical factors in the success of the response to a threatening epidemic outbreak are the degree of responsibility of the main political actors involved in the response and the population compliance to the proposed measures. The Behavioural epidemiology literature has focused on the latter factor but largely disregarded the former. The multiple failures in COVID-19 control and the lack of consensus that still surround the main response options (i.e., the elimination-suppression-mitigation trichotomy) highlight the importance of considering the political layer in preparedness activities.

Methods: We integrate a simple transmission model into a game-theoretic framework for the interaction between the main political actors involved in the response, namely a government, its opposition and lobbies. The aim is to provide a conceptual framework allowing one to identify the political factors promoting a timely and effective response.

Results: Low degrees of responsibility (i.e., prioritizing consensus over health protection) of political agents can delay or de-potentiate the response until when epidemic growth eventually overtakes the agents' payoffs, thereby forcing them to switch towards the higher degree of responsibility needed to promote an adequate response. When both the government and the opposition are only "partly" responsible, a stall in the response decision-making process likely arises, further delaying the response. Policy and epidemiological parameters amplifying the response delay are ranked by a sensitivity analysis.

Conclusions: Promoting a high degree of responsibility of political actors and lobbies during emergency situations should be a key target of preparedness. Therefore, future pandemic plans should also include, beyond technical indications, ethical statements "guiding" political entities to cooperation.

Background: Although tuberculosis mortality has dramatically decreased over the last decade, tuberculosis remains the world's biggest cause of death. Conflict affected nations hold vast majority of malnourished people globally, where many people die each year of tuberculosis. With regard to the global burden of tuberculosis, Ethiopia ranks third in the African continent and seventh overall. But in the research arena, the severity of the issue is not as well understood. Therefore, the current study aimed to assess undernutrition and the determinant factors among adult TB-patients receiving treatment in public health facilities in conflict affected zones of Southern.

Methods: A multicenter facility-based cross-sectional study was conducted from 27/08/2023-28/ 09/2023 among 414 randomly selected adult (age ≥18 years) TB-patients receiving treatment at public health facilities in conflict affected zones of Southern Ethiopia. An interviewer-administered questionnaire and anthropometric measurements were used to collect data from study participants after written informed consent provision. By using SPSS Version 25, bivariate and multivariable logistic regression models were employed to determine the factors related to nutritional status.

Results: Overall, 33.3% of study participants had undernutrition, with a [95% CI (28.8%-38.1%)]. Factors such as cigarette smoking [AOR = 2.02, 95% CI; 1.22, 3.34] chat chewing [AOR = 2.50, 95% CI; 1.59, 3.93] regular cheka drinking [AOR = 1.82; 95% CI, 1.22-2.71] and household food insecurity [AOR = 1.78, 95% CI; 1.19, 2.66] had significant association with undernutrition.

Conclusions: The results of this study show that undernutrition affects one in three adult TB patients. Lifestyle factors such as smoking and chewing, and dietary factors like cheka eating and household food security had significant association with undernutrition. In order to improve the quality of life for TB patients, it is imperative that all stakeholders should prioritize addressing the lifestyle and nutritional aspects that are essential to the effectiveness of TB control and prevention initiatives.

Background: Evidence is limited on the impact of temperature variability (TV) on health in low-and-middle-income countries (LMICs), such as South Africa. This study examined the association between TV and cardiovascular disease (CVD) and respiratory disease (RD) mortality in five South African cities.

Methods: Daily mortality and meteorological data in five South African cities (Bloemfontein, Cape Town, Durban, Johannesburg, and Gqeberha) were collected from Statistics South Africa and the South African Weather Service for the period 2006-2016. TV was calculated as the standard deviation of the daily minimum and maximum temperatures over the exposure period. City-specific risks were estimated using quasi-Poisson regression models combined with distributed lag nonlinear models, adjusting for potential confounders. A meta-analysis was then conducted to pool the overall estimates across cities. Additionally, stratified analyses by age group and sex were performed to assess effect modification.

Results: A total of 213,875 cardiovascular and 114,887 respiratory deaths were recorded in the five cities during the study period. The risks with increasing TV were higher for RD mortality as compared to CVD mortality. The pooled estimates showed the highest and significant increase in RD mortality of 1.21(95% CI: 1.04;1.38) per an increase in TV at 0-2 days from the 25th to the 50th percentile for all ages combined. The elderly appeared more vulnerable to RD mortality than <65 years age group, with significant mortality risks per increase in TV at 0-2 days (RR = 1.18, 95% CI: 1.04; 1.32),0-3 days (RR = 1.16, 95% CI: 1.04; 1.28) and at 0-7 days (RR = 1.12, 95% CI: 1.02; 1.22) from the 50th to the 75th percentile. A stratified analysis showed the elderly and women as more vulnerable. The pooled results across the five cities suggested no statistically significant TV effect on CVD mortality.

Conclusion: This study found a short-term association between temperature variability and respiratory mortality, especially among elderly individuals and women, in five South African cities. No significant effect was observed for cardiovascular mortality. The findings support targeted public health strategies that account for temperature-related risks in vulnerable populations.

Introduction: Long COVID (LC) is a multisystem condition with prolonged symptoms persisting beyond acute SARS-CoV-2 infection. However, prevalence estimates vary widely due to differences in case definitions and sampling methodologies. This study aims to determine the prevalence of LC across different definitions and correct for selection bias using advanced statistical modeling.

Methods: We conducted a retrospective, observational study at Luigi Sacco Hospital (Milan, Italy), analyzing 3,344 COVID-19 patients from two pandemic waves (2020-2021). Participants included 1,537 outpatients from the ARCOVID clinic and 1,807 hospitalized patients. LC was defined based on WHO and NICE criteria, as well as two alternative definitions: symptoms persisting at 3 and 6 months post-infection. We used a bivariate censored Probit model to account for selection bias and estimate adjusted LC prevalence.

Results: LC prevalence varied across definitions: 67.4% (WHO), 76.3% (NICE), 80.2% (3 months), and 79.6% (6 months). Adjusted prevalence estimates remained consistent across definitions. The most common symptoms were fatigue (58.6%), dyspnea (41.1%), and joint/muscle pain (39.2%). Risk factors included female sex (OR 2.165-2.379), metabolic disease (OR 1.587-1.629), and older age (40-50 years, OR 1.847). Protective factors included antiplatelets (OR 0.640-0.689), statins (OR 0.616), and hypoglycemics (OR 0.593-0.706). Vaccination, hydroxychloroquine, and antibiotics were associated with an increased risk of LC. Selection bias significantly influenced prevalence estimates, underscoring the need for robust statistical adjustments.

Discussion: Our findings highlight the high prevalence of LC, particularly among specific subgroups, with strong selection effects influencing outpatient participation. Differences in prevalence estimates emphasize the impact of case definitions and study designs on LC research. The identification of risk and protective factors supports targeted interventions and patient management strategies.

Conclusion: This study provides one of the most comprehensive analyses of LC prevalence while accounting for selection bias. Our findings call for standardized LC definitions, improved epidemiological methodologies, and targeted prevention strategies. Future research should explore prospective cohorts to refine LC prevalence estimates and investigate long-term health outcomes.

Background: Loneliness is a growing public health issue, particularly among older adults, owing to various internal and external factors related to ageing. However; evidence regarding this segment of the Ethiopian population is scarce. Therefore, this study aimed to assess the prevalence of loneliness and its associated factors among older adults in Ethiopia.

Methods: This community-based cross-sectional study was conducted from April 20 to May 20, 2023. A multistage systematic sampling technique, using an interviewer-administered questionnaire, was used. Data were entered into Epi Data version 4.6.0.0 and exported to the Stata version 14 software for analysis. A binary logistic regression analysis was conducted. Variables with a p-value < 0.20 in the Bivariable analysis were entered into multivariable regression and variables with a p-value < 0.05, with a 95% confidence interval (CI) were considered statistically significant.

Results: A total of 840 older adults took part with a 99.2% response rate. The overall prevalence of loneliness among older adults was 48.69%, with 95% CI = 45.31-52.07%. Living alone (OR=2.59, 95% CI = 1.11-6.05), the presence of chronic illness (OR = 1.69, 95% CI = 1.12-2.54), sleep time greater than 9 h (OR = 1.56, 95% CI = 1.08-2.22), impairment (OR = 5.09, 95% CI = 3.17-8.19), and poor social support (OR = 4.38, 95% CI = 2.53-7.59) were positively, but family size <5 (OR = 0.62, 95% CI = 0.45-0.85) and good health status (OR = 0.43, 95% CI = 0.27-0.66) were negatively associated with loneliness among older adults.

Conclusions: Nearly half of the participants felt lonely. Hence, every concerned body should pay special attention to this sidelined segment of the population by creating better social support networks, providing a conducive living environment, and providing aid to impaired older adults.

Background: Lower respiratory infections are the leading cause of paediatric morbidity and mortality. This study documents the incidence and etiology of influenza-like illness (ILI) among young children in rural eastern India.

Methods: We conducted a surveillance of ILI in children visiting paediatric clinics of two hospitals in District West Midnapore, West Bengal from April 1, 2022 to March 31, 2023. Nasopharyngeal swabs were collected from children 0-2 years of age with ILI and tested for influenza, respiratory syncytial virus (RSV), and SARS-CoV-2; a representative set for the respiratory panel.

Results: Of 9,923 outpatient children 0-14 years of age screened, 1,001 had ILI, of which 439 (44%) were in 0-2-year-olds. The ILI incidence was 439/4,310 [10.2% (95% CI: 9.29-11.1)] in the 0-2-year-olds, 288/2,473 [11.6% (10.4-12.9)] in >2-5-year-olds, and, 274/3,140 [8.7% (7.7-9.7)] in >5-14-year-olds. Of 390 enrolled children (median age: 12 months), viruses were identified in 23.3%, occurring singly (15%) or with other viruses (1.3%). RSV was the most common virus (12.6%), followed by influenza (6.6%) and SARS-CoV-2 (0.77%). Influenza subtypes included IA/H3 (50%), IA/H1N1pdm2009 (34.6%) and IB (15.4%). IA/H1N1pdm09 predominated during the 2022 monsoon, RSV during 2022 autumn and A/H3 and B during 2023 winters. Cough and difficulty breathing were associated with RSV. The major bacteria detected were Streptococcus pneumoniae (55.5%), Haemophilus influenzae (29%) and Moraxella catarrhalis (3.7%). Other viruses were parainfluenza virus 3 (4.4%), bocavirus (3.7%) and adenovirus (3%). Viral-bacterial co-detections were frequent (20%). Seventeen children required hospitalization, with difficulty breathing increasing hospitalization risk (OR = 4.47, 95% CI: 1.67-12). Children with RSV had increased odds of hospitalization (OR = 3.11, 95% CI: 1-9.26).

Conclusions: The majority of ILI was observed in children aged 0-2 years, with RSV and influenza as major viral causes associated with ILI. RSV increased the risk of hospitalization. These findings contribute to building the evidence base for maternal RSV immunization policy in India.

Background: Access to emergency care (EC) services is crucial for severe anaemia outcome. Limited information exists on the association between travel times to EC services and the presentation and severity of anaemia upon hospital admission. Here, we investigate the association between travel time and presentation of severe anaemia (compared to mild/moderate anaemia) at admission in western Kenya.

Methods: Data from January 2020 to July 2023 from Busia County Referral Hospital were assembled for paediatric admissions aged 1-59 months residing in Busia County. Travel time from a patient's village to the hospital was calculated using a least cost path algorithm. Anaemia severity was categorised as mild (Hb ≥ 7-<10 g dl^-1), moderate (Hb ≥ 5-<7 g dl^-1) and severe (Hb < 5 g dl^-1). We fitted a geostatistical model accounting for covariates to estimate the association between travel times to EC services and severe anaemia presentation.

Results: Severe anaemia admissions had the highest median travel time of 36 min (IQR: 25,54) (p-value: <0.001). Compared to children living within a 30 min travel time to the hospital, the adjusted odds ratio (AOR) of severe anaemia presentation relative to mild/moderate anaemia was 2.44 (95% CI: 1.63-3.55) for those residing within 30-59 min. For travel times of 60-89 min, the AOR was 3.55 (95% CI: 1.86-6.10) and for ≥90 min, the AOR was 3.41 (95% CI: 1.49-7.67).

Conclusion: Travel time is significantly associated with the severity of paediatric anaemia presentations at hospitals. Addressing disparities in travel times such as strategic bolstering of lower-level facilities to offer EC services, is crucial for implementing new interventions and optimizing existing hospital-linked interventions to enhance healthcare delivery.

We present Regional Psychologically Valid Agents (R-PVAs) as a modeling approach to predicting transmission-reducing behaviors and epidemiology. The approach builds upon computational cognitive theory and formalizes aspects of theories of individual-level behavior change. We present R-PVA models of social distancing and mask wearing in response to dynamics in the physical and information environments in the 50 U.S. states. The models achieve strong goodness-of-fits for predicting day-to-day mask-wearing (R ² = 0.93) and social distancing (R ² = 0.62) for the first three waves of COVID-19, prior to the rollout of vaccines.

Background: Multiple myeloma (MM) is a malignant hematologic disorder characterized by the abnormal clonal proliferation of bone marrow plasma cells and excessive production of immunoglobulins, often leading to severe organ damage. Due to its high incidence, recurrence, and death rates, MM poses a significant burden on individuals and global healthcare systems. This study leverages the latest data from the Global Burden of Disease Study 2021 (GBD 2021) to analyze the epidemiological trends of MM and propose effective preventive strategies.

Methods: Using data from GBD 2021, we analyzed the age-standardized incidence rate (ASIR), death rate (ASDR), and disability-adjusted life years (DALYs) of MM, evaluating temporal trends through estimated annual percentage change (EAPC). Pearson correlation analysis was employed to explore the relationship between age-standardized rates (ASRs) and the Sociodemographic Index (SDI). Additionally, frontier analysis was conducted. Finally, Bayesian age-period-cohort models were utilized to predict the trends of MM ASRs through 2040.

Results: In 2021, the global number of new MM cases was 148,755 (95% UI: 131,780.4-162,049.2), with 116,359.6 deaths (95% UI: 103,078.6-128,470.6) and 2,595,595 DALYs (95% UI: 2,270,483.6-2,889,968.2). Age-standardized rates increased with age. Between 1990 and 2021, the global burden of MM exhibited a consistent upward trend across all populations, with males and older adults bearing the highest burden. The analysis demonstrated a positive correlation between ASRs and the SDI. Frontier analysis indicated regions with medium-to-high SDI have the greatest potential for reducing ASRs. Among all risk factors, high body mass index (BMI) was identified as the most significant contributor to MM. Projections suggest that by 2040, the global burden of MM may experience a decline.

Conclusion: Driven by population aging and advancements in diagnostic capabilities, the global burden of multiple myeloma continues to rise. Targeted prevention and treatment strategies, particularly for elderly and high-risk populations, are essential to alleviate the disease burden and improve patient outcomes.