Infectious Diseases of Poverty最新文献

Background: Coronavirus disease 2019 (COVID-19) is still ongoing spreading globally, machine learning techniques were used in disease diagnosis and to predict treatment outcomes, which showed favorable performance. The present study aims to predict COVID-19 severity at admission by different machine learning techniques including random forest (RF), support vector machine (SVM), and logistic regression (LR). Feature importance to COVID-19 severity were further identified.

Methods: A retrospective design was adopted in the JinYinTan Hospital from January 26 to March 28, 2020, eighty-six demographic, clinical, and laboratory features were selected with LassoCV method, Spearman's rank correlation, experts' opinions, and literature evaluation. RF, SVM, and LR were performed to predict severe COVID-19, the performance of the models was compared by the area under curve (AUC). Additionally, feature importance to COVID-19 severity were analyzed by the best performance model.

Results: A total of 287 patients were enrolled with 36.6% severe cases and 63.4% non-severe cases. The median age was 60.0 years (interquartile range: 49.0-68.0 years). Three models were established using 23 features including 1 clinical, 1 chest computed tomography (CT) and 21 laboratory features. Among three models, RF yielded better overall performance with the highest AUC of 0.970 than SVM of 0.948 and LR of 0.928, RF also achieved a favorable sensitivity of 96.7%, specificity of 69.5%, and accuracy of 84.5%. SVM had sensitivity of 93.9%, specificity of 79.0%, and accuracy of 88.5%. LR also achieved a favorable sensitivity of 92.3%, specificity of 72.3%, and accuracy of 85.2%. Additionally, chest-CT had highest importance to illness severity, and the following features were neutrophil to lymphocyte ratio, lactate dehydrogenase, and D-dimer, respectively.

Conclusions: Our results indicated that RF could be a useful predictive tool to identify patients with severe COVID-19, which may facilitate effective care and further optimize resources.

Background: Triatomines in Latin America are natural Chagas disease (ChD) vectors. Triatomine domiciliation is one of the main factors increasing the occurrence risk of this disease in humans. There are 66 triatomine species in Brazil, with three genera of significant epidemiological importance-Panstrongylus, Rhodnius, and Triatoma. Among the Triatoma species, Triatoma williami, a wild species, has been reported in Goiás, Mato Grosso, and Mato Grosso do Sul. In the Barra do Garças, Mato Grosso, the invasion by triatomines has been reported, with T. williami being the most common species. This study aimed to survey triatomine fauna and determine the Trypanosoma cruzi natural infection rates in triatomines in the urban area of Barra do Garças, Mato Grosso, Brazil.

Methods: Triatomine specimens were sampled by passive surveillance or active search by agents combating endemic diseases from 2019 to 2020. A parasitological feces diagnosis was performed to detect the presence of T. cruzi after the specimens were identified. Concerning T. cruzi identification, molecular diagnosis and genetic sequencing were performed to determine the strain, also called discrete typing units (DTUs).

Results: The 211 triatomines were collected, distributed in specimens of T. williami (84.4%), P. geniculatus (3.3%), P. diasi (1.4%), and R. neglectus (10.9%). Two colonies of T. williami were found through morphological analyses. These insects were sampled inside domiciles in an urban area neighboring Jardim Pitaluga (15° 51'57.7″ N, 052° 16' 04.5 E). The records were sampled in September 2019 and January 2021. The rate of natural infection by T. cruzi was 39.4%. Two T. williami specimens from the sampled colonies were positive for the T. cruzi strain DTU IV.

Conclusions: This is the first time that T. williami has been confirmed in an urban area of Barra do Garças, Mato Grosso, Brazil. Further studies are needed for a clearer understanding of the ecology of this species for prevention and control mechanisms since its sampled specimens had a high rate of natural infection by T. cruzi.

It is unequivocal that human influence has warmed the planet, which is seriously affecting the planetary health including human health. Adapting climate change should not only be a slogan, but requires a united, holistic action and a paradigm shift from crisis response to an ambitious and integrated approach immediately. Recognizing the urgent needs to tackle the risk connection between climate change and One Health, the four key messages and recommendations that with the intent to guide further research and to promote international cooperation to achieve a more climate-resilient world are provided.

Background: Few data are available regarding the long-term case-fatality rate (CFR) among people living with HIV (PLWH) with nontuberculous mycobacteria (NTM) disease. The aim of this study is to analyze the long-term CFR in patients with NTM disease and to identify risk factors for their death.

Methods: A retrospective cohort study of 379 cases of microbiologically confirmed NTM disease in PLWH was conducted from January 1, 2012, to December 31, 2020, in Shanghai, China. We used Kaplan-Meier survival analysis and the log-rank test to compare the long-term CFR in patients with disseminated NTM (DNTM) and localized NTM disease. Univariate Cox proportional hazards regression analysis and a stepwise Cox proportional hazards regression model were used to estimate the predictors of long-term CFR.

Results: The cohort was followed up for a median of 26 months. The total CFR was 15.7% by one year and increased to 22.6% at 5 years after the diagnosis of NTM disease. The 5-year CFR of PLWH with DNTM was significantly higher than that of PLWH with localized NTM (26.7% vs 19.6% for DNTM and localized NTM disease, respectively). Older age [hazard ratio (HR) = 1.04, 95% confidence interval (CI): 1.02-1.06, P < 0.001], comorbidity (HR = 2.05, 95% CI: 1.21-3.49, P < 0.01), DNTM (HR = 2.08, 95% CI: 1.17-3.68, P < 0.05), and HIV viral load (HR = 1.32, 95% CI: 1.12-1.55, P < 0.001) were all independent risk factors for long-term CFR. In the subgroup analysis, time to culture positivity was negatively correlated with CFR in patients with DNTM (HR = 0.90, 95% CI: 0.82-0.98, P < 0.05).

Conclusions: NTM was associated with a high long-term CFR in PLWH. Further approaches to prevent NTM disease in PLWH are urgently needed.

Background: COVID-19 pandemic continues, clarifying signatures in clinical characters and antibody responses between severe and non-severe COVID-19 cases would benefit the prognosis and treatment.

Methods: In this study, 119 serum samples from 37 severe or non-severe COVID-19 patients from the First People's Hospital of Yueyang were collected between January 25 and February 18 2020. The clinical features, antibody responses targeting SARS-CoV-2 spike protein (S) and its different domains, SARS-CoV-2-specific Ig isotypes, IgG subclasses, ACE2 competitive antibodies, binding titers with FcγIIa and FcγIIb receptors, and 14 cytokines were comprehensively investigated. The differences between severe and non-severe groups were analyzed using Mann-Whitney U test or Fisher's exact test.

Results: Severe group including 9 patients represented lower lymphocyte count, higher neutrophil count, higher level of LDH, total bile acid (TBA) (P < 1 × 10^-4), r-glutaminase (P = 0.011), adenosine deaminase (P < 1 × 10^-4), procalcitonin (P = 0.004), C-reactive protein (P < 1 × 10^-4) and D-dimer (P = 0.049) compared to non-severe group (28 patients). Significantly, higher-level Igs targeting S, different S domains (RBD, RBM, NTD, and CTD), FcγRIIa and FcγRIIb binding capability were observed in a severe group than that of a non-severe group, of which IgG1 and IgG3 were the main IgG subclasses. RBD-IgG were strongly correlated with S-IgG both in severe and non-severe group. Additionally, CTD-IgG was strongly correlated with S-IgG in a non-severe group. Positive RBD-ACE2 binding inhibition was strongly associated with high titers of antibody (S-IgG1, S-IgG3, NTD-IgG, RBD-IgA, NTD-IgA, and CTD-IgA) especially RBD-IgG and CTD-IgG in the severe group, while in the non-severe group, S-IgG3, RBD-IgG, NTD-IgG, and NTD-IgM were correlated with ACE2 blocking rate. S-IgG1, NTD-IgM and S-IgM were negatively associated with illness day in a severe group, while S-IgG3, RBD-IgA, CTD-IgA in the severe group (r = 0.363, P = 0.011) and S-IgG1, NTD-IgA, CTD-IgA in the non-severe group were positively associated with illness day. Moreover, GRO-α, IL-6, IL-8, IP-10, MCP-1, MCP-3, MIG, and BAFF were also significantly elevated in the severe group.

Conclusion: Antibody detection provides important clinical information in the COVID-19 process. The different signatures in Ig isotypes, IgG subclasses, antibody specificity between the COVID-19 severe and non-severe group will contribute to future therapeutic and preventive measures development.

Background: Non-pharmaceutical interventions (NPIs) are a crucial suite of measures to prevent and control infectious disease outbreaks. Despite being particularly important for crisis-affected populations and those living in informal settlements, who typically reside in overcrowded and resource limited settings with inadequate access to healthcare, guidance on NPI implementation rarely takes the specific needs of such populations into account. We therefore conducted a systematic scoping review of the published evidence to describe the landscape of research and identify evidence gaps concerning the acceptability, feasibility, and effectiveness of NPIs among crisis-affected populations and informal settlements.

Methods: We systematically reviewed peer-reviewed articles published between 1970 and 2020 to collate available evidence on the feasibility, acceptability, and effectiveness of NPIs in crisis-affected populations and informal settlements. We performed quality assessments of each study using a standardised questionnaire. We analysed the data to produce descriptive summaries according to a number of categories: date of publication; geographical region of intervention; typology of crisis, shelter, modes of transmission, NPI, research design; study design; and study quality.

Results: Our review included 158 studies published in 85 peer-reviewed articles. Most research used low quality study designs. The acceptability, feasibility, and effectiveness of NPIs was highly context dependent. In general, simple and cost-effective interventions such as community-level environmental cleaning and provision of water, sanitation and hygiene services, and distribution of items for personal protection such as insecticide-treated nets, were both highly feasible and acceptable. Logistical, financial, and human resource constraints affected both the implementation and sustainability of measures. Community engagement emerged as a strong factor contributing to the effectiveness of NPIs. Conversely, measures that involve potential restriction on personal liberty such as case isolation and patient care and burial restrictions were found to be less acceptable, despite apparent effectiveness.

Conclusions: Overall, the evidence base was variable, with substantial knowledge gaps which varied between settings and pathogens. Based on the current landscape, robust evidence-based guidance is not possible, and a research agenda is urgently required that focusses on these specific vulnerable populations. Although implementation of NPIs presents unique practical challenges in these settings, it is critical that such an agenda is put in place, and that the lessons learned from historical and present experiences are documented to build a firm evidence base.

The World Health Organization's first roadmap and the London Declaration on neglected tropical diseases (NTDs) have allowed an unprecedented expansion of interventions to control and eliminate this group of infectious diseases that primarily affects vulnerable or marginalised communities. The 2021-2030 NTD roadmap sustains a further acceleration of interventions but also introduces a broader and more ambitious agenda, calling to be accompanied by a new political declaration. Sponsored by the Government of Rwanda, the Kigali Declaration on neglected tropical diseases will be launched in 2022 to renew and reinvigorate commitments to end NTDs, also in the wake of the current setback caused by the COVID-19 pandemic. Starting on World NTD Day 2022, a global campaign "100% Committed" will call on a broad range of stakeholders to sign the declaration and make bold financial and political commitments towards achieving the 2030 roadmap and Sustainable Development Goals' targets for NTDs.

Background: One of the effective ways to attract social collaboration to provide effective, prompt, and coordinated interventions in emergencies is through social innovation. The present study seeks to identify the factors affecting the implementation of the social innovation plan based on the collaboration between government and non-governmental organizations (NGOs) for saving people's lives in crises. The initial idea of this research was obtained from the best practice "Every Home Is a Health Base" which was implemented in Iran.

Methods: The Grounded Theory strategy has been used in this study. The statistical population of the study is health experts from the Ministry of Health and Medical Education of Iran. The study time span is during the first half of 2020. Exploratory analysis was used to identify the factors of social innovation. By selecting and reviewing 68 research in-depth, the initial framework was prepared. Then, through a semi-structured interview with experts, the framework was adapted and reviewed. Based on the analysis of the collected data, 39 open codes were extracted and the factors affecting the implementation of the social innovation were identified.

Results: The eight axis codes as the factors affecting the implementation of the social innovation plan based on the collaboration between government and NGOs are as follows: Paying attention to the components of the NGOs collaboration effectiveness, investment to attract NGOs collaboration, the ability to manage the implementation, the ability of networking, the ability of policymaking, providing the necessary cultural and educational infrastructure; Existence of capable legal organizations to solve the executive problems of the plan and facilitate coordination, and controlling, containing and reducing the effects of the crisis, as consequences.

Conclusions: Lessons learned from the COVID-19 pandemic have shown the world that the current governmental and social structures are not efficient enough to respond quickly to the emergence of global challenges. Social innovation is a solution to this problem. The findings of this study also confirm this and identify the factors affecting the implementation of the social innovation plan based on collaboration between governments and NGOs in crises. The results of this research give governments and policymakers an efficient solution by involving NGOs, especially in times of widespread crises. Also, they can be used in planning for social development.

Background: In recent years, a programme of vector control, screening and treatment of gambiense human African trypanosomiasis (gHAT) infections led to a rapid decline in cases in the Mandoul focus of Chad. To represent the biology of transmission between humans and tsetse, we previously developed a mechanistic transmission model, fitted to data between 2000 and 2013 which suggested that transmission was interrupted by 2015. The present study outlines refinements to the model to: (1) Assess whether elimination of transmission has already been achieved despite low-level case reporting; (2) quantify the role of intensified interventions in transmission reduction; and (3) predict the trajectory of gHAT in Mandoul for the next decade under different strategies.

Method: Our previous gHAT transmission model for Mandoul was updated using human case data (2000-2019) and a series of model refinements. These include how diagnostic specificity is incorporated into the model and improvements to the fitting method (increased variance in observed case reporting and how underreporting and improvements to passive screening are captured). A side-by-side comparison of fitting to case data was performed between the models.

Results: We estimated that passive detection rates have increased due to improvements in diagnostic availability in fixed health facilities since 2015, by 2.1-fold for stage 1 detection, and 1.5-fold for stage 2. We find that whilst the diagnostic algorithm for active screening is estimated to be highly specific (95% credible interval (CI) 99.9-100%, Specificity = 99.9%), the high screening and low infection levels mean that some recently reported cases with no parasitological confirmation might be false positives. We also find that the focus-wide tsetse reduction estimated through model fitting (95% CI 96.1-99.6%, Reduction = 99.1%) is comparable to the reduction previously measured by the decline in tsetse catches from monitoring traps. In line with previous results, the model suggests that transmission was interrupted in 2015 due to intensified interventions.

Conclusions: We recommend that additional confirmatory testing is performed in Mandoul to ensure the endgame can be carefully monitored. More specific measurement of cases, would better inform when it is safe to stop active screening and vector control, provided there is a strong passive surveillance system in place.

Background: Overseas imported dengue fever is an important factor in local outbreaks of this disease in the mainland of China. To better prevent and control such local outbreaks, the epidemiological characteristics and temporal-spatial distribution of overseas imported dengue fever cases in provincial-level administrative divisions (PLADs) where dengue fever is outbreak in the mainland of China were explored.

Methods: Using the Chinese National Notifiable Infectious Disease Reporting Information System (CNNDS), we identified overseas imported dengue fever cases in dengue fever outbreak areas in the mainland of China from 2005 to 2019 to draw the epidemic curve and population characteristic distribution of overseas imported cases in each PLAD. Based on spatial autocorrelation analysis of ArcGIS 10.5 and temporal-spatial scanning analysis of SaTScan 9.5, we analyzed the temporal-spatial distribution of overseas imported dengue fever in dengue fever outbreak areas in the mainland of China.

Results: A total of 11,407 imported cases, mainly from Southeast Asia, were recorded from 2005 to 2019 in these 13 PLADs. Of which 62.1% were imported into Yunnan and Guangdong Provinces. Among the imported cases, there were more males than females, mainly from the 21-50 age group. The hot spots were concentrated in parts of Yunnan, Guangdong and Fujian Provinces. We found the cluster of infected areas were expanding northward.

Conclusions: Based on the analysis of overseas imported dengue cases in 13 PLADs of the mainland of China from 2005 to 2019, we obtained the epidemiological characteristics and spatial distribution of imported dengue cases. Border controls need to pay attention to key population sectors, such as 21-50 years old men and education of key populations on dengue prevention. There is a need to improve the awareness of the prevention and control of imported cases in border areas. At the same time, northern regions cannot relax their vigilance.