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Generalized cure rate model for infectious diseases with possible co-infections.

ABSTRACT: This research mainly aims to develop a generalized cure rate model, estimate the proportion of cured patients and their survival rate, and identify the risk factors associated with infectious diseases. The generalized cure rate model is based on bounded cumulative hazard function, which is a non-mixture model, and is developed using a two-parameter Weibull distribution as the baseline distribution, to estimate the cure rate using maximum likelihood method and real data with R and STATA software. The results showed that the cure rate of tuberculosis (TB) patients was 26.3%, which was higher than that of TB patients coinfected with human immunodeficiency virus (HIV; 23.1%). The non-parametric median survival time of TB patients was 51 months, while that of TB patients co-infected with HIV was 33 months. Moreover, no risk factors were associated with TB patients co-infected with HIV, while age was a significant risk factor for TB patients among the suspected risk factors considered. Furthermore, the bounded cumulative hazard function was extended to accommodate infectious diseases with co-infections by deriving an appropriate probability density function, determining the distribution, and using real data. Governments and related health authorities are also encouraged to take appropriate actions to combat infectious diseases with possible co-infections.

SUBMITTER: Balogun OS

PROVIDER: S-EPMC7485820 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Generalized cure rate model for infectious diseases with possible co-infections.

Balogun Oluwafemi Samson OS Gao Xiao-Zhi XZ Jolayemi Emmanuel Teju ET Olaleye Sunday Adewale SA

PloS one 20200911 9

This research mainly aims to develop a generalized cure rate model, estimate the proportion of cured patients and their survival rate, and identify the risk factors associated with infectious diseases. The generalized cure rate model is based on bounded cumulative hazard function, which is a non-mixture model, and is developed using a two-parameter Weibull distribution as the baseline distribution, to estimate the cure rate using maximum likelihood method and real data with R and STATA software. ...[more]

PMID: 32915903

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Project description:BackgroundVector-borne infectious diseases are a significant cause of human and animal mortality and morbidity. Modeling studies predict that changes in climate that accompany global warming will alter the transmission risk of many vector-borne infectious diseases in different parts of the world. Global warming will also raise sea levels, which will lead to an increase in saline and brackish water bodies in coastal areas. The potential impact of rising sea levels, as opposed to climate change, on the prevalence of vector-borne infectious diseases has hitherto been unrecognised.Presentation of the hypothesisMosquito species possessing salinity-tolerant larvae and pupae, and capable of transmitting arboviruses and parasites are found in many parts of the world. An expansion of brackish and saline water bodies in coastal areas, associated with rising sea levels, can increase densities of salinity-tolerant vector mosquitoes and lead to the adaptation of freshwater vectors to breed in brackish and saline waters. The breeding of non-mosquito vectors may also be influenced by salinity changes in coastal habitats. Higher vector densities can increase transmission of vector-borne infectious diseases in coastal localities, which can then spread to other areas.Testing the hypothesisThe demonstration of increases in vector populations and disease prevalence that is related to an expansion of brackish/saline water bodies in coastal areas will provide the necessary supportive evidence. However the implementation of specific vector and disease control measures to counter the threat will confound the expected findings.Implications of the hypothesisRising sea levels can act synergistically with climate change and then interact in a complex manner with other environmental and socio-economic factors to generate a greater potential for the transmission of vector-borne infectious diseases. The resulting health impacts are likely to be particularly significant in resource-poor countries in the tropics and semi-tropics. Some measures to meet this threat are outlined.

Project description:BackgroundThe microbiome plays a pivotal role in mediating immune deviation during the development of early-life viral infections. Recurrent infections in children are considered a risk factor for disease development. This study delves into the metagenomics of the microbiome in children suffering from severe infections, seeking to identify potential sources of these infections.AimsThe aim of this study was to identify the specific microorganisms and factors that significantly influence the treatment duration in patients suffering from severe infections. We sought to understand how these microbial communities and other variables may affect the treatment duration and the use of antibiotics of these patients with severe infections.MethodWhole-genome shotgun sequencing was conducted on samples collected from children aged 0-14 years with severe infections, admitted to the Pediatrics Department of Xiamen First Hospital. The Kraken2 algorithm was used for taxonomic identification from sequence reads, and linear mixed models were employed to identify significant microorganisms influencing treatment duration. Colwellia, Cryptococcus, and Citrobacter were found to significantly correlate with the duration of clinical treatment. Further analysis using propensity score matching (PSM) and rank-sum test identified clinical indicators significantly associated with the presence of these microorganisms.ResultsUsing a linear mixed model after removed the outliers, we identified that the abundance of Colwellia, Cryptococcus, and Citrobacter significantly influences the treatment duration. The presence of these microorganisms is associated with a longer treatment duration for patients. Furthermore, these microorganisms were found to impact various clinical measures. Notably, an increase in hospitalization durations and medication costs was observed in patients with these microorganisms. In patients with Colwellia, Cryptococcus, and Citrobacter, we discover significant differences in platelets levels. We also find that in patients with Cryptococcus, white blood cells, hemoglobin, and neutrophils levels are lower.ConclusionThese findings suggest that Colwellia, Cryptococcus, and Citrobacter, particularly Cryptococcus, could potentially contribute to the severity of infections observed in this cohort, possibly as co-infections. These microorganisms warrant further investigation into their pathogenic roles and mechanisms of action, as their presence in combination with disease-causing organisms may have a synergistic effect on disease severity. Understanding the interplay between these microorganisms and pathogenic agents could provide valuable insights into the complex nature of severe pediatric infections and guide the development of targeted therapeutic strategies.

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Generalized cure rate model for infectious diseases with possible co-infections.

Publications

Generalized cure rate model for infectious diseases with possible co-infections.

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