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Spatial and Prediction Models for Addressing Challenges in Pediatric Tuberculosis Control and Care.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Spatial and Prediction Models for Addressing Challenges in Pediatric Tuberculosis Control and Care./
作者:	Gunasekera, Kenneth Suranga.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
面頁冊數:	219 p.
附註:	Source: Dissertations Abstracts International, Volume: 84-01, Section: B.
Contained By:	Dissertations Abstracts International84-01B.
標題:	Public health. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29060271
ISBN:	9798837551444

Spatial and Prediction Models for Addressing Challenges in Pediatric Tuberculosis Control and Care.
Gunasekera, Kenneth Suranga.

Spatial and Prediction Models for Addressing Challenges in Pediatric Tuberculosis Control and Care. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 219 p.

Source: Dissertations Abstracts International, Volume: 84-01, Section: B.

Thesis (Ph.D.)--Yale University, 2022.

This item must not be sold to any third party vendors.

Tuberculosis (TB) is among the leading causes of global mortality among children < 5 years. Each year, over 1 million TB cases occur among children < 15 years worldwide, and nearly one quarter of those children die; approximately 80% of those deaths occur among children < 5 years. Alleviating the burden of pediatric TB and mortality requires 1) enhanced efforts to prevent transmission to children and 2) treating more children for TB.Targeting resources to children with a known TB exposure has been a cornerstone of the public health response to prevent transmission and detect cases early. Infectious adults must be diagnosed and treated earlier to prevent transmission to their child contacts. Modeling studies suggest that targeting community-level active case-finding to areas with high local transmission intensity may demonstrate population-level reductions in TB incidence. However, obtaining conclusive evidence of concentrated transmission requires access to spatial and genomic data, which is often only available under research conditions in high TB-incidence settings.In chapter 1, I use Bayesian spatial modeling methods to probe routinely collected, age-disaggregated TB notification data to demonstrate that overrepresentation of young child cases co-locate with areas of high local transmission intensity, identified by molecular evidence of transmission from a prospective cohort study in the same setting. This finding suggests that the use of models that leverage widely available notification data should be explored as tools to target case-finding and treatment efforts in high-transmission locations to maximize the direct and indirect benefits of active screening approaches. In chapter 2, I leverage data from a large prevalence survey to investigate a poorly understood form of TB that may frustrate symptom-based active case-finding efforts.Given that modeling estimates suggest that 96% of global childhood mortality due to TB occurs among children not receiving anti-tuberculosis treatment, identifying and treating more cases of pediatric TB provide an opportunity to reduce child mortality. Diagnostic tools for pediatric pulmonary TB are limited by paucibacillary disease in children as well by resource constraints in many high TB-incidence settings. This contributes to poorer treatment outcomes through missed diagnoses and treatment delays.In chapter 3, I describe the analysis of a cohort of children being evaluated for TB from Cape Town, South Africa to demonstrate that a majority of anti-tuberculosis treatment-decisions could be made using clinical evidence alone, without the need for additional diagnostic testing. In chapter 4, I describe the assembly of a large cohort of pediatric TB diagnostic evaluation data sourced from multiple geographically diverse, high TB-incidence settings to develop a prediction model for TB and investigate its validity and generalizability. As part of this work, I describe efforts in partnership with the World Health Organization to operationalize the prediction model as a treatment-decision algorithm to guide the evaluation of children with presumptive pulmonary TB.

ISBN: 9798837551444Subjects--Topical Terms:

534748
Public health.
Subjects--Index Terms:

Algorithm

Spatial and Prediction Models for Addressing Challenges in Pediatric Tuberculosis Control and Care.
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300 $a 219 p.
500 $a Source: Dissertations Abstracts International, Volume: 84-01, Section: B.
500 $a Advisor: Cohen, Theodore.
502 $a Thesis (Ph.D.)--Yale University, 2022.
506 $a This item must not be sold to any third party vendors.
520 $a Tuberculosis (TB) is among the leading causes of global mortality among children < 5 years. Each year, over 1 million TB cases occur among children < 15 years worldwide, and nearly one quarter of those children die; approximately 80% of those deaths occur among children < 5 years. Alleviating the burden of pediatric TB and mortality requires 1) enhanced efforts to prevent transmission to children and 2) treating more children for TB.Targeting resources to children with a known TB exposure has been a cornerstone of the public health response to prevent transmission and detect cases early. Infectious adults must be diagnosed and treated earlier to prevent transmission to their child contacts. Modeling studies suggest that targeting community-level active case-finding to areas with high local transmission intensity may demonstrate population-level reductions in TB incidence. However, obtaining conclusive evidence of concentrated transmission requires access to spatial and genomic data, which is often only available under research conditions in high TB-incidence settings.In chapter 1, I use Bayesian spatial modeling methods to probe routinely collected, age-disaggregated TB notification data to demonstrate that overrepresentation of young child cases co-locate with areas of high local transmission intensity, identified by molecular evidence of transmission from a prospective cohort study in the same setting. This finding suggests that the use of models that leverage widely available notification data should be explored as tools to target case-finding and treatment efforts in high-transmission locations to maximize the direct and indirect benefits of active screening approaches. In chapter 2, I leverage data from a large prevalence survey to investigate a poorly understood form of TB that may frustrate symptom-based active case-finding efforts.Given that modeling estimates suggest that 96% of global childhood mortality due to TB occurs among children not receiving anti-tuberculosis treatment, identifying and treating more cases of pediatric TB provide an opportunity to reduce child mortality. Diagnostic tools for pediatric pulmonary TB are limited by paucibacillary disease in children as well by resource constraints in many high TB-incidence settings. This contributes to poorer treatment outcomes through missed diagnoses and treatment delays.In chapter 3, I describe the analysis of a cohort of children being evaluated for TB from Cape Town, South Africa to demonstrate that a majority of anti-tuberculosis treatment-decisions could be made using clinical evidence alone, without the need for additional diagnostic testing. In chapter 4, I describe the assembly of a large cohort of pediatric TB diagnostic evaluation data sourced from multiple geographically diverse, high TB-incidence settings to develop a prediction model for TB and investigate its validity and generalizability. As part of this work, I describe efforts in partnership with the World Health Organization to operationalize the prediction model as a treatment-decision algorithm to guide the evaluation of children with presumptive pulmonary TB.
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650 4 $a Computer science. $3 523869
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653 $a Infectious disease
653 $a Pediatric medicine
653 $a Tuberculosis
653 $a Spatial models
653 $a Predictive models
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