Milken Institute School of Public Health Poster Presentations (Marvin Center & Video)
Climate-Driven Models of Valley Fever Incidence: A Systematic Review
Poster Number
43
Document Type
Poster
Status
Graduate Student - Masters
Abstract Category
Environmental and Occupational Health
Keywords
Valley Fever, climate, infectious disease, epidemiology
Publication Date
Spring 2018
Abstract
Background: Valley Fever incidence has risen dramatically in the Southwest United States over the past two decades. Current hypotheses of Valley Fever infection implicate dust as a vector and climate as an influencing factor on seasonal and annual disease incidence. Climate-driven models have aided the understanding of other infectious disease such as meningococcal meningitis and Rift Valley Fever.
Objectives: To evaluate model design of climate-driven models predicting Valley Fever incidence by through the framework of a systematic review.
Methods: We conducted a systematic literature review using both the PRISMA and Navigation guides. Web of Science, Pubmed, Embase, ProQuest, and Scopus were searched for all articles published in English after 1997 pertaining to climate and Valley Fever. Only studies utilizing county, state, and national level exposure and case information from the Southwest United States were included in this review.
Results: Eight studies modeling Valley Fever incidence by climate variables were identified and reviewed. Exploratory analysis revealed bimodal peaks in both incidence and precipitation throughout the study areas. Adjusting for disease incubation and grouping incident cases by season provided the best estimate of a case’s exposure window. Detrending annual incident case data increases model sensitivity as recent linear increases in incidence cannot be explained solely by climate. Seasonal incidence terms and seasonal climate parameters provided the most accurate and precise model results, with a maximum full model R2 of 0.8.
Discussion: Application of climate-driven Valley Fever models in public health can enhance preventative and diagnostic measures. Use of large exposure windows or seasons improves model accuracy and accounts for the varied nature of disease report dates.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Open Access
1
Climate-Driven Models of Valley Fever Incidence: A Systematic Review
Background: Valley Fever incidence has risen dramatically in the Southwest United States over the past two decades. Current hypotheses of Valley Fever infection implicate dust as a vector and climate as an influencing factor on seasonal and annual disease incidence. Climate-driven models have aided the understanding of other infectious disease such as meningococcal meningitis and Rift Valley Fever.
Objectives: To evaluate model design of climate-driven models predicting Valley Fever incidence by through the framework of a systematic review.
Methods: We conducted a systematic literature review using both the PRISMA and Navigation guides. Web of Science, Pubmed, Embase, ProQuest, and Scopus were searched for all articles published in English after 1997 pertaining to climate and Valley Fever. Only studies utilizing county, state, and national level exposure and case information from the Southwest United States were included in this review.
Results: Eight studies modeling Valley Fever incidence by climate variables were identified and reviewed. Exploratory analysis revealed bimodal peaks in both incidence and precipitation throughout the study areas. Adjusting for disease incubation and grouping incident cases by season provided the best estimate of a case’s exposure window. Detrending annual incident case data increases model sensitivity as recent linear increases in incidence cannot be explained solely by climate. Seasonal incidence terms and seasonal climate parameters provided the most accurate and precise model results, with a maximum full model R2 of 0.8.
Discussion: Application of climate-driven Valley Fever models in public health can enhance preventative and diagnostic measures. Use of large exposure windows or seasons improves model accuracy and accounts for the varied nature of disease report dates.