Coccidioidomycosis (Valley Fever) Case Data for the Southwestern United States

Morgan E. Gorris; Linh Anh Cat; Melissa Matlock; Oladele A. Ogunseitan; Kathleen K. Treseder; James T. Randerson; Charles S. Zender

doi:10.5334/ohd.31

Authors:

Morgan E. Gorris ,

Department of Earth System Science, University of California, Irvine, US

Linh Anh Cat,

Department of Ecology and Evolutionary Biology, University of California, Irvine, US

Melissa Matlock,

Department of Population Health and Disease Prevention, University of California, Irvine, US

Oladele A. Ogunseitan,

Department of Population Health and Disease Prevention, University of California, Irvine, US

Kathleen K. Treseder,

Department of Ecology and Evolutionary Biology, University of California, Irvine, US

James T. Randerson,

Department of Earth System Science, University of California, Irvine, US

Charles S. Zender

Department of Earth System Science, University of California, Irvine, US

Abstract

We compiled a coccidioidomycosis (Valley fever) case database for three states in the southwestern United States (US). Currently, county-level, monthly case counts are available from 2000–2015 for Arizona, California, and Nevada. We collected these data from each respective state public health agency. The Valley fever case database is available on GitHub, at https://github.com/valleyfever/valleyfevercasedata. This database may be used to examine relationships between the number of Valley fever cases and hypothesized explanatory variables such as environmental conditions, social determinants, human behavior, occupational activities, public policies, or other risk factors. We aim to provide regular updates to this database and include more states as data become available.

Funding statement: M. E. Gorris received support from a Department of Defense (DoD), National Defense Science & Engineering Graduate Fellowship (32 CFR 168a). M. E. Gorris, L. A. Cat, and M. Matlock thank the UC Irvine Data Science Initiative for their funding and support. L. A. Cat acknowledges funding and support from the UC-Mexico Initiative. M. Matlock is also supported by Water UCI and the UCI Graduate Division. K. K. Treseder is supported by US NSF (EAR-1411942 and DEB-1457160) and the US Department of Energy, Office of Science, Office of Biological and Environmental Research (BER), under Award Numbers DE-PS02-09ER09-25 and DE-SC001641. J. T. Randerson received support from the Gordon and Betty Moore Foundation (GBMF#3269), NASA Soil Moisture and Interdisciplinary Science Program, and the U.S. Dept. of Energy Office of Science RUBISCO Science Focus Area. C. S. Zender acknowledges support from the Borrego Valley Endowment Fund and DOE ACME DE-SC0012998.

How to Cite: Gorris, M.E., Cat, L.A., Matlock, M., Ogunseitan, O.A., Treseder, K.K., Randerson, J.T. and Zender, C.S., 2020. Coccidioidomycosis (Valley Fever) Case Data for the Southwestern United States. Open Health Data, 7(1), p.1. DOI: http://doi.org/10.5334/ohd.31

1. Overview

Introduction/Study Description

Coccidioidomycosis, also known as Valley fever, is an infectious disease endemic to parts of North, Central, and South America [1]. Humans contract Valley fever when they inhale spores of the fungal genus Coccidioides. In the US, the Centers for Disease Control and Prevention (CDC) estimates Valley fever is currently endemic to southwestern states and arid regions in the Pacific Northwest [2].

We compiled county-level Valley fever case data by month from 2000–2015 in Arizona, California, and Nevada. We collected data from each respective state health agency.

This database will allow researchers and health officials to access aggregated US Valley fever case numbers, foregoing the time it takes to contact each state health agency individually. Access to Valley fever case numbers will accelerate research aiming to study the relationships between Valley fever case numbers and other explanatory variables.

We have previously used this database in a study by Gorris et al. 2018 to examine the relationships between climate dynamics and Valley fever incidence throughout the southwestern US [3] and as a basis for projections of Valley fever in response to climate change [4].

Data for New Mexico are available but were not permitted to be released. These data may be obtained by contacting the New Mexico State Department of Health:

New Mexico Department of Health

1190 St. Francis Drive, Runnels N1361

Santa Fe, New Mexico 87502

https://nmhealth.org/

Epidemiology and Response Division phone number: 1-800-879-3421

Data for Utah are available but were not permitted to be released. These data may be obtained by contacting the Utah Department of Health:

Bureau of Epidemiology

Utah Department of Health

PO Box 142104

Salt Lake City, UT 84114-2104

http://health.utah.gov/epi/

Bureau of Epidemiology phone number: 1-801-538-6191

epi@utah.gov

2. Context

Spatial coverage

Description

Valley fever case data are available at the county level from the states of Arizona, California, and Nevada in the United States. Counties within the US are assigned a unique Federal Information Processing Standards (FIPS) code for identification.

Temporal coverage

Data are currently available at the monthly level from 2000/01 to 2015/12. We used the data format (yyyy/mm).

Species

The data are the number of reported coccidioidomycosis cases caused by the fungi Coccidioides spp. and contracted by humans (Homo sapiens) in each given month.

3. Methods

Steps

We collected monthly, county-level data from 2000/01 to 2015/12 by means of personal contact from the following state health agencies:

Arizona Department of Health Services

150 N 18th Ave, Ste 140

Phoenix, AZ 85007

California Department of Public Health

PO Box 997377, MS 0500

Sacramento, CA 95899

Nevada Department of Health and Human Services

4126 Technology Way

Carson City, NV 89706

Quality Control

Quality control of the data was completed by the respective state health agency and varies by agency.

Constraints

There are multiple considerations to take when analyzing Valley fever case data. First, techniques for reporting Valley fever cases have changed through time and may have led to increased numbers of Valley fever cases [5].

Second, each Valley fever case was dated corresponding to the month and year which the diagnosing health institution submitted the official Valley fever case report. However, there may be a lag between when someone was infected with Coccidioides spp., when symptoms occurred, and when the individual was diagnosed with Valley fever. This lag has been estimated to be between 1 and 1.5 months [6, 7, 8].

Third, the location of infection may have not occurred where the official case report was filed. For example, someone may have been exposed to Coccidioides spp. during travel.

Privacy

This data includes Valley fever case numbers only, excluding any personal identifying or demographic information.

Ethics

Data was collected according to standard ethical principles.

4. Dataset description

Object name

coccidioidomycosis_m2000_2015_v0.1.csv

Data type

Secondary data

Format names and versions

CSV, Version 0.1

Creation dates

Creation of this database began 2015/10

Dataset creators

Morgan E. Gorris, Linh Anh Cat, and Melissa Matlock

Language

English

License

CC-BY 4.0

Repository location

https://github.com/valleyfever/valleyfevercasedata

Publication date

(2019/03/01)

To contribute data

To share data, please submit a pull request. See the GitHub link under Repository location for further instructions.

5. Reuse potential

Valley fever cases in the US have been increasing, causing concern (Figure 1) [3]. The number of Valley fever cases fluctuate by region and through time (Table 1).

Figure 1

The number of Valley fever cases in the US has been increasing through time.

Table 1

Valley fever statistics by state (2000–2015).

State	Number of Counties	Mean annual number of cases	Median annual number of cases	Range of annual number of cases

Arizona	15	6433	5180	1922–16473
California	58	2786	2688	818–5219
Nevada	17	68	63	28–116

This database may be used to examine relationships between the number of Valley fever cases and any hypothesized explanatory variable. Some examples include environmental conditions, social determinants, human behaviour, occupational activities, public policies, or other health risk factors. The data can be used by epidemiologists to compare disease trends across the southwestern US. It can also be used to educate health care providers on the historical amounts of Valley fever in their region.

The data can be aggregated to examine case number at the state level. The data can also be aggregated to examine data on the annual time-scale. We aim to provide regular updates to this database and include more states as data become available.

Additional File

The additional file for this article can be found as follows:

Dataset 1

Coccidioidomycosis (valley fever) case data. DOI: https://doi.org/10.5334/ohd.31.s1

Acknowledgements

We thank Shane Brady from the Arizona Department of Health Services, Colleen McLellan and Curtis Fritz from the California Department of Public Health, and Jennifer Thompson from the Nevada Department of Health and Human Services for providing us with Valley fever case data from their respective state health agencies.

References

Centers for Disease Control and Prevention. Valley Fever (Coccidioidomycosis) Risk & Prevention. 2019 [online]. Available at: https://www.cdc.gov/fungal/diseases/coccidioidomycosis/risk-prevention.html [Accessed 2 Feb. 2019].

Centers for Disease Control and Prevention. Sources of Valley Fever (Coccidioidomycosis). 2019 [online]. Available at: https://www.cdc.gov/fungal/diseases/coccidioidomycosis/causes.html [Accessed 2 Feb. 2019].

Gorris ME, Cat LA, Zender CS, Treseder KK, Randerson JT. Coccidioidomycosis dynamics in relation to climate in the southwestern United States. GeoHealth. 2018; 2(1): 6–24. DOI: https://doi.org/10.1002/2017GH000095

Gorris ME, Treseder KK, Zender CS, Randerson JT. Expansion of coccidioidomycosis endemic regions in the United States in response to climate change. GeoHealth. 2019; 3(10): 308–327. DOI: https://doi.org/10.1029/2019GH000209

Hector RF, Rutherford GW, Tsang CA, Erhart LM, McCotter O, Anderson SM, Komatsu K, Tabnak F, Vugia DJ, Yang Y, Galgiani JN. The public health impact of coccidioidomycosis in Arizona and California. International journal of environmental research and public health. 2011; 8(4): 1150–1173. DOI: https://doi.org/10.3390/ijerph8041150

Smith CE, Beard RR, Rosenberger HG, Whiting EG. Effect of season and dust control on coccidioidomycosis. Journal of the American Medical Association. 1946; 132(14): 833–838. DOI: https://doi.org/10.1001/jama.1946.02870490011003

Comrie AC. Climate factors influencing coccidioidomycosis seasonality and outbreaks. Environmental health perspectives. 2005; 113(6): 688–692. DOI: https://doi.org/10.1289/ehp.7786

Tsang CA, Anderson SM, Imholte SB, Erhart LM, Chen S, Park BJ, Christ C, Komatsu KK, Chiller T, Sunenshine RH. Enhanced surveillance of coccidioidomycosis, Arizona, USA, 2007–2008. Emerging infectious diseases. 2010; 16(11): 1738–1744. DOI: https://doi.org/10.3201/eid1611.100475

[B1] Centers for Disease Control and Prevention. Valley Fever (Coccidioidomycosis) Risk & Prevention. 2019 [online]. Available at: https://www.cdc.gov/fungal/diseases/coccidioidomycosis/risk-prevention.html [Accessed 2 Feb. 2019].

[B2] Centers for Disease Control and Prevention. Sources of Valley Fever (Coccidioidomycosis). 2019 [online]. Available at: https://www.cdc.gov/fungal/diseases/coccidioidomycosis/causes.html [Accessed 2 Feb. 2019].

[B3] Gorris ME, Cat LA, Zender CS, Treseder KK, Randerson JT. Coccidioidomycosis dynamics in relation to climate in the southwestern United States. GeoHealth. 2018; 2(1): 6–24. DOI: https://doi.org/10.1002/2017GH000095

[B4] Gorris ME, Treseder KK, Zender CS, Randerson JT. Expansion of coccidioidomycosis endemic regions in the United States in response to climate change. GeoHealth. 2019; 3(10): 308–327. DOI: https://doi.org/10.1029/2019GH000209

[B5] Hector RF, Rutherford GW, Tsang CA, Erhart LM, McCotter O, Anderson SM, Komatsu K, Tabnak F, Vugia DJ, Yang Y, Galgiani JN. The public health impact of coccidioidomycosis in Arizona and California. International journal of environmental research and public health. 2011; 8(4): 1150–1173. DOI: https://doi.org/10.3390/ijerph8041150

[B6] Smith CE, Beard RR, Rosenberger HG, Whiting EG. Effect of season and dust control on coccidioidomycosis. Journal of the American Medical Association. 1946; 132(14): 833–838. DOI: https://doi.org/10.1001/jama.1946.02870490011003

[B7] Comrie AC. Climate factors influencing coccidioidomycosis seasonality and outbreaks. Environmental health perspectives. 2005; 113(6): 688–692. DOI: https://doi.org/10.1289/ehp.7786

[B8] Tsang CA, Anderson SM, Imholte SB, Erhart LM, Chen S, Park BJ, Christ C, Komatsu KK, Chiller T, Sunenshine RH. Enhanced surveillance of coccidioidomycosis, Arizona, USA, 2007–2008. Emerging infectious diseases. 2010; 16(11): 1738–1744. DOI: https://doi.org/10.3201/eid1611.100475

Reading: Coccidioidomycosis (Valley Fever) Case Data for the Southwestern United States

Data papers

Coccidioidomycosis (Valley Fever) Case Data for the Southwestern United States

Authors:

Morgan E. Gorris ,

Department of Earth System Science, University of California, Irvine, US

X close

Linh Anh Cat,

Department of Ecology and Evolutionary Biology, University of California, Irvine, US

X close

Melissa Matlock,

Department of Population Health and Disease Prevention, University of California, Irvine, US

X close

Oladele A. Ogunseitan,

Department of Population Health and Disease Prevention, University of California, Irvine, US

X close

Kathleen K. Treseder,

Department of Ecology and Evolutionary Biology, University of California, Irvine, US

X close

James T. Randerson,

Department of Earth System Science, University of California, Irvine, US

X close

Charles S. Zender

Department of Earth System Science, University of California, Irvine, US

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Abstract

1. Overview

Introduction/Study Description

2. Context

Spatial coverage

Description

Temporal coverage

Species

3. Methods

Steps

Quality Control

Constraints

Privacy

Ethics

4. Dataset description

Object name

Data type

Format names and versions

Creation dates

Dataset creators

Language

License

Repository location

Publication date

To contribute data

5. Reuse potential

Additional File

Acknowledgements

Competing Interests

References