Infodemiological data concerning silicosis in the USA in the period 2004–2010 correlating with real-world statistical data

Nicola Luigi Bragazzi; Guglielmo Dini; Alessandra Toletone; Francesco Brigo; Paolo Durando

doi:10.1016/j.dib.2016.11.021

. 2016 Nov 13;10:457–464. doi: 10.1016/j.dib.2016.11.021

Infodemiological data concerning silicosis in the USA in the period 2004–2010 correlating with real-world statistical data

Nicola Luigi Bragazzi ^a,^⁎, Guglielmo Dini ^b, Alessandra Toletone ^c, Francesco Brigo ^d,^e, Paolo Durando ^b,^f

PMCID: PMC5198853 PMID: 28054008

Abstract

This article reports data concerning silicosis-related web-activities using Google Trends (GT) capturing the Internet behavior in the USA for the period 2004–2010. GT-generated data were then compared with the most recent available epidemiological data of silicosis mortality obtained from the Centers for Disease Control and Prevention for the same study period. Statistically significant correlations with epidemiological data of silicosis (r=0.805, p-value <0.05) and other related web searches were found. The temporal trend well correlated with the epidemiological data, as well as the geospatial distribution of the web-activities with the geographic epidemiology of silicosis.

Keywords: Infodemiology and infoveillance, Internet, Occupational medicine and hygiene, Web 2.0, Work-related diseases

Specifications Table

Subject area	Medicine
More specific subject area	Occupational medicine
Type of data	Figure, tables
How data was acquired	Outsourcing of Google Trends site and the Centers for Disease Control and Prevention (CDC) site
Data format	Raw, analyzed
Experimental factors	Google Trends search volumes were obtained through graphs and heat-maps
Experimental features	Validation of Google Trends-based data with “real-world” data taken from the CDC site was performed by means of correlational analysis
Data source location	USA
Data accessibility	Data are within this article

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Value of the data

•
Google Trends (GT)-based data (infodemiological data) could be useful for scientific community, researchers and occupational physicians in that they show good correlation with “real world” data obtained from the Centers for Disease Control and Prevention site, thus proving to be reliable.
•
These data could be further statistically processed, analyzed, refined and validated in such a way to complement traditional surveillance of silicosis, providing data quicker and in real time.
•
These data could be used to understand occupational diseases-related web activities.
•
To our knowledge, this is the first analysis of web search behavior related to an occupational disease, namely silicosis, carried out both in quantitative and qualitative terms.

1. Data

This article contains infodemiological data on silicosis searched in the USA in the study period 2004–2010, obtained from Google Trends (GT) (Fig. 1). These data well correlated with “real-world” data obtained from the Centers for Disease Control and Prevention (CDC) site for the same study period (Table 1, Table 2, Table 3).

Table 1.

Pearson׳s correlation between Google Trends-based data and epidemiological data in the study period 2004–2010.

Variable		GT-based silicosis (Disease)	GT-based silicosis (search term)
Gender

Female	Correlation coefficient	−0.145	−0.144
Female	Significance level P	0.7562	0.7588

Male	Correlation coefficient	0.778^⁎	0.765^⁎
Male	Significance level P	0.0394	0.0453

Ethnicities

White	Correlation coefficient	0.713	0.696
White	Significance level P	0.0720	0.0825

Female	Correlation coefficient	0.010	−0.001
Female	Significance level P	0.9832	0.9980

Male	Correlation coefficient	0.767^⁎	0.755^⁎
Male	Significance level P	0.0441	0.0498

Black	Correlation coefficient	0.841^⁎	0.847^⁎
Black	Significance level P	0.0177	0.0162

Female	Correlation coefficient	−0.176	−0.162
Female	Significance level P	0.7066	0.7281

Male	Correlation coefficient	0.855^⁎	0.859^⁎
Male	Significance level P	0.0143	0.0132

Other	Correlation coefficient	−0.135	−0.162
Other	Significance level P	0.7731	0.7286

Female	Correlation coefficient	−0.292	−0.254
Female	Significance level P	0.5249	0.5833

Male	Correlation coefficient	−0.019	−0.055
Male	Significance level P	0.9676	0.9074

Adjusted white

Female	Correlation coefficient	−0.015	−0.007
Female	Significance level P	0.9751	0.9876

Male	Correlation coefficient	0.787^⁎	0.778^⁎
Male	Significance level P	0.0357	0.0396

Adjusted black

Female	Correlation coefficient	−0.155	−0.149
Female	Significance Level P	0.7396	0.7507

Male	Correlation coefficient	0.864^⁎	0.867^⁎
Male	Significance level P	0.0122	0.0116

Adjusted other

Female	Correlation coefficient	−0.292	−0.254
Female	Significance level P	0.5249	0.5833

Male	Correlation coefficient	0.030	0.004
Male	Significance level P	0.9490	0.9939

Adjusted overall	Correlation coefficient	0.823^⁎	0.816^⁎
Adjusted overall	Significance level P	0.0231	0.0253

Age

age 15–24	Correlation coefficient	−0.070	−0.018
age 15–24	Significance level P	0.8813	0.9695

age 25–34	Correlation coefficient	−0.657	−0.656
age 25–34	Significance level P	0.1091	0.1092

age 35–44	Correlation coefficient	0.501	0.533
age 35–44	Significance level P	0.2520	0.2179

age 45–54	Correlation coefficient	0.308	0.278
age 45–54	Significance Level P	0.5017	0.5466

age 55–64	Correlation coefficient	0.457	0.468
age 55–64	Significance level P	0.3031	0.2898

age 65–74	Correlation coefficient	0.619	0.622
age 65–74	Significance level P	0.1379	0.1357

age 75–84	Correlation coefficient	0.701	0.677
age 75–84	Significance level P	0.0792	0.0949

age 85-on	Correlation coefficient	0.462	0.442
age 85-on	Significance level P	0.2966	0.3208

No. >45	Correlation coefficient	0.747	0.730
No. >45	Significance level P	0.0535	0.0623

No. 15–44	Correlation coefficient	0.291	0.334
No. 15–44	Significance level P	0.5262	0.4636

Underlying	Correlation coefficient	−0.850^⁎	−0.832^⁎
Underlying	Significance level P	0.0154	0.0203

Number of deaths	Correlation coefficient	0.759^⁎	0.746
Number of deaths	Significance level P	0.0477	0.0542

Death rate	Correlation coefficient	0.805^⁎	0.794^⁎
Death rate	Significance level P	0.0291	0.0329

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^⁎

Statistically significant, with p-value <0.05.

Table 2.

Pearson׳s correlation between GT-based data and clinical symptoms/diseases associated with silicosis.

Variable		GT-based silicosis (Disease)	GT-based silicosis (search term)
Associated diseases

Lung cancer	Correlation coefficient	0.714	0.740
Lung cancer	Significance level P	0.0712	0.0574

Laryngeal cancer	Correlation coefficient	−0.749	−0.786
Laryngeal cancer	Significance level P	0.0526	0.0360

Rheumatoid arthritis	Correlation coefficient	0.793^⁎	0.767^⁎
Rheumatoid arthritis	Significance level P	0.0333	0.0443

Systemic Lupus Erythematosus	Correlation coefficient	0.869^⁎	0.865^⁎
Systemic Lupus Erythematosus	Significance level P	0.0112	0.0120

Scleroderma	Correlation coefficient	0.918^⁎	0.934^⁎
Scleroderma	Significance level P	0.0035	0.0021

Tubercolosis	Correlation coefficient	0.083	0.106
Tubercolosis	Significance level P	0.8588	0.8217

Symptoms

Anorexia	Correlation coefficient	0.220	0.184
Anorexia	Significance level P	0.6348	0.6931

Cough	Correlation coefficient	−0.740	−0.770^⁎
Cough	Significance level P	0.0571	0.0429

Dyspnea	Correlation coefficient	−0.725	−0.757^⁎
Dyspnea	Significance level P	0.0654	0.0490

Fatigue	Correlation coefficient	−0.576	−0.612
Fatigue	Significance level P	0.1756	0.1438

Fever	Correlation coefficient	−0.848^⁎	−0.869^⁎
Fever	Significance level P	0.0158	0.0110

Respiratory failure	Correlation coefficient	−0.939^⁎⁎	−0.939^⁎⁎
Respiratory failure	Significance level P	0.0017	0.0017

Tachipnea	Correlation coefficient	−0.937^⁎⁎	−0.941^⁎⁎
Tachipnea	Significance level P	0.0018	0.0016

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^⁎

Statistically significant, with p-value <0.05;

^⁎⁎

Statistically significant, with p-value <0.01.

Table 3.

Pearson׳s correlation between GT-based data concerning clinical symptoms/diseases associated with silicosis and silicosis epidemiological data (namely, death rate and number of deaths) in the study period 2004–2010.

Variable		GT-based silicosis (Disease)	GT-based silicosis (search term)
Associated diseases

Lung cancer	Correlation coefficient	0.736	0.697
Lung cancer	Significance level P	0.0595	0.0818

Laryngeal cancer	Correlation coefficient	−0.680	−0.628
Laryngeal cancer	Significance level P	0.0929	0.1308

Rheumatoid arthritis	Correlation coefficient	0.476	0.445
Rheumatoid arthritis	Significance level P	0.2797	0.3165

Systemic Lupus Erythematosus	Correlation coefficient	0.455	0.399
Systemic Lupus Erythematosus	Significance level P	0.3051	0.3755

Scleroderma	Correlation coefficient	0.861^⁎	0.823^⁎
Scleroderma	Significance level P	0.0129	0.0230

Tubercolosis	Correlation coefficient	−0.007	−0.030
Tubercolosis	Significance level P	0.9879	0.9484

Symptoms

Anorexia	Correlation coefficient	−0.161	−0.175
Anorexia	Significance level P	0.7299	0.7080

Cough	Correlation coefficient	−0.817^⁎	−0.784^⁎
Cough	Significance level P	0.0247	0.0370

Dyspnea	Correlation coefficient	−0.790^⁎	−0.754
Dyspnea	Significance level P	0.0347	0.0503

Fatigue	Correlation coefficient	−0.753	−0.729
Fatigue	Significance level P	0.0505	0.0632

Fever	Correlation coefficient	−0.820^⁎	−0.776
Fever	Significance level P	0.0240	0.0401

Respiratory failure	Correlation coefficient	−0.864^⁎	−0.825^⁎
Respiratory failure	Significance level P	0.0121	0.0225

Tachipnea	Correlation coefficient	−0.902^⁎⁎	−0.867^⁎
Tachipnea	Significance level P	0.0054	0.0115

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^⁎

Statistically significant, with p-value <0.05;

^⁎⁎

Statistically significant, with p-value <0.01.

2. Experimental design, materials and methods

GT (available at https://www.google.com/trends) was exploited in order to capture Internet activities and interest related to silicosis. GT was mined in the USA, looking for “silicosis” as keyword, and using both “search term” (data directly available at https://www.google.com/trends/explore?date=2004-01-01%202010-12-31&geo=US&q=Silicosis) and “search topic” [Disease] (data directly available at https://www.google.com/trends/explore?date=2004-01-01%202010-12-31&geo=US&q=%2Fm%2F02yw8n) as search strategy options, from 2004 to 2010. Data downloadable from GT are available as monthly data, in comma-separated values (CSV) format.

“Real-world” statistical data, both raw and adjusted, were collected from the CDC site for the same study period 2004–2010 [1], [2], [3], [4], [5].

Correlational analysis was carried out between the GT-based search volumes and the “real-world” statistical data about silicosis. A list of silicosis-related terms (clinical symptoms and other associated diseases) was further searched and their flux volumes were correlated with the silicosis hit-search data and the epidemiological data (namely, death rate and number of deaths).

All statistical analyses were carried out using the Statistical Package for Social Science version 23.0 (SPSS, IBM, IL, USA) and STATISTICA version 12 (StatSoft Inc., Tulsa, OK, USA). Figures with a p-value <0.05 were considered significant.

For further details, the reader is referred to [6].

Footnotes

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Transparency data associated with this article can be found in the online version at doi:10.1016/j.dib.2016.11.021.

Transparency document. Supplementary material

Supplementary material

mmc1.pdf^{(138KB, pdf)}

References

1.Centers for Disease Control and Prevention (CDC). Silicosis mortality trends and new exposures to respirable crystalline silica — United States, 2001–2010. Available at 〈http://www.cdc.gov/mmwr/preview/mmwrhtml/mm6405a1.htm〉 [PMC free article] [PubMed]
2.Centers for Disease Control and Prevention (CDC). Silicosis: Death rates (per million population) by race and sex, U.S. residents age 15 and over, 2001–2010. Available at 〈http://wwwn.cdc.gov/eworld/Data/Silicosis_Death_rates_per_million_population_by_race_and_sex_US_residents_age_15_and_over_20012010/770〉
3.Centers for Disease Control and Prevention (CDC). Silicosis: Number of deaths by sex, race, age group, and median age at death, U.S. residents age 15 and over, 2001–2010. Available at 〈http://wwwn.cdc.gov/eworld/Data/Silicosis_Number_of_deaths_by_sex_race_age_group_and_median_age_at_death_US_residents_age_15_and_over_20012010/769〉
4.Center for Disease Control and Prevention (CDC). Silicosis: Age-adjusted death rates by state, U.S. residents age 15 and over, 1996–2005. Available at 〈http://wwwn.cdc.gov/eworld/Data/Silicosis_Age-adjusted_death_rates_by_state_US_residents_age_15_and_over_1996-2005/297〉
5.Centers for Disease Control and Prevention (CDC). Silicosis: Number of deaths by state, U.S. residents age 15 and over, 2001–2010. Available at 〈http://wwwn.cdc.gov/eworld/Data/Silicosis_Number_of_deaths_by_state_US_residents_age_15_and_over_20012010/772〉
6.Bragazzi N.L., Dini G., Toletone A., Brigo F., Durando P. Leveraging Big Data for exploring occupational diseases-related interest at the level of scientific community, media coverage and novel data streams: the example of silicosis as a pilot study. PLoS. One. 2016;11(11) doi: 10.1371/journal.pone.0166051. Available at 〈https://www.ncbi.nlm.nih.gov/pubmed/27806115〉 e0166051. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary material

mmc1.pdf^{(138KB, pdf)}

[bib1] 1.Centers for Disease Control and Prevention (CDC). Silicosis mortality trends and new exposures to respirable crystalline silica — United States, 2001–2010. Available at 〈http://www.cdc.gov/mmwr/preview/mmwrhtml/mm6405a1.htm〉 [PMC free article] [PubMed]

[bib2] 2.Centers for Disease Control and Prevention (CDC). Silicosis: Death rates (per million population) by race and sex, U.S. residents age 15 and over, 2001–2010. Available at 〈http://wwwn.cdc.gov/eworld/Data/Silicosis_Death_rates_per_million_population_by_race_and_sex_US_residents_age_15_and_over_20012010/770〉

[bib3] 3.Centers for Disease Control and Prevention (CDC). Silicosis: Number of deaths by sex, race, age group, and median age at death, U.S. residents age 15 and over, 2001–2010. Available at 〈http://wwwn.cdc.gov/eworld/Data/Silicosis_Number_of_deaths_by_sex_race_age_group_and_median_age_at_death_US_residents_age_15_and_over_20012010/769〉

[bib4] 4.Center for Disease Control and Prevention (CDC). Silicosis: Age-adjusted death rates by state, U.S. residents age 15 and over, 1996–2005. Available at 〈http://wwwn.cdc.gov/eworld/Data/Silicosis_Age-adjusted_death_rates_by_state_US_residents_age_15_and_over_1996-2005/297〉

[bib5] 5.Centers for Disease Control and Prevention (CDC). Silicosis: Number of deaths by state, U.S. residents age 15 and over, 2001–2010. Available at 〈http://wwwn.cdc.gov/eworld/Data/Silicosis_Number_of_deaths_by_state_US_residents_age_15_and_over_20012010/772〉

[bib6] 6.Bragazzi N.L., Dini G., Toletone A., Brigo F., Durando P. Leveraging Big Data for exploring occupational diseases-related interest at the level of scientific community, media coverage and novel data streams: the example of silicosis as a pilot study. PLoS. One. 2016;11(11) doi: 10.1371/journal.pone.0166051. Available at 〈https://www.ncbi.nlm.nih.gov/pubmed/27806115〉 e0166051. [DOI] [PMC free article] [PubMed] [Google Scholar]

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Infodemiological data concerning silicosis in the USA in the period 2004–2010 correlating with real-world statistical data

Nicola Luigi Bragazzi

Guglielmo Dini

Alessandra Toletone

Francesco Brigo

Paolo Durando

Abstract

1. Data

Fig. 1.

Table 1.

Table 2.

Table 3.

2. Experimental design, materials and methods

Footnotes

Transparency document. Supplementary material

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

Cite

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PERMALINK

Infodemiological data concerning silicosis in the USA in the period 2004–2010 correlating with real-world statistical data

Nicola Luigi Bragazzi

Guglielmo Dini

Alessandra Toletone

Francesco Brigo

Paolo Durando

Abstract

1. Data

Fig. 1.

Table 1.

Table 2.

Table 3.

2. Experimental design, materials and methods

Footnotes

Transparency document. Supplementary material

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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