Community Risk Factors in the COVID-19 Incidence and Mortality in Catalonia (Spain). A Population-Based Study
Abstract
:1. Introduction
- Analyse the associations between both COVID-19 incidence and mortality and long-term exposure to pollutant concentration (NO2 and PM10), while adjusting for demographic information, socioeconomic status and general health status (cardiovascular diseases, psychological disorders and all-cause cancer);
- Explore the potential links between agri-food industry and COVID-19 incidence and mortality as observed from the outbreaks in these particular industries;
- Screen, for the very first time, the potential use of the overall Land Use and Cover data on describing the geographical COVID-19 incidence and mortality.
2. Materials and Methods
2.1. COVID-19 Cases and Deaths
2.2. Comorbidities
2.3. Demographic and Socioeconomic Data
2.4. Air Pollution
2.5. Agri-Food Industry
2.6. Land Use and Land Cover Data
2.7. Statistical Analysis
3. Results
Main Model Adjustment
4. Discussion
4.1. Demographics
4.2. Socioeconomics
4.3. Comorbidities
4.4. Air Pollution
4.5. Forest, Meat, and Leather and Fur Industry
4.6. Land Use and Cover
4.7. Limitations
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Urban Areas | Industrial, Commercial and Transport Units | Agricultural Areas | Forest and Semi-NATURAL Areas |
---|---|---|---|
Discontinuous urban fabric | Industrial or commercial units | Permanently irrigated land | Lowland natural grasslands |
Continuous urban fabric | Road and rail networks and associated land | Non-irrigated arable land | Montane natural grasslands |
Unirrigated Fruit tress | Alpine natural grasslands | ||
Irrigated Fruit trees | Transitional woodland/shrub | ||
Vineyards | Wetland vegetation | ||
Rice fields | Coniferous forest | ||
Citrus trees | Broad-leaved forest | ||
Sclerophyll forest |
Covariate (Units) | Description |
---|---|
Demographics, socioeconomic status, and comorbidity (Main model) | |
Sex: Females | Categorical variable comparing females to males, used as a reference level. |
Percent > 65 (%) | Percentage of people aged above 65 years. |
SES A | Socioeconomic status categorised with 5 levels, comparing very high, high, low and very low (A, B, D, E) socioeconomic status to normal (C), used as the reference level. Data from 2014. |
SES B | |
SES D | |
SES E | |
Cardiovascular diseases (%) | Group variable. Percentage of people with congestive heart failure, hypertension, ischemic cardiomyopathy or who suffered cerebrovascular accident in 2014. |
Psychological disorders (%) | Group variable. Percentage of people with depression, schizophrenia, intellectual disability, conduct disorder, attention deficit disorder or psychosis in 2014. |
All-cause cancer (%) | Group variable. Percentage of people with any type of cancer in 2014. |
Human activity | |
NO2 (µg/m3) * | Nitrogen dioxide annual weighed average in 2016. |
PM10 (µg/m3) * | Particulate matter with diameter of 10 µm annual weighed average in 2016. |
Meat industry * | Number of industries based on slaughtering of livestock, conservation and elaboration of meat products in 2020. |
Fish industry * | Number of industries based on preparation and conservation of fish, crustaceans and molluscs in 2020. |
Vegetable industry * | Number of industries based on preparation and preservation of fruits and vegetables in 2020. |
Animal oils and fats * | Number of industries based on manufacturing of vegetable and animal oils and fats in 2020. |
Milk products * | Number of industries based on manufacturing of milk products in 2020. |
Grain mill industry * | Number of industries based on manufacturing of grain mill products, starches and starch products in 2020. |
Bakery industry * | Number of industries based on manufacturing of bakery and pasta products in 2020. |
Other food products * | Number of industries based on manufacturing of other food products in 2020. |
Animal feeding * | Number of industries based on manufacturing of products for animal feeding in 2020. |
Beverage industry * | Number of industries based on manufacturing of beverages in 2020. |
Forest industry * | Number of forest industries in 2020. |
Leather and fur industry * | Number of industries based on preparation, tanning and dyeing animal skins in 2020. |
Garden industry * | Number of industries based on seed conditioning and handling, substrate production and ornamental plant conservation in 2020. |
Land use and Land cover | |
ilr-Urban areas * | Isometric logratio (ilr) transformation of the percentage of urban areas in a given BHA. Numerical variable. |
ilr-Industrial areas * | Isometric logratio (ilr) transformation of the percentage of industrial, commercial and transport unit areas in a given BHA. Numerical variable. |
ilr-Agricultural areas * | Isometric logratio (ilr) transformation of the percentage of agricultural areas in a given BHA. Numerical variable. |
ilr-Forested areas * | Isometric logratio (ilr) transformation of the percentage of forested and semi-natural areas in a given BHA. Numerical variable. |
Mean ± SD | Statistical Results | ||||
---|---|---|---|---|---|
Variables | 2016 Concentration Levels | 2018/2019 Concentration Levels | df | t | p-Value |
NO2 | 20.23 ± 12.163 | 21.37 ± 10.700 | 246 | 0.792 | 0.428 |
PM10 | 21.52 ± 4.397 | 20.72 ± 5.241 | 351.37 | −1.559 | 0.119 |
Incidence of COVID-19 | Mortality of COVID-19 | |||||||
---|---|---|---|---|---|---|---|---|
Adjusted Main Model | Unadjusted | Adjusted Main Model | Unadjusted | |||||
Covariates | Odds Ratio (95% CI) | p-Value | Odds Ratio (95% CI) | p-Value | Odds Ratio (95% CI) | p-Value | Odds Ratio (95% CI) | p-Value |
Main Model | ||||||||
Sex: Female | 1.772 (1.7577–1.7870) | *** | 1.723 (1.7087–1.7366) | *** | 1.034 (0.9974–1.0724) | - | 0.990 (0.9551–1.0257) | - |
Percent > 65 | 1.006 (1.0047–1.0072) | *** | 1.018 (1.0171–1.0189) | *** | 1.023 (1.0171–1.0281) | *** | 1.052 (1.0481–1.0562) | *** |
SES A (very high) | 1.199 (1.1832–1.2150) | *** | 1.171 (1.1568–1.1848) | *** | 1.547 (1.4556–1.6434) | *** | 1.523 (1.4414–1.6093) | *** |
SES B (high) | 1.126 (1.1116–1.1402) | *** | 1.153 (1.1387–1.1674) | *** | 1.241 (1.1696–1.3166) | *** | 1.346 (1.2702–1.4271) | *** |
SES D (low) | 0.967 (0.9542–0.9800) | *** | 0.998 (0.9849–1.0114) | - | 0.914 (0.8573–0.9754) | * | 1.015 (0.9517–1.0815) | - |
SES E (very low) | 0.956 (0.9432–0.9688) | *** | 0.994 (0.9806–1.0067) | - | 0.908 (0.8511–0.9677) | ** | 1.011 (0.9493–1.0778) | - |
Cardiovascular diseases | 1.003 (1.0020–1.0049) | *** | 1.016 (1.0153–1.0173) | *** | 1.007 (1.0006–1.0136) | * | 1.038 (1.0336–1.0423) | *** |
Psychological disorders | 1.148 (1.1418–1.1545) | *** | 1.057 (1.0517–1.0627) | *** | 1.312 (1.2809–1.3435) | *** | 1.255 (1.2282–1.2827) | *** |
All-cause cancer | 1.021 (1.0153–1.0258) | *** | 1.084 (1.0805–1.0883) | *** | 1.102 (1.0774–1.1272) | *** | 1.239 (1.2205–1.2584) | *** |
Human activity | ||||||||
NO2 | 0.999 (0.9989–0.9996) | *** | 1.002 (1.0014–1.0020) | *** | 1.013 (1.0118–1.0151) | *** | 1.017 (1.0154–1.0182) | *** |
PM10 | 1.003 (1.0015–1.0038) | *** | 1.009 (1.0077–1.0098) | *** | 1.048 (1.0421–1.0541) | *** | 1.050 (1.0451–1.0559) | *** |
Meat industry | 1.002 (1.0012–1.0019) | *** | 1.001 (1.0006–1.0014) | *** | 0.995 (0.9926–0.9965) | *** | 0.992 (0.9900–0.9938) | *** |
Fish industry | 0.993 (0.9911–0.9951) | *** | 0.982 (0.9799–0.9840) | *** | 0.964 (0.9536–0.9755) | *** | 0.929 (0.9177–0.9412) | *** |
Vegetable industry | 0.988 (0.9867–0.9885) | *** | 0.985 (0.9839–0.9856) | *** | 0.941 (0.9340–0.9478) | *** | 0.923 (0.9154–0.9300) | *** |
Animal oils and fats | 0.982 (0.9812–0.9836) | *** | 0.980 (0.9789–0.9813) | *** | 0.909 (0.8988–0.9189) | *** | 0.888 (0.8781–0.8991) | *** |
Milk products | 1.000 (0.9982–1.0013) | - | 1.001 (0.9995–1.0024) | - | 0.973 (0.9650–0.9806) | *** | 0.975 (0.9675–0.9822) | *** |
Grain mill industry | 0.948 (0.9441–0.9523) | *** | 0.944 (0.9397–0.9478) | *** | 0.777 (0.7502–0.8047) | *** | 0.753 (0.7266–0.7811) | *** |
Bakery industry | 0.984 (0.9809–0.9873) | *** | 0.977 (0.9740–0.9801) | *** | 0.974 (0.9589–0.9891) | ** | 0.938 (0.9236–0.9517) | *** |
Other food products | 0.984 (0.9829–0.9861) | *** | 0.977 (0.9752–0.9783) | *** | 0.933 (0.9244–0.9412) | *** | 0.910 (0.9019–0.9178) | *** |
Animal feeding | 0.998 (0.9967–0.9994) | ** | 0.999 (0.9975–1.0001) | - | 0.970 (0.9630–0.9768) | *** | 0.967 (0.9605–0.9739) | *** |
Beverage industry | 0.999 (0.9994–0.9996) | *** | 0.999 (0.9994–0.9996) | *** | 0.998 (0.9970–0.9983) | *** | 0.997 (0.9963–0.9978) | *** |
Forest industry | 1.004 (1.0011–1.0077) | * | 0.990 (0.9869–0.9931) | *** | 0.945 (0.9278–0.9632) | *** | 0.907 (0.8911–0.9240) | *** |
Leather and fur industry | 1.070 (1.0624–1.0779) | *** | 1.078 (1.0702–1.0856) | *** | 1.110 (1.0776–1.1441) | *** | 1.115 (1.0823–1.1489) | *** |
Garden industry | 0.922 (0.9122–0.9329) | *** | 0.922 (0.9119–0.9321) | *** | 0.717 (0.6715–0.7649) | *** | 0.709 (0.6649–0.7560) | *** |
Land use and cover | ||||||||
ilr-Urban areas | 1.006 (1.0048–1.0076) | *** | 1.013 (1.0114–1.0136) | *** | 1.050 (1.0440–1.0569) | *** | 1.062 (1.0566–1.0669) | *** |
ilr-Industrial areas | 0.990 (0.9884–0.9921) | *** | 0.991 (0.9892–0.9926) | *** | 1.039 (1.0304–1.0477) | *** | 1.036 (1.0281–1.0442) | *** |
ilr-Agricultural areas | 0.982 (0.9806–0.9835) | *** | 0.977 (0.9762–0.9786) | *** | 0.936 (0.9303–0.9422) | *** | 0.925 (0.9200–0.9300) | *** |
ilr-Forested areas | 1.014 (1.0131–1.0158) | *** | 1.012 (1.0111–1.0136) | *** | 0.991 (0.9856–0.9971) | ** | 0.987 (0.9816–0.9925) | *** |
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Zaldo-Aubanell, Q.; Campillo i López, F.; Bach, A.; Serra, I.; Olivet-Vila, J.; Saez, M.; Pino, D.; Maneja, R. Community Risk Factors in the COVID-19 Incidence and Mortality in Catalonia (Spain). A Population-Based Study. Int. J. Environ. Res. Public Health 2021, 18, 3768. https://doi.org/10.3390/ijerph18073768
Zaldo-Aubanell Q, Campillo i López F, Bach A, Serra I, Olivet-Vila J, Saez M, Pino D, Maneja R. Community Risk Factors in the COVID-19 Incidence and Mortality in Catalonia (Spain). A Population-Based Study. International Journal of Environmental Research and Public Health. 2021; 18(7):3768. https://doi.org/10.3390/ijerph18073768
Chicago/Turabian StyleZaldo-Aubanell, Quim, Ferran Campillo i López, Albert Bach, Isabel Serra, Joan Olivet-Vila, Marc Saez, David Pino, and Roser Maneja. 2021. "Community Risk Factors in the COVID-19 Incidence and Mortality in Catalonia (Spain). A Population-Based Study" International Journal of Environmental Research and Public Health 18, no. 7: 3768. https://doi.org/10.3390/ijerph18073768