| Autor: |
Raymond F Palmer, Tatjana Walker, Roger B Perales, Rodolfo Rincon, Carlos Roberto Jaén, Claudia S Miller |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
Environmental Disease, Vol 6, Iss 4, Pp 134-141 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2468-5704 |
| DOI: |
10.4103/ed.ed_18_21 |
| Popis: |
Background: Chemical intolerance (CI) is characterized by multisystem symptoms initiated by a one-time high-dose or a persistent low-dose exposure to environmental toxicants. Prior studies have investigated symptom clusters rather than defined comorbid disease clusters. We use a latent class modeling approach to determine the number and type of comorbid disease clusters associated with CI. Methods: Two hundred respondents with and without CI were recruited to complete the Quick Environmental Exposure and Sensitivity Inventory (QEESI), and a 17-item comorbid disease checklist. A logistic regression model was used to predict the odds of comorbid disease conditions between groups. A latent class analysis was used to inspect the pattern of dichotomous item responses from the 17 comorbid diseases. Results: Those with the highest QEESI scores had significantly greater probability of each comorbid disease compared to the lowest scoring individuals (P < 0.0001). Three latent class disease clusters were found. Class 1 (17% of the sample) was characterized by a cluster consisting of irritable bowel syndrome (IBS), arthritis, depression, anxiety, fibromyalgia, and chronic fatigue. The second class (53% of the sample) was characterized by a low probability of any of the co-morbid diseases. The third class (30% of the sample) was characterized only by allergy. Discussion: We have demonstrated that several salient comorbid diseases form a unique statistical cluster among a subset of individuals with CI. Understanding these disease clusters may help physicians and other health care workers to gain a better understanding of individuals with CI. As such, assessing their patients for CI may help identify the salient initiators and triggers of their CI symptoms—therefore guide potential treatment efforts. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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