| Literature DB >> 34291035 |
Li-Peng Yao1, Ran Tao2.
Abstract
During recent decades, the elevation of workers' health has become the utmost point of concern since it is considered among the primary indicators of economies. The economies, especially the emerging ones, are primarily focusing on every indicator to uplift their businesses. Along with the other aspects, it is also required to assess the impact of Chronic Obstructive Pulmonary Disease (COPD) on workers' health conditions in small- and medium-scale enterprises (SMEs). With this aim, we are presenting a detailed analysis to reveal useful insights regarding the COPD-workers' health nexus. The sample set of 1,008 workers is working in various SMEs in Beijing and Tianjin from September, 2020. The findings infer that a rise in COPD concerning wages will uplift the worker health problems due to COPD affecting worker health. Whereas, the working condition and tools, smoking years, and health safety training have a statistically adverse effect on workers' health concerning wages. The outcomes in terms of insights would be useful for planning future perspectives.Entities:
Keywords: COPD; China; SMEs; probit model; workers health
Year: 2021 PMID: 34291035 PMCID: PMC8287322 DOI: 10.3389/fpubh.2021.711629
Source DB: PubMed Journal: Front Public Health ISSN: 2296-2565
Figure 1COPD symptoms in Chinese SMEs.
Figure 2Workers' age distribution of Chinese SMEs.
Correlations matrix.
| WH | 1.000 | |||||
| COPD | 0.009 | 1.000 | ||||
| WCT | −0.085 | 0.075 | 1.000 | |||
| SY | −0.295 | −0.066 | 0.027 | 1.000 | ||
| WG | −0.085 | 0.075 | 1.000 | 0.027 | 1.000 | |
| HS | −0.092 | 0.047 | 0.102 | 0.111 | 0.102 | 1.000 |
Logistics regression estimates (N = 1,008).
| COPD | 0.0646 | 0.0182 | 3.55 | 0.00 |
| WCT | −0.0439 | 0.0167 | −2.63 | 0.00 |
| SY | −0.2892 | 0.0339 | −8.51 | 0.00 |
| HS | −0.0294 | 0.0136 | −2.16 | 0.031 |
| Logistic GOF | 194.15 | |||
| Pearson chi2 | 0.0004 | |||
| Link Test | ||||
| _hat | 0.7986 | 0.1336 | 5.98 | 0.00 |
| _hatsq | −0.3613 | 0.1655 | −2.18 | 0.029 |
| _cons | 0.1373 | 0.0931 | 1.47 | 0.140 |
| COPD | 1.03 | 0.966532 | ||
| WCT | 1.01 | 0.986672 | ||
| SY | 1.04 | 0.964581 | ||
| HS | 1.03 | 0.967583 | ||
| Mean VIF | 1.03 | |||
1% and
5% significant levels.
Sensitivity and specificity analysis.
| 296 | 193 | 489 | |||
| 157 | 362 | 519 | |||
| Total | 453 | 555 | 1,008 | ||
| Sensitivity | Pr (+|D) | 65.34% | Positive predictive value | Pr (+|D) | 60.53% |
| Specificity | Pr (–|~D) | 65.23% | Negative predictive value | Pr (–|~D) | 69.75% |
| False – rate for true D | Pr (–|D) | 34.77% | |||
| False + rate for true ~D | Pr (+|~D) | 34.66% | |||
| False + rate for classified | Pr (~D|+) | 39.47% | |||
| False – rate for classified | Pr (D|–) | 30.25% | |||
| Correctly classified | 65.28% | ||||
Figure 3Model validity and accuracy analysis.
Figure 4ROC analysis.