The healthy diver: A cross-sectional survey to evaluate the health status of recreational scuba diver members of Divers Alert Network (DAN)

<div><p>Background</p><p>Scuba diver fitness is paramount to confront environmental stressors of diving. However, the diving population is aging and the increasing prevalence of diseases may be a concern for diver fitness.</p><p>Purpose</p><p>The purpose of this study is to assess the demographics, lifestyle factors, disease prevalence, and healthcare access and utilization of Divers Alert Network (DAN) members and compare them with those from the general population.</p><p>Methods</p><p>DAN membership health survey (DMHS) was administered online in 2011 to DAN members in the United States (US). Health status of DMHS respondents was compared with the general US population data from the Center for Disease Control and Prevention’s Behavioral Risk Factor Surveillance System using two-sided student’s <i>t</i>-tests and Mantel-Haenszel chi-square tests. Univariate and multivariate logistic regression analyses were conducted to identify factors associated with healthcare utilization among the DMHS participants.</p><p>Results</p><p>Compared to the general US population, the DMHS population had lower prevalence of asthma, heart attack, angina, stroke, diabetes, hypertension, hypercholesterolemia, and disabilities (p<0.01); more heavy alcohol drinkers, and fewer smokers (p<0.01); and greater access and utilization (routine checkup) of healthcare (p<0.01). Healthcare utilization in males was lower than among females. Increasing age and increase in the number of chronic illnesses were associated with increased healthcare utilization.</p><p>Conclusions</p><p>DAN members are healthier than the general US population. DAN members also have better access to healthcare and utilize healthcare for preventive purposes more often than the general population. DAN members appear to have a better fitness level than their non-diving peers.</p></div

Factors affecting healthcare utilization among DAN survey participants.

<p>Factors affecting healthcare utilization among DAN survey participants.</p

Comparison of prevalence of diseases and healthcare utilization between DAN survey respondents and the general US population (BRFSS data).

<p>Comparison of prevalence of diseases and healthcare utilization between DAN survey respondents and the general US population (BRFSS data).</p

Adjusted odds ratio and 95% CI for healthcare utilization in the past year by age, adjusted for sex, income and chronic health conditions.

<p>Adjusted odds ratio and 95% CI for healthcare utilization in the past year by age, adjusted for sex, income and chronic health conditions.</p

Physical and medical characteristics and brain MRI lesions in 12 Ama divers.

<p>*: parenthesis: numbers of lesion.</p

Magnetic resonance images of brains of three Ama divers: hyperintense area on T2-weighted image (circle), corresponding to hypointensity on T1-weighted image (inset).

<p>A patchy shadow in the left parietal cortex (A, No. 2), a linear subcortical lesion in the right frontal lobe (B, No. 5), and deformity of bilateral caudate heads and subdural fluid collection (allow heads) (C, No. 11).</p

Distribution of Perceived Severity Index (PSI) in the BIG292 data set with corresponding Type I/II and Type A/B classifications [19, 20].

<p>Distribution of Perceived Severity Index (PSI) in the BIG292 data set with corresponding Type I/II and Type A/B classifications [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0172665#pone.0172665.ref019" target="_blank">19</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0172665#pone.0172665.ref020" target="_blank">20</a>].</p

DCS occurrences and binomial and trinomial model (Type A/B classification) predictions for the BIG292 data set.

<p>DCS occurrences and binomial and trinomial model (Type A/B classification) predictions for the BIG292 data set.</p

Parameter values and 95% confidence intervals for the binomial and trinomial (Type A/B classification) variants of the LE1nt model.

<p>For the log likelihood values, the underlined results can be directly compared. The trinomial model offers a highly significant improvement over the binomial model.</p

Example random process in which a pair of independent uniform numbers trigger event A if <i>R</i>_<i>A</i> < 0.2 and event B if <i>R</i>_<i>B</i> < 0.2.

<p>If <i>R</i>_<i>A</i> is observed before <i>R</i>_<i>B</i> and event A masks event B, then the hierarchical probability of observing event A is <i>P</i>^<i>h</i>(<i>A</i>) = 0.2 and the hierarchical probability of observing event B is <i>P</i>^<i>h</i>(<i>B</i>) = 0.16.</p