Dataset Information

Skin fairness is a better predictor for impaired physical and mental health than hair redness.

ABSTRACT: About 1-2% of people of European origin have red hair. Especially female redheads are known to suffer higher pain sensitivity and higher incidence of some disorders, including skin cancer, Parkinson's disease and endometriosis. Recently, an explorative study performed on 7,000 subjects showed that both male and female redheads score worse on many health-related variables and express a higher incidence of cancer. Here, we ran the preregistered study on a population of 4,117 subjects who took part in an anonymous electronic survey. We confirmed that the intensity of hair redness negatively correlated with physical health, mental health, fecundity and sexual desire, and positively with the number of kinds of drugs prescribed by a doctor currently taken, and with reported symptoms of impaired mental health. It also positively correlated with certain neuropsychiatric disorders, most strongly with learning disabilities disorder and phobic disorder in men and general anxiety disorder in women. However, most of these associations disappeared when the darkness of skin was included in the models, suggesting that skin fairness, not hair redness, is responsible for the associations. We discussed two possible explanations for the observed pattern, the first based on vitamin D deficiency due to the avoidance of sunbathing by subjects with sensitive skin, including some redheads, and second based on folic acid depletion in fair skinned subjects, again including some (a different subpopulation of) redheads. It must be emphasized, however, that both of these explanations are only hypothetical as no data on the concentration of vitamin D or folic acid are available for our subjects. Our results, as well as the conclusions of current reviews, suggest that the new empirical studies on the concentration of vitamin D and folic acids in relation to skin and hair pigmentation are urgently needed.

SUBMITTER: Flegr J

PROVIDER: S-EPMC6888829 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Skin fairness is a better predictor for impaired physical and mental health than hair redness.

Flegr Jaroslav J Sýkorová Kateřina K

Scientific reports 20191202 1

About 1-2% of people of European origin have red hair. Especially female redheads are known to suffer higher pain sensitivity and higher incidence of some disorders, including skin cancer, Parkinson's disease and endometriosis. Recently, an explorative study performed on 7,000 subjects showed that both male and female redheads score worse on many health-related variables and express a higher incidence of cancer. Here, we ran the preregistered study on a population of 4,117 subjects who took part ...[more]

PMID: 31792316

Similar Datasets

Project description:BackgroundApproximately 1 in 5 American adults experience mental illness every year. Thus, mobile phone-based mental health prediction apps that use phone data and artificial intelligence techniques for mental health assessment have become increasingly important and are being rapidly developed. At the same time, multiple artificial intelligence-related technologies (eg, face recognition and search results) have recently been reported to be biased regarding age, gender, and race. This study moves this discussion to a new domain: phone-based mental health assessment algorithms. It is important to ensure that such algorithms do not contribute to gender disparities through biased predictions across gender groups.ObjectiveThis research aimed to analyze the susceptibility of multiple commonly used machine learning approaches for gender bias in mobile mental health assessment and explore the use of an algorithmic disparate impact remover (DIR) approach to reduce bias levels while maintaining high accuracy.MethodsFirst, we performed preprocessing and model training using the data set (N=55) obtained from a previous study. Accuracy levels and differences in accuracy across genders were computed using 5 different machine learning models. We selected the random forest model, which yielded the highest accuracy, for a more detailed audit and computed multiple metrics that are commonly used for fairness in the machine learning literature. Finally, we applied the DIR approach to reduce bias in the mental health assessment algorithm.ResultsThe highest observed accuracy for the mental health assessment was 78.57%. Although this accuracy level raises optimism, the audit based on gender revealed that the performance of the algorithm was statistically significantly different between the male and female groups (eg, difference in accuracy across genders was 15.85%; P<.001). Similar trends were obtained for other fairness metrics. This disparity in performance was found to reduce significantly after the application of the DIR approach by adapting the data used for modeling (eg, the difference in accuracy across genders was 1.66%, and the reduction is statistically significant with P<.001).ConclusionsThis study grounds the need for algorithmic auditing in phone-based mental health assessment algorithms and the use of gender as a protected attribute to study fairness in such settings. Such audits and remedial steps are the building blocks for the widespread adoption of fair and accurate mental health assessment algorithms in the future.

Project description:PurposeHealth-related quality of life (HRQOL) in older persons is influenced by physical and mental health, as well as by their social contacts and social support. Older women and men have disparate types of social networks; they each value social ties differently and experience loneliness in unique and personal ways. The aim of this study is, therefore, to determine the longitudinal association between loneliness and social isolation with HRQOL in older people-separated by gender.MethodsData stem from the third and fourth follow-up of the ESTHER study-a population-based cohort study of the older population in Germany. A sample of 2171 older women and men (mean age: 69.3 years, range 57-84 years) were included in this study; HRQOL was assessed by using the Short Form-12 questionnaire (SF-12). Data on physical and mental health, loneliness, and social networks were examined in the course of comprehensive home visits by trained study doctors. Gender-specific linear regression analyses were performed to predict physical quality of life (measured by the PCS, physical component score of the SF-12) and mental quality of life (measured by the MCS, mental component score) after three years, adjusted by socioeconomic variables as well as physical, mental, and social well-being.ResultsAt baseline, PCS was 41.3 (SD: 10.0) in women and 42.2 (SD: 9.6) in men (p = .04). MCS was 47.0 (SD: 10.2) in women and 49.6 (SD: 8.6) in men (p < .001). In both genders, PCS and MCS were lower three years later. Loneliness at t0 was negatively associated with both PCS and MCS after three years (t1) among women, and with MCS but not PCS after three years among men. In both genders, the strongest predictor of PCS after three years was PCS at t0 (p < .001), while the strongest predictors of MCS after three years were MCS and PCS at t0.ConclusionHRQOL in elderly women and men is predicted by different biopsychosocial factors. Loneliness predicts decreased MCS after three years in both genders, but decreased PCS after three years only in women. Thus, a greater impact of loneliness on the health of older women can be surmised and should therefore be considered in the context of their medical care.

Project description:OBJECTIVES:To prospectively examine the health and health-related behaviors of Army Special Forces personnel in comparison with two distinct, but functionally similar Army groups. METHODS:Special Forces, Ranger Qualified, and General Purposes Forces enrolled in the Millennium Cohort Study were identified using data from the Defense Manpower Data Center. Using prospective survey data (2001-2014), we estimated the association of Army specialization with mental health, social support, physical health, and health-related behaviors with multivariable regression models. RESULTS:Among the 5,392 eligible participants (84.4% General Purposes Forces, 10.0% Special Forces, 5.6% Ranger Qualified), Special Forces personnel reported the lowest prevalence of mental disorders, physical health problems, and unhealthy behaviors. In the multivariable models, Special Forces personnel were less likely to report mental health problems, multiple somatic symptoms, and unhealthy behaviors compared with General Purpose Forces infantrymen (odds ratios [OR]: 0.20-0.54, p-values < .01). Overall, Special Forces personnel were similar in terms of mental and physical health compared with Ranger Qualified infantrymen, but were less likely to sleep < 5 hours/night (OR: 0.60, 95% confidence intervals: 0.40, 0.92) and have 5 or more multiple somatic symptoms (OR: 0.69, 95% CI: 0.49, 0.98). Both Special Forces personnel and Ranger Qualified infantrymen engaged in more healthy behaviors compared with General Purpose Forces infantrymen (OR: 2.57-6.22, p-values<0.05). Engagement in more healthy behaviors reduced the odds of subsequent adverse health outcomes, regardless of specialization. CONCLUSIONS:Army Special Forces personnel were found to be mentally and physically healthier than General Purpose Forces infantrymen, which may in part be due to their tendency to engage in healthy behaviors. Findings indicate that engagement in a greater number of healthy behaviors may reduce odds for subsequent adverse outcomes.

Project description:Incarceration is definitively linked to poor health, and upon release from prison, many individuals experience difficulty in maintaining good health. Given the complexity of the reentry process, one's health status, both in and out of prison, likely influences additional aspects of reentry, such as abstaining from crime or adhering to parole terms. The purpose of this study is to determine whether in-prison physical and mental health, as well as changes to an individual's health upon release from prison, are related to the likelihood of recidivating. We employ the Serious and Violent Reentry Initiative (SVORI) data, a multi-state sample of formerly incarcerated males who are followed from prison to release into the community and interviewed about a number of post-prison release issues, including health. We use hierarchical logistic and multinomial regressions, where survey waves are nested within people, to assess if in-prison physical and mental health and post-release changes to health are associated with recidivism in two ways: general re-incarceration and re-incarceration due to either a technical violation of parole or a new conviction. With right-censoring due to recidivism or "failure," our final sample size is 2180 person-periods (i.e., waves) nested within 871 respondents. We find that better physical health, both in-prison and changes in health post-release, is related to a higher likelihood of recidivating. Better mental health, both in-prison and changes to mental health post-release, is related to a decrease in the likelihood of recidivating. Individuals with poor mental health in-prison who make significant improvements after release see the largest reduction in their odds of recidivating. Finally, the combination of better mental health in-prison and increases in mental health post-release is associated with reductions in the likelihood of re-offending for both technical violations and new convictions. In sum, in-prison health continues to influence individuals after prison and is associated with their odds of recidivating, thus contributing to the churning of individuals through the prison system.

Dataset Information

Skin fairness is a better predictor for impaired physical and mental health than hair redness.

Publications

Skin fairness is a better predictor for impaired physical and mental health than hair redness.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets