Poor tap water quality experiences and poor sleep quality during the Flint, Michigan Municipal Water Crisis

Sleep Health ◽  
2017 ◽  
Vol 3 (4) ◽  
pp. 241-243 ◽  
Author(s):  
Daniel J. Kruger ◽  
Gergana D. Kodjebacheva ◽  
Suzanne Cupal
Keyword(s):  
Water Quality ◽  
Sleep Quality ◽  
Tap Water ◽  
Poor Sleep ◽  
Water Crisis ◽  
Poor Sleep Quality ◽  
Municipal Water ◽  
Tap Water Quality

scholarly journals Data Visualization to Promote Understanding of the Community Health Impact of the Flint Water Crisis

2021 ◽  
Vol 10 (3) ◽  
Author(s):  
Annika Nikhar ◽  
Daniel Kruger
Keyword(s):  
Water Quality ◽  
Tap Water ◽  
Water Source ◽  
Health Impact ◽  
Water Crisis ◽  
Adverse Health Effects ◽  
Health Related ◽  
Tap Water Quality ◽  
The Times

Between 2014 to 2015, the city of Flint suffered from critically contaminated water that caused long-term adverse health effects for many of its residents. In 2017, the “Speak to Your Health!” Community Survey assessed various aspects of adult Flint residents’ health. In addition to the health-related assessments, there were also questions about residents’ tap water quality, including during the times of Flint’s water crisis. This project used results from the survey on how water turbidity levels affected the number of days when poor mental and physical health interfered with daily activities. Diagramming software was used to create images representing this relationship in intuitive formats. These data visualizations are intended to boost data literacy among non-researchers, particularly the people and policymakers of Flint. Given that the crisis ensued after Flint’s water source was switched without proper infrastructure in place to ensure appropriate water quality, causing the population to suffer long term health, social, and economic complications, it is hoped that these results will be used to empower the population of Flint to advocate for continued investment in remediation and prevent future similar health crises.  

scholarly journals Perceived Water Quality and Reported Health among Adults during the Flint, MI Water Crisis

2017 ◽  
Vol 15 (1) ◽  
pp. 56-61 ◽  
Author(s):  
Daniel J. Kruger ◽  
Suzanne Cupal ◽  
Gergana D. Kodjebacheva ◽  
Thomas V. Fockler
Keyword(s):  
Water Quality ◽  
Physical Health ◽  
Tap Water ◽  
Water Source ◽  
Behavioral Risk ◽  
Water Crisis ◽  
Physical And Mental Health ◽  
Online Social Media ◽  
Tap Water Quality ◽  
Mental And Physical Health

Background and Purpose: In April 2014, the municipal water source for Flint, Michigan was changed from Lake Huron to the Flint River. Although residents reported concerns about the quality of tap water and resulting health problems, officials insisted that the water was safe. This study examined relationships between self-reported tap water quality during the water crisis and health conditions among Flint residents. Methods: Participants from each residential Census Tract in the City of Flint were recruited via address lists, online social media, and community-based events. The survey included mental and physical health items from the CDC’s Behavioral Risk Factor Surveillance System and an item on tap water quarter quality experiences. Analyses were weighted to be demographically representative. Results: Participants (N = 277) rated their tap water quality (taste, smell, appearance) as Poor (57%), Fair (20%), Good (13%), Very Good (6%), and Excellent (3%). Controlling for age, gender, years of education, whether respondents were African American or Hispanic/Latino/a, and population demographics, lower perceived tap water quality was associated with worse mental and physical health across all indicators. Conclusion: This study demonstrates associations of tap water quality experiences with reported poor physical and mental health among adults in Flint during the Flint Water Crisis.

Association of severity of depressive symptoms with sleep quality, social support and stress among Pakistani medical and dental students: A cross-sectional study

2019 ◽  
Vol 2 (2) ◽  
pp. 211-220
Author(s):  
Ahmed Waqas ◽  
Aqsa Iftikhar ◽  
Zahra Malik ◽  
Kapil Kiran Aedma ◽  
Hafsa Meraj ◽  
...  
Keyword(s):  
Social Support ◽  
Depressive Symptoms ◽  
Medical Students ◽  
Sleep Quality ◽  
Severe Depression ◽  
Dental Students ◽  
Poor Sleep ◽  
Poor Sleep Quality ◽  
Cross Sectional ◽  
Medical And Dental Students

AbstractObjectivesThis study has been designed to elucidate the prevalence of stress, depression and poor sleep among medical students in a Pakistani medical school. There is a paucity of data on social support among medical students in Pakistan; an important predictor of depressive symptoms. Therefore, this study was also aimed to demonstrate the direct and indirect impact of social support in alleviating depressive symptoms in the study sample.MethodsThis observational cross-sectional study was conducted in Lahore, Pakistan, where a total of 400 students at a medical school were approached between 1st January to 31st March 2018 to participate in the study. The study sample comprised of medical and dental students enrolled at a privately financed Pakistani medical and dental school. The participants responded to a self-administered survey comprising of five parts: a) demographics, b) Pittsburgh Sleep Quality Index (PSQI), c) Patient Health Questionnaire-9 (PHQ-9), d) Multidimensional Scale of Perceived Social Support (MSPSS) and e) Perceived Stress Scale-4 (PSS-4). All data were analysed using SPSS v. 20. Linear regression analysis was used to reveal the predictors of depression.ResultsIn total, 353 medical students participated, yielding a response rate of 88.25%. Overall, poor sleep quality was experienced by 205 (58.1%) students. Mild to severe depression was reported by 83% of the respondents: mild depression by 104 (29.5%), moderate depression by 104 (29.5%), moderately severe depression by 54 (15.3%) and severe depression by 31 (8.8%) respondents. Subjective sleep quality, sleep latency, daytime dysfunction and stress levels were significantly associated with depression symptoms. Social support was not significantly associated with depressive symptoms in the regression model (Beta = -0.08, P < 0.09); however, it acted as a significant mediator, reducing the strength of the relationship between depressive symptoms and sleep quality and stress.ConclusionsAccording to our study, a large proportion of healthcare (medical and dental) students were found to be suffering from mild to moderate depression and experienced poor sleep quality. It is concluded that social support is an important variable in predicting depressive symptomatology by ameliorating the effects of poor sleep quality and high stress levels.

scholarly journals Impact and Correlates of Poor Sleep Quality in Japanese White-Collar Employees

SLEEP ◽  
2003 ◽  
Vol 26 (4) ◽  
pp. 467-471 ◽  
Author(s):  
Yuriko Doi ◽  
Masumi Minowa ◽  
Toshiro Tango
Keyword(s):  
Sleep Quality ◽  
White Collar ◽  
Poor Sleep ◽  
Poor Sleep Quality

scholarly journals The prevalence and risk factors for depressive symptoms in frontline nurses under COVID-19 pandemic based on a large cross-sectional study using the propensity score-matched method

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hongyan Wang ◽  
Xiaoling Dai ◽  
Zichuan Yao ◽  
Xianqing Zhu ◽  
Yunzhong Jiang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Depressive Symptoms ◽  
Sleep Quality ◽  
Psychological Capital ◽  
Cross Sectional Study ◽  
Poor Sleep ◽  
Sectional Study ◽  
Poor Sleep Quality ◽  
Related Factors ◽  
Cross Sectional

Abstract Introduction To explore the prevalence of depressive symptoms and the associated risk factors in frontline nurses under COVID-19 pandemic. Methods This cross-sectional study was conducted from February 20, 2020 to March 20, 2020 and involved 562 frontline nurses. The effective response rate was 87.68%. After propensity score matched, there were 498 participants left. Extensive characteristics, including demographics, dietary habits, life-related factors, work-related factors, and psychological factors were collected based on a self-reported questionnaire. Specific scales measured the levels of sleep quality, physical activity, depressive symptoms, perceived organization support and psychological capital. Adjusted odds ratios and 95% confidence intervals were determined by binary paired logistic regression. Results Of the nurses enrolled in the study, 50.90% had depressive symptoms. Three independent risk factors were identified: poor sleep quality (OR = 1.608, 95% CI: 1.384–1.896), lower optimism of psychological capital (OR = 0.879, 95% CI: 0.805–0.960) and no visiting friend constantly (OR = 0.513, 95% CI: 0.286–0.920). Conclusions This study revealed a considerable high prevalence of depressive symptoms in frontline nurses during the COVID-19 outbreak, and identified three risk factors, which were poor sleep quality, lower optimism of psychological capital, and no visiting friend constantly. Protecting mental health of nurses is important for COVID-19 pandemic control and their wellbeing. These findings enrich the existing theoretical model of depression and demonstrated a critical need for additional strategies that could address the mental health in frontline nurses for policymakers.

scholarly journals Measuring Subjective Sleep Quality: A Review

2021 ◽  
Vol 18 (3) ◽  
pp. 1082
Author(s):  
Marco Fabbri ◽  
Alessia Beracci ◽  
Monica Martoni ◽  
Debora Meneo ◽  
Lorenzo Tonetti ◽  
...  
Keyword(s):  
Construct Validity ◽  
Sleep Quality ◽  
Psychometric Properties ◽  
Internal Consistency ◽  
Self Report ◽  
Measurement Properties ◽  
Poor Sleep ◽  
Daily Routine ◽  
Factorial Structure ◽  
Poor Sleep Quality

Sleep quality is an important clinical construct since it is increasingly common for people to complain about poor sleep quality and its impact on daytime functioning. Moreover, poor sleep quality can be an important symptom of many sleep and medical disorders. However, objective measures of sleep quality, such as polysomnography, are not readily available to most clinicians in their daily routine, and are expensive, time-consuming, and impractical for epidemiological and research studies., Several self-report questionnaires have, however, been developed. The present review aims to address their psychometric properties, construct validity, and factorial structure while presenting, comparing, and discussing the measurement properties of these sleep quality questionnaires. A systematic literature search, from 2008 to 2020, was performed using the electronic databases PubMed and Scopus, with predefined search terms. In total, 49 articles were analyzed from the 5734 articles found. The psychometric properties and factor structure of the following are reported: Pittsburgh Sleep Quality Index (PSQI), Athens Insomnia Scale (AIS), Insomnia Severity Index (ISI), Mini-Sleep Questionnaire (MSQ), Jenkins Sleep Scale (JSS), Leeds Sleep Evaluation Questionnaire (LSEQ), SLEEP-50 Questionnaire, and Epworth Sleepiness Scale (ESS). As the most frequently used subjective measurement of sleep quality, the PSQI reported good internal reliability and validity; however, different factorial structures were found in a variety of samples, casting doubt on the usefulness of total score in detecting poor and good sleepers. The sleep disorder scales (AIS, ISI, MSQ, JSS, LSEQ and SLEEP-50) reported good psychometric properties; nevertheless, AIS and ISI reported a variety of factorial models whereas LSEQ and SLEEP-50 appeared to be less useful for epidemiological and research settings due to the length of the questionnaires and their scoring. The MSQ and JSS seemed to be inexpensive and easy to administer, complete, and score, but further validation studies are needed. Finally, the ESS had good internal consistency and construct validity, while the main challenges were in its factorial structure, known-group difference and estimation of reliable cut-offs. Overall, the self-report questionnaires assessing sleep quality from different perspectives have good psychometric properties, with high internal consistency and test-retest reliability, as well as convergent/divergent validity with sleep, psychological, and socio-demographic variables. However, a clear definition of the factor model underlying the tools is recommended and reliable cut-off values should be indicated in order for clinicians to discriminate poor and good sleepers.

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