Are waste pickers at risk for hepatitis B and C infections because of poverty or environmental exposures?

Scarponi, Cristiane Faria de Oliveira; Zolnikov, Tara Rava; Mol, Marcos Paulo Gomes

doi:10.1590/0037-8682-0123-2019

Cited by 8 publications

(5 citation statements)

References 12 publications

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“…Although various factors contributed to poverty, such as age or education levels, and are closely related to infectious disease spread [ 34 ], considering the low self-awareness of their condition among impoverished people, the poverty group could show a progressive increase in the risk of disease such as hepatitis [ 35 ]. In support of this, in Brazil, a previous study also showed hepatitis B susceptibility rates ranging from approximately 32% among individuals living with low income [ 36 ]. Therefore, poverty in people with complications such as DM is the cause of increasing HBV or HCV and is also likely to be an important factor in the incidence rate because there is a cost burden [ 37 ].…”

Section: Discussionmentioning

confidence: 57%

Hepatitis Risk in Diabetes Compared to Non-Diabetes and Relevant Factors: A Cross-Sectional Study with National Health and Nutrition Examination Survey (NHANES), 2013–2018

Han

Kwon

Kim

et al. 2023

IJERPH

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This study aimed to identify the development of hepatitis B or C infection in diabetes patients compared to those without and to elucidate factors associated with the prevalence of hepatitis B or C infection in diabetes. We conducted a cross-sectional study using data from the National Health and Nutrition Examination Survey (NHANES) 2013–2018. As evaluation factors, we included variables such as age, race, illicit drug use, and poverty. The diabetic group had a significantly higher prevalence of hepatitis B or C infection than the non-diabetic group (odds ratio (OR) = 1.73; 95% confidence interval (CI), 1.36–2.21, p < 0.01). In multivariate Cox regression, non-poverty and non-illicit drug use were lower risk factors contributing to hepatitis development in diabetes (hazard ratio (HR) = 0.50; 95% CI, 0.32–0.79, p < 0.01, and HR = 0.05; 95% CI, 0.03–0.08, p < 0.01, respectively). Logistic regression also showed that these factors were significant contributors to hepatitis development in the diabetic group (p < 0.01). In patients with diabetes, the development of hepatitis was higher than that in those without, and hepatitis development was influenced by poverty and illicit drug use. This may provide supporting evidence of response strategies for diabetes to care for hepatitis development in advance.

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Section: Discussionmentioning

confidence: 57%

Hepatitis Risk in Diabetes Compared to Non-Diabetes and Relevant Factors: A Cross-Sectional Study with National Health and Nutrition Examination Survey (NHANES), 2013–2018

Han

Kwon

Kim

et al. 2023

IJERPH

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“…Waste pickers have a higher risk of acquiring a number of diseases and injuries compared to the general population. This is because of exposure to cuts (lesions to the skin) and toxic substances like chemicals, bacteria, dust inhalation, and medical wastes [9][10][11][12]. Diseases they most commonly face include respiratory infections, skin diseases, mental illness, and chronic diseases [11,13].…”

Section: Introductionmentioning

confidence: 99%

Illness, Self-Rated Health and Access to Medical Care among Waste Pickers in Landfill Sites in Johannesburg, South Africa

Made

Ntlebi

Kootbodien

et al. 2020

IJERPH

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Waste pickers are exposed to various environmental health hazards, and self-rated health (SRH) could influence their medical care access. This study investigated the association between illness, clinic visits and SRH, and assessed if SRH can increase clinic visits. A cross-sectional study was conducted. SRH was defined as “very good”, “good”, “fair”, and “poor”. The illnesses were mental health, infectious, and chronic diseases. Medical care access included clinic visits in the previous 12 months. An ordinal logistic regression model was fitted to assess the association. There were 361 participants, 265 (73.41%) were males. Median age was 31 years, (interquartile range (IQR): 27–39). SRH: poor (29.89%), fair (15.92%), good (43.30%) very good (10.89%). Ever smoked (adjusted odds ratio (AOR): 1.72; 95% confidence interval (CI): 1.11–2.66), mental health (AOR: 1.87; 95% CI: 1.22–2.84), chronic (AOR: 2.34; 95% CI:1.47–3.68) and infectious (AOR: 2.07; 95% CI: 1.77–3.63) diseases were significantly associated with increased odds of reporting poor health. Clinic visit was not associated with SRH. From 99 (31%) individuals who rated their health as poor and ill, 40% visited a clinic (p = 0.0606). Acute and chronic illnesses were associated with poor SRH but this did not increase clinic visits. Provision of mobile clinic services at the landfill sites could increase access to medical care.

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“…In particular, based on the American Association for the Study of Liver Disease (AASLD), HBV and HCV are the leading infectious diseases that are closely related to poverty 54,55 . Greene et al 56 analyzed surveillance data in New York City and reported that chronic hepatitis C was included in diseases that were related to severe poverty in people with a low income and hepatitis B susceptibility rates are approximately 32% among individuals with a low income in Brazil 57 .…”

Section: Discussionmentioning

confidence: 99%

Machine learning for predicting hepatitis B or C virus infection in diabetic patients

Kim,

Park,

Lee

2023

Sci Rep

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Highly prevalent hepatitis B and hepatitis C virus (HBV and HCV) infections have been reported among individuals with diabetes. Given the frequently asymptomatic nature of hepatitis and the challenges associated with screening in some vulnerable populations such as diabetes patients, we conducted an investigation into the performance of various machine learning models for the identification of hepatitis in diabetic patients while also evaluating the significance of features. Analyzing NHANES data from 2013 to 2018, machine learning models were evaluated; random forest (RF), support vector machine (SVM), eXtreme Gradient Boosting (XGBoost), and least absolute shrinkage and selection operator (LASSO) along with stacked ensemble model. We performed hyperparameter tuning to improve the performance of the model, and selected important predictors using the best performance model. LASSO showed the highest predictive performance (AUC-ROC = 0.810) rather than other models. Illicit drug use, poverty, and race were highly ranked as predictive factors for developing hepatitis in diabetes patients. Our study demonstrated that a machine-learning-based model performed optimally in the detection of hepatitis among diabetes patients, achieving high performance. Furthermore, models and predictors evaluated from the current study, we expect, could be supportive information for developing screening or treatment methods for hepatitis care in diabetes patients.

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Are waste pickers at risk for hepatitis B and C infections because of poverty or environmental exposures?

Cited by 8 publications

References 12 publications

Hepatitis Risk in Diabetes Compared to Non-Diabetes and Relevant Factors: A Cross-Sectional Study with National Health and Nutrition Examination Survey (NHANES), 2013–2018

Hepatitis Risk in Diabetes Compared to Non-Diabetes and Relevant Factors: A Cross-Sectional Study with National Health and Nutrition Examination Survey (NHANES), 2013–2018

Illness, Self-Rated Health and Access to Medical Care among Waste Pickers in Landfill Sites in Johannesburg, South Africa

Machine learning for predicting hepatitis B or C virus infection in diabetic patients

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