Prof. Amal K. Mitra
Department of Public Health, Texas Tech University Health Sciences Center, Lubbock, Texas, USA
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Academic Career Length
30Years (1997-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
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Journal Article
Due to lack of sufficient data on the psychological toll of the COVID-19 pandemic on adolescent mental health, this systematic analysis aims to evaluate the impact of the pandemic on adolescent mental health. This study follows the PRISMA guidelines for systematic reviews of 16 quantitative studies conducted in 2019–2021 with 40,076 participants. Globally, adolescents of varying backgrounds experience higher rates of anxiety, depression, and stress due to the pandemic. Secondly, adolescents also have a higher frequency of using alcohol and cannabis during the COVID-19 pandemic. However, social support, positive coping skills, home quarantining, and parent–child discussions seem to positively impact adolescent mental health during this period of crisis. Whether in the United States or abroad, the COVID-19 pandemic has impacted adolescent mental health. Therefore, it is important to seek and to use all of the available resources and therapies to help adolescents mediate the adjustments caused by the pandemic.
Journal Article
Amal K. Mitra
Anthony MawsonMore than a billion people—one-sixth of the world’s population, mostly in developing countries—are infected with one or more of the neglected tropical diseases (NTDs). Several national and international programs (e.g., the World Health Organization’s Global NTD Programs, the Centers for Disease Control and Prevention’s Global NTD Program, the United States Global Health Initiative, the United States Agency for International Development’s NTD Program, and others) are focusing on NTDs, and fighting to control or eliminate them. This review identifies the risk factors of major NTDs, and describes the global burden of the diseases in terms of disability-adjusted life years (DALYs).
Journal Article
Obstructive sleep apnea (OSA) is a serious, potentially life-threatening condition. Epidemiologic studies show that sleep apnea increases cardiovascular diseases risk factors including hypertension, obesity, and diabetes mellitus. OSA is also responsible for serious illnesses such as congestive heart failure, stroke, arrhythmias, and bronchial asthma. The aim of this systematic review is to evaluate evidence for the association between OSA and cardiovascular disease morbidities and identify risk factors for the conditions. In a review of 34 studies conducted in 28 countries with a sample of 37,599 people, several comorbidities were identified in patients with severe OSA—these were: heart disease, stroke, kidney disease, asthma, COPD, acute heart failure, chronic heart failure, hyperlipidemia, thyroid disease, cerebral infarct or embolism, myocardial infarction, and psychological comorbidities including stress and depression. Important risk factors contributing to OSA included: age > 35 years; BMI ≥ 25 kg/m2; alcoholism; higher Epworth sleepiness scale (ESS); mean apnea duration; oxygen desaturation index (ODI); and nocturnal oxygen desaturation (NOD). Severe OSA (AHI ≥ 30) was significantly associated with excessive daytime sleepiness and oxygen desaturation index. The risk of OSA and associated disease morbidities can be reduced by controlling overweight/obesity, alcoholism, smoking, hypertension, diabetes mellitus, and hyperlipidemia.
1 June 2004 in Maternal and Child Health Journal
Journal Article
Amal K. MitraAKAmal J. Khoury
Amal J. Khoury
AHAgnes W. Hinton
Agnes W. Hinton
CCCathy Carothers
Cathy Carothers
Journal Article
Amal K. MitraAHAkhlaque Haque
Akhlaque HaqueMIManirul Islam
Manirul IslamSBS. A. M. K. Bashar
S. A. M. K. BasharTo assess the risk of lead poisoning among preschool and school-aged children in Bangladesh, 345 children were screened for blood lead levels (BLLs) from one rural and two urban areas in Bangladesh from September 2007 through January 2008. An urban industrial area at Tongi was identified as a disaster area, where 99% (104/105) of those tested had BLLs ≥10 µg/dL. Industrial emissions and use of leaded gasoline by two-stroke engine vehicles were identified as possible sources of lead in that area. A rural nonindustrial area at Chirirbandar, Dinajpur was identified as another high-risk area, where 14% of the children screened had BLLs ≥10 µg/dL. BLLs at the urban industrial area were significantly higher than those at the rural and urban nonindustrial areas (24.58 ± 10.32, 7.24 ± 6.31, and 2.47 ± 3.32 µg/dL, respectively; p <0.001). Weight-for-age z-scores of the urban children were significantly lower than that of the rural children (-1.41 ± 1.88 vs. 0.20 ± 1.16, p <0.001). Children with elevated BLLs had poorer nutritional status (p = 0.05) than those with normal BLLs. Over 90% of the parents did not know that lead causes health problems. In conclusion, the problem of lead poisoning in children was found to be high in both urban and rural Bangladesh. A universal lead screening for preschool and school-aged children and a lead education program for parents are recommended for implementation in Bangladesh.
Journal Article
The tragic pandemic of COVID-19, due to the Severe Acute Respiratory Syndrome coronavirus-2 or SARS-CoV-2, has shaken the entire world, and has significantly disrupted healthcare systems in many countries. Because of the existing challenges and controversies to testing for COVID-19, improved and cost-effective methods are needed to detect the disease. For this purpose, machine learning (ML) has emerged as a strong forecasting method for detecting COVID-19 from chest X-ray images. In this paper, we used a Deep Learning Method (DLM) to detect COVID-19 using chest X-ray (CXR) images. Radiographic images are readily available and can be used effectively for COVID-19 detection compared to other expensive and time-consuming pathological tests. We used a dataset of 10,040 samples, of which 2143 had COVID-19, 3674 had pneumonia (but not COVID-19), and 4223 were normal (not COVID-19 or pneumonia). Our model had a detection accuracy of 96.43% and a sensitivity of 93.68%. The area under the ROC curve was 99% for COVID-19, 97% for pneumonia (but not COVID-19 positive), and 98% for normal cases. In conclusion, ML approaches may be used for rapid analysis of CXR images and thus enable radiologists to filter potential candidates in a time-effective manner to detect COVID-19.
About Me
Dr. Amal Mitra is an internationally recognized scholar and academic leader with distinguished background in medicine and public health. As a tenured Professor of Public Health at Texas Tech University Health Sciences Center's Julia Jones Matthews School of Population and Public Health, he combines expertise in epidemiology, biostatistics, and social and behavioral health with a proven track record of founding and directing transformative academic programs. His collaborative research spans institutions across Bangladesh, Hong Kong, India, Kuwait, Malaysia, and Nepal. Dr. Mitra's distinguished career includes numerous accolades, notably the 2026 Gordon-Wyon Award for Community-Oriented Public Health from the American Public Health Association (APHA) International Section; 2025 Mentorship Award from AAAS; 2024 John Snow Award from the APHA Epidemiology Section; the 2022-2023 Fulbright-Nehru Academic and Professional Excellence Award; and the 2007 U.S. Fulbright Scholar Award. He has authored three influential books: Essentials of Clinical and Public Health Nutrition (Springer Nature 2025), Statistical Approaches for Epidemiology: From Concept to Application (Springer Nature, 2024) and Epidemiology for Dummies (John Wiley & Sons, 2023), and more than 125 peer-reviewed scientific articles. His current areas of research interest include cancer prevention, mental health of adolescents, autism spectrum disorder, low birth weight, and lead poisoning.
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