|Year : 2017 | Volume
| Issue : 2 | Page : 88-96
Burden of exposure to lead as a risk factor for mental illness in Indian children 1990–2015: A systematic analysis based on global burden of disease approach
Mohandoss Arunachalam Anusa1, Thavarajah Rooban2
1 Department of Psychiatry, Shri Sathya Sai Medical College and Research Institute, Kanchipuram, Tamil Nadu, India
2 Department of Oral Pathology, Director and Senior Consultant, Marundeeshwara Oral Pathology Services and Analytics, Chennai, Tamil Nadu, India
|Date of Web Publication||8-Dec-2017|
Mohandoss Arunachalam Anusa
Department of Psychiatry, Shri Sathya Sai Medical College and Research Institute, Kanchipuram, Tamil Nadu
Source of Support: None, Conflict of Interest: None
Background: The risk of lead exposure for mental illness and its burden on Indian children, as a society, is not studied till date. This study aims to present the same as well as to compare the risk and burden between 1990 and 2015. Materials and Methods: Using India-specific, Global Burden of Disease 2015 data health metrics-disability-adjusted life years (DALYs), we estimated the burden of mental illness and exposure to lead as a risk factor for the same. Descriptive estimates of prevalence and DALY for mental illness and risk of lead exposure are presented for various age groups of Indian children (0–14 years) of either gender. Results: In 2015, 26,450,345 Indian children were affected with mental illness contributing to DALYs of 2,453,344. In 2015, 162,492.1 DALYs were lost to mental illness due to risk factors and 130,429 lost DALYs due to risk attributed to lead exposure. The risk rate attribution and the difference of burden in 1990–2015 are presented. Years of life lost due to lead exposure for mental illness is high as compared to all attributed risks. Lead was identified as risk factor for intellectual disability among children. Conclusions: Lead continues to pose a significant overall health risk and specifically for mental illness. The estimated burden of mental illness and extent of association of risk indicate the urgent need of clear policies to reduce lead from our immediate environment.
Keywords: Attributed risk, disability-adjusted life year, India, lead, mental illness
|How to cite this article:|
Anusa MA, Rooban T. Burden of exposure to lead as a risk factor for mental illness in Indian children 1990–2015: A systematic analysis based on global burden of disease approach. Ann Indian Psychiatry 2017;1:88-96
|How to cite this URL:|
Anusa MA, Rooban T. Burden of exposure to lead as a risk factor for mental illness in Indian children 1990–2015: A systematic analysis based on global burden of disease approach. Ann Indian Psychiatry [serial online] 2017 [cited 2021 Dec 1];1:88-96. Available from: https://www.anip.co.in/text.asp?2017/1/2/88/220243
| Introduction|| |
Lead has been one of the most widely used metals by humans. Its effects on health have been described even in the Roman civilization. Common sources of lead exposure are lead-based paints, contaminated soil, household dust, old water pipes, paints, lead-glazed ceramics, industrial pollutants, and automobile fuels. Recent review indicates that even low-level exposure (blood lead levels ≤10 μg/dL) results in cognitive dysfunction, neurobehavioral disorders, neurological damage, hypertension, and renal impairment. Lead is imbibed in the human system as inorganic and organic forms, mainly through ingestion and inhalation. In rare conditions, such as smelting, battery industries, or pesticide contact, it may be through a cutaneous route.
Recent reviews have highlighted the effect of lead on the neural and mental health and concluded that there is no safe threshold for lead toxicity. Since the 1970s, reduction in lead usage has been called for, which has resulted in drastic reduction of lead use in population level at least in the developed nations, while the developing nations still suffer from the deluge of lead-related health hazards. Several focused manuscripts have advocated the need to reduce lead exposure and poisoning., Lead has been a subject matter of investigation as an etiological agent or a risk factor for several psychiatric illnesses owing to its close association with neurological damage.,,,,
India is one of the fastest growing economies with a 1.3 billion population. Utilization of natural resources by the unorganized sector has rendered India as a leading polluter. Lead is found as a part of anthropogenic emission by a factor of more than 100 times than the natural emission. The literature on lead as a risk factor for mental disorders in Indian children is limited. Even such available limited literature is only regional in appeal and concerns include limited sample size, poor follow-up, besides nonconsideration of numerous other.,,
To develop focused policies, the sum total of mental disease burden that Indian society faces along with the risk attributed to the exposure of lead needs to be studied. Parameters such as disability-adjusted life year (DALY), years lost to disability (YLD), and years lost to death (YLL) have been introduced in this regard and successfully used by global burden of disease (GBD) studies., The aim of the present study is to use the data of GBD 2015 to study the risk of lead exposure for mental illness in Indian children in 2015, in terms of DALYs, YLL, and YLDs as well as to observe the changes during 1990–2015.
| Materials and Methods|| |
The GBD 2015 study (http://ghdx.healthdata.org/gbd-results-tool) included mental diseases (including substance use disorder) as defined by the International Classification of Diseases, Tenth Revision (ICD-10). The risk and cause factors implied and all definitions of the GBD 2015 study were retained for the purpose of this study. The age of the children, for the study purpose, was classified as those <5 years, 5–9 years, and 10–14 years. The detailed methods are described below.
The GBD 2015 study analyzed about 195 countries and territories. The effects of lead on mental health and illness emanate from prominent studies.,,, The prevalence of mental illness and lead exposure came from 74 main sources and 4196 ancillary data sources, accessed from http://ghdx.healthdata.org/about-ghdx/data-type-definitions, and the terms of the definitions in the GBD 2015 as well as the present study which are available at http://www.healthdata.org/terms-defined.
Data visualizations and collection for mental illness with lead exposure as a risk factor were collected from http://vizhub.healthdata.org/gbd-compare. The GBD 2015 is reported to utilize advanced versions of the DisMod-MR 2.0 (Disease Modeling Meta-Regression Tool- 2, Free tool, Jan Barendregt, Department of Public Health of Erasmus University in the Netherlands, Available from Epigear.com, 2016) which uses complex computer coding in Python rendering the process efficient and to achieve stable and better internal consistency from selected data supplied by the GBD 2015. The exact algorithm of lead exposure can be accessed at http://ghdx.healthdata.org/global-burden-disease-study-2015 -gbd-2015-risk-factors-code-2 and those of mental illness can be accessed at http://ghdx.healthdata.org/gbd-2015-code with specific names. The results of each mental illness group are combined to give the values displayed in the program. The program computes the outcome variables as the prevalence of each mental illness-specific sequel – in each age group, sex, and year – times in the absence/presence of a risk factor – lead exposure in the present study. A corresponding appropriate GBD disability weight derived from a population survey is applied. This method ensures that disease burden is a function of public perception about disease severity rather than the interpretation of the health-care personnel.
We report total mental illness DALYs and rates (per 100,000 population) in addition to prevalence estimates of Indian children (0–14 years) suffering from mental illness in 1990 and 2015. One DALY is described as “a year of healthy life lost due to either premature mortality or disability” and the sum of DALYs as “the gap between the population's current health status and an ideal situation where the entire population lives to an advanced age, free of disease.” Uncertainty, arising from data inputs and the calculations of DALYs, was subjected through Monte Carlo simulation techniques. Monte Carlo simulation is a computerized mathematical technique that facilitates to account for risk in quantitative analysis and decision-making. Widely used in disparate fields, the technique furnishes the researchers with a range of possible outcomes and the probabilities they will occur for any choice of action. It performs risk analysis by incrementally building models of a variety of possible results. It substitutes a range of values – a probability distribution – for any factor that has inherent uncertainty. It then calculates results over and over, each time using a different set of random values from the probability functions. Depending on the number of uncertainties and the ranges specified for them, a Monte Carlo simulation could involve thousands or tens of thousands of recalculations before it is complete. Monte Carlo simulation produces distributions of possible outcome values. This technique is supposed to use 1000 draws for each age, sex, country, year, and cause. Subsequent calculations were made at the level of the 1000 draws for all estimates. The 95% uncertainty interval around each value reported as the 25th and 975th draws of the parameter studied, presented as upper to lower, unless specified. Ratio of lead exposure to all risk for mental illness and all risk to mental illness was also calculated.
| Results|| |
As per the GBD 2015 report, the prevalence of the mental illness in Indian children increased after accounting for all variations including population growth. In 2015, 26,450,345 Indian children were affected with mental illness contributing to 2,453,344 DALYs. In 2015, 162,492.1 DALYs were lost to mental illness due to risk factors. [Table 1a] and [Table 1b] provides the prevalence – overall and in each of the major mental illness group (ICD-10) within the age groups in 1990 and 2015. The number of children (of either gender) diagnosed with the mental illness shows that schizophrenia, drug use disorder, and bipolar disorders are not reported <10 years of age. The major mental illness affecting the Indian children that remains is the autistic spectrum disorder. An approximate 30% increase in cases of autistic spectrum disorder has been reported between 1990 and 2015. On considering the DALYs, increase in DALYs has been noted during the study period across all mental illness age group. In India, by 1990, for both genders, the age-standardized DALY per 100,000 for mental disorders attributable to lead exposure was 11.65 which in 2015 changed to 8.91. In 2015, the age-standardized rate for males was 8.72 while for females was 9.13.
|Table 1a: Comparison of prevalence and disability-adjusted life years of various mental disorders in Indian Children as per Global Burden of Diseases-1990|
Click here to view
|Table 1b: Comparison of prevalence and disability-adjusted life years of various mental disorders in Indian Children as per Global Burden of Diseases-2015|
Click here to view
India is one of the few countries where lead exposure showed a significant risk for mental illness [Figure 1] and [Figure 2] a, b]. India in 2015, accounting for all ages and all diseases, lead posed as a significant risk factor the ischemic heart diseases risk factor attributes was 2.71%(1.08–4.65), hemorrhagic stroke 2.79% (0.98–5.11), chronic kidney disease due to hypertension 1.85% (0.71–3.39), hypertensive heart diseases 4.75% (1.39–10.74), rheumatic heart diseases 0.78% (0.21–1.69), and idiopathic developmental intellectual disability 14.48% (9.51–18.6). On further analyzing the tree maps, it was identified that lead exposure caused a risk factor attribution of 3.13%, 5–14 years age group, both genders and 8.9% of all DALYs, 50.88% to intellectual disability while the same was 9.5% for < 5 years, both genders accounting for 0.18% of all DALYs in the age group [Figure 3]a and [Figure 3]b.
|Figure 1: Global map showing countries with lead exposure as a risk factor for mental illness. (a) In children, either gender, below 5 years of age. (b) In children, either gender, 5–14 years of age. Image credit: Institute for Health Metrics and Evaluation (www.healthdata.org)|
Click here to view
|Figure 2: Comparison of Countries with high lead exposure as a risk factor for mental illness, measured in disability-adjusted life years rate, per lakh people (a) In children, either gender, below 5 years of age. Image credit: Institute for Health Metrics and Evaluation (www.healthdata.org). (b) In children, either gender, 5–14 years of age. Image credit: Institute for Health Metrics and Evaluation (www.healthdata.org)|
Click here to view
|Figure 3: Tree map images showing lead as risk factor for various illness Indian children of either gender. (a) Children under 5 years of age. (b) Children between aged between 5–14 years. Image credit: Institute for Health Metrics and Evaluation (www.healthdata.org)|
Click here to view
Comparison of the YLDs, DALYs, and YLLs for the year 2015 for mental illness with lead exposure both at India and global level is tabulated [Table 2], [Table 3], [Table 4]. The lead exposure to all risk exposure ratio was calculated for both YLDs and DALYs. The comparison of the lead to all risk exposure ratio between India and global level showed no difference between the India and global level in terms of YLDs and DALYs. All risk attributed to mental illness was ≤0.13 for YLDs and ≤0.14 in India and ≤0.1 globally. The YLLs [Table 4] showed the effect of lead exposure on Indian children. The YLL was higher consistently across all age-group studied in India, while at the global level, it was nil. The YLLs progressively increased till <10 years after which there is a reduction. [Table 5] shows the DALYs due to mental illness with lead exposure as a risk factor among age groups and between genders.
|Table 2: Pediatric age group with years lived with disability and disability-adjusted life years for mental illness with all risk exposure and lead exposure risk in India|
Click here to view
|Table 3: Pediatric age group with years lived with disability and disability-adjusted life years for mental illness with all risk exposure and lead exposure risk at global level|
Click here to view
|Table 4: Pediatric age group with years of life lost for mental illness with all risk exposure and lead exposure risk at India and global level|
Click here to view
|Table 5: Disability-adjusted life years by the mental disorders with risk of lead exposure|
Click here to view
| Discussion|| |
For an average Indian child, there are a numerous potential sources of lead exposure which include ingestion of flaking paint from toys, consumption of food cooked in cheap aluminum/brass utensils, ingestion of contaminated soil/water from lead/PVC pipes, inhalation of pollutants from industrial/automotive exhaust, and improper disposal of batteries. In addition, the high prevalence of childhood anemia poses a significant problem. Anemia facilitates the absorption of lead and similar metals from the gut. Use of childhood eye cosmetics called as kajal, surma, and kohl is not uncommon in India. These materials are a potential source of lead toxicity. Besides these, Indian folk/traditional medicines are also reported to be a source of lead exposure. Lead pollution is now recognized as a public health issue of concern in India. Elevated blood lead levels have been studied in Indian children, and few studies have investigated factors that could be correlated with this finding.,, To the best of our knowledge, only very few studies have focused on measuring the health burden of lead exposure in terms of all mental illness and at Pan-India level and no studies on the comparison of the burden of lead exposure over a period. We believed that the influence of the lead exposure on mental illness can be better expressed by use of the DALYs, YLLs, and YLDs than conventional epidemiological measures, as previously demonstrated in other studies.,,,,,
The Indian mental health survey 2016 is the nearest available data to compare the prevalence of the mental illness. The report does not provide numbers to compare the present results; hence the findings of the present study cannot be compared in the light of existing literature. The rapid increase of numbers, especially autism spectrum and eating disorder, in 2015 could be due to the increasing awareness or actual increase in prevalence of the disorder. As such there is not much of a difference between 1990 and 2015, though the prevalence of mental illness, even after GBD accounting for population increase, the prevalence has increased. The 10–14-year age group appears to be more vulnerable with high loss of DALYs to mental illness. While comparing the prevalence with DALYs, one must be aware of the inclusion of disability weight in GBD calculations. The spectrum of disability weight (measured on a scale from 0 to 1, where 0 equals a state of full health and 1 equals death) is wide in mental illness, for example, it ranges from 0.011 (borderline idiopathic developmental intellectual disability) to 0.778 (schizophrenia-acute state). For the same reason, the influence of ratio of risk attribution to mental illness on DALYs/YLDs for mental illness appears to be less (≤0.014) [Table 2] and [Table 3]. Most of mental illness in acute phase will reduce DALYs/YLDs while in remission phase would not contribute to parameters. Furthermore, the number of patients suffering from severe form of diseases will be less than those suffering from mild degrees. Together, this would on DALYs/YLDs and this phenomenon specifically warrants further study.
India fares better with less DALYs due to mental illness as compared to global values, which may be due to bias with under-reporting of prevalence of mental illness owing to stigma attached in India. The same could be established only when the exact prevalence of mental illness in Indian children is estimated. However, until reliable data emerge, the GBD data can be used to make robust estimates.
High attributed risk of lead exposure in mental illness in Indian children as compared globally is shown in [Table 2], [Table 3], [Table 4]. Lead could cause reduction in cognition, learning difficulties caused by its on prefrontal cortex. Ratio of lead to all risk exposure shows that 10–14 years age group is more vulnerable in India, probably due to high lead pollution [Table 2] and [Table 3], while the YLL does not show any difference globally but high in India [Table 4]. Lead is known to cause neurological and psychiatric abnormalities after crossing the blood–brain barrier. Lead mimics calcium ions and interferes with mitochondrial structure/function, impairing cellular respiration, signaling, and other physiological process. It also damages the brain microvasculature by disrupting protein kinase C pathway, thereby causing damage to prefrontal cortical dysfunction such as distractibility, impaired judgment, and thought disorder rendering children more vulnerable. Lead in brain is reported to interfere with glutamatergic, cholinergic, and dopaminergic systems disrupting learning process. It is also reported that lead exerts its effect on acetylcholine and decrease cholinergic function and interferes with γ-amino butyric acid neurotransmission through heme-synthesis inhibition. Effect of lead is also observed through serotonin, hypothalamic-pituitary-adrenal axis, depleting antioxidants, and creating-free radicals. It is reported that there is brain volume reduction in the cognitive and emotional territories of anterior cingulate cortex, accounting for neural abnormalities with significant gender differences.
From 1990 to 2000, the effect of lead, observed as DALYs, was increasing which then started reducing across age groups and gender [Table 4], probably by the result of governmental policies. Rules such as Air (Prevention and Control of Pollution) Act, 1981 and The Environment (Protection) Act, 1986 of India enforcing have probably contributed to this change. Considering the present status, in 2016, India has enacted laws to reduce lead in paints. The South African experience indicates that even after efforts made in removing lead from petrol and paint, informal occupational sector (automobile/battery repair and spray-painting businesses) continue to contribute to lead pollution. These are at-risk sources for lead emission as they are is used in residential settings exposing community to lead. Such industry are often unregistered and unregulated impacting environment at large., The same conditions may apply to India too. Emerging body of literature indicates the young adolescents who are exposed to lead in initial phases of life are more prone to violent behaviors and often indulge in crime. The exposure was identified to be related directly to the exposure. Such studies highlight the effect of lead on developing neural system and possible influence on mental well-being.
The primary focus of prevention of mental illness and its risk factors such as lead exposure should include implementation of population-level program, accounting for dynamic social determinants of mental health over targeted treatment-focused intervention. This could be the way forward to prevent the high burden of mental illness on existing fragile Indian mental health care system with inadequate number of psychiatrists, financial resources, and noncoverage of insurance.
The weakness of the present study, like any other GBD studies, include poor incidence data from regional sources as well as data representativeness index., Although the GBD algorithm accounts for these factors, the extent of extrapolation is not clearly known. Role of genetics and other risk factors for mental illness need to be factored in before extrapolation of results. Furthermore, the study does not account or consider the blood lead levels and qualitatively relate the parameters besides not including the types of mental illness. Most of the risk comes from intellectual disability [Figure 3]. Further studies need to focus on the types of illness too. The study underlines the need for formulation of cost-effective, noninvasive methods of lead detection from body fluids that is easy to handle, which is compact and less technique sensitive.
| Conclusions|| |
Lead poses a significant risk for mental illness spectrum, especially for intellectual disability in Indian children. We have presented the collective burden posed by the lead on mental health in Indian environment. India needs to reduce its lead level in environment through stringent policies. Sensitization of the need of early detection of lead toxicity to all involved stakeholders may be the initial step in this direction. In-depth focused studies need to be performed to identify the exact risk of incremental exposure of lead on developing nervous system in children to prevent mental illness.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Vorvolakos T, Arseniou S, Samakouri M. There is no safe threshold for lead exposure: A literature review. Psychiatriki 2016;27:204-14.
Meyer PA, Brown MJ, Falk H. Global approach to reducing lead exposure and poisoning. Mutat Res 2008;659:166-75.
Guilarte TR, Opler M, Pletnikov M. Is lead exposure in early life an environmental risk factor for schizophrenia? Neurobiological connections and testable hypotheses. Neurotoxicology 2012;33:560-74.
Bakulski KM, Rozek LS, Dolinoy DC, Paulson HL, Hu H. Alzheimer's disease and environmental exposure to lead: The epidemiologic evidence and potential role of epigenetics. Curr Alzheimer Res 2012;9:563-73.
Toscano CD, Guilarte TR. Lead neurotoxicity: From exposure to molecular effects. Brain Res Brain Res Rev 2005;49:529-54.
Yolton K, Cornelius M, Ornoy A, McGough J, Makris S, Schantz S, et al.
Exposure to neurotoxicants and the development of attention deficit hyperactivity disorder and its related behaviors in childhood. Neurotoxicol Teratol 2014;44:30-45.
Lidsky TI, Schneider JS. Adverse effects of childhood lead poisoning: The clinical neuropsychological perspective. Environ Res 2006;100:284-93.
Saha JK, Selladurai R, Coumar MV, Dotaniya ML, Kundu S, Patra AK. Urban activities in India leading to soil pollution. In: Soil Pollution - An Emerging Threat to Agriculture, A Part of Environmental Chemistry for a Sustainable World Series. 1st
ed., Vol. 10. India: Springer; 2017. p. 193-228.
Mohan VR, Sharma S, Ramanujam K, Babji S, Koshy B, Bondu JD, et al.
Effects of elevated blood lead levels in preschool children in urban Vellore. Indian Pediatr 2014;51:621-5.
Jain NB, Hu H. Childhood correlates of blood lead levels in Mumbai and Delhi. Environ Health Perspect 2006;114:466-70.
Kalra V, Chitralekha KT, Dua T, Pandey RM, Gupta Y. Blood lead levels and risk factors for lead toxicity in children from schools and an urban slum in Delhi. J Trop Pediatr 2003;49:121-3.
GBD 2015 Mortality and Causes of Death Collaborators. Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980-2015: A systematic analysis for the global burden of disease study 2015. Lancet 2016;388:1459-544.
Charlson FJ, Baxter AJ, Cheng HG, Shidhaye R, Whiteford HA. The burden of mental, neurological, and substance use disorders in china and India: A systematic analysis of community representative epidemiological studies. Lancet 2016;388:376-89.
Hu H, Shih R, Rothenberg S, Schwartz BS. The epidemiology of lead toxicity in adults: Measuring dose and consideration of other methodologic issues. Environ Health Perspect 2007;115:455-62.
Navas-Acien A, Schwartz BS, Rothenberg SJ, Hu H, Silbergeld EK, Guallar E, et al.
Bone lead levels and blood pressure endpoints: A meta-analysis. Epidemiology 2008;19:496-504.
Budtz-Jørgensen E, Bellinger D, Lanphear B, Grandjean P, International Pooled Lead Study Investigators. An international pooled analysis for obtaining a benchmark dose for environmental lead exposure in children. Risk Anal 2013;33:450-61.
Flaxman A, Murray C, Vos T, editors. Integrated Meta-regression Framework for Descriptive Epidemiology. Seattle, WA: University of Washington Press; 2014.
Kassebaum NJ, Smith AGC, Bernabé E, Fleming TD, Reynolds AE, Vos T, et al.
Global, regional, and national prevalence, incidence, and disability-adjusted life years for oral conditions for 195 countries, 1990-2015: A systematic analysis for the global burden of diseases, injuries, and risk factors. J Dent Res 2017;96:380-7.
Harrison RL. Introduction to Monte Carlo simulation. AIP Conf Proc 2010;1204:17-21.
Ray TK, Laskar AR. Selected summaries-lead intoxication in children: A global concern. Natl Med J India 2004;17:320-1.
Caravanos J, Carrelli J, Dowling R, Pavilonis B, Ericson B, Fuller R, et al.
Burden of disease resulting from lead exposure at toxic waste sites in Argentina, Mexico and Uruguay. Environ Health 2016;15:72.
Chatham-Stephens K, Caravanos J, Ericson B, Landrigan P, Fuller R. The pediatric burden of disease from lead exposure at toxic waste sites in low and middle income countries. Environ Res 2014;132:379-83.
Gururaj G, Varghese M, Benegal V, Rao GN, Pathak K, Singh LK, et al
. National Mental Health Survey of India, 2015-2016: Summary. National Institute of Mental Health and Neuro Sciences. 1st
ed. Bengaluru: NIMHANS Publication; 2016. p. 128.
Mathee A. Towards the prevention of lead exposure in South Africa: Contemporary and emerging challenges. Neurotoxicology 2014;45:220-3.
Barrett KL. Exploring community levels of lead (Pb) and youth violence. Sociol Spectr 2017:37;205-22.
Mohandoss AA, Thavarajah R. An audit of Indian health insurance claims for mental illness from pooled insurance information bureau's macroindicator data. Indian J Psychol Med 2017;39:254-61.
] [Full text]
[Figure 1], [Figure 2], [Figure 3]
[Table 1a], [Table 1b], [Table 2], [Table 3], [Table 4], [Table 5]
|This article has been cited by|
||Beyond the looking glass: recent advances in understanding the impact of environmental exposures on neuropsychiatric disease
| ||Jonathan A. Hollander,Deborah A. Cory-Slechta,Felice N. Jacka,Steven T. Szabo,Tomás R. Guilarte,Staci D. Bilbo,Carolyn J. Mattingly,Sheryl S. Moy,Ebrahim Haroon,Mady Hornig,Edward D. Levin,Mikhail V. Pletnikov,Julia L. Zehr,Kimberly A. McAllister,Anika L. Dzierlenga,Amanda E. Garton,Cindy P. Lawler,Christine Ladd-Acosta |
| ||Neuropsychopharmacology. 2020; |
|[Pubmed] | [DOI]|