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Hair Analysis

Number: 0300



Policy

Aetna considers chemical hair analysis experimental and investigational, except for diagnosis of suspected chronic arsenic poisoning. 

Note: Microscopic evaluation of hair structure (trichogram) may be medically necessary as part of the work-up of members with alopecia or abnormal-appearing or abnormally growing hair. 

Note: Although hair samples may be used for verifying abuse of illicit substances in persons who wish to evade detection, its use in this situation is not considered treatment of disease.

Background

Hair analysis has been proposed as an aid in the diagnosis of disorders such as mineral or protein deficiency or mineral toxicity.  Hair analysis has not been proven to be effective in ascertaining mineral or metabolic imbalances or IgE-mediated allergic diseases.  Hair analysis has also not been proven to be of use in either the diagnosis or treatment of autism.

Because arsenic is taken up and bound in hair and fingernails, analysis of hair and nails have been used to detect chronic arsenic exposure (Goldman, 2008).  However, in a setting in which air exposure is a consideration, as in an industrial environment, it is very difficult to remove exogenous arsenic from hair, and therefore to get a reliable reading.  Also a lack of standardization of analysis contributes to the variability of results from hair and nail testing.  Goldman (2008) explained that commercial laboratory hair analyses for multiple elements including arsenic are highly inaccurate.  Determination of arsenic in hair and nails has been most useful in epidemiological studies performed to evaluate environmental exposures of populations to inorganic arsenic; it is less useful in the evaluation of an individual patient (Goldman, 2008).

Microscopic evaluation of hair structure (trichogram) may be indicated as part of the work-up of members with alopecia or abnormal-appearing or abnormally growing hair.  Hair may be examined under the microscope to determine the number of hairs that are actively growing (anagen phase) versus the resting phase (telogen).  In addition, microscopic examination of clipped hair can reveal structural abnormalities of the hair bulb or shaft.

Rahman et al (2009) determined the concentration of trace elements present in scalp hair sample of schizophrenic patients and examined the relationship between trace elements level and nutritional status or socioeconomic factors.  The study was conducted among 30 schizophrenic male patients and 30 healthy male volunteers.  Hair trace element concentrations were determined by flame atomic absorption spectroscopy and analyzed by independent t-test, Pearson's correlation analysis, regression analysis, and analysis of variance (ANOVA).  Mn, Zn, Ca, Cu, and Cd concentrations of schizophrenic patients were 3.8 +/- 2.31 microg/gm, 171.6 +/- 59.04 microg/gm, 396.23 +/- 157.83 microg/gm, 15.40 +/- 5.68 microg/gm, and 1.14 +/- 0.89 microg/gm of hair sample, while those of control subjects were 4.4 +/- 2.32 microg/gm, 199.16 +/- 27.85 microg/gm, 620.9 +/- 181.55 microg/gm, 12.23 +/- 4.56 microg/gm, and 0.47 +/- 0.32 microg/gm of hair sample, respectively.  The hair concentration of Zn and Ca decreased significantly (p = 0.024; p = 0.000, respectively) and the concentration of Cu and Cd increased significantly (p = 0.021; p = 0.000, respectively) in schizophrenic patients while the concentration of Mn (p = 0.321) remain unchanged.  Socioeconomic data reveals that most of the patients were poor, middle-aged and divorced.  Mean body mass indices (BMIs) of the control group (22.26 +/- 1.91 kg/m(2)) and the patient group (20.42 +/- 3.16 kg/m(2)) were within the normal range (18.5 to 25.0 kg/m(2)).  Pearson's correlation analysis suggested that only Ca concentration of patients had a significant positive correlation with the BMI (r = 0.597; p = 0.000) which was further justified from the regression analysis (R (2) = 44 %; t = 3.59; p = 0.002) and 1-way ANOVA test (F = 3.62; p = 0.015).  A significant decrease in the hair concentration of Zn and Ca as well as a significant increase in the hair concentration of Cu and Cd in schizophrenic patients than that of its control group was observed, which may provide prognostic tool for the diagnosis and treatment of this disease.  However, further work with larger population is suggested to examine the exact correlation between trace element level and the degree of disorder.

Guidelines from the National Institute for Health and Clinical Excellence (2011) recommended against the use of hair analysis in the diagnosis of food allergy.  An NIAID expert panel (Boyce et a., 2010) made similar recommendations against the use of hair analysis in food allergy.  Guidelines from the American Academy of Asthma, Allergy and Immunology (Wallace et al, 2008) state that hair analysis has no diagnostic validity in rhinitis. Autism guidelines from the Singapore Ministry of Health (2010) recommend against the use of hair mineral analysis for autism.

Albar et al (2013) stated that recently, hair cortisol has become a topic of global interest as a biomarker of chronic stress.  Different research groups have been using different methods for extraction and analysis, making it difficult to compare results across studies.  A critical examination of the reported analytical methods is important to facilitate standardization and allow for a uniform interpretation.  These researchers qualitatively compared 4 published procedures from laboratories in Germany, the Netherlands, the United States of America and Canada.  Multiple aspects of their procedures were compared.  A major difference among the laboratories was the enzyme-linked immunosorbent assay (ELISA) kit used: the Canadian laboratory used the kit from ALPCO Diagnostics (Salem, MA), the American laboratory used the kit from DRG International (Springfield, NJ), the German laboratory used the kit from DRG Instruments GmbH (Marburg, Germany), or IBL (Hamburg, Germany), and the Dutch used the kit from Salimetrics (Suffolk, UK).  In addition, there were noted differences in hair mass used as well as washing and extraction procedures.  The range of hair cortisol levels determined in healthy volunteers by the 4 groups was within 2.3-fold: Koren, 46.1 pg/mg; Van Rossum, 29.72 pg/mg; Kirschbaum, 20 pg/mg and Laudenslager ~ 27 pg/mg.  The authors concluded that the relative similarities in hair cortisol values in volunteers among the 4 laboratories should facilitate a quality assurance exchange program, as a necessary step toward clinical use of this novel test.

Karlen and colleagues (2013) examined cortisol concentrations in hair as biomarker of prolonged stress in young children (n = 100) and their mothers and the relation to perinatal and socio-demographic factors.  Prolonged stress levels were assessed through cortisol in hair.  A questionnaire covered perinatal and socio-demographic factors during the child's first year of life.  Maternal hair cortisol during the 2nd and 3rd trimester and child hair cortisol at year 1 and 3 correlated.  Child cortisol in hair levels decreased over time and correlated to each succeeding age, between years 1 and 3 (r = 0.30, p = 0.002), 3 and 5 (r = 0.39, p < 0.001), and 5 and 8 (r = 0.44, p < 0.001).  Repeated measures gave a significant linear association over time (p < 0.001).  There was an association between high levels of hair cortisol and birth weight (β = 0.224, p = 0.020), non-appropriate size for gestational age (β = 0.231, p = 0.017), and living in an apartment compared with a house (β = 0.200, p = 0.049).  In addition, these investigators found high levels of cortisol in hair related to other factors associated with psychosocial stress exposure.  The authors concluded that correlation between hair cortisol levels in mothers and their children suggested a heritable trait or maternal calibration of the child's hypothalamic-pituitary-adrenocortical axis.  Cortisol output gradually stabilizes and seems to have a stable trait.  They stated that cortisol concentration in hair has the potential to become a biomarker of prolonged stress, especially applicable as a non-invasive method when studying how stress influences children's health.

Russell et al (2014) noted that cortisol is assumed to incorporate into hair via serum, sebum, and sweat sources; however, the extent to which sweat contributes to hair cortisol content is unknown.  In this study, sweat and saliva samples were collected from 17 subjects after a period of intensive exercise and analyzed by salivary ELISA.  Subsequently, an in-vitro test on exposure of hair to hydrocortisone was conducted.  Residual hair samples were immersed in a 50-ng/ml hydrocortisone solution for periods lasting 15 mins to 24 hrs, followed by a wash or no-wash condition.  Hair cortisol content was determined using the authors’ modified protocol for a salivary ELISA.  Post-exercise control sweat cortisol concentrations ranged from 8.16 to 141.7 ng/ml and correlated significantly with the log-transformed time of day.  Sweat cortisol levels significantly correlated with salivary cortisol concentrations.  In-vitro hair exposure to a 50-ng/ml hydrocortisone solution (mimicking sweat) for 60 mins or more resulted in significantly increased hair cortisol concentrations.  Washing with isopropanol did not affect immersion-increased hair cortisol concentrations.  The authors concluded that human sweat contains cortisol in concentrations comparable with salivary cortisol levels.  The findings of this study suggested that perfuse sweating after intense exercise may increase cortisol concentrations detected in hair.  This increase likely cannot be effectively decreased with conventional washing procedures and should be considered carefully in studies using hair cortisol as a biomarker of chronic stress.

Boscolo-Berto et al (2014) evaluated the diagnostic performance of ethyl glucuronide in the 3-cm proximal scalp hair fraction (HEtG) as a marker of chronic excessive drinking.  In July 2012/May 2013, after a systematic search through the MEDLINE, OVID/EMBASE, WEB OF SCIENCE, and SCOPUS databases, 8 studies were included in the pooled analysis that reported raw single data on HEtG concentration and self-reported daily alcohol intake (SDAI).  A receiver operating characteristic curve analysis and a Spearman rank-order correlation test were used.  A meta-analysis was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and Cochrane recommendations, comprising quality and bias assessments.  The pooled analysis showed that 30 pg/mg could be a useful cut-off value for HEtG to detect an SDAI greater than 60 g/d and demonstrated a parabolic direct correlation between HEtG and SDAI data [rho 0.79; 95 % confidence interval (CI): 0.69 to 0.87; p < 0.001].  The meta-analysis found an overall HEtG sensitivity of 0.96 (95 % CI: 0.72 to 1.00) and a specificity of 0.99 (95 % CI: 0.92 to 1.00); a nomogram to predict the post-test probability of exhibiting the targeted condition in the general population was built.  Significant variability among the included studies was detected, which was mainly explained by true heterogeneity in the presence of publication bias.  The authors concluded that HEtG is a promising marker for identifying chronic excessive drinking.  Nonetheless, they stated that larger and well-designed population studies are needed to draw any definitive conclusions on the significance and appropriateness of its application in the forensic setting.

Mao et al (2014) stated that trace elements play an important role in maintaining the normal metabolic and immune function.  The onset of recurrent respiratory tract infection (RRI) is associated with the immune function, genetic factors and nutritional status.  However, the association between the levels of trace elements and RRI remains inconclusive.  These researchers investigated the alterations of hair levels of zinc (Zn), copper (Cu) and iron (Fe) in Chinese children with RRI by performing a meta-analysis.  A pre-defined electronic databases search was performed to identify eligible studies for the analysis of hair Zn, Cu or Fe levels in Chinese children with RRI.  A total of 13 studies were included; RRI patients displayed significantly lower levels of hair Zn (13 studies, random effects SMD: - 1.215, 95 % CI: - 1.704 to - 0.725, p < 0.0001), Cu (11 studies, random effects SMD: - 0.384, 95 % CI: - 0.717 to - 0.052, p = 0.023) and Fe (12 studies, random effects SMD: - 0.569, 95 % CI: - 0.827 to - 0.312, p < 0.0001) compared with controls.  No evidence of publication bias was observed.  Sensitivity analysis did not change the results significantly.  The authors concluded that deficiency of Zn, Cu and Fe may be contributing factors for the susceptibility of RRI in Chinese children.  However, more studies in different ethnicities should be performed in the future.

Wei et al (2015) compared the hair cortisol levels between patients with depression and healthy controls.  Hair cortisol levels of 22 first-episodic and 13 recurrent female patients with depression and 30 healthy controls were measured and compared using the electrochemiluminescence immunoassay.  The relationship between hair cortisol levels and Hamilton depression scale (HAMD) or Hamilton anxiety scale (HAMA) scores were also examined.  Before disease episode, no significant differences were observed among healthy controls, first-episodic patients and recurrent patients.  In disease episode, the hair cortisol level in first-episodic patients was significantly higher than that in healthy controls or recurrent patients, while no significant difference was observed between recurrent patients and healthy controls.  No significant correlation was found between HAMD or HAMA scores and hair cortisol levels in patients.  The authors concluded that these findings indicated that hair cortisol levels increased in disease episode in first-episodic, but not recurrent patients with depression, which may suggest that episodes of disease have influence on cortisol levels.  The main drawbacks of this study were: (i) long-term effects of anti-depressants on the results cannot be excluded without detailed medication information of the recurrent patients, and (ii) sample sizes were relatively small.

Liu et al (2015) examined the association between Zn levels and myocardial infarction (MI) using a meta-analysis approach.  These investigators searched articles in the PubMed, OVID, and ScienceDirect published as of November 2014.  A total of 13 eligible articles with 2,886 subjects from 41 case-control studies were identified.  Overall, pooled analysis indicated that subjects with MI had lower Zn levels than healthy controls (standardized mean difference (SMD) = -1.848, 95 % CI: -2.365 to -1.331).  Further subgroup analysis found that subjects with MI had lower Zn levels than healthy controls in serum (SMD = -1.764, 95 % CI: -2.417 to 1.112) and hair (SMD = -3.326, 95 % CI: -4.616 to -2.036), but not in toenail (SMD = -0.396, 95 % CI: -1.114 to 0.322).  The subgroup analysis stratified by type of Zn measurement found a similar pattern in inductively coupled plasma-atomic absorption spectrometry (ICP-AAS) (SMD = -2.442, 95 % CI: -3.092 to -1.753), but not in neutron activation analysis (NAA) (SMD = -0.449, 95 % CI: -1.127 to 0.230]).  Lower Zn levels in MI patients were found both in male (SMD = -3.350, 95 % CI: -4.531 to -2.169]) and female (SMD = -2.681, 95 % CI: -3.440 to -1.922]).  And the difference of Zn levels according to MI in Asia (SMD = -2.555, 95 % CI: -3.267 to -1.844) was greater to that among the population in Europe (SMD = -0.745, 95 % CI: -1.386 to -0.104), but no difference was found in Oceania (SMD = -0.255, 95 % CI: -0.600 to 0.089]).  The authors concluded that this meta-analysis indicated that there is a significant association between Zn deficiency and MI.  They suggested that a community-based, long-term observation in a cohort design should be performed to obtain better understanding of causal relationships between Zn and MI, through measuring hair Zn at baseline to investigate whether the highest Zn category versus lowest was associated with MI risk.

CPT Codes / HCPCS Codes / ICD-9 Codes
Chemical hair analysis:
CPT codes covered if selection criteria are met:
82175 Arsenic
83015 Heavy metal (e.g., arsenic, barium, beryllium, bismuth, antimony, mercury); screen
HCPCS codes not covered for indications listed in the CPB:
P2031 Hair analysis (excluding arsenic)
ICD-9 codes covered if selection criteria are met:
961.1 Poisoning by arsenical anti-infectives
985.1 Toxic effect of arsenic and its compounds
E866.3 Accidental poisoning by arsenic and its compounds and fumes
V82.5 Special screening for chemical poisoning and other contamination [suspected chronic arsenic poisoning]
ICD-9 codes not covered for indications listed in the CPB (not all-inclusive):
269.3 Mineral deficiency, not elsewhere classified
270.0 - 277.9 Other metabolic disorders
281.4 Protein-deficiency anemia
299.00, 299.01 Autistic disorder
477.0 - 477.9 Allergic rhinitis
493.00 - 493.92 Asthma
691.8 Other atopic dermatitis and related conditions
693.1 Dermatitis due to food taken internally
708.0 - 708.9 Urticaria
796.0 Nonspecific abnormal toxicological findings
989.5 Toxic effect of venom
995.0 Other anaphylactic shock
995.1 Angioneurotic edema
995.20 - 995.29 Other and unspecified adverse effect of drug, medicinal and biological substance
995.3 Allergy, unspecified
995.60 - 995.69 Anaphylactic shock due to adverse food reaction
995.7 Other adverse food reactions, not elsewhere classified
V15.01 - V15.09 Allergy, other than medicinal agents
V77.99 Special screening for other and unspecified endocrine, nutritional, metabolic, and immunity disorders
Microscopic evaluation of hair structure (trichogram):
CPT codes covered if selection criteria are met:
96902 Microscopic examination of hairs plucked or clipped by the examiner (excluding hair collected by the patient) to determine telogen and anagen counts, or structural hair shaft abnormality
ICD-9 codes covered if selection criteria are met:
704.09 Alopecia, other [abnormal alopecia]
704.2 Abnormalities of hair
704.8 - 704.9 Other and unspecified diseases of hair and hair follicles
CPT Codes / HCPCS Codes / ICD-10 Codes
Information in the [brackets] below has been added for clarification purposes.   Codes requiring a 7th character are represented by "+":
ICD-10 codes will become effective as of October 1, 2015 :
Chemical hair analysis:
CPT codes covered if selection criteria are met:
82175 Arsenic
83015 Heavy metal (e.g., arsenic, barium, beryllium, bismuth, antimony, mercury); screen
HCPCS codes not covered for indications listed in the CPB:
P2031 Hair analysis (excluding arsenic)
ICD-10 codes covered if selection criteria are met:
T37.8x1+ - T37.96x+ Poisoning by, adverse effect of and underdosing of other and unspecified systemic anti-infectives and antiparasitics
T57.0x1+ - T57.0x4+ Toxic effect of arsenic and its compounds
Z13.88 Encounter for screening for disorder due to exposure contaminants [suspected chronic arsenic poisoning]
ICD-10 codes not covered for indications listed in the CPB (not all-inclusive):
D53.0 Protein deficiency anemia
E58, E59, E60, E61.0 - E61.9 Mineral deficiencies
E70.0 - E88.9 Metabolic disorders
F84 Autistic disorder
J30.0 - J30.9 Vasomotor and allergic rhinitis
J45.20 - J45.998 Asthma
L20.0 - L20.9 Atopic dermatitis
L27.2 Dermatitis due to ingested food
L50.0 - L50.9 Urticaria
R82.5 - R82.6, R89.2 - R89.3 Other and unspecified abnormal findings in urine
T36.0x1+ - T37.5x6+, T38.0x1+ - T50.996+ Poisoning by, adverse effects of and underdosing of drugs, medicaments and biological substances
T63.001 - T63.94x+ Toxic effect of contact with venomous animals and plants
T78.00x+ - T78.2xx+
T88.6xx+
Anaphylactic shock
T78.3xx+ Angioneurotic edema
T78.40x+ Allergy, unspecified
Z13.21 - Z13.29 Encounter for screening for nutritional, metabolic and other endocrine disorders
Z91.010 - Z91.09 Allergy status, other than to drugs and biological substances
Microscopic evaluation of hair structure (trichogram):
CPT codes covered if selection criteria are met:
96902 Microscopic examination of hairs plucked or clipped by the examiner (excluding hair collected by the patient) to determine telogen and anagen counts, or structural hair shaft abnormality
ICD-10 codes covered if selection criteria are met:
L65.0 - L65.9 Other nonscarring hair loss [abnormal alopecia]
L67.0, L67.8 - L67.9 Hair color and hair shaft abnormalities
L73.1, L73.8 - L73.9 Other follicular disorders


The above policy is based on the following references:
    1. U.S. Department of Health and Human Services, Health Care Financing Administration (HCFA). Hair analysis -- not covered. Medicare Coverage Issues Manual §50-24. Baltimore, MD: HCFA; 2000.
    2. Lazar P. Hair analysis: What does it tell us? JAMA. 1974;229:1908-1909.
    3. Hambidge KM. Hair analyses: Worthless for vitamins, limited for minerals. Am J Clin Nutr. 1983;36:943-949.
    4. Klevay LM, Bistrian BR, Fleming CR, Neumann CG. Hair analysis in clinical and experimental medicine. Am J Clin Nutr. 1987;46(2):233-236.
    5. Barrett S. Commercial hair analysis: Science or scam? JAMA. 1985;254:1041-1045.
    6. Filipek PA, Accardo PJ, Ashwal S, et al. Practice parameter: Screening and diagnosis of autism. Report of the Quality Standards Subcommittee of the American Academy of Neurology and the Child Neurology Society. Neurology. 2000;55(4):468-479.
    7. Kruse-Jarres JD. Limited usefulness of essential trace element analyses in hair. Am Clin Lab. 2000;19(5):8-10.
    8. Hu H. Exposure to metals. Prim Care. 2000;27(4):983-996.
    9. Hindmarsh JT. Caveats in hair analysis in chronic arsenic poisoning. Clin Biochem. 2002;35(1):1-11.
    10. Niggemann B, Gruber C. Unproven diagnostic procedures in IgE-mediated allergic diseases. Allergy. 2004;59(8):806-808.
    11. Tsatsakis A, Tutudaki M. Progress in pesticide and POPs hair analysis for the assessment of exposure. Forensic Sci Int. 2004;145(2-3):195-199.
    12. Dolan K, Rouen D, Kimber J. An overview of the use of urine, hair, sweat and saliva to detect drug use. Drug Alcohol Rev. 2004;23(2):213-217.
    13. Passalacqua G, Compalati E, Schiappoli M, Senna G. Complementary and alternative medicine for the treatment and diagnosis of asthma and allergic diseases. Monaldi Arch Chest Dis. 2005;63(1):47-54.
    14. Savvopoulos MA, Pallis E, Tzatzarakis MN, et al. Legal issues of addiction assessment: The experience with hair testing in Greece. J Appl Toxicol. 2005;25(2):143-152.
    15. Gambelunghe C, Rossi R, Ferranti C, et al. Hair analysis by GC/MS/MS to verify abuse of drugs. J Appl Toxicol. 2005;25(3):205-211.
    16. Kapaj S, Peterson H, Liber K, Bhattacharya P. Human health effects from chronic arsenic poisoning -- a review. J Environ Sci Health A Tox Hazard Subst Environ Eng. 2006;41(10):2399-2428.
    17. Caprara DL, Klein J, Koren G. Diagnosis of fetal alcohol spectrum disorder (FASD): Fatty acid ethyl esters and neonatal hair analysis. Ann Ist Super Sanita. 2006;42(1):39-45.
    18. Ng DK, Chan CH, Soo MT, Lee RS. Low-level chronic mercury exposure in children and adolescents: Meta-analysis. Pediatr Int. 2007;49(1):80-87.
    19. Goldman RH. Arsenic exposure and poisoning. Waltham, MA: UpToDate [online serial]; 2008.
    20. Wallace DV, Dykewicz MS, Bernstein DI, et al.; Joint Task Force on Practice, American Academy of Allergy, Asthma & Immunology, American College of Allergy, Asthma and Immunology, Joint Council of Allergy, Asthma and Immunology. The diagnosis and management of rhinitis: An updated practice parameter. J Allergy Clin Immunol. 2008;122(2 Suppl):S1-S8.
    21. Rahman A, Azad MA, Hossain I, et al. Zinc, manganese, calcium, copper, and cadmium level in scalp hair samples of schizophrenic patients. Biol Trace Elem Res. 2009;127(2):102-108.
    22. Gow R, Thomson S, Rieder M, et al. An assessment of cortisol analysis in hair and its clinical applications. Forensic Sci Int. 2010;196(1-3):32-37.
    23. Boyce JA, Assa'ad A, Burks AW, et al.; NIAID-Sponsored Expert Panel. Guidelines for the diagnosis and management of food allergy in the United States: Report of the NIAID-sponsored expert panel. J Allergy Clin Immunol. 2010;126(6 Suppl):S1-S58.
    24. Singapore Ministry of Health (MOH). Autism spectrum disorders in preschool children. Singapore: MOH; March 2010.
    25. National Institute for Health and Clinical Excellence (NICE). Food allergy in children and young people. Clinical Guideline 116. London, UK: NICE; February 2011.
    26. Aleksa K, Liesivuori J, Koren G. Hair as a biomarker of polybrominated diethyl ethers' exposure in infants, children and adults. Toxicol Lett. 2012;210(2):198-202.
    27. Appenzeller BM, Tsatsakis AM. Hair analysis for biomonitoring of environmental and occupational exposure to organic pollutants: State of the art, critical review and future needs. Toxicol Lett. 2012;210(2):119-140.
    28. Staufenbiel SM, Penninx BW, Spijker AT, et al. Hair cortisol, stress exposure, and mental health in humans: A systematic review. Psychoneuroendocrinology. 2013;38(8):1220-1235.
    29. Wolowiec P, Michalak I, Chojnacka K, Mikulewicz M. Hair analysis in health assessment. Clin Chim Acta. 2013;419:139-171.
    30. Albar WF, Russell EW, Koren G, et al. Human hair cortisol analysis: Comparison of the internationally-reported ELISA methods. Clin Invest Med. 2013;36(6):E312-E316.
    31. Karlen J, Frostell A, Theodorsson E, et al. Maternal influence on child HPA axis: A prospective study of cortisol levels in hair. Pediatrics. 2013;132(5):e1333-e1340.
    32. Russell E, Koren G, Rieder M, Van Uum SH. The detection of cortisol in human sweat: Implications for measurement of cortisol in hair. Ther Drug Monit. 2014;36(1):30-34.
    33. Boscolo-Berto R, Favretto D, Cecchetto G, et al. Sensitivity and specificity of EtG in hair as a marker of chronic excessive drinking: Pooled analysis of raw data and meta-analysis of diagnostic accuracy studies. Ther Drug Monit. 2014;36(5):5605-75.
    34. Mao S, Zhang A, Huang S. Meta-analysis of Zn, Cu and Fe in the hair of Chinese children with recurrent respiratory tract infection. Scand J Clin Lab Invest. 2014;74(7):561-567.
    35. Wei J, Sun G, Zhao L, et al. Analysis of hair cortisol level in first-episodic and recurrent female patients with depression compared to healthy controls. J Affect Disord. 2015;175C:299-302.
    36. Liu B, Cai ZQ, Zhou YM. Deficient zinc levels and myocardial infarction: Association between deficient zinc levels and myocardial infarction: A meta-analysis. Biol Trace Elem Res. 2015 Jan 28 [Epub ahead of print].


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