Author: toxlaw

A wastewater treatment plant may substantially reduce a methamphetamine concentration while still releasing measurable residues. A 2025 Iranian study provides a practical example of why “removal,” “destruction” and “not detected” should remain separate terms.

What did investigators collect?

Asadi and colleagues sampled influent and effluent at three wastewater treatment plants serving Kermanshah, Hamadan and Sanandaj. Collections covered cold and warm seasons, with three sampling occasions in one week during each seasonal period. Wastewater samples were 12-hour composites, built from aliquots every 15 minutes. Receiving-river samples were grab samples.

Solid-phase extraction and liquid chromatography–tandem mass spectrometry measured specific drugs and metabolites. These were environmental water specimens, not human blood or urine. River grabs also have different temporal coverage from the composite wastewater samples.

How was removal calculated?

The study calculated removal as 100 multiplied by the difference between mean influent and mean effluent concentration, divided by mean influent concentration. It was therefore a concentration-based comparison in ng/L.

For example, cold-season methamphetamine means at Sanandaj were 129 ± 100 ng/L in influent and 3.4 ± 2.7 ng/L in effluent, reported as mean ± standard deviation. Those means correspond to an approximately 97.4% decrease. The effluent still contained measured methamphetamine. A large percentage decrease and a positive effluent result can both be correct.

In the warm season at the same plant, the corresponding means were 50.9 ± 66.2 and 29.2 ± 35.6 ng/L, an approximately 42.6% decrease calculated from the reported means. The large variability and limited sampling make a single universal treatment-performance percentage inappropriate.

What does an effluent result below detection establish?

Cold-season methamphetamine at Hamadan was reported below the detection limit in effluent, with a mean influent concentration of 98.7 ± 136 ng/L. Below detection is not a measured concentration of zero. A calculated removal approaching 100% depends in part on how censored concentrations are handled.

The paper reports analytical detection and quantitation limits across the assay panel, but those limits should not be treated as ecological safety thresholds. Demonstrating that an instrument cannot detect the parent drug at its reporting limit is a different task from demonstrating no biologically relevant residual exposure.

Did the experiment establish complete chemical destruction?

No. A decline in the dissolved parent-drug concentration can reflect degradation, transformation, partitioning or other processes. The concentration equation alone is not a complete mass balance across water, solids and transformation products. This study did not establish mineralization of every removed methamphetamine molecule.

Operational conditions also mattered. The authors excluded cold-season Kermanshah results from their removal-efficiency presentation because that plant was not operating at steady state during repairs. That exclusion prevents treating every sampled plant-season as an equivalent test of routine performance.

Can river concentrations be assigned solely to the plant?

The study detected methamphetamine at selected river locations, including upstream locations. A downstream measurement is evidence of occurrence at that sampling point, but does not isolate the plant’s contribution from other inputs, dilution and transport. Repeated sampling and source-specific measurements would strengthen source attribution.

The findings justify examining treatment performance and environmental fate. They do not establish human impairment from environmental exposure, identify individual drug users or calculate a safe drinking-water concentration. This article’s percentage calculations describe the reported concentration means, not an independently validated treatment guarantee.

Source

Asadi A et al. Illicit drug use derived from wastewater-based epidemiology in Iran, their removal during wastewater treatment, and occurrence in receiving waters. Heliyon. 2025;11:e42516. doi:10.1016/j.heliyon.2025.e42516. Observational environmental sampling evidence.

An experiment can show an overall treatment effect while individual dose-by-time comparisons remain uncertain. That distinction is central to a 2015 mouse study frequently summarized as showing glial activation at every methamphetamine dose.

What exposure did the experiment use?

McConnell and colleagues studied adult male C57BL/6 mice aged 12–14 weeks. Animals received four intraperitoneal injections, two hours apart, containing saline or methamphetamine at 2, 4, 6 or 8 mg/kg per injection. The corresponding cumulative methamphetamine amounts were 8, 16, 24 or 32 mg/kg over the dosing session.

The starting treatment-group counts were 56 saline controls and 60, 60, 72 and 72 mice across the increasing methamphetamine doses. These animals were distributed across time points and assays; the starting totals are not the denominator for every reported comparison. Deaths before planned sacrifice were one, one, two, three and nine, respectively, and those animals were excluded from neurotransmitter and histology analyses.

What did the glial measurements show?

Researchers used GFAP staining for astrocytes and Iba-1 staining for microglia in the striatum. They considered staining distribution and cell morphology as well as quantitative area measurements. These are cellular-response markers, not direct counts of dead neurons.

For GFAP area, the overall methamphetamine effect was significant, but no individual methamphetamine-versus-saline comparison survived Bonferroni correction. For Iba-1 area, overall dose, time and interaction effects were significant; the day-one 8 mg/kg comparison was significant after correction, while the other individual treatment-versus-control comparisons were not.

The authors also described activated-looking glial morphology. That observation and the overall statistical tests are informative, but they do not justify calling every dose and time point a separately confirmed quantitative effect. Conversely, the nonsignificant pairwise tests do not prove absence of a response.

Did all measured systems recover together?

No. Higher-dose animals were less active one day after dosing. By day seven, locomotor comparisons with saline controls were no longer significant. Meanwhile, striatal dopamine and related markers remained altered over portions of the observation period.

Tyrosine hydroxylase and dopamine-transporter staining decreased after the higher doses and tended toward recovery by day 14. Microglial activation also diminished, whereas the described astrocytic response persisted longer. Behavioral normalization, protein-marker recovery and glial changes were therefore different observations on different schedules.

Was this proof that inflammation caused terminal destruction?

No. The study characterized dose and timing relationships; it did not independently manipulate glial activation to isolate its causal contribution. The authors noted that additional degeneration-specific staining would be needed to distinguish terminal damage from regulatory changes in the markers measured.

Hyperthermia also accompanied dosing. The experiment did not convert all of these linked responses into one established causal pathway. Nor did it demonstrate that reduced locomotion was caused solely by dopamine depletion; exhaustion, withdrawal-related effects and compensatory mechanisms were discussed as possibilities.

What limits human extrapolation?

This was a male-only mouse model with a specific injection schedule, substantial high-dose mortality and laboratory tissue endpoints. Cortical monoamine findings did not consistently mirror the striatal changes. A regional result should not become a claim of uniform whole-brain injury.

The experiment helps formulate mechanistic hypotheses. It cannot supply a human blood concentration for injury, a recovery deadline or an impairment threshold. Its strongest lesson for interpretation is to keep the endpoint, tissue, dose, survival and statistical comparison attached to each result.

Source

McConnell SEA et al. Characterization of binge-dosed methamphetamine-induced neurotoxicity and neuroinflammation. Neurotoxicology. 2015;50:131–141. doi:10.1016/j.neuro.2015.08.006. Historical controlled animal evidence.

A medication trial can show both improved mood scores and reduced drug use without proving that the mood improvement caused the drug-use change. A secondary ADAPT-2 analysis examined that possibility using mediation models, producing a promising hypothesis with substantial uncertainty.

Was this an independent treatment trial?

No. Jha and colleagues reanalyzed the existing ADAPT-2 cohort, using only the first six-week stage and only participants with at least mild depressive symptoms at baseline: a Patient Health Questionnaire-9 score of five or higher. The analytic sample was 326, including 84 assigned to active medication and 242 to placebo.

The active regimen combined 380 mg extended-release naltrexone by intramuscular injection every three weeks with oral extended-release bupropion targeted to 450 mg daily. This selected, opioid-free-at-randomization population had moderate or severe methamphetamine use disorder. The analysis does not establish effects in all people with depression or all people using methamphetamine.

What improved, and how was it measured?

Depressive symptoms were measured weekly by self-reported PHQ-9, using a one-week recall period rather than the usual two-week period. The estimated medication-versus-placebo difference at week four was −2.52 PHQ-9 points, standard error 0.81. The study also examined depression response, defined as at least a 50% score reduction, and remission, defined as a score below five.

These are related but different outcomes. A continuous trajectory of improvement can carry information that a threshold-based response label discards. PHQ-9 symptom categories are also not an independent diagnostic interview establishing major depressive disorder in each participant.

How was mood related to later methamphetamine response?

Methamphetamine treatment response required at least three negative urine screens among four possible screens during weeks five and six. Twenty-three of 326 participants met that endpoint: 14/84 with active medication and 9/242 with placebo. It was not a definition of continuous abstinence throughout treatment.

Greater early PHQ-9 improvement, represented by a modeled slope, was associated with later urine response: odds ratio 3.74, 95% confidence limits 1.28–10.93. By contrast, the separate week-four depression-response and remission categories were not significantly associated with methamphetamine response: their confidence intervals included one. Both the positive slope result and those null categorical findings belong in the account.

What does “24.8% mediated” mean?

The mediation model estimated that early depression change accounted for 24.8% of the medication effect on the defined urine endpoint. Its bootstrapped 95% confidence interval was 6.7%–60.3%. That wide interval signals considerable uncertainty; it is not a precise decomposition of how the medicines work.

Treatment assignment was randomized, but the amount of each person’s mood improvement was not. Mood change may share causes with reduced use, better engagement or other changes during treatment. The temporal ordering helps frame a mechanism hypothesis, but does not by itself eliminate mediator–outcome confounding or establish that directly treating depression would reproduce the same drug-use benefit.

What additional checks temper the interpretation?

The exploratory cross-lagged analysis found one significant mood-response-to-next-visit urine association, from week three to week four; other cross-domain estimates were nonsignificant. The paper also reported missing PHQ-9 values, increasing to 69 at week four. Missing mood outcomes and the selected baseline symptom threshold constrain inference.

This analysis adds a mechanism question to the original ADAPT-2 efficacy result; it is not an independent replication. It does not validate choosing an antidepressant solely to reduce methamphetamine use or using mood improvement as proof of abstinence or functional recovery.

Source

Jha MK et al. Early Change in Depressive Symptom Severity With Naltrexone-Bupropion Combination and Its Association With Reduction in Methamphetamine Use in ADAPT-2 Trial. Journal of Clinical Psychiatry. 2025;86(3). doi:10.4088/JCP.25m15825. Secondary analysis of historical randomized human-trial data.

An early treatment marker can predict a later outcome without proving that acting on the marker improves care. A 2012 analysis of bupropion trials for methamphetamine dependence is a useful example: it proposed early decision rules, but never randomized participants to follow those rules.

Which patients supplied the prediction data?

Brensilver and colleagues retrospectively combined 55 participants from the active-bupropion arms of two earlier randomized trials. All reported methamphetamine use on fewer than 19 of the preceding 30 days. They represented 48% of the combined bupropion-treated samples, rather than every participant in the parent trials.

The historical regimen used oral sustained-release bupropion, 150 mg daily initially and then 150 mg twice daily, during 12 weeks of treatment. Both trials included cognitive behavioral therapy and incentives, but the counseling schedules and incentive contingencies differed. This secondary analysis was not a new placebo-controlled efficacy trial.

What counted as later treatment failure?

End-of-treatment abstinence required no positive methamphetamine urine results in the final two weeks, allowing only one missing specimen per week. Participants with a positive result or more extensive missingness were classified as persistent users. Fourteen of 55 met the abstinence definition; only 28 submitted any specimen in the final week.

That endpoint combined evidence of drug use with remaining available for testing. Someone who left the study could fail the endpoint without a positive final specimen. Describing every failure as chemically confirmed continued use would therefore misstate the measurement.

How well did early results discriminate?

For thrice-weekly urine testing, the area under the receiver operating characteristic curve was 0.801 after two weeks, with a 95% confidence interval of 0.675–0.926. Three weeks produced an AUC of 0.808, without a significant improvement over two weeks. An AUC summarizes ranking discrimination between later outcome groups; it is not the percentage of patients correctly classified by every possible decision threshold.

The authors reported a greater than 90% likelihood of endpoint failure among people not achieving the proposed early-negative-sample pattern. With only the first specimen of each week, failure to provide at least two negative specimens in the first three weekly visits had a reported positive predictive value of 88% for failure. Predicting success was weaker: all-negative results in those three weekly samples had a reported negative predictive value of 51%.

Why can those predictive values change in another clinic?

The underlying failure rate was high in this selected sample. Positive and negative predictive values depend on how common the target outcome is in the population being tested. They are not fixed properties of a urine assay.

Missing early samples were counted as positive. Testing frequency also changed the information available: six opportunities over two weeks are not equivalent to three opportunities over three weeks. The parent studies used somewhat different laboratory procedures, including different confirmation practices. A proposed clinical rule needs those details, not just a memorable percentage.

Did the analysis validate medication-specific resistance?

No. Early response had similar predictive characteristics in placebo-treated participants. That observation suggests the marker may reflect broader engagement or treatment responsiveness, rather than a specific biological inability to benefit from bupropion.

The small pooled sample, empirically selected threshold and absence of an independent prospective validation limit transportability. Most importantly, the analysis did not compare continuing treatment against switching or augmenting it when the marker was unfavorable. It cannot establish that discontinuation improves outcomes, and should not be presented as a current treatment instruction.

Source

Brensilver M, Heinzerling KG, Swanson AN, Shoptaw SJ. A Retrospective Analysis of Two Randomized Trials of Bupropion for Methamphetamine Dependence: Suggested Guidelines for Treatment Discontinuation/Augmentation. Drug and Alcohol Dependence. 2012;125:169–172. doi:10.1016/j.drugalcdep.2012.03.027. Historical retrospective prediction analysis.

In historical toxicology papers, “mg%” commonly means milligrams per 100 mL, not the percentage of ethanol lost. Chang and colleagues reported blood ethanol losses often falling around 20–40 mg% after years of storage. Reading that as a 20–40% loss confuses an absolute concentration change with a relative change.

What the original study measured

The 1984 paper examined whole-blood specimens collected for driving investigations, stored at room temperature in evacuated tubes with approximately 0.36% weight/volume sodium fluoride. It was not a study of ethanol formation in decomposed bodies.

One series included 37 previously unopened second tubes tested after three years. Original concentrations ranged from 84 to 348 mg/100 mL. Losses ranged from 3 to 37 mg/100 mL, averaging 19 mg/100 mL.

A second series included 57 unopened specimens stored for 6.75 years. Initial concentrations ranged from 12 to 352 mg/100 mL. Losses ranged from 3 to 72 mg/100 mL, averaging 33 mg/100 mL. In 32 cases, previously opened tubes were also examined; their losses ranged from 34 to 93 mg/100 mL, averaging 61 mg/100 mL.

Absolute and relative changes answer different questions

A decrease of 20 mg/100 mL from an initial 100 mg/100 mL is a 20% reduction. The same absolute decrease from 200 mg/100 mL is a 10% reduction. These are illustrative calculations, not additional study observations.

Likewise, an average loss of 33 mg/100 mL is equivalent to 0.033 g/dL; it is not an average 33% loss. To calculate a relative loss, the original concentration must be the denominator for the same specimen. A group mean absolute loss cannot be relabeled as a group mean relative loss.

Method changes qualify the historical comparison

The three-year series used the same headspace GC instrument for initial and repeat analyses. The 6.75-year comparison crossed two instrument configurations, internal standards and dilution procedures. Both used flame-ionization detection.

The reported coefficients of variation were 4.6% for the older configuration and 2.0% for the newer one. These are measures of analytical variability, not storage-loss percentages. The paper does not provide a complete modern validation dataset for detection limits, quantification limits and every matrix interference.

The typical range was not a guarantee

The authors described many losses as moderately predictable around 20–40 mg/100 mL and suggested a role for oxygen availability and oxidation. Their actual ranges included substantially smaller and larger changes, particularly in opened tubes. They did not directly measure the oxidative mechanism in each sample or validate a universally applicable add-back.

The specimens came from different cases, with differing starting concentrations and container histories. The observed direction of loss does not establish that all forensic blood must behave the same way. The study did not determine anatomical collection sites or postmortem intervals because it concerned specimens collected during life.

Applying old data to a present report

When citing a historical storage paper, transcribe the units in full and identify whether the number is a concentration, an absolute difference, a relative percentage or an analytical CV. Then compare the actual matrix, preservative, container, opening history and temperature.

This paper supports attention to ethanol loss during long storage. It does not establish a person’s earlier impairment or supply a correction for autopsy blood. A separate experiment showing both increases and decreases under warm putrefactive conditions is discussed in ethanol change during blood putrefaction.

Primary source

Chang RB, Smith WA, Walkin E, Reynolds PC. The Stability of Ethyl Alcohol in Forensic Blood Specimens. Journal of Analytical Toxicology. 1984;8:66–67. doi:10.1093/jat/8.2.66.

A statistically significant ethanol decrease can coexist with a result judged acceptable under a chosen analytical tolerance. Kocak and colleagues demonstrated that distinction in 80 frozen plasma specimens. Their findings should not be simplified to “freezing prevents change” or applied automatically to postmortem whole blood.

Four groups, not repeated testing of every sample

The hospital study used specimens from intoxicated drivers in Turkey during November 2013–April 2014. Twenty different specimens were assigned to each storage period: two, three, four or five months at −20°C. The same 80 specimens were not repeatedly opened and tested at every interval.

Blood was collected into sodium-fluoride/K3EDTA tubes, mixed and centrifuged. Plasma was divided into an immediately tested aliquot and a tightly stoppered polystyrene storage tube. Frozen aliquots were thawed and reanalyzed on one day. Hemolyzed, clotted, improperly filled or otherwise unsuitable collection specimens were rejected.

The measured matrix and method were specific

Ethanol was measured in plasma by an alcohol-dehydrogenase enzymatic assay on a Roche Cobas C501, in duplicate with internal controls. This was not chromatographic confirmation of ethanol in decomposed autopsy whole blood.

The study included initial concentrations above the reagent’s stated lower detection limit of 2.17 mmol/L, equivalent to 0.1 g/L. Reported intra-assay CVs were 8.27% and 2.36% at the two control levels. The article did not independently validate every possible interference associated with postmortem matrices.

All four groups showed statistically significant decreases

Median plasma ethanol changed from 31.4 to 29.1 mmol/L after two months, 34.3 to 30.5 after three, 24.3 to 19.2 after four and 39.3 to 25.8 after five. Each paired group comparison had P < 0.001.

Those medians describe different groups, with different initial concentration distributions. They do not establish a precise month-by-month loss curve for one individual specimen. The study also reported no ethanol generation in the tested specimens under these conditions.

The authors applied a separate 25% criterion

The paper compared deviations with a ±25% allowable-total-error criterion that it attributed to CLIA’88 proficiency-testing limits. This was the authors’ selected evaluation rule, not a conclusion that a 25% discrepancy is acceptable for every forensic opinion or legal purpose.

The abstract emphasizes the five-month group, but the detailed results report individual deviations exceeding the criterion in 11 of 20 five-month tubes and four of 20 four-month tubes. No such exceedances were reported in the two- and three-month groups. A broad statement that every four-month specimen remained within the chosen tolerance would omit a material result.

Why the distinction matters for interpretation

A significance test asks whether the data support a change beyond random variation under its model. An analytical tolerance asks a different question about the size of a discrepancy. Neither decides whether a disputed case result supports impairment, timing of alcohol use or cause of death.

The authors concluded that plasma could be stored for approximately three to four months under suitable conditions, while recommending laboratory-specific criteria. The individual four-month exceedances qualify that practical conclusion. The study did not demonstrate stability across other containers, temperatures, matrices or postmortem conditions.

For death investigations involving a hospital specimen, preserve its plasma designation and enzymatic method when comparing it with later whole blood. Our discussion of hospital ethanol specimens addresses additional matrix and method differences that should remain separate from storage loss.

Primary source

Kocak FE, Isiklar OO, Kocak H, Meral A. Comparison of blood ethanol stabilities in different storage periods. Biochemia Medica. 2015;25:57–63. doi:10.11613/BM.2015.006.

The history of a blood tube can materially affect ethanol reanalysis years later. Tiscione and colleagues found greater losses in previously opened tubes and tubes with lower blood volumes. Their research used preserved antemortem whole blood, so it does not establish how every decomposed autopsy specimen behaves.

The comparison involved paired tubes and two laboratories

The study used 117 forensic cases, 110 with a second tube. Blood had been collected during life for driving investigations in nominal 10 mL glass tubes containing 100 mg sodium fluoride and 20 mg potassium oxalate. Anatomical venipuncture sites were not specified.

Tube A was initially tested and later reanalyzed by the original laboratory after 5.4–10.3 years at room temperature. Previously unopened Tube B was analyzed by another laboratory after 5.6–10.5 years. The second laboratory used different instrumentation and methodology. This was authentic evidence-storage research, not a randomized experiment isolating opening as its only variable.

Prior opening was associated with greater loss

Among 100 matched ethanol-positive tube pairs used for the opening comparison, mean losses were 0.047 g/dL in previously opened Tube A and 0.035 g/dL in unopened Tube B. The paired difference was significant at P < 0.001.

Those matched-pair means should not be confused with the means for all initially positive tubes: 0.051 g/dL for 110 Tube A specimens and 0.035 g/dL for 104 Tube B specimens. The denominators answer different comparisons.

The paper’s actual volume categories matter

The table classified volumes as no more than 6 mL versus more than 6 mL. Although the narrative also describes these as approximately half-full categories, the operational 6 mL boundary should be retained when describing the results.

Previously opened tubes with no more than 6 mL lost a mean 0.080 g/dL, compared with 0.044 g/dL for higher-volume tubes. Unopened tubes lost means of 0.065 and 0.027 g/dL in the corresponding categories. Volume-group differences were significant at P < 0.001.

The authors proposed greater oxygen availability as an explanation, consistent with oxidation of ethanol. They did not directly measure the amount lost by oxidation versus every other possible mechanism in each tube.

Some initially positive tubes became negative

Six of 110 initially positive Tube A specimens and three of 104 initially positive Tube B specimens were negative after storage. Eight of these nine tubes were in the lower-volume category. Thus a much later negative result did not necessarily establish that the original specimen contained no ethanol.

Seven initially negative cases remained negative. That observation does not prove that microbial ethanol formation is impossible in every forensic specimen, particularly postmortem blood under different conditions.

Analytical controls addressed another source of difference

The original laboratory used dual-column headspace GC–FID. The second used headspace GC with simultaneous FID and mass-spectrometric detection, with FID quantitation. Its reported detection and quantification limits were 0.005 and 0.010 g/dL, and replicate CV was below 3.1%. Shared standard lots showed no significant difference at the study’s stated 99.9% confidence criterion.

That comparison strengthened the storage interpretation, but did not make the methods, storage durations and opening histories identical. No universal numerical add-back was validated.

Application in death investigations

For an archived specimen collected before death, record its fill volume, opening history, preservatives, storage and method before comparing results. Do not transfer these antemortem loss figures directly to autopsy blood, where additional formation and decomposition processes may operate. See the contrasting serial increase in a preserved autopsy specimen. Neither a stored concentration nor its change alone establishes impairment or cause of death.

Primary source

Tiscione NB, Vacha RE, Alford I, Yeatman DT, Shan X. Long-Term Blood Alcohol Stability in Forensic Antemortem Whole Blood Samples. Journal of Analytical Toxicology. 2015;39:419–425. doi:10.1093/jat/bkv037.

An uninterpretable postmortem carbohydrate-deficient transferrin result cannot be classified as a normal result or evidence against chronic excessive drinking. Rainio and colleagues obtained interpretable electrophoresis results from only 41% of submitted serum specimens. Their study illustrates why analytical success must be separated from diagnostic sensitivity.

The study attempted four specimens per autopsy

The researchers collected blood from 70 corpses: an early femoral sample after mortuary arrival and left femoral, right femoral and inferior-vena-cava samples at autopsy one to six days later. The total was 280 specimens, not 280 independent deceased people.

Blood was centrifuged and serum frozen at −20°C without preservative until testing approximately two months later. All specimens underwent capillary zone electrophoresis. A subset of 76 specimens from 19 people was selected for parallel HPLC analysis.

What prevented a numerical result?

The analyte index depended on identifying transferrin isoform peaks and quantifying disialotransferrin relative to their summed peak areas. When the peaks could not be identified, the result was classified as non-interpretable. This was not a concentration below an established detection limit.

With electrophoresis, 115 of 280 specimens yielded interpretable results, reported as 41%. The authors considered hemolysis the most likely explanation for many failures, but did not experimentally establish the cause of every failed separation. Inferior-vena-cava specimens accounted for 34% of the non-interpretable electrophoresis results; that is a share of failures, not a 34% failure rate for that vessel.

HPLC had three outcome categories

Of 76 specimens intended for HPLC, 41 yielded interpretable results, 24 were non-interpretable and 11 were exhausted by previous analyses. The reported 54% analytical yield includes the specimens no longer available for testing in its denominator.

It would be misleading to describe all 35 remaining specimens as demonstrated HPLC analytical failures. Some could not be analyzed because there was insufficient material. Nor does the 54% figure establish superior diagnostic sensitivity to electrophoresis: the HPLC subset was smaller and the study was not a diagnostic-accuracy trial.

Case-level success is another denominator

At least one interpretable result was obtained in 57 of 70 autopsies, or 81%. Multiple collection sites gave more opportunities for a usable result. That case-level figure does not mean that 81% of people with independently verified heavy drinking were identified.

The investigators explicitly did not compare CDT with drinking histories or relevant autopsy findings to determine sensitivity and specificity. The paper’s analytical yield cannot be repurposed as either measure.

Method agreement did not eliminate specimen failure

Electrophoresis used ultraviolet detection at 200 nm; HPLC used anion-exchange separation with detection at 460 nm after iron saturation and lipoprotein precipitation. The study reported a significant inter-method association, with R² = 0.9128, and no systematic direction of inter-method difference in the compared results.

Agreement among measurable specimens does not supply a value for an uninterpretable one. The article does not provide a complete numerical postmortem validation dataset for detection limits, quantification limits and imprecision, and does not establish an automatic rescue procedure for failed specimens.

How the finding should be communicated

A report should distinguish a measured CDT index, an uninterpretable separation and a specimen unavailable because it was exhausted. None of these categories alone establishes acute impairment or cause of death; CDT addresses a different biological question from an ethanol concentration.

The same reporting principle appears with another analyte in unquantifiable postmortem THC results. The specific causes and analytical methods differ, but a failed measurement is not a negative exposure finding.

Primary source

Rainio J, De Paoli G, Druid H, et al. Post-mortem stability and redistribution of carbohydrate-deficient transferrin (CDT). Forensic Science International. 2008;174:161–165. doi:10.1016/j.forsciint.2007.03.020.

Carbohydrate-deficient transferrin did not show significant group changes with delayed sampling or collection site in a 70-autopsy study by Rainio and colleagues. That finding supports relative stability under the conditions studied. It does not prove that every postmortem specimen is usable or that a CDT result establishes acute intoxication.

A marker of drinking history, not a blood ethanol measurement

CDT concerns less glycosylated forms of the iron-transport protein transferrin. The study quantified disialotransferrin relative to selected transferrin isoforms, reporting a percentage index. This is analytically different from measuring ethanol in g/L or mg/100 mL.

The research question concerned whether a marker used in assessing chronic excessive alcohol consumption remained stable after death. The investigators did not validate CDT as a measure of ethanol concentration at death, the time of a last drink, behavioral impairment or cause of death.

How sequential specimens were collected

Blood was obtained from 70 consecutive corpses at the Karolinska forensic medicine department. An initial femoral sample was collected transcutaneously as soon as practicable after arrival at the mortuary. At autopsy one to six days later, separate samples were obtained by cutting the left and right femoral veins and inferior vena cava.

The resulting 280 specimens were centrifuged. Serum was stored without preservative at −20°C until analysis approximately two months later. The delay while the body remained in cold storage and the later storage of separated serum are different parts of the specimen history.

What the paired comparison showed

Initial and autopsy values could be compared in 29 cases. The interval between collections ranged from 24.8 to 145.3 hours, with a median of 73 hours. Estimated time in warm conditions before mortuary transport ranged from 1.7 to 38.0 hours; the initial sample was collected 4.5–66.1 hours after death.

For capillary zone electrophoresis, reported changes ranged from −0.8 to +0.9 percentage points, with a median of −0.1. For HPLC, changes ranged from −0.6 to +0.4 percentage points, with a median of −0.2. A paired t-test found no significant difference at the study’s 0.05 threshold.

These are changes in the percentage index, not relative percentage losses of the original value. A nonsignificant group result also does not establish that every individual value remained identical.

Site comparisons supported the same limited conclusion

ANOVA found no significant differences among left femoral, right femoral and inferior-vena-cava results. The authors interpreted the findings as showing no major redistribution of CDT under the tested conditions. This cannot be generalized to unrelated drugs, whose distribution and postmortem behavior differ.

The methods were capillary zone electrophoresis with ultraviolet detection and anion-exchange HPLC with visible detection. Quantitation used the disialotransferrin peak area divided by the summed areas of specified transferrin isoforms. The article did not provide a full numerical detection-limit, quantification-limit and precision dataset for this postmortem application.

Missing results limit the inference

Only 115 of 280 electrophoresis analyses were interpretable. At least one usable result was obtained in 57 of 70 autopsies. Uninterpretable specimens cannot be treated as normal CDT values, and the stability findings necessarily depend on the subset that could be measured.

The study did not compare CDT with independently established drinking histories to calculate diagnostic sensitivity or specificity. Its evidence concerns analytical feasibility, stability and site comparisons. For a contrasting analyte-specific distribution problem, see different postmortem distributions of co-detected drugs.

Primary source

Rainio J, De Paoli G, Druid H, et al. Post-mortem stability and redistribution of carbohydrate-deficient transferrin (CDT). Forensic Science International. 2008;174:161–165. doi:10.1016/j.forsciint.2007.03.020.

Two million urine specimens can provide substantial information about a laboratory’s tested population without becoming a representative survey of everyone in the United States. A historical clinical-testing study illustrates both the value and the limits of large toxicology datasets.

What was the denominator?

LaRue and colleagues examined clinical specimens collected from January 2014 through December 2019. They selected a convenience sample of two million adult patient specimens with definitive methamphetamine test orders and used one specimen per person, based on the earliest collection date. Samples came from healthcare practices in all 50 states and the District of Columbia.

Testing used liquid chromatography–tandem mass spectrometry at one commercial laboratory. The investigators excluded results consistent with prescribed medications as reported by the ordering healthcare professional. That process depended on prescription information supplied with the order; it was not an independent verification of every person’s entire medication history.

Which positivity numbers are comparable?

Across the six-year dataset, 106,405 of two million specimens were methamphetamine-positive: 5.32% raw positivity. Separately, the regression model estimated marginal positivity probabilities of 1.0% in 2014 and 4.4% in 2019 after accounting for demographic, regional and practice-specialty variables. Their reported 95% confidence intervals were 1.0%–1.1% and 4.2%–4.6%, respectively.

The authors described that modeled change as a 340% relative increase. It is also a 3.4-percentage-point absolute increase. Neither is the same quantity as the pooled 5.32% raw figure, which covers all six years and reflects the actual mixture of sampled patients. Calling these numbers contradictory, or combining them into a single trend calculation, would mix estimands.

Why is this not national prevalence?

People were tested because healthcare professionals considered testing medically necessary. The dataset therefore selected for entry into care, laboratory use, a methamphetamine order and the participating practices. Random selection within that available dataset does not make the underlying clinical population nationally representative.

Adjusting for age, sex, census division and practice specialty helps address recorded differences. It cannot account fully for why a clinician ordered a test or for changes in unmeasured patient characteristics. The study is strong evidence about trends in this testing system, with a narrower basis for population-wide claims.

Why do copositivity percentages need another denominator?

Additional substances were not ordered for all two million specimens. Among methamphetamine-positive specimens, 78,510 had fentanyl testing, with 9.03% positivity. The relevant comparison is the subset tested for fentanyl, not all 106,405 methamphetamine-positive specimens. Heroin assessment used 6-monoacetylmorphine; alcohol assessment used ethyl glucuronide and ethyl sulfate. Those analytes are not interchangeable with blood parent-drug concentrations.

The study also modeled adjusted odds of other-drug detection in methamphetamine-positive versus methamphetamine-negative groups. An odds ratio is not a percentage of samples, an overdose probability or proof that one substance caused use of another.

Does a copositive urine establish a mixed dose?

No. Detection of two analytes in one urine specimen may reflect exposures at different times because analytes have different formation and elimination patterns. These data cannot distinguish intentional simultaneous administration, sequential use or unrecognized exposure in an individual case.

The findings also do not establish impairment at collection or at an earlier event. The authors’ commercial-laboratory affiliations and funding are relevant context: most authors were laboratory employees and one was a consultant. That does not negate the measurements, but transparency about data source and sampling is essential when presenting the results as evidence of broader trends.

Source

LaRue L et al. The Cyclical and Evolving Nature of Methamphetamine Use: An Evaluation of Near Real-time Urine Drug Test Results. Journal of Addiction Medicine. 2021;15:396–405; copyright/online record 2020. doi:10.1097/ADM.0000000000000778. Historical cross-sectional clinical laboratory evidence.