Buza Reloaded: Court Shifts Ground But Again Invalidates California’s DNA-on-arrest Law

Buza I For the reasons we have set forth, we conclude that the DNA Act ... unreasonably intrudes on such arrestees' expectation of privacy and is invalid under the Fourth Amendment of the United States Constitution.

Buza II For the reasons we have set forth, we conclude that the DNA Act ... unreasonably intrudes on such arrestees’ expectation of privacy and is invalid under article I, section 13, of the [California] Constitution.

Nearly seven years ago, Mark Buza set a San Francisco police car on fire to protest what he said was a corrupt government. Spotted running from the burning car, he was promptly arrested and brought to the county jail. There, he engaged in a second act of defiance, refusing to allow police to swab the inside of his cheeks to acquire a DNA sample. (He did, however, acquiesce in fingerprinting and writing a signature.) After his conviction for arson-related crimes as well as the separate offense of refusing to submit to DNA sampling, a court ordered him to give a sample before sentencing. In a third act of resistance, he refused, but when the court issued a further order allowing police to use force, he finally submitted to the swabbing. His DNA profile entered the California database, apparently without incriminating him in any other crimes.

The California Court of Appeal reversed the conviction for resisting “the seizure of his DNA at a time when he was entitled to the presumption of innocence, and [when] there had been no judicial determination of probable cause to believe he committed the offense for which he was arrested ... .” California's DNA-collection law, the court reasoned, “violated his Fourth Amendment right to be free from unreasonable searches and seizures.” The court expressly declined to consider whether compelling DNA collection before a judicial finding of probable cause violated Buza’s “right to privacy under article I, section 1, of the California Constitution.”

The California Supreme Court granted review, vacated the appellate court’s judgment, and ordered that court to reconsider its reversal of the DNA-refusal conviction in light of the United States Supreme Court’s decision in Maryland v. King, 133 S.Ct. 1958 (2013).

In a lengthy opinion issued four days ago, the Court of Appeal, like Buza himself, stuck to its guns. For a second time, it held that the California initiative that mandates DNA sampling on arrest (and that has been upheld by several other courts) is unconstitutional. However, the court shifted ground. Now Presiding Justice Anthony Kline wrote: “we question whether King establishes the validity of the California Act’s application to arrestees under the Fourth Amendment. We base our decision, however, solely upon article I, section 13, of the California Constitution, which in our view undoubtedly prohibits the search and seizure at issue.” (Emphasis added.)

I’ll leave it to California’s bar, bench, and scholars to debate how and why the Court of Appeal can be so certain that California’s constitution, which essentially reproduces the words of the Fourth Amendment, compels a different result than King might warrant. Here, I want to consider the Court of Appeal's analysis of the outcome that should follow under the U.S. Constitution as construed in King.

[Next installment]

Closely related postings

• Buza Reloaded: The Fourth Amendment Framework, December 22, 2014
• Buza Reloaded: Fourth Amendment Balancing, January 1, 2015
• Buza Reloaded: California Balancing, January 4, 2015
• The Constitutionality of DNA Collection Before Conviction: An Updated Scorecard, July 17, 2014
• The Impact of Medina's Marginal Balancing on DNA Databases and Sample Retention, July 16, 2014
• Kansas Court of Appeals Rejects Post-King Challenge to DNA Collection on Arrest, June 8, 2014
• What Is Wrong with People v. Lowe? More on DNA-on-Arrest Laws After Maryland v. King, March 24, 2014
• The Ninth Circuit's Minimal Opinion in Haskell v. Harris, March 22, 2014
• Get Serious: The US Department of Justice's Amicus Brief in Haskell v. Harris, December 6, 2013

References

• People v. Buza, No. A125542 (Cal. Ct. App., 1st Dist., Dec. 3, 2014) (pdf)
• People v. Buza, 129 Cal.Rptr.3d 753 (Ct. App., 1st Dist., 2011), rev. granted, 262 P.3d 854 (Cal. 2011), vacated, 302 P.3d 1051 (Cal. 2013) (ordering reconsideration in light of Maryland v. King)
• David H. Kaye, Why So Contrived? DNA Databases After Maryland v. King, 104 J. Crim. L. & Criminology 535 (2014)
• David H. Kaye, Maryland v. King: Per Se Unreasonableness, the Golden Rule, and the Future of DNA Databases, 127 Harv. L. Rev. Forum 39 (2013)

Another Disgusting DNA Case: Please Flush!

A quick update to the disgusting DNA report of last March (The Whooper Stopper). Following up on a report from KOAT-TV in Albuquerque, the Associated Press reported that a man helped himself to $250,000 worth of jewelry from a home while the owners were on vacation and neglected to flush whatever he left behind in the toilet. Is there is a lesson to criminalists here: swab toilet seats and handles to catch more considerate burglars as well? Probably not. Cf. Peter Gill, Misleading DNA Evidence: Reasons for Miscarriages of Justice (2014) (discussing the interpretation of touch DNA).

Far more offensive is a case reported by the same TV station in 2011. It seems that a 31-year-old Sunflower Market employee offered a woman what he said was a free yogurt sample. Incredibly, it was his semen. After pleading guilty, he was sentenced to a two-year prison term.

PS: On August 9, 2017, the AP reported yet another case of police flushing out a burglar who didn't. "The suspect 'did his business and didn't flush it' during the October break-in in the city of Thousand Oaks, said Detective Tim Lohman of the Ventura County Sheriff's Office. That ... DNA profile ... matched another profile in a national database ... ." Apparently, the detective does not follow this subcategory of "abandoned DNA" cases, for he said that "it's the first DNA burglary match case he knows of with fecal evidence collected from a toilet."

The Supreme Sound of Silence: Same-Sex Marriage and DNA Databases

The big news among Supreme Court watchers is the big dog that did not bark in the night — the Court’s denial of petitions for certiorari in seven cases striking down bans on same-sex marriage in Indiana, Wisconsin, Utah, Oklahoma, and Virginia. [1] A denial of a cert petition has no precedential value. It does not mean that the Court approves of the decision below—or that it disapproves of it. It means that, for unstated and often banal reasons (the Court receives some 10,000 petitions a year [4]), no more than three Justices voted in favor of review the decision below. (By convention, it takes four votes to grant the writ that triggers the Court’s review of the case on the merits.)

The Court watchers are treating the rejection of the writs here as a “tacit win to gay marriage” on the theory that it means that if and when the Court chooses to confront the issue, a majority of states will have sanctioned same-sex marriage, making it more likely that the Court will accept the argument that the Constitution forbids limiting the institution of marriage to couples of the opposite sex. [3]

This predicted dynamic was evident in the Court’s handling of laws requiring routine DNA collection for law enforcement databases. No appellate court ever struck down a law requiring convicted offenders to provide samples, and for some thirty years, the Court invariably denied petitions for review in those cases. Only after Maryland’s highest court essentially invalidated that state’s law providing for DNA collection on arrest did the Supreme Court step in. By that time, every state had a DNA database for convicted offenders, and a majority had extended them to require pre-conviction DNA sampling. Every state signed an amicus brief urging the Court to uphold the practice. The Court split 5–4 on the constitutionality of pre-conviction DNA testing. Had the states and the federal executive branch not presented so unified a front in favor of expansive DNA collection, the outcome could have been different. [2]

References

1. Amy Howe, Today’s Orders: Same-sex Marriage Petitions Denied, SCOTUSblog, Oct. 6, 2014, 10:41 AM, http://www.scotusblog.com/2014/10/todays-orders-same-sex-marriage-petitins-denied/
2. David H. Kaye, Why So Contrived? DNA Databases After Maryland v. King, 104 J. Crim. L. & Criminology 535 (2014), available at http://ssrn.com/abstract=2376467
3. Adam Liptak, Supreme Court Delivers Tacit Win to Gay Marriage, N.Y. Times, Oct. 7, 2014, at A1, http://www.nytimes.com/2014/10/07/us/denying-review-justices-clear-way-for-gay-marriage-in-5-states.html
4. Robert M. Yablon, Justice Sotomayor and the Supreme Court’s Certiorari Process, 123 Yale L.J. F. 551 (2014), http://yalelawjournal.org /forum/justice-sotomayor-and-the-supreme-courts-certiorari-process.html

Bayes in Our Times

Today's New York Times has an article on "a once obscure field known as Bayesian statistics."^1/ It is an informative piece by Faye Flam, a science journalist with an uncommonly good grasp of science. But a quantum of confusion infects the effort to contrast "Bayesian statistics" with "the more traditional or 'classical' approach, known as frequentist statistics."

The article presents the solution to famous Monty Hall problem (known to "classical" probabilists as the three-curtains problem long before its appearance in the TV game show) as especially amenable to "Bayesian statistics." But frequentist thinking works quite well here. In the long run, the strategy of switching beats the strategy of not switching. This is easily proved with classical, objective probabilities.

Indeed, it is not clear that the Monty Hall problem is even a problem in statistical inference.^2/ There are no statistical (sample) data to consider and no sense in which the use of Bayes' rule to solve the probability problem "counter[s] pure objectivity." How, then, do "[t]he two methods approach the same problem[] from different angles"?

Of course, the Monty Hall problem is nice for illustrating the power of Bayes' rule in working with conditional probabilities. I have used it in this way in my courses, and that may have been the reason it appears in the article. But it does not illustrate the philosophical divide between frequentists and Bayesians.

To this extent, it is disappointing that the Times (but probably not the author) chose to start the online version of the article with a large photograph of Monty Hall captioned "Bayesian statistics can help solve the Monty Hall problem of winning a car." It would have been equally accurate to report that "Frequentist statistics can help solve the Monty Hall problem of winning a car." But that is is hardly news fit to print.

Notes

1.Faye D. Flam, The Odds, Continually Updated, N.Y. Times, Sept. 30, 2014, at D1.

2. On the distinction between a "problem of statistical inference or, more simply, a statistics problem," and a probability problem, see, for example, Morris H. DeGroot, Probability and Statistics 257 (1975).

Hazard Ratios and Heart Failure

Today’s big news in medicine is a new drug, designated LCZ696 by its manufacturer, Novartis. According to the New York Times, LCZ696 “has shown a striking efficacy in prolonging the lives of people with heart failure and could replace what has been the bedrock treatment for more than 20 years.” [1] Specifically, more than 8,400 patients in 47 countries enrolled in a randomized, double-blind experiment in which they received either LCZ696 or an ACE inhibitor called enalapril (in addition to whatever else their doctors prescribed).

The trial was halted after a median follow-up time of 27 months “ because the boundary for an overwhelming benefit with LCZ696 had been crossed.” [2] “By that point, 21.8 percent of those who received LCZ696 had died from a cardiovascular cause or had been hospitalized for worsening heart failure. That figure was 26.5 percent for those receiving enalapril. That represents a 20 percent relative reduction in risk using a statistical measure called the hazard ratio.” [1]

This is good news for patients (if the drug receives regulatory approval and performs as expected in practice). But the account in the Times poses a small statistical puzzle. How does the difference between 21.8 and 26.5 percentage points translate into “a 20 percent relative reduction in risk”? The average risk across patients dropped by 26.5 – 21.8 = 4.7 percentage points. This absolute reduction is appreciable, but 4.7 percentage points is not 20% of the original 26.5 percent risk of hospitalizations or deaths in the control group (4.7 / 26.5 = 17.7%). What accounts for the discrepancy?

The answer lies in the details of a technique known in biostatistics as survival analysis. The statistical technique is not limited to the analysis of death rates. It can be applied to all sorts of situations involving different times to some outcome. The outcome can be the overruling of a Supreme Court case, the firing of a worker, or the exoneration of a prison inmate sentenced to die, to pick a few examples from forensic statistics.

So what does the 20% “relative reduction in risk” cited in the Times article mean? Well, a hazard function is the probability that if you survive to a given time t (the event in question has not already occurred), you will survive in the next instant. A hazard ratio is the ratio of the hazard in the treatment group to the hazard in the control group at t. The heart failure study used an estimation procedure known as proportional hazards regression, which assumes that the hazard in one group is a constant proportion of the hazard in the other group. Under this assumption, in a clinical trial where death is the endpoint, the hazard ratio indicates the relative likelihood of death in treated versus control subjects at any given point in time.

Thus, unlike the ordinary relative risk discussed in many court opinions, the “hazard ratio” is not simply the proportion with a disease in an exposed group divided by the proportion in an unexposed group. In the LCZ696 study, the hazard ratio was 0.80, meaning that the probability that a randomly selected patient taking LCZ696 would die from or be hospitalized for heart failure the next day is 80% of the probability for a randomly selected patient taking enalapril. To put it another way, the probability of hospitalization or death tomorrow from heart failure drops by 20% when LCZ696 is substituted for enalapril.

Yet a third formulation is that the odds that a randomly selected patient treated with LCZ696 will be hospitalized or die sooner than a randomly selected control patient are 0.8 (to 1) — that's 4 to 5, corresponding to a probability of 4/9 = 44%. [3]

How long either patient can expect to live and avoid hospitalization from heart failure is another story. As one article on hazard ratios explains, “[t]he difference between hazard-based and time-based measures is analogous to the odds of winning a race and the margin of victory.” [3] By itself, the hazard ratio picks the winning horse (probably), but it does not give the number of lengths for its expected success.

References

1. Andrew Pollack, New Novartis Drug Effective in Treating Heart Failure, N.Y. Times, Aug.31, 2014, at A4
2. John J.V. McMurray et al., Angiotensin–Neprilysin Inhibition versus Enalapril in Heart Failure, New Engl. J. Med., Aug. 30, 2014
3. Spotswood L. Spruance et al., Hazard Ratio in Clinical Trials, 48 Antimicrobial Agents and Chemotherapy 2787 (2004)