Archives for June 2017

Spontaneous Remission from Addiction: Definitions and Implications for Treatment

Spontaneous remission from addiction, when people recover from substance use disorders without treatment, is the ardent wish of people with addiction and of those who love them, particularly if remission occurs readily, speedily, and without complications. Yet, what exactly defines spontaneous remission, what causes it, and how often it occurs are mostly unknown. Estimates of the rate of spontaneous remission from addiction have ranged from 4% to nearly 60%.

Poppy in the setting sun

Why do some people spontaneously recover and others don’t? Does it mean there is hope for people who are addicted that they will one day be able to “just say no”? (Should we be angry with them if they don’t?) And what about those difficult cases treatment providers see, that never seem to improve despite the provider’s best efforts? And if someone claims a treatment cured them, might they have just “spontaneously remitted” and not know it?

Unfortunately, precise estimates of spontaneous remission are notoriously difficult to obtain because of the limits of the data available. Very little is actually known about spontaneous remission.

For one thing, different researchers use different terms to describe the phenomenon: “spontaneous remission,” “spontaneous recovery,” “aging out,” “maturing out,” “natural recovery,” “selfremitting,” “unassisted recovery,” “recovery without treatment,” and “selfchange.” Each term has slightly different connotations, and definitions may differ as to specifying complete abstinence or some moderation of use. In general, researchers seem to agree that the terms refer to people who are achieving abstinence or moderation on their own.

The definition of addiction itself is a moving target. Which people are most susceptible to which drugs with what severity changes, perhaps based on economic and social conditions. (In the 80s and 90s in the U.S., the primary concern was crack cocaine in inner cities, and in 2017 the primary concern is prescription opioids and heroin laced with fentanyl in rural areas.) Some of the older epidemiological surveys available use outdated diagnostic criteria (e.g., DSM-III or DSM-IV) to diagnose addiction, such that heavy use and physiological dependence may have qualified as addiction. Now, with DSM-Vwe distinguish between simple dependence and more complex addiction. People who are dependent but not addicted to a substance are probably more likely to remit on their own, without treatment, than people who are heavily addicted.

To determine whether remission was spontaneous, researchers also need to be able to pin down what is considered treatment, and what isn’t. For example, some researchers consider 12-step approaches to be treatment, while others don’t, accordingly influencing estimates of people who recover without treatment.

As if all of this isn’t enough of a puzzle, researchers don’t follow up with study subjects after the same amount of time. Some researchers follow up after a year, while others try to find people every five or ten years. Depending on the length of time, some subjects might have remitted and relapsed, while others might have remitted for good; the percent of subjects in remission is likely to change over time. Some subjects cannot be found again at all, and their data is typically removed from consideration, thus influencing estimates of spontaneous remission rates.

The information that is available comes mainly from three different kinds of evidence, each of which has strengths and drawbacks: individual case studies, nationwide epidemiological surveys, and meta-analyses of smaller-scale studies.

1. Individual Case Studies

Spontaneous remission is a known phenomenon in part because of the people who have stepped forward and told their stories. Maia Szalavitz wrote about one of the most famous examples, Oliver Sacks, who, “found in writing an alternative source of pleasure and purpose. His ability to take joy in this work—even when it was not his primary source of income—replaced the ‘vapid mania of amphetamines’; more critically, writing was more meaningful than taking drugs.”

Szalavitz, author of Unbroken Brain: A Revolutionary New Way of Understanding Addiction, has done extensive journalistic research into the phenomenon of spontaneous recovery. In an interview, she speculated on why and how spontaneous remission from addiction happens:

“I think in some cases, a lot of times, it’s life events, like you fall in love with somebody and because you’re just in love with somebody at that moment, you are able to give it up for them, whereas if you fell in love at another time, it wouldn’t work. Or you just got the job you’ve always wanted. Or the structure of your life changes. For a lot of people, you can’t really party the way you did in college [at age] 30. And that structurally helps a lot of people to recover, just the fact that in order to earn a living, you have to show up somewhere at 9 or 10 in the morning. And maybe those people have less severe addictions.”
Maia Szalavitz

Individual case studies and anecdotes are helpful for observing that a phenomenon sometimes occurs, but we cannot draw conclusions on how often it usually happens.  As Szalavitz notes, no generalizations can be drawn from individual anecdotes.

2. Nationwide Epidemiological Surveys

National epidemiological surveys are typically designed to estimate the prevalence of various disorders and/or diseases in the population. They are not well-suited to describing the course of a disease or disorder over a person’s life, or how often people get better from the disease or disorder (whether on their own or through treatment).

Four epidemiological surveys have been analyzed related to spontaneous remission from addiction: the Epidemiological Catchment Area survey (ECA, 1980-84), the National Comorbidity Survey (NCS-1, 1990-92), the National Comorbidity Survey Replication (NCS-R, 2001-03), and the National Epidemiological Survey on Alcohol and Related Conditions (NESARC, 2001-02). The first three were generally performed to determine the predominance of various mental health illnesses in the United States. Only the fourth was aimed at people with addiction, and it was focused on alcohol-related problems.

In-depth analysis or review of these four surveys is beyond the scope of this article, but one researcher, Gene Heyman, performed an analysis of all four in 2013, in order to try to answer questions about spontaneous remission. Based on his analysis of these four nationwide surveys, Heyman concluded that a consistent percent of people with addiction remit over time, and that most people do so by age 30. He estimated lifetime recovery rates to be around 80%.

Unfortunately, his conclusions are limited by the nature of the data he examined. Large-scale surveys are notoriously fickle when one tries to conclude anything from them beyond what they were designed to estimate.

  • They are observational, and therefore often unable to distinguish between lurking variables. For example, these surveys did not adequately answer whether people were in treatment or not when they remitted. Heyman’s estimate of 80% is not an estimate of spontaneous recovery, only lifetime remission. From these surveys, we can only say that people remitted, not why or how they did so.
  • They are a snapshot in time, not longitudinal. Therefore, the surveys are unable to show how long addiction persisted for individuals. His conclusion that a consistent number of people remit every year is based on a snapshot of people in different generations, not based on a longitudinal study following a group of people over time.
  • Surveys are also prone to misrepresenting populations. In the case of the four examined by Heyman, the people who are most severely addicted, living in impoverished neighborhoods, homeless, or in prison, were less likely to be sampled. He noted that the ECA survey attempted to compensate for this by over-sampling prison populations, but the other three did not. None of the surveys accounted for people who may have died in the course of their addictions. Heyman’s 80% statistic is only valid if the surveys adequately represent the total population of people with addiction in the U.S.

Heyman was aware of all of these limitations, and adjusted his estimate to 64% based on the critique of researchers who estimated that these surveys missed about 25% of all people with addiction. He tried to then assume that, if the missing people remitted by age 30, the rate would jump back up to 74%, but the “age 30” estimate was based on the same data that is biased by missing people. He claimed that to adjust the statistic below 64% to account for missing people, “would imply that approximately one in ten adult Americans had become addicted to an illicit drug and that most were currently addicted.” In fact, 1 in 10 was the estimate for the number of Americans over the age of 12 who were currently addicted in 2007, and about 1 in 13 (7.8%) Americans over the age of 12 were currently addicted in 2015.

While Heyman establishes a hopeful picture for overall remission rates for people with addiction, caution is warranted in trusting his conclusions. The estimates for lifetime remission, whether 64%, 74%, or 80%, are cause to be optimistic about the ability of people to recover from addiction. They are not, however, reason to become impatient with people who suffer from addiction. His statistics are also not applicable to the question of spontaneous remission, given that the surveys did not examine whether the people who remitted were in treatment or not.

Heyman’s results are potentially grounds for someone to apply for grants to do a large-scale longitudinal study. If remission rates without treatment can be known, researchers may be better able to evaluate treatment effectiveness. A longitudinal study could help determine which treatments are effective above and beyond spontaneous remission.

3. Meta-Studies

Where the nationwide surveys lack the power to describe how addiction progresses in an individual or why spontaneous recovery happens, individual studies typically lack the large sample sizes needed to generalize for all of America. For this reason, some researchers choose to use complex statistical analyses to compare results across studies and draw conclusions from their combined data, a process called meta-analysis.

In 2009, Glenn Walters did a quantitative review of the literature to determine the extent of spontaneous remission, and whether people who spontaneously remit are different in any obvious way from people who don’t. Walters included 12-step approaches under the umbrella of “formal intervention,” or treatment. He also performed the analysis for a broad definition of spontaneous remission, that the subjects had reduced the amount and/or frequency of drug intake and were free of negative consequences for 6 months, and a narrow definition of spontaneous remission, that the subjects were entirely abstinent from the substance of choice for 6 months.

Walters found that the average prevalence of spontaneous remission from alcohol, tobacco, or other drugs was 26.2% using a broad definition of remission, and 18.2% using a narrow definition of remission. Walters also managed to evaluate the principal reasons people who spontaneously remitted reported for why they quit using.

The top four reasons people reported for stopping alcohol/drugs and staying stopped were:

  1. “support from family/friends”
  2. “find new relationships/avoid old relations”
  3. “transform identity/reject addict identity”
  4. “willpower/resist the urge to use”

The top four reasons people reported for stopping tobacco and staying stopped were:

  1. “willpower/resist the urge to use”
  2. “substitute activities/dependencies”
  3. “self-confidence”
  4. tied: “change in recreational/leisure activities” and “exercise/physical fitness”

In Walters’ review, people who were able to remit (spontaneously or otherwise) were not less severely addicted or otherwise meaningfully different from those who did not remit. The exception is that, for tobacco, there is some evidence that those who had been smoking longer/more intensively were less likely to remit.

To draw conclusions with greater confidence, however, one would need to look into the sampling methodology of the studies Walters reviewed, to see whether sampling bias occurred. For instance, Walters’ estimates of spontaneous remission are much lower than those that come from the nationwide surveys. Aside from the fact that his estimates are of spontaneous remission rather than total lifetime remission, Walters’ estimates may also be more accurate due to better/more representative sampling or less accurate due to more biased sampling.

Walters himself noted that, to really understand how and why spontaneous remission occurs, and at what specific rates, a study with more rigorous methodology is needed.

“A longitudinally designed investigation of a large unselected group of untreated substance abusers would go a long way toward filling many of the gaps in our current knowledge of spontaneous remission. This group of individuals could be followed and periodically reinterviewed to determine changes in their use of substances. Such a study would allow more precise calculation of patterns of spontaneous remission, treatment remission, and relapse.”
Glenn Walters

In fact, while such a longitudinal study would be extremely expensive, it would be cost-effective in the long run, both in terms of dollars and relieving human suffering. Researchers need to know natural remission rates if they are to adequately determine the effectiveness of various treatments that surpass those natural rates. Furthermore, if spontaneous remission rates are indeed as high as 50%, then treatment funds can be used to potentially accelerate the process of spontaneous remission, or to focus on people who are unlikely to remit without help.

For now, we can only estimate spontaneous remission rates to range between 4% and 60%, and speculate about the reasons why people spontaneously remit. We can say, confidently, that spontaneous remission does happen, and possibly at fairly high frequencies. That alone has hopeful implications for the treatment of addiction.

Jennifer West from Virginia Tech’s Laboratory for Interdisciplinary Statistical Analysis contributed to this post.

This content is for informational purposes only and is not a substitute for medical or professional advice. Consult a qualified health care professional for personalized medical and professional advice.