There has been an increase in the interest in patient compliance in the past two decades (Trostle 1997
; Bloom 2001
; Wosinska 2005
In general, compliance is defined as following or adhering to medical advice.2
Clinicians generally agree that patient compliance is an integral part of effective medical care, but the degree of compliance is low. Patients frequently do not take prescribed medicines, do not keep office appointments, do not follow through with treatment programs, and do not adjust lifestyles according to medical conditions. Begg (1984)
reports 6–20% of patients do not even redeem their prescriptions. Smith and Yawn (1994)
document that 19–28% of appointments are cancelled or missed, while Sellers et al. (1979)
laments that 70% of clients in behavioral programs (such as substance abuse or diet control) fail to complete the programs. Noncompliance has been reported across many diseases.3
Failure to comply implies the absence of key inputs in health production (Keller et. al. 1982
; Ellickson et al. 1999
). In addition, noncompliance may necessitate more expensive treatment later.4
Noncompliance also may lead to medical errors because physicians may be misinformed about patients’ behaviors (see Melnikow and Kiefe 1994
). The evidence suggests that lack of compliance leads to negative health outcomes and higher healthcare costs. In one study, noncompliance is claimed to lead to 125,000 premature deaths each year in the United States (Loden and Schooler 2000
). The cost of noncompliance in the U.S. due to hospital re-admissions and lost productivity has been estimated at around $100 billion a year (National Pharmaceutical Council 1992
; Johnson and Bootman 1995
Clearly, understanding why
patients do not comply is important. Many have viewed noncompliance as resulting from patients’ irrational behavior (Haynes 1979b
; Trostle 1997
). Increasingly, however, studies have turned attention to more objective factors, such as treatment complexity, side effects, and physician-patient interactions (Haynes 1979b
; Conrad 1985
). A more balanced approach regards patient compliance as the client’s decision in light of the benefits and costs of continued treatment.5
We hypothesize that if a patient perceives good progress and expects benefits, he is more likely to comply. This is a natural hypothesis from the standpoint of a patient’s costs and benefits. If a patient has been making good progress during a treatment episode, it seems reasonable to expect him to continue. To test this hypothesis, we study office visits for alcohol problems. Compliance is measured by keeping scheduled visits and continuing with treatment. Our progress variables are whether a client’s drinking problem has improved or whether there has been a relapse since the previous visit, as reported by clinicians and patients.
We use the intertemporal structure in our data to identify the causal effect of treatment progress on compliance. We use treatment progress in an on-going treatment episode to explain compliance in a future
This allows us to test whether good progress in the past predicts compliance in the future. As far as we know, this is the first attempt to draw a causal relationship between treatment progress and compliance in alcohol outpatient treatments.
We control for a number of patient covariates in our study. Substantial research, starting with Haynes (1979a
, demonstrates the importance of patient’s knowledge of therapeutic regimes, interactions between patients and doctors, as well as motivation. Other papers have stressed the importance of patients’ medical knowledge by comparing compliance between clients with and without educational training about therapeutic regimes (Wintraub et al. 1973
; Brown et al. 1987
; Seltzer 1980
; Ley and Llewellyn 1995
). Patient characteristics and previous experiences of alcohol abuse treatment will capture these effects. Finally, we control for unobserved heterogeneity of patients using random-effect, fixed-effect, and finite-mixture models (Heckman and Singer 1984
; Cutler 1995
Our results show that treatment progress affects patient compliance: a relapse in the previous visit increases the chance of dropping out of treatment, while making progress reduces it. On average, a relapse into drinking increases the chance of dropping out of a treatment program by about 9 percent, while making progress reduces it by 2.7 percent. These magnitudes are small but statistically significant. The results are robust when unobserved client heterogeneity is controlled for. Nevertheless, we do not find evidence that lack of progress or relapse in an earlier visit reduces the chance of missing the next scheduled visit for clients who stay in the program. Perhaps the decision regarding an upcoming visit is more likely subject to factors we do not observe, but the decision to remain in treatment is subject to systematic influence of progress in therapy.
The rest of the paper is organized as follows. Section 2 describes the data, defines measures of patient compliance and treatment progress, and presents summary statistics. Section 3 outlines the estimation strategy. Section 4 presents our main findings and robustness checks. We draw some conclusions and discuss future research in Section 5.