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and Health

Evidence from Vancouver Injection

Drug Users

Chris Riddell

Rosemarie Riddell

A B S T R A C T

This paper investigates the link between welfare payments and drug use among injection drug users. We find an increase in the likelihood of an over-dose in the days following check arrival, and in the probability of leaving the hospital against medical advice (AMA) on check day. Using the check arrival date as an instrument, we estimate the Local Average Treatment Effect of leaving AMA on subsequent readmission and the probability of a drug overdose. The results indicate that, for individuals influenced by check day, leaving AMA leads to readmission much sooner than planned dis-charge, longer subsequent stays in the hospital, and a substantial increase in the probability of a drug overdose.

I. Introduction

The relationship between social assistance and substance abuse has been a topic of much debate in the United States where, in 1996, Public Law 104-121 terminated Supplemental Security Income and Social Security Disability Income (SSI/DI) for recipients with a primary diagnosis related to substance abuse. The rationale for the law was a belief that such recipients were spending the money on drugs and alcohol such that welfare was effectively “aiding and abetting addiction” (Cohen 1994). Canada has made no such change in the social assistance legislation.1

Chris Riddell is an assistant professor in the School of Policy Studies at Queen’s University, and Rosemarie Riddell is a Clinical Nurse Specialist at St. Paul’s Hospital. The authors thank Mike Campolieti, David Green, Morley Gunderson, Doug Hyatt, Craig Riddell, and Aloysius Siow for useful discussions, and especially Tom Crossley, Harry Krashinsky, and two anonymous referees for very helpful comments.

[Submitted May 2003; accepted June 2005]

ISSN 022-166X E-ISSN 1548-8004 © 2006 by the Board of Regents of the University of Wisconsin System 1. Income assistance laws are under provincial jurisdiction in Canada. Our reading of the provincial laws for the five largest provinces is that only Ontario (in a legislative change made in 1997) has a strong restriction

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A few studies in the public health literature have shown evidence of so-called “check effects”; that is, a relationship between the day welfare checks are released and subsequent increases in outcomes such as hospital admissions, drug-related fatal-ities and 911 calls (for instance, Shaner et al. 1995; Philips, Christenfeld, and Ryan 1999).

In addition, economists have examined the relationship between identifiable, pre-announced income changes and consumption (for instance, Wilcox 1989 and Parker 1999 in the case of social security-related payments). One such study directly tests for a consumption-related check effect among welfare recipients, and finds that the prob-ability and amount of daily expenditures increases markedly on the day of check arrival (Stephens 2003).

This paper contributes to the check-effect literature by conducting a unique test using hospital admissions data on injection drug users (IDU) from the Canadian city of Vancouver. We first examine the distribution of drug overdose admissions over time as a way of identifying any link between welfare payments and drug consumption. We then address the question of whether drug users are more likely to leave the hospital against medical advice (AMA)—thereby interrupting their treatment—on the day that welfare checks are released. Finally, we test whether check day is an environmental cue that triggers drug use by using “Welfare Wednesday” as an instrument to estimate the Local Average Treatment Effect of leaving AMA on subsequent readmission and the likelihood of a subsequent drug overdose.

II. Background

The situation in Vancouver’s Downtown Eastside is dire. In 1993 drug overdoses from heroin became the leading cause of death among men 30–49 years of age (Cain 1994). Through the 1990s, Vancouver has consistently had the highest over-all levels of overdose deaths in Canada with over 300 in 1998 alone and over 2000 between 1991 and 1998 (Riley 1998). According to a statement by the British Columbia General Coroner, Vancouver’s overdose death rate is “very likely” the high-est in North America (The Washington Post1997). The prevalence of HIV infection among Vancouver’s injection drug users is conservatively estimated to be 25 percent (McLean 2000) with some articles in the media quoting a rate as high as 50 percent (for instance, The Canadian Press 2000). Even with the conservative estimates, Vancouver is widely believed to have one of the highest levels of HIV infection among injection drug users in North America, and similar to cities such as New York and Bangkok (Riley 1998; Stevens 2000). The federal government is starting to take

Riddell and Riddell 139

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notice: The Health Minister announced in July 2001 a $7 million commitment to the city of Vancouver for community health promotion efforts.

The IDU population has a very high incidence of welfare receipt and hospital use, as will be discussed further in Section IV. Under B.C. Benefits Legislation, rates for income assistance for an employable single adult effective October 1, 2002 include $325 for shelter and $185 for support allowance.2 Individuals who have a severe mental/physical impairment (requires extensive assistance to perform daily tasks, additional transportation costs, special diets) that is confirmed by a physician to con-tinue for two years are eligible for full disability benefits (Level II). Rates for Level II benefits effective October 2002 include $325 for shelter and $461.42 for support allowance, which is the third highest in the country after Ontario and Alberta. Many IDUs are eligible for Level II Disability due to advanced HIV/AIDS-related illnesses. Welfare checks are distributed once a month in the province of British Columbia, usually on the last Wednesday. In the “Downtown Eastside” of Vancouver—home to around 10,000 IDUs (McClean 2000)—the notion that “Welfare Wednesday” is associ-ated with a number of days of binging on heroin, cocaine and alcohol has reached myth-ical proportions in both the media and medmyth-ical communities. Indeed, the weekend following Welfare Wednesday also is known as “Mardi Gras Weekend” (Stevens 2000).

III. Welfare Checks and Drug Consumption

In the United States, over 200,000 individuals received SSI/DI payments based on “drug and alcohol addiction” in 1996 (Davies et al. 2000). Following P.L. 104-121—passed on March 29, 1996—the Social Security Administration ceased to issue SSI/DI payments to individuals whose drug or alcohol addiction (DA&A) was a signifi-cant factor in their disability. As well, as of January 1, 1997, eligibility for SSI/DI was terminated for DA&A cases.3It is noteworthy that even prior to 1996, receiving SSI/DI in the United States was more difficult for individuals with a drug or alcohol dependency than in Canada. Since 1972, DA&A beneficiaries could only receive payment through a representative payee and had to participate in a treatment program (if appropriate treat-ment was available). Then in 1994, a three-year time limit on SSI/DI benefits was placed on DA&A beneficiaries. Outside the realm of Social Security, the 1996 “Gramm Amendment” imposed a lifetime ban on Food Stamps and Temporary Assistance for Needy Families (known as TANF, formerly the Aid to Families with Dependent Children program) to individuals with a drug felony conviction.

Essentially, the rationale behind the 1996-97 changes was that the federal govern-ment was subsidizing substance abuse in American communities in the first week of the month (Cohen 1994). However, as noted by Catalano and McConnell (1999), very

2. See the B.C. Ministry of Human Resources website (www.mhr.bc.ca) for further information on B.C.’s income assistance laws. In September 2003, sweeping changes were made to the Income Assistance laws in British Columbia. The data in this paper cover the 1995 to 2000 period where no major amendments were made.

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little scholarly research was cited in the debate with the notable exception of Shaner et al. (1995). Those authors study a very small, and highly specialized, cohort of 105 schizophrenic cocaine users in Los Angeles and find that drug use, psychiatric symp-toms, and hospital admissions all increased around the first of the month—when wel-fare checks (in the United States) are released.4

From a theoretical perspective, the possible relationship between welfare checks and drug consumption has various components. One component is the pure income effect story. Based on Cohen (1994), it is clear that the 1997 change in U.S. law was predi-cated on the notion that terminating SSI/DI benefits may lead to reduced drug con-sumption. To date, we are unaware of any study that has convincingly examined the income effect side of the substance use-welfare debate. Indeed, we are unable to directly test for income effects in this paper since we do not observe an individual’s benefit level. In this paper we address the question of whether the distribution of welfare pay-ments affects the distribution of drug consumption over time. The addiction literature suggests that the key reason why we may see spikes in drug consumption in the days following welfare day is due to the link between environmental cues and the neuro-physiology of drug use (for literature reviews see Gawin 1991 for cocaine addiction and Goldstein 1991 for heroin addiction).5

The more rapid a drug’s onset of action, the greater the euphoria users experience and the more they “like” the drug. Cocaine and heroin are especially rapid in their delivery to the brain, particularly when administered via smoking and intravenous methods. Moreover, in the case of cocaine, the rate of removal from the brain is extremely fast.6 Thus, there is something inherent to using cocaine that makes it more likely an individ-ual will want to use the drug again immediately after a hit. This is believed to be a key reason why individuals who become addicted to cocaine tend to move toward a “binge and crash” pattern of use, in addition to moving from intranasal or oral administration toward smoking and intravenous administration.7Individuals who progress toward a fully developed cocaine addiction will typically start in the evening, readminister cocaine every 10 to 30 minutes, transition toward high-dose, and use the drug continu-ously until all cocaine on hand is gone with binges lasting as long as 200 hours.8

What factors trigger a cocaine binge? The addiction literature overwhelming points to the importance of environmental cues. Addicts experience powerful urges to use drugs when they encounter certain cues that they associate with prior drug use. Such conditionedcues can include everything from mood states, to certain people or certain

4. However, Catalano and McConnell (1999) examine psychiatric admissions at a San Francisco hospital from 1994 to March 1998 and find strong evidence of check effects both before and afterthe change in law. 5. Liquidity constraints also could be a factor. IDUs are likely poor savers with limited access to credit markets. 6. In essence, cocaine provides a very powerful but very brief euphoric effect. In fact, cocaine also blocks the removal of dopamine from the brain, resulting in an accumulation that causes continuous pleasurable stimulation of brain cells. Cocaine results in a high that last about 20 minutes with a half-life of about one hour (Gawin 1991).

7. Of course, not all individuals who try cocaine become addicts; in fact, it is estimated that only between 10 to 15 percent of those who try cocaine (usually intranasally) become addicted (Gawin 1991). And then, not all addicts progress to the fully developed addiction stage characterized by high-dose, smoking/ intravenous delivery, long duration binging.

8. In animal-based experiments, continuous and rapid cocaine use is observed if access is unrestricted, with the animal typically dying in approximately two weeks. When access is restricted, an animal will “press a lever a thousand times for a single cocaine dose” (Gawin 1991).

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places. (Bernheim and Rangel 2004 provide an extensive review.) This notion implies that once a culture of drug use has been established—such as in the Downtown Eastside of Vancouver—it would be very difficult for a user to resist drug use once exposed to that culture, even if their intention was not to use drugs. Indeed, Welfare Wednesday itself may have become an environmental cue for some IDUs. As will be discussed later, a strong majority of the nonhomeless drug users in the data live in the Downtown Eastside, making it difficult for them to escape environmental cues.9

Heroin addiction typically involves a different pattern of use; specifically, a smooth consumption pattern that resembles someone taking medication. Nevertheless, heroin binges do occur, and also have been linked to environmental cues. That said, for many IDUs—who, because they have made the transition toward intravenous use, have well-advanced addictions—heterogeneity in drug type is not a critical issue because they either use cocaine and heroin simultaneously (as in a “speedball”), in the same session (along with other drugs), or specialize in either cocaine or heroin for a short time, but regularly switch back and forth. For example, cocaine users typically com-bine cocaine with other drugs—primarily heroin, alcohol, and marijuana—in order to mitigate certain negative characteristics of the cocaine experience, especially anxiety. Environmental cues are a key component of recent addiction theories. For instance, Laibson (2001) incorporates “taste” shocks into the Becker and Murphy (1988) rational additional model where a taste shock is a cue-driven craving. With taste shocks, Laibson’s theory can explain the phenomenon of addicts—after exiting rehab or incarceration—being more likely to resume use if they return to their original envi-ronment. In Bernheim and Rangel’s (2004) theory of addiction, drug-related cues “trick” the brain into skipping its usual step in the decision-making process where every option is considered and appropriate expectations about future consequences are considered. Instead, the brain only considers the pleasurable aspects of drug use. The authors’ theory explains many of the stylized facts of drug use including “ran-dom” binges, and the importance of heterogeneity in drug type and method of deliv-ery. Both the above theories are consistent with binging on check day provided that Welfare Wednesday either explicitly or implicitly leads to cue-driven cravings.

There is a case to expect binging after welfare day. The remainder of the paper tests this hypothesis. A key difference between a spike in drug consumption on welfare day relative to a spike in overall consumption on welfare day (as in Stephens 2003) is that “lumpy” drug consumption is likely more harmful than “smooth” drug consump-tion—and thus there may be a role for public policy. For instance, the likelihood of overdosing may be greater when drugs are consumed in a binge and crash pattern.10

9. Moreover, it is widely believed that most homeless IDUs live in the Downtown Eastside.

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As well, the long-term health consequences to the individual may be worse if they systematically use drugs in a lumpy manner (Warner-Smith et al. 2001). Evidence from (nonfatal) heroin overdose cases suggests that such users may suffer permanent pulmonary impairment, and an increased probability of developing pneumonia. For heroin and cocaine, there is evidence that users suffer permanent cognitive impair-ments and muscular complications proportional to their overdose experiences. Finally, lumpy drug use deteriorates your state of awareness and thus increases the chance of sharing needles (Mandell et al. 1994)—a key risk factor in contracting HIV.

IV. Data

The data were compiled from the records of St. Paul’s Hospital, Vancouver’s only downtown hospital, which almost exclusively handles the city’s injection drug user population. The final data were based on two sources: the chart file and the administrative file, which are merged using an individual’s identification num-ber. When a patient is admitted to the hospital for the first time they are given an iden-tification number. This ideniden-tification number is unique to every individual and can be used to track an individual over time if they have subsequent admissions.

The administrative file contains the individual’s personal information such as name, address (if they have a fixed address, indicates homeless otherwise), date of birth, gen-der as well as the identification number. The administrative file also contains impor-tant medical information not necessarily specific to a particular admission/discharge such as whether the patient is an injection drug user and HIV status. Each time a patient is admitted they are given a chart, which in addition to the identification num-ber, contains the information relevant to that particular admission such as the patient’s diagnosis, length of stay, and discharge status (planned discharge versus AMA). Our data include all admissions from injection drug users, and thus can be considered a census of IDU admissions to St. Paul’s Hospital. Medical records staff (employees specialized in the coding of medical statistics) flag patients as being IDUs upon chart review if they have injected drugs within the last six months (that is, the IDU flag is a self-reported measure combined with an assessment and diagnosis by an addiction medical specialist).

The data cover fiscal year 1996 (March 1 1995 to February 29 1996) to fiscal year 2000, and includes a total of 4,760 records from 2,432 individuals. Summary statis-tics on all relevant information available in the data are presented in Table 1. There are 1491 individuals for whom we have no longitudinal information (only one admis-sion). For the 941 individuals that had multiple admissions over the sample period, the average number of observations per person is 3.5 with the minimum being two observations and the maximum being 23.

A striking feature of the data is the AMA rate—the proportion of cases involving a patient leaving against medical advice—which, at 26 percent, is extremely high. That is, one in every four admissions involves an IDU failing to complete treatment and leaving the hospital against medical advice. This AMA rate is far greater than the AMA rate for St. Paul’s Hospital as a whole, which is 1.3 percent—similar to U.S. evidence on AMA rates (for instance, 1.2 percent in Smith and Telles 1991; 2.2 per-cent in Jeremiah, O’Sullivan, and Stein 1995).

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Table 1

Length of stay in hospital (in days) 9.07

(15.09)

Readmitted to hospital within 2 weeks 0.111

(0.310)

Sample size (number of admissions/discharges) 4,760

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The main drawback of the data is that we do not observe whether an individual is a social assistance recipient. However, an additional advantage of testing for check effects among IDUs in Vancouver is that we know from other sources that social assistance receipt is likely to be extremely high in our data. For example, in June 1999, of the 405 IDUs in the provincially funded Vancouver Injection Drug-User Program, 89 percent were welfare recipients (Palepu et al. 2001). Unfortunately, it is not possible to match our data with social assistance records from the Ministry of Human Resources due to confidentiality concerns.

We also conducted our own test of social assistance receipt. We randomly chose a week in June 2002 and asked a welfare caseload worker from the Ministry to assist us in a survey of all IDUs that were admitted to the hospital on that day. We first col-lected information (including detailed interviews) on the IDUs admitted, and then called the worker at the Ministry the following week to confirm the relevant social assistance information. Of the 25 IDUs admitted, 22 were receiving Assistance (two of three not receiving assistance indicated homelessness) including four on basic, four on Disability I, and 14 on Disability II.

We also have information on housing status, which will be used in the analysis to provide a test of social assistance receipt. To collect social assistance on a consistent basis, one must have a fixed address. Based on our discussions with Ministry staff, some recipients have been cut off from support because they were unable (unwilling) to find shelter. Our own experience with this population is that the homeless IDUs do not keep the Ministry updated on information required for continuing benefits, such as efforts to find a job or alternative income sources. Moreover, British Columbia law eliminates the “comfort money” component of the support allowance payment for recipients defined as transients, which includes those without depend-ent children who have no fixed address thereby leaving such recipidepend-ents with only the food component.11

V. Welfare Wednesday and the Distribution

of Overdose Admissions

In this section, we examine the relationship between the distribution of welfare payments and the distribution of drug consumption. The dates of check arrival were obtained from the British Columbia Ministry of Human Resources. The date of check arrival must fall on the Wednesday that has at least three working days before the end of the month.12This typically amounts to the last Wednesday of the month with the exception of December, where check arrival is always the second to last Wednesday of the month due to the holidays. Including December, welfare checks are released on the second to last Wednesday in about 40 percent of cases. The Ministry releases a listing of Welfare Wednesdays (posted at local offices and the web) at the beginning of each year.

11. See Section 10 of Schedule A of the British Columbia Benefits Regulations.

12. This rule is because it takes three working days for checks to clear the bank, and thus there is time for a check to be replaced if lost or stolen.

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We use drug overdose admissions as a proxy for drug consumption, which includes all drug poisoning admissions.13Figure 1 presents the distribution of overdose admis-sions over time. A spike in overdose admisadmis-sions is seen on Welfare Wednesday through Welfare Friday; there is no evidence of a “Mardi-Gras Weekend” effect.

To further explore the pattern in Figure 1 we estimate the following probit regression:

(1) OVERDOSEit= f(γ0+ γ1WDAYSit+ γ2 Xit+ γ3 DAYit+ γ4WEEKit + γ5 MONTHit+ γ6YEARit+ µit)

where OVERDOSEequals one if the ith individual was admitted to the hospital on the tth admission with a principal diagnosis of a drug overdose, zero if any other diagnosis; WDAYS is a vector of five welfare days (Welfare Wednesday through Welfare Sunday corresponding to Figure 1) dummies; Xis a vector of demographic variables (age, gender, homeless, HIV status); DAY, WEEK, MONTH, and YEARare sets of six day-of-the week, three week-of-the-month14(last week of month, penultimate week of month, and so forth), 11 calendar-month, and four fiscal-year dummy variables respectively; and µis an error.

As noted above, Welfare Wednesday is not always during the last week of the month. In fact, about 40 percent of checks over the sample period were distributed on an earlier Wednesday. Equation 1, and other regressions estimated later in the paper, exploit this variation and allow us to address any general end-of-the-month effects unrelated to welfare checks per se.

Table 2 presents the results. The estimates are consistent with the pattern seen in Figure 1 although the Welfare Wednesday variable is just barely outside of conven-tional significant levels. The interpretation from Column 1 is somewhat unwieldy. For instance, overdoses are about six percentage points more likely to occur on Welfare Thursday relative to any other Thursday, and three (0.06−0.03) percentage points more likely to occur relative to Sunday (the omitted day). Ceteris paribus, overdoses are less likely to occur during the week relative to the weekend. A test of the equality of all day-of-the-week dummies can be rejected at the one percent level (χ2= 15.4), but an equality test on the five weekday dummies cannot be rejected (χ2= 2.0) and so we alter the specification as given in the second column. The welfare day variables now can be evaluated relative to any nonwelfare-week weekday. Overdoses admission are about five percentage points more likely to occur on a Welfare Thursday and about four percentage points more likely to occur on the Welfare Friday. No statistically sig-nificant effects are found for any other day during the welfare week. The homeless are more likely to overdose while women and HIV positive individuals are less likely.

13. The primary diagnosis is the “most responsible” diagnosis. Formally, the drug overdose category includes “poisoning by heroin,” “poisoning by cocaine,” and some other miscellaneous drug poisonings. Our drug overdose approach is thus much cleaner than in most studies where broad and generally hard-to-diagnose drug-related hard-to-diagnoses are considered (in particular, drug-related psychological hard-to-diagnoses). For our analysis that uses drug overdoses (results discussed in Section V and Section VII), we get smaller effects if we include these psychological diagnoses as overdose cases.

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Riddell and Riddell 147

0 2 4 6 8 10 12 14 16 18

M onday before

Tuesday before

Wednesday before

Thursday before

F riday before

Saturday before

Sunday before

Welfare M onday

Welfare Tuesday

Welfare Wednesday

Welfare Thursday

Welfare F riday

Welfare Saturday

Welfare Sunday

M onday after

Tuesday after

Wednesday after

Thursday after

F riday after

Saturday after

Sunday after

Figur

e 1

Distrib

ution of Drug Over

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Table 2

Estimates of the Change in Probability of a Drug Overdose Admission

Variable Specification

[1] [2]

Welfare Wednesday 0.028 0.028

(0.019) (0.017)

Welfare Thursday 0.057*** 0.048***

(0.028) (0.022)

Welfare Friday 0.039** 0.041***

(0.023) (0.021)

Welfare Saturday −0.029* −0.027

(0.012) (0.013)

Welfare Sunday 0.002 −0.003

(0.022) (0.019)

Homeless 0.026*** 0.025***

(0.011) (0.012)

Downtown Eastside postal code 0.007 0.007

(0.009) (0.009)

Other downtown Vancouver postal code −0.001 −0.001

(0.009) (0.009)

Female −0.017*** −0.017***

(0.006) (0.006)

HIV positive −0.027*** −0.027***

(0.006) (0.006)

Monday −0.028*** —

(0.009)

Tuesday −0.017 —

(0.010)

Wednesday −0.023** —

(0.010)

Thursday −0.027** —

(0.010)

Friday −0.022** —

(0.010)

Saturday 0.011 —

(0.017)

Weekend — 0.041***

(0.012)

Log likelihood −897.6 −898.9

χ2 100.5 97.9

Number observations 4,760

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There are two types of overdose cases that we do not observe in our data: (a) fatal over-doses, and (b) nonfatal overdoses that do not result in a hospital admission.15One of the current views—with considerable empirical support—in the literature on drug overdoses is that most fatal drug overdoses could be prevented because fellow users are unlikely to call for an ambulance (Warner-Smith et al. 2001). For the current analysis, unobserved overdoses are problematic if the distribution of overdoses that result in a hospital admis-sion differs from the distribution of unobserved overdoses. Overall, we believe this is unlikely; however, one possibility is that the spike in overdose cases in Figure 1 is due to a greater police presence in the Downtown Eastside during the welfare week and thus a higher probability of the police finding an individual who has overdosed and calling paramedics (resulting in a hospital admission that would not normally occur). However, the argument could go the other way—with a greater police presence in what is a very small geographic area, users may be even less likely to call for an ambulance.

There appears to be no previous evidence on the link between welfare day and either drug overdose hospital admissions or fatal drug overdoses. Phillips, Christenfeld, and Ryan (1999) examine U.S. death certificates from 1973 to 1988 and find an overall increase of 1 percent in the number of deaths in the first week of the month (the U.S. wel-fare week) relative to the last week of the previous month, and a 14 percent increase in substance abuse-related deaths (which includes a wide variety of causes of death). From Canada, there is only evidence from 1993 for British Columbia where Verheul, Singer, and Christenson (1997) find a 50 percent increase in coroner-reported deaths on welfare day. The authors also find increases in detox center admissions and 911 calls on welfare day.

VI. Welfare Wednesday and the Distribution

of Hospital Discharges

In this section, we examine whether Welfare Wednesday induces IDUs to leave the hospital against medical advice—interrupting their treatment. Figure 2 shows the distribution of discharges (AMA discharges relative to planned discharges, which together equal total discharges) over the sample period. A clear spike is observed on the day welfare checks are released. We see an increase on the Tuesday and Thursday as well, but clearly the Wednesday effect dominates. The distribution of planned discharges is very different on the weekends. In fact, the AMA rate is as high on the Welfare Saturday and Welfare Sunday as it is on Wednesdays, but this is entirely due to substantially fewer planned discharges on the weekend. The fall in planned dis-charges is due to two factors: (a) there are fewer staff working on the weekend who are involved in discharge planning for this population (such as social workers and com-munity liaison nurses), and (b) IDUs require considerable comcom-munity support (home-care nursing, pharmacies for methadone) that may be unavailable on the weekend.

15. The extent of these unobserved overdoses may be very large, but it is very difficult to estimate the number of nonfatal, nonhospital admission overdoses per year. In 1998, there were 300 fatal overdoses in British Columbia. In our data there were 65 overdose admissions in 1998. This is only overdose admissions for St. Paul’s Hospital, but, as noted, the latter treats a majority of the drug users in the province. Based on the overdose liter-ature (see Section III), in a typical cross-section of IDUs about 20 percent report overdosing (nonfatally) in the preceding 12 months. Recall that it is believed that there are approximately 10,000 IDUs in Vancouver alone.

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The Journal of Human Resources 150 M onday before

Tuesday before

Wednesday before

Thursday before

F riday before

Saturday before

Sunday before

Welfare M onday

Welfare Tuesday

Welfare Wednesday

Welfare Thursday

Welfare F riday

Welfare Saturday

Welfare Sunday

M onday after

Tuesday after

Wednesday after

Thursday after

F riday after

Saturday after

Sunday after

P

la

n

n

ed

A

M

A

s

e 2

ution of Disc

har

g

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We now further examine the pattern in Figure 2, and exploit the homeless variable to test for social assistance receipt. To do so, we estimate:

(2) AMAit= f(β0+ β1WWit+ β2 WWit*HOMELESSit+ β3 Xit+ β4 HOSPITALit + β5 DAYit+ β6 WEEKit+ β7 MONTHit+ β8 YEARit+ εit)

where AMAequals one if the admission involved the patient leaving AMA, zero if a planned discharge; WWis a dummy variable indicating a check arrival day, zero if any other day; HOMELESSequals one if the patient reported no fixed address; Xis the same vector of demographics used in Equation 1; HOSPITALis a set of medical-related controls including the hospital ward where the individual was treated, and a set of dummies for the nature of the illness associated with the admission16; DAY, WEEK, MONTH, YEAR are the same sets of day-of-the-week, week-of-the-month, calendar-month and fiscal year dummies as in Equation 1; and εis an error.17

We also estimate Equation 2 using the “welfare week” instead of the Wednesday (the Monday through Sunday of the welfare day). We might expect check day to induce some people to leave on the Tuesday or the Thursday through Sunday of welfare week, although Figure 2 shows that virtually all of the action is on the Wednesday.

Table 3 presents the results. With the day-of-the-week dummies, the results in the first column indicate that there is nearly a 16 percentage point increase in the likeli-hood of an AMA on Welfare Wednesday relative to any other Wednesday, and a six percentage point increase (0.16−0.10) relative to Sunday (the omitted day-of-the-week). When estimated using the welfare week, the estimated marginal effect on the welfare week dummy is 0.07.

Our AMA check effect results are qualitatively similar to the findings of Anis et al. (2002). The authors estimate a regression similar to Equation 2 using similar data to us from the same hospital; however, they only have access to HIV positive patients (IDUs and nondrug users).18 Leaving AMA and income assistance receipt are rare among the non-IDU HIV-positive population, and so their choice of sample is

16. The diagnosis variables listed in Table 1 indicate the principal diagnosis and are used, along with the length of stay variable, to control for the severity of the illness. The diagnoses are categorized using the World Heath Organization”s International Classification of Diseases (9th Revision, Clinical Modification ICD-9), which are used to classify illnesses for morbidity and mortality data, indexing medical records, med-ical care review and so forth. The codes were aggregated in appropriate cases into higher order diagnoses; in particular, the diagnoses selected account for the most responsible diagnoses for this patient population. The omitted category is “other” diagnosis.

17. As with Equation 1, we use a simple probit model to estimate Equation 2. While there may be unob-served factors such as drug type (cocaine users are more likely to leave AMA since no analogous medica-tion to methadone exists for cocaine, but users combine drugs frequently so its importance is unclear) that are in the error term we can think of no convincing story for an unobserved factor that also is correlated with the explanatory variables. In any event, for robustness purposes, we estimate Equation 2 using random and fixed-effects estimators as well. The disadvantage of the latter is that we lose observations for those who only had one admission as well as those that had no variation in the AMA variable over time. For brevity, the ran-dom and fixed-effects estimates are not presented, but qualitatively—and quantitatively in cases where we can compare marginal effects such as with the random effects model—the results are unchanged. 18. The authors also restrict the sample to the unit of analysis being the individual (that is, using the 1997–99 period, they choose the first admission seen in the data for a given individual), and so end up with only 448 IDU admissions, and only 36 Welfare Wednesday admissions (and do not exploit the variation of where check day falls within the month).

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Advice

Variable Specification

[1] [2]

Welfare Wednesday 0.157*** —

(0.045)

Welfare Wednesday * homeless −0.122** —

(0.059)

Welfare week — 0.069***

(0.024)

Welfare week * homeless — −0.056

(0.037)

Homeless 0.105*** 0.115***

(0.024) (0.026)

Downtown Eastside postal code 0.093*** 0.093***

(0.022) (0.022)

Other downtown Vancouver postal code 0.044** 0.045**

(0.022) (0.022)

Female 0.015 0.016

(0.014) (0.014)

HIV Positive 0.032** 0.034**

(0.015) (0.015)

Monday −0.072*** −0.074***

(0.024) (0.024)

Tuesday −0.088*** −0.089***

(0.024) (0.024)

Wednesday −0.105*** −0.077***

(0.024) (0.024)

Thursday −0.062*** −0.063***

(0.025) (0.025)

Friday −0.124*** −0.126***

(0.022) (0.022)

Saturday 0.043 0.041

(0.035) (0.034)

Last week of the month 0.008 −0.016

(0.018) (0.023)

Third week of the month −0.044** −0.052***

(0.018) (0.018)

Second week of the month −0.038** −0.028

(0.016) (0.021)

Log likelihood −2567.2 −2557.3

χ2 365.3 385.2

Number of observations 4,760

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somewhat peculiar.19They find a positive correlation between welfare day and the AMA rate, but give no sense—either in levels as in Figure 2 or as a marginal effect— of the magnitude of their result.

The day dummies indicate that AMAs are more likely to occur on the weekend. A hypothesis test on the equality of the six day dummies is rejected at the one percent level (χ2= 46.9), and an equality test for the five weekday dummies can be rejected as well (χ2= 13.1). However, as noted, the difference between the weekday dummies and the weekend (there is no statistical difference between Saturday and Sunday) is attributa-ble to the nature of hospital discharge planning. AMAs are evaluated relative to planned discharges, which fall on the weekend because of reduced staff and community-based clo-sures. AMAs do not respond in a similar way on the weekend since it is believed that the decision to leave AMA has very little to do with the hospital staff or community support. Ceteris paribus, the homeless are more likely to leave the hospital AMA, and the interaction term suggests that there is only a very small check effect for the homeless—as anticipated given that welfare receipt should be quite low for this group.20HIV positive IDUs are more likely to leave AMA. AMAs are more likely to occur in the last week of the month and the first week of the month (the omitted cat-egory) relative to the two middle weeks (an equality test on the two middle weeks can-not be rejected; χ2= 0.07).

VII. Welfare Wednesday as an Environmental Cue

As a final test of the link between welfare payments and drug use, we investigate whether individuals induced to leave AMA by Welfare Wednesday are more likely to subsequently use drugs than IDUs who leave the hospital on a planned discharge. We implement this test by estimating the effect of leaving AMA on the likelihood of rapid readmission to the hospital (readmission within 14 days) and the likelihood of that readmission being due to a drug overdose using Welfare Wednesday as an instrument for leaving AMA. Specifically, we estimate:

(3) READMISSIONit+1= f(α0+ α1 AMAit+ α3 Zit+ υit)

(4) OVERDOSEit+1= f(ζ0+ ζ1 AMAit+ ζ3 Zit+ ηit)

where READMISSIONequals one if the ith individual was admitted to the hospital on the t+1th admission within 14 days following the tth admission; OVERDOSEequals one if the ith individual was admitted to the hospital on the t+1th admission with a primary diagnosis of a drug overdose, AMA is as previously defined; Zare vectors of controls which include all information in Equation 2 except the Welfare Wednesday variable; υand ηare disturbance terms.

19. For instance, the AMA rate for non-IDUs in their sample is 1.3 percent—identical to the AMA rate for the hospital as a whole.

20. We also tested for welfare receipt by estimating Equation 2 for the nonhomeless and included an interac-tion term between welfare Wednesday and HIV status. HIV positive IDUs may be more likely to be on Disability I or II, which yield a much higher benefit rate (see Section II). But, the results indicate that the AMA check effect is the same for HIV positive patients as for HIV negative patients. There may simply be insuffi-cient variation among the nonhomeless in welfare benefits; the results from our interviews support this conclu-sion (recall 18 of the 22 patients on income assistance were receiving Disability I or II with only four on basic).

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Note that Equations 3 and 4 do not require “complete spells.” Individuals are defined as equaling zero (for both dependent variables) if they have not been readmitted to the hospital (or with an overdose) within 14 days of the admission of interest. That would therefore include individuals who were readmitted, for example, two months follow-ing the initial discharge as well as individuals who were never seen in the data again. As a robustness check for Equation 2, we estimate the readmission regression using alternative definitions of the dependent variable (such as readmission within seven days and readmission within one month), and also by using a set of duration models.21 Recall that many overdoses (all fatal but also most nonfatal) are unobserved to us; some individuals who leave AMA may overdose, but not be subsequently admitted to the hospital and thus do not appear in the data. Unlike the analysis in Section V, where it is difficult to be certain what the implications of unobserved overdoses are for the results, our estimates here will understateany check effect.

We use Welfare Wednesday as an instrument for AMA. It is plausible to believe that the impact of leaving AMA on future outcomes such as drug use is not the same for everybody. In the presence of heterogeneous impacts, we cannot estimate the aver-age causal effect of leaving AMA for the IDU population. We can, however, estimate the Local Average Treatment Effect (LATE) of leaving AMA on subsequent readmis-sion and overdose outcomes, where the LATErefers to the subset of the population whose behavior was influenced by Welfare Wednesday (Imbens and Angrist 1994; Heckman 1997).

There are three conditions that must hold for a consistent LATEestimator: (a) Welfare Wednesday has a direct effect on leaving AMA, (b) leaving the hospital on a Wel-fare Wednesday only affects readmission/overdoses through its effect on increasing the probability of leaving AMA, and (c) the monotonicity assumption (Imbens and Angrist 1994). Figure 2 offers evidence in favor of the first condition. The second condition should be met given that the date on which you become ill (that is, whether you are in the hospital around a Welfare Wednesday) is an exogenous event. Alternatively stated, we believe there is no reason to expect a relationship between the day of the week that you leave the hospital on a planneddischarge and subsequent health outcomes. It might be tempting to think that individuals leaving the hospital on a planned discharge on Welfare Wednesday are more likely to binge following welfare receipt. But, imagine two IDUs, both of whom receive social assistance—one who is properly discharged on the Monday preceding Welfare Wednesday, and one who is properly discharged on the Welfare Wednesday. Both individuals will receive their welfare payment on the Wednesday, both will be in the community following receipt, and both may be subject to cravings. We see no plausible reason to expect that the individual properly discharged on welfare day is any more or less likely to binge than the individual properly dis-charged on the Monday preceding welfare day. The third condition implies that Welfare Wednesday can only induce people to leave AMA who would not otherwise, and does not induce people to stay in the hospital (until a planned discharge) who otherwise would leave AMA—the latter of which seems highly unlikely.

Table 4 presents the results. Beginning with the readmission regression, note the mean of the dependent variable: 11 percent of IDUs are readmitted to the hospital

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Riddell and Riddell

155

Table 4

Estimates of the Change in Probability of Readmission

Overdose Readmission of Length of Stay

Variable Readmission Readmission Same Diagnosis on Readmission

Probit IV Probit Probit IV Probit Probit IV Probit OLS IV

Left against medical advice 0.133*** 0.354*** 0.006** 0.072*** 0.092*** 0.231*** −0.979* 8.51***

(0.013) (0.101) (0.002) (0.016) (0.010) (0.050) (0.603) (2.31)

Homeless 0.008 −0.020 −0.001 −0.006 0.004 −0.013 −1.69* −3.22**

(0.018) (0.021) (0.005) (0.003) (0.011) (0.011) (1.00) (1.37)

Female −0.014* — −0.005* −0.004 −0.004 −0.005 0.914* 0.809

(0.008) 0.017** (0.003) (0.003) (0.005) (0.006) (0.534) (0.587)

(0.009)

HIV positive 0.054*** 0.047** 0.001 −0.002 0.015*** 0.015** 0.822 0.486

(0.010) (0.011) (0.002) (0.002) (0.005) (0.007) (0.621) (0.648)

Log likelihood −1515.1 −1582.3 −245.8 −239.7 −863.1 −931.4 — —

χ2 295.2 161.0 63.5 75.7 347.8 211.2

Adjusted R2 0.08 0.08

Number of observations 4,760 2,328

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within two weeks (8 percent within one week and 16 percent within one month). The simple probit model indicates that leaving AMA increases the likelihood of readmis-sion within two weeks by 13 percentage points.22However, the IV estimate is much higher with an estimated impact of around 35 percentage points.23

From the overdose regression, a similar story emerges. Based on the simple probit, leaving AMA implies just under a one percentage point increase in the likelihood of a subsequent overdose, which is understated given that there are likely individuals who leave AMA and subsequently overdose but are not admitted to the hospital. When we instrument for AMA, the LATE is seven percentage points.

Overall, the LATE estimates suggest that those IDUs induced to leave AMA because of Welfare Wednesday are more likely to subsequently binge on drugs than IDUs leaving the hospital on a planned discharge.24It is possible that this result is picking up a type of income effect; specifically, it is the IDUs receiving welfare pay-ments who are induced to leave. However, we are confident that social assistance receipt is extremely high among this population. We believe the more convincing explanation is that Welfare Wednesday is an environmental cue for some IDUs.

A seven percentage point increase in the probability of an overdose due to Welfare Wednesday strikes us as a troubling result—particularly if this finding can be extrap-olated to those individuals induced to leave AMA by Welfare Wednesday who over-dose fatally or overover-dose nonfatally but survive without admission to the hospital. However, an important question remains: what can be said about the remaining 28 (35 − 7 = 28) percentage points of the Welfare Wednesday induced readmission effect? The fact that one in ten IDUs are readmitted to the hospital within two weeks (almost two in ten within one month) itself is striking, and illustrates the strain on the health care system that this population creates. The further finding that Welfare Wednesday may generate a situation where IDUs have a one in three chance of return-ing to the hospital within two weeks is, we believe, astoundreturn-ing. But to fully appreci-ate the implications of the results we need to know how much of the 28 percentage points is binging versus planned behavior unrelated to drug use such as paying rent.

To address this issue, we offer the following evidence. First, we asked the Ministry of Human Resources about check theft—an area of concern in the United States.25 About 1 percent of checks go missing, which includes both lost checks and stolen checks. Unfortunately, the Ministry was unable to provide us with the incidence of missing checks for IDUs (or for Disability Level II recipients for example), which could be much higher. Thus, we can make no concrete conclusions about IDUs want-ing to pick up their checks for fear of theft, although the incidence of theft appears much lower than in the United States.

22. The estimate using a one week definition is 11 percentage points. The result that patients who leave AMA are readmitted sooner than planned discharge cases has been shown previously in both Canada and the United States. Anis et al. (2002) estimate the latter relationship and find a positive correlation, but do not tie readmission (or any other outcome such as drug overdose for that matter) to Welfare Wednesday. 23. The results are very similar if we use a linear probability model. For the linear IV case, the F-stat from the first stage is 19.03.

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We also have some evidence on the incidence of direct deposit. While the Ministry told us that, overall, direct deposit is very common in British Columbia, based on our experience with this population, and our interview-based study (none on direct deposit) the use of direct deposit is rare among IDUs. In fact, many do not have bank accounts. It may therefore be the case that individuals leave the hospital to cash their check, pay bills, and then return thereafter without using drugs. On the other hand, due to variation in when Welfare Wednesday falls within the month, our results con-trol for general end-of-the-month effects such as paying rent. The findings also indi-cated that individuals were no more likely to leave AMA in the last week of the month relative to the first week of the month. Moreover, in our interview-based study, 16 of the 22 IDUs on income assistance had the housing component of their check mailed directly to their hotel, an option offered by the Ministry.

One final complexity is that B.C. law has a provision where the support component of an individual’s income assistance payment can be reduced by time spent in the hos-pital. The Ministry appears to rely entirely on patients themselves informing the Ministry of a hospital stay. As far as we can tell, there is no enforcement mechanism. In particular, because there is no information-sharing agreement between hospitals and the Ministry of Human Resources, hospital staff are not permitted to notify welfare workers that a patient is in the hospital without that patient’s consent. Nevertheless, if the threat is viewed as credible, this could be an additional reason for the AMA check effect.

As a final piece of evidence, we conduct two more analyses. First, we estimate the same regression as in the above cases of Equations 3 and 4, but with a dependent vari-able that is defined as equaling one if the individual is readmitted to the hospital with the identical principal diagnosis as they had on the initial visit.26If planned behavior unrelated to drug use is driving the results, we should see patients readmitted with the same medical condition. The third column of Table 4 presents the results. We see that 23 of the remaining 28 percentage point effect is driven by individuals being re-admitted with the identical principal diagnosis. But these results can only be taken so far. For some medical conditions—such as pneumonia, endocarditis (heart valve infection), osteomyelitis (bone infection), and septicemia (blood infection), among others—the illness is so serious that the individual’s behavior outside the hospital is largely irrelevant: They will be back in the hospital with the same condition regard-less of their drug use or lack thereof.

To further pursue the above results, we examine length of a stay in the hospital, a common proxy in the medical literature for the severity of an illness. In particular, we ask whether individuals induced to leave AMA by Welfare Wednesday have longer lengths of stay on their next admission. For this analysis, we condition on being re-admitted (that is, only uses the 941 individuals for whom we have longitudinal infor-mation, which amounts to 2,328 admission observations).

The final column of Table 4 presents the results. From OLS estimation, we see a negative correlation between leaving AMA and the length of your next hospital stay— not a surprising result given that patients who leave AMA are, in general, completing

26. For this analysis, we use the full ICD codes (up to the five-digit level) as opposed to the broad categories defined in Table 1 or more aggregated ICD coding, and thus the dependent variable only equals one if the principal diagnosis is truly identical.

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treatment relating to their prior admission. The IV regression, however, yields very different results. Patients induced to leave AMA by Welfare Wednesday have rela-tively longer hospital stays—by around eight days, a very large effect (relative to the mean of around nine days). This suggests that such individuals are engaging in behav-ior that negatively affects their health status. Drug use, and particularly heavy drug use, is a prime candidate. For instance, many IDUs have a central-venous catheter (an intravenous line leading to the heart) in place for antibiotics. Individuals leaving the hospital AMA would still have the central-venous catheter in place, and would likely use this line if they were to inject drugs, thereby exposing themselves to complica-tions such as further infection or an air embolism (air in the blood stream, which can be fatal).27

Overall, we do not have sufficient evidence to conclude whether any of the remaining 28 percentage points is of drug-related policy concern or is purely a check administration issue, but the length of stay findings suggest the former.28 It should be emphasized that even if all of the 28 percentage points is planned behav-ior unrelated to drug consumption, attention from policy-makers is still required. Many of the patients involved have a severe medical condition, and thus even a few days out of the hospital can be highly problematic or fatal. Moreover, there is a pub-lic cost, given that the AMA will likely cause a setback in the patient’s recovery leading to a longer overall stay in hospital than would have otherwise occurred. As well, the 26 percent AMA rate is a constant source of frustration and morale prob-lems among hospital staff.

VIII. Summary and Policy Discussion

This paper conducted a test of check effects by using hospital data on injection drug users from the Canadian city of Vancouver, which arguably has one of the worst IDU epidemics in the world. We find an increase in: (a) the likelihood of an overdose in the days following check day, and (b) the likelihood of leaving AMA on check day. Using the date of check arrival as an instrument, we then estimate the Local Average Treatment Effect of leaving AMA on subsequent readmission. The results indicate that patients induced to leave AMA by Welfare Wednesday are readmitted much sooner and are more likely to overdose relative to IDUs properly discharged.

The results are consistent with recent addiction theories, including Laibson (2001) and Bernheim and Rangel (2004), which emphasize the robust result from the med-ical literature that environmental cues trigger drug consumption and binges. Moreover, some drugs—particularly cocaine when used intravenously—are inher-ently prone to binging behavior; users will be hard-pressed not to spend all money on hand once consumption begins.

Given that there are private and public costs involved in the welfare check and drug consumption link, there may be a role for public policy. Based on the theory of why

27. This is principally because IDUs have systematic difficulty finding useable veins.

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binges occur there appears to be two main legislative possibilities—that would be viable for most Canadian provinces—for individuals with a primary diagnosis relat-ing to drug abuse. One policy response would be to “break” the Welfare Wednesday environmental cue by issuing checks in a different way. For instance, checks could be distributed on the individual’s birthday (irony aside, the Ministry has suggested to us that they may experiment with this option). If Welfare Wednesday is a sufficiently important cue, this could be an effective policy response.

On the other hand, it may be the case that it is simply access to funds that ultimately matters. A second policy response would then be to split the monthly welfare payment into several separate payments.29 The most recent evidence on drug prices in Vancouver is from 2002 when the median price per “point” (single shot) for injection-based cocaine was $20 and for injection-injection-based heroin $10 (Wood et al. 2003). Recall that, in 2002, an individual on basic income assistance received $185 in (nonhousing) support money while individuals on full disability (level II) received about $460 in support.30It is generally believed that a majority of this population is on full disabil-ity due to advanced HIV. In the case of cocaine, once addiction is relatively developed and users have made the transition to high-dose and long-duration use, the typical pat-tern of use is readministering every ten to 30 minutes for a period ranging from four to 24 hours (Gawin 1991). If we use the median pattern of use (20 minutes, 14 hours) a back-of-the envelope calculation shows that a cocaine user on full disability cannot currently afford their “optimal” amount following welfare day. Splitting the check into several smaller payments may be a promising way of reducing binging.

Other, more costly policy responses in the form of money management programs have been suggested. For instance, sophisticated programs exist in the United States ranging from cases where patients can earn back social security benefit credits after demonstrating money management skills and subject to participation in treatment pro-grams to cases where an arm’s length representative manages the money for the patient. However, the scope of such programs is limited at this time, and we are unaware of any systematic study of them. Due to the evidence in favour of check effects and the extent of the IDU epidemic in Vancouver—as well as increasing IDU-related activity in other major cities in Canada, Australia, Europe, Asia and the United States—some form of policy response and subsequent evaluation merits serious consideration.

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30. This policy option is not feasible for users who only collect basic support since it would be very difficult to connect them to drug use. In the case of Disability I and Disability II a primary diagnosis must be made.

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Gambar

Table 1
Figure 1Monday before0
Table 2
Figure 20100Monday before20406080
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