Substance Use Patterns and Opioid Abstinence Rates Among Patients on Methadone Treatment at a Referral Hospital in Nairobi, Kenya.

doi:10.21203/rs.3.rs-3742901/v1

Background

Several evidence-based assessments show that the Methadone Maintenance Treatment (MMT) is beneficial. However, patients using multiple substances at the time of enrolment has affected the outcomes of the program. Studies have indicated that patients continue to use opioids and other substances while in the program. We aimed to determine substance use patterns and opioid abstinence rates among patients who were attending the Medically-Assisted Therapy Clinic at Mathari National Teaching and Referral Hospital, Nairobi.

Methods

We conducted a retrospective cohort study of 713 patients who had enrolled in the Medically Assisted Therapy (MAT) clinic between December 2014 and February 2018. Data for each participant was collected over 24 months. A researcher-designed socio-demographic form and data collecting form were used to collect data. Data was analyzed using SPSS version 26.0.

Results

Most participants were males 611(85.7%). The mean age was 34.2 years (SD 8.55). The majority had attained primary level of education 333(46.7%), were employed 424(59.47%), and were separated 322(45.1%). All the participants had used at least one other substance apart from opioids. The commonest substances were; tobacco 649(91%), followed by cannabis 591(82.9%) and benzodiazepines 367(51.5%). At 6, 12, 18, and 24 months, opioid abstinence rates were 61.3%, 76.0%, 73.5%, and 81.4%, respectively. Abstinence rates increased from 61.3% at 6 months to 81.4% at 24 months, which was statistically significant (p < 0.001).

Conclusions

Majority of the patients attending the MAT clinic use more than one substance. There is a need to scale up the program to also offer free management for other substances. This study replicated the evidence of MMT among opioid users. However, there is still a need to explore further why some patients continue to use opioids while in the program.

Psychiatry

Opioids

methadone

medically assisted therapy

methadone maintenance treatment

Kenya

Substance use is prevalent and associated with a significant burden of disease globally. According to the United Nations Office on Drugs and Crime (UNODC), 296 million people had used substances globally in 2021, accounting for 5.8% of people aged 15–64 years [1] Opioids use was estimated 60 million people in 2021, 31.5 million of whom used opiates especially heroin [1]. Opioids remain the substances with the highest severe drug-related harm, especially fatal overdoses [1] Despite the low number, opioids were responsible for 70 percent of the estimated 128,000 deaths in 2019 attributable to substance use disorders. Opioids also contributed to 18 million years of lost productivity due to drug use-related impairment or premature death and disability [1]. Other major drugs reported were; cannabis, amphetamines and prescription stimulants, ecstasy and cocaine. This drug use has been increasing more rapidly in developing than in developed countries [1]. A national survey on the Status of Substance Use in Kenya showed that more than 50% of the population had ever used a substance in their lifetime [2]. The national survey showed that alcohol was the most widely used substance followed by tobacco, khat, cannabis, prescription drugs, inhalants, heroin, cocaine, and hashish. The survey noted that household surveys could not produce reliable estimates of heroin, cocaine, and other users of illegal substances prevalence as it is a hidden population [2].

Several studies have highlighted multiple drug use and continued use of opioids while in the Methadone Maintenance Treatment program as a significant problem that negatively impacts retention [3–5]. The UNODC report showed regions having higher than the global average of opioid use disorder as North America 3%, Australia and New Zealand at 3.3%, Near and the Middle East and South-West Asia at 2.6% and South Asia at 2.0% [6]. A study done in the USA at 26 MMT facilities showed concurrent substance use among opioid users as benzodiazepines 26.4%, cannabinoids 20.7%, cocaine 10.8% and amphetamines 9.1% [7]. A study in Australia from 19 agencies treating opioid users showed concurrent substance use as tobacco 96%, cannabis 69%, alcohol 51% and benzodiazepines 50% [8]. In Asia, several studies showed concurrent substance use among opioid users [4, 9]. In Africa there is a paucity of published data concerning Opioids Substitution Therapy (OST), the studies showed multiple substance use among opioid users [10, 11]. A study in South Africa showed concurrent substance use as cannabis 5.4%, methamphetamine 48.6%, cannabis + any other 17.65, methamphetamine + any other 41.9% any two drugs 25.7% and any three drugs 17.6% [10]. In Kenya, a study on the prevalence of concurrent cannabis use among opioid users was 84.8% [12]. Few studies have been done regarding opioid abstinence rates. The study at the 26 MMTs in the USA found opioid abstinence rates at 3 months 2.8%, at 6 months 6.9%, at 9 months 16.4% and at 12 months 70.4% [7]. Another study in Malaysia showed in the first year of follow-up, 2.4% tested positive for opioids while in the second year it was 1.4% [13].

Several assessments of evidence have shown a variety of benefits from the Methadone Maintenance Treatment (MMT) [14–16] MMT worldwide is cost-effective [15, 17]. The benefits cut across many domains including physical health, psychological, social, financial, and legal issues. Results from research have been consistent showing a reduction of HIV and Hepatitis C infection, reductions in premature deaths attributed to overdose, reduction in the use of illicit drugs, and reduction in criminal behaviors [7, 10, 18, 19]. MMT during pregnancy is also associated with reduced complications and increased fetal outcomes [14].

Despite the success of many MMT programs, some patients who attend methadone clinics continue using opioids and other drugs. Some research attributes this continued opioid use to the opiate craving which can lead to lapse and relapse [20]. The majority of OST programs have focused on Opioids Use Disorders only, rather than a holistic approach inclusive of other substances since a majority of opioid users use multiple substances [21]. The UNODC report indicated that there was limited access to treatment for drug use disorders, especially in low or intermediate-economy countries. Globally, only one in eight persons with drug use disorder gets treatment annually [22].

The objective of this study was to determine substance use patterns among patients attending Medically Assisted Therapy clinic and opioid abstinence rates at a national referral hospital in Nairobi.

Study design

This study was a retrospective cohort study from December 2014 to February 2020. Data of each participant was collected for a maximum of 24 months from the time that the patient was enrolled in the program.

Study site

The study was conducted at the Medically-Assisted Therapy (MAT) Clinic located at Mathari National Teaching and Referral Hospital in Nairobi, Kenya. This was the first MAT clinic in Kenya and it became operational in December 2014. The catchment area is most parts of Nairobi City a cosmopolitan city as the facility is located approximately 5 kilometres from Nairobi City Centre. All the patients are on methadone the oral syrup formulation. Patients are referred to the facility from drop-in centres where they have undergone psychosocial education, support and educated on other harm reduction practices like not sharing needles nor syringes, no flushing, and safe sex among others. This is in line with the national guidelines on MAT treatment [23].

Study population and sample size

We conducted a census of the 727 patients enrolled from December 2014 to February 2018. All the participants were adults aged 18 years and above. The last period of February 2018 was informed by the COVID-19 pandemic getting to Kenya in early March 2020. Since February 2020 there were no new enrolments of patients in the clinic nor the random urine drug screen testing. Therefore, for the patients who were enrolled in February 2018, we were able to get their 24-month data until February 2020.

Ethical considerations

Ethical approval to conduct the study was granted by the Ethics and Research Committee of Kenyatta National Hospital-University of Nairobi and Mathari National Teaching and Referral Hospital Research Committee.

Data collection procedures

Medical records of the patients enrolled from December 2014 to February 2018 were retrieved based on the register. Researcher-designed sociodemographic and data collection forms were used to collect data. Socio-demographic data collected included age, gender, education level, marital status, occupation, housing status, and legal status. The data collection form included: the age at which the patient first used opioids; duration of use; severity of the Opioids Use Disorder; other drugs at enrolment and urine drug screen results at 6 months, 12 months, 18 months, and 24 months.

Out of the 727 files intended for the study, we found 713 files; 14 files were missing. Table 1 below shows the sociodemographic characteristics of the participants, clinical profile, maximum methadone dose and retention status of the participants at 24 months. Majority of participants were male, had a primary level of education and 81% were retained in treatment at 24 months.

Table 1

Sociodemographic characteristics, clinical profile, maximum methadone dose and retention status at 24 months
Variable	Category	Frequency (n)	Percentage (%)
Gender	Male	611	85.7
	Female	102	14.3
Age	≤ 20yrs	19	2.7
	21–30	227	31.8
	31–40	310	43.5
	41–50	178	18
	51–60	25	3.5
	61–70	3	0.4
	> 70	1	0.1
Education level	Primary	333	46.7
	Secondary	290	40.67
	Tertiary	75	10.52
	None	15	2.1
Marital status	Separated	322	45.1
	Single	225	31.6
	Married	154	21.6
	Widowed	12	1.7
Occupation	Unemployed	195	27.35
	Employed	424	59.47
	Self-employed	94	13.18
Housing status	Rental	234	32.82
	Unstable	196	27.49
	Homeless	120	16.83
	Friends	104	14.59
	Relatives	55	7.71
	Own house	4	0.56
Legal history	Yes	548	64.24
	No	255	36.76
Medical comorbidity	HIV	107	15.0
	Hepatitis C	81	11.4
	Syphilis	44	6.2
	Hepatitis B	13	1.8
	Tuberculosis	2	0.3
	Others (asthma, hypertension)	14	2.0
Maximum methadone dose (mg)	Less than 50	49	6.9
	51–100	217	30.4
	101–150	308	43.2
	151–200	116	16.3
	> 200	23	3.2
Retention status at 24 months	Active	580	81.3
	Lost to follow up	95	13.3
	Deceased	16	2.2
	Transferred out	11	1.5
	Voluntary termination	7	1.0
	Weaned off	4	0.6

Substance use among study participants

a) Opioids use disorder

The mean age at first use of opioids was 22.21 years (standard deviation 6.14, range 8–77 years). Majority 338(47.4%) of the participants were in the age group 21–30 years. The mean duration of use of opioids before enrolling in MAT was 11.87 years (standard deviation 7.28 years, range 1 to 40 years). Most patients 360(50.5%) had used opioids for less than 11 years. Almost all the patients had severe opioid use disorder 712(99.9%). Table 2 below shows the patterns of opioid use among the participants at baseline.

Table 2

Patterns of Opioids use at baseline
Variable	Frequency (n)	Percentage (%)
Age at first use of opioids (years)
10 and below	5	0.7
11–20	323	45.3
21–30	338	47.4
31–40	41	5.8
41–50	4	0.6
> 50	2	0.3
Duration of opioid use (years)
10 and below	360	50.5
11–20	261	36.6
21–30	84	11.8
31–40	8	1.1
Routes of administration
Intravenous	398	55.8
Smoking	158	22.2
Both	157	22.0
Severity of opioid use disorder
Severe	712	99.9
Moderate	1	0.1

b) Other substances used at induction

All the participants used at least one other substance apart from opioids. The commonest substances used were tobacco 649(91.0%), cannabis 591(82.9%) and benzodiazepines 367(51.5%) as shown in Fig. 1. The benzodiazepines commonly used were diazepam,clonazepam and flunitrazepam. “Others” in the figure included barbiturates, glue, and chlorpromazine.

c) Number of other substances per participant

Regarding the total number of substances used per participant apart from opioids, the majority were using three 248(34.8%), followed by two 245(34.4%) and four 97(13.6%) as shown in Table 3.

Table 3

Number of other substances used per participant
Number of substances	Frequency (N)	Percent (%)
0	1	0.1
1	88	12.2
2	245	34.4
3	248	34.8
4	97	13.6
5	29	4.1
6	5	0.7
7	1	0.1
Total	713	100.0

Association between sociodemographic characteristics & other substance use on bivariate analysis.

During bivariate analysis, the factors associated with substance use were as follows: Tobacco; gender, housing, and legal status, Cannabis; age at first use of heroin and duration of heroin use, Benzodiazepine; age at first use of heroin, Khat; gender, legal history, Benzhexol; occupation. For alcohol and cocaine, no sociodemographic factor was associated with use. Table 4 below shows the bivariate analysis.

Table 4

Association between sociodemographic characteristics & other substance use on bivariate analysis.
Variable	Category	Tobacco n(%)	p-value	Cannabis n(%)	p-value	Benzodiazepine n(%)	p-value	Alcohol n(%)	p-value	Miraa n (%)	p-value	Benzhexol n(%)	P value	Cocaine n(%)	P-value
Gender	Male	564(92.3)	0.003	508(83.1%)	0.660	317(51.88%)	0.592	121(19.8%)	0.189	57(9.33%)	0.005	33(5.4%)	0.137	15(2.45%)	0.395
Gender	Female	85(83.33%)		83 (81.4%)		50(49.02%)		26(25.49%)		19(18.63%)		2(1.96%)		4(3.92%)
Age (years)	< 20	17(89.47%)	0.561	16(84.2%)	0.031	8(42.11%)	0.148	3(15.79%)	0.449	1(5.26%)	0.753	0	0.831	0	0.418
	21–30	202(88.98%		197(86.8%)		127(55.95%)		41(18.06%)		29(12.78%)		14(6.17%)		5(2.2%)
	31–40	286(92.26%)		259(83.5%)		159(51.29%)		63(20.32%)		34(10.97%)		13(4.19%)		13(4.19%)
	41–50	115(89.84%)		100(78.1%)		65(50.78%)		35(27.34%)		10(10.97%)		6(4.69%)		1(0.78%)
	51–60	25(100%)		17(68.0%)		7(28.0%)		5(20.0%)		2(8.0%)		2(8.0%)		0
	61–70	3(100)		2(66.7%)		1(33.33%)		0		0		0		0
	> 70	1(100)		0(0%)		0		0		0		0		0
Education level	None	15(100%)	0.116	14(93.3%)	0.582	8(53.33%)	0.618	0	0.148	1(6.67%)	0.328	1(6.67%)	0.499	0	0.303
	Primary	310(93.09%)		277(83.2%)		168(50.45%)		71(21.32%)		37(11.11%)		18(5.41%)		6(1.8%)
	Secondary	256(88.28%)		236(81.4%)		147(50.69%)		64(22.07%)		26(8.97%)		15(5.17%)		9(3.1%)
	Tertiary	68(90.67%)		64(85.3%)		4458.67%)		12(16.0%)		12(16.0%)		19(1.33%)		4(5.33%)
Marital status	Single	201(89.33%)	0.754	188(83.6%)	0.682	126(56.0%)	0.087	44(19.56%)	0.801	31(13.78%)	0.086	9(4.0%)	0.293	10(4.44%)	0.202
	Married	142(92.21%)		130(84.4%)		66(42.86%)		29(18.83%)		14(9.09%)		5(3.25%)		4(2.6%)
	Separated	295(91.61%)		262(81.4%)		169(52.48%)		71(22.05%)		28(8.7%)		21(6.52%)		5(1.55%)
	Widowed	11(91.67%)		11(91.7%)		6(50.0%)		3(25.0%)		3(25.0%)		0		0
Occupation Status	Unemployed	173(88.72%)	0.147	163(83.6%)	0.574	107(54.87%)	0.528	36(18.46%)	0.378	22(11.28%)	0.471	16(8.21%)	0.033	3(1.54%)	0.384
	Self-employed	90(95.74%)		81(86.2%)		48(51.06%)		24(25.53%)		13(13.83%)		2(2.13%)		4(4.26%)
	Employed	386(91.04%)		347(81.8%)		212(50.0%)		87(20.52%)		41(9.67%)		17(4.01%)		12(2.83%)
Housing status	Homeless	114(95.0%)	0.010	102(85.0%)	0.808	58(48.33%)	0.159	24(20.0%)	0.665	12(10.0%)	0.830	7(5.83%)	0.278	2(1.67%)	0.969
	Friends	88(84.62%)		84(80.8%)		61(58.65%)		17(16.35%)		13(12.5%)		7(6.73%)		3(2.88%)
	Relatives	46(83.64%)		45(81.8%)		30(54.55%)		11(20.0%)		6(10.91%)		3(5.45%)		2(3.64%)
	Rented house	220(94.02%)		197(84.2%)		108(46.15%)		49(20.94%)		28(11.97%)		5(2.14%)		6(2.56%)
	Own house	3(75.0%)		4(100.0%)		1(25.0%)		0		0		0		0
	Unstable	178(90.82%)		159(81.1%)		109(55.61%)		46(23.47%)		17(8.67%)		13(6.63%)		6(3.06%)
Legal history	Yes	434(94.76%)	< 0.001	373(81.4%)	0.169	234(51.09%)	0.785	91(19.87%)	0.508	37(8.08%)	0.003	24(5.24%)	0.583	13(2.84%)	0.700
Legal history	No	215(84.31%)		218(85.5%)		133(52.16%)		56(21.96%)		39(15.29%)		11(4.31%)		6(2.35%)
Age at first opioid use	≤ 10	5(100)	0.836	5(100.0%)	0.034	4(25.5%)	0.032	1(20.0%)	0.327	0	0.328	0	0.895	0	0.818
	11–20	292(90.0)		270(83.6%)		184(56.97%)		70(21.67%)		41(12.69%)		19(5.88%)		11(3.41%)
	21–30	307(90.8)		277(82.0%)		157(46.45%)		62(18.34%)		28(8.28%)		14(4.14%)		8(2.37%)
	31–40	39(95.1)		35(85.4%)		21(51.22%)		12(29.27%)		6(14.63%)		2(4.88%)		0
	41–50	4(100%)		4(100.0%)		1(25.5%)		2(50.0%)		1(25.0%)		0		0
	> 50	2(100)		0(.0%)		0		0		0		0		0
Duration of opioid use	≤ 10	327(90.83%)	0.737	312(86.7%)	0.030	183(50.83%)	0.503	74(20.56%)	0.701	42(11.67%)	0.663	16(4.44%)	0.687	6(1.67%)	0.257
	11–20	236(90.42%)		208(79.7%)		132(50.57%)		50(19.16%)		26(9.96%)		13(4.98%)		11(4.21%)
	21–30	78(92.86%)		66(78.6%)		49(58.33%)		21(25.0%)		8(9.52%)		6(7.14%)		2(2.38%)
	31–40	8(100%)		5(62.5%)		3(37.5%)		2(25.0%)		0		0		0

Association between sociodemographic characteristics & other substance use on multivariate analysis.

On multivariate analysis, the factors associated with substance use were: gender- whereby being female was associated with a high likelihood of using tobacco and reduced odds for miraa use (OR 2.01 and 0.49) respectively, occupation, whereby those employed more likely to use benzhexol; legal history, those with no legal history more likely to smoke and less likely to use khat. Table 5 below shows the multivariate analysis.

Table 5

**Association between sociodemographic characteristics & other substance use on multivariate analysis**.
Variable	Reference category	Tobacco		cannabis		Benzodiazepines		miraa		Benzhexol
		aOR (95%CI)	P value	aOR (95%CI)	P value	aOR(95%CI)	P value	aOR (95%CI)	P value	aOR (95%CI)	P value
Gender	Female versus male	2.01(1.08–3.74)	0.028	-		-		0.49(0.24–0.87)	0.014	-
Age	Less than 20 versus above 20 years	-		1.06(0.94–1.19)	0.380	-		-		-
Housing arrangement	Unstable versus Homeless	2.30(0.87–6.10)	0.094	-		-		-		-
	Unstable versus friends	0.79(0.37–1.68)	0.537	-		-		-		-
	Unstable versus relatives	0.75(0.31–1.84)	0.530	-		-		-		-
	Unstable versus rental	2.06(0.97–4.38)	0.061	-		-		-		-
	Unstable versus own house	0.48(0.05–5.15)	0.544	-		-		-
Occupation	Employed versus unemployed	-		-		-		-		2.14(1.06–4.33)	0.034
	Employed versus self-employed	-		-		-		-		0.52(0.12–2.29)	0.388
Legal history	No versus Yes	3.23(1.85–5.66)	< 0.001	-		-		0.49(0.30–79)	0.003	-
Age at first use of opioids	≤ 10 versus > 10 years	-		0.92(0.82–1.04)	0.196	0.96(0.93–0.98)				-
Duration of opioid use in years	≤ 10 versus > 10 years	-		0.92(0.81–1.03)	0.141	-				-

Opioids abstinence rates

The abstinence rate increased from 61.3% at 6 months to 81.4% at 24 months, this was statistically significant (p < 0.001). In the sixth month while in the MAT program, among the 437 participants who had a urine drug screen (UDS) done, a majority had abstained from opioids 268(61.3%) while 169(38.7%) were still using opioids. In the twelfth month, among the 387 participants who had a UDS done majority had abstained from opioids 294(76.0%) while 93(24.0%) were still using opioids. At eighteen months, among the 321 participants who had a UDS done majority had abstained from opioids 236(73.5%) while 85(26.5%) were still using opioids. In the twenty-fourth month, among the 354 participants who had a UDS done, a majority had abstained from opioids 288(81.4%) while 66(18.6%) were still using opioids. These results are shown in Fig. 2 and Table 6 below.

Table 6

Abstinence rates at 6, 12, 18, and 24 months
	Opioids abstinence		Chi-Square	df	P-value.
	Yes	No	Chi-Square	df	P-value.
6 Months	268(61.33%)	169(38.67%)	22.428	1	< 0.001
12 Months	294(75.97%)	93(24.03%)	104.395	1	< 0.001
18 Months	236(73.52%)	85(26.48%)	71.031	1	< 0.001
24 months	288(81.36%)	66(18.64%)	139.22	1	< 0.001

Significant Socio-demographic characteristics of study participants

This study found the mean age of the participants was 34.22 years (SD 8.55). This was comparable with other studies which had also shown that most participants were around 30–40 years. This pattern is similar to what has been seen in previous studies carried out at different geographical locations with different study designs [9–11, 17, 24]. This is a worrying trend as this age group is expected to be the most productive in society but they are using their time, energy and resources using substances. Most of the participants in this study were males 611(85.7%). This finding is consistent with most findings of other studies [9–11, 13, 24] The reasons could be that culturally males are less condemned than women when they use substances. Women are vulnerable, those using substances might have undergone trauma, domestic violence, or are suffering from depression, hence this needs to be explored further.

In terms of marital status, this study found a majority of the participants were separated 322(45/1%). Similar studies showed mixed results [4, 9, 10, 13]. The results in this study could be possibly due to the negative impacts of substance use disorders which cause disintegration of the family unit. The different results in other studies could be due to differences in culture and impairments in interpersonal relationships.

Concurrent substance use

This study found that almost all the participants 712(99.9%) using opioids at induction were also using at least one other substance commonly tobacco, cannabis, benzodiazepines and alcohol. This is in agreement with past studies whereby a pattern of multiple substance use has been observed among individuals on methadone treatment. Some of these drugs when combined in high doses can lead to severe central nervous system depression. The specific substance may vary depending on the location of the studies e.g. in Vietnam and Australia showed the commonest substance was tobacco [8, 17] while studies done in Finland and the USA showed methamphetamine and benzodiazepine as the commonest substance [3, 7]. Most of the other similar studies have shown different findings from this study [9–11].

The variations in patterns of substance use in these studies could be due to different study designs, geographical location, availability of the substances, rules of a country and socioeconomic status. Nicotine binds nicotinic acetylcholine receptors in the central nervous system and stimulates the release of dopamine in the nucleus accumbens potentiating the euphoria that opioids cause [25]. Nicotine has also been found to relieve the withdrawal symptoms of opioids including irritability, restlessness and depression [25]. Cannabis and tobacco are also regarded as gateway drugs into opioid use with patients continuing to use even after having started using opioids [6, 21, 25]

Abstinence rates among opioid users attending MAT

In this study, the opioids abstinence rates were 61.3%, 76%, 73.5% and 81.4% at 6, 12, 18 and 24 months respectively. Other similar studies done at different periods of the patients being in the program showed mixed results. A retrospective study done in the USA found the opioid abstinence rate at 12 months was 89.8% [7]. A 36-month prospective cohort study done in Australia found the opioid abstinence rate at 36 months was 10% [8]. Another study in Malaysia found the opioid abstinence rate at 12 months and 24 months was 97.6% and 98.6% respectively [26]. The variations in abstinence rates could be due to different study designs, different methods of testing drugs and different intervals at which tests were done. Substance Abuse and Mental Health Services Administration (SAMHSA) recommends that random drug screens should be conducted at least once per month for patients attending MAT clinics [16].

Strengths and limitations

This study being a cohort longitudinal study provided insightful findings that would inform policymakers in the field of drug addiction to implement more measures to reduce the burden of substance use disorders.

This study had some limitations. It was conducted in a hospital setting among patients with opioid use disorders, therefore, the results cannot be generalized to the general population. The UDS being done at random time points in the course of the program and the lack of UDS kits affected the abstinence rates results. The COVID-19 pandemic limited the duration of the study to February 2020, because since March 2020 no UDS was being done in the clinic.

In conclusion, this study found that almost all the patients attending the MAT program used at least one other drug apart from opioids during enrolment. The commonest drugs used were tobacco, cannabis, benzodiazepines and alcohol. Majority of the participants used at least three other drugs. Some of these drugs when combined in high doses can lead to severe central nervous system depression. This calls for the inclusion of free management of these other substances in the MAT program.

This study showed encouraging evidence for the use of methadone in the management of opioid use disorder. It has replicated the findings of other studies on the efficacy of methadone.

We recommend that the Ministry of Health together with other stakeholders in the MAT program should scale up the provisions of the free services. These should include the management of other substances commonly used by opioid users. The Ministry of Health and other stakeholders to implement preventive measures for the early onset of opioid use. The healthcare workers in the MAT program to utilize software that identifies patients due for a UDS on a specific day on a random interval schedule. Further prospective studies to assess other outcome measures such as relapse rates, mortality, criminality and generally patient's quality of life.

MAT

Medically Assisted Therapy/Medication-Assisted Treatment

MMT

Methadone Maintenance Treatment

OST

Opioids Substitution Treatment

OUD

Opioids Use Disorder

UDS

Urine Drug Screen

UNODC

United Nations Office of Drugs and Crime

Acknowledgements

None

Ethics approval and consent to participate

The study was performed in accordance with the Declaration of Helsinki. Ethical approval to conduct this study from the Scientific Ethics and Research Committee of the University of Nairobi, Kenyatta National Hospital (KNH-UON ERC) was obtained before conducting the study (approval number P687/12/2020). In addition, permission was sought to carry out the study from the management of MNTRH and the MMT clinic. The ethics approval letter is attached in supplementary material.

This was a retrospective study, involving data abstraction from patient records, with no direct interviews with patients, hence no consent to participate was required.

Consent for publication

Not applicable

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Competing interests declaration

The authors declare that they have no competing interests.

Funding

Not applicable

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Substance Use Patterns and Opioid Abstinence Rates Among Patients on Methadone Treatment at a Referral Hospital in Nairobi, Kenya.

Status:

Version 1

Abstract

Figures

Introduction

Methods

Study design

Study site

Study population and sample size

Ethical considerations

Data collection procedures

Results

a) Opioids use disorder

b) Other substances used at induction

c) Number of other substances per participant

Opioids abstinence rates

Discussion

Significant Socio-demographic characteristics of study participants

Concurrent substance use

Abstinence rates among opioid users attending MAT

Strengths and limitations

Conclusion

Recommendations

List Of Abbreviations

Declarations

References

Status:

Version 1