Stigma-related mental health knowledge and attitudes among primary health workers and community health volunteers in rural Kenya

Abstract

Background:

The study was conducted in rural Kenya and assessed stigma in health workers from primary health facilities.

Aims:

This study compared variations in stigma-related mental health knowledge and attitudes between primary health workers (HWs) and community health volunteers (CHVs).

Methods:

Participants (n = 44 HWs and n = 60 CHVs) completed the self-report Mental Health Knowledge Schedule and the Reported and Intended Behavior Scale, along with sociodemographic questions. Multiple regression models were used to assess predictors of mental health knowledge and stigmatizing behaviors.

Results:

HWs had significantly higher mean mental health knowledge scores than CHVs, p < .001, and significantly higher mean positive attitudes scores than CHVs, p = .042. When controlling for relevant covariates, higher positive attitudes was the only significant predictor of higher mental health knowledge, and self-rating of sense of belonging to the community and mental health knowledge remained the main predictors of positive attitudes.

Conclusion:

Results suggest that stigma-related mental health knowledge and attitudes are associated, and interventions should target these areas with health workers. There is scope for intervention to increase knowledge and positive attitudes for individuals who feel a strong sense of community belonging. Future studies should test feasible ways to reduce stigma in this population.

Keywords

Stigma mental illness Kenya rural community health

Introduction

Mental, neurological and substance use disorders (MNS) comprise a significant proportion of the global disease burden, with depressive disorders estimated to be the leading cause of worldwide disease burden by 2020 (Ferrari et al., 2013; van der Werf, 2012; Vos et al., 2012; Walker, McGee, & Druss, 2015). Yet, as many as three out of four patients with MNS disorders in low- and middle-income countries (LMICs) do not receive effective care (Becker & Kleinman, 2013; Kohn, Saxena, Levav, & Saraceno, 2004). While a lack of mental health services is one explanation for this treatment gap, barriers to accessing existing mental health services, such as lack of geographical or financial access, as well as cultural barriers and stigma also play a significant role in this process (Collins, Insel, Chockalingam, Daar, & Maddox, 2013; Simon, Fleck, Lucas, & Bushnell, 2004; Wang et al., 2007).

Task-sharing using non-mental health specialists is a promising strategy for bridging this treatment gap (van Ginneken et al., 2013). However, a recent systematic review found that mental health–related stigma and biases of healthcare workers (internalized and societal) were great barriers to help-seeking for individuals with mental illness (Clement et al., 2015). The benefits of task-sharing might be unrealized if non-specialists create a barrier to seeking care because of their unwelcoming perceptions, attitudes and behaviors toward mental illness. This study seeks to understand the mental health knowledge and stigmatizing behaviors in two types of non-specialist workers – lay health workers and primary care clinicians in Kenya.

Stigma can often lead to individuals delaying access to services until there is severe impairment of functioning and exacerbation of clinical symptoms, at which point treatment efficacy may be reduced, thus negatively impacting prognosis and clinical outcomes (Henderson, Evans-Lacko, & Thornicroft, 2013). In sub-Saharan Africa, mental health–related stigma is widespread and relatively high (Barke, Nyarko, & Klecha, 2011) and is exacerbated by cultural beliefs about the role of evil spirits and demons in the etiology of mental illness (Gureje, Lasebikan, Ephraim-Oluwanuga, Olley, & Kola, 2005; Ndetei, Khasakhala, Mutiso, & Mbwayo, 2011). Studies from Europe and Asia have shown that stigmatizing attitudes and behaviors are common among healthcare staff (Jie, Juan, Thornicroft, & Huang, 2014; Martensson, Jacobsson, & Engstrom, 2014; Ungar & Knaak, 2013; Van Boekel, Brouwers, Van Weeghel, & Garretsen, 2013), and this finding has been documented within Kenya, where stigma and low mental health knowledge were found to be prevalent among hospital staff (Ndetei et al., 2011), potentially adversely affecting healthcare delivery, patient self-esteem and clinical outcomes.

Kenyan community-based lay health workers (HWs), often called ‘community health volunteers’ (CHVs), are non-professional workers who provide essential health-related services to the community such as identifying and referring cases, educating people on best practices to prevent the spread of diseases and ensuring the overall well-being of the community (Jenkins et al., 2010). CHVs can also deliver adapted evidence-based psychotherapeutic interventions with favorable treatment outcomes (Balaji et al., 2012; Chowdhary et al., 2014; Patel et al., 2010; van Ginneken et al., 2013; Viswanathan et al., 2010). Because of their large numbers relative to professional medical staff, and their familiarity with the communities they reside in, CHVs provide a feasible pathway to increase access to mental health services in LMICs. Nurses in Kenyan health facilities have endorsed task-sharing with CHVs as a means of improving access to mental health services (Mendenhall et al., 2016).

The purpose of this study was to compare the levels of stigma-related mental health knowledge and stigmatizing attitudes among a sample of formal primary HWs and lay HWs (CHVs) in Kenya to identify differences between HW type and other demographic variables. To our knowledge, this is the first study to compare these stigma-related outcomes among subsections of HWs in rural Kenya. This baseline data has the capacity to demonstrate the types of stigma that need to be addressed through intervention. A greater understanding of the differences between these two cohorts is essential for determining whether the development of effective anti-stigma interventions can be applied broadly to both groups of HWs, or if there is a need for more tailored anti-stigma initiatives.

Methods

This is a cross-sectional comparative survey conducted between December 2015 and March 2016 in Makueni County, one of 47 counties in Kenya. The sample included 104 participants (44 HWs and 60 CHVs) drawn from 20 primary health facilities, selected because they were serving a population wider than their geographical catchment area. Three CHVs from each of the 20 community health units were nominated by the health facility in charge or by a Community Health Extension Worker (CHEW) to participate in the study. Generally, CHVs in Kenya are recruited with input from CHEWs who are based at the health facility and members from the village, sub-location and district (Rachlis et al., 2016).

Primary HWs in this sample were licensed professionals (nurses and clinicians) attached to each of the 20 facilities. Each facility was asked to nominate at least two HWs based on staffing capacities and availability. Random selection was conducted for two or more HWs who met these criteria. HWs had a minimum of post-secondary level training while CHVs had completed primary level education and had previously undergone a 2-week standardized Community Strategy training in Kenya implemented by the Ministry of Health (National Coordinating Agency for Population and Development [Kenya], Ministry of Medical Services [Kenya], Ministry of Public Health and Sanitation [Kenya], Kenya National Bureau of Statistics [Kenya], & ICF Macro, 2011). Details of this government regulated strategy are described elsewhere (National Coordinating Agency for Population and Development [Kenya] et al., 2011). Data are based on self-report.

Ethics and informed consent

Informed consent was obtained from all participants. Ethical approval for this study was obtained from the Maseno University and the New York State Psychiatric Institute/Columbia University Medical Center Institutional Review Boards.

Measures

Sociodemographic information

Sociodemographic information was collected from each participant including age, gender, religious affiliation, participant type (HW or CHV), number of years of practice, highest level of education, self-rating of current health status, self-rating of current life satisfaction, self-rating of current mental status, self-rating of current sense of belonging to the community and socioeconomic status. To create a wealth index, we used factor scores derived through principal component analysis (PCA) as the weights of sociodemographic items in its asset index. Factors scores are a measure of the strength of association of an item with the first principal component. It is assumed that this factor represents economic status. The presence of electricity in one’s home, ownership of items (refrigerator, TV, bicycle, motorcycle, cell phone, radio and motor vehicle), how food is cooked in their house (gas stove, kerosene stove, electric stove, charcoal and wood), the type of residence (tenant, home owner, staying with family and other) and the wall material in the residence they live in (mud, plastered mud, iron sheets, wood and brick/stone)were used in constructing each participant’s wealth index. The percentage of variance explained by the first factor was 20.2%. The socioeconomic scores were grouped together to create three indices: (1) low (34%), (2) middle (33%) and (3) high (33%).

Mental Health Knowledge Schedule

Stigma-related and disorder-specific mental health knowledge were assessed using the Mental Health Knowledge Schedule (MAKS; Evans-Lacko et al., 2010). MAKS is a 12-item scale, with the first 6 items assessing stigma-related knowledge, scored on a 5-point Likert scale from strongly disagree to strongly agree. Item 6 is reverse coded. A MAKS total score is calculated for items 1–6 with a higher total score indicating greater mental health knowledge; the highest possible score being 30. Items 7–12 are scored similarly to items 1–6 and assess knowledge about specific psychiatric disorders by measuring correct identification of various conditions as illnesses. Participants are presented with the following words – Depression, Stress, Schizophrenia, Bipolar Disorder, Drug Addiction and Grief – and then asked to what extent each word is a mental illness. They rate each word on a 5-point Likert scale from strongly disagree to strongly agree. Items 8 (Stress) and 12 (Grief) are reverse coded. The MAKS was found to have adequate test–retest reliability (.71 Lin’s concordance statistic) and moderate internal consistency (Cronbach’s α = .65) (Evans-Lacko et al., 2010). The MAKS has been used in a variety of mental health knowledge and stigma-related studies globally (Chisholm et al., 2016; Evans-Lacko, Henderson, & Thornicroft, 2013; Jie et al., 2014; Martensson et al., 2014). It was designed to be used in conjunction with scales measuring other facets of stigma such as those measured by the Reported and Intended Behavior Scale (RIBS) (Evans-Lacko et al., 2010).

RIBS

RIBS was used to measure stigma-related attitudes (Evans-Lacko et al., 2011) and is an 8-item scale assessing mental health-related reported and predicted discriminatory behavior in four main domains: (1) living alongside, (2) working alongside, (3) living nearby and (4) having a close relationship with an individual with a mental illness. Items 1–4 assess the current prevalence of behaviors in these domains, while items 5–8 are used to calculate intended interactions with an individual with a mental illness in these same domains. Items 1–4 are trichotomous, with answer choices yes, no and don’t know. Don’t know and no are coded (0) and yes is coded (1). Items 1–4 can be scored to determine the level of personal contact participants have had with an individual with mental illness (range, 0–4). Items 5–8 are scored on a 5-point Likert scale similar to the MAKS, where 1 represents strongly disagree and 5 represents strongly agree. The sum of items 5–8 is used to calculate a RIBS-intended total score; the highest possible score is 20. A higher RIBS-intended score indicates a likelihood to engage in more positive behaviors with an individual with a mental illness. Internal consistency was high (Cronbach’s α = .85) and test–retest reliability was good (Lin’s concordance statistic = .75) in the sample in which the scale was developed (Evans-Lacko et al., 2011).

Statistical analysis

Data were analyzed using IBM SPSS 23. Descriptive statistics were run to determine sociodemographic characteristics as well as on selected items of the MAKS and RIBS. Chi-square and t-tests were used to assess the differences between HW and CHV depending on the distribution of the sociodemographics, self-rating to current, health status; mental health status; life satisfaction and sense of belonging to community variables, MAKS scores and RIBS-intended scores. Linear regression was used to determine univariate predictors of MAKS and RIBS-intended scores. Items which were significantly associated with MAKS and RIBS scores at the univariate level (p < .100) were entered into multiple linear regression for adjusted analyses of the predictors of MAKS and RIBS-intended scores. Prior to the multiple regression analysis, all assumptions were checked and no serious issues were found. The amount of missing data was found to be less than 2%, which ensures the quality of data and generalizability of research conclusions.

Results

The sociodemographic characteristics of the sample are presented in Table 1. On average, CHVs were older (p = .019), less educated (p < .001), lower on the wealth index (p < .001) and reported more life dissatisfaction (p = .001) than HWs. No significant differences were found in other sociodemographic variables between groups.

Table 1.

Sociodemographic characteristics of the respondents.

Variable	Category	Overall (N = 103)	HW (N = 44)	CHV (N = 59)	Group differences
Variable	Category	n (%)	n (%)	n (%)	Group differences
Gender	Male	41 (39.8)	20 (48.8)	21 (51.2)	χ²_{(1, 103)} = 1.02; p = .312
Gender	Female	62 (60.2)	24 (38.7)	38 (61.3)	χ²_{(1, 103)} = 1.02; p = .312
Education level	Primary	12 (11.7)	0 (0.0)	12 (100.0)	χ²_{(2, 102)} = 90.50; p < .001
	Secondary	44 (42.7)	0 (0.0)	44 (100.0)
	University/College	46 (44.7)	43 (93.5)	3 (6.5)
	Missing	1 (1.0)
Marital status	Married	78 (75.7)	31 (39.7)	47 (60.3)	χ²_{(1, 103)} = 1.16; p = .281
Marital status	Living alone	25 (24.3)	13 (52.0)	12 (48.0)	χ²_{(1, 103)} = 1.16; p = .281
Socio-economic index	Low	35 (34.0)	1 (2.9)	34 (97.1)	χ²_{(2, 103)} = 47.95; p < .001
	Middle	34 (33.0)	14 (41.2)	20 (58.8)
	High	34 (33.0)	29 (85.3)	5 (14.7)
Age	Mean (SD)	39.9 (10.9)	37.0 (12.6)	42.1 (9.0)	t₍₁₀₁₎ = −2.39; p = .019
Self-rating to current health status	Excellent	18 (17.5)	12 (66.7)	6 (33.3)	χ²_{(2, 103)} = 5.15; p = .076
	Satisfactory	60 (58.3)	23 (38.3)	37 (61.7)
	Unsatisfactory	25 (24.3)	9 (36.0)	16 (64.0)
Self-rating to current life satisfaction	Satisfied	67 (65.0)	37 (55.2)	30 (44.8)	χ²_{(1, 102)} = 11.63; p = .001
	Dissatisfied	35 (34.0)	7 (20.0)	28 (80.0)
	Missing	1 (1.0)
Self-rating to current mental health status	Excellent	17 (16.5)	9 (52.9)	8 (47.1)	χ²_{(2, 103)} = 2.06; p = .358
	Good	71 (68.9)	27 (38.0)	44 (62.0)
	Fair	15 (14.6)	8 (53.3)	7 (46.7)
Self-rating to sense of belonging to the community	Strong	28 (27.2)	13 (46.4)	15 (53.6)	χ²_{(2, 103)} = 2.36; p = .308
	Somewhat strong	62 (60.2)	28 (45.2)	34 (54.8)
	Somewhat weak	13 (12.6)	3 (23.1)	10 (76.9)
MAKS score	Mean (SD)	23.9 (3.0)	25.3 (2.3)	22.8 (3.1)	t₍₁₀₁₎ = 4.54; p < .001
RIBS-intended score	Mean (SD)	16.2 (3.1)	16.9 (3.0)	15.7 (3.1)	t₍₁₀₁₎ = −2.05; p = .042
Worker type	HW	44 (42.7)	NA	NA	NA
Worker type	CHV	59 (57.3)	NA	NA	NA

HW: health worker; CHV: community health volunteer; SD: standard deviation; MAKS: Mental Health Knowledge Schedule; RIBS: Reported and Intended Behavior Scale; NA: not applicable.

Significant p values (p < .05) are in bold.

Table 2 outlines the response frequencies for disorder-specific knowledge items on the MAKS, broken down by participant type. As shown in Table 2, HWs have a high rate of accuracy (⩾90%) in characterizing depression, schizophrenia, bipolar disorder and drug addiction as mental disorders. CHVs are less accurate at characterizing these disorders, particularly bipolar disorder, with only 51.8% agreeing that it is a mental illness. Over 90% of HWs and over 50% of CHVs incorrectly identified grief and stress as mental disorders. In the overall sample, 81.2% of participants incorrectly identified stress and 66.4% incorrectly identified grief as mental disorders, demonstrating low mental health knowledge in these two domains (Table 2).

Table 2.

Response frequencies for MAKS items 7–12.

	Strongly agree (%)		Slightly agree (%)		Neither agree nor disagree (%)		Slightly disagree (%)		Strongly disagree (%)		Don’t know (%)
	HW^a	CHV	HW^a	CHV	HW^a	CHV	HW^a	CHV	HW^a	CHV	HW^a	CHV
Depression^c	43 (97.7)	32 (55.2)	1 (2.3)	6 (10.3)	0	6 (10.3)	0	6 (10.3)	0	4 (6.7)	0	4 (6.7)
Stress^d	38 (86.4)	31 (54.4)	4 (9.1)	9 (15.8)	0	9 (15.8)	1 (2.3)	3 (5.3)	1(2.3)	4 (7.0)	0	1 (1.8)
Schizophrenia^e	44 (100)	44 (80.0)	0	5 (9.1)	0	2 (3.6)	0	2 (3.6)	0	0	0	2 (3.6)
Bipolar disorder^f	44 (100)	22 (39.3)	0	7 (12.5)	0	4 (07.1)	0	5 (8.9)	0	0	0	18 (32.1)
Drug addiction^d	40 (90.9)	37 (64.9)	3 (6.8)	9 (15.8)	1 (2.3)	3 (5.3)	0	5 (8.8)	0	1 (1.8)	0	2 (3.5)
Grief^b,c	29 (67.4)	24 (41.4)	8 (18.2)	6 (10.3)	0	9 (15.5)	3 (7.0)	13 (22.4)	1 (2.3)	3 (5.2)	2 (4.7)	3 (5.2)

HW: health worker; CHV: community health volunteer; MAKS: Mental Health Knowledge Schedule.

Some participants lacked data for specific variables and hence the sample size for specific calculations differs as follows:

n = 44.

For HW Grief, n = 43.

For CHV Depression and Grief, n = 58.

For CHV Stress and Drug addiction, n = 57.

For CHV Schizophrenia, n = 55.

For CHV Bipolar disorder, n = 56.

Overall, a high proportion of both HWs and CHVs reported having had at least one interaction with individuals with mental problems, measured using the RIBS. In all, 45.5% of HWs and 42.4% of CHVs have lived with someone with a mental health problem. Similarly, 43.2% of HWs and 40.7% of CHVs have worked with someone with a mental health problem. In all, 81.8%of HWs and 86.2% of CHVs have had a neighbor with a mental health problem, and 54.5% of HWs and 66.1% of CHVs have had a close friend with a mental health problem. Thus, the mean personal contact (RIBS-reported) score for this sample was 2.30 (SD = 1.20). Although CHVs had higher personal contact scores (M = 2.34, SD = 1.18) compared to HWs (M = 2.25, SD = 1.22), these differences were not statistically significant. Thus, both worker types reported similar levels of interaction with individuals with a mental health problem.

Mean MAKS and RIBS-intended scores for the overall sample were 23.9 (SD = 3.0) and 16.2 (SD = 3.1), respectively (Table 1). In univariate analyses, HWs had significantly higher mean MAKS scores (M = 25.3, SD = 2.3) than CHVs (M = 22.8, SD = 3.1), p < .001, indicating that HWs have a higher level of mental health knowledge than their CHV counterparts. Furthermore, HWs had significantly higher mean RIBS-intended scores (M = 16.9, SD = 3.0) than CHVs (M = 15.7, SD = 3.1), t(101) = 2.05, p = .042, indicating that HWs are more willing to engage with individuals with mental health problems. Mean scores on the MAKS were significantly positively correlated with mean scores on the RIBS-intended, r (102) = 0.277, p < .05. Thus, greater mental health knowledge is associated with higher self-reported future (intended) positive behaviors toward individuals with mental health problems. Personal contact (RIBS-reported) and RIBS-intended did not demonstrate a significant correlation. Therefore, having previously interacted with people with mental health problems is not indicative of a greater willingness to interact with such persons in the future.

Table 3 shows the results of unadjusted and adjusted linear regression analyses for MAKS scores. In the unadjusted analyses, having a secondary education or university education, having a high wealth index and having a higher RIBS-intended score predicted greater mental health knowledge, p < .05. Being a CHV, reporting unsatisfactory health status, reporting life dissatisfaction and reporting a weak sense of belonging to the community predicted lower mental health knowledge, p < .05. In the adjusted analyses however, only willingness to engage with people with mental health problems (RIBS-intended scores) significantly predicted higher mental health knowledge.

Table 3.

Unadjusted and adjusted factors associated with MAKS scores.

Variable	Category	Mean	MAKS
			Unadjusted			Adjusted
			Beta	95% CI	p-Value	Beta	95% CI	p-Value
Worker type	HW	25.3	Ref.
Worker type	CHV	22.8	−2.52	(−3.61 to −1.42)	0.000	−0.84	(−4.23 to 2.59)	.630
Gender	Male	24.2	Ref.			NS
Gender	Female	23.6	−0.65	(−1.86 to 0.56)	0.292
Education level	Primary	21.3	Ref.
	Secondary	23.1	1.73	(−0.04 to 3.51)	0.055	0.96	(−0.23 to 2.82)	0.306
	University/college	25.2	3.86	(2.10 to 5.63)	0.000	1.83	(−1.23 to 5.35)	0.306
Marital status	Married	23.6	Ref.			NS
Marital status	Living alone	24.5	0.88	(−0.50 to 2.26)	0.210
Wealth index	Low	23.2	Ref.
	Middle	23.1	−0.11	(−1.49 to 1.26)	0.871	−1.41	(−2.23 to 0.13)	0.073
	High	25.4	2.18	(0.81 to 3.56)	0.002	0.06	(−1.23 to 1.83)	0.944
Age	Mean (SD)	39.9	−0.02	(−0.07 to 0.03)	0.469	NS
Self-rating of current health status	Excellent	24.7	Ref.
	Satisfactory	24.0	−0.74	(−2.34 to 0.86)	0.363	1.00	(−0.23 to 2.55)	0.199
	Unsatisfactory	22.9	−1.80	(−3.65 to 0.04)	0.055	0.09	(−1.23 to 1.91)	0.926
Self-rating of current life satisfaction	Satisfied	24.6	Ref.
Self-rating of current life satisfaction	Dissatisfied	22.5	−2.14	(−3.34 to −0.94)	0.001	−0.70	(−1.23 to 0.57)	0.279
Self-rating of current mental health status	Excellent	24.4	Ref.			NS
	Good	23.7	−0.66	(−2.30 to 0.97)	0.424
	Fair	24.1	−0.29	(−2.43 to 1.86)	0.792
Self-rating of sense of belonging to the community	Strong	24.6	Ref.			NS
	Somewhat strong	24.0	−0.59	(−1.92 to 0.74)	0.382	0.00	(−1.23 to 1.28)	0.999
	Somewhat weak	21.7	−2.88	(−4.84 to −0.92)	0.004	−1.39	(−3.23 to 0.75)	0.199
RIBS-intended score	Mean	16.2	0.30	(0.12 to 0.48)	0.002	0.28	(−0.23 to 0.46)	0.003

Ref.: reference category; NS: not significant; MAKS: Mental Health Knowledge Schedule; CI: confidence interval; SD: standard deviation; RIBS: Reported and Intended Behavior Scale; HW: health worker; CHV: community health volunteer.

Significant p values (p < .05) are in bold.

Table 4 outlines the unadjusted and adjusted regression model for RIBS-intended scores. In the unadjusted analyses, having a middle wealth index and higher mental health knowledge (MAKS scores) predicted a greater likelihood of engaging with individuals with a mental health problem. Being a CHV and reporting a weak or somewhat strong sense of belonging to the community (as compared to a strong sense) predicted lower RIBS-intended scores. In the adjusted analyses, higher mental health knowledge continued to predict higher RIBS-intended scores and reporting a sense of belonging to the community as ‘somewhat strong’ predicted lower RIBS-intended scores.

Table 4.

Unadjusted and adjusted factors associated with RIBS-intended scores.

Variable	Category	Mean	RIBS-intended scores
Variable	Category	Mean	Unadjusted			Adjusted
			Beta	95% CI	p-Value	Beta	95% CI	p-Value
Worker type	HW	16.93	Ref.			Ref.
Worker type	CHV	15.68	−1.25	(−2.46 to −0.04)	0.042	−0.47	(−2.10 to 1.17)	0.572
Gender	Male	16.44	Ref.
Gender	Female	16.06	−0.37	(−1.62 to 0.87)	0.552
Education level	Primary	16.17	Ref.			NS
	Secondary	15.43	−0.73	(−2.69 to 1.22)	0.458
	University/college	17.07	0.90	(−1.05 to 2.85)	0.363
Marital status	Married	16.09	Ref.
Marital status	Living alone	16.60	0.51	(−0.91 to 1.93)	0.478
Wealth index	Low	15.29	Ref.			Ref.
	Middle	16.88	1.60	(0.13 to 3.06)	0.033	1.54	(0.00 to 3.09)	0.050
	High	16.50	1.21	(−0.25 to 2.68)	0.103	0.05	(−1.85 to 1.94)	0.963
Age	Mean	39.90	0.00	(−0.05 to 0.06)	0.946
Self-rating of current health status	Excellent	17.06	Ref.			NS
	Satisfactory	15.75	−1.31	(−2.95 to 0.34)	0.119
	Unsatisfactory	16.72	−0.34	(−2.23 to 1.56)	0.726
Self-rating of current life satisfaction	Satisfied	16.46				NS
Self-rating of current life satisfaction	Dissatisfied	15.80	−0.66	(−1.95 to 0.63)	0.311
Self-rating of current mental health status	Excellent	16.94	Ref.			NS
	Good	15.86	−1.08	(−2.74 to 0.58)	0.199
	Fair	17.07	0.13	(−2.05 to 2.30)	0.909
Self-rating of sense of belonging to the community	Strong	17.54	Ref.			Ref.
	Somewhat strong	15.89	−1.65	(−3.01 to −0.29)	0.018	−1.68	(−2.99 to −0.37)	0.013
	Somewhat weak	14.92	−2.61	(−4.62 to −0.60)	0.011	−1.63	(−3.65 to 0.39)	0.113
MAKS scores	Mean	23.9	0.31	(0.12 to 0.51)	0.002	0.29	(0.07 to 0.51)	0.009

Ref.: reference category; NS: not significant; MAKS: Mental Health Knowledge Schedule; CI: confidence interval; RIBS: Reported and Intended Behavior Scale; HW: health worker; CHV: community health volunteer.

Significant p values (p < .05) are in bold.

Discussion

This study examined stigma-related mental health knowledge and attitudes in CHVs and formal HWs in rural Kenya. Overall, levels of stigma-related mental health knowledge and behaviors were relatively high. The mean level of knowledge was 23.9 (range, 0–30), and the mean level of positive attitudes was 16.2 (range, 0–20). There is a paucity of data on this subject from this region and hence it impossible to compare these numbers to population norms. However, a study with community mental health staff in China found the mean level of knowledge to be 16.8 (SD = 5.4) and the mean level of positive attitudes to be 12.0 (SD = 3.4). These comparisons should be made with caution since the study sites and samples differ in many ways. Nonetheless, it appears that participants in our study sample had relatively higher scores than those in this Chinese sample. Our hope is that findings from this study are able to provide baseline data for future stigma research to build on.

Results suggest that when controlling for relevant covariates, higher stigma-related mental health knowledge is solely predicted by a greater willingness to interact with individuals with mental health problems. Furthermore, results also suggest that a greater willingness to engage in anti-stigmatizing behaviors is predicted by higher mental health knowledge and can be hindered by a sense of belongingness to the community. This indicates that stigma-related knowledge and attitudes share a bidirectional and complementary relationship, and variations in one affect the other. Moreover, when HWs (CHV and HW) report a sense of belongingness to the community, this actually leads to a lower willingness to engage with individuals with mental health problems. This is an interesting and novel finding, and one possible reason could be that individuals with strong ties to the community might not want to jeopardize their standing by associating themselves with individuals with mental illness who are often stigmatized and ostracized. Further qualitative research is required to assess the veracity of this claim and to investigate specific reasons for this counterintuitive finding.

Our other findings are in line with prior evidence that greater mental health knowledge predicts more positive attitudes (Martensson et al., 2014). Interestingly, our results contradict previous research that high personal contact and higher education are also strong predictors of positive attitudes (Aznar-Lou, Serrano-Blanco, Fernández, Luciano, & Rubio-Valera, 2016; Barke et al., 2011). Level of education was not significant in predicting knowledge or attitudes in adjusted analyses in our sample. It is also important to note that although HWs consistently demonstrated higher knowledge and more positive attitudes than CHVs, these differences were not significant when controlling for relevant covariates. Thus, it appears that anti-stigma interventions do not need to be tailored differently for both groups. Rather, anti-stigma interventions might benefit from identifying ways in which to increase mental health knowledge leading to benefits in attitudes. Campaigns focusing on biological correlates of mental illness might be effective for reducing stigma among healthcare workers (Ungar & Knaak, 2013). More research is needed to determine the efficacy of such interventions in Kenya. Furthermore, it appears that anti-stigma campaigns should pay special attention to groups that feel a strong sense of community belongingness as these groups might be more prone to perpetuating stigmatizing attitudes. Interventions specifically targeting these groups might yield effective outcomes.

Additionally, our findings from the MAKS shed light on differences in awareness of specific mental health problems between HWs and CHVs. It appears that HWs display accurate recognition of Depression, Schizophrenia, Bipolar Disorder and Drug Addiction as mental illnesses. However, CHVs demonstrate more variability in their responses. Bipolar Disorder appears to be the least well understood among CHVs with 32.1% indicating that they do not know if it is a mental illness. It is possible that a lack of local idioms for this disorder and lower prevalence rates as compared to other illnesses might have contributed to the low knowledge about this disorder (Aillon et al., 2014; Read, 2013). Moreover, the MAKS does not give descriptions of mental illnesses, rather it presents single names such as ‘Depression’ or ‘Schizophrenia’. Low levels of correct identification might thus be due to unfamiliarity with these terms rather than with the illnesses themselves. It would be important to conduct a follow-up study explaining Bipolar Disorder in terms of symptoms to better understand if CHVs are still unable to categorize it as a mental illness. Another possible explanation is that the ability to efficiently recognize psychotic and impulse control disorders as mental illness while displaying more uncertainty toward affective disorders could be related to sociocultural conceptions of mental illness. A recent study investigating knowledge of psychiatric terms among Kenyan youth found that symptoms of mental illness were mainly conceptualized as bizarre or incoherent behavior, or as a consequence of the frequent misuse of drugs and alcohol (Mamah et al., 2013). This perception of mental illness better describes individuals suffering from Schizophrenia or Substance Use Disorders rather than individuals suffering from an Affective Disorder. Hence, if mental illness is commonly viewed as leading to erratic behaviors and hallucinations, signs of Depression or Anxiety may not be recognized as mental illnesses.

Furthermore, Grief and Stress were frequently identified as mental illnesses in this sample by both CHVs and HWs. Although Posttraumatic Stress Disorder, Acute Stress Disorder and Adjustment Disorder are present in the Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5) and the International Classification of Diseases, 10th edition (ICD-10), the experience of general life stress is not classified as a mental illness (Maercker et al., 2013). Likewise, Grief and Bereavement are not classified as mental illnesses, although prolonged or abnormal reactions can become psychopathological (Bandini, 2015; Pies & Zisook, 2010). These findings have important implications for misdiagnosis and the possible medicalization of typical emotional reactions in these communities. Individuals suffering from non-pathological bereavement or stress might be misdiagnosed and incorrectly treated for a mental illness. Such misdiagnosis can adversely affect a patient by labeling them as ‘mentally ill’ leading to stigmatization and discrimination (First, 2011; Fox & Jones, 2013).

It is also interesting to note that results from the RIBS-reported suggest a high prevalence of social interactions with individuals with mental illness in both HWs and CHVs. Previous studies place the point prevalence of common mental illness and the prevalence of psychosis features in Kenya at 10.8% and 13.9%, respectively (Jenkins et al., 2012; Jenkins et al., 2015). Moreover, the prevalence of common mental illness in primary care is thought to be as high as 56.3% (Aillon et al., 2014). It is unsurprising then that exposure to individuals with mental illness is relatively common for this sample and possibly the larger community. There is hence a huge potential to increase access to services by engaging HWs and CHVs in mental healthcare. This could allow the referral and treatment of individuals suffering in community settings who might not have sought help through formal channels and who may be unsure of the services available for treatment.

Limitations

Participants in this study were recruited from a rural setting in Kenya and hence the results are not generalizable to the urban population. It would be useful to develop a matched case–control study comparing HWs with the general population to determine if the results from this study are indicative of the perceptions and attitudes of the general population as a whole. Furthermore, since HWs were nominated to participate in this study, there is the possibility of selection-bias skewing the results more favorably. There is also the risk of social desirability bias which has been documented with the use of the MAKS and the RIBS (Henderson, Evans-Lacko, Flach, & Thornicroft, 2012). Although this can be mitigated using Internet-based practices, which grants anonymity, this was not feasible given that the study was conducted in rural Kenya where routine access to web-based platforms is uncommon. Finally, Epilepsy was not included in our study since it is classified as a neurological condition rather than a mental illness, and it is also not part of the MAKS. However, it is likely that many HWs have cultural beliefs and stigmatizing attitudes toward Epilepsy that may delay treatment-seeking for patients. Thus, future studies should include Epilepsy to better understand how it is related to mental health stigma.

Conclusion

Findings from this study suggest that stigma-related mental health knowledge and attitudes are intrinsically related, and hence interventions should focus on targeting both these avenues with HWs in rural Kenya. Generally, levels of mental health knowledge and positive behaviors were high, indicating that stigma may not be as prevalent in this sample of healthcare workers as one might expect. There is scope for intervention to improve knowledge among HWs and CHVs through training on diagnosis of priority mental illnesses and to increase positive attitudes targeted specifically at those who have strong ties to the community and feel a strong sense of belonging. Future research is warranted to test feasible and sustainable ways to implement these interventions and improve mental healthcare in the region.

Footnotes

Acknowledgements

The authors are grateful to all the participants for their cooperation and willingness to participate in the study.

Availability of data and materials

Available on request from the corresponding author.

Ethical standards

The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by Grand Challenges Canada (GCC) (grant number 0739-05) through the first author and implemented by Africa Mental Health Foundation, Kenya in collaboration with Columbia University, New York. All authors were instrumental in the research process. GCC had no role in the study design, data collection, analysis, interpretation, writing of the manuscript or in the decision to submit the manuscript for publication.

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