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Abstract

Health interventions, including those directed at ameliorating symptoms of mental disorders, can contribute significantly to realising the goal of sustainable development. The Strategic Development Goal of ensuring healthy lives and well-being for all, at all ages, pertains all health conditions, including those affecting mental health. Considering the low ratio of researchers to the population of many low- and middle-income countries, there is a specific need to build capacity for research so as to ensure good quality data so that social policies can be data-informed. This article outlines four considerations for trial investigators assessing the effectiveness of mental health interventions in low- and middle-income countries, namely, task sharing, scaling up, structural barriers, and the transformation imperative. Task sharing is an arrangement in which non-specialist health workers receive training and supervision to screen for and diagnose mental disorders and intervene with persons affected by them. Scaling up a proof of concept is appropriate when trials yield positive results showing effectiveness of the intervention. Structural barriers such as transport difficulties, long waiting times in clinics, food insecurity, competing demands on people’s time, childcare concerns, and poor health literacy play an important role in driving health behaviours and should be considered in intervention design. Transformation of the cadre of researchers to include those from oppressed and marginalised groups will yield investigators who are able to frame research questions and develop methodologies that reflect the lived realities of these communities.

Keywords

Design interventions low-resource contexts mental health conditions

This article outlines four considerations for trial investigators assessing the effectiveness of mental health interventions in low- and middle-income countries (LMICs). These considerations are task sharing, scaling up, structural barriers, and the transformation imperative. Data on the prevalence and incidence of health conditions in resource-constrained contexts, including mental health conditions, have burgeoned over the past several years. Indeed, several studies have yielded data on various mental health conditions in LMICs, where the effects of inequality and lack of access to resources create and exacerbate conditions for the onset and sustainability of mental health conditions, including emotional distress and alcohol and drug abuse. Health interventions, including those directed at ameliorating symptoms of mental disorders, can contribute significantly to realising the goal of sustainable development. Indeed, the Strategic Development Goal of ensuring healthy lives and well-being for all, at all ages, pertains to all health conditions.

Considerable data have shown the large burden of disease of mental and substance use conditions (Patel et al., 2018), dementia (Nichols et al., 2022), and suicide (Moitra et al., 2021). A recent systematic review showed that mental disorders were among the top 10 leading causes of burden of disease in the world from the 1990s to 2019 (Mental Disorders Collaborators, 2022). Depression is associated with most disability-adjusted life years (DALYs) for both sexes, with higher rates in women while substance use disorders had higher rates among men (Rehm & Shield, 2019).

In a systematic review of 174 surveys across 63 countries providing period and lifetime prevalence estimates, 17.6% (95% confidence interval [CI] = [16.3%, 18.9%]) met the criteria for a common mental disorder (CMD) during the 12 months preceding assessment and 29.2% (95% CI = [25.9%, 32.6%]) were identified as having had a CMD at some time during their lifetimes (Steel et al., 2014). A systematic review and meta-analysis to estimate the prevalence of depression among university students in LMICs found that nearly one quarter (24.4%) had elevated symptoms of depression (Akhtar et al., 2020). In a systematic review of mental health conditions among young persons living with HIV, up to 40.8% were found to have major depression, up to 52.6% had elevated depressive symptoms, and as much as 25.0% had elevated anxiety symptoms. Anxiety disorder was estimated at 45.6% (Too et al., 2021). Among youth in LMICs, 28% had significant symptoms of depression or anxiety, and up to 87% had symptoms of post-traumatic stress disorder (PTSD) among those exposed to traumatic experiences (Yatham et al., 2018).

Prison populations worldwide have disproportionate numbers of persons living with mental conditions (Butler et al., 2022), and most of the global prison population resides in LMICs (Hill et al., 2022), which makes some comments on this population appropriate in this article. In a systematic review and meta-analysis of prevalence studies among prisoners in LMICs, the estimated pooled 1 year prevalence rates was 6.2% (95% CI = [4.0, 8.6]) for psychosis, 16.0% (95% CI = [11.7, 20.8]) for major depression, 3.8% (95% CI = [1.2, 7.6]) for alcohol use disorders, and 5.1% (95% CI = [2.9, 7.8]) for drug use disorders (Baranyi et al., 2019). A rapid review exploring suicide and mental health prevalence among prison populations in LMICs found that the prevalence of depression ranged from 25% to 85%, the prevalence of anxiety ranged from 7.2% to 61.9%, and the prevalence of PTSD ranged from 10% to 15.7%. Also, the prevalence of psychosis among prisoners in LMICs was 5.5%, compared to 3.5% in high-income countries (HICs; Hill et al., 2022). Among the Ethiopian migrant returnees from the Middle East and South Africa, the prevalence of CMD among migrant returnees was found to be 27.6% as assessed by the 20-item Self-Reporting Questionnaire (SRQ-20). The symptoms that were most commonly endorsed were headaches (40.6%), poor appetite (39.4), fatigue (35.8%), difficulty sleeping (36.9%), feeling unhappy (37.6%), and feeling nervous or tense (32%) (Habtamu et al., 2017).

By all accounts, mental health conditions contribute significantly to the burden of disease in LMICs. Yet, considering the low ratio of researchers to the population of many LMICs (OECD, 2016), there is a specific need to build capacity for research to ensure good quality data so that social policies can be data-informed. In particular, there is a need for capacity-building in designing studies to build an evidence base for interventions to ameliorate mental health conditions. Various interventions have been tested, some with success and others not. For example, group interpersonal psychotherapy was shown to be highly efficacious in reducing depression and functional impairment among participants in rural Uganda (Bolton et al., 2003). Among persons who screened positive for a CMD in Zimbabwe, problem-solving therapy provided by lay health workers showed improvement at 6 months compared with standard care (Chibanda et al., 2016).

Several intervention studies have been conducted among persons living with HIV. In a 2015 review of interventions, 13 trials were conducted in sub-Saharan Africa, 7 in Asia, and 2 in the Middle East (Sikkema et al., 2015). Of these interventions, 18 used cognitive behavioural therapy, 2 used family interventions, and 2 used pharmacological treatments. In the review, 4 trials reported significant intervention effects in mental health outcomes, and 11 studies demonstrated promising findings (Sikkema et al., 2015).

Evidence-informed interventions have a significant role to play in offering guidance to clinicians, health care decision-makers, and policymakers in LMICs. To this extent, the studies that generate an evidence base for mental health interventions have the potential to influence the lives of millions of people in LMICs who live with a mental health condition. Based on my experience, over the past many years, there are various aspects of interventions that may be borne in mind by trial investigators when designing mental health intervention studies in LMICs. Four are reviewed in this article, namely, task sharing, scaling up of interventions, structural barriers to conducting intervention trials, and the imperative for transformation of the cadre of trial investigators.

Task sharing

Task sharing is an arrangement in which generalists (non-specialist health professionals, lay workers, affected individuals, or informal caregivers) receive training and appropriate supervision by mental health specialists. These non-specialist health workers screen for or diagnose CMDs, treat or monitor people affected by them, and refer to appropriate specialists when this is needed. Task sharing is especially appropriate for consideration in resource-constrained contexts where the mental health gap is wide and where there are too few mental health professionals – such as psychologists, psychiatrists, and professional counsellors – to provide services to persons requiring treatment. To this extent, non-specialist health providers – who are already integrated into health systems in LMICs, such as general nurses, lay counsellors, and community health workers – can be appropriately trained for some mental health services with a referral system for complex cases. Task sharing is especially relevant in resource-constrained contexts where there are insufficient trained professionals to provide services. Indeed, there is a considerable shortage and lack of equitable distribution of psychiatrists in LMICs which accounts for people with mental disorders not having access to treatment (Patel, 2009). Two examples of task-sharing intervention trials are discussed in the following, with considerably different outcomes.

In the first example, Safren et al. (2021) showed that depressed persons living with HIV who had defaulted on first-line antiretroviral therapy (ART) and received nurse-delivered cognitive behaviour therapy for adherence (CBT-AD) had improved depression scores and adherence to second-line HIV treatment compared to those who received treatment as usual. The study was a two-arm randomised controlled effectiveness trial in which 161 participants with clinical depression and unsuppressed viral loads were recruited from primary care clinics providing HIV care in a South African township. At 4 months scores on the Hamilton Depression Scale (HAM-D) improved by an estimated 4.88 points more than the control group. At follow-up points of 4, 8, and 12 months, the CBT-AD intervention group had an estimated 5.63 lower HAM-D scores and 23.56 percentage points higher adherence than the treatment as usual condition. The authors found that the odds of undetectable viremia were 2.51 (CI = [1.01, 6.66], p = .047) at 12 months and 3.54 greater over all of the follow-ups (adjusted odds ratio [OR] = 3.54, CI = [1.59, 20.50]; p = .038) for those assigned to the CBT-AD arm. These results provide robust evidence that nurse-delivered cognitive behaviour therapy was effective in improving clinical depression and ART adherence.

The second example of task sharing is a study among South African women that sought to determine the effectiveness of a task-sharing psychological treatment for perinatal depression using non-specialist community health workers (Lund et al., 2020). This double-blind individual randomised controlled trial conducted in two antenatal clinics in a peri-urban area found no significant differences on the Hamilton Depression Rating Scale (HDRS) at 3 months post-partum between the intervention and control arms. The study authors speculated on a range of reasons for these null findings. First, there may have been insufficient contact time with service users, as there were only six sessions including problem-solving, behavioural activation, and cognitive reframing. About half of the participants in the intervention arm completed all six sessions, suggesting that the intensity of the intervention may have been too low to show an effect. Second, the authors considered that the delivery of the intervention may not have been ideal. They found a fidelity rating of 62.8% of the intervention, which while not low, indicated that the community health workers could have benefitted from more rigorous training and enhanced supervision. Third, measurement factors might also have been at play as the Edinburgh Postnatal Depression Scale (EPDS) was used to recruit participants into the trial, but symptom improvement was measured using the HDRS. Of those enrolled, 60% screened below the cut-off of 17 on the HDRS which indicated moderate depression and over half did not meet the criteria for major depression, suggesting a possible floor effect. Finally, the authors speculate that the enhanced usual care condition may have been an effective intervention in and of itself so that the task-shared intervention appeared to be less effective than it actually was in comparison (Lund et al., 2020).

Scaling up

Scaling up refers to ‘deliberate efforts to increase the impact of successfully tested health innovations so as to benefit more people and to foster policy and programme development on a lasting basis’ (World Health Organization [WHO], 2010). It entails expanding small-scale projects to have a greater reach and thus broaden the effects of an intervention so that society as a whole can derive benefits from it (WHO, 2016). Often health interventions that are innovative are implemented on a small scale as a proof-of-concept study or because of budgetary restrictions. Interventions of this nature seek to reduce the burden of disease, reduce health inequities, arrive at more optimal social determinants of health, and improve the likelihood of implementing the Sustainable Development Goals (Morton et al., 2017). Scaling up often takes the form of expansion and replication. Expansion entails extending the provision of services to new geographical regions or populations. Replication entails implementing new and innovative practices in independent contexts (WHO, 2016). The components of scaling up include dissemination and advocacy, that is, communicating and promoting the concept by means of training, technical assistance, and communication with health workers; focusing on organisational processes by means of integrating new partners; mobilising resources by analysing the cost of scaling up and ensuring that resources are available; and monitoring and evaluating the effects of the scale up to as to assess the outcomes of the intervention (Fixsen et al., 2013).

Scaling up a proof of concept is appropriate to the extent that trial findings detect a positive response among persons receiving the tested intervention compared to the control group, not only in terms of statistical significance but also in terms of effect size. Task sharing, therefore, can and should only be scaled up when findings indicate that it is appropriate. In the context of a resource-constrained environment, when conducting a proof-of-concept trial, there is a need to think ahead in terms of feasibility and sustainability when designing and testing an intervention, for example, using general nurses or community health workers rather than psychologists and psychiatrists. General nursing staff already exist in the health system, whereas psychologists and psychiatrists are fewer in number and often command higher salaries than nurses and counsellors. Thus, the potential for scale up is good if the proof-of-concept trial is successful. Given the possibility of null findings in some well-designed trials, it is fair to assume that task sharing is not a panacea and good evidence is required for task-sharing interventions to be considered for scale up.

Structural factors

Many interventions are based on social cognitive models of behaviour change. Yet, structural barriers also play an important role in driving health behaviours and should be considered when designing interventions. Structural factors are often relevant in LMICs where economic, social, and political realities are often more constraining on individual behaviour than in HICs (Kagee et al., 2011). Social Action Theory, for example, focuses on contextual influences on behaviour, which include the settings and systems that activate goals and support personal capabilities, social engagement, motivational appraisals, and actions (Ewart, 1991). For example, structural factors such as transport difficulties, long waiting times in clinics, food insecurity, competing demands on people’s time, childcare concerns, poor health literacy, and the salience of the need for the intervention may inhibit the uptake of mental health interventions. Political and social barriers to the uptake of interventions can include stigma about seeking mental health care (Crowe et al., 2018), xenophobia towards refugees and migrants (Vromans et al., 2011), traditional and cultural beliefs that inform explanatory models of mental distress (Mayston et al., 2020), and limited mental health literacy (Furnham & Swami, 2018). For some conditions such as HIV and mental health problems, the overarching structural barrier of stigma influences treatment seeking, acceptance, and adherence. To this extent, there is a need to negotiate stigma in the context of enrolling and recruiting research participants when designing interventions among persons with these conditions and to consider the roll out and scale up of interventions if trials are successful.

Another structural factor related to conducting research in a resource-constrained environment is that many trial participants live under conditions of poverty. I have observed occasions when research participants may have enrolled in more than one study at the same time to benefit from the financial incentives being offered. In the event of simultaneous enrolment in the context of intervention studies, contamination between interventions can occur that can threaten the validity of a study and lead to spurious findings. It would be difficult to ascertain whether the data yielded at a study end point is due to the intervention of that specific study or the intervention of another, or the combination of the interventions of the studies in which the participant may have enrolled.

In the context of engaging with structural barriers, especially in LMICs, implementation science is relevant. Implementation science involves identifying and engaging with barriers and facilitators to the uptake of health innovations for which evidence exists (Bauer & Kirchner, 2020). The fact that an intervention has been demonstrated to be effective in ameliorating psychological symptoms does not automatically translate into its application in clinical settings, and for most health innovations, uptake is often limited. To this extent, the scientific study of the systematic uptake of research findings into routine practice and hence to improve the quality and effectiveness of health service is necessary (Eccles & Mittman, 2006).

Transformation among trial investigators

With the growing importance of mental health in many LMICs, people from these countries remain under-represented as authors of publications and recipients of research grants. As quoted in the Lancet, a tweet by a scholar from Michigan State University stated: ‘Far too long, global mental health has been an exclusive group of scholars from selected countries and @TheLancetPsych is enabling it by giving them the platform and not even caring about the missing voices’ (Dalglish, 2020). In their appeal for equity in global mental health research, Chibanda et al. (2021) identify four kinds of racial discrimination, namely, interpersonal, internalised, institutional, and systematic. Interpersonal racism refers to ways that beliefs about race emerge in one-on-one or small-group interactions, which can often involve microaggressions. Internalised racism refers to personal understandings of race that can change the way in which roles, hierarchy, and self-worth are perceived. Institutional racism involves a focus on the procedures and norms within institutions such as universities that disadvantage Black and other minority ethnicities. Systemic racism that draws together the different aspects of racism so that they mutually reinforce one another (Chibanda et al., 2021). To address these inequities requires considerable effort on the part of multiple role players that include funding agencies, trial investigators, journal editors, and departments of global mental health, public health, psychiatry, and psychology to agitate for a change in policies.

Historically, in both HICs and LMICs, the research community has been drawn from the educated middle class. Training researchers is a long process and requires many years of study, thus requiring financial support and assistance up to and beyond the doctoral level. As a consequence, in LMICs, groups who experience varying levels of oppression, marginalisation, and inequality have historically enjoyed a few opportunities to attend university for an extended period and are thus less likely to make their way into senior positions at universities and research institutions than those from better resourced backgrounds. To this extent, transformation of the cadre of researchers requires the inclusion of persons from marginalised or oppressed groups such as persons of colour, women, LGBTI+ (lesbian, gay, bisexual, transgender, and intersex) and gender non-binary persons, disabled persons, persons living with HIV, persons with a refugee background, migrants, and non-first-language English speakers. Transformation of such a nature would yield researchers who are able to frame research questions and develop methodologies that reflect the realities of these groups and others, thus addressing the needs of historically neglected communities. To transform the field, a focus of equity is necessary in various aspects of the research process, such as funding priorities, the composition of journal editorial boards, decisions about the nature of the research questions of studies, attendance at international conferences, and support for authorship from LMICs (Chibanda et al., 2021).

Relatedly, building research capacity among key informants, community workers, and interviewers enhances skill level, employability, and intellectual capital among poor and marginalised communities. To this extent, partnerships with non-governmental and non-profit organisations can play a role in facilitating research capacity-building and the acquisition and development of intellectual capital in communities.

Concluding comments

Generating an evidence base to inform mental health interventions in LMICs is expensive and challenging. Often funding for such research comes from HICs, which creates a power differential between investigators and the people whom they research. Research studies that involve task sharing, consider the need for scaling up, address structural barriers, and embrace the transformation imperative can strengthen the body of evidence for interventions to ameliorate mental health conditions in LMICs.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Ashraf Kagee

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Designing interventions to ameliorate mental health conditions in resource-constrained contexts: some considerations

Abstract

Keywords

Task sharing

Scaling up

Structural factors

Transformation among trial investigators

Concluding comments

Footnotes

Declaration of conflicting interests

Funding

ORCID iD

References