Skip to main content

Collaborative care for the detection and management of depression among adults with hypertension in South Africa: study protocol for the PRIME-SA randomised controlled trial

Abstract

Background

The high co-morbidity of mental disorders, particularly depression, with non-communicable diseases (NCDs) such as cardiovascular disease (CVD), is concerning given the rising burden of NCDs globally, and the role depression plays in confounding prevention and treatment of NCDs. The objective of this randomised control trial (RCT) is to determine the real-world effectiveness of strengthened depression identification and management on depression outcomes in hypertensive patients attending primary health care (PHC) facilities in South Africa (SA).

Methods/design

The study design is a pragmatic, two-arm, parallel-cluster RCT, the unit of randomisation being the clinics, with outcomes being measured for individual participants. The 20 largest eligible clinics from one district in the North West Province are enrolled in the trial. Equal numbers of hypertensive patients (n = 50) identified as having depression using the Patient Health Questionnaire (PHQ-9) are enrolled from each clinic, making up a total of 1000 participants with 500 in each arm. The nurse clinicians in the control facilities receive the standard training in Primary Care 101 (PC101), a clinical decision support tool for integrated chronic care that includes guidelines for hypertension and depression care. Referral pathways available include referrals to PHC physicians, clinical or counselling psychologists and outpatient psychiatric and psychological services. In the intervention clinics, this training is supplemented with strengthened training in the depression components of PC101 as well as training in clinical communication skills for nurse-led chronic care. Referral pathways are strengthened through the introduction of a facility-based behavioural health counsellor, trained to provide structured manualised counselling for depression and adherence counselling for all chronic conditions. The primary outcome is defined as at least 50% reduction in PHQ-9 score measured at 6 months.

Discussion

This trial should provide evidence of the real world effectiveness of strengtheneddepression identification and collaborative management on health outcomes of hypertensive patients withcomorbid depression attending PHC facilities in South Africa.

Trial registration

South African National Clinical Trial Register: SANCTR (http://www.sanctr.gov.za/SAClinicalTrials) (DOH-27-0916-5051). Registered on 9 April 2015. ClinicalTrials.gov: ID: NCT02425124. Registered on 22 April 2015.

Peer Review reports

Background

Burden and significance of co-morbid depression in patients with hypertension

Cardiovascular disease (CVD) (including coronary heart disease (CHD), hypertension and stroke) is the leading cause of mortality in the world [1]. Even in contexts such as South Africa, which has a large human immunodeficiency virus (HIV) burden, CVD is among the top three causes of years of life lost (YLLs) [2]. In relation to disability-adjusted life years (DALYs), which include quantification of YLLs and years lived with disability (YLD), data from South Africa shows a rapid rise in NCDs [2]. Estimates by the World Health Organisation (WHO) using DALYs suggest that NCDs were responsible for 28% of the total burden of disease in South Africa in 2004 [3]. These conditions are becoming more prevalent with the diffusion of urban lifestyle risk factors, including tobacco smoking, unhealthy diet, alcohol consumption and physical inactivity [4,5,6].

The prevalence of hypertension, at 20% of the adult population, is of particular concern [7], with CVD projected to increase by over 40% in the 35–65 years age group by 2030 [6]. A survey of over 18,000 encounters showed that hypertension follow-up was the leading reason for attending primary care clinics, accounting for 10% of all consultations among adults [8]. Similarly, in a study at a primary care clinic in Khayelitsha, Cape Town, hypertension was the most common morbidity (65%) among 14,364 adult chronic disease patients with tuberculosis (TB), HIV, diabetes or hypertension [9].

NCDs often co-exist with one another, as well as with mental disorders. Results from 245,404 participants from the World Health Surveys in 60 countries revealed that co-morbid depression was present in respondents with diabetes (9.3%), arthritis (10.7%), angina (15%) and asthma (18.1%) [10]. Prevalence rates of depression were highest when there was co-morbidity of two or more chronic physical conditions (23%). Relatively high rates of co-morbidity of NCDs are increasingly being reported in patients in South Africa [11], with a local study also revealing a similar pattern where multiple co-morbidities were associated with increased psychological symptoms [12].

Prevalence of depression in respondents from the World Health Surveys without chronic diseases on the other hand was less than 5% [13]. This suggests that people with NCDs are two to five times more likely to suffer from depression than those without NCDs, with the likelihood increasing with multiple co-morbidities. Depression co-existing with other NCDs was also shown to result in greater health decrements than when one or more physical conditions were present [10]. Mental disorders have a mutually reinforcing relationship with NCDs [14], compromising both prevention and treatment through exacerbating modifiable risk factors, and by compromising adherence and self-care, respectively [15]. In addition, through compromising the endocrine and immune systems, depression and anxiety can have a negative interactive effect at the biological level, exacerbating vulnerability to, and the course of, existing NCDs [16].

With respect to CVD in particular, co-morbid depression increases the risk of CHD and stroke [17,18,19,20,21,22,23,24]. Risk factors for CHD and stroke are related to unhealthy lifestyles which are more common among people with depression or chronic ill-health. Depressed people are more likely to smoke and less likely to exercise, adhere to chronic medication, or eat healthily [25, 26]. It is also possible that there are common causal physiological pathways, such as inflammation, which underlie both CVD and depression [16]. With regard to the impact of common mental disorders on adherence in hypertensive patients specifically, some studies from high-income countries (HICs) show higher non-adherence in patients with co-morbid depression [27]. However, it should be noted that a recent study in Ghana found that there was no relationship between co-morbid depression and/or anxiety and non-adherence, although they did find a relationship between moderate to severe stress symptoms and non-adherence [28].

Health system reforms to accommodate the rise in co-existing chronic conditions

In response to the rising burden of co-existing chronic diseases that includes HIV, TB and NCDs, the South African National Department of Health has piloted a multi-disease, integrated, chronic services approach [29]. Care for chronic conditions requires collaborative care, which extends beyond the biomedical model that characterises acute care, to incorporate the following elements: self-management support and access to behavioural-change programmes (to help patients have control over their illness and live with their condition); clinical decision support (to improve providers’ expertise and skills to provide the most evidence-informed care); clinical information systems (capturing patient-level information that will help clinical care and follow-up); delivery system design to promote chronic care (promoting planned team-based care and identifying the roles and responsibilities of the different members of the health care team so that they can work synergistically); linkages to community resources such as support groups; and quality improvement to improve the organisation of health care as a whole [30,31,32].

Such integrated collaborative care for clusters of co-existing illnesses described above, especially physical and mental disorders, has been shown to be more effective than usual care in HICs. Exemplary evidence of this is provided by the TEAMcare trial in the United States which targeted patients with depression and poorly controlled diabetes, heart disease or both, using nurse-led, multi-disease collaborative care [33]. The TEAMcare intervention combined pharmacotherapy with psychosocial interventions, supported by self-help materials, to assist patients to solve problems and set goals to improve adherence and self-care.

In South Africa, at the facility level, the integrated chronic care approach which is part of the integrated clinical management service, aims to strengthen the quality of care for chronic conditions through, inter alia: (1) facility re-organisation to improve the quality of care and servicing all chronic care patients at one service point and (2) strengthening clinical decision support through onsite in-service training using an integrated set of algorithms and checklists developed to support nurse-led identification and management of all common chronic diseases whether communicable, NCDs or mental illness – called PC101 [34]. At the community level, well-controlled patients are supported to engage in self-management by community health worker (CHW)-driven, ward-based outreach teams (WBOTs), which are part of the primary health care (PHC) re-engineering framework in South Africa. At a population level, health promotion and population screening is envisaged to promote an informed and activated population [35].

While mental health problems managed at the PHC level are included in the basket of chronic disease categories catered for by this integrated approach, findings suggest that the limited mental health component included in the basic PC101 training is insufficient to improve PHC nurses’ knowledge of mental health conditions [36], and was not associated with increased case detection of depression in a previous trial focussing on NCDs [37]. There is thus a need to strengthen the mental health component of this integrated approach in order to close the large treatment gap for mental disorders in South Africa, particularly in the chronic care population. While one in six adults experience a common mental disorder (depression, anxiety disorders and substance use disorders) over a 12-month period [38], only one in four receives treatment of any kind [39].

The Programme for improving mental health care (PRIME) [40] in South Africa has been working in one of the districts where this integrated model has been piloted for the past 5 years to develop and evaluate the feasibility of an integrated collaborative-care package for depression and alcohol use disorders (AUD). The package includes strengthening of the mental health component of PC101 training and referral pathways for management of AUD and depression [41]; the use of lay behavioural health counsellors to provide manual-based counselling for chronic care patients with depression, shown to have promising outcomes in a pilot trial [42]; strengthened referral pathways for initiation of psychotropic medication by primary health care physicians; as well as referral to mental health specialists for more severe and treatment resistant depression. In addition, systems strengthening innovations to support these efforts have been introduced through the EMERALD (Emerging Mental Health Systems in Low- and Middle-income Countries (LIMCs)) programme [43], including clinical communication skills for nurse-led chronic care and a strengthened employee assistance programme for primary health care providers experiencing personal problems and burn-out.

Development of the strengthened mental health intervention has been iterative, guided by the United Kingdom (UK) Medical Research Council (MRC) framework for the development of complex interventions [44] and refined during two successive pilots, the first in a single clinic, and the second in a further three clinics. The resultant intervention has been scaled up to 10 clinics in the Dr. Kenneth Kaunda (DKK) District and a further 10 clinics in the adjacent Bojanala Platinum (BP) District during the period 2014–2017. The intervention targets depression co-morbid with all common chronic conditions managed at primary care facilities.

This paper describes one of a pair of trials evaluating the effects of this intervention on mental and physical outcomes among chronic care patients. The other trial (Co-morbid Affective Disorders, HIV and Long-term Health – CobALT), focusses on chronically ill patients receiving antiretroviral (ART) treatment. These two conditions covered by this pair of trials account for most chronic care attendances in South Africa. Similarities and differences between the two trials are described in Table 1. The main distinction is the restriction of the trial reported in this paper to one of these districts, the eligibility criteria for patient participants and the choice of physical outcomes.

Table 1 Comparison of the pair of trials evaluating the intervention developed during PRIME-SA

Objectives

The primary objective of the trial is to determine the real-world effectiveness of strengthened integrated depression care among patients with co-existing hypertension, in a district where the integrated chronic care approach is being piloted. The primary hypothesis of the trial is that patients attending intervention facilities will demonstrate improved depression outcomes, as defined by a 50% reduction in depressive symptoms measured on the Patient Health Questionnaire (PHQ-9) at 6 months post baseline, compared to patients who attend clinics where the strengthened mental health component has not been added to the existing services.

As one of the secondary outcomes, we will determine the impact of the intervention on physical outcomes for hypertension as the trial was under-powered to have this as a primary outcome. We hypothesise that patients attending intervention facilities will demonstrate reductions in their blood pressure at 6 and 12 months post baseline, compared to patients who attend control clinics. Other secondary outcomes for the trial are listed under the ‘Outcome measures’ section.

Funding and all administrative information required by the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) Checklist [45] including the structure, function and composition of all the trial committees, are summarised in the administrative files supplied as Additional files 1 and 2 of the web-based supplementary files (see Additional file 1 for details, Additional file 2 for the SPIRIT Checklist and Fig. 1 for the SPIRIT Figure). The trial is funded from the UK Department of International Development (DFID) through PRIME, supported through grant agreement HRPC10. The funder did not contribute to the study design, data collection and management and will not contribute to data analysis, interpretation, write-up or decisions to submit the results for publication.

Fig. 1
figure 1

Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) Figure

Methods/design

Trial design

The study design is a pragmatic, two-arm, parallel-cluster randomised controlled trial (RCT) stratified by sub-district. The unit of randomisation is the clinics, and we measure outcomes on individual participants.

Setting

The study site is the DKK District in the North West Province of South Africa located adjacent to and west of the populous Gauteng region (see Fig. 2). The South African National Department of Health guided selection of the site as it is a priority district for several initiatives to strengthen primary care, including the integrated chronic care approach and the establishment of ward-based teams of CHWs. DKK is also one of 11 districts where national health insurance is being phased in over a 14-year period [46]. DKK comprises four sub-districts, with a population of 796,823 [41]. The main economic activities are mining and agriculture. Public health facilities include regional hospitals, primary health care facilities and one specialist in-patient mental health facility.

Fig. 2
figure 2

Location of the Dr. Kenneth Kaunda and Bojanala Districts in relation to South Africa

Interventions

Control arm: the integrated chronic care approach implemented by the Department of Health (described in the introduction) forms the control comparison and has been implemented in both intervention and control clinics in the year prior to the introduction of the study intervention with various levels of technical support provided by the Department of Health. At facility level the main interventions have been the re-organisation of chronic care services and training of clinic staff in PC101. Queues, appointment systems and waiting rooms for communicable disease services (HIV, TB once the intensive phase of treatment has been completed) have been integrated with services catering for NCDs and mental health, resulting in a single chronic care service aimed at reducing fragmentation and promoting integrated care [47]. Nurses in the control facilities are able to refer patients identified as having depression to PHC physicians for the initiation of antidepressants (as they are not authorised to prescribe antidepressant medications in South Africa); as well as to mental health specialists. The latter are, however, limited with only two district-level PHC psychologists available to service all PHC facilities in the DKK District. Limited specialist psychological and psychiatric care is also available at the district hospital.

Intervention arm: in the intervention clinics, we strengthened the training of PHC nurse clinicians in the identification and management of depression through training in clinical communication skills and supplementary PC101 mental health sessions (see Additional file 3 of web-based supplementary material for examples of PC101 guidelines). We trained facility trainers in the intervention facilities to deliver this supplementary training material. Facility-based trainers in the control facilities are not exposed to this training in the delivery of this supplementary material. In addition, we strengthened referral pathways for treatment and counselling (see the stepped-up collaborative-care model depicted in Fig. 3). For treatment, we provided supplementary training in mental health care for PHC physicians. For counselling, we introduced lay behavioural health counsellors into the intervention clinics, with structured supervision from the project psychologist and district-based psychologists that includes individual and weekly group supervision. A comparison of the training provided in the control and intervention clinics is contained in Table 2. The intervention coordinator is responsible for monitoring the intervention to ensure that each component is functioning optimally at the facility level. A detailed monthly monitoring document was developed and is used to monitor coverage of nurse training in the supplementary material given high nurse turnover, nurse referrals to counsellors and counsellor integration within the facility.

Fig. 3
figure 3

Collaborative-care package for depression

Table 2 Comparison of training provided to control and intervention clinics

Clinic participants

We considered all 60 public sector primary care clinics in the DKK District for enrolment in the trial and excluded clinics if they were mobile or satellite units, saw fewer than 10,000 attendances per year, or participated in the piloting of the intervention or data collection. We enrolled the largest 20 eligible clinics in the trial.

Randomisation, blinding and allocation concealment

We randomised clinics, their staff and patients to one of two parallel groups, with equal numbers of clinics (n = 10) in each group, arranging clinics into two strata determined by their sub-district to avoid potential confounding resulting from geographically determined differences in sub-district management. Twelve clinics serving the central Matlosana sub-district comprised one strata, and eight more rurally situated clinics from the Tlokwe, Maquassi Hills and Ventersdorp sub-districts the second stratum. We randomised clinics in each stratum on a 1:1 ratio yielding 10 clinics per group. Randomisation was completed using nQuery Advisor by the trial statistician independently of the managers who gave permission for the trial, and before implementation of the intervention or patient recruitment to ensure allocation concealment.

Intervention clinic staff cannot be blinded to the clinic’s treatment status given that they have to agree to additional training activities over and above the standard PC101 training; as well as the introduction of behavioural health counsellors. Although the outcome assessors (the fieldworkers) are not informed as to which clinics are in the intervention arm and which are in the control arm, it is not possible to guarantee complete blinding of the fieldworkers given the abovementioned intervention activities at the intervention clinics. To minimise the potential for bias, all patient screening, recruitment, interviewing, follow-up and quality assurance procedures are standardised across both arms to ensure that these data collection activities and procedures are applied equally in all clinics. The data collection team also operates independently of the intervention team, so as to limit exposure to the intervention.

Patient participants

We recruit patient participants from the ambulatory primary care population attending chronic care services at the trial clinics. Participants are considered to be eligible for the trial if they are aged 18 years or older, report receiving treatment for hypertension and screen positive for depression with a total score of 9 or more on the (PHQ-9) [48] (see Fig. 4). We chose the PHQ-9 given that it has been commonly used in other trials on depression [42, 49,50,51], and is relatively short and easy to train fieldworkers to administer. Most importantly, it has also been validated on the target population in South Africa [52]. We did consider other depression measures such as the Hamilton Rating Scale for Depression (HAM-D). The HAM-D, however, can take up to 15 to 30 min to administer, is ideally clinician administered and is also complicated to score, requiring substantial training in getting reasonable inter-rater agreement [53]. Given that this is a pragmatic trial, our preference was to use a measure that could be relatively easier to administer and which had been validated in the population of interest. The PHQ-9 is aligned with the Diagnostic and Statistical Manual of Mental Disorders-Text Revision (DSM-IV-TR) diagnostic criteria for major depressive disorder and the cut-off of 9 has been determined for the study population in a preliminary validation study involving 676 chronic care patients also from the DKK District. A cut-off of 9 and above was shown through the validation study to be the optimal cut-off for sensitivity and specificity indices against the ‘gold standard’ of a Structured Clinical Interview for a DSM-IV (SCID) diagnosis of major depression [51]. The trials findings will, therefore, apply to patients with major depressive symptoms at baseline. In this validation study, performance of a Setswana translation of the PHQ-9 with an adapted response to improve understanding of the time period when symptoms were experienced was compared against a structured clinical interview administered by a clinical psychologist [52] (see Table 3 for a comparison of the original and localised validated version). Mean PHQ-9 scores at baseline will be compared between intervention and control arms to assess for any difference which could indicate potential selection bias. We exclude participants if they are in need of urgent medical attention, too ill to provide informed consent or plan to move outside the vicinity of the clinic during the 12 month follow-up period (see Fig. 4). We expect some patients to meet eligibility criteria for both pairs of trials owing to the growing overlap between HIV and hypertension, recognise that it is important to represent this co-morbidity within the trials, and so invite such patients to participate in both trials.

Fig. 4
figure 4

Flow of participants

Table 3 Patient Health Questionnaire-9: general and localised versions

We will enrol roughly equal numbers of depressed hypertensive patients into the study using the PHQ-9, across the 20 clinics with a target of 50 participants per clinic, making up a total of over 1000 participants with over 500 in each arm (see Fig. 4).

Patient screening and recruitment

In the intervention clinics, we recruit patients following training of nurses and counsellors in the intervention as well as a 2-month embedding period. Trained fieldworkers enrol participants in the study independent of clinical care or participation in the counselling intervention for depression. Screening follows a three-stage process:

  1. 1.

    Trained fieldworkers (recruiters) will explain the study to patients seated in the chronic care clinic waiting room and invite them to participate. No mention of depression or hypertension is made at this stage to avoid possible stigma being attached to patients who identify themselves as interested. Fieldworkers then ask these patients one-on-one whether they are receiving hypertension treatment and if they plan to reside in the area for the next 12 months.

  2. 2.

    Fieldworkers then escort patients who respond positively to both these questions to another fieldworker, trained to perform the interview, located in a private space in the clinic. The interviewers brief potential participants about the psychological content of the questions in the PHQ-9 and obtain verbal consent before continuing (this pre-consent process is contained in Additional file 4 of web-based supplementary files). Patients who score 9 or more on the PHQ-9 are then taken through the detailed information sheet and asked to provide written informed consent (see Additional file 5 of the web-based supplementary files for more detail). We collect patient identifiers only once written consent is provided. We consider this opt-out verbal consent process necessary to balance the risks associated with the emotional content of the screening questions and the practicalities of administering written consent to many thousands of patients, as preliminary work in the district indicated that we could expect around 12% of patients to screen positive for depression using the PHQ-9.

  3. 3.

    We collect participant identifiers only once written consent is provided, and request national identity numbers to assist linkage with the national population register, which records deaths in SA.

    We ask illiterate participants to confirm that they have understood each consent line and mark a cross (x) in each consent line which an independent witness then witnesses and countersigns.

If patients show signs of suicidality through a positive response to the suicidal question of the PHQ-9, we repeat the question to confirm the response. We give patients who report having suicidal thoughts in the past 2 weeks psychoeducational material on suicidal thoughts and contact information about where to get help. We refer patients with suicidal thoughts on more than 7 days in the past 2 weeks to the nurse on the same day for assessment and onward referral if necessary.

Data collection and outcome measures

Participants provide written consent before completing the full baseline questionnaire in the language of their choice, predominantly Setswana although some interviews are also conducted in Afrikaans or English. This questionnaire includes items on demographics and multiple instruments (outlined below, Table 4) (see Additional file 6 of supplementary files for full questionnaire). Anthropometric measurements include height, weight, waist circumference and blood pressure. Trained fieldworkers measure the latter using a digital sphygmomanometer, and take readings while the participant is seated at three time points during the course of the interview. We use the ‘Averaging Rules for Determining Mean Blood Pressure’ from the National Health and Nutrition Examination Survey (NHANES) Health Technology/Blood Pressure Procedures Manual [54] to determine the average blood pressure whereby the first measurement is discarded and the second two readings used to calculate a mean blood pressure for each assessment (baseline, 6 months, 12 months).

Table 4 Schema of patient-level data collection

The primary outcome measure is a reduction in PHQ-9 score of 50% or more compared with baseline [49,50,51, 55, 56], evaluated at 6 months. Thus, we use the PHQ-9 to determine eligibility for the trial as well as to measure mental health outcomes at 6 and 12 months.

Secondary outcomes (see Table 4) include: (1) response at 12 months defined as a 50% reduction in the score on the PHQ-9, (2) remission at 12 months defined as a score of < 5 on the PHQ-9, (3) mean PHQ-9 scores at 6 and 12 months, (4) antidepressant treatment, including initiation or intensification of antidepressant medication, (5) referral to a counsellor for depression counselling, (6) referral to a mental health specialist (clinical psychologist, psychiatrist or secondary care services), (7) blood pressure at 6 and 12 months, (8) disability measured at 12 months using the WHO Disability Assessment Schedule version 2.0 (WHODAS 2), (9) stress symptoms measured at 12 months using the Perceived Stress Scale (PSS), (10) patient assessment of quality of chronic illness care received measured at 12 months using the Patient Assessment of Care for Chronic Conditions (PACIC) scale, (11) health care utilisation including clinic visits and hospital admissions, (12) resource use and economic outcomes measured using a Service Use Questionnaire and (13) all-cause mortality measured through follow-up at the clinic and linkage to the South African population register.

We obtain demographic information through self-report at baseline, including questions on sex, age, educational and employment status, income and household composition. We repeat some of the questions at 6- and 12-month interviews to track changes in economic status.

The WHODAS 2 [57] is a generic assessment instrument for disability for diseases including mental disorders. We use the 12-item interviewer administered version which has been previously used on a South African ART patient population [58].

The Perceived Stress Measure (PSS) [59] is a 10-item self-reported measure that provides an assessment of the extent to which situations in a person’s life are appraised as stressful. It has been previously used in South Africa with perceptions of high levels of stress associated with higher risk for developing some mental disorders [60].

The Patient Assessment of Care for Chronic Conditions (PACIC) scale is a self-reported measure of quality of patient-centred chronic clinical services received in the past 6 months, which emphasises self-management support in the form of collaborative goal setting, problem-solving and follow-up [61]. The original 20-item scale was adapted for the local context and reduced to 10 items.

Limitations

Limitations of the trial design include dependence on self-reporting of hypertensive medication for inclusion in the trial, lack of detailed information on prescribed medication for hypertension, and limited data on adherence to medication. While the broad inclusion criteria and light-touch follow-up are consistent with the pragmatic design and are intended to maximise generalisability to an intervention in real-world circumstances, it can be difficult to assess whether all the study participants were eligible for the intervention and to quantify the processes of care in a causal pathway. A qualitative process evaluation will be conducted alongside the RCT to address these gaps and to understand how integrating mental health services for depression affects care of physical conditions like hypertension.

Data management

Fieldworkers use handheld devices to collect data that is uploaded onto a secure server. The Trial Management Team manage data quality assurance through weekly telephonic data meetings when uploaded data is reviewed, as well as bimonthly face-to-face meetings of the Data Management Team comprising the data manager, principal investigators (PIs) and invited co-investigators, including the statistician depending on the need. Data is collected alongside the first phase of the CobALT trial in the DKK District, and is subject to the same standard operating procedures for data management for the CobALT trial. These include intensive fieldworker training in the administration of the interview, and onsite supervision by three fieldwork supervisors who perform daily quality checks on interviews conducted, observe interviews weekly to ensure the interviewer administers the questionnaire and performs the measurements as trained, and assists in addressing fieldwork problems that may have impeded data collection.

Patients receive a ZAR50 voucher (just under US$3) in appreciation for their time. We schedule follow-up appointments for 6 and 12 months at the clinic, with reminders and confirmation of follow-up interviews provided using text messaging and, where appropriate, telephone calls.

Sample size and power calculations

We calculated sample size on the following assumptions: we considered the primary outcome as the proportion of respondents considered to have at least a 50% reduction in PHQ-9 scores at 6 months compared with baseline; an intraclass correlation coefficient (ICC) of 0.04 for this outcome and is based on a similar trial in a LIMC setting [62], a significance level of 0.05; and loss to follow-up of 15% to make provision for patients who may leave the area, die or refuse to take part in the study at follow-up. This loss-to-follow-up rate was determined on the basis of a previous trial in South Africa involving chronic care patients, including those with depression [63]. The trial statistician calculated a sample size comprising 50 patients in each of the 20 facilities making up 1000 patients (500 patients in the intervention arm and 500 patients in the control arm) to provide 90% power to detect an effect size of 17% (60% control versus 77% intervention), and 80% power to detect an effect size of 15% (60% control versus 75% intervention).

Statistical methods

We will analyse data using the STATA statistical package and will blind all analysis pertaining to the primary and secondary objectives until the analysis is finalised. At baseline, we will calculate descriptive statistics including frequencies, means and standard deviations on both the intervention and control arms to establish comparability of the arms. For the intention-to-treat analysis of the primary outcome, we will use binomial regression to estimate differences in proportions of participants considered having at least 50% reduction in depressive symptoms, as measured using PHQ-9 at 6 months. We will take into account the clustering of participants within facilities for any inference done. We will complete secondary analyses whereby we adjust for baseline PHQ-9 scores, and will test for interaction effects between physical conditions. Secondary analysis will also include a comparison of outcomes in intervention-group participants who did and did not receive the intervention with adjustment for baseline covariates; as well as sub-group analysis of blood pressure outcomes in patients who had uncontrolled blood pressure at baseline.

Process evaluation

The UK MRC framework for the process evaluation of complex evaluations [64] informs the process evaluation design. We have adopted a mixed-methods approach that combines quantitative process variables collected during the 12-month period of the trial and post hoc in-depth qualitative process evaluation interviews across the clusters that follow analysis of the trial outcome data. Through monitoring changes at a facility and district level on a regular basis, we will gather information related to contextual factors that may influence outcomes in the intervention and control clusters. To monitor fidelity and quality of implementation we will collect (1) process indicators on coverage of training sessions and supervision received across the intervention clusters for PHC providers and behavioural health counsellors, (2) data on the total number of patients identified and referred for treatment of depression, and (3) fidelity checks of the counselling implementation through analysing audio-recordings of counselling sessions. We will employ in-depth qualitative process evaluation interviews to examine variations in the trial outcomes associated with potential causal mechanisms and contextual factors. These interviews will be informed by the 6-month depression primary outcome data, and conducted post hoc on purposefully sampled clusters and individuals to understand variations in the clusters that may emerge in the outcome data and analysed using thematic analysis [65].

Ethical considerations

In order to protect the confidentiality of the information provided by research participants, the research team, including fieldworkers, are required to sign a confidentiality agreement. The hand-held data collection devices are also password protected and data from interviews are uploaded immediately onto a secure server which is permission and access controlled, at which point they are erased from the hand-held device. Access to the uploaded data is restricted to the PIs, data manager, data architect, site project coordinator and research manager. All data collected will also be anonymised for analysis purposes.

Monitoring of adverse events

The Data and Safety Monitoring Board (DSMB) for the COBALT trial also provides safety monitoring for the PRIME-SA trial. Specific adverse events, applicable to the PRIME-SA trial, and protocols for management and reporting are specified in a DSMB Charter and are detailed in Table 5. Of note, given the high rate of positive responses to the ninth item of the PHQ-9 (‘Thoughts that you would be better off dead or of hurting yourself in some way’) during the past 2 weeks in the formative PRIME work, and a low rate of reported deaths by suicide in the North West Province, the DSMB advised against over-interpretation of a single item in a questionnaire. We thus introduced a standard operating procedure previously described that distinguishes patients needing psychoeducation, where patients are provided with information on their condition and where to get help from local community resources and helplines, from those requiring immediate assessment by the clinic nurse and onward referral. Death is identified during the follow-up periods and the cause of death investigated by the fieldworkers through contacting the family and through reviewing the clinic files and the National Death Registry. Death by suicide is considered a serious adverse event as it could be associated with the intervention and is reported to the COBALT DSMB and UKZN BREC within 7 days of knowledge of confirmed suicide.

Table 5 Defining, monitoring and reporting of harm in the PRIME trial

Further, while hospitalisation, prolonged hospitalisation and death would normally be considered serious adverse events, these events are expected to occur fairly frequently among the participant population given that they all suffer from hypertension which may also be co-morbid with other chronic conditions, thus increasing their vulnerability to hospitalisation and premature death. We thus monitor hospitalisations and deaths at 6- and 12-month follow-up interviews as well as link hospitalisation data on a quarterly basis, with a once-off linkage with the national population register being planned as well. The frequency of these events are reviewed at the 6-monthly DSMB meetings, which are then reported to the relevant Institutional Review Board (UKZN) with the DSMB Letter of Recommendation.

Dissemination

The PIs have established a Trial Steering Committee comprising key national, provincial and district stakeholders including policy-makers, planners and managers (see Additional file 1 supplementary web-based material for more detail) where trial progress is being reported and provides a platform to disseminate the trial results and partake in discussions around policy implications. These stakeholders have expressed their commitment to implementing the intervention package should the trial results be successful. Scale-up would require, inter alia, the expansion of the PC101 training to include clinical communication skills; a dedicated mental health module; and role clarification of the lay behavioural health counsellors within the collaborative-care model.

Further, the results of the trial will be written up in a main outcome publication within a year of the finalisation of the fieldwork, together with additional papers detailing secondary outcomes and sub-group analyses. Authorship will be determined on the basis of contributions made to the design, intervention, data collection and analysis.

Discussion

In the context of the rising burden of NCDs in LMICs, and CVD specifically in South Africa, the high prevalence of co-morbid depression is a major and growing public health threat. It interferes with treatment adherence as well as health-enhancing lifestyle changes, and has the potential to undermine investments in health care for these conditions. There is an urgent need to close the treatment gap for common mental disorders in LMICs and South Africa specifically, especially in patients who have multiple co-morbid chronic conditions – given the negative health impacts and economic costs. It is particularly important to know whether such an intervention leads to cost saving both to the health care system (through improved physical health outcomes and reduced health care utilisation) and broader society (through improved functioning, productivity and income).

Given the paucity of mental health specialists in LMICs, task sharing has been recommended as a strategy to close this gap globally as well as locally in South Africa [66, 67]. PRIME in South Africa, in collaboration with the Department of Health has developed a collaborative-team-based package for depression care in the chronic care population that strengthens and builds on existing task-sharing strategies. The need for this RCT to evaluate the real-world effectiveness of this integrated collaborative approach is spurred by the lack of evidence on the effectiveness of collaborative-care models on mental and physical health outcomes in chronic populations in LMICs. The proposed trial will be the first RCT evaluating the real-world effectiveness of a task-shared collaborative team approach to caring for hypertensive patients with co-morbid depression in a LMIC.

Trial status

At the time of submission (September 2016) the trial is in follow-up stage. Recruitment and baseline data collection began in April 2015, and will continue until the end of October 2016.

Abbreviations

ART:

Antiretroviral treatment

AUD:

Alcohol use disorders

BP:

Bojanala Platinum District

BREC:

Biomedical Research Ethics Committee

CHD:

Coronary heart disease

CHW:

Community health worker

CobALT:

Comorbid Affective Disorders AIDS/HIV and Long Term Health

CVD:

Cardiovascular disease

DALYs:

Disability-adjusted Life Years

DFID:

Department for International Development

DKK:

Dr. Kenneth Kaunda District

DSMB:

Data and Safety Monitoring Board

DSM-IV:

Diagnostic and Statistical Manual of Mental Disorders

EMERALD:

Emerging Mental Health Systems in Low- and Middle-income Countries

HIC:

High-income countries

HIV:

Human immunodeficiency virus

HREC:

Human Research Ethics Committee

ICC:

Intraclass correlation coefficient

LMIC:

Low- and middle-income countries

MRC:

Medical Research Council

NCDs:

Non-communicable diseases

NHANES:

National Health and Nutrition Examination Survey

NIHR:

National Institute for Health Research

PACIC:

Patient Assessment of Care for Chronic Conditions

PC101:

Primary Care 101

PHC:

Primary health care

PHQ-9:

Patient Health Questionnaire

PI:

Principle investigator

PRIME:

Programme for Improving Mental Health Care

PSS:

Perceived Stress Scale

RCT:

Randomised controlled trial

TB:

Tuberculosis

UK:

United Kingdom

UKZN:

University of KwaZulu-Natal

VAS:

Visual Analogue Scale

WBOT:

Ward-based outreach team

WHO:

World Health Organiszation

WHODAS:

World Health Organiszation Disability Assessment Schedule

YLD:

Years lived with disability

YLLs:

Years of life lost

References

  1. Yach D, Hawkes C, Gould CL, Hofman KJ. The global burden of chronic diseases: overcoming impediments to prevention and control. JAMA. 2004;291(21):2616–22.

    Article  CAS  PubMed  Google Scholar 

  2. Mortality GBD. Causes of Death C. Global, regional, and national age-sex specific all-cause and cause-specific mortality for 240 causes of death, 1990–2013: a systematic analysis for the global burden of disease study 2013. Lancet. 2015;385(9963):117–71.

    Article  Google Scholar 

  3. Mayosi BM, Flisher AJ, Lalloo UG, Sitas F, Tollman SM, Bradshaw D. The burden of non-communicable diseases in South Africa. Lancet. 2009;374(9693):934–47.

    Article  PubMed  Google Scholar 

  4. van Zyl S, et al. Risk-factor profiles for chronic diseases of lifestyle and metabolic syndrome in an urban and rural setting in South Africa. 2012;4:2012.

  5. Levitt NS. Diabetes in Africa: epidemiology, management and healthcare challenges. Heart. 2008;94(11):1376–82. Epub 2 Jun 2008

    Article  CAS  PubMed  Google Scholar 

  6. Maredza M, Hofman KJ, et al. A hidden menace: cardiovascular disease in South Africa and the costs of an inadequate policy response. SA Heart. 2011;8:48–57.

    Google Scholar 

  7. Steyn K, Gaziano TA, Bradshaw D, Laubscher R, Fourie J, South African D, Health CT. Hypertension in south African adults: results from the demographic and health survey, 1998. J Hypertens. 2001;19(10):1717–25.

    Article  CAS  PubMed  Google Scholar 

  8. Mash B, Fairall L, Adejayan O, Ikpefan O, Kumari J, Mathee S, Okun R, Yogolelo W. A morbidity survey of south African primary care. PLoS One. 2012;7(3):e32358.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Oni T, Youngblood E, Boulle A, McGrath N, Wilkinson RJ, Levitt NS. Patterns of HIV, TB, and non-communicable disease multi-morbidity in peri-urban South Africa – a cross sectional study. BMC Infect Dis. 2015;15:20.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Moussavi S, Chatterji S, Verdes E, Tandon A, Patel V, Ustun B. Depression, chronic diseases, and decrements in health: results from the world health surveys. Lancet. 2007;370(9590):851–8.

    Article  PubMed  Google Scholar 

  11. Folb N, Timmerman V, Levitt NS, Steyn K, Bachmann MO, Lund C, Bateman ED, Lombard C, Gaziano TA, Zwarenstein M, et al. Multimorbidity, control and treatment of noncommunicable diseases among primary healthcare attenders in the western cape, South Africa. South African Med J = Suid-Afrikaanse tydskrif vir geneeskunde. 2015;105(8):642–7.

    Google Scholar 

  12. Mendenhall E, Richter LM, Stein A, Norris SA. Psychological and physical co-morbidity among urban south African women. PLoS One. 2013;8(10):e78803.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Thornicroft G, Chatterji S, Evans-Lacko S, Gruber M, Sampson N, Aguilar-Gaxiola S, Al-Hamzawi A, Alonso J, Andrade L, Borges G, et al. Under-treatment of people with major depressive disorder in 21 countries. Br J Psychiatry. (in press)

  14. Prince M, Patel V, Saxena S, Maj M, Maselko J, Phillips MR, Rahman A. No health without mental health. Lancet. 2007;370(9590):859–77.

    Article  PubMed  Google Scholar 

  15. Ngo VK, Rubinstein A, Ganju V, Kanellis P, Loza N, Rabadan-Diehl C, Daar AS. Grand challenges: integrating mental health care into the non-communicable disease agenda. PLoS Med. 2013;10(5):e1001443.

    Article  PubMed  PubMed Central  Google Scholar 

  16. World Health Organization and Calouste Gulbenkian Foundation. Integrating the response to mental disorders and other chronic diseases in health care systems. Geneva: World Health Organization; 2014.

  17. Gan Y, et al. Depression and the risk of coronary heart disease: a meta-analysis of prospective cohort studies. BMC Psychiatry. 2014;14. p. 371. https://0-doi-org.brum.beds.ac.uk/10.1186/s12888-014-0371-z.

  18. Triant VA. HIV infection and coronary heart disease: an intersection of epidemics. J Infect Dis. 2015;205(Suppl 3):S355–61.

  19. Triant VA, Lee H, Hadigan C, Grinspoon SK. Increased acute myocardial infarction rates and cardiovascular risk factors among patients with human immunodeficiency virus disease. J Clin Endocrinol Metab. 2007;92(7):2506–12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Sen S, Rabinstein AA, Elkind MSV, Powers WJ. Recent developments regarding human immunodeficiency virus infection and stroke. Cerebrovasc Dis (Basel, Switzerland). 33(3):209–18.

  21. Longo-Mbenza B, Longokolo Mashi M, Lelo Tshikwela M, Mokondjimobe E, Gombet T, Ellenga-Mbolla B, Nge Okwe A, Kangola Kabangu N, Mbungu FS. Relationship between younger age, autoimmunity, cardiometabolic risk, oxidative stress, HAART, and ischemic stroke in Africans with HIV/AIDS. ISRN Cardiology. 2011:897908–8.

  22. van Wijk JPH, Cabezas MC. Hypertriglyceridemia, metabolic syndrome, and cardiovascular disease in HIV-infected patients: effects of antiretroviral therapy and adipose tissue distribution. Int J Vasc Med. 2012:201027–7.

  23. Law M, Friis-Møller N, Weber R, Reiss P, Thiebaut R, Kirk O, d’Arminio Monforte A, Pradier C, Morfeldt L, Calvo G, et al. Modelling the 3-year risk of myocardial infarction among participants in the data collection on adverse events of anti-HIV drugs (DAD) study. HIV Med. 2003;4(1):1–10.

    Article  CAS  PubMed  Google Scholar 

  24. Bloomfield GS, Hogan JW, Keter A, Sang E, Carter EJ, Velazquez EJ, Kimaiyo S. Hypertension and obesity as cardiovascular risk factors among HIV seropositive patients in western Kenya. PLoS One. 2011;6(7):e22288. Epub 14 Jul 2011.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Glassman AH. Cigarette smoking: implications for psychiatric illness. Am J Psychiatry. 1993;150(4):546–53.

    Article  CAS  PubMed  Google Scholar 

  26. Ziegelstein RC, Fauerbach JA, Stevens SS, Romanelli J, Richter DP, Bush DE. Patients with depression are less likely to follow recommendations to reduce cardiac risk during recovery from a myocardial infarction. Arch Intern Med. 2000;160(12):1818–23.

    Article  CAS  PubMed  Google Scholar 

  27. Krousel-Wood MA, Frohlich ED. Hypertension and depression: coexisting barriers to medication adherence. J Clin Hypertens. 2010;12(7):481–6.

    Google Scholar 

  28. Kretchy IA, Owusu-Daaku FT, Danquah SA. Mental health in hypertension: assessing symptoms of anxiety, depression and stress on anti-hypertensive medication adherence. Int J Ment Heal Syst. 2014;8:25.

    Article  Google Scholar 

  29. Mahomed OH, Asmall S. Development and implementation of an integrated chronic disease model in South Africa: lessons in the management of change through improving the quality of clinical practice. Int J Integr Care. 2015;15:e038.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Bonomi AE, Wagner EH, Glasgow RE, VonKorff M. Assessment of chronic illness care (ACIC): a practical tool to measure quality improvement. Health Serv Res. 2002;37(3):791–820.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Evans-Lacko S, Little K, Meltzer H, Rose D, Rhydderch D, Henderson C, Thornicroft G. Development and psychometric properties of the mental health knowledge schedule. Can J Psychiatr. 2010;55(7):440–8.

    Article  Google Scholar 

  32. Rose D, Thornicroft G. Service user perspectives on the impact of a mental illness diagnosis. Epidemiologia e psichiatria sociale. 2010;19(2):140–7.

    Article  PubMed  Google Scholar 

  33. Katon WJ, Lin EH, Von Korff M, Ciechanowski P, Ludman EJ, Young B, Peterson D, Rutter CM, McGregor M, McCulloch D. Collaborative care for patients with depression and chronic illnesses. N Engl J Med. 2010;363(27):2611–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Fairall L, Bateman E, Cornick R, Faris G, Timmerman V, Folb N, Bachmann M, Zwarenstein M, Smith R. Innovating to improve primary care in less developed countries: towards a global model. BMJ innovations. 2015;1(4):196–203.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Asmal S, Mahomed O. Integrated chronic disease management manual. Pretoria: Department of Health; ND.

  36. Naidoo S, Mahomed OH, Asmall S, Taylor M. Nurses’ knowledge of chronic disease management. Health SA Gesondheid. 2014;19(1):1–8.

  37. Fairall LR. Integrating prevention and treatment for NCDs into PHC. In: Changing patterns of non-communicable diseases: Crowne Plaz Johannesburg; 2013. Retrieved 1 March 2018 at http://www.iamp-online.org/changing-patterns-non-communicable-diseases.

  38. Herman AA, Stein DJ, Seedat S, Heeringa SG, Moomal H, Williams DR. The South African Stress and Health (SASH) study: 12-month and lifetime prevalence of common mental disorders. S Afr Med J. 2009;99(5 Pt 2):339–44.

    PubMed  PubMed Central  Google Scholar 

  39. Seedat S, Williams DR, Herman AA, Moomal H, Williams SL, Jackson PB, Myer L, Stein DJ. Mental health service use among south Africans for mood, anxiety and substance use disorders. South African Med J = Suid-Afrikaanse tydskrif vir geneeskunde. 2009;99(5 Pt 2):346–52.

    Google Scholar 

  40. Lund C, Tomlinson M, De Silva M, Fekadu A, Shidhaye R, Jordans M, Petersen I, Bhana A, Kigozi F, Prince M, et al. PRIME: a programme to reduce the treatment gap for mental disorders in five low- and middle-income countries. PLoS Med. 2012;9(12):e1001359.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Petersen I, Fairall L, Bhana A, Kathree T, Selohilwe O, Brooke-Sumner C, Faris G, Breuer E, Sibanyoni N, Lund C, et al. Integrating mental health into chronic care in South Africa: the development of a district mental healthcare plan. Br J Psychiatry J Ment Sci. 2016;208(Suppl 56):s29–39.

    Article  Google Scholar 

  42. Petersen I, Hanass Hancock J, Bhana A, Govender K. A group-based counselling intervention for depression comorbid with HIV/AIDS using a task shifting approach in South Africa: a randomized controlled pilot study. J Affect Disord. 2014;158:78–84.

    Article  CAS  PubMed  Google Scholar 

  43. Semrau M, Evans-Lacko S, Alem A, Ayuso-Mateos JL, Chisholm D, Gureje O, Hanlon C, Jordans M, Kigozi F, Lempp H, et al. Strengthening mental health systems in low- and middle-income countries: the EMERALD programme. BMC Med. 2015;13:79.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Craig P, Dieppe P, Macintyre S, Michie S, Nazareth I, Petticrew M, Medical Research Council G. Developing and evaluating complex interventions: the new Medical Research Council guidance. BMJ. 2008;337:a1655.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Chan AW, Tetzlaff JM, Gotzsche PC, Altman DG, Mann H, Berlin JA, Dickersin K, Hrobjartsson A, Schulz KF, Parulekar WR, et al. SPIRIT 2013 explanation and elaboration: guidance for protocols of clinical trials. BMJ. 2013;346:e7586.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Department of Health. National Health Insurance in South Arica. Pretoria: Department of Health; 2011.

    Google Scholar 

  47. Mahomed OH, Asmall S, Freeman M. An integrated chronic disease management model: a diagonal approach to health systems strengthening in South Africa. J Health Care Poor Underserved. 2014;25(4):1723–9.

    Article  PubMed  Google Scholar 

  48. Kroenke K, Spitzer RL, Williams JB, Lowe B. The patient health questionnaire somatic, anxiety, and depressive symptom scales: a systematic review. Gen Hosp Psychiatry. 2010;32(4):345–59. Epub 7 May 2010

    Article  PubMed  Google Scholar 

  49. Ell K, Xie B, Quon B, Quinn DI, Dwight-Johnson M, Lee PJ. Randomized controlled trial of collaborative care management of depression among low-income patients with cancer. J Clin Oncol Off J Am Soc Clin Oncol. 2008;26(27):4488–96.

    Article  Google Scholar 

  50. Richards DA, Hill JJ, Gask L, Lovell K, Chew-Graham C, Bower P, Cape J, Pilling S, Araya R, Kessler D, et al. Clinical effectiveness of collaborative care for depression in UK primary care (CADET): cluster randomised controlled trial. BMJ. 2013;347:f4913.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Menchetti M, Sighinolfi C, Di Michele V, Peloso P, Nespeca C, Bandieri PV, Bologna M, Fioritti A, Fravega R, Ghio L, et al. Effectiveness of collaborative care for depression in Italy. A randomized controlled trial. Gen Hosp Psychiatry. 2013;35(6):579–86.

    Article  PubMed  Google Scholar 

  52. Bhana A, Rathod SD, Selohilwe O, Kathree T, Petersen I. The validity of the patient health questionnaire for screening depression in chronic care patients in primary health care in South Africa. BMC Psychiatry. 2015;15(1):118.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606–13.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Prevention. CfDCa: National Health And Nutrition Examination Survey (NHANES) health tech/blood pressure procedures manual. 2009.

  55. Crane PK, Gibbons LE, Willig JH, Mugavero MJ, Lawrence ST, Schumacher JE, Saag MS, Kitahata MM, Crane HM. Measuring depression levels in HIV-infected patients as part of routine clinical care using the nine-item patient health questionnaire (PHQ-9). AIDS Care. 2010;22(7):874–85.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Lowe B, Kroenke K, Herzog W, Grafe K. Measuring depression outcome with a brief self-report instrument: sensitivity to change of the Patient Health Questionnaire (PHQ-9). J Affect Disord. 2004;81(1):61–6.

    Article  PubMed  Google Scholar 

  57. Garin O, Ayuso-Mateos JL, Almansa J, Nieto M, Chatterji S, Vilagut G, Alonso J, Cieza A, Svetskova O, Burger H, et al. Validation of the ‘World Health Organization disability assessment schedule, WHODAS-2’ in patients with chronic diseases. Health Qual Life Outcomes. 2010;8:51.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Hanass-Hancock J, Myezwa H, Carpenter B. Disability and living with HIV: baseline from a cohort of people on long term ART in South Africa. PLoS One. 2015;10(12):e0143936.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Cohen S, Kamarck T, Mermelstein R. A global measure of perceived stress. J Health Soc Behav. 1983;24(4):385–96.

    Article  CAS  PubMed  Google Scholar 

  60. Vythilingum B, Roos A, Faure SC, Geerts L, Stein DJ. Risk factors for substance use in pregnant women in South Africa. S Afr Med J. 2012;102(11 Pt 1):851–4.

    Article  PubMed  Google Scholar 

  61. Glasgow RE, Wagner EH, Schaefer J, Mahoney LD, Reid RJ, Greene SM. Development and validation of the patient assessment of chronic illness care (PACIC). Med Care. 2005;43(5):436–44.

    Article  PubMed  Google Scholar 

  62. Rahman A, Malik A, Sikander S, Roberts C, Creed F. Cognitive behaviour therapy-based intervention by community health workers for mothers with depression and their infants in rural Pakistan: a cluster-randomised controlled trial. Lancet. 2008;372(9642):902–9.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Fairall LR, Folb N, Timmerman V, Lombard C, Steyn K, Bachmann MO, Bateman ED, Lund C, Cornick R, Faris G, et al. Educational outreach with an integrated clinical tool for nurse-led non-communicable chronic disease management in primary care in South Africa: a pragmatic cluster randomised controlled trial. PLoS Med. 2016;13(11):e1002178.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Moore GF, Audrey S, Barker M, Bond L, Bonell C, Hardeman W, Moore L, O’Cathain A, Tinati T, Wight D, et al. Process evaluation of complex interventions: Medical Research Council guidance. BMJ. 2015;350:h1258.

    Article  PubMed  PubMed Central  Google Scholar 

  65. Grant A, Treweek S, Dreischulte T, Foy R, Guthrie B. Process evaluations for cluster-randomised trials of complex interventions: a proposed framework for design and reporting. Trials. 2013;14:15.

    Article  PubMed  PubMed Central  Google Scholar 

  66. World Health Organization. Comprehensive mental health action plan 2013–2020. Geneva: World Health Organization; 2013.

    Google Scholar 

  67. Department of Health. Mental health policy framework and strategic plan. Pretoria: Department of Health; 2013.

    Google Scholar 

  68. Dua T, Barbui C, Clark N, Fleischmann A, Poznyak V, van Ommeren M, Yasamy MT, Ayuso-Mateos JL, Birbeck GL, Drummond C, et al. Evidence-based guidelines for mental, neurological, and substance use disorders in low- and middle-income countries: summary of WHO recommendations. PLoS Med. 2011;8(11):e1001122.

  69. Patel V, Belkin GS, Chockalingam A, Cooper J, Saxena S, Unutzer J. Grand challenges: integrating mental health services into priority health care platforms. PLoS Med. 2013;10(5):e1001448.

  70. Petersen I, Hanass Hancock J, Bhana A, Govender K. Closing the treatment gap for depression co-morbid with HIV in South Africa: Voices of afflicted women. Health. 2013;5(3A):557–66.

  71. Petersen I, Bhana A, Baillie K. The Feasibility of Adapted Group-Based Interpersonal Therapy (IPT) for the Treatment of Depression by Community Health Workers Within the Context of Task Shifting in South Africa. Community Ment Health J. 2011;48:336–41.

  72. McEvoy PM, Nathan P, Noton PJ: Efficacy of Transdiagnostic Treatments: A Review of Published Outcome Studies and Future Research Directions. Journal of Cognitive Psychotherapy: An International Quarterly 2009;23(1):20–33.

Download references

Acknowledgements

The authors thank the pilot and intervention clinic nurses and counsellors who were willing to take on additional clinical responsibilities despite their tremendous workloads; physicians and clinic managers at the study facilities; the North West Provincial Department of Health; and the North West district management.

PRIME-SA research team contributors are listed in administrative file Additional file 1.

Funding

This study is an output of the Programme forimproving mental health care (PRIME). This work was supported by the UK Department for International Development [201446]. The views expressed do not necessarily reflect the UK Government’s official policies. GT is supported by the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care South London at King’s College London Foundation Trust. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. GT acknowledges financial support from the Department of Health via the National Institute for Health Research (NIHR) Biomedical Research Centre and Dementia Unit awarded to South London and Maudsley NHS Foundation Trust in partnership with King’s College London and King’s College Hospital NHS Foundation Trust. GT is supported by the European Union Seventh Framework Programme (FP7/2007-2013) Emerald project.

Availability of data and materials

The anonymised participant-level data will be made publically available 1 year after publication of the main trial outcome paper, in accordance with PRIME publication and data management policies.

Author information

Authors and Affiliations

Authors

Consortia

Contributions

IP led the conceptualisation of the study and participated in designing the study; applying for funding; developing, piloting and implementing the intervention; and planning the data collection. AB participated in conceptualising and designing the study; applying for funding; developing, piloting and implementing the intervention; and planning the data collection. NF participated in designing the study; developing, piloting and implementing the intervention; and planning the data collection. GT participated in conceptualising and designing the study; developing, piloting and implementing the intervention; and planning the data collection. BZ participated in designing the study and planning the data collection. OS participated in developing, piloting and implementing the intervention; and planning the data collection. RP participated in developing, piloting and implementing the intervention. NM participated in developing, piloting and implementing the intervention. DGP participated in developing, piloting and implementing the intervention; and planning the data collection. TK participated in applying for funding; and developing, piloting and implementing the intervention. CLu participated in conceptualising and designing the study; and applying for funding. CLo participated in designing the study. MB participated in designing the study. TG participated in designing the study. NL participated in designing the study. LF led the conceptualisation and design of the study, and participated in applying for funding; developing, piloting and implementing the intervention; and planning the data collection. All authors reviewed the manuscript and approved the final version.

Corresponding author

Correspondence to Inge Petersen.

Ethics declarations

Ethics approval and consent to participate

Ethical approval for the study was obtained from the University of KwaZulu-Natal’s Biomedical Research Ethics Committee (BREC) (reference BFC049/15); the University of Cape Town’s Faculty of Health Sciences Human Research Ethics Committee (reference HREC: 412/2011); as well as the Department of Health in the North West Province of South Africa. All amendments to the protocol are approved by BREC at the University of KwaZulu-Natal and amended on the trial registries. Given that randomisation of the clinics to the intervention and control condition was carried out prior to data collection, informed consent was only obtained from patients to participate in individual data collection and not for randomisation. Informed consent procedures have been described in detail in the recruitment section. Informed consent required that patients understood that their participation was voluntary and that they could withdraw at any stage; were required to sit for three interviews (baseline, 6 months and 12 months), and permit access to their patient records for research purposes.

Consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Additional files

Additional file 1:

Administration file_1 Jun 2017. (DOCX 40 kb)

Additional file 2:

SPIRIT Checklist. (DOCX 23277 kb)

Additional file 3:

PC101 pages. (DOCX 37 kb)

Additional file 4:

Pre-consent process. (DOCX 492 kb)

Additional file 5:

PRIME and COBALT consent forms. (PDF 7265 kb)

Additional file 6:

Baseline questionnaire. (DOC 130 kb)

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Petersen, I., Bhana, A., Folb, N. et al. Collaborative care for the detection and management of depression among adults with hypertension in South Africa: study protocol for the PRIME-SA randomised controlled trial. Trials 19, 192 (2018). https://0-doi-org.brum.beds.ac.uk/10.1186/s13063-018-2518-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://0-doi-org.brum.beds.ac.uk/10.1186/s13063-018-2518-6

Keywords