https://orcid.org/0000-0001-5037-0833
Figures
Abstract
Background
The prevalence of hypertension continues to rise in low- and middle-income- countries (LMICs) where scalable, evidence-based interventions (EBIs) that are designed to reduce morbidity and mortality attributed to hypertension have yet to be fully adopted or disseminated. We sought to evaluate evidence from published randomized controlled trials using EBIs for hypertension control implemented in LMICs, and identify the WHO/ExpandNet scale-up components that are relevant for consideration during “scale-up” implementation planning.
Methods
Systematic review of RCTs reporting EBIs for hypertension control implemented in LMICs that stated “scale-up” or a variation of scale-up; using the following data sources PubMed/Medline, Web of Science Biosis Citation Index (BCI), CINAHL, EMBASE, Global Health, Google Scholar, PsycINFO; the grey literature and clinicaltrials.gov from inception through June 2021 without any restrictions on publication date. Two reviewers independently assessed studies for inclusion, conducted data extraction using the WHO/ExpandNet Scale-up components as a guide and assessed the risk of bias using the Cochrane risk-of-bias tool. We provide intervention characteristics for each EBI, BP results, and other relevant scale-up descriptions.
Main results
Thirty-one RCTs were identified and reviewed. Studies reported clinically significant differences in BP, with 23 studies reporting statistically significant mean differences in BP (p < .05) following implementation. Only six studies provided descriptions that captured all of the nine WHO/ExpandNet components. Multi-component interventions, including drug therapy and health education, provided the most benefit to participants. The studies were yet to be scaled and we observed limited reporting on translation of the interventions into existing institutional policy (n = 11), cost-effectiveness analyses (n = 2), and sustainability measurements (n = 3).
Citation: Gyamfi J, Vieira D, Iwelunmor J, Watkins BX, Williams O, Peprah E, et al. (2022) Assessing descriptions of scalability for hypertension control interventions implemented in low-and middle-income countries: A systematic review. PLoS ONE 17(7): e0272071. https://doi.org/10.1371/journal.pone.0272071
Editor: Aneesh Basheer, DM Wayanad Institute of Medical Sciences, INDIA
Received: December 16, 2021; Accepted: July 12, 2022; Published: July 28, 2022
Copyright: © 2022 Gyamfi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the manuscript, Supporting Information files and in Figshare, a data repository. Link to data is https://doi.org/10.6084/m9.figshare.19411190.v3.
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Cardiovascular disease-related deaths in low- and middle-income countries (LMICs) is increasing rapidly [1] with hypertension expected to cause 7.5 million deaths per year [2]. Globally, the prevalence of hypertension is estimated at 40% [3], and continues to rise in LMICs where scalable, evidence-based interventions (EBIs) that are designed to reduce morbidity and mortality attributed to hypertension have yet to be fully adopted or disseminated. Multiple barriers at the systems, provider, and patient levels impede hypertension control [4, 5], which is complicated by low disease-related knowledge and literacy [5–8] and fragile healthcare systems. Failing to implement effective, evidence-based strategies will result in a projected 68% (125.5 million) increase in hypertension by 2025 [2] and unacceptably low rates of hypertension control in LMICs. Therefore, innovative approaches to curtail hypertension in LMICs are warranted and should be scaled both vertical and horizontal [9].
Scaling-up EBIs at all levels is necessary for effective long-term control of hypertension in LMICs. The WHO/ExpandNet, a global health network of public health professionals and scientists whose focus is to advance the practice and science of successful health innovations, defines scaling-up as “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” [9]. However, optimal implementation and program expansion has received minimal attention [10]. Moreover, there is no uniform definition with which to measure scalability across studies [11].
Using the WHO/ExpandNet scale-up components as a guide, the objectives of this study were to evaluate evidence from published randomized controlled trials using EBIs for hypertension control implemented in LMICs and identify the WHO/ExpandNet scale-up components that are relevant for consideration in “scale-up” implementation planning.
Methods
This review is registered in PROSPERO (CRD42019117750) https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=117750.
Search strategy
We developed a comprehensive search strategy to identify published trials that met predefined inclusion criteria using the Standard Cochrane Collaboration systematic review technique [12] and the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) [13]; World Bank criteria [14] were used to define LMICs. The search terms used were similar to those used by Ogedegbe et al. 2014 [4]. The final strategy included MeSH, Emtree and PsycINFO subject headings, keywords, and author-generated keywords (S1 File). The following databases were searched: Web of Science Biosis Citation Index (BCI), CINAHL, EMBASE, Global Health, Google Scholar, PubMed/Medline, and PsycINFO; the grey literature and clinicaltrials.gov were also searched. The initial search was conducted on July 7, 2018, and updated June 3, 2021.
Inclusion and exclusion criteria
Studies were included if they: 1) were published RCTs implemented in LMICs, 2) reported on hypertension control intervention(s), 3) stated a primary and/or secondary outcome of change in blood pressure, 4) included “scale-up” or a variation of the term “scale,” and 5) were published in English. No limitation was placed on publication year or age of participants included in the study, and non-randomized studies, protocols, and systematic reviews were excluded.
Data extraction
All citations were downloaded to EndNote and then exported to Google Sheets. Titles and abstracts of all articles were independently screened and rated (by JG and DV) to determine if they met inclusion criteria. Discrepancies regarding eligibility of studies were resolved by discussion between the two raters (J.G. and D.V.), and, if necessary, a third party. We then conducted full-text article review and extracted relevant information from the articles that met all of the study’s inclusion criteria. Specifically, the following study characteristics were retrieved and coded: country, setting, design, sample size, intervention type, duration, professional implementing the intervention, primary and or secondary blood pressure outcome, blood pressure findings, including mean systolic blood pressure (SBP) and diastolic blood pressure (DBP), mean difference in SBP and DBP between the treatment and control groups, and WHO/ExpandNet components discussed, with extraction of specific descriptions addressing each component. Identification of WHO/ExpandNet components [15] were mostly implied rather than explicitly stated. We applied the definition of each component (Table 1) to identify relevant information from each eligible article. Data were stored in Excel and analyzed with SPSS statistical software.
Quality assessment methods
Risk of bias and quality of studies were assessed by two reviewers using the Cochrane Handbook for Systematic Review of Interventions, Version 5.1.0 [12] and the Cochrane risk-of-bias tool. Biases assessed included random sequence generation (selection bias), allocation concealment (selection bias), blinding of participants and personnel (performance bias), blinding of outcome assessment (detection bias), incomplete outcome data (attrition bias), and selective reporting (reporting bias). Quality of all trials was categorized as low/high/unclear risk of bias for each item mentioned above individually. Low risk of bias indicated that the item was well described and accounted for in the study; high risk of bias indicated the item was not sufficiently described in the study; and unclear risk of bias indicated that there was no information provided in the article to enable determination of the specific item of bias. All data were analyzed in Review Manager (RevMan 5.3).
Results
A total of 2,491 articles were identified (Fig 1). After removing duplicates, 1,951 titles and abstracts were independently screened. Of these, 1,698 articles were excluded, yielding 253 articles for which full texts were obtained and reviewed. During full-text review, 222 studies were excluded for the following reasons: No-scale-up plans described (n = 54), not conducted in LMICs (n = 21), not an RCT (n = 82), primary/secondary outcome was not change in blood pressure (n = 51), reported updated information for the same study–older version excluded (n = 1), animal study (n = 1), abstract only (n = 7), and non-English articles (n = 5). Thus, 31 papers met all study inclusion criteria and were eligible for the final review [3, 16–45]. All of the interventions reviewed were yet to be brought to full scale; however, the authors mentioned future plans for scale-up.
The majority of the studies (n = 19) were implemented mainly in South and East Asia, including Bangladesh (n = 1), Bangladesh, Pakistan, and Sri Lanka (n = 1), China (n = 9), India (n = 2), China and India (n = 1), Pakistan (n = 3),Vietnam (n = 1), and Mongolia (n = 1) (Table 2). The other studies were conducted in South America (Argentina n = 1; Brazil, n = 1; Chile, n = 1), sub-Saharan Africa (Ghana, n = 2), Nigeria and China (n = 1), Nigeria and Jamaica (n = 1), and the Middle East (Turkey, n = 5; Iran, n = 1)). The settings included: hospitals (n = 9), health centers/community setting (n = 16); urban settings (n = 18), rural settings (n = 5), both urban and rural settings (n = 2), and not reported (n = 6). Interventions were delivered by various health professionals, including physicians, nurse case managers, pharmacists, nutritionists, community health nurses, community health workers, and other primary healthcare workers.
Interventions
Of the RCTs reviewed, the interventions reported included diet, physical activity, medication, behavioral lifestyle counselling, and/or a combination of health education and health promotion strategies (Table 2). Duration of intervention lasted from less than one month to 36 months, with the exception of the Turkish medication study, which lasted two hours [43]. The interventions provided clinically significant reduction in blood pressure for study participants. The studies conducted in Pakistan and Chile, used salt reduction strategy, lifestyle counselling, and health education through text messaging, respectively; however, the mean difference in SBP and/or DBP or the significance was not reported [34, 42]. The primary or secondary outcomes of all studies included change in SBP and/or DBP. Sample size ranged from 39 to 4,023 participants.
The trial participants were adults over 18 years of age, with equal distribution of males and females. Eleven studies reported mean participant income: six studies, low income ($1,025 or less/month) [16, 21, 29, 30, 38, 40]; four studies, low-middle income ($1,026-$4,035/month) [18, 20, 22, 35]; and one upper-middle income ($4,036 - $12,475) [32]. The dominant study design employed was parallel design (n = 16), with the remaining studies using a cluster design (n = 13), cross-over design (n = 1) and step-wedge (n = 1). All of the studies used non-probability sampling, with the exception of five studies conducted in Brazil, China, China/India, Vietnam, and Turkey [18, 24,37, 39, 41].
Only four studies [3, 17, 25, 38] involved local or national stakeholders. Two of these consulted local health officials to guide the study site selection in Nigeria and China, and Ghana [3, 38]; one consulted traditional music specialists in the community prior to the music selection for the intervention [17]; and one study selected health centers in Argentina based on expert recommendations from the country’s national public health system [25].
Interventions and blood pressure control
The majority of the 31 studies reported clinically significant decreases in SBP and DBP among participants exposed to the intervention (Table 2). Multicomponent interventions comprising education about healthy lifestyle (i.e., diet, physical activity, etc.), providing antihypertensive medications or education about medication adherence, and implemented over a longer duration (6–12 months) were more effective in reducing blood pressure. Moreover, 23 (74%) of the studies found a statistically significant mean difference in SBP and/or DBP (p < 0.05) with the greatest mean SBP and DBP difference of -22.5 mmHg (p < 0.001) and 11.62 mmHg (p < 0.000), respectively [24, 31]. The studies that did not reach statistical significance were single-focused interventions (diet, physical activity, or medication solely), and/or were of shorter duration (2 hours to 6 months) [17, 23, 43]. The majority of the studies reported using automated machines to measure blood pressure, while others utilized a manual mercury sphygmomanometer.
Descriptions of evidence of scalability components
Of the 31 articles that mentioned a plan for scale-up, the majority briefly described the need for expansion or replication (horizontal scaling-up). As displayed in Table 3, only two studies provided descriptions suggestive of policy, political, legal, regulatory, budgetary, or other health systems changes necessary to institutionalize the interventions at a national level (vertical scale-up) [16, 38]. One study [38] reported future plans for horizontal and vertical scale-up, which included a cost-effectiveness evaluation of the task-shifting strategy for hypertension control in various health centers and community-based health planning and service (CHPS) compounds in Ghana, and its potential scale-up across Ghana and other countries in Sub-Saharan Africa, while engaging policymakers. Another study [16] described scale-up as the feasibility of implementation; large-scale disease prevention; and engagement of private and public interest, political support, and policy and administrative institutions that can initiate, implement, sustain, and evaluate such programs for improving the long-term health of the population.
The remaining studies described scalability using the following phrases: large scale improvement of clinical practice; reduce illness and disease prevention (impact); sustainment of intervention over time to decrease burden of disease; larger samples to assess effectiveness; emphasizing objective measures, rigorous protocol, and training of health professionals; adaptation of the intervention and integration into the existing health care system, widespread acceptability of intervention; maintenance of long-term effects in other patients; repetition of intervention in other healthcare settings; global application/expansion to other primary care facilities; human resources, training, and consideration of other operative expenses that can potentially impact spread. Among the studies with clear descriptions of scalability, the descriptions were based on WHO/ExpandNet components of scalability. On average, studies described a range of five to nine scalability components, with the majority focusing more on patient outcomes and horizontal scale-up than on the vertical scale-up.
WHO/ExpandNet scale-up components
Table 4 summarizes the WHO/ExpandNet scalability components reported by the studies.
Input.
All of the articles reported on material and equipment inputs. Moreover, some studies described in detail the training provided to health professionals implementing the intervention. Twenty-one articles provided information about health professional training, including training in the study protocol, standardization of blood pressure measurements, motivational interviewing, lifestyle behavioral counseling, interview procedures, and data collection forms.
Output.
The majority of the services offered included health promotion and health education (n = 19), medication therapy (n = 12), physical activity (n = 6), music therapy (n = 1), and meditation (n = 1). Eighteen studies used multicomponent interventions provided to participants over a duration ranging from <1 day to 36 months. Baseline and follow-up visits occurred every three months in most cases over the course of the treatment to check for intervention effects.
Outcome.
All studies described outcomes. In ten studies, multisite (hospitals, health centers, and community) recruitment strategies were used to ensure that all patients who were eligible were screened. Studies focusing on a specific population were limited to a certain recruitment site (e.g., hospital or nursing home). Across the studies, sample size ranged from 17 to 4,023 participants, aged 18 years and over.
Impact.
Twenty-three studies (74%) reported statistically significant mean differences in SBP and/or DBP between the intervention and the control participants.
Equity.
Most studies were implemented in urban settings (n = 18), rural settings (n = 5), both urban and rural settings (n = 2), six studies did not provide sufficient information to determine the setting.
Embedded within current health organization policy.
Only 11 out of 31 articles (35%) provided this information. One study [3] stated consulting the local health officials for the selection of health facilities, and included sites that had the infrastructure, resources, and healthcare staff to implement the protocol effectively. Similarly, in another study [38], Ghana’s CHPS program was used as a platform to engage community health nurses already employed at the health facilities to participate in the intervention implementation process. The other articles discussed matching components of their program with existing flow of care, integrating the new intervention into the facilities’ disease management system, and amending existing policy to accommodate new interventions.
Costs/Cost effectiveness.
Only 14 (45%) studies mentioned cost or cost effectiveness of the intervention and/or future plans to assess the interventions’ cost effectiveness. Authors discussed assessing the cost of intervention(s) (i.e., affordability of fruits and vegetables and/or medications for the population), cost-effectiveness compared to other interventions, cost of medication, and suggested future studies to assess cost-effectiveness of the intervention. However, only two studies conducted a cost-effectiveness analysis [25, 28].
Monitoring/Evaluation.
Twenty-six studies included monitoring of medication adherence through pill counts, frequent documentation of adverse events, use of patient diaries, patient self-reported intervention engagement, and case management.
Sustainability.
Twenty-two (71%) of the articles referenced sustainability (e.g., uptake and maintenance of intervention, sustained decrease in post-trial blood pressure). However, only three studies assessed sustainability post-intervention delivery: the TASSH study in Ghana demonstrated sustainability of intervention effects 12 months post-trial implementation [38]; a study in Mongolia assessed continuation of the intervention three months post-intervention conclusion [16]; and a study in China found that participants continued and sustained their physical activity regimen 30 months post-intervention [23].
Overall, six studies provided descriptions that documented all nine WHO/ExpandNet components [3, 16, 25, 28, 38, 41]. Few studies focused on the importance of embedding the intervention into an already established healthcare system or a consideration of costs and cost-effectiveness; only 13% of the studies assessed post-implementation sustainability.
Risk of bias assessment.
Based on the Cochrane Risk of Bias criteria [12] most studies (~75%) were classified as low risk of bias for random sequence generation (selection bias), and Selective reporting (reporting bias); however, over 70% of the studies did not provide information on allocation concealment (Figs 2 and 3). Approximately, 25 percent of the studies were rated high risk of bias for blinding of participant, personnel, and outcome assessment, mainly due to the nature of the study interventions not allowing for blinding to be possible. Participant withdrawal or dropout was reported by 19 studies (61%), and all of the trials reported following up the participants for at least two time points throughout the intervention.
Review authors’ judgments about each risk of bias item presented as percentages across all included studies (n = 31 studies).
Review authors’ judgments about each risk of bias item for each article (n = 31 studies).
Discussion
We assessed the descriptions of scalability from published RCTs of EBIs for hypertension control in LMICs. Although most of the studies highlighted the need for horizontal scale-up, only two studies [16, 38] clearly described plans for scale-up using the WHO/ExpandNet recommended vertical and horizontal scale-up strategy. The WHO/ExpandNet scale-up recommendations were not available until 2010, therefore older studies could not reference the recommendations because they did not exist at the time of this review.
Overall, most of the studies focused on reporting patient outcomes rather than delineating plans for the scale-up process. This is consistent with one systematic review of 14 studies conducted in primary care settings in 11 LMICs and three HICs for the prevention of mostly infectious diseases [46]. The WHO/ExpandNet components that were frequently described included Inputs, Outputs, Outcomes, Impacts, Equity, Monitoring, and Evaluation; however, few studies focused on the importance of embedding the study into an already established healthcare system or on cost effectiveness and sustainability, with only two studies [25, 28] providing cost-effectiveness data. Furthermore, despite the need for EBI scale-up in LMICs, lack of capacity due to fragile health care systems as a result of limited human and material resources, and inadequately trained health professionals in implementation science may hinder the scale-up process.
Several observations emerge from this review. First, multicomponent interventions, including those comprising medications and health education for lifestyle behavioral changes (i.e., physical activity, diet, weight loss, smoking cessation), proved effective in decreasing blood pressure. The decrease in SBP ranged from -0.04 to -22.5 mmHg, and the mean difference for DBP ranged from -0.1 to 11.62 mmHg across all studies. Our findings are consistent with previous reviews showing that multicomponent intervention integrated into an already existing system, and that is tailored to the practice context [47], can be an effective means to improve hypertension control [48].
Second, incorporating EBIs into the regular operations of the healthcare system is key to scaling up. This eliminates the prospect of segmented care and increases the possibility of getting greater buy-in from stakeholders and the professionals implementing the intervention [49]. Incorporating health education as part of the intervention into an established system is effective for lowering blood pressure and long-term disease prevention [24, 30]. Furthermore, adapting the intervention to the changing context and in response to input from the leadership within an organization, throughout the process of development to implementation, is likely to foster greater support and ensure program adoption and sustainability. Individual, institutional, and systemic capacity building [50] including training of medical/public health researchers on scale-up approaches in resource-constrained settings is paramount.
Third, an understanding of the challenges LMICs face in scaling up effective interventions for the [51, 52] management of hypertension is critical to improving physician and healthcare worker practice; and to improving both patient satisfaction and health outcomes. Human resource, health system capacity, and stakeholder acceptance of intervention is key to successful scale-up. Further, building capacity for health professionals to be trained in scale-up of EBIs is a desired outcome in implementation science to advance intervention reach and adoption.
Fourth, considering the lack of a clear definition of scale-up and inconsistency in the descriptions of scale-up components provided in the articles reviewed, we propose the following definition of scale-up: “Widespread expansion and or replication of an evidence-based, context-tailored health interventions for various population groups with guaranteed human and material resources, while engaging all key stakeholders (i.e., patients, community members, providers, health care policy makers), throughout the implementation process to ensure sustainability.” This definition draws on existing definitions while incorporating elements that include context-specific interventions, availability of resources, continuous stakeholder involvement at all levels, and sustainability considerations. Moreover, interventions being scaled in the context of low resource settings should be cost-effective; and implementation should be sensitive of the cost burden to the setting in relation to health outcomes. Finally, we recognize that the WHO/ExpandNet guides are designed for actual scale-up studies, however, using it as a guide to identify scale-up components of studies yet to be scaled will provide guidance to implementers for successful scale-up efforts. Scale-up should be considered early, preferably during intervention planning and protocol design; and descriptions of the WHO/ExpandNet components should include sufficient details to enable replication. Concerted efforts should be made to improve the reporting of conceptualization, operationalization, and measurement of scalability in published literature by drawing on the terminologies used in the implementation science literature.
This review has some limitations. First, we assessed only RCTs that were published in English. Second, studies using designs other than RCT were not included, which may also have scalability descriptions, and therefore for future research, we recommend a scoping review of non-RCTs to capture other scalable interventions for hypertension control reported elsewhere. Finally, because of heterogeneity in terms of treatment and comparators across studies, we were unable to obtain the pooled means for SBP and DBP, and a meta-analysis of the effect sizes was not performed. Finally, the conclusions should be interpreted with some caution due to the lack of a clear definition of scale-up across studies.
Despite these limitations, we believe the review has several important strengths. We conducted a rigorous systematic search, using a priori inclusion and exclusion criteria to retrieve articles that reported studies using EBIs for hypertension control in LMICs across multiple databases and assessed the WHO/ExpandNet scale-up components reported within the studies that met the inclusion criteria. We also conducted a risk of bias assessment for identified studies and found few examples of bias in the studies included in the evidence synthesis for scale-up (Figs 2 and 3). Our review provides a robust description of the scale-up terminology used in the studies that heretofore has not been reported. Finally, because there were no restrictions on article publication date, we captured a broad range of literature that describes the current state of scale-up terminologies within the implementation science field.
Conclusion
Although this review failed to identify interventions that were brought to full scale for hypertension control in LMICs, it highlights the limited available data on intervention scalability for hypertension control in LMICs and demonstrates the need for scale-up metrics and processes for resource-constrained settings. The findings thus set the stage for a valid, reliable, and reproducible set of metrics for assessing scale-up potential of EBIs for HTN and other conditions.
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