Article Text
PDF
Abstract
Background Studies assessing the indirect impact of COVID-19 using mathematical models have increased in recent years. This scoping review aims to identify modelling studies assessing the potential impact of disruptions to essential health services caused by COVID-19 and to summarise the characteristics of disruption and the models used to assess the disruptions.
Methods Eligible studies were included if they used any models to assess the impact of COVID-19 disruptions on any health services. Articles published from January 2020 to December 2022 were identified from PubMed, Embase and CINAHL, using detailed searches with key concepts including COVID-19, modelling and healthcare disruptions. Two reviewers independently extracted the data in four domains. A descriptive analysis of the included studies was performed under the format of a narrative report.
Results This scoping review has identified a total of 52 modelling studies that employed several models (n=116) to assess the potential impact of disruptions to essential health services. The majority of the models were simulation models (n=86; 74.1%). Studies covered a wide range of health conditions from infectious diseases to non-communicable diseases. COVID-19 has been reported to disrupt supply of health services, demand for health services and social change affecting factors that influence health. The most common outcomes reported in the studies were clinical outcomes such as mortality and morbidity. Twenty-five studies modelled various mitigation strategies; maintaining critical services by ensuring resources and access to services are found to be a priority for reducing the overall impact.
Conclusion A number of models were used to assess the potential impact of disruptions to essential health services on various outcomes. There is a need for collaboration among stakeholders to enhance the usefulness of any modelling. Future studies should consider disparity issues for more comprehensive findings that could ultimately facilitate policy decision-making to maximise benefits to all.
- Covid-19
- public health
- scoping review
Data availability statement
All data relevant to the study are included in the article or uploaded as supplementary information. All data underlying the results are available as part of the article and no additional source data are required.
This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
Statistics from Altmetric.com
STRENGTH AND LIMITATIONS OF THIS STUDY
This review will be the first review aimed to identify modelling studies assessing the potential impact of disruptions to essential health services caused by COVID-19 and to summarise the characteristics of disruption and the models used to assess the disruptions.
A thorough literature search of three major electronic databases and reporting as per the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols extension for scoping reviews guidelines.
A search of grey literature has not been performed, perhaps excluding significant contributions not published in commercial publications.
No comparisons of the findings of the studies were included in this review directly because of the large heterogeneity on which the models were built.
Background
COVID-19 had a wide range of effects. The COVID-19 pandemic and actions taken in response to it have far-reaching indirect consequences on other diseases because of their substantial disruptions to healthcare services.1 Disruptions include mitigation measures being undertaken in response to the COVID-19 pandemic, leading to the scaling back of certain actions and care-seeking; reduced capabilities of the healthcare system due to overwhelmingly high demand for the care of patients with COVID-19; and interruptions in commodity supply as a result of effects on both domestic and international supply chains.2 It is critical to understand these consequences and how policies might eliminate, diminish or mitigate them.
Epidemiological or mathematical models have provided forecasts of the pandemic based on different policy scenarios; they have supported the planning of healthcare resources to meet the COVID-19 demand and have supported countries in understanding COVID-19 transmission mechanics.3 Several models have been employed to assess the potential impact of disruptions to essential health services caused by COVID-19 pandemic on morbidity, mortality and other outcomes.2 4–6 Modellers develop new models and review and improve existing ones in order to support decision-makers formulating policies to combat this devastating pandemic and to apply in future pandemics. Studies assessing the indirect impact of COVID-19 using mathematical models have increased in recent years.7 Yet no scoping reviews summarising the description of such models assessed the effects of disruption to essential health services for several conditions other than COVID-19 in various settings exist in the literature. Since the field is rapidly developing, a review of the literature provides an overview, and description of existing models was considered important.
This scoping review aims to identify modelling studies assessing the potential impact of disruptions to essential health services caused by COVID-19 and to summarise the characteristics of disruption and the models used to assess the disruptions.
Methods
This scoping review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols extension for scoping reviews (PRISMA-ScR)8 and the protocol was registered with Open Science Framework (OSF) (https://osf.io/d5ymb). There are no competing interests for any author.
Data source and search strategy
A detailed literature search was carried out in PubMed, Embase and CINAHL from January 2020 to December 2022 for studies to include in the review. Searches were performed using index terms and phrases related to COVID-19, modelling and healthcare disruptions (online supplemental appendix 1, search strategy). No language restrictions were applied. References cited in identified sources were examined for additional studies meeting our eligibility criteria.
Supplemental material
Study selection
Modelling studies that were published in peer-reviewed journals assessing any impacts of disruptions to health services caused by COVID-19 were included in this review. Detailed descriptions of categories of disruption3 7 and models3 7 9 are provided in online supplemental appendix tables 2 and 3, respectively. Reviews, commentaries, editorials, letters to the editor, documents, conference abstracts and case reports were excluded, unless they provided novel modelling analyses or outcomes. Studies that applied time-series and regression analyses to assess the impact of disruptions to health services during COVID-19 were not included in this review. Two reviewers (SKV and LS) independently screened the titles, abstracts and full texts identified in the literature search using predefined screening criteria as stated above. Any disagreements between the two reviewers were resolved by a third independent reviewer (NC).
Data charting process
A standardised data charting form was designed for the study. Two reviewers (LS and SKV) independently extracted the data. In case of any disagreement, a designated third reviewer (NC) cross-checked the data and the discrepancy was resolved through mutual consultation. The following characteristics were extracted in four domains: (1) general characteristics: author, year of publication, country, the population of interest, study setting, health condition, funding source and investigator group information; (2) nature of disruption: type of health service disrupted, category of disruption and possible reasons, effects on health services and other outcomes including clinical, economic, humanistic and public health goals; (3) model characteristics: specific name and type of model (classification provided in online supplemental appendix table 2) and its subclass as reported by the author, model application level, study and disruption time frames, time horizon, data sources, details on stakeholders involvement and assumptions employed; and (4) mitigation strategies: characteristics of mitigation strategies tested and its outcomes and overall recommendations.
Data synthesis
A qualitative analysis of the included studies was performed under the format of a narrative report. Findings were structured according to a primary description of the general characteristics of the included studies, followed by a comprehensive description of models and their findings. Effects of disruption were categorised as effects on demand for health services and clinical (eg, mortality and morbidity), humanistic, economic and public health goal (eg, delay in reaching disease elimination targets) outcomes. Demand for health services may change (increase or decrease) because of fear of seeking services, because of COVID-19 or difficulties in accessing them because of several reasons including disruptions to transportation or lack of funding for transportation. Mitigation strategies were conceptualised into three categories: (1) behavioural modifications including strategies to reduce social change affecting factors that influence health; (2) ensuring resources for services; and (3) ensuring access to services which are on demand. Appropriate data were presented in the form of summary tables. Because the purpose of this study is to summarise the characteristics of disruption and the models, quality assessment of included studies is least important; hence, it is not undertaken.
Patient and public involvement
Patients were not involved in this scoping review.
Results
Study selection
A total of 4677 records, comprising 4077 unique articles after removal of duplicates, were identified. Title and abstract screening removed 3896 articles, yielding 243 articles for review at full text. Another 191 were excluded due to other reasons listed in the PRISMA-ScR flow diagram (figure 1). Finally, 52 studies were included in this review.2 4–6 10–57
Study characteristics
All 52 studies were published between 2020 and 2022. Twenty-eight (53.8%) studies10–23 25–38 analysed single countries and 24 (46.2%) analysed multiple countries (ranging from 2 to 190).2 4–6 24 39–57 While four studies investigated settings with disease burden not specific to a certain country or a region.11 18 19 53 Fourteen studies (26.9%) had models set for only low-income or middle-income countries (LMICs),2 4–6 23 24 39 42–44 47 48 50 54 and 29 (55.8%) studies for only high-income countries (HICs).10 12–17 20–22 25–38 40 45 46 52 55 Five (9.6%) studies focused on both HIC and LMICs.41 49 51 56 57 There were 43 (82.7%) population-based studies2 4–6 10–12 14–21 23 24 27 28 30 32 34–36 39–57 and 9 (17.3%) hospital-based studies.13 22 25 26 29 31 33 37 38 Twenty-six (50%) studies considered the general population,2 4 10 12 15 19 22 27–29 34–36 39–41 44 46 48–51 53 54 56 57 while the remaining studies included the population with or at risk for a disease condition, the older population, and women and children (online supplemental appendix table 4). Majority (45, 86.5%) of studies were funded. Among the 52 studies, 25 (48.1%) assessed the impact of both disruption and mitigation strategies on health services,4–6 10–13 15 18 19 26 28 32 35 39–41 43 46–48 50 53 54 56 while the remaining assessed only the impact of disrupted services. The main characteristics of all the included studies are provided in table 1. Additional information is provided in online supplemental appendix table 4.
Summary of characteristics of included studies
The disruptions caused by COVID-19 have been studied for several health conditions (HIV infection (n=10),2 5 12 16 20 24 41–43 52 tuberculosis (TB) (n=5),2 27 49 50 54 and malaria (n=3)),2 6 57 vaccine preventable (n=2)39 44 and other infectious diseases (n=12),11 15 18 19 21 26 29 47 48 51 53 56 cancers (n=11),10 13 17 25 28 30 32 34 40 45 46 cardiovascular disease (n=2),33 35 maternal and child health (n=2)4 23 and many other disease conditions (n=7)2 4 20 22 23 30 32 36 37 39 44 56 (table 1 and online supplemental appendix table 5). Twelve (23.1%) studies modelled multiple health conditions2 4 20 22 23 30 32 36 37 39 44 56 while remaining focused on specific health condition or area. Disruption to the health service modelled in the studies fall into three categories7: (1) disruption to social change affecting factors that influence health (n=23; 44.2%)2 4 5 12 16 20 21 23–25 27 29–31 33–35 41 43 49 50 52 55; (2) disruption to supply of health services (n=51; 98.1%)2 5 6 10–22 24–57; and (3) disruption affecting demand for health services (n=28; 53.8%).2 4 5 10 12–14 24 25 28–32 34–37 40 42 43 45 49 50 52 55–57 Shifts in behaviour (n=18;34.6%),2 5 12 16 20 21 23–25 27 29 31 41 43 49 50 52 55 government-enforced lockdowns that limit health service provision (n=23 ;44.2%),10–23 25–38 and shortage of income for households (n=2; 3.8%)4 35 were the factors that affected social change. Lack of resources (n=50; 96.2 %)2 4–6 10–22 24–28 30–57 and health personnel (n=46; 88.5%)2 4–6 10–22 24–28 30–37 40–42 44–56 were the main reasons for the disruption to supply of health services modelled in the studies. Fear of seeking services (n=14; 26.9%),2 4 5 10 12–14 24 28 36 37 45 49 50 or difficulties in accessing (eg, disruptions to transportation) (n=19; 36.5%),2 4 10 12–14 25 28 30 32 34 35 40 42 43 50 52 56 57 were the main reasons for change in demand for health services. More details on the nature of COVID-19 disruptions are provided in online supplemental appendix table 5.
Description of models
The types of models used in the included studies were provided in table 1 and online supplemental appendix table 6. A total of 116 models were employed in the included 52 studies. Eighteen2 5 6 13 17 30 32 40 41 43 44 46–48 53 55–57 studies employed more than one model (ranging from 2 to 7). The majority of the models were simulation models (n=86; 74.1%) including microsimulation (n=4538.8),5 10 11 13 17 26 28–30 32 34 35 38 40–42 44–48 53 56 compartmental (n=30, 250.9%),2 5 6 12 15 16 18–22 24 27 40 41 43 44 52 agent-based (n=4),5 41 44 discrete-event (n=3),14 25 37 Markov (n=4),31 50 51 55 and a few other models (n=30)4 23 30 33 36 39 49 50 54 56 57 such as models from Vaccine Impact Modelling Consortium (eg, Lives Saved Tool (LiST)4 23). Most models were applicable at the national level (38.8%),6 10 12 13 15–17 20–23 25–27 30–36 38 48–50 52 57 followed by global (24.1%),2 4 11 18 39 41 44 45 51 53 54 regional (31.9%),5 19 24 40 42 46 47 55 56 and local (5.2%)14 28 29 37 43 settings (table 1 and online supplemental appendix table 6). Two analytical study time frames were identified. First, the time frame of disruption itself applied in the models was from 2 months to a maximum of 1.5 years. Second, the time frame of the impact of disruption was measured from 4 months up to 50 years in these models. Among the 116 models, 54 and 108 models performed parameters sensitivity analyses (univariate,2 6 10 12 13 15 16 18 21 22 24–26 34 35 39–43 46 48 51 multivariate6 20 31 and probabilistic28 52) and scenario analyses,2 6 11–20 22 24–38 41–48 51–53 55–57 respectively.
The impact of disruption during specific waves of the COVID-19 pandemic (ie, Omicron, Delta variants) was not considered in any models. Twelve studies considered stakeholder involvement.2 5 10 19 25 26 28 32 40 44 47 50 Three studies engaged stakeholders to guide model parameters and project disruptions.10 28 32 Stakeholders include clinical specialists10 25 26 40 50 or advisory groups2 5 10 19 28 44 and various agencies.44 47 Three studies (gender28 32 35 and ethnicity35) taken into account the equity considerations while assessing the impact of disruption. The impact of disruption during the pre-vaccination and post-vaccination era was not considered in any models.
Effects of COVID-19 disruptions modelled
Demand for health services was reported to change due to disruption in utilisation of health services. A total of 18 (34.6%) studies reported change in demand for health services.2 4 5 10 12–14 22 24 25 28 30 40 42 43 45 49 50 Eight studies reported increase in demand22 25 28 30 40 42 43 50 and 10 reported decreases in demand.2 4 5 10 12–14 24 45 49 The impact of COVID-19 has been reported to disrupt several components of health services, including prevention,2 4–6 11 12 15 18–20 24 28 36 39 41–44 47 48 50 52–54 56 57 screening,12–14 17 20 26 30 34 40 45 46 51 52 diagnosis,2 5 6 10 13 16 17 21 25–27 32 36 37 40 41 45 46 49–52 treatment,2 4–6 10 12 13 16 17 20 24–27 31 33 36 42 43 45 49–52 55 57 vaccination services,15 39 44 54 hospital admissions,22 mass drug administrations,11 18 19 47 48 56 and elective surgery,10 33 and others.4 36 38 Specific disruption effects reported by all 52 studies were categorised as clinical, humanistic, economic and public health goal outcomes (table 2 and online supplemental table 7). Forty-four studies reported clinical outcomes such as mortality and morbidity.2 4–6 10 12–17 20 21 23–37 39–46 49–52 54–57 Fifteen studies reported public health goal outcomes (eg, delay in reaching disease elimination targets).11 18 19 25 26 33 35 37 44 47 48 53 54 56 57 Seventeen articles reported humanistic outcomes (such as change in risky behaviour2 5 12 14 16 20 21 23 24 29 35 41 43 49 50 52 and quality of life28) and four articles22 29 35 38 reported economic outcome (eg, financial loss). Fourteen studies reported positive outcomes associated with COVID-19 disruptions such as decrease in risky behaviour,2 5 12 16 20 21 24 27 29 41 43 49 50 52 reduction in morbidity29 and financial saving.29
Effects of disruption and mitigation strategies
Effects of mitigation strategies
Twenty-five (48.1%) studies assessed the effect of mitigation strategies that can reduce the impact of COVID-19 disruptions on health services.4–6 10–13 15 18 19 26 28 32 35 39–41 43 46–48 50 53 54 56 Mitigation strategies were classified into three categories7: (1) behavioural modifications including strategies to reduce social change affecting factors that influence health (n=3),5 12 43 (2) ensuring resources for services (n=19)4 6 10 11 13 15 18 19 26 28 32 35 39 46–48 50 53 56; (3) ensuring access to services which are on demand (eg, reduce fear of seeking services, improve disruption to transport and prioritisation of population for health service) (n=5).13 40 41 50 54 Twenty studies reported improved clinical outcomes such as mortality and morbidity.4–6 10 12 13 15 26 28 32 35 39–41 43 46 47 50 54 56 Six studies reported public health goal outcomes (eg, improvement in reaching elimination target and periodic intensification of routine immunisation).11 18 19 48 53 56 One article reported a humanistic outcome (additional job creation)35 and one article28 reported economic outcome (eg, Maximise cost-effectiveness). One study39 reported improved outcomes in health services such as fully vaccinated people.39 Detailed description on the effects of mitigation strategies is provided in table 2 and online supplemental appendix table 8. Governments (n=16), policy makers (n=9), the health system (n=0.8), professional bodies (n=2), communities (n=1) and others (n=1) are among the audiences suggested by study authors for their findings (online supplemental appendix table 8).
Discussion
The COVID-19 pandemic, and actions taken in response to it, will have far-reaching consequences on other diseases, poverty, food security and economic growth. Essential healthcare services are frequently interrupted across the world during the COVID-19 pandemic. This scoping review has identified a total of 52 modelling studies that assessed the potential impact of disruptions to essential health services including health promotion, preventive, diagnosis and treatment services. Studies employed several mathematical models including compartment, agent-based, discrete-event simulation, Markov, regression and time series models to assess the impact on various outcomes. Studies covered a wide range of health conditions from infectious diseases to non-communicable diseases. The impact of COVID-19 has been reported to disrupt the supply of health services, demand for health services and social change affecting factors that influence health. All studies showed that disruption in health services focusing on different health conditions and services during the pandemic generally caused a greater loss of life and an increase in the prevalence of disease conditions studied. Health system resilience is the ability to prepare, manage and learn from shocks such as the COVID-19 pandemic. Twenty-eight studies in this review modelled various mitigation strategies to manage the impact of COVID-19. The findings of these studies show that one way to lessen the indirect impacts of the COVID-19 pandemic is to maintain essential health services by ensuring resources and access to services (eg, reducing fear of seeking services, improving transport) and prioritising the population who are at risk (eg, cancer screening).
Healthcare delivery was impacted in several different ways. The demand for health services could be influenced by fear of contracting COVID-19 and/or difficulties accessing services,7 while the supply of services especially operation of health services may be affected by shifting resources to fight the COVID-19 pandemic and/or by closing health services or healthcare facilities. It can be also due to the disruption to the supply of medicines and commodities. Additionally, social and public health measures due to the pandemic such as a stringent lockdown, may have an impact on people’s socioeconomic status as well as their capacity to access the healthcare they require.58 These included those for communicable diseases, non-communicable diseases, mental health, maternal and child health, routine immunisation services, and cancer diagnosis and treatment. It is important to understand which components of the health system as a whole were disrupted more severely and what the main contributing factors were. It is also important to track any changes in disruption that may be occurring as the outbreak progresses along its various stages. However, there was no attempt to assess the impact of disruption on the overall health system across all diseases and services in any of the included studies. It is important to have national or regional data on the impact of the pandemic from studies to improve understanding of the perceived extent of disruptions across all services, and the reasons for disruptions. This information can help to plan for mitigation strategies and policies and support decision-makers at various levels to advocate for resources and investment throughout the course of the pandemic for the most affected settings and populations.
Several studies in this review modelled various mitigation strategies to determine how they might affect COVID-19’s impact on a specific healthcare setting. Based on a recent survey,58 WHO suggested strategies to mitigate disruptions to services, such as triaging to identify priorities, shifting to online patient consultations, changes to prescribing practices and supply chain strategies, and refocusing public health information communications. However, focusing on only one area could have a detrimental effect on services provided in other contexts. It is crucial to carefully weigh the benefits and risks of pursuing mitigation strategies in an overall health system and recognise which strategies work best throughout different stages of the pandemic. RAPID (Rapid Assessment of Pandemic Indirect Impacts and Mitigating Interventions for Decision-making) for the State of California made such efforts, identifying and assessing the impact of mitigation strategies for six health conditions that severely deteriorated because of the COVID-19 pandemic and presenting a menu of alternatives for enhancing community health and generating cost savings.59 The optimal trade-offs between safety (ie, reducing risk of exposure to COVID) and limiting disruption through various mitigation strategies should also be established using models.
Implementing evidence-based mitigation strategies should be a policy priority, especially given how the pandemic has exacerbated disparity across several socioeconomic circumstances. Compliance by the public with mitigation strategies is largely exogenous, especially in democratic societies. It is important to investigate how mitigation strategies and compliance might operate in parallel to improve the implementation process. A recent modelling study looked at how compliance tailored to the US conditions and mitigation strategies can work together to reduce the spread of COVID-19.60 Further research on the short-term and long-term effects of these strategies, and approaches to address community acceptability and barriers to implementation is crucial. It is important to involve key stakeholders at local and national levels, including government and non-governmental agencies, to advocate for the proper allocation and regulation of available health resources while implementing mitigation strategies.
This review demonstrates the large heterogeneity on which the models were built, especially in the conceptualisation of disruption and mitigation, the structure of the models, and the underlying data used. It is difficult to compare the results of the models directly because the time periods of estimations, outcome measures, and underlying assumptions and model structures vary. In the context of a pandemic, models are often refined and updated in response to new information and data; therefore, allowing end-users to raise questions, comment and offer feedback to modellers can enhance future iterations. An appropriate stakeholder engagement manages to meet the expectations of both end-users, often policy-makers, and modellers on what models can achieve and adherence of models to culturally relevant and socially acceptable policy options based on the local context.61 In addition, stakeholders can advise on data gaps or assumptions; as it is particularly important in instances where models are not locally developed such as some of the models we have seen in this review. Recently, a framework was available to illustrate the collaborative process between modellers and stakeholders to address challenges including transparency, inclusive decision-making, and accountability that hinder successful policy implementation.62 Of the studies examined in this review, only one has sufficiently engaged stakeholders.10 We recognise that stakeholder engagement will be extremely difficult to employ in an emergency context due to capacity and resource constraints on the part of all stakeholders, not just researchers.
After living with the COVID-19 pandemic for almost 3 years, it has become evident that the pandemic had serious adverse health effects, especially on the vulnerable populations, such as children, the elderly, people living with chronic conditions or disabilities, and minority groups.58 The effects of the COVID-19 pandemic on mortality and health disparities are underestimated when only deaths directly attributed to COVID-19 are considered. Vulnerable patient populations have faced greater challenges in accessing health services during the COVID-19 pandemic.63 Of the studies included in this review, only a few studies incorporated focusing on vulnerable patient populations into the model. This is because of the unavailability of reliable data on indirect effects for different populations (eg, by geographic location, race/ethnicity and economic status) at the time of analysis. Future studies on pandemic, not limited to COVID-19, should consider the disparity issues in the modelling to estimate more comprehensive results that could ultimately facilitate policy decision-making to maximise benefits to all. Other considerations, such as the impact during specific waves of the COVID-19 pandemic (ie, Omicron, Delta variants) or before or after the vaccination era were not considered in any studies. Future models also should comprehend whether disruption’s indirect impacts have any effects since the period of vaccination or during waves of the COVID-19 pandemic. A similar review may require to be carried out after some time when more data are available and more models incorporating these aspects are published.
To the best of our knowledge, this is the first scoping review that aims to identify modelling studies assessing and/or predicting the potential impact of disruptions to essential health services caused by COVID-19 and to summarise the characteristics of disruption and the models used to assess the disruptions. One limitation of our work is that a search of grey literature has not been performed, perhaps excluding significant contributions not published in commercial publications. We were unable to provide a precise categorisation of models in this review if the authors had not reported enough model descriptions. Another limitation is that we failed to include conference abstracts because there was insufficient data on key model characteristics required as per our objective. Finally, we did not compare the results of the studies included in this review directly because of the large heterogeneity on which the models were built, especially in the conceptualisation of disruption and mitigation, the model application level, the structure of the models and the underlying data used.
Conclusion
This scoping review summarised modelling studies assessing the potential impact of disruptions to essential health services published from January 2020 to August 2022. Regardless of what model is used, all studies show that disruption in health services focusing on a specific health condition or setting generally causes a greater loss of life and an increase in disease prevalence during the pandemic. It is important to assess the impact of disruption on the overall health system across all diseases and services because such information can help to plan mitigation strategies and policies and support decision-makers at various levels in advocating for resources and investment for the most affected settings and populations throughout the course of the pandemic. There is a need for collaboration among stakeholders to enhance the usefulness of any modelling; a process framework that articulates the roles and responsibilities of various stakeholders to enhance accountability needs to be developed. Implementing mitigation strategies should be a policy priority, particularly as the pandemic has exacerbated inequality in a wide range of socioeconomic conditions. For the next pandemic, future models should consider these aspects that could ultimately facilitate culturally relevant and socially acceptable policy options to maximise benefits to all.
Data availability statement
All data relevant to the study are included in the article or uploaded as supplementary information. All data underlying the results are available as part of the article and no additional source data are required.
Ethics statements
Patient consent for publication
Acknowledgments
Thank you to all key informants for excellent feedback and for taking the time to respond and comment on our results. A special thanks to Sulfath Thekkumcheril Sidhick for her efforts to try to provide full text for identified studies and helping in updating the search.
References
Supplementary materials
Supplementary Data
This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.
Footnotes
Contributors SKV, RH and NC are responsible for the conception of the study. SKV, LS, RH and NC contributed to the development of the design. SKV, LS and WK screened citations, reviewed full-text articles and achieved consensus on the final included studies. SKV, LS and WK extracted the data. SKV and LS drafted the manuscript. DT, MS, RH and NC provided important intellectual contributions and guidance throughout the development of the manuscript. All authors contributed, edited and approved the final version of this manuscript. NC and RH acted as guarantors.
Funding This research received funding from the World Health Organization (WHO) and Centers for Disease Control and Prevention (CDC) (SHEPheRD 2021 Domain 1-A015). The views expressed solely represent those of the authors, and they are not the official views of the World Health Organization and Centers for Disease Control and Prevention
Competing interests None declared.
Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Provenance and peer review Not commissioned; externally peer reviewed.
Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.