Multimorbidity-associated emergency hospital admissions: a “screen and link” strategy to improve outcomes for high-risk patients in sub-Saharan Africa: a prospective multicentre cohort study protocol

Multimorbidity, non-communicable diseases, hospital care, sub-Saharan Africa, health related quality of life, patient costs, health system costs ALL Metrics - Views Downloads How to cite this article Spencer SA, Rutta A, Hyuha G et al. Multimorbidity-associated emergency hospital admissions: a “screen and link” strategy to improve outcomes for high-risk patients in sub-Saharan Africa: a prospective multicentre cohort study protocol [version 2; peer review: 1 approved, 2 approved with reservations]. NIHR Open Res 2024, 4:2 (https://doi.org/10.3310/nihropenres.13512.2) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente Select a format first ▬ ✚ Study Protocol Revised Multimorbidity-associated emergency hospital admissions: a “screen and link” strategy to improve outcomes for high-risk patients in sub-Saharan Africa: a prospective multicentre cohort study protocol [version 2; peer review: 1 approved, 2 approved with reservations] Stephen A. Spencer https://orcid.org/0000-0001-8451-7514 1,2*, Alice Rutta https://orcid.org/0000-0001-5217-5249 3*, Gimbo Hyuha https://orcid.org/0000-0002-2419-4386 4*, [...] Gift Treighcy Banda https://orcid.org/0000-0002-7465-0730 1,2, Augustine Choko1, Paul Dark5, Julian T. Hertz6, Blandina T. Mmbaga https://orcid.org/0000-0002-5550-1916 3, Juma Mfinanga4, Rhona Mijumbi1, Adamson Muula7, Mulinda Nyirenda7, Laura Rosu2, Matthew Rubach3,6, Sangwani Salimu1,2, Francis Sakita3,8, Charity Salima9, Hendry Sawe https://orcid.org/0000-0002-0395-5385 4, Ibrahim Simiyu https://orcid.org/0000-0002-2130-0673 2,4, Miriam Taegtmeyer https://orcid.org/0000-0002-5377-2536 2, Sarah Urasa3,8, Sarah White2, Nateiya M. Yongolo https://orcid.org/0000-0002-9659-7926 2,3, Jamie Rylance1,2*, Ben Morton https://orcid.org/0000-0002-6164-2854 2*, Eve Worrall https://orcid.org/0000-0001-9147-3388 2*, Felix Limbani https://orcid.org/0000-0003-0176-7011 1*, MultiLink ConsortiumStephen A. Spencer https://orcid.org/0000-0001-8451-7514 1,2*, Alice Rutta https://orcid.org/0000-0001-5217-5249 3*, [...] Gimbo Hyuha https://orcid.org/0000-0002-2419-4386 4*, Gift Treighcy Banda https://orcid.org/0000-0002-7465-0730 1,2, Augustine Choko1, Paul Dark5, Julian T. Hertz6, Blandina T. Mmbaga https://orcid.org/0000-0002-5550-1916 3, Juma Mfinanga4, Rhona Mijumbi1, Adamson Muula7, Mulinda Nyirenda7, Laura Rosu2, Matthew Rubach3,6, Sangwani Salimu1,2, Francis Sakita3,8, Charity Salima9, Hendry Sawe https://orcid.org/0000-0002-0395-5385 4, Ibrahim Simiyu https://orcid.org/0000-0002-2130-0673 2,4, Miriam Taegtmeyer https://orcid.org/0000-0002-5377-2536 2, Sarah Urasa3,8, Sarah White2, Nateiya M. Yongolo https://orcid.org/0000-0002-9659-7926 2,3, Jamie Rylance1,2*, Ben Morton https://orcid.org/0000-0002-6164-2854 2*, Eve Worrall https://orcid.org/0000-0001-9147-3388 2*, Felix Limbani https://orcid.org/0000-0003-0176-7011 1*, MultiLink Consortium * Equal contributors PUBLISHED 08 May 2024 Author details Author details 1 Malawi-Liverpool-Wellcome Programme, Blantyre, Malawi 2 Liverpool School of Tropical Medicine, University of Liverpool, Liverpool, England, UK 3 Kilimanjaro Clinical Research Institute, Moshi, Tanzania 4 Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania 5 Humanitarian and Conflict Response Institute, The University of Manchester, Manchester, England, UK 6 Duke University School of Medicine, Duke University, Durham, North Carolina, USA 7 The Kamuzu University of Health Sciences, Blantyre, Malawi 8 Kilimanjaro Christian Medical University College, Moshi, Tanzania 9 Achikondi Women Community Clinic, Lilongwe, Malawi 2 Liverpool School of Tropical Medicine, University of Liverpool, Liverpool, England, UK 3 Kilimanjaro Clinical Research Institute, Moshi, Tanzania 4 Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania 5 Humanitarian and Conflict Response Institute, The University of Manchester, Manchester, England, UK 6 Duke University School of Medicine, Duke University, Durham, North Carolina, USA 7 The Kamuzu University of Health Sciences, Blantyre, Malawi 8 Kilimanjaro Christian Medical University College, Moshi, Tanzania 9 Achikondi Women Community Clinic, Lilongwe, Malawi Stephen A. Spencer Roles: Conceptualization, Data Curation, Funding Acquisition, Investigation, Methodology, Project Administration, Software, Writing – Original Draft Preparation, Writing – Review & Editing Roles: Conceptualization, Data Curation, Funding Acquisition, Investigation, Methodology, Project Administration, Software, Writing – Original Draft Preparation, Writing – Review & Editing Alice Rutta Roles: Investigation, Project Administration, Writing – Original Draft Preparation, Writing – Review & Editing Roles: Investigation, Project Administration, Writing – Original Draft Preparation, Writing – Review & Editing Gimbo Hyuha Roles: Investigation, Project Administration, Writing – Original Draft Preparation, Writing – Review & Editing Roles: Investigation, Project Administration, Writing – Original Draft Preparation, Writing – Review & Editing Gift Treighcy Banda Roles: Data Curation, Investigation, Methodology, Writing – Review & Editing Roles: Data Curation, Investigation, Methodology, Writing – Review & Editing Augustine Choko Roles: Conceptualization, Funding Acquisition, Methodology, Writing – Review & Editing Roles: Conceptualization, Funding Acquisition, Methodology, Writing – Review & Editing Paul Dark Roles: Conceptualization, Funding Acquisition, Methodology, Supervision, Writing – Review & Editing Roles: Conceptualization, Funding Acquisition, Methodology, Supervision, Writing – Review & Editing Julian T. Hertz Roles: Conceptualization, Methodology, Project Administration, Writing – Review & Editing Roles: Conceptualization, Methodology, Project Administration, Writing – Review & Editing Blandina T. Mmbaga Roles: Conceptualization, Methodology, Project Administration, Writing – Review & Editing Roles: Conceptualization, Methodology, Project Administration, Writing – Review & Editing Juma Mfinanga Roles: Methodology, Project Administration, Writing – Review & Editing Roles: Methodology, Project Administration, Writing – Review & Editing Rhona Mijumbi Roles: Supervision, Writing – Review & Editing Roles: Supervision, Writing – Review & Editing Adamson Muula Roles: Conceptualization, Funding Acquisition, Methodology, Writing – Review & Editing Roles: Conceptualization, Funding Acquisition, Methodology, Writing – Review & Editing Mulinda Nyirenda Roles: Conceptualization, Funding Acquisition, Methodology, Writing – Review & Editing Roles: Conceptualization, Funding Acquisition, Methodology, Writing – Review & Editing Laura Rosu Roles: Data Curation, Investigation, Methodology, Supervision, Writing – Review & Editing Roles: Data Curation, Investigation, Methodology, Supervision, Writing – Review & Editing Matthew Rubach Roles: Conceptualization, Funding Acquisition, Methodology, Project Administration, Supervision, Writing – Review & Editing Roles: Conceptualization, Funding Acquisition, Methodology, Project Administration, Supervision, Writing – Review & Editing Sangwani Salimu Roles: Data Curation, Investigation, Methodology, Writing – Review & Editing Roles: Data Curation, Investigation, Methodology, Writing – Review & Editing Francis Sakita Roles: Conceptualization, Methodology, Writing – Review & Editing Roles: Conceptualization, Methodology, Writing – Review & Editing Charity Salima Roles: Conceptualization, Funding Acquisition, Methodology, Writing – Review & Editing Roles: Conceptualization, Funding Acquisition, Methodology, Writing – Review & Editing Hendry Sawe Roles: Conceptualization, Funding Acquisition, Methodology, Project Administration, Supervision, Writing – Review & Editing Roles: Conceptualization, Funding Acquisition, Methodology, Project Administration, Supervision, Writing – Review & Editing Ibrahim Simiyu Roles: Data Curation, Investigation, Methodology, Writing – Review & Editing Roles: Data Curation, Investigation, Methodology, Writing – Review & Editing Miriam Taegtmeyer Roles: Conceptualization, Funding Acquisition, Methodology, Supervision, Writing – Review & Editing Roles: Conceptualization, Funding Acquisition, Methodology, Supervision, Writing – Review & Editing Sarah Urasa Roles: Conceptualization, Methodology, Writing – Review & Editing Roles: Conceptualization, Methodology, Writing – Review & Editing Sarah White Roles: Conceptualization, Data Curation, Funding Acquisition, Methodology, Writing – Review & Editing Roles: Conceptualization, Data Curation, Funding Acquisition, Methodology, Writing – Review & Editing Nateiya M. Yongolo Roles: Data Curation, Investigation, Methodology, Writing – Review & Editing Roles: Data Curation, Investigation, Methodology, Writing – Review & Editing Jamie Rylance Roles: Conceptualization, Funding Acquisition, Methodology, Project Administration, Software, Supervision, Writing – Review & Editing Roles: Conceptualization, Funding Acquisition, Methodology, Project Administration, Software, Supervision, Writing – Review & Editing Ben Morton Roles: Conceptualization, Funding Acquisition, Methodology, Project Administration, Software, Supervision, Writing – Review & Editing Roles: Conceptualization, Funding Acquisition, Methodology, Project Administration, Software, Supervision, Writing – Review & Editing Eve Worrall Roles: Conceptualization, Funding Acquisition, Methodology, Project Administration, Software, Supervision, Writing – Review & Editing Roles: Conceptualization, Funding Acquisition, Methodology, Project Administration, Software, Supervision, Writing – Review & Editing Felix Limbani Roles: Conceptualization, Funding Acquisition, Methodology, Project Administration, Software, Supervision, Writing – Review & Editing Roles: Conceptualization, Funding Acquisition, Methodology, Project Administration, Software, Supervision, Writing – Review & Editing OPEN PEER REVIEW REVIEWER STATUS The prevalence of multimorbidity (the presence of two or more chronic health conditions) is rapidly increasing in sub–Saharan Africa. Hospital care pathways that focus on single presenting complaints do not address this pressing problem. This has the potential to precipitate frequent hospital readmissions, increase health system and out-of-pocket expenses, and may lead to premature disability and death. We aim to present a description of inpatient multimorbidity in a multicentre prospective cohort study in Malawi and Tanzania. Determine prevalence of multimorbid disease among adult medical admissions and measure patient outcomes. Health Economic: Measure economic costs incurred and changes in health-related quality of life (HRQoL) at 90 days post-admission. Situation analysis: Qualitatively describe pathways of patients with multimorbidity through the health system. Determine hospital readmission free survival and markers of disease control 90 days after admission. Health Economic: Present economic costs from patient and health system perspective, sub-analyse costs and HRQoL according to presence of different diseases. Situation analysis: Understand health literacy related to their own diseases and experience of care for patients with multimorbidity and their caregivers. This is a prospective longitudinal cohort study of adult (≥18 years) acute medical hospital admissions with nested health economic and situation analysis in four hospitals: 1) Queen Elizabeth Central Hospital, Blantyre, Malawi; 2) Chiradzulu District Hospital, Malawi; 3) Hai District Hospital, Boma Ng’ombe, Tanzania; 4) Muhimbili National Hospital, Dar-es-Salaam, Tanzania. Follow-up duration will be 90 days from hospital admission. We will use consecutive recruitment within 24 hours of emergency presentation and stratified recruitment across four sites. We will use point-of-care tests to refine estimates of disease pathology. We will conduct qualitative interviews with patients, caregivers, healthcare providers and policymakers; focus group discussions with patients and caregivers, and observations of hospital care pathways.

In sub-Saharan Africa, multimorbidity (defined as people living with two or more chronic health conditions) is increasing due to high infectious (e.g., human immunodeficiency virus (HIV)) and non-communicable (e.g., high blood pressure and diabetes) disease burdens. Multimorbidity increases as people live longer and can be worsened by HIV and HIV-medications. Patients delay seeking help until they are severely ill, meaning hospitals are key to healthcare delivery for chronic diseases, however hospital clinicians often focus on a single disease. Failure to identify and treat multimorbidity may lead to frequent readmissions, high costs, preventable disability and death. Aim This cohort study is the first in a three-phase study with the overarching goal to design and test a system to identify patients suffering from multimorbidity when they seek emergency care in sub-Saharan African hospitals. This could improve early disease treatment (reducing death), ensure better follow-up and prevent disability, readmission and excess costs. The cohort study aims to determine multimorbidity prevalence, outcomes and costs. The results will help us co-create with key stakeholders the most cost-effective way to deliver improved care for patients before testing this strategy in a randomised trial.

in Brief In Malawi and Tanzania, we will identify multimorbidity among patients admitted to hospital (focusing on high blood pressure, diabetes, HIV and chronic kidney disease), by enhancing diagnostic tests in hospital departments treating acutely admitted medical patients. With the help of healthcare professional, patients and community groups we will find how best to link patients to long-term care and improve self-management. After mapping health system pathways, we will work with stakeholders (policymakers, healthcare worker representatives, community and patient groups) to co-develop an intervention to improve outcomes for patients with multimorbidity. This study will allow us to collect clinical, health economic and health system data to inform this process. In sub-Saharan Africa, multimorbidity (defined as people living with two or more chronic health conditions) is increasing due to high infectious (e.g., human immunodeficiency virus (HIV)) and non-communicable (e.g., high blood pressure and diabetes) disease burdens. Multimorbidity increases as people live longer and can be worsened by HIV and HIV-medications. Patients delay seeking help until they are severely ill, meaning hospitals are key to healthcare delivery for chronic diseases, however hospital clinicians often focus on a single disease. Failure to identify and treat multimorbidity may lead to frequent readmissions, high costs, preventable disability and death. Aim This cohort study is the first in a three-phase study with the overarching goal to design and test a system to identify patients suffering from multimorbidity when they seek emergency care in sub-Saharan African hospitals. This could improve early disease treatment (reducing death), ensure better follow-up and prevent disability, readmission and excess costs. The cohort study aims to determine multimorbidity prevalence, outcomes and costs. The results will help us co-create with key stakeholders the most cost-effective way to deliver improved care for patients before testing this strategy in a randomised trial.

in Brief In Malawi and Tanzania, we will identify multimorbidity among patients admitted to hospital (focusing on high blood pressure, diabetes, HIV and chronic kidney disease), by enhancing diagnostic tests in hospital departments treating acutely admitted medical patients. With the help of healthcare professional, patients and community groups we will find how best to link patients to long-term care and improve self-management. After mapping health system pathways, we will work with stakeholders (policymakers, healthcare worker representatives, community and patient groups) to co-develop an intervention to improve outcomes for patients with multimorbidity. This study will allow us to collect clinical, health economic and health system data to inform this process. Multimorbidity, non-communicable diseases, hospital care, sub-Saharan Africa, health related quality of life, patient costs, health system costs Corresponding Author(s) Stephen A. Spencer ([email protected]) Alice Rutta ([email protected]) Gimbo Hyuha ([email protected]) Ben Morton ([email protected]) Eve Worrall ([email protected]) Felix Limbani ([email protected]) Alice Rutta ([email protected]) Gimbo Hyuha ([email protected]) Ben Morton ([email protected]) Eve Worrall ([email protected]) Felix Limbani ([email protected]) Grant information: This project is funded by the National Institute for Health and Care Research (NIHR) under its ‘Research and Innovation for Global Health Transformation’ (Grant Reference Number NIHR201708). This research was funded by the NIHR (NIHR201708) using UK aid from the UK Government to support global health research. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. In addition, this research is supported by a number of individual grants. This research is funded in part by the Wellcome Trust [Wellcome Trust Clinical PhD Fellowship awarded to SAS: Grant number 203919]. PD is also supported by a NIHR Senior Investigator award [NIHR203745]. This publication is associated with the Research, Evidence and Development Initiative (READ-It). READ-It (project number 300342-104) is funded by UK aid from the UK government; however, the views expressed do not necessarily reflect the UK government’s official policies. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2024 Spencer SA 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 work is properly cited. How to cite: Spencer SA, Rutta A, Hyuha G et al. Multimorbidity-associated emergency hospital admissions: a “screen and link” strategy to improve outcomes for high-risk patients in sub-Saharan Africa: a prospective multicentre cohort study protocol [version 2; peer review: 1 approved, 2 approved with reservations]. NIHR Open Res 2024, 4:2 (https://doi.org/10.3310/nihropenres.13512.2) First published: 18 Jan 2024, 4:2 (https://doi.org/10.3310/nihropenres.13512.1) Latest published: 08 May 2024, 4:2 (https://doi.org/10.3310/nihropenres.13512.2) The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The revised protocol manuscript includes changes based on the important feedback included in the reviewer reports. In the introduction, we have provided further explanation regarding the definition of multimorbidity, emphasizing its variability across literature to the context and setting. In this study, our focus lies on diseases that are important to the study population (HIV, hypertension, diabetes, and chronic kidney disease), and which treatment and control can be modifiable within the existing health systems. Our objectives now elaborate on our focus to gather baseline data on these four pre-selected diseases to inform the design of our planned trial. We have also clarified that both self-reported and clinically reported chronic diseases will be documented. In the eligibility criteria, we have detailed our approach to include participants with acute medical diagnoses identified from medical records using ICD codes; and provided justification for the exclusion of participants with trauma, as other research groups are addressing health system interventions for these patients. The analysis section now includes a statement that pre-designed statistical analysis plans will be developed will be published as a component of our reporting. These revisions enhance the clarity and comprehensiveness of our protocol manuscript. The revised protocol manuscript includes changes based on the important feedback included in the reviewer reports. In the introduction, we have provided further explanation regarding the definition of multimorbidity, emphasizing its variability across literature to the context and setting. In this study, our focus lies on diseases that are important to the study population (HIV, hypertension, diabetes, and chronic kidney disease), and which treatment and control can be modifiable within the existing health systems. Our objectives now elaborate on our focus to gather baseline data on these four pre-selected diseases to inform the design of our planned trial. We have also clarified that both self-reported and clinically reported chronic diseases will be documented. In the eligibility criteria, we have detailed our approach to include participants with acute medical diagnoses identified from medical records using ICD codes; and provided justification for the exclusion of participants with trauma, as other research groups are addressing health system interventions for these patients. The analysis section now includes a statement that pre-designed statistical analysis plans will be developed will be published as a component of our reporting. These revisions enhance the clarity and comprehensiveness of our protocol manuscript. See the authors' detailed response to the review by Rifqah Abeeda Roomaney See the authors' detailed response to the review by Claire Rosen Non-communicable diseases (NCDs), which can be accelerated by human immunodeficiency virus (HIV) infection and its therapy, are a major health problem in sub-Saharan Africa (sSA)1. Diagnosis is frequently delayed until emergency hospital presentation, when people are already severely unwell2. In hospitals, unrecognised multimorbidity (“the co-existence of two or more chronic conditions”)3 results in frequent re-admission, high out-of-pocket expenses, disability and preventable death4. In sSA, co-existent infectious and non-communicable disease pathology is common5. Some studies suggest a higher burden of multimorbidity among females compared to males6,7. Existing risk stratification and screening frameworks from High Income Countries are poorly calibrated for people in sSA and underestimate severity. For example, diabetes and hypertension are more prevalent in African populations at younger ages8. Acute medical admission is frequently the index presentation of multimorbid disease in areas of poverty9. The patient pathway in sSA regions may be protracted and complex10. People frequently seek healthcare late for reasons including opportunity cost, poor health literacy and prior consultation with informal care providers. This leads to high prevalence of poorly controlled multi-morbid disease in hospital emergency care where recognition of multimorbidity is delayed or prevented by weak triage systems, resource limitations, and a focus on a single primary diagnosis11. Commitments to Universal Health Coverage and the World Health Organization Model List of Essential Medicines12 mean that HIV and NCDs are treatable through improved access to essential medicines. However, significant barriers include a lack of patient empowerment and health literacy, shortage of healthcare workers (HCW) and limited training13. Malawi (low income) and Tanzania (low-middle income) are neighbouring countries in the sub-Saharan African region that have experienced dramatic increases in life expectancy between 2000 and 2020 (Malawi 45.6 to 64.3 and Tanzania 50.8 to 65.5 years of age)14,15. This is largely due to improvements in prevention and treatment of infectious diseases, such as HIV, tuberculosis and malaria, as well as reductions in maternal and infant mortality16. The increase in life-expectancy has been accompanied by increased rates of non-communicable diseases such as hypertension and type 2 diabetes, however, the health systems of both countries are currently weighted toward communicable disease control17 with insufficient provision for the management of multimorbid conditions. Roll-out of anti-retroviral treatment for HIV has been a success, and as patients with HIV live longer, there is increasing risk of frequent and complex interaction between HIV and NCDs; in particular hypertension and diabetes18. Although multimorbidity is commonly defined as the co-existence of two or more chronic conditions in a single person, there are recognised limitations in the utility of this definition requiring to different settings and disciplines19,20. In this study, we focus on multimorbidity based on diseases that are important to the study population, and which treatment and control can be modifiable within the existing health systems. Based on data and clinical reports of medical patient cohorts in Tanzania and Malawi, three frequently co-existing diseases warrant immediate focus: HIV infection, hypertension and diabetes21–26. A systematic review and meta-analysis also confirmed high prevalence of these conditions among medical in-patients in sub-Saharan hospitals (HIV: 36.4%; hypertension: 24.7%; diabetes: 11.9%)27, however, these data are limited in scope and taken from multiple sources, targeted to single rather than multimorbid diagnoses. Chronic kidney disease was another common condition (7.7% prevalence)27, but which lacked accurate data on chronicity. Inconsistent use of diagnostic methodology and criteria are common in sSA due to under-resourced diagnostic and laboratory services27,28. The primary objectives of this mixed methods (clinical, health economics and qualitative) study are: Clinical: Determine the prevalence of multimorbid disease in adults admitted to hospital with acute medical conditions in Malawi and Tanzania. Health economic: Measure economic costs incurred and changes in health-related quality of life between admission and 90 days post admission. Situation analysis: Describe current care for multimorbidity, enabling and hindering factors to the delivery of patient centred care. We aim to use the findings to inform the development and design of a randomised controlled trial. The secondary objectives are: Clinical: 1. Measure prevalence of important pre-selected individual conditions, including chronic non-communicable (hypertension, diabetes mellitus and chronic kidney disease) and communicable (HIV infection) conditions using gold-standard diagnostic criteria. This background information will inform the design of our planned follow-up trial. 2. Measure prevalence chronic conditions that are self-reported or captured in clinical records. 3. Measure hospital readmission rate 30 and 90 days after admission. 4. Measure survival 30 and 90 days after admission. 5. Measure a composite of readmission free survival 30 and 90 days after admission. 6. Measure markers of disease control 90 days after admission. 7. Measure the rate of end-organ damage (such as cerebrovascular accident and myocardial infarction). Health economic: 1. Disaggregate economic costs from patient and health system perspectives and sub-analyse according to the presence of multimorbidity and other socio-economic factors. 2. Model how patients with different multimorbidity incur costs as they pass through the health system. 3. Analyse changes in Health-related Quality of Life according to presence of different diseases. 4. Estimate the potential cost-effectiveness of a selected intervention to improve diagnosis and treatment for people with multimorbidity. Situation analysis: 1. Describe healthcare pathways for adults during and after acute medical admission using structured observational data during patient admission. 2. Qualitatively describe (i) patients’ level of health literacy related to their own conditions and (ii) healthcare engagement for patients with multimorbidity using key-informant interviews. Our funded programme of work is divided into three phases: (1) a prospective longitudinal clinical cohort study of acute medical admissions with nested health economics study and situational analysis, (2) co-creation of a complex intervention aiming at delivering improved care for people with multimorbidity and (3) a cluster randomised trial with nested process and economic evaluation to evaluate the intervention. This protocol focuses on phases 1 and 2. Phase 1 incorporates three distinct methodologies (see Figure 1): 1 A) Prospective longitudinal cohort study; 1 B) health economic evaluation focussing on costs (patient and health-system) and consequences (health related quality of life); 1 C) patient-centred situation analysis with mixed qualitative methods including key informant interviews, focus group discussions and observation of patient care pathways. Phase 2 incorporates intervention cost-effectiveness modelling, to support co-creation, design and selection of an intervention to be evaluated subsequently through a cluster randomised clinical trial (phase 3). This study will be conducted in four sites across two countries (Malawi and Tanzania). Within each country we will recruit patients from a central referral hospital and a district hospital to develop generalisable results, broadly applicable within both contexts. The study sites are: Queen Elizabeth Central Hospital, Blantyre, Malawi; 2) Chiradzulu District Hospital, Southern Region, Malawi; 3) Hai District Hospital, Tanzania; 4) Muhimbili National Hospital, Dar-es-Salaam, Tanzania. The study is planned to take approximately 15 months (12 months recruitment, with an additional three months to complete follow up) and will be conducted in parallel in all sites. We partnered with established organisations working in the sphere of NCDs in Malawi and Tanzania. These include the Malawi and Tanzania NCD Alliances, Community for Disease Prevention and Management, Malawi Health Equity Network and community research advisory boards. These structures have direct contact with beneficiaries and community leaders. Prior to the development of the study, we conducted community forums with patients with NCDs, care givers, leaders of peer support groups and members of community research advisory board. These groups reflected on their experience with multimorbidity in their home, barriers and facilitators to diagnosis, treatment, and care for multimorbidity in health facilities and proposals for improvement. Their experiences informed the development of our situation analysis questions. We also engaged policy stakeholders from Ministries of Health in both countries in a need analysis process. This helped us to gain: the core national indicators for monitoring progress of NCDs and the status of their ongoing progress; national, local and community setup of structures for governing and managing NCD and multi-morbidity services: current NCD policies, programmes, and implementation strategies. Proposals and issues that came up from both the policy and community consultation will be part useful during the second and third stage of the study when we develop and implement the intervention (with continued engagement). The needs analysis helped in identifying key stakeholders whom to engage during this period as community and policy members from community advisory boards and national advisory groups. Phase 1 A: Prospective longitudinal cohort study. Patients will be screened for enrolment at the primary point of entry for hospital admission. Cohort study participants will be consecutively recruited within 24 hours of emergency presentation, and stratified recruitment across four sites. Eligibility criteria are as follows: Inclusion: 1. Adult patients (aged ≥18 years). 2. Decision to admit to hospital. 3. Physician diagnosis of an acute medical disorder captured from medical records, using International Classification of Diseases (ICD) codes. 4. Residence within the study catchment area (predefined for each hospital site). 5. Contactable on discharge by phone (either directly or through a carer). Exclusion: 1. Pregnancy (other research groups are specifically addressing this). 2. Planned medical admission. 3. Detainees or prisoners. 4. Admission for primary trauma, obstetric or gynaecological condition (other research groups are addressing health system interventions for these groups). 5. Patient or carer declines consent to take part. The study procedures and schedule for follow-up are described in Table 1. | Study Visit | A | B | C | D | E | F | G | |---|---|---|---|---|---|---|---| | Day post admission | 0 | 2 | 5 | 7 | Discharge | 30 | 90 | | Deferred consent | x | |||||| | Consent (verbal) | x | x | x | x | x | x | | | Consent (written) | 3 | 3 | 3 | |||| | Vital signs (including blood pressure) | x | x | x | x | x | x | | | Medical history, function (including grip strength) and outcome assessment | x | 4 | x | |||| | Screen for Adverse Events (AEs) | x | x | x | x | x | x | | | HIV POC* | x | x | ||||| | Where HIV, VL, urinary LAM, serum CrAg* | x | 1 | ||||| | Blood glucose POC | x | x | x | x | x | || | HBA1c POC | x | x | ||||| | Creatinine POC | x | x | ||||| | Urinary dipstick | x | x | ||||| | Serum save 5mL) | x | x | ||||| | Situation analysis | x | x | x | x | x | x | | | Patient and health system costs | x | x | x | |||| | EQ5D questionnaire | x | x | x | |||| | Qualitative interviews | x | x | AE: adverse event; POC: point of care blood test; HIV: human immunodeficiency virus; VL: viral load; urine LAM: urinary lipoarabinomannan; CrAg: cryptococcal antigen; HbA1c: haemoglobin A1c (glycated haemoglobin). Additional contact with the participants may be made up to approximately 12 months after hospital admission to check the longer-term outcomes. This will be by telephone where possible. Visit F (hospital discharge) visit data will be collected, this may occur at any point before or after the 2, 5 and 7 day follow up visits. Visit G (90 day) will occur in a specific research outpatient follow-up clinic. There will be flexibility of ±10 working days for this follow up visit to take place. * Diagnostic HIV test and/or HIV viral load will be collected as part of routine clinical service. Parentheses indicate only if required to confirm diagnosis (rely on concurrent or earlier test). 1: Samples for urinary LAM and serum CrAg test will be collected and processed as part of routine clinical service to screen for opportunistic infection in line with WHO guidance at admission. 2: Qualitative interviews will take place with healthcare workers in a portion of treating providers after patient discharge. Qualitative interviews will take place with a proportion of patients after follow up visit at 90 days. 3: Written consent will be taken at the earliest possible opportunity, either directly from the patient or from a proxy (if patient lacks capacity). 4: Telephone or home visit follow-up (in order to maximise retention and data completeness) Phase 1 B: Health economic evaluation. One third of participants recruited to the cohort study will be randomised to the health economics study, with follow-up and data collection as per cohort study, or as detailed in section (Variables, below). Phase 1 C: Situation analysis. In depth interviews (IDIs) with patients and caregivers will include patients purposively sampled from the overall cohort whilst still an in-patient, at the point of hospital discharge or during the 90-day follow up clinic. Carers will include those supporting patients whilst they are being treated in hospital or who supported a patient who died (during their hospital stay or within 90 days after admission). During interviews, caregivers will be linked/matched to patients in approximately half of the cases (caregivers of the patients we will interview). This will help to diversify the data and facilitate recall for patients who may have experienced reduced capacity during the early stages of hospital admission. Focus group discussions (FGDs) will be conducted with patients who have recovered from acute medical admission and their caregivers, in the community, after hospital discharge. We will hold female and male specific FGDs to ensure equal participation, especially for women who may be inhibited from disclosure in the presence of men. We will approach and recruit the caregivers of acute medical patients (those staying full time with the patients at the hospital and providing day to day care) during their hospital admission. Patients and caregivers will take part in either FGD or in-depth interviews, but not both. IDIs with healthcare workers (HCWs) and policy makers will include HCWs directly involved with acute medical patients – physicians (senior clinicians (consultants), juniors), nurses, ward attendants, reception clerks and/or frontline administrative staff. Policy makers will include those working in decision-making roles at the district and national hospitals and service delivery level of the government-funded health system. Inclusion criteria 1. Patients (≥18 years) recruited from cohort study (separate consent sought for this sub-study). 2. Carers/guardians caring for patients within the cohort study. 3. Healthcare workers who provide direct care for patients recruited to the cohort study. 4. Healthcare workers taking part in the training as part of the programme roll-out. 5. Policymakers that have occupied their current or previous policymaker position for 3 months or more. Exclusion criteria 1. Patients and carers who do not speak a language understood by one of the field research team (not anticipated, as team speak both English and the predominant local languages). 2. Patients, carers and healthcare workers who are judged likely to be negatively affected by participation e.g., due to distress. 3. Patients who are too sick to be out of bed and participate in an interview in a private area. 4. Policymakers that are yet to be involved in policy development. During screening, participants will give informed written consent to their data being used for clinical cohort and HE study (consented together prior to allocation to HE study) and the modelling (Phase 2). In cases where prospective consent not possible, deferred consent model will be offered (to allow those who initially lack capacity to be represented in the cohort). A separate written consent will be sought for the participants recruited to the situation analysis including health care workers. We will obtain informed consent from patients if their physical and mental capacity allows based on the following questions: 1) can the patient retain information; 2) can the patient weigh the information; 3) can the patient communicate a decision. Where there is uncertainty, these can be assessed through: 1) asking the patient to reflect the given information back to the study staff, including the purpose, procedures and risks of the study; 2) asking the patient to convey the alternatives of participation in the study, including understanding the voluntary nature of inclusion. For fully competent patients, an information sheet will be provided, the study will be discussed and written consent obtained. Illiterate patients will be read the information sheet and mark the consent form with either a cross or thumbprint to indicate their agreement in the presence of an independent witness. If a patient is unable to give consent, we will obtain consent from a proxy (a relative or representative) in the same manner. Where the patient subsequently regains capacity, we will approach them to discuss and obtain retrospective consent. If no proxy is available and local regulations allow, we will use a deferred consent process. If deferred consent is used, we will tell the patient about the study as soon as possible and obtained consent for use of the data collected. If the patient does not have capacity or if a significant delay to regaining capacity (>7 days) is anticipated, then we will seek retrospective assent from their proxy. Justification for this consent model is that patients stand to benefit from early use of enhanced diagnostics using CE marked and commercially available point of care tests. Delayed diagnosis in this context has potentially detrimental effects for patients who otherwise would not have access to the enhanced diagnostic package. Patients will be at minimal risk through study procedures. From a scientific perspective, this model will reduce the risk of recruitment bias. There is precedence for deferred consent in the Malawian context with existing data describing the acceptability of this approach29. Deferred consent has also been used in Tanzania for a randomised controlled trial of tranexamic acid versus placebo after post-partum haemorrhage30. All participant information sheets and consent forms will be translated into local languages (kiSwahili in Tanzania, and Chichewa in Malawi). Phase 1 A: Prospective longitudinal cohort study. The main outcome is to define the characteristics of multimorbidity. We will employ enhanced point of care test (POC) diagnostics for patients admitted to the study to determine the prevalence of multimorbid diseases of interest (specifically HIV, hypertension, diabetes and chronic kidney disease). All POCs used in the study are CE marked and commercially available. We will make the results of these tests available to treating healthcare providers using a standardised proforma. No treatment recommendation will be provided with this information and clinicians will be able to interpret and use this information independently. Structured data extraction of clinical information (including potential predictors of patient outcomes) will be performed at intervals (described in Table 1 from patient records). Phase 1 B: Health economic evaluation. To explore the impact of socioeconomic status (SES) on patient costs, clinical outcomes and health-related quality of life (HRQoL) we will calculate wealth scores and use these to assign all clinical cohort study participants to SES quintiles. For participants randomised to the health economic study, we will collect data on patient costs at admission (in person), discharge (in person), 30 and 90 days post discharge (in person or by telephone). Health system costs for patients in the cohort study will be modelled using top down and bottom up costing methods. HRQoL will be estimated in quality adjusted life years (QALYs). Phase 1 C: Situation analysis In depth interviews (IDI) and focus group discussions (FGD) with patients recruited to the cohort study, and their care givers will be conducted to understand their experiences, preferences, and priorities, including level of health literacy and engagement with healthcare providers for long term management of chronic conditions. Exploration of detailed experiences from patients and carers will provide specific concrete examples of their expectations, and any concerns they have, as well as their priorities and perceptions of how the hospital currently manages acute medical patients with multimorbidity, problems and how best to address them. In-depth interviews and structured discussions with health care providers who provided direct care for patients recruited in the cohort study will be conducted to determine “nodes” in the treatment pathway where an intervention could effectively be delivered. Key informant interviews with policymakers at national and district level will explore the pathways of health care for people living with multimorbidity, their perspectives of the policy environment around the management of multimorbidity, and how an intervention could be incorporated into current practice. Observations of patient care pathways in a purposefully selected sample of participants, will describe the patient pathway, and the effect of hospital context on acute medical care for multimorbid disease. We will observe the functioning of the wards, the relationship among healthcare workers (HCWs) and between HCWs and the patients. We will conduct semi-structured observation of hospital activities, patient pathways and patient consultations for adults admitted with acute medical conditions. Phase 1 A: Prospective longitudinal cohort study - baseline and clinical data. The primary outcome of the clinical cohort study will be to collect data on conditions known to be highly prevalent among medical in-patients in sSA, and are likely to be disease constituents of multimorbidity. Following results from a recent systematic review27, we will therefore focus on HIV, hypertension, diabetes and chronic kidney disease. Importantly these conditions can be diagnosed using available point-of-care tools. Although heart failure is also a common in these settings, diagnosis is technically more challenging and is being explored separately. We will apply standardised point of care testing to participants and record these primary outcome conditions both in the electronic data capture tools and in the patient notes, making these explicitly available to treating clinical teams. HIV: HIV will be tested using locally available HIV rapid diagnostics tests. For HIV positive patients we will also test the HIV viral load using existing laboratory infrastructure in each site. Hypertension: In patients with a regular pulse rhythm, automated electronic sphygmomanometer will be used. In patients with an irregular pulse rhythm, manual aneroid sphygmomanometer will be used. Patients will be semi-recumbent or supine, legs uncrossed, relaxed for at least five minutes after the start of the consultation, and not talking. On the first visit, the blood pressure will be taken from both arms and using the arm with the highest reading thereafter. At each visit two readings will be taken from this arm, recording the second reading. Diabetes: HbA1c levels will be measured using the POC Hemocue® HbA1c 501 Analyzer and cartridges (HemoCue AB, Ängelholm, Sweden). CKD: Abbott iSTAT point-of-care device (Abbott Point of Care Inc, Illinois, USA) with Chem8+ cartridges will be used to measure creatinine from venous blood, directly following the manufacturer’s iSTAT User Guide. Other conditions are expected to contribute to multimorbidity, for example hearing and visual impairment, causes of impaired mobility such as injury at birth, or disability from trauma, surgical procedures and others later in life. A key criterion for inclusion in the planned intervention study will be potential for improved morbidity or mortality from linkage to care. We will therefore collect data on disability using standardised questionnaires, with key components from Washington Group on functioning, Patient Health Questionnaire (PHQ)-9 for depression in addition to data tools used in previous cohort studies for the collection of standardised clinical information, and allow this to inform the intervention design. Clinical outcomes, coded from a structured case summary and the available medical notes, will be established as follows: 1. Medical diagnosis and supporting evidence. 2. In-patient treatment of these diagnoses, including time of appropriate treatment establishment. 3. Vital signs, including pulse oximetry (using a A310 finger oximeter), respiratory rate, pulse rate, blood pressure and temperature. 4. Routine laboratory data supporting these diagnoses (such as urine lipoarabinomannan [LAM], serum CrAg). 5. Clinical outcomes will also be ascertained at hospital discharge, 30 days and 90 days as: 6. Death (coded from all available records). 7. Readmission to hospital. Data will be collected by a study nurse using electronic data capture. This will describe the patient characteristics, medical history and ‘patient journey’ through the healthcare system. Age, date of birth and sex will be documented from patient reports and/or clinical notes. To ensure complete data collection, we will prospectively collect data on vital signs, details of resuscitation, medication administration, results of clinical, laboratory, and radiologic examinations. All clinically relevant findings will be entered into the medical notes to ensure availability to the responsible clinicians. As a component of the functional assessment, we will measure hand grip strength at day 90 follow-up using a GRIPX Digital Hand Dynamometer, which is a known predictor of health and overall strength31. Grip strength will be measured seated on a chair without elbow rests, with the elbow loosely flexed at 900, and the wrist in a neutral position32,33. Grip strength will be measured as the mean of three tests on both hands with 60 seconds of recovery between each attempt. Phase 1 B: Health economic data sources and measurement. All consenting study participants will be asked asset ownership, house construction and demographic questions extracted from the most recent relevant national Demographic and Health Survey (DHS). Patient cost data will be captured before, during, and for 90 days after hospital admission using an amended version of the STOP-TB tool, a widely-used questionnaire for measuring TB patient costs34. We will capture direct medical costs (e.g., drugs, diagnostics, consultation and inpatient fees), non-medical costs (e.g., food and transport), indirect costs (lost time for patients and guardians). Where a patient is readmitted to hospital, we will also capture these costs. Costs will be captured in local currency and presented in 2024 United States dollars. Health system costs will be collected using top-down and bottom-up (micro) costing methods. We will develop and validate (with relevant hospital staff) a visual model of individual patient pathways through the study hospitals. This will be supplemented with data on waiting times, and time spent with various health workers from the sample of patients that are included in observations conducted as part of the situation analysis. Costs for each stage of the patient pathway (triage, admission, diagnostics, treatment, discharge, readmission etc.) will be based on unit costs for tests, staff time, hospital bed days and other inputs, obtained from hospital data, tests and consumables suppliers, and Ministry of Health (MoH). Any other costs deemed relevant during the data collection stage will also be included. Top-down costing data on hospital overheads (utilities, building costs, maintenance, etc.) will be obtained for each hospital. HRQoL data will be collected using the EQ-5D-5L questionnaire in English, Chichewa (Malawi) and kiSwahili (Tanzania). The questionnaire will be administered, at admission, 30 and 90 days post admission in person or (day 30) by phone (as for patient costs). Phase 1 C: Situation analysis. Experienced social science fieldworkers fluent in the predominant local language (kiSwahili in Tanzania and Chichewa in Malawi) will collect data with guidance from the clinical academic teams. Observation tools and interview topic guides will be piloted to ensure understandability, and promote effective dialogue and data generation. We will conduct one pilot observation and interview for each of the patient, caregivers and HCW groups. After this, early observation notes and interview transcriptions will be reviewed by the senior academic team to verify and refine as required the phrasing of questions and level of probing. Interviews and focus group discussions will be audio-recorded. During focus group discussions, a note-taker will assist the facilitator and record differences and similarities among group members in their reaction and response to questions. These steps will promote diversity in data collection. Phase 1 A: Prospective longitudinal cohort study. The primary outcome will be disease prevalence. At 5% prevalence, a precision of 1.5% and power of 90% at α=0.05 is expected (precision will reduce as actual estimate rises towards 50%). We will use 5% prevalence as the lower boundary for inclusion of the disease during intervention development. We expect that HIV, hypertension and diabetes will meet this threshold. The cohort will allow us confidence to decide on the inclusion of chronic kidney disease. Prevalence will be assessed in both countries at the district and central hospital level to determine if findings are shared across settings. Given these assumptions, sample size is 1544 patients, with a target of 1600 to account for drop out. We will recruit these patients across the four hospitals (with numbers varying between sites due to different patient loads). Phase 1 B: Health economic evaluation. A randomised sub-sample of one third of the cohort study participants will be selected for participation in patient costing and HRQoL analysis (approximately 133 participants per hospital). Phase 1 C: Situation analysis. Sampling strategies for patients and caregivers is designed to include those individuals representing: those admitted for the first time and those who are returnees; those on HDU, ICU and ordinary wards; different age categories; male and female. We will ensure inclusion of patients with multiple known pathologies representing “multimorbidity” according to the final definition. Sample sizes for interviews are preliminary; they will continue until a point of saturation is reached. Table 2 shows estimates of the number of patient and carer interviews required. For healthcare workers, we will include different cadres working in different departments. | PER HOSPITAL SITE | In-depth interviews | Focussed group discussions | Structured discussions | |---|---|---|---| | Patients with multimorbidity* | 10 | 24 | 10 | | Caregivers to patients with multimorbidity* | 10 | 24 | 10 | | Doctors | 2 | - | 4 | | Nurses | 4 | - | 8 | | Ward attendants | 2 | - | 6 | | Reception / frontline administration staff | 2 | - | 2 | | Total | 30 | 48 | 40 | Observations will be conducted at different days of the week and at different times of the day including both the morning and afternoon hours (examining variation over time). In each day, the observer will spend four to five hours conducting these hospital observations with a further two to three hours of reflection to document and expand the observation notes. We will observe 40 patients consultations (10 patients per hospital), including a sequence of observations throughout their follow-up to establish pathways, timings and bottlenecks in the system. Observers will balance male and female patients, observing different consultation rooms and patients admitted at different times of day. Phase 1 A: Prospective longitudinal cohort study - quantitative statistical analysis. We will report the study in accordance with STROBE guidelines. A CONSORT diagram will summarise participant enrolment and follow-up. We will report descriptive statistics, including N (sample size of analysis population), n (sample size of analysis population without missing values). For continuous data we will report the mean and standard deviation for normally distributed data, or median and interquartile range for data that are not normally distributed. The proportion of observed levels will be reported for all binary and categorical measures alongside corresponding exact, binomial exact 95% confidence intervals (CIs) for proportions when appropriate. Results will be disaggregated by sex. Where appropriate, we will use multiple imputations using chained equations for missing data. Will provide in-depth baseline epidemiologic and clinical management data across the different sites. Similarities and differences across sites will be recorded. Analysis will include evaluation of the association between clinical management and patient outcomes. Univariate and multivariate strength of association between variables and patient outcomes will be tested by logistic regression modelling. Statistical significance will be defined as p < 0.05. Pre-designed statistical analysis plans will be developed and will be published as a component of our reporting. Prevalences will be given as a proportion with 95% confidence intervals of the population estimate. Combinations of more than one will be assessed for their “joint prevalence” in order to understand multimorbidity disease clusters. Phase 1B: Health economic. Principle components analysis (PCA) will be used to construct wealth scores using individuals’ asset and other data, and wealth quintiles in the study population. Additionally, participants will be assigned to quintiles in reference to most recent DHS survey, allowing socioeconomic assessment of the study population relative to the national population. Patient costs will be estimated for each individual patient including any costs incurred by their guardians in accompanying them to hospital or subsequent health care visits. Indirect costs (e.g., lost time for paid or unpaid work, including housework) will be estimated based on the number of days of reported inability to work multiplied by the pre-disease wage given by patients during their baseline interview. The patient pathway model will be parameterised with unit costs and used to estimate individual patient and health system costs for a set of the most frequently observed multimorbidity disease combinations and patient types. Capital costs extending beyond one year will be annualised over their expected lifespan. Results will be presented as means and 95% confidence intervals. Deterministic and probabilistic sensitivity analyses will be conducted to test the robustness of the results. Multiple logistic regression will be used to compare patient costs by multimorbid condition(s) and wealth quintile to identify any significant differences. Additionally, we will examine the relationship between patient costs and age and sex. Change in HRQoL, expressed as QALYs, between admission, day 30 and day 90 interviews will be estimated using the area under the curve method. QALYs will be calculated according to the most relevant available tariff based on both geographical proximity and economic context. In our study we will use Uganda’s tariff for Tanzania, and Zimbabwe’s tariff for Malawi. Updated tariffs will be used if these become available before the analysis. The QALY calculations will also consider mortality during follow-up by attributing the lowest value from each value set (which corresponds to death), from the date of death until the end of the follow-up period. Missing patient cost or HRQoL data will be imputed using multiple chained imputations modelling with a predictive mean matching algorithm and relevant baseline variables. Health economic analysis will be performed in Stata v15.4 and Excel. Phase 1 C: Situation analysis. Transcription and translation will be performed in line with local standard procedures. For accuracy, during the first 10 transcriptions a researcher will read the transcribed interviews and focus groups while listening to the audio recordings, and then check translations. Further checks will depend on the quality of these initial evaluations. For in-depth interviews and focus group discussions, all data collected will be transferred to a qualitative data analysis software package (NVivo), to enable analysis. Data will be analysed following broad deductively defined themes and inductively derived sub-themes. We will employ a combination of thematic and framework coding to compare perspectives between different stakeholders. Two researchers will undertake initial coding of a small number of transcripts, and then discuss and agree themes for further coding. Analysis will be performed concurrently with fieldwork using an iterative approach to identify emerging themes that can be clarified or explored further through later data collection. Data will be triangulated between methods and participant groups to cross-check information and provide a more comprehensive analysis. Consent will be documented on paper or electronic forms. A paper copy will be given to the participant or guardian, and any original paper will be stored in a locked cabinet at the local hospital site, accessible only to the study team. All study data will be collected electronically. Baseline, follow-up questionnaire data, and physiological observations (including pulse, respiratory rate, blood pressure, and oxygen saturations), and any point-of-care measures will be captured direct to Open Data Kit35 software on devices with password encryption. Clinical information including presenting complaints, history of presenting complaints, past medical history and drug history will be collected. Data on differential diagnosis and final diagnosis will be collected including clinical actions (e.g., prescribed medication) and patient response. We will also collect information on prescribed medication at hospital discharge, including where possible out of pocket expenses, health literacy and drug dispensary access (including stock outs). Laboratory test results will be directly electronically exported from laboratory information systems where possible. The study will adhere to LSTM (sponsor) data management and security policies, and encryption guidelines. This allows the data from each site to be collated, and presented at summary level in near real-time for the purposes of monitoring recruitment across both countries. During the study, individual participant level data will be available by the study team only, for the purposes of data validation and completeness checking. Access to this will be controlled by the same data security policies, with journaling of data change requests to allow full data audit. After successful import of external data, and formal closure of the study, data will be fully anonymised by removing identifiers (including embedded electronic data) and granulating data which might enable an individual to be triangulated (area of habitation will be retained at “ward” level, and age rounded to the nearest year). Data management for the patient costs, and HRQoL will be as per the clinical cohort study. Health system cost data will be collected and stored in custom built Excel spreadsheets. Qualitative data. Audio files will be stored on a secure network drive and transfer a copy to the project transcription and translation team using encrypted connection. Transcriptions and audio files will only be accessible to the study team. Any paper copies will be stored in a locked filing cabinet. Participant names will not be included in transcripts or file names; instead, participants will be given ID numbers. Quality assurance measures will include piloting of the approaches described by the topic guides, and informal feedback from all participants on how to improve the structure, style and content of the sessions. Data availability. ODK will be used to collect data on Android devices. The data from all sites will be synchronised to a server hosted by the Malawi Liverpool Wellcome programme (MLW), Blantyre Malawi. Data will be of high quality and in a format that can be shared with other interested researchers through access of data dictionaries. Handling of data requests will follow the relevant standard operating procedures at MLW. All data will be managed according to a data management plan, and transferred in accordance with a data management plan. Ethics was obtained from LSTM (21-086; approved on 10.05.2022); College of Medicine Research and Ethics Committee (COMREC), Malawi (P.11/21/3462; approved on 15.10.2021); National Institute for Medical Research (NIMR), Tanzania (NIMR/HQ/R.8a/Vol.IX/4008; approved on 13.05.2022); Kilimanjaro Christian Medical Centre (KCMC) (2570; approved on 15.12.2022). Our reflexivity statement (available here: doi.org/10.7910/DVN/CKSYSW) describes how we have promoted equity in our international research partnership and authorship within the MultiLink Consortium36. The findings from this study will be disseminated amongst the scientific community. Results will be feedback and discussed with hospital staff and patient groups. Within the MultiLink consortium, we have dedicated leads for both community and policy engagement, through community advisory boards and policy stakeholder ‘thinktank’ meetings. We intend to publish our findings in peer reviewed scientific journals and present data at appropriate local, national and international conferences. We will produce a close-out report for the local research ethics committees and LSTM (sponsors) at the end of the study and a final report once data are published. In addition, we will produce a lay report of our findings which will be made available to all participants. We will directly summarise the results to all ethics bodies, including COMREC. Data generated from this study will be used to inform design of a complex intervention in collaboration with key stakeholders as a part of structured dissemination activities. There are a number of limitations with this study, which we have attempted to mitigate. First, there are a limited number of sites in two countries. In order to generate broadly generalisable results, the chosen sites reflect a mix of tertiary / referral hospitals and district hospitals in each country. Second, we have decided to systematically screen for four conditions (HIV, hypertension, diabetes and CKD) using point-of-care diagnostics. This may miss multimorbidity from other chronic conditions, which require additional infrastructure to confirm diagnoses (such as clinical imaging or enhanced laboratories), but we will collect clinical data on all diagnosed chronic conditions. A recent systematic review indicates a high burden of disease from these four conditions among hospitals patients, which can importantly be diagnosed using available point-of-care tools27. Third, CKD diagnoses require evidence of chronicity of disease at three months and therefore data in this condition is prone to survivorship bias. Fourth, EQ5D-5L data and resulting QALY estimates may not reflect the most accurate picture of HRQoL in our study context. However, through our eCRF, we will collect rich clinical data with additional scales (such as the Patient Health Questionnaire-9 [PHQ-9] screening tool for depression37; and clinical frailty scale)38 to explore the possible limitations and interrelationships. Fifth, our planned situation analysis will provide a cross-sectional picture of the health system at the time of the study, which is not able to incorporate the dynamic and adaptive nature of health systems. However, the MultiLink consortium includes researchers from each country and site, with a rich understanding of the context. We will share the information and learning from clinical, health economic and qualitative situation analysis and use relevant national treatment guidelines to develop a set of diagnostic and treatment algorithms which seek to optimise care for people with multimorbidity. Group discussions will be held at all recruitment sites to ensure we have accurately understood and synthesized the issues identified through situation analysis, and to explore stakeholder perceptions on the feasibility and desirability of our proposed intervention for the RCT phase. We will work with stakeholders (frontline healthcare workers, hospital managers and patients/their representatives) in both countries to explain our proposed intervention and get their feedback on practicality, risks and possible improvements. This will consider quantitative resourcing requirements (e.g., in relation to staffing levels and bed space) as well as feedback from healthcare workers and patients on practical and emotive considerations. The preferred intervention package will then be tested under Phase 3 (cRCT) under a separate protocol. SAS, AR and GH were equally contributing first authors. JR, BM, EW and FL were equally contributing senior authors. Kilimanjaro Clinical Research Institute, Moshi, Tanzania Sanjura Biswaro Yesse Bumija Robert Chuwa Rose Freddy Mwamini Kacheuka Frank Kimaro Zanuni Kweka Rachel Mangoni Martha Oshoseni Philoteus Sakasaka Constantine Tarimo Gidion Tesha Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania Safina Baleche Yusuph Chimpaye Frank Gugu Grasiana Kimario Naftari Mahimbo Ramadhani Mashoka Vicky Mlele Hussein R. Moremi Noela Mpili Herieth Cliff Mushi Nsajigwa Mwakyambiki Benjamin Paulo Mwenda Abdulaziz Abdallah Nassoro Nuhu Richard Chiku Simbano Malawi-Liverpool-Wellcome Programme, Blantyre, Malawi Marlen Chewani Beatrice Chinoko Stephen Gordon Sylvester Kaimba Maureen Kandiero Lucy Keyala Florence Malowa Peter Mandala Mercy Mkandawire Matthew Mlongoti Bright Mnesa Albert Mukatipa Alfred Muyaya Deborah Nyirenda Jacob Phulusa Kamuzu University of Health Sciences (KUHeS), Blantyre, Malawi Diana Msindira Genesis Chowe Liverpool School of Tropical Medicine, Liverpool, UK Nicola Desmond Amy Smith The University of Manchester Yusuf Iqbal Joanna Jozefiak Faculty Opinions recommendedReferences - 1. Mathabire Rücker SC, Tayea A, Bitilinyu-Bangoh J, et al.: High rates of hypertension, diabetes, elevated low-density lipoprotein cholesterol, and cardiovascular disease risk factors in HIV-infected patients in Malawi. AIDS. 2018; 32(2): 253–60. PubMed Abstract | Publisher Full Text | Free Full Text - 2. 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PubMed Abstract | Publisher Full Text Author details Author details 1 Malawi-Liverpool-Wellcome Programme, Blantyre, Malawi 2 Liverpool School of Tropical Medicine, University of Liverpool, Liverpool, England, UK 3 Kilimanjaro Clinical Research Institute, Moshi, Tanzania 4 Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania 5 Humanitarian and Conflict Response Institute, The University of Manchester, Manchester, England, UK 6 Duke University School of Medicine, Duke University, Durham, North Carolina, USA 7 The Kamuzu University of Health Sciences, Blantyre, Malawi 8 Kilimanjaro Christian Medical University College, Moshi, Tanzania 9 Achikondi Women Community Clinic, Lilongwe, Malawi 2 Liverpool School of Tropical Medicine, University of Liverpool, Liverpool, England, UK 3 Kilimanjaro Clinical Research Institute, Moshi, Tanzania 4 Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania 5 Humanitarian and Conflict Response Institute, The University of Manchester, Manchester, England, UK 6 Duke University School of Medicine, Duke University, Durham, North Carolina, USA 7 The Kamuzu University of Health Sciences, Blantyre, Malawi 8 Kilimanjaro Christian Medical University College, Moshi, Tanzania 9 Achikondi Women Community Clinic, Lilongwe, Malawi Stephen A. Spencer Roles: Conceptualization, Data Curation, Funding Acquisition, Investigation, Methodology, Project Administration, Software, Writing – Original Draft Preparation, Writing – Review & Editing Roles: Conceptualization, Data Curation, Funding Acquisition, Investigation, Methodology, Project Administration, Software, Writing – Original Draft Preparation, Writing – Review & Editing Alice Rutta Roles: Investigation, Project Administration, Writing – Original Draft Preparation, Writing – Review & Editing Roles: Investigation, Project Administration, Writing – Original Draft Preparation, Writing – Review & Editing Gimbo Hyuha Roles: Investigation, Project Administration, Writing – Original Draft Preparation, Writing – Review & Editing Roles: Investigation, Project Administration, Writing – Original Draft Preparation, Writing – Review & Editing Gift Treighcy Banda Roles: Data Curation, Investigation, Methodology, Writing – Review & Editing Roles: Data Curation, Investigation, Methodology, Writing – Review & Editing Augustine Choko Roles: Conceptualization, Funding Acquisition, Methodology, Writing – Review & Editing Roles: Conceptualization, Funding Acquisition, Methodology, Writing – Review & Editing Paul Dark Roles: Conceptualization, Funding Acquisition, Methodology, Supervision, Writing – Review & Editing Roles: Conceptualization, Funding Acquisition, Methodology, Supervision, Writing – Review & Editing Julian T. Hertz Roles: Conceptualization, Methodology, Project Administration, Writing – Review & Editing Roles: Conceptualization, Methodology, Project Administration, Writing – Review & Editing Blandina T. Mmbaga Roles: Conceptualization, Methodology, Project Administration, Writing – Review & Editing Roles: Conceptualization, Methodology, Project Administration, Writing – Review & Editing Juma Mfinanga Roles: Methodology, Project Administration, Writing – Review & Editing Roles: Methodology, Project Administration, Writing – Review & Editing Rhona Mijumbi Roles: Supervision, Writing – Review & Editing Roles: Supervision, Writing – Review & Editing Adamson Muula Roles: Conceptualization, Funding Acquisition, Methodology, Writing – Review & Editing Roles: Conceptualization, Funding Acquisition, Methodology, Writing – Review & Editing Mulinda Nyirenda Roles: Conceptualization, Funding Acquisition, Methodology, Writing – Review & Editing Roles: Conceptualization, Funding Acquisition, Methodology, Writing – Review & Editing Laura Rosu Roles: Data Curation, Investigation, Methodology, Supervision, Writing – Review & Editing Roles: Data Curation, Investigation, Methodology, Supervision, Writing – Review & Editing Matthew Rubach Roles: Conceptualization, Funding Acquisition, Methodology, Project Administration, Supervision, Writing – Review & Editing Roles: Conceptualization, Funding Acquisition, Methodology, Project Administration, Supervision, Writing – Review & Editing Sangwani Salimu Roles: Data Curation, Investigation, Methodology, Writing – Review & Editing Roles: Data Curation, Investigation, Methodology, Writing – Review & Editing Francis Sakita Roles: Conceptualization, Methodology, Writing – Review & Editing Roles: Conceptualization, Methodology, Writing – Review & Editing Charity Salima Roles: Conceptualization, Funding Acquisition, Methodology, Writing – Review & Editing Roles: Conceptualization, Funding Acquisition, Methodology, Writing – Review & Editing Hendry Sawe Roles: Conceptualization, Funding Acquisition, Methodology, Project Administration, Supervision, Writing – Review & Editing Roles: Conceptualization, Funding Acquisition, Methodology, Project Administration, Supervision, Writing – Review & Editing Ibrahim Simiyu Roles: Data Curation, Investigation, Methodology, Writing – Review & Editing Roles: Data Curation, Investigation, Methodology, Writing – Review & Editing Miriam Taegtmeyer Roles: Conceptualization, Funding Acquisition, Methodology, Supervision, Writing – Review & Editing Roles: Conceptualization, Funding Acquisition, Methodology, Supervision, Writing – Review & Editing Sarah Urasa Roles: Conceptualization, Methodology, Writing – Review & Editing Roles: Conceptualization, Methodology, Writing – Review & Editing Sarah White Roles: Conceptualization, Data Curation, Funding Acquisition, Methodology, Writing – Review & Editing Roles: Conceptualization, Data Curation, Funding Acquisition, Methodology, Writing – Review & Editing Nateiya M. Yongolo Roles: Data Curation, Investigation, Methodology, Writing – Review & Editing Roles: Data Curation, Investigation, Methodology, Writing – Review & Editing Jamie Rylance Roles: Conceptualization, Funding Acquisition, Methodology, Project Administration, Software, Supervision, Writing – Review & Editing Roles: Conceptualization, Funding Acquisition, Methodology, Project Administration, Software, Supervision, Writing – Review & Editing Ben Morton Roles: Conceptualization, Funding Acquisition, Methodology, Project Administration, Software, Supervision, Writing – Review & Editing Roles: Conceptualization, Funding Acquisition, Methodology, Project Administration, Software, Supervision, Writing – Review & Editing Eve Worrall Roles: Conceptualization, Funding Acquisition, Methodology, Project Administration, Software, Supervision, Writing – Review & Editing Roles: Conceptualization, Funding Acquisition, Methodology, Project Administration, Software, Supervision, Writing – Review & Editing Felix Limbani Roles: Conceptualization, Funding Acquisition, Methodology, Project Administration, Software, Supervision, Writing – Review & Editing Roles: Conceptualization, Funding Acquisition, Methodology, Project Administration, Software, Supervision, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information This project is funded by the National Institute for Health and Care Research (NIHR) under its ‘Research and Innovation for Global Health Transformation’ (Grant Reference Number NIHR201708). This research was funded by the NIHR (NIHR201708) using UK aid from the UK Government to support global health research. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. In addition, this research is supported by a number of individual grants. This research is funded in part by the Wellcome Trust [Wellcome Trust Clinical PhD Fellowship awarded to SAS: Grant number 203919]. PD is also supported by a NIHR Senior Investigator award [NIHR203745]. This publication is associated with the Research, Evidence and Development Initiative (READ-It). READ-It (project number 300342-104) is funded by UK aid from the UK government; however, the views expressed do not necessarily reflect the UK government’s official policies. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Article Versions (2) Copyright © 2024 Spencer SA 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 work is properly cited. metrics VIEWS $counts.viewCount downloads Citations CITE how to cite this article Spencer SA, Rutta A, Hyuha G et al. Multimorbidity-associated emergency hospital admissions: a “screen and link” strategy to improve outcomes for high-risk patients in sub-Saharan Africa: a prospective multicentre cohort study protocol [version 2; peer review: 1 approved, 2 approved with reservations]. NIHR Open Res 2024, 4:2 (https://doi.org/10.3310/nihropenres.13512.2) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. track receive updates on this article Track an article to receive email alerts on any updates to this article. Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE ApprovedThe paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approvedFundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 18 Jan 2024 Views 0 How to cite this report: Tran PB. Reviewer Report For: Multimorbidity-associated emergency hospital admissions: a “screen and link” strategy to improve outcomes for high-risk patients in sub-Saharan Africa: a prospective multicentre cohort study protocol [version 2; peer review: 1 approved, 2 approved with reservations]. NIHR Open Res 2024, 4:2 (https://doi.org/10.3310/nihropenres.14663.r31249) The direct URL for this report is: https://openresearch.nihr.ac.uk/articles/4-2/v1#referee-response-31249 https://openresearch.nihr.ac.uk/articles/4-2/v1#referee-response-31249 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Reviewer Report 22 Apr 2024 Approved with Reservations VIEWS 0 Summary: The topic of multimorbidity is an important one for study settings in sub-Saharan Africa due to the increasing burden of chronic and infectious diseases, limited healthcare resources, and the need for effective strategies to improve patient outcomes and ... Continue reading 2. Beckett K, Farr M, Kothari A, Wye L, et al.: Embracing complexity and uncertainty to create impact: exploring the processes and transformative potential of co-produced research through development of a social impact model.Health Res Policy Syst. 2018; 16 (1): 118 PubMed Abstract | Publisher Full Text 3. Fortin M, Stewart M, Poitras ME, Almirall J, et al.: A systematic review of prevalence studies on multimorbidity: toward a more uniform methodology.Ann Fam Med. 2012; 10 (2): 142-51 PubMed Abstract | Publisher Full Text 4. Triolo F, Harber-Aschan L, Belvederi Murri M, Calderón-Larrañaga A, et al.: The complex interplay between depression and multimorbidity in late life: risks and pathways.Mech Ageing Dev. 2020; 192: 111383 PubMed Abstract | Publisher Full Text 5. Cicek M, Hayhoe B, Otis M, Nicholls D, et al.: Depression and unplanned secondary healthcare use in patients with multimorbidity: A systematic review.PLoS One. 2022; 17 (4): e0266605 PubMed Abstract | Publisher Full Text 6. Birk JL, Kronish IM, Moise N, Falzon L, et al.: Depression and multimorbidity: Considering temporal characteristics of the associations between depression and multiple chronic diseases.Health Psychol. 2019; 38 (9): 802-811 PubMed Abstract | Publisher Full Text 7. Eack SM, Greeno CG, Lee BJ: Limitations of the Patient Health Questionnaire in Identifying Anxiety and Depression: Many Cases Are Undetected.Res Soc Work Pract. 2006; 16 (6): 625-631 PubMed Abstract | Publisher Full Text 8. Vidyasagaran A, Ayesha R, Boehnke J, Kirkham J, et al.: Core outcome sets for trials of interventions to prevent and to treat multimorbidity in low- and middle-income countries: the COSMOS study. medRxiv. 2024. Publisher Full Text I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close The topic of multimorbidity is an important one for study settings in sub-Saharan Africa due to the increasing burden of chronic and infectious diseases, limited healthcare resources, and the need for effective strategies to improve patient outcomes and ... Continue reading Summary: The topic of multimorbidity is an important one for study settings in sub-Saharan Africa due to the increasing burden of chronic and infectious diseases, limited healthcare resources, and the need for effective strategies to improve patient outcomes and reduce healthcare costs. This cohort study aims to identify multimorbidity among emergency care patients in sub-Saharan African hospitals, facilitating early treatment, improved follow-up, and reduced disability, readmission, and costs. The study seeks to co-create cost-effective strategies for enhanced patient care. While the protocol demonstrates thorough planning and detail, several concerns persist. Foremost is the absence of a theoretical framework guiding the study's conceptualization and implementation, particularly crucial in implementation research. Additionally, the narrow scope of multimorbidity prevalence assessment and the limited focus on a specific study population (i.e., high-risk/severe-stage patients) are notable limitations. The absence of a primary care component, essential for integrated patient-centered care in multimorbidity management, is a critical gap. Despite labelling the project as a "co-creation," there appears to be a perceived limited level of patient and public involvement, warranting further attention and clarification. Please see details below. Fairness: Theoretical framework Study population: Study setting: Scope of multimorbidity: Outcomes of interest: Data collection: Analysis plan: Other: The topic of multimorbidity is an important one for study settings in sub-Saharan Africa due to the increasing burden of chronic and infectious diseases, limited healthcare resources, and the need for effective strategies to improve patient outcomes and reduce healthcare costs. This cohort study aims to identify multimorbidity among emergency care patients in sub-Saharan African hospitals, facilitating early treatment, improved follow-up, and reduced disability, readmission, and costs. The study seeks to co-create cost-effective strategies for enhanced patient care. While the protocol demonstrates thorough planning and detail, several concerns persist. Foremost is the absence of a theoretical framework guiding the study's conceptualization and implementation, particularly crucial in implementation research. Additionally, the narrow scope of multimorbidity prevalence assessment and the limited focus on a specific study population (i.e., high-risk/severe-stage patients) are notable limitations. The absence of a primary care component, essential for integrated patient-centered care in multimorbidity management, is a critical gap. Despite labelling the project as a "co-creation," there appears to be a perceived limited level of patient and public involvement, warranting further attention and clarification. Please see details below. Fairness: - In the protocol, LSTM is designated as the "sponsor". However, the reviewer requests further clarification regarding the involvement, roles, and decision-making power of all partner institutions, as well as the nature of the collaborative efforts. Emphasis is placed on outlining the equitable nature of these partnerships, aligning with the principles of ethical global health research partnerships. Given the predominant role of academic institutions in the Global North as major sponsors of research, and their control over vital resources for global health research (Monette et al., 20211), it is imperative to illustrate how power dynamics are addressed to ensure fairness and inclusivity in this project. The reflexivity statement merely states that local researchers are “co-applicants” in the funding application. - Patient and public involvement (PPI) is stated to be integrated into the project and the project is described as a process of co-creation. However, the extent to which research can be said to be ‘co-produced’ is a factor of the number of research stages involved in, stakeholder types, the scale of their contribution, and ‘adherence’ to the principles and practice of co-production (see Figure 1 in Beckett et al., 20182). It is essential to clarify whether PPI spans all research/project stages of the co-production continuum (e.g., from topic identification to process evaluation) or selective ones, and the level of influence the patients/public have at each stage. Are they considered ‘participants’ or ‘co-researchers’? (If the latter, are any patient/member of the public named author on this publication?) Are they allowed to 'express a view' only or 'contribute to decision making'? (See Levels of Participation Pyramid by Kirby, 1999). If there will be limited involvement of patients and the public, it is important to acknowledge this limitation. Additionally, the term "co-creation" should be used carefully and with nuance, recognizing the extent of involvement and avoiding overstatement of collaborative processes where they may be lacking. Theoretical framework - A critical concern is the absence of a theoretical framework to guide the study, underpin concepts and provide a structured foundation for interpreting the findings. This gap becomes particularly pronounced considering the planned development of an implementation strategy aimed at enhancing the quality of care and health outcomes for individuals living with multimorbidity. Relevant to this project may be the Chronic Care Model by Wagner et al., which provides a framework for improving care for patients with chronic conditions. It emphasizes the need for a proactive, planned approach to care that is patient-centered, population-based, and community-oriented. - The importance of integrated care in managing multimorbidity, particularly at the primary care level, is well-established. However, this critical aspect is noticeably absent in the protocol, from background/rationale to the development of intervention strategies to deliver improved care and health outcomes for patients with multimorbidity. Primary care is not represented among the study sites and no health workers or patients will be identified from this setting. This omission further underscores the need for a theoretical framework to guide both research and implementation strategies, ensuring comprehensive consideration of all relevant components for effective multimorbidity management. Study population: - The term "high-risk patients" is included in the title, is this designation warranted due to the study population being limited to those admitted to emergency care? - It remains ambiguous whether patients previously diagnosed with the four preselected conditions at baseline and already undergoing treatment for those conditions will continue to be enrolled in the study. Will they still receive point-of-care (POC) testing? Moreover, is the focus primarily on prevalence or incidence? The emphasis appears to be on identifying undetected or undiagnosed cases among those with one or more of the four preselected conditions to initiate treatment. Additionally, the study seems to exclude individuals already hospitalized at the start of the study period. However, prevalence rates are consistently mentioned throughout the protocol, does the study intend to focus instead on estimating incidence rates? - Could the authors clarify the definition of "acute medical hospital admissions" and explain whether the specific health reason for hospital admission is a predictor of interest in the analysis, and if so, how will it be utilized? - The study's exclusive recruitment of patients admitted to emergency care for acute medical hospital admissions suggests a focus solely on severe-stage patients. This approach represents a missed opportunity to identify individuals in earlier stages of disease progression and enable their linkages to care. - Would a person be enrolled in the study if they were discharged on the same day as their emergency admission? - The criteria of including only participants who are contactable on discharge by phone inadvertently marginalizes groups lacking access to phones/technology. Study setting: - Could the authors please provide additional information regarding the urban or rural context of the hospitals selected as study sites and provide more information on the approximate size of the catchment area served by each of the selected hospitals in Malawi and Tanzania? Additionally, how do factors such as population density and accessibility to healthcare services vary across the catchment areas of these hospitals? Scope of multimorbidity: - The scope of multimorbidity prevalence assessment in this project is limited, focusing solely on four preselected conditions: high blood pressure, diabetes, HIV, and chronic kidney disease. However, Fortin et al. advocated for a broader approach and proposed including at least 12 conditions to assess multimorbidity prevalence (Fortin et al., 20123). By incorporating a more comprehensive range of conditions, biases and underestimations can be minimized, providing a more accurate representation of the true burden of multimorbidity within the studied population. - The plain summary, as well as other sections, mentioned that "The cohort study aims to determine multimorbidity prevalence". However, considering the narrow scope of the study focusing on a limited set of chronic conditions, it might be more accurate to reflect this through precise wording throughout the protocol. Therefore, instead of "multimorbidity prevalence", it could be more appropriate to use phrases like "common comorbidity occurrences in hospital-admitted patients" or "a limited scope of multimorbidity prevalence assessment based on a list of four preselected high-burden/high-prevalent chronic conditions". This adjustment ensures alignment between the study's objectives and the terminology used throughout the protocol. - The prevalence of mental health disorders among patients with multimorbidity (https://moseschikoti.com/wp-content/uploads/2019/07/Multimorbidity-and-depression-A-systematic-review-and-meta-analysis.pdf), the bidirectional interplay between depression and multimorbidity (Triolo et al., 20204), and the significant impact/treatment burden of physical-mental health multimorbidity (Cicek et al., 20225) are widely recognized in multimorbidity research. Particularly in sub-Saharan Africa, mental health conditions are frequently undiagnosed and undertreated. Depression's pivotal role in multimorbidity patterns justifies focused screening and treatment for at-risk individuals (Birk et al., 20196). However, while outcomes related to mental health may be partially captured through instruments like the EQ5D and the Patient Health Questionnaire (PHQ)-9 for depression (a rapid assessment tool with various limitations, Eack et al., 20067), their importance warrants greater attention and focus. Presently, mental disorders are neither included in the list of selected conditions for the assessment of multimorbidity prevalence nor identified as a primary clinical outcome of interest in the study, underscoring the need for its inclusion to ensure a comprehensive assessment of multimorbidity prevalence, patient health status and outcomes. Outcomes of interest: - While the below named study is currently only available in preprint format, it will soon be published in BMJ Global Health. The reviewer recommends that the authors look into this study and consider integrating core outcome sets tailored for trials of interventions aimed at preventing and treating multimorbidity in low- and middle-income countries. Notably, "adherence to treatment" appears to be absent from the current protocol and merits inclusion to comprehensively evaluate intervention efficacy. Vidyasagaran et al. (20248). A comprehensive Core Outcome Set (COS) for multimorbidity, particularly tailored to low- and middle-income countries (LMICs), is lacking. This study followed stringent guidelines to develop COS specific to LMICs. The research identified four essential outcomes for prevention interventions and four for treatment interventions in LMIC settings. These outcomes encompass adverse events, quality of life, development of new comorbidities, health risk behavior, adherence to treatment, and out-of-pocket expenditure. A context-sensitive COS has the potential to minimize research redundancy, standardize outcome measures, and propel multimorbidity research in LMICs, ultimately improving health outcomes for people living with multimorbidity. Data collection: - Will there also be an EQ5D5L questionnaire administered at point of discharge (E)? What is shown for EQ5D5L at timepoint E (discharge) in Table 1 do not align with the following texts: “The questionnaire will be administered, at admission, discharge, 30 and 90 days post admission in person or (day 30) by phone (as for patient costs).” - What’s the difference between structured discussions and focused group interviews (Table 2)? - Could the authors please provide more details regarding the measures that will be taken to ensure the objectivity and reliability of the structured observations conducted during patient admission - specifically, any steps taken to minimize potential biases in this data collection process? Analysis plan: - What statistical methodologies will be employed to estimate the costs associated with specific combinations of morbidity, ensuring that the influence of other comorbidities and covariates is effectively controlled for? Other: - In the context of multimorbidity, when discussing various combinations of co-existing conditions, it may be more appropriate to use terms such as "disease combinations", "morbidity combinations" or, in the case of two conditions, "pairs/dyads" and in the case of three, "triads", etc. The term "multimorbidity combination" being used lacks phrasal-semantic coherence. - Minor: Some abbreviations were not defined at first instance of use, but later on (IDI, FGD, HCW…) and some were not defined at all. - Minor: Various closing brackets missing. - Is the rationale for, and objectives of, the study clearly described? Yes - Is the study design appropriate for the research question? Partly - Are sufficient details of the methods provided to allow replication by others? Partly - Are the datasets clearly presented in a useable and accessible format? Not applicable

1. Monette EM, McHugh D, Smith MJ, Canas E, et al.: Informing 'good' global health research partnerships: A scoping review of guiding principles.Glob Health Action. 2021; 14 (1): 1892308 PubMed Abstract | Publisher Full Text2. Beckett K, Farr M, Kothari A, Wye L, et al.: Embracing complexity and uncertainty to create impact: exploring the processes and transformative potential of co-produced research through development of a social impact model.Health Res Policy Syst. 2018; 16 (1): 118 PubMed Abstract | Publisher Full Text 3. Fortin M, Stewart M, Poitras ME, Almirall J, et al.: A systematic review of prevalence studies on multimorbidity: toward a more uniform methodology.Ann Fam Med. 2012; 10 (2): 142-51 PubMed Abstract | Publisher Full Text 4. Triolo F, Harber-Aschan L, Belvederi Murri M, Calderón-Larrañaga A, et al.: The complex interplay between depression and multimorbidity in late life: risks and pathways.Mech Ageing Dev. 2020; 192: 111383 PubMed Abstract | Publisher Full Text 5. Cicek M, Hayhoe B, Otis M, Nicholls D, et al.: Depression and unplanned secondary healthcare use in patients with multimorbidity: A systematic review.PLoS One. 2022; 17 (4): e0266605 PubMed Abstract | Publisher Full Text 6. Birk JL, Kronish IM, Moise N, Falzon L, et al.: Depression and multimorbidity: Considering temporal characteristics of the associations between depression and multiple chronic diseases.Health Psychol. 2019; 38 (9): 802-811 PubMed Abstract | Publisher Full Text 7. Eack SM, Greeno CG, Lee BJ: Limitations of the Patient Health Questionnaire in Identifying Anxiety and Depression: Many Cases Are Undetected.Res Soc Work Pract. 2006; 16 (6): 625-631 PubMed Abstract | Publisher Full Text 8. Vidyasagaran A, Ayesha R, Boehnke J, Kirkham J, et al.: Core outcome sets for trials of interventions to prevent and to treat multimorbidity in low- and middle-income countries: the COSMOS study. medRxiv. 2024. Publisher Full Text Competing Interests: No competing interests were disclosed. Reviewer Expertise: My work focuses on health economics, primary care, infectious diseases, and multimorbidity. CITE HOW TO CITE THIS REPORT Tran PB. Reviewer Report For: Multimorbidity-associated emergency hospital admissions: a “screen and link” strategy to improve outcomes for high-risk patients in sub-Saharan Africa: a prospective multicentre cohort study protocol [version 2; peer review: 1 approved, 2 approved with reservations]. NIHR Open Res 2024, 4:2 (https://doi.org/10.3310/nihropenres.14663.r31249) The direct URL for this report is: https://openresearch.nihr.ac.uk/articles/4-2/v1#referee-response-31249 https://openresearch.nihr.ac.uk/articles/4-2/v1#referee-response-31249 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. Views 0 How to cite this report: Rosen C. Reviewer Report For: Multimorbidity-associated emergency hospital admissions: a “screen and link” strategy to improve outcomes for high-risk patients in sub-Saharan Africa: a prospective multicentre cohort study protocol [version 2; peer review: 1 approved, 2 approved with reservations]. NIHR Open Res 2024, 4:2 (https://doi.org/10.3310/nihropenres.14663.r31103) The direct URL for this report is: https://openresearch.nihr.ac.uk/articles/4-2/v1#referee-response-31103 https://openresearch.nihr.ac.uk/articles/4-2/v1#referee-response-31103 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Reviewer Report 12 Apr 2024 Claire Rosen, University of Pennsylvania, Philadelphia, PA, USA Approved with Reservations VIEWS 0 The proposed study protocol outlines, in detail, the rational behind the first two phases of a three-phase mixed-methods investigation to: I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close - Describe the prevalence of HIV, hypertension, diabetes, and chronic kidney disease among patients admitted to 4 The proposed study protocol outlines, in detail, the rational behind the first two phases of a three-phase mixed-methods investigation to: I would like to begin by applauding the investigators on this well thought-out research proposal, and specifically for their robust community engagement in designing the study. I am impressed by breadth of investigation that the authors plan to undertake. I have a few small suggestions that I think would improve this project. To start, the definition of multimorbidity in the literature is not strictly defined as "two or more comorbidities". In fact, there has been a decent amount of debate in the literature over the ideal definition of multimorbidity. Furthermore, the authors are specifically looking at HIV, hypertension, diabetes, and chronic kidney disease. As such, it would be more honest of an approach to describe the aims and study to be that of understanding the prevalence of specific common comorbid conditions to the populations of interest, and the impact of those comorbidities on outcomes. Either way, a clarification that "multimorbidity" is not universally defined (e.g. "though there are multiple proposed definitions of multimorbidity, we choose to define multimorbidity as X because Y") and that the study is specifically looking at diseases of importance to the study population (HIV, hypertension, diabetes, and chronic kidney disease) is necessary. In your methods for this study, you mention that you will include patients with a "physician diagnosis of an acute medical disorder". How will this be judged? Is it from the clinical records? Claims data? Do you have a proposed list of medical disorders that you plan to include? Finally, in your plans for Phase 1a analysis, you note that you plan to use logistic regression. It would be helpful to clearly define your planned covariates, or how you intend to select covariates for your regression model. Further, you mention multiple outcomes - as such, would be sure to clarify how you will define statistical or clinical significance, and how this will be adjusted for multiple outcomes (e.g. Bonferroni). Thank you for the opportunity to review your planned investigation - I wish you the best of luck with your endeavors! - Describe the prevalence of HIV, hypertension, diabetes, and chronic kidney disease among patients admitted to 4 hospitals (two in Malawi and two in Tanzania) over the span of one calendar year and to examine the association between these comorbidities and clinical outcomes. - Measure and describe the costs incurred and changes in health-related quality of life at 90 days post-admission - Qualitatively study patients and carer's experience within the healthcare system, and specifically regarding the impact of common comorbidities on care seeking, care navigation, and care adherence. I would like to begin by applauding the investigators on this well thought-out research proposal, and specifically for their robust community engagement in designing the study. I am impressed by breadth of investigation that the authors plan to undertake. I have a few small suggestions that I think would improve this project. To start, the definition of multimorbidity in the literature is not strictly defined as "two or more comorbidities". In fact, there has been a decent amount of debate in the literature over the ideal definition of multimorbidity. Furthermore, the authors are specifically looking at HIV, hypertension, diabetes, and chronic kidney disease. As such, it would be more honest of an approach to describe the aims and study to be that of understanding the prevalence of specific common comorbid conditions to the populations of interest, and the impact of those comorbidities on outcomes. Either way, a clarification that "multimorbidity" is not universally defined (e.g. "though there are multiple proposed definitions of multimorbidity, we choose to define multimorbidity as X because Y") and that the study is specifically looking at diseases of importance to the study population (HIV, hypertension, diabetes, and chronic kidney disease) is necessary. In your methods for this study, you mention that you will include patients with a "physician diagnosis of an acute medical disorder". How will this be judged? Is it from the clinical records? Claims data? Do you have a proposed list of medical disorders that you plan to include? Finally, in your plans for Phase 1a analysis, you note that you plan to use logistic regression. It would be helpful to clearly define your planned covariates, or how you intend to select covariates for your regression model. Further, you mention multiple outcomes - as such, would be sure to clarify how you will define statistical or clinical significance, and how this will be adjusted for multiple outcomes (e.g. Bonferroni). Thank you for the opportunity to review your planned investigation - I wish you the best of luck with your endeavors! - Is the rationale for, and objectives of, the study clearly described? Yes - Is the study design appropriate for the research question? Yes - Are sufficient details of the methods provided to allow replication by others? Yes - Are the datasets clearly presented in a useable and accessible format? Not applicable Competing Interests: No competing interests were disclosed. Reviewer Expertise: Health services research and multimorbidity CITE HOW TO CITE THIS REPORT Rosen C. Reviewer Report For: Multimorbidity-associated emergency hospital admissions: a “screen and link” strategy to improve outcomes for high-risk patients in sub-Saharan Africa: a prospective multicentre cohort study protocol [version 2; peer review: 1 approved, 2 approved with reservations]. NIHR Open Res 2024, 4:2 (https://doi.org/10.3310/nihropenres.14663.r31103) The direct URL for this report is: https://openresearch.nihr.ac.uk/articles/4-2/v1#referee-response-31103 https://openresearch.nihr.ac.uk/articles/4-2/v1#referee-response-31103 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. - Author Response 23 May 2024Stephen Spencer, Malawi-Liverpool-Wellcome Programme, Blantyre, Malawi23 May 2024Author ResponseReviewer’s comment I have a few small suggestions that I think would improve this project. To start, the definition of multimorbidity in the literature is not strictly defined as "two ... Continue reading Reviewer’s comment I have a few small suggestions that I think would improve this project. To start, the definition of multimorbidity in the literature is not strictly defined as "two or more comorbidities". In fact, there has been a decent amount of debate in the literature over the ideal definition of multimorbidity. Furthermore, the authors are specifically looking at HIV, hypertension, diabetes, and chronic kidney disease. As such, it would be more honest of an approach to describe the aims and study to be that of understanding the prevalence of specific common comorbid conditions to the populations of interest, and the impact of those comorbidities on outcomes. Either way, a clarification that "multimorbidity" is not universally defined (e.g. "though there are multiple proposed definitions of multimorbidity, we choose to define multimorbidity as X because Y") and that the study is specifically looking at diseases of importance to the study population (HIV, hypertension, diabetes, and chronic kidney disease) is necessary. Authors’ reply Thank you very much for your suggestion. We agree and have modified the final paragraph in the introduction section; we have highlighted that there limitations with the definition, particularly to different contexts (and provided an additional reference); and clarified that we will focus on HIV, hypertension, diabetes and chronic kidney disease. Reviewer’s comment In your methods for this study, you mention that you will include patients with a "physician diagnosis of an acute medical disorder". How will this be judged? Is it from the clinical records? Claims data? Do you have a proposed list of medical disorders that you plan to include? Authors’ reply Thank you for highlighting this was not clear. Inclusion was based on capture of acute medical disorder from medical records, using ICD codes. The Inclusion criteria in the manuscript has been updated to reflect this. Reviewer’s comment Finally, in your plans for Phase 1a analysis, you note that you plan to use logistic regression. It would be helpful to clearly define your planned covariates, or how you intend to select covariates for your regression model. Further, you mention multiple outcomes - as such, would be sure to clarify how you will define statistical or clinical significance, and how this will be adjusted for multiple outcomes (e.g. Bonferroni). Authors’ reply Thank you for raising this. We will publish a statistical analysis plan (SAP) as a component of our reporting, which is over-seen by the study statistician (Dr Sarah White). We plan to publish this separately on an online repository (see updated ‘Analysis’ section).Reviewer’s commentCompeting Interests: No competing interests were disclosed. Close I have a few small suggestions that I think would improve this project. To start, the definition of multimorbidity in the literature is not strictly defined as "two or more comorbidities". In fact, there has been a decent amount of debate in the literature over the ideal definition of multimorbidity. Furthermore, the authors are specifically looking at HIV, hypertension, diabetes, and chronic kidney disease. As such, it would be more honest of an approach to describe the aims and study to be that of understanding the prevalence of specific common comorbid conditions to the populations of interest, and the impact of those comorbidities on outcomes. Either way, a clarification that "multimorbidity" is not universally defined (e.g. "though there are multiple proposed definitions of multimorbidity, we choose to define multimorbidity as X because Y") and that the study is specifically looking at diseases of importance to the study population (HIV, hypertension, diabetes, and chronic kidney disease) is necessary. Authors’ reply Thank you very much for your suggestion. We agree and have modified the final paragraph in the introduction section; we have highlighted that there limitations with the definition, particularly to different contexts (and provided an additional reference); and clarified that we will focus on HIV, hypertension, diabetes and chronic kidney disease. Reviewer’s comment In your methods for this study, you mention that you will include patients with a "physician diagnosis of an acute medical disorder". How will this be judged? Is it from the clinical records? Claims data? Do you have a proposed list of medical disorders that you plan to include? Authors’ reply Thank you for highlighting this was not clear. Inclusion was based on capture of acute medical disorder from medical records, using ICD codes. The Inclusion criteria in the manuscript has been updated to reflect this. Reviewer’s comment Finally, in your plans for Phase 1a analysis, you note that you plan to use logistic regression. It would be helpful to clearly define your planned covariates, or how you intend to select covariates for your regression model. Further, you mention multiple outcomes - as such, would be sure to clarify how you will define statistical or clinical significance, and how this will be adjusted for multiple outcomes (e.g. Bonferroni). Authors’ reply Thank you for raising this. We will publish a statistical analysis plan (SAP) as a component of our reporting, which is over-seen by the study statistician (Dr Sarah White). We plan to publish this separately on an online repository (see updated ‘Analysis’ section). COMMENTS ON THIS REPORT - Author Response 23 May 2024Stephen Spencer, Malawi-Liverpool-Wellcome Programme, Blantyre, Malawi23 May 2024Author ResponseReviewer’s comment I have a few small suggestions that I think would improve this project. To start, the definition of multimorbidity in the literature is not strictly defined as "two ... Continue reading Reviewer’s comment I have a few small suggestions that I think would improve this project. To start, the definition of multimorbidity in the literature is not strictly defined as "two or more comorbidities". In fact, there has been a decent amount of debate in the literature over the ideal definition of multimorbidity. Furthermore, the authors are specifically looking at HIV, hypertension, diabetes, and chronic kidney disease. As such, it would be more honest of an approach to describe the aims and study to be that of understanding the prevalence of specific common comorbid conditions to the populations of interest, and the impact of those comorbidities on outcomes. Either way, a clarification that "multimorbidity" is not universally defined (e.g. "though there are multiple proposed definitions of multimorbidity, we choose to define multimorbidity as X because Y") and that the study is specifically looking at diseases of importance to the study population (HIV, hypertension, diabetes, and chronic kidney disease) is necessary. Authors’ reply Thank you very much for your suggestion. We agree and have modified the final paragraph in the introduction section; we have highlighted that there limitations with the definition, particularly to different contexts (and provided an additional reference); and clarified that we will focus on HIV, hypertension, diabetes and chronic kidney disease. Reviewer’s comment In your methods for this study, you mention that you will include patients with a "physician diagnosis of an acute medical disorder". How will this be judged? Is it from the clinical records? Claims data? Do you have a proposed list of medical disorders that you plan to include? Authors’ reply Thank you for highlighting this was not clear. Inclusion was based on capture of acute medical disorder from medical records, using ICD codes. The Inclusion criteria in the manuscript has been updated to reflect this. Reviewer’s comment Finally, in your plans for Phase 1a analysis, you note that you plan to use logistic regression. It would be helpful to clearly define your planned covariates, or how you intend to select covariates for your regression model. Further, you mention multiple outcomes - as such, would be sure to clarify how you will define statistical or clinical significance, and how this will be adjusted for multiple outcomes (e.g. Bonferroni). Authors’ reply Thank you for raising this. We will publish a statistical analysis plan (SAP) as a component of our reporting, which is over-seen by the study statistician (Dr Sarah White). We plan to publish this separately on an online repository (see updated ‘Analysis’ section).Reviewer’s commentCompeting Interests: No competing interests were disclosed. Close I have a few small suggestions that I think would improve this project. To start, the definition of multimorbidity in the literature is not strictly defined as "two or more comorbidities". In fact, there has been a decent amount of debate in the literature over the ideal definition of multimorbidity. Furthermore, the authors are specifically looking at HIV, hypertension, diabetes, and chronic kidney disease. As such, it would be more honest of an approach to describe the aims and study to be that of understanding the prevalence of specific common comorbid conditions to the populations of interest, and the impact of those comorbidities on outcomes. Either way, a clarification that "multimorbidity" is not universally defined (e.g. "though there are multiple proposed definitions of multimorbidity, we choose to define multimorbidity as X because Y") and that the study is specifically looking at diseases of importance to the study population (HIV, hypertension, diabetes, and chronic kidney disease) is necessary. Authors’ reply Thank you very much for your suggestion. We agree and have modified the final paragraph in the introduction section; we have highlighted that there limitations with the definition, particularly to different contexts (and provided an additional reference); and clarified that we will focus on HIV, hypertension, diabetes and chronic kidney disease. Reviewer’s comment In your methods for this study, you mention that you will include patients with a "physician diagnosis of an acute medical disorder". How will this be judged? Is it from the clinical records? Claims data? Do you have a proposed list of medical disorders that you plan to include? Authors’ reply Thank you for highlighting this was not clear. Inclusion was based on capture of acute medical disorder from medical records, using ICD codes. The Inclusion criteria in the manuscript has been updated to reflect this. Reviewer’s comment Finally, in your plans for Phase 1a analysis, you note that you plan to use logistic regression. It would be helpful to clearly define your planned covariates, or how you intend to select covariates for your regression model. Further, you mention multiple outcomes - as such, would be sure to clarify how you will define statistical or clinical significance, and how this will be adjusted for multiple outcomes (e.g. Bonferroni). Authors’ reply Thank you for raising this. We will publish a statistical analysis plan (SAP) as a component of our reporting, which is over-seen by the study statistician (Dr Sarah White). We plan to publish this separately on an online repository (see updated ‘Analysis’ section). Views 0 How to cite this report: Roomaney RA. Reviewer Report For: Multimorbidity-associated emergency hospital admissions: a “screen and link” strategy to improve outcomes for high-risk patients in sub-Saharan Africa: a prospective multicentre cohort study protocol [version 2; peer review: 1 approved, 2 approved with reservations]. NIHR Open Res 2024, 4:2 (https://doi.org/10.3310/nihropenres.14663.r31181) The direct URL for this report is: https://openresearch.nihr.ac.uk/articles/4-2/v1#referee-response-31181 https://openresearch.nihr.ac.uk/articles/4-2/v1#referee-response-31181 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Reviewer Report 14 Mar 2024 Approved VIEWS 0 Thank you for the opportunity to review the study protocol titled “Multimorbidity-associated emergency hospital admissions: a “screen and link” strategy to improve outcomes for high-risk patients in sub-Saharan Africa: a prospective multicentre cohort study protocol” by Spencer et al., in ... Continue reading I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close Thank you for the opportunity to review the study protocol titled “Multimorbidity-associated emergency hospital admissions: a “screen and link” strategy to improve outcomes for high-risk patients in sub-Saharan Africa: a prospective multicentre cohort study protocol” by Spencer et al., in consideration of publication in the NIHR Open Research Journal. Multi-morbidity plays an important role in the disease burden in Africa. I am unaware of such a large-scale study that has taken place in Sub-Saharan Africa and thus the authors are addressing a knowledge gap i.e. multi-morbidity in emergency hospital admissions and the assessment of HRQoL and the related economic costs. As far as I am aware, injuries have not been well incorporated into studies of multi-morbidity. Given that the setting is hospital-based, the authors may want to incorporate some information on injuries in their analysis. The protocol is well-written, thorough and clear. I commend the authors for publishing a protocol, so that similar studies could theoretically be performed elsewhere. I look forward to reading the outcomes of the study. This study will be a great addition to the knowledge base in sub-Saharan Africa and answer many important questions. Minor:

- Can the authors elaborate on why there has been an increase in life expectancy in Malawi and Tanzania? Although I can guess why, it would be better for the authors to state.

- The authors have justified why they are interested in certain pre-selected diseases. They may want to consider the existence of other diseases (even if self-report) to control for confounding in the analysis. The same goes for body mass index. I note that this is addressed by the authors in the limitations. - Can the authors elaborate on why admissions for primary trauma are excluded? Multi-morbidity plays an important role in the disease burden in Africa. I am unaware of such a large-scale study that has taken place in Sub-Saharan Africa and thus the authors are addressing a knowledge gap i.e. multi-morbidity in emergency hospital admissions and the assessment of HRQoL and the related economic costs. As far as I am aware, injuries have not been well incorporated into studies of multi-morbidity. Given that the setting is hospital-based, the authors may want to incorporate some information on injuries in their analysis. The protocol is well-written, thorough and clear. I commend the authors for publishing a protocol, so that similar studies could theoretically be performed elsewhere. I look forward to reading the outcomes of the study. This study will be a great addition to the knowledge base in sub-Saharan Africa and answer many important questions. Minor:

- Can the authors elaborate on why there has been an increase in life expectancy in Malawi and Tanzania? Although I can guess why, it would be better for the authors to state.

- The authors have justified why they are interested in certain pre-selected diseases. They may want to consider the existence of other diseases (even if self-report) to control for confounding in the analysis. The same goes for body mass index. I note that this is addressed by the authors in the limitations. - Can the authors elaborate on why admissions for primary trauma are excluded? - Is the rationale for, and objectives of, the study clearly described? Yes - Is the study design appropriate for the research question? Yes - Are sufficient details of the methods provided to allow replication by others? Yes - Are the datasets clearly presented in a useable and accessible format? Not applicable Competing Interests: No competing interests were disclosed. Reviewer Expertise: Multimorbidity, injuries and risk factors CITE HOW TO CITE THIS REPORT Roomaney RA. Reviewer Report For: Multimorbidity-associated emergency hospital admissions: a “screen and link” strategy to improve outcomes for high-risk patients in sub-Saharan Africa: a prospective multicentre cohort study protocol [version 2; peer review: 1 approved, 2 approved with reservations]. NIHR Open Res 2024, 4:2 (https://doi.org/10.3310/nihropenres.14663.r31181) The direct URL for this report is: https://openresearch.nihr.ac.uk/articles/4-2/v1#referee-response-31181 https://openresearch.nihr.ac.uk/articles/4-2/v1#referee-response-31181 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. - Author Response 27 May 2024Stephen Spencer, Malawi-Liverpool-Wellcome Programme, Blantyre, Malawi27 May 2024Author ResponseWe would like to take the opportunity to thank the reviewer for their time and thoughtful consideration. We have carefully addressed all comments and updated the manuscript which we believe ... Continue reading We would like to take the opportunity to thank the reviewer for their time and thoughtful consideration. We have carefully addressed all comments and updated the manuscript which we believe is now much improved. Reviewer’s comment

Can the authors elaborate on why there has been an increase in life expectancy in Malawi and Tanzania? Although I can guess why, it would be better for the authors to state. Authors’ reply Thank you for this suggestion. We have provided explanations (with references) for improvements in life expectancy in Malawi and Tanzania (improvements in prevention and treatment for infectious diseases, particularly HIV/AIDS, Tuberculosis and Malaria, as well as improvements in maternal and child survival). Reviewer’s comment

The authors have justified why they are interested in certain pre-selected diseases. They may want to consider the existence of other diseases (even if self-report) to control for confounding in the analysis. The same goes for body mass index. I note that this is addressed by the authors in the limitations. Authors’ reply We agree. In the objectives section we have highlighted that we are focussing on the four pre-selected diseases using gold-standard diagnostic criteria to inform the design of our planned trial. We have added an additional objective: to measure the prevalence of self-reported / clinically-reported chronic diseases. Reviewer’s comment Can the authors elaborate on why admissions for primary trauma are excluded? Authors’ reply We acknowledge and agree that trauma is an increasing burden of health systems and an important area to develop context-sensitive policies to improve outcomes. We opted to focus on medical populations as there are existing studies in this context (e.g. https://doi.org/10.1016/S2214-109X(23)00346-7; doi: 10.2471/BLT.23.290755), and health system interventions are for these groups are being addressed. We have made this clearer in the list of exclusions.We would like to take the opportunity to thank the reviewer for their time and thoughtful consideration. We have carefully addressed all comments and updated the manuscript which we believe is now much improved.Competing Interests: None Close Reviewer’s comment

Can the authors elaborate on why there has been an increase in life expectancy in Malawi and Tanzania? Although I can guess why, it would be better for the authors to state. Authors’ reply Thank you for this suggestion. We have provided explanations (with references) for improvements in life expectancy in Malawi and Tanzania (improvements in prevention and treatment for infectious diseases, particularly HIV/AIDS, Tuberculosis and Malaria, as well as improvements in maternal and child survival). Reviewer’s comment

The authors have justified why they are interested in certain pre-selected diseases. They may want to consider the existence of other diseases (even if self-report) to control for confounding in the analysis. The same goes for body mass index. I note that this is addressed by the authors in the limitations. Authors’ reply We agree. In the objectives section we have highlighted that we are focussing on the four pre-selected diseases using gold-standard diagnostic criteria to inform the design of our planned trial. We have added an additional objective: to measure the prevalence of self-reported / clinically-reported chronic diseases. Reviewer’s comment Can the authors elaborate on why admissions for primary trauma are excluded? Authors’ reply We acknowledge and agree that trauma is an increasing burden of health systems and an important area to develop context-sensitive policies to improve outcomes. We opted to focus on medical populations as there are existing studies in this context (e.g. https://doi.org/10.1016/S2214-109X(23)00346-7; doi: 10.2471/BLT.23.290755), and health system interventions are for these groups are being addressed. We have made this clearer in the list of exclusions. COMMENTS ON THIS REPORT - Author Response 27 May 2024Stephen Spencer, Malawi-Liverpool-Wellcome Programme, Blantyre, Malawi27 May 2024Author ResponseWe would like to take the opportunity to thank the reviewer for their time and thoughtful consideration. We have carefully addressed all comments and updated the manuscript which we believe ... Continue reading We would like to take the opportunity to thank the reviewer for their time and thoughtful consideration. We have carefully addressed all comments and updated the manuscript which we believe is now much improved. Reviewer’s comment

Can the authors elaborate on why there has been an increase in life expectancy in Malawi and Tanzania? Although I can guess why, it would be better for the authors to state. Authors’ reply Thank you for this suggestion. We have provided explanations (with references) for improvements in life expectancy in Malawi and Tanzania (improvements in prevention and treatment for infectious diseases, particularly HIV/AIDS, Tuberculosis and Malaria, as well as improvements in maternal and child survival). Reviewer’s comment

The authors have justified why they are interested in certain pre-selected diseases. They may want to consider the existence of other diseases (even if self-report) to control for confounding in the analysis. The same goes for body mass index. I note that this is addressed by the authors in the limitations. Authors’ reply We agree. In the objectives section we have highlighted that we are focussing on the four pre-selected diseases using gold-standard diagnostic criteria to inform the design of our planned trial. We have added an additional objective: to measure the prevalence of self-reported / clinically-reported chronic diseases. Reviewer’s comment Can the authors elaborate on why admissions for primary trauma are excluded? Authors’ reply We acknowledge and agree that trauma is an increasing burden of health systems and an important area to develop context-sensitive policies to improve outcomes. We opted to focus on medical populations as there are existing studies in this context (e.g. https://doi.org/10.1016/S2214-109X(23)00346-7; doi: 10.2471/BLT.23.290755), and health system interventions are for these groups are being addressed. We have made this clearer in the list of exclusions.We would like to take the opportunity to thank the reviewer for their time and thoughtful consideration. We have carefully addressed all comments and updated the manuscript which we believe is now much improved.Competing Interests: None Close Reviewer’s comment

Can the authors elaborate on why there has been an increase in life expectancy in Malawi and Tanzania? Although I can guess why, it would be better for the authors to state. Authors’ reply Thank you for this suggestion. We have provided explanations (with references) for improvements in life expectancy in Malawi and Tanzania (improvements in prevention and treatment for infectious diseases, particularly HIV/AIDS, Tuberculosis and Malaria, as well as improvements in maternal and child survival). Reviewer’s comment

The authors have justified why they are interested in certain pre-selected diseases. They may want to consider the existence of other diseases (even if self-report) to control for confounding in the analysis. The same goes for body mass index. I note that this is addressed by the authors in the limitations. Authors’ reply We agree. In the objectives section we have highlighted that we are focussing on the four pre-selected diseases using gold-standard diagnostic criteria to inform the design of our planned trial. We have added an additional objective: to measure the prevalence of self-reported / clinically-reported chronic diseases. Reviewer’s comment Can the authors elaborate on why admissions for primary trauma are excluded? Authors’ reply We acknowledge and agree that trauma is an increasing burden of health systems and an important area to develop context-sensitive policies to improve outcomes. We opted to focus on medical populations as there are existing studies in this context (e.g. https://doi.org/10.1016/S2214-109X(23)00346-7; doi: 10.2471/BLT.23.290755), and health system interventions are for these groups are being addressed. We have made this clearer in the list of exclusions. Alongside their report, reviewers assign a status to the article: - Approved - Approved with reservations - Not approved | Invited Reviewers | ||| |---|---|---|---| | 1 | 2 | 3 | | | Version 2 (revision) 08 May 24 | ||| | Version 1 18 Jan 24 | read | read | read | Sign up for content alerts You are now signed up to receive this alert Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list: Examples of 'Non-Financial Competing Interests' - Within the past 4 years, you have held joint grants, published or collaborated with any of the authors of the selected paper. - You have a close personal relationship (e.g. parent, spouse, sibling, or domestic partner) with any of the authors. - You are a close professional associate of any of the authors (e.g. scientific mentor, recent student). - You work at the same institute as any of the authors. - You hope/expect to benefit (e.g. favour or employment) as a result of your submission. - You are an Editor for the journal in which the article is published. Examples of 'Financial Competing Interests' - You expect to receive, or in the past 4 years have received, any of the following from any commercial organisation that may gain financially from your submission: a salary, fees, funding, reimbursements. - You expect to receive, or in the past 4 years have received, shared grant support or other funding with any of the authors. - You hold, or are currently applying for, any patents or significant stocks/shares relating to the subject matter of the paper you are commenting on. Sign up for content alerts and receive a weekly or monthly email with all newly published articles Register with NIHR Open Research Already registered? Sign in close Error If you are a previous or current NIHR award holder, sign up for information about developments, publishing and publications from NIHR Open Research. We'll keep you updated on any major new updates to NIHR Open Research Sign In If you've forgotten your password, please enter your email address below and we'll send you instructions on how to reset your password. Email us for further assistance. The email address should be the one you originally registered with F1000. Email address not valid, please try again You registered with F1000 via Google, so we cannot reset your password. To sign in, please click here. If you still need help with your Google account password, please click here. You registered with F1000 via Facebook, so we cannot reset your password. To sign in, please click here. If you still need help with your Facebook account password, please click here. Code not correct, please try again Server error, please try again. If your email address is registered with us, we will email you instructions to reset your password. If you think you should have received this email but it has not arrived, please check your spam filters and/or contact for further assistance. Please wait...