Operations research in global health: a scoping review with a focus on the themes of health equity and impact

Impact of targeted drug administration and intermittent preventive treatment for forest goers using artesunate-pyronaridine to control malaria outbreaks in Cambodia

INTRODUCTION

The national malaria programme of Cambodia targets the rapid elimination of all human malaria by 2025. As clinical cases decline to near-elimination levels, a key strategy is the rapid identification of malaria outbreaks triggering effective action to interrupt local transmission. We report a comprehensive, multipronged management approach in response to a  2022 Plasmodium falciparum outbreak in Kravanh district, western Cambodia.

METHODS

The provincial health department of Pursat in conjunction with the Center for Parasitology, Entomology and Malaria Control (CNM) identified villages where transmission was occurring using clinical records, and initiated various interventions, including the distribution of insecticide-treated bed nets, running awareness campaigns, and implementing fever screening with targeted drug administration. Health stations were set up at forest entry points, and later, targeted drug administrations with artesunate-pyronaridine (Pyramax) and intermittent preventive treatment for forest goers (IPTf) were implemented in specific village foci. Data related to adherence and adverse events from IPTf and TDA were collected. The coverage rates of interventions were calculated, and local malaria infections were monitored.

RESULTS

A total of 942 individuals were screened through active fever surveillance in villages where IPTf and TDA were conducted. The study demonstrated high coverage and adherence rates in the targeted villages, with 92% (553/600) coverage in round one and 65% (387/600) in round two. Adherence rate was 99% (551/553) in round one and 98% (377/387) in round two. The study found that forest goers preferred taking Pyramax over repeated testing consistent with the coverage rates: 92% in round one compared to 65% in round two. All individuals reachable through health stations or mobile teams reported complete IPTf uptake. No severe adverse events were reported. Only six individuals reported mild adverse events, such as loss of energy, fever, abdominal pain, diarrhoea, and muscle aches. Two individuals attributed their symptoms to heavy alcohol intake following prophylaxis.

CONCLUSIONS

The targeted malaria outbreak response demonstrated high acceptability, safety, and feasibility of the selected interventions. Malaria transmission was rapidly controlled using the available community resources. This experience suggests the effectiveness of the programmatic response for future outbreaks.

Markov models for clinical decision-making in radiation oncology: A systematic review

The intrinsic stochasticity of patients' response to treatment is a major consideration for clinical decision-making in radiation therapy. Markov models are powerful tools to capture this stochasticity and render effective treatment decisions. This paper provides an overview of the Markov models for clinical decision analysis in radiation oncology. A comprehensive literature search was conducted within MEDLINE using PubMed, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Only studies published from 2000 to 2023 were considered. Selected publications were summarized in two categories: (i) studies that compare two (or more) fixed treatment policies using Monte Carlo simulation and (ii) studies that seek an optimal treatment policy through Markov Decision Processes (MDPs). Relevant to the scope of this study, 61 publications were selected for detailed review. The majority of these publications (n = 56) focused on comparative analysis of two or more fixed treatment policies using Monte Carlo simulation. Classifications based on cancer site, utility measures and the type of sensitivity analysis are presented. Five publications considered MDPs with the aim of computing an optimal treatment policy; a detailed statement of the analysis and results is provided for each work. As an extension of Markov model-based simulation analysis, MDP offers a flexible framework to identify an optimal treatment policy among a possibly large set of treatment policies. However, the applications of MDPs to oncological decision-making have been understudied, and the full capacity of this framework to render complex optimal treatment decisions warrants further consideration.

Unmasking the Hidden Burden: A Delayed Diagnosis of Leprosy Patients With Grade 2 Disability and Its Effects on the Healthcare System in Bangladesh

Introduction Leprosy remains a significant cause of preventable disability worldwide. Early diagnosis and treatment of leprosy are critical not only to stop its spread but also to prevent physical and social complications and reduce the disease burden. Objectives The study aims to evaluate the factors that lead to a delayed leprosy diagnosis. Methods This study was conducted in the outpatient departments of Leprosy Control Institute and Hospital, Dhaka, Bangladesh, and at Medical College for Women and Hospital, Dhaka, Bangladesh, from March 2023 to June 2023. A total number of 252 male (148) and female (104) patients were selected with any sign of leprosy, including disability, age ranging from 15 to 74 years. Data was collected in a pre-designed structured questionnaire by the researchers. To assess the risk of independent exposures of Grade 2 leprosy disabilities, we used a logistic regression model. A chi-square test showed the association between significant effects and leprosy disabilities. A p-value of 0.05 was considered as significant. For statistical analysis, STATA version 15 (StataCorp LLC, College Station, Texas, USA) was used. Results The study participants exhibited a higher percentage of disability, with a rate of 25.8% for Grade 2 disabilities. In addition to this, males represented a more considerable proportion, 58.7%, than females among leprosy and disability patients across all levels of disability. In our study, lack of money and painless symptoms showed a significant association (p<0.001) with Grade 2 disability. Conclusion The study reveals that Grade 2 disabilities are more common in males and are particularly prevalent in lower socioeconomic groups.

Expanding the roles of community health workers to sustain programmes during malaria elimination: a meeting report on operational research in Southeast Asia

In Southeast Asia malaria elimination is targeted by 2030. Cambodia aims to achieve this by 2025, driven in large part by the urgent need to control the spread of artemisinin-resistant falciparum malaria infections. Rapid elimination depends on sustaining early access to diagnosis and effective treatment. In much of Cambodia, rapid elimination will rely on a village malaria worker (VMW) network. Yet as malaria declines and is no longer a common cause of febrile illness, VMWs may become less popular with febrile patients, as VMWs do not diagnose or treat other conditions at present. There is a risk that VMWs become inactive and malaria rebounds before the complete interruption of transmission is achieved.During 2021-23 a large-scale operational research study was conducted in western Cambodia to explore how a VMW network could be sustained by including health activities that cover non-malarial illnesses to encourage febrile patients to continue to attend. 105 VMWs received new rapid diagnostic tests (including dengue antigen-antibody and combined malaria/C-reactive protein tests), were trained in electronic data collection, and attended health education packages on hygiene and sanitation, disease surveillance and first aid, management of mild illness, and vaccination and antenatal care.In August 2023 the National Malaria Control Programme of Cambodia convened a stakeholder meeting in Battambang, Cambodia. Findings from the study were reviewed in the context of current malaria elimination strategies. The discussions informed policy options to sustain the relevance of the VMW network in Cambodia, and the potential for its integration with other health worker networks. This expansion could ensure VMWs remain active and relevant until malaria elimination is accomplished.

An operations research approach to automated patient scheduling for eye care using a multi-criteria decision support tool

Inefficient management of resources and waiting lists for high-risk ophthalmology patients can contribute to sight loss. The aim was to develop a decision support tool which determines an optimal patient schedule for ophthalmology patients. Our approach considers available booking slots as well as patient-specific factors. Using standard software (Microsoft Excel and OpenSolver), an operations research approach was used to formulate a mathematical model. Given a set of patients and clinic capacities, the model objective was to schedule patients efficiently depending on eyecare measure risk factors, referral-to-treatment times and targets, patient locations and slot availabilities over a pre-defined planning horizon. Our decision support tool can feedback whether or not a patient is scheduled. If a patient is scheduled, the tool determines the optimal date and location to book the patients' appointments, with a score provided to show the associated value of the decisions made. Our dataset from 519 patients showed optimal prioritization based on location, risk of serious vision loss/damage and the referral-to-treatment time. Given the constraints of available slots, managers can input hospital-specific parameters such as demand and capacity into our model. The model can be applied and implemented immediately, without the need for additional software, to generate an optimized patient schedule.

Pharmacogenomics driven decision support prototype with machine learning: A framework for improving patient care

Introduction

A growing number of healthcare providers make complex treatment decisions guided by electronic health record (EHR) software interfaces. Many interfaces integrate multiple sources of data (e.g., labs, pharmacy, diagnoses) successfully, though relatively few have incorporated genetic data.

Method

This study utilizes informatics methods with predictive modeling to create and validate algorithms to enable informed pharmacogenomic decision-making at the point of care in near real-time. The proposed framework integrates EHR and genetic data relevant to the patient's current medications including decision support mechanisms based on predictive modeling. We created a prototype with EHR and linked genetic data from the Department of Veterans Affairs (VA), the largest integrated healthcare system in the US. The EHR data included diagnoses, medication fills, and outpatient clinic visits for 2,600 people with HIV and matched uninfected controls linked to prototypic genetic data (variations in single or multiple positions in the DNA sequence). We then mapped the medications that patients were prescribed to medications defined in the drug-gene interaction mapping of the Clinical Pharmacogenomics Implementation Consortium's (CPIC) level A (i.e., sufficient evidence for at least one prescribing action) guidelines that predict adverse events. CPIC is a National Institute of Health funded group of experts who develop evidence based pharmacogenomic guidelines. Preventable adverse events (PAE) can be defined as a harmful outcome from an intervention that could have been prevented. For this study, we focused on potential PAEs resulting from a medication-gene interaction.

Results

The final model showed AUC scores of 0.972 with an F1 score of 0.97 with genetic data as compared to 0.766 and 0.73 respectively, without genetic data integration.

Discussion

Over 98% of people in the cohort were on at least one medication with CPIC level a guideline in their lifetime. We compared predictive power of machine learning models to detect a PAE between five modeling methods: Random Forest, Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), K Nearest neighbors (KNN), and Decision Tree. We found that XGBoost performed best for the prototype when genetic data was added to the framework and improved prediction of PAE. We compared area under the curve (AUC) between the models in the testing dataset.

Applying Discrete Event Simulation to Reduce Patient Wait Times and Crowding: The Case of a Specialist Outpatient Clinic with Dual Practice System

Long wait times and crowding are major issues affecting outpatient service delivery, but it is unclear how these affect patients in dual practice settings. This study aims to evaluate the effects of changing consultation start time and patient arrival on wait times and crowding in an outpatient clinic with a dual practice system. A discrete event simulation (DES) model was developed based on real-world data from an Obstetrics and Gynaecology (O&G) clinic in a public hospital. Data on patient flow, resource availability, and time taken for registration and clinic processes for public and private patients were sourced from stakeholder discussion and time-motion study (TMS), while arrival times were sourced from the hospital’s information system database. Probability distributions were used to fit these input data in the model. Scenario analyses involved configurations on consultation start time/staggered patient arrival. The median registration and clinic turnaround times (TT) were significantly different between public and private patients (p < 0.01). Public patients have longer wait times than private patients in this study’s dual practice setting. Scenario analyses showed that early consultation start time that matches patient arrival time and staggered arrival could reduce the overall TT for public and private patients by 40% and 21%, respectively. Similarly, the number of patients waiting at the clinic per hour could be reduced by 10–21% during clinic peak hours. Matching consultation start time with staggered patient arrival can potentially reduce wait times and crowding, especially for public patients, without incurring additional resource needs and help narrow the wait time gap between public and private patients. Healthcare managers and policymakers can consider simulation approaches for the monitoring and improvement of healthcare operational efficiency to meet rising healthcare demand and costs.

Effects of minimum unit pricing for alcohol in South Africa across different drinker groups and wealth quintiles: a modelling study

OBJECTIVES

To quantify the potential impact of minimum unit pricing (MUP) for alcohol on alcohol consumption, spending and health in South Africa. We provide these estimates disaggregated by different drinker groups and wealth quintiles.

DESIGN

We developed an epidemiological policy appraisal model to estimate the effects of MUP across sex, drinker groups (moderate, occasional binge, heavy) and wealth quintiles. Stakeholder interviews and workshops informed model development and ensured policy relevance.

SETTING

South African drinking population aged 15+.

PARTICIPANTS

The population (aged 15+) of South Africa in 2018 stratified by drinking group and wealth quintiles, with a model time horizon of 20 years.

MAIN OUTCOME MEASURES

Change in standard drinks (SDs) (12 g of ethanol) consumed, weekly spend on alcohol, annual number of cases and deaths for five alcohol-related health conditions (HIV, intentional injury, road injury, liver cirrhosis and breast cancer), reported by drinker groups and wealth quintile.

RESULTS

We estimate an MUP of R10 per SD would lead to an immediate reduction in consumption of 4.40% (-0.93 SD/week) and an increase in spend of 18.09%. The absolute reduction is greatest for heavy drinkers (-1.48 SD/week), followed by occasional binge drinkers (-0.41 SD/week) and moderate drinkers (-0.40 SD/week). Over 20 years, we estimate 20 585 fewer deaths and 9 00 332 cases averted across the five health-modelled harms.Poorer drinkers would see greater impacts from the policy (consumption: -7.75% in the poorest quintile, -3.19% in richest quintile). Among the heavy drinkers, 85% of the cases averted and 86% of the lives saved accrue to the bottom three wealth quintiles.

CONCLUSIONS

We estimate that MUP would reduce alcohol consumption in South Africa, improving health outcomes while raising retail and tax revenue. Consumption and harm reductions would be greater in poorer groups.

Operational research: A multidisciplinary approach for the management of infectious disease in a global context

Infectious diseases, both established and emerging, impose a significant burden globally. Successful management of infectious diseases requires considerable effort and a multidisciplinary approach to tackle the complex web of interconnected biological, public health and economic systems. Through a wide range of problem-solving techniques and computational methods, operational research can strengthen health systems and support decision-making at all levels of disease control. From improved understanding of disease biology, intervention planning and implementation, assessing economic feasibility of new strategies, identifying opportunities for cost reductions in routine processes, and informing health policy, this paper highlights areas of opportunity for operational research to contribute to effective and efficient infectious disease management and improved health outcomes.

Radiotherapy treatment scheduling: Implementing operations research into clinical practice

Background

Every week, radiotherapy centers face the complex task of scheduling hundreds of treatment sessions amongst the available linear accelerators. With the increase in cancer patient numbers, manually creating a feasible and efficient schedule has shown to be a difficult, time-consuming task. Although operations research models have been increasingly reported upon to optimize patient care logistics, there is almost no scientific evidence of implementation in practice.

Methods

A mathematical operations research model was adapted to generate radiotherapy treatment schedules in two Dutch centers. The model was iteratively adjusted to fulfill the technical and medical constraints of each center until a valid model was attained. Patient data was collected for the planning horizon of one week, and the feasibility of the obtained schedules was verified by the staff of each center. The resulting optimized solutions are compared with the ones manually developed in practice.

Results

The weekly schedule was improved in both centers by decreasing the average standard deviation between sessions’ starting times from 103.0 to 50.4 minutes (51%) in one center, and the number of gaps in the schedule from 18 to 5 (72%) in the other. The number of patients requiring linac switching between sessions has also decreased from 71 to 0 patients in one center, and from 43 to 2 in the other. The automated process required 5 minutes and 1.5 hours of computation time to find an optimal weekly patient schedule, respectively, as opposed to approximately 1.5 days when performed manually for both centers.

Conclusions

The practical application of a theoretical operations research model for radiotherapy treatment scheduling has provided radiotherapy planners a feasible, high-quality schedule in an automated way. Iterative model adaptations performed in small steps, early engagement of stakeholders, and constant communication proved to facilitate the implementation of operations research models into clinical practice.

“What’s the evidence?”—Towards more empirical evaluations of the impact of OR interventions in healthcare

Despite an increasing number of papers reporting applications of operational research (OR) to problems in healthcare, there remains little empirical evidence of OR improving healthcare delivery in practice. Without such evidence it is harder both to justify the usefulness of OR to a healthcare audience and to learn and continuously improve our approaches. To progress, we need to build the evidence-base on whether and how OR improves healthcare delivery through careful empirical evaluation. This position paper reviews evaluation standards in healthcare improvement research and dispels some common myths about evaluation. It highlights the current lack of robust evaluation of healthcare OR and makes the case for addressing this. It then proposes possible ways for building better empirical evaluations of OR interventions in healthcare.

Operational research to support equitable non-communicable disease policy in low-income and middle-income countries in the sustainable development era: a scoping review

INTRODUCTION

Non-communicable diseases (NCDs) represent a growing health burden in low-income and middle-income countries (LMICs). Operational research (OR) has been used globally to support the design of effective and efficient public policies. Equity is emphasised in the Sustainable Development Goal (SDG) framework introduced in 2015 and can be analysed within OR studies.

METHODS

We systematically searched MEDLINE, Embase, Scopus and Web of Science for studies published between 2015 and 2018 at the intersection of five domains (OR, LMICs, NCDs, health and decision-making and/or policy-making). We categorised the type of policy intervention and described any concern for equity, which we defined as either analysis of differential impact by subgroups or, policy focus on disadvantaged groups or promoting universal health coverage (UHC).

RESULTS

A total of 149 papers met the inclusion criteria. The papers covered a number of policy types and a broad range of NCDs, although not in proportion to their relative disease burden. A concern for equity was demonstrated by 88 of the 149 papers (59%), with 8 (5%) demonstrating differential impact, 47 (32%) targeting disadvantaged groups, and 68 (46%) promoting UHC.

CONCLUSION

Overall, OR for NCD health policy in the SDG era is being applied to a diverse set of interventions and conditions across LMICs and researchers appear to be concerned with equity. However, the current focus of published research does not fully reflect population needs and the analysis of differential impact within populations is rare.

Mapping the evidence on health equity considerations in economic evaluations of health interventions: a scoping review protocol

BACKGROUND

Equity in health has become an important policy agenda around the world, prompting health economists to advance methods to enable the inclusion of equity in economic evaluations. Among the methods that have been proposed to explicitly include equity are the weighting analysis, equity impact analysis, and equity trade-off analysis. This is a new development and a comprehensive overview of trends and concepts of health equity in economic evaluations is lacking. Thus, our objective is to map the current state of the literature with respect to how health equity is considered in economic evaluations of health interventions reported in the academic and gray literature.

METHODS

We will conduct a scoping review to identify and map evidence on how health equity is considered in economic evaluations of health interventions. We will search relevant electronic, gray literature and key journals. We developed a search strategy using text words and Medical Subject Headings terms related to health equity and economic evaluations of health interventions. Articles retrieved will be uploaded to reference manager software for screening and data extraction. Two reviewers will independently screen the articles based on their titles and abstracts for inclusion, and then will independently screen a full text to ascertain final inclusion. A simple numerical count will be used to quantify the data and a content analysis will be conducted to present the narrative; that is, a thematic summary of the data collected.

DISCUSSION

The results of this scoping review will provide a comprehensive overview of the current evidence on how health equity is considered in economic evaluations of health interventions and its research gaps. It will also provide key information to decision-makers and policy-makers to understand ways to include health equity into the prioritization of health interventions when aiming for a more equitable distribution of health resources.

SYSTEMATIC REVIEW REGISTRATION

This protocol was registered with Open Science Framework (OSF) Registry on August 14, 2019 (https://osf.io/9my2z/registrations).

'Knowledge for better health' revisited - the increasing significance of health research systems: a review by departing Editors-in-Chief

How can nations organise research investments to obtain the best bundle of knowledge and the maximum level of improved health, spread as equitably as possible? This question was the central focus of a major initiative from WHO led by Prof Tikki Pang, which resulted in a range of developments, including the publication of a conceptual framework for national health research systems - Knowledge for better health - in 2003, and in the founding of the journal Health Research Policy and Systems (HARPS). As Editors-in-Chief of the journal since 2006, we mark our retirement by tracking both the progress of the journal and the development of national health research systems. HARPS has maintained its focus on a range of central themes that are key components of a national health research system in any country. These include building capacity to conduct and use health research, identifying appropriate priorities, securing funds and allocating them accountably, producing scientifically valid research outputs, promoting the use of research in polices and practice in order to improve health, and monitoring and evaluating the health research system. Some of the themes covered in HARPS are now receiving increased attention and, for example, with the assessment of research impact and development of knowledge translation platforms, the journal has covered their progress throughout that expansion of interest. In addition, there is increasing recognition of new imperatives, including the importance of promoting gender equality in health research if benefits are to be maximised. In this Editorial, we outline some of the diverse and developing perspectives considered within each theme, as well as considering how they are held together by the growing desire to build effective health research systems in all countries.From 2003 until mid-June 2017, HARPS published 590 articles on the above and related themes, with authors being located in 76 countries. We present quantitative data tracing the journal's growth and the increasing external recognition of its role. We thank the many colleagues who have kindly contributed to the journal's success, and finish on an exciting note by welcoming the new Editors-in-Chief who will take HARPS forward.