Establishing research impact assessment in Iran: The first report from a non-high-income country.

Background: This study presents the first report on research impact assessment (RIA) in non-high-income countries, undertaken as a pilot initiative in 2021. Within it, we aimed to explore the feasibility of employing the 'payback' model for evaluating the impact of health research and enhancing the accountability of universities. We focussed on three key impact domains: 'production of decision support documents and knowledge-based products,' 'implementation of research results,' and 'health and economic impact.' Methods: We adopted a case study approach to assess the impact of 5334 health research projects conducted by researchers from 18 universities from 2018 to 2020. Researchers were required to submit evidence related to at least one of the specified impact domains; six scientific committees verified and scored claimed impacts at the national level. Results: Only 25% of the assessed projects achieved impact in at least one domain, with the production of decision support documents and knowledge products being the most reported impact. Notably, economic impact was verified in only three projects, indicating room for improvement in this area. Technology research exhibited the highest acceptance rate of claimed impact, suggesting a positive correlation between technology-focused projects and impactful outcomes. Conclusions: This study demonstrates the feasibility of employing a case study approach and the 'payback' model to evaluate the impact of health research, even within the constraints of a moderately equipped research infrastructure. These findings underscore the potential of integrating RIA into the governance of health research in Iran and other non-high-income countries, as well as the importance of using RIA to assess the accountability of health research systems, guide the allocation of research funding, and advocate for the advancement of health research. The study sets a precedent for future assessments in similar contexts and contributes to the ongoing global dialogue on the societal impact of health research.

Neural oscillatory characteristics of feedback-associated activity in globus pallidus interna.

Neural oscillatory activities in basal ganglia have prominent roles in cognitive processes. However, the characteristics of oscillatory activities during cognitive tasks have not been extensively explored in human Globus Pallidus internus (GPi). This study aimed to compare oscillatory characteristics of GPi between dystonia and Parkinson's Disease (PD). A dystonia and a PD patient performed the Intra-Extra-Dimension shift (IED) task during both on and off-medication states. During the IED task, patients had to correctly choose between two visual stimuli containing shapes or lines based on a hidden rule via trial and error. Immediate auditory and visual feedback was provided upon the choice to inform participants if they chose correctly. Bilateral GPi Local Field Potentials (LFP) activity was recorded via externalized DBS leads. Transient high gamma activity (~ 100-150 Hz) was observed immediately after feedback in the dystonia patient. Moreover, these bursts were phase synchronous between left and right GPi with an antiphase clustering of phase differences. In contrast, no synchronous high gamma activity was detected in the PD patient with or without dopamine administration. The off-med PD patient also displayed enhanced low frequency clusters, which were ameliorated by medication. The current study provides a rare report of antiphase homotopic synchrony in human GPi, potentially related to incorporating and processing feedback information. The absence of these activities in off and on-med PD patient indicates the potential presence of impaired medication independent feedback processing circuits. Together, these findings suggest a potential role for GPi's synchronized activity in shaping feedback processing mechanisms required in cognitive tasks.

Hybrid feature engineering of medical data via variational autoencoders with triplet loss: a COVID-19 prognosis study.

Medical machine learning frameworks have received much attention in recent years. The recent COVID-19 pandemic was also accompanied by a surge in proposed machine learning algorithms for tasks such as diagnosis and mortality prognosis. Machine learning frameworks can be helpful medical assistants by extracting data patterns that are otherwise hard to detect by humans. Efficient feature engineering and dimensionality reduction are major challenges in most medical machine learning frameworks. Autoencoders are novel unsupervised tools that can perform data-driven dimensionality reduction with minimum prior assumptions. This study, in a novel approach, investigated the predictive power of latent representations obtained from a hybrid autoencoder (HAE) framework combining variational autoencoder (VAE) characteristics with mean squared error (MSE) and triplet loss for forecasting COVID-19 patients with high mortality risk in a retrospective framework. Electronic laboratory and clinical data of 1474 patients were used in the study. Logistic regression with elastic net regularization (EN) and random forest (RF) models were used as final classifiers. Moreover, we also investigated the contribution of utilized features towards latent representations via mutual information analysis. HAE Latent representations model achieved decent performance with an area under ROC curve of 0.921 (±0.027) and 0.910 (±0.036) with EN and RF predictors, respectively, over the hold-out data in comparison with the raw (AUC EN: 0.913 (±0.022); RF: 0.903 (±0.020)) models. The study aims to provide an interpretable feature engineering framework for the medical environment with the potential to integrate imaging data for efficient feature engineering in rapid triage and other clinical predictive models.

National and subnational burden of brain and central nervous system cancers in Iran, 1990-2019: Results from the global burden of disease study 2019.

INTRODUCTION: Central nervous system cancers (CNS cancers) impose a significant burden upon healthcare systems worldwide. Currently, the lack of a comprehensive study to assess various epidemiological indexes of CNS cancers on national and subnational scales in Iran can hamper healthcare planning and resource allocation in this regard. This study aims to fill this gap by providing estimates of CNS cancer epidemiological measures on national and subnational levels in Iran from 1990 to 2019. MATERIALS AND METHODS: This study is a part of Global Burden of Disease (GBD) 2019 that contains epidemiological measures including prevalence, incidence, mortality, Disability-Adjusted Life Years (DALYs), Years Lived with Disability (YLDs), and Years of Life Lost (YLLs) of CNS cancers. Age standardization was utilized for comparing different provinces. RESULTS: In 2019, 5811 (95% Uncertainty Interval: 2942-7046) national new cases and 3494 (1751-4173) deaths due to CNS cancers were reported. National age-standardized incidence (ASIR), deaths (ASDR), and DALYs rates were 7.3 (3.7-8.8), 4.6 (2.3-5.5), and 156.4 (82.0-187.0) per 100,000 in 2019, respectively. Subnational results revealed that ASDR and ASIR have increased in the past 30 years in all provinces. Although incidence rates have increased in all age groups and genders since 1990, death rates have remained the same for most age groups and genders except for young patients aged under 15, where a decrease in mortality and YLLs can be observed. CONCLUSION: The incidence, deaths, and DALYs of CNS cancers increased at national and subnational levels. These findings should be considered for planning and resource allocation.

Automated prediction of COVID-19 mortality outcome using clinical and laboratory data based on hierarchical feature selection and random forest classifier.

Early prediction of COVID-19 mortality outcome can decrease expiration risk by alerting healthcare personnel to assure efficient resource allocation and treatment planning. This study introduces a machine learning framework for the prediction of COVID-19 mortality using demographics, vital signs, and laboratory blood tests (complete blood count (CBC), coagulation, kidney, liver, blood gas, and general). 41 features from 244 COVID-19 patients were recorded on the first day of admission. In this study, first, the features in each of the eight categories were investigated. Afterward, features that have an area under the receiver operating characteristic curve (AUC) above 0.6 and the p-value criterion from the Wilcoxon rank-sum test below 0.005 were used as selected features for further analysis. Then five feature reduction methods, Forward Feature selection, minimum Redundancy Maximum Relevance, Relieff, Linear Discriminant Analysis, and Neighborhood Component Analysis were utilized to select the best combination of features. Finally, seven classifiers frameworks, random forest (RF), support vector machine, logistic regression (LR), K nearest neighbors, Artifical neural network, bagging, and boosting were used to predict the mortality outcome of COVID-19 patients. The results revealed that the combination of features in CBC and then vital signs had the highest mortality classification parameters, respectively. Furthermore, the RF classifier with hierarchical feature selection algorithms via Forward Feature selection had the highest classification power with an accuracy of 92.08 ± 2.56. Therefore, our proposed method can be confidently used as a valuable assistant prognostic tool to sieve patients with high mortality risks.

Inequality in prevalence, awareness, treatment, and control of hypertension in Iran: the analysis of national households' data.

BACKGROUND: Providing an equitable Universal Health Coverage (UHC) is key for progressing towards the sustainable development goals in the health systems. To help policymakers make hypertension services more equitable with existing (limited) resources in Iran, we examined the inequality of the prevalence, awareness, treatment, and control (PATC) of hypertension as the four indicators of hypertension UHC in Iran.  METHODS: This research was a cross-sectional study of inequality of PATC of hypertension using a representative sample of Iranians aged ≥ 25 years from the Iran 2016 STEP wise approach to Surveillance study (STEPS). Outcome variables consisted of PATC of hypertension. Covariates were demographic (age, sex, and marital status) and living standard (area of residence, wealth status, education, and health insurance) indicators. We drew concentration curves (CC) and estimated concentration indices (C). We also conducted normalized Erreygers decomposition analysis for binary outcomes to identify covariates that explain the wealth-related inequality in the outcomes. Analysis was conducted in STATA 14.1. RESULTS: The normalized concentration index of hypertension prevalence and control was -0.066 (p < .001) and 0.082 (p < .001), respectively. The C of awareness and treatment showed nonsignificant difference between the richest and poorest. Inequality in the hypertension prevalence of females was significantly higher than males (C = -0.103 vs. male C = -0.023, p < .001). Our analyses explained 33% of variation in the C of hypertension prevalence and 99.7% of variation in the C of control. Education, wealth index, and complementary insurance explained most inequality in the prevalence. Area of residence, education, wealth status, and complementary insurance had the largest contribution to C of control by 30%, 28%, 26%, and 21%, respectively. CONCLUSIONS: This study showed a pro-rich inequality in the prevalence and control of hypertension in Iran. We call for expanding the coverage of complementary insurance to reduce inequality of hypertension prevalence and control as compared with other factors it can be manipulated in short run. We furthermore advocate for interventions to reduce the inequality of hypertension control between rural and urban areas.

Individual and institutional capacity-building for evidence-informed health policy-making in Iran: a mix of local and global evidence.

BACKGROUND: Providing valid evidence to policy-makers is a key factor in the development of evidence-informed policy-making (EIPM). This study aims to review interventions used to promote researchers' and knowledge-producing organizations' knowledge and skills in the production and translation of evidence to policy-making and explore the interventions at the individual and institutional level in the Iranian health system to strengthen EIPM. METHODS: The study was conducted in two main phases: a systematic review and a qualitative study. First, to conduct the systematic review, the PubMed and Scopus databases were searched. Quality appraisal was done using the Joanna Briggs Institute checklists. Second, semi-structured interviews and document review were used to collect local data. Purposive sampling was used and continued until data saturation. A qualitative content analysis approach was used for data analysis. RESULTS: From a total of 11,514 retrieved articles, 18 papers were eligible for the analysis. Based on the global evidence, face-to-face training workshops for researchers was the most widely used intervention for strengthening researchers' capacity regarding EIPM. Target audiences in almost all of the training programmes were researchers. Setting up joint training sessions that helped empower researchers in understanding the needs of health policy-makers had a considerable effect on strengthening EIPM. Based on the local collected evidence, the main interventions for individual and institutional capacity-building were educational and training programmes or courses related to the health system, policy-making and policy analysis, and research cycle management. To implement the individual and institutional interventions, health system planners and authorities and the community were found to have a key role as facilitating factors. CONCLUSION: The use of evidence-based interventions for strengthening research centres, such as training health researchers on knowledge translation and tackling institutional barriers that can prevent well-trained researchers from translating their knowledge, as well as the use of mechanisms and networks for effective interactions among policy-makers at the macro and meso (organizational) level and the research centre, will be constructive for individual and institutional capacity-building. The health system needs to strengthen its strategic capacity to facilitate an educational and training culture in order to motivate researchers in producing appropriate evidence for policy-makers.

Policy options for strengthening evidence-informed health policy-making in Iran: overall SASHA project findings.

BACKGROUND: The institutionalization of evidence-informed health policy-making (EIHP) is complex and complicated. It is complex because it has many players and is complicated because its institutionalization will require many changes that will be challenging to make. Like many other issues, strengthening EIHP needs a road map, which should consider challenges and address them through effective, harmonized and contextualized strategies. This study aims to develop a road map for enhancing EIHP in Iran based on steps of planning. METHODS: This study consisted of three phases: (1) identifying barriers to EIHP, (2) recognizing interventions and (3) measuring the use of evidence in Iran's health policy-making. A set of activities was established for conducting these, including foresight, systematic review and policy dialogue, to identify the current and potential barriers for the first phase. For the second phase, an evidence synthesis was performed through a scoping review, by searching the websites of benchmark institutions which had good examples of EIHP practices in order to extract and identify interventions, and through eight policy dialogues and two broad opinion polls to contextualize the list of interventions. Simultaneously, two qualitative-quantitative studies were conducted to design and use a tool for assessing EIHP in the third phase. RESULTS: We identified 97 barriers to EIHP and categorized them into three groups, including 35 barriers on the "generation of evidence" (push side), 41 on the "use of evidence" (pull side) and 21 on the "interaction between these two" (exchange side). The list of 41 interventions identified through evidence synthesis and eight policy dialogues was reduced to 32 interventions after two expert opinion polling rounds. These interventions were classified into four main strategies for strengthening (1) the education and training system (6 interventions), (2) the incentives programmes (7 interventions), (3) the structure of policy support organizations (4 interventions) and (4) the enabling processes to support EIHP (15 interventions). CONCLUSION: The policy options developed in the study provide a comprehensive framework to chart a path for strengthening the country's EIHP considering both global practices and the context of Iran. It is recommended that operational plans be prepared for road map interventions, and the necessary resources provided for their implementation. The implementation of the road map will require attention to the principles of good governance, with a focus on transparency and accountability. Video abstract.

Developing Framework and Strategies for Capacity Building to Apply Evidence-Informed Health Policy-Making in Iran: Mixed Methods Study of SAHSHA Project.

BACKGROUND: SASHA, which stands for "evidence-informed health policy-making (EIHP)" in Persian, is a national project to draw a roadmap for strengthening EIHP in Iran. As a part of SASHA, this research aimed to develop evidence-based and context-aware policy options for increasing the capacity of decision-makers to apply EIHP in Iran. METHODS: This was a qualitative study, which was informed by a literature review of pull efforts' capacity building programs. Based on the review, we developed policy options and validated them through an expert panel that involved twelve experts. Data were analyzed using a content analysis method. RESULTS: We extracted data from 11 articles. The objectives of capacity building programs were: single-skill development, personal/professional development, and organizational development. According to these objectives, the contents and training methods of the programs vary. Capacity building programs have shown positive impacts on individual knowledge/attitudes to use EIHP. However, the impacts of programs at the organizational or the health system level remain under-researched. We followed several threads from the literature review through to the expert panel that included training the management team, instead of training managers, training for problem-solving skills, and designing tailored programs. Barriers of capacity building for EIHP regard the context of the health system (weak accountability and the widespread conflict of interest) and healthcare organizational structures (decision support systems, knowledge management infrastructures, and lack of management team). Experts suggested interventions on the barriers, particularly on resolving the conflict of interests before launching new programs. A proposed framework to increase the capacity of health policy-makers incorporates strategies at three levels: capacity building program, organizational structure, and health system context. CONCLUSION: To prepare the context of Iranian healthcare organizations for capacity building programs, the conflict of interests needs to be resolved, decision-makers should be made more accountable, and healthcare organizations need to provide more knowledge management infrastructures and decision support systems.

Qualitative analysis of Iranian sixth five-year economic, social, and cultural development plan from universal health coverage perspective.

BACKGROUND: This research analyzed the Sixth Five-Year Economic, Social, and Cultural Development Plan of the Islamic Republic of Iran (6NPD) to shed light on how the plan addresses the Universal Health Coverage (UHC). METHODS: This research was a qualitative study. We systematically analyzed 'Secs. 14 -Health, Insurance, Health & Women, and Family' in the 6NPD. Through a content analysis, we converted this section into meaning units and coded them. Coding was guided through the conceptual framework 'Six Building Blocks of Health System' and the key principles of UHC. RESULTS: Six themes and twenty-one subthemes were identified. The subthemes of financing include a fair and secured process of resource pooling, payment methods, revenue generation for the health sector, and a definition of a basic benefits package. The subthemes of governance and leadership consist of social insurance policies' integration, compliance of providers, a designation of the Ministry of Health and Medical Education (MoHME) as the regulator and the steward of health resources, a payer-provider split, and stakeholders' participation. The subthemes of health workforce emphasizes balancing the quality and quantity of the health workforce with populations' health needs and the health system's requirements. The subthemes of health information systems consist of the electronic health records for Iranians, information systems for organization and delivery functions, and information systems for the financing function. The subthemes of the organization and delivery consider improving effectiveness and efficiency of healthcare delivery, strengthening the family physician program and referral system, and extending the pre-hospital emergency system. Lastly, access to medicine focuses on the design and implementation of an essential drug list and drug systems for approving the coverage and provision of generic medicine. CONCLUSIONS: The 6NPD introduced policies for strengthening the 6 building blocks of the health system. It introduced policies to improve financing particularly resource pooling and the sustainability of financial resources. As mandated by 6NPD, centering the health system's governance/leadership in MoHME may exacerbate the existing conflict of interests and provoke various arguments, which impede the enforcement of rules and regulation. The 6NPD is a step forward in terms of improving financial protection, yet several other policies need to be made to adequately meet the requirement of UHC regarding equity and effective coverage.

Identifying associations between health services operational factors and health experience for patients with type 2 diabetes in Iran.

BACKGROUND: Facing limited health resources, healthcare providers need to rely on health service delivery models that produce the best clinical outcomes and patient experience. We aimed to contribute to developing a patient experience-based type 2 diabetes service delivery model by identifying operational structures and processes of care that were associated with clinical outcome, health experience, and service experience. METHODS: We conducted a cross-sectional survey of type 2 diabetes patients between January 2019 to February 2020. Having adjusted for demand variables, we examined relationships between independent variables (behaviours, services/processes, and structures) and three categories of dependent variables; clinical outcomes (HbA1c and fasting blood glucose), health experience (EuroQol quality of life (EQ-5D), evaluation of quality of life (visual analgene scale of EQ-5D), and satisfaction with overall health status), and service experience (evaluation of diabetes services in comparison with worst and best imaginable diabetes services and satisfaction with diabetes services). We analysed data using multivariate linear regression models using Stata software. RESULTS: After adjusting for demand variables; structures, diabetes-specific health behaviours, and processes explained up to 22, 12, and 9% of the variance in the outcomes, respectively. Based on significant associations between the diabetes service operations and outcomes, the components of an experience-based service delivery model included the structural elements (continuity of care, redistribution of task to low-cost resources, and improved access to provider), behaviours (improved patient awareness and adherence), and process elements (reduced variation in service utilization, increased responsiveness, caring, comprehensiveness of care, and shared decision-making). CONCLUSIONS: Based on the extent of explained variance and identified significant variables, health services operational factors that determine patient-reported outcomes for patients with type 2 diabetes in Iran were identified, which focus on improving continuity of care and access to providers at the first place, improving adherence to care at the second, and various operational process variables at the third place.

Introducing an efficient sampling method for national surveys with limited sample sizes: application to a national study to determine quality and cost of healthcare.

BACKGROUND: Sampling a small number of participants from an entire country is not straightforward. In this case, researchers reluctantly sample from a single setting or few settings, which limits the generalizability of findings. Therefore, there is a need to design efficient sampling method for small sample size surveys that can produce generalizable results at the country level. METHODS: Data comprised of twenty proxy variables to measure health services demands, structures, and outcomes of 413 districts of Iran. We used two data mining methods (hierarchical clustering method (HCM) and model-based clustering method (MCM)) to create homogenous groups of districts, i.e., strata based on these variables. We compared the internal and stability validity of the methods by statistical indices. An expert group checked the face validity of the methods, particularly regarding the total number of strata and the combination of districts in each stratum. The efficiency of selected method, which is measured by the inverse of variance, was compared with a simple random sampling (SRS) through simulation. The sampling design was tested in a national study in Iran, which aimed to evaluate the quality and costs of medical care for eight selected diseases by only recruiting 300 participants per disease at the country level. RESULTS: MCM and HCM divided the districts into eight and two clusters, respectively. The measures of internal and stability validity showed that clusters created by MCM were more separated, compact, and stable, thus forming our optimum strata. The probability of death from stroke, chronic obstructive pulmonary disease, and in-hospital mortality rate were the most important indicators that distinguished the eight strata. Based on the simulation results, MCM increased the efficiency of the sampling design up to 1.7 times compared to SRS. CONCLUSIONS: The use of data mining improved the efficiency of sampling up to 1.7 times greater than SRS and markedly reduced the number of strata to eight in the entire country. The proposed sampling design also identified key variables that could be used to classify districts in Iran for sampling from these target populations in the future studies.

A machine learning based exploration of COVID-19 mortality risk.

Early prediction of patient mortality risks during a pandemic can decrease mortality by assuring efficient resource allocation and treatment planning. This study aimed to develop and compare prognosis prediction machine learning models based on invasive laboratory and noninvasive clinical and demographic data from patients' day of admission. Three Support Vector Machine (SVM) models were developed and compared using invasive, non-invasive, and both groups. The results suggested that non-invasive features could provide mortality predictions that are similar to the invasive and roughly on par with the joint model. Feature inspection results from SVM-RFE and sparsity analysis displayed that, compared with the invasive model, the non-invasive model can provide better performances with a fewer number of features, pointing to the presence of high predictive information contents in several non-invasive features, including SPO2, age, and cardiovascular disorders. Furthermore, while the invasive model was able to provide better mortality predictions for the imminent future, non-invasive features displayed better performance for more distant expiration intervals. Early mortality prediction using non-invasive models can give us insights as to where and with whom to intervene. Combined with novel technologies, such as wireless wearable devices, these models can create powerful frameworks for various medical assignments and patient triage.

Rethinking health services operations to embrace patient experience of healthcare journey.

Patient experience is assumed pivotal for improving health services operations. The patient experience of healthcare services in Iran has been mostly assessed through the satisfaction and quality of single services or activities at individual providers, clinical departments, or health facilities. However, given the rise of chronic and multi-morbid conditions, health services for these conditions consist of several activities and interactions through a journey that patients take in the health system. To fill in this gap, we propose focusing on the assessment of patient experience on the patient journey through the health system. We advocate that there is much potential for improving the patient experience by rethinking the operations management of health services to embrace the patient experience of the healthcare journey. Rethinking health operations management may include an exhaustive list of interventions. Concisely, at the strategic level, policy-makers while understanding the need for shifting towards the patient experience, make sure that operational level management is experience oriented. This would be pursued through a strategic approach to patient experience, reconsidering qualifications for operational management, and benchmarking to identify and share best practices. Lessons learnt from previous quality improvement programmes are also considered as a capacity to establish the experience orientation.

Investigating the prevalence of primary thyroid dysfunction in obese and overweight individuals: Tehran thyroid study.

BACKGROUND: Due to the increasing worldwide prevalence of obesity, it is essential to determine the prevalence of obesity-related thyroid dysfunctions. The purpose of this study was to investigate the prevalence of thyroid dysfunctions, namely hypothyroidism and hyperthyroidism, and their association with BMI among adult Iranian overweight and obese individuals. METHOD: This cross-sectional study was carried out within the framework of the Tehran Thyroid Study (TTS); 5353 participants (57.5% female) entered our study. Anthropometric measurements were performed. Serum levels of thyroid-stimulating hormone (TSH), free thyroxine (FT4), and thyroid peroxidase antibody (TPOAb) were assayed. We categorized individuals into 3 BMI groups (normal-weight, overweight and obese), then calculated prevalence rate, odds ratio (OR), and 95% confidence interval (CI) for outcomes in overweight and obese groups. The normal-weight group was used as the control group. RESULTS: We found a higher prevalence of hypothyroidism (11.6% vs 8.2% Total, 4.0% vs 1.1% overt and 7.6% vs 7.1% subclinical, P < 0.001) and TPOAb positivity (17.3% vs 11.6%, P < 0.001) in obese participants compared with normal-weight participants. Hyperthyroidism's overall prevalence was 4.2, 5.7, and 4.9% in obese, overweight, and normal-weight groups, respectively. Obesity was associated with higher odds of overt hypothyroidism (OR: 2.0, 95% CI: 1.15-3.49, P < 0.05) and TPOAb positivity (OR: 1.29, 95% CI: 1.04-1.60, P < 0.05) after adjusting for confounding variables. In contrast, no association was observed between the overweight group and the odds of hypothyroidism and TPOAb positivity in the adjusted results. CONCLUSIONS: Obesity was associated with an increased risk of overt hypothyroidism and TPOAb positivity.

Public-private partnerships in primary health care: a scoping review.

BACKGROUND: The Astana Declaration on Primary Health Care reiterated that PHC is a cornerstone of a sustainable health system for universal health coverage (UHC) and health-related Sustainable Development Goals. It called for governments to give high priority to PHC in partnership with their public and private sector organisations and other stakeholders. Each country has a unique path towards UHC, and different models for public-private partnerships (PPPs) are possible. The goal of this paper is to examine evidence on the use of PPPs in the provision of PHC services, reported challenges and recommendations. METHODS: We systematically reviewed peer-reviewed studies in six databases (ScienceDirect, Ovid Medline, PubMed, Web of Science, Embase, and Scopus) and supplemented it by the search of grey literature. PRISMA reporting guidelines were followed. RESULTS: Sixty-one studies were included in the final review. Results showed that most PPPs projects were conducted to increase access and to facilitate the provision of prevention and treatment services (i.e., tuberculosis, education and health promotion, malaria, and HIV/AIDS services) for certain target groups. Most projects reported challenges of providing PHC via PPPs in the starting and implementation phases. The reported challenges and recommendations on how to overcome them related to education, management, human resources, financial resources, information, and technology systems aspects. CONCLUSION: Despite various challenges, PPPs in PHC can facilitate access to health care services, especially in remote areas. Governments should consider long-term plans and sustainable policies to start PPPs in PHC and should not ignore local needs and context.

Spatio-temporal analysis of misaligned burden of disease data using a geo-statistical approach.

Data used to estimate the burden of diseases (BOD) are usually sparse, noisy, and heterogeneous. These data are collected from surveys, registries, and systematic reviews that have different areal units, are conducted at different times, and are reported for different age groups. In this study, we developed a Bayesian geo-statistical model to combine aggregated sparse, noisy BOD data from different sources with misaligned areal units. Our model incorporates the correlation of space, time, and age to estimate health indicators for areas with no data or a small number of observations. The model also considers the heterogeneity of data sources and the measurement errors of input data in the final estimates and uncertainty intervals. We applied the model to combine data from nine different sources of body mass index in a national and sub-national BOD study. The cross-validation results confirmed a high out-of-sample predictive ability in sparse and noisy data. The proposed model can be used by other BOD studies especially at the sub-national level when the areal units are subject to misalignment.

Modeling Patient Journeys for Demand Segments in Chronic Care, With an Illustration to Type 2 Diabetes.

Chronic care is an important area for cost-effective and efficient health service delivery. Matching demand and services for chronic care is not easy as patients may have different needs in different stages of the disease. More insight is needed into the complete patient journey to do justice to the services required in each stage of the disease, to the different experiences of patients in each part of the journey, and to outcomes in each stage. With patient journey we refer to the "journey" of the patient along the services received within a demand segment of chronic care. We developed a generic framework for describing patient journeys and provider networks, based on an extension of the well-known model of Donabedian, to relate demand, services, resources, behavior, and outcomes. We also developed a generic operational model for the detailed modeling of services and resources, allowing for insight into costs. The generic operational model can be tailored to the specific characteristics of patient groups. We applied this modeling approach to type 2 diabetes (T2D) patients. Diabetes care is a form of chronic care for patients suffering diabetes mellitus. We studied the performance of T2D networks, using a descriptive model template. To identify and describe demand we made use of the following demand segments within the diabetes type 2 population: patients targeted for prevention; patients with stage 1 diabetes treated by their GP with lifestyle advice; patients with diabetes stage 2 treated by their GP with lifestyle advice and oral medication; patients with stage 3 diabetes treated by their GP with lifestyle advice, oral medication, and insulin injections; patients with stage 4 diabetes with complications (treated by internal medicine specialists). We used a Markov model to describe the transitions between the different health states. The model enables the patient journey through the health care system for cohorts of newly diagnosed T2D patients to be described, and to make a projection of the resource requirements of the different demand segments over the years. We illustrate our approach with a case study on a T2D care network in The Netherlands and reflect on the role of demand segmentation to analyse the case study results, with the objective of improving the T2D service delivery.

How health transformation plan was designed and implemented in the Islamic Republic of Iran?

Following his inauguration in late 2013, President Rouhani aimed to boost quality and equity in the health care delivery system. To fulfill this aim, a set of interventions, called Health Transformation Plan (HTP), were implemented. So far, it has been a heated debate whether HTP breathes a spirit of a new reform. HTP has targeted long-standing historical deficits of the Iranian health system as well as urgent problems, both of which have been, to some extent, resolved. To decrease Out-Of-Pocket (OOP) health expenditures, HTP has presented new financing mechanisms to expand a safety net to Iranian citizens fundamentally. HTP also encompassed interventions to overcome problems in the provision of health care by recruitment of health workforces, establishing new health facilities, and expanding primary health care to urban and peri-urban areas. Furthermore, performance indicators including access, quality, and patient satisfaction have been affected. Given these changes, HTP is entitled to be a health system reform. However, a new agenda within HTP is required so that the Iranian health system can obtain better value for money that is to be spending on it.

Insight into blood pressure targets for universal coverage of hypertension services in Iran: the 2017 ACC/AHA versus JNC 8 hypertension guidelines.

BACKGROUND: We compared the prevalence, awareness, treatment, and control of hypertension in Iran based on two hypertension guidelines; the 2017 ACC/AHA -with an aggressive blood pressure target of 130/80 mmHg- and the commonly used JNC8 guideline cut-off of 140/90 mmHg. We shed light on the implications of the 2017 ACC/AHA for population subgroups and high-risk individuals who were eligible for non-pharmacologic and pharmacologic therapies. METHODS: Data was obtained from the Iran national STEPS 2016 study. Participants included 27,738 adults aged ≥25 years as a representative sample of Iranians. Regression models of survey design were used to examine the determinants of prevalence, awareness, treatment, and control of hypertension. RESULTS: The prevalence of hypertension based on JNC8 was 29.9% (95% CI: 29.2-30.6), which soared to 53.7% (52.9-54.4) based on the 2017 ACC/AHA. The percentage of awareness, treatment, and control were 59.2% (58.0-60.3), 80.2% (78.9-81.4), and 39.1% (37.4-40.7) based on JNC8, which dropped to 37.1% (36.2-38.0), 71.3% (69.9-72.7), and 19.6% (18.3-21.0), respectively, by applying the 2017 ACC/AHA. Based on the new guideline, adults aged 25-34 years had the largest increase in prevalence (from 7.3 to 30.7%). They also had the lowest awareness and treatment rate, contrary to the highest control rate (36.5%) between age groups. Compared with JNC8, based on the 2017 ACC/AHA, 24, 15, 17, and 11% more individuals with dyslipidaemia, high triglycerides, diabetes, and cardiovascular disease events, respectively, fell into the hypertensive category. Yet, based on the 2017 ACC/AHA, 68.2% of individuals falling into the hypertensive category were eligible for receiving pharmacologic therapy (versus 95.7% in JNC8). LDL cholesterol< 130 mg/dL, sufficient physical activity (Metabolic Equivalents≥600/week), and Body Mass Index were found to change blood pressure by - 3.56(- 4.38, - 2.74), - 2.04(- 2.58, - 1.50), and 0.48(0.42, 0.53) mmHg, respectively. CONCLUSIONS: Switching from JNC8 to 2017 ACC/AHA sharply increased the prevalence and drastically decreased the awareness, treatment, and control in Iran. Based on the 2017 ACC/AHA, more young adults and those with chronic comorbidities fell into the hypertensive category; these individuals might benefit from earlier interventions such as lifestyle modifications. The low control rate among individuals receiving treatment warrants a critical review of hypertension services.