Hesam Ghiasvand

Background: The aging population has resulted in more people living longer with musculoskeletal conditions who require hip and knee replacement surgery. Lengthening waiting lists continue to be a challenge for patients and health care services. Objective: This pragmatic study aimed to develop and test a digital self-management intervention (the HOPE [Help Overcome Problems Effectively] program) to better prepare patients waiting for hip and knee replacement surgery. Methods: The study used a pragmatic, pre-post with follow-up, single-arm design. All intervention and data collection components were delivered online. Patients were recruited from those on the waiting list for hip or knee surgery. Following iterative co-development of the intervention, the content was refined and optimized into a final version for testing. The resulting program was an 8-week intervention delivered via the HOPE 4 The Community (H4C) digital platform (powered by H4C). Data were collected at baseline (pre-HOPE program), 8 weeks (post-HOPE program), and 6-month follow-up. Patient-reported outcome measures related to preparation for surgery, quality of life, physical function, pain, mental well-being, self-efficacy, and physical activity. Resource usage data were collected to calculate health and social care costs. System Usability Scale data were collected post-HOPE program. Results: One hundred participants enrolled in the HOPE program. Of these, 57 (57%) consented to take part in the evaluation and returned the baseline questionnaire. Thirty-nine participants completed ≥5 of the 8 sessions and all surveys. Among the 25 participants who had surgery at 6 months, 23 (92%) felt better prepared due to the HOPE program. Median improvements in most outcomes were observed at 8 weeks, with several continuing to improve at 6 months. The Friedman test showed significant improvements over 6 months in self-efficacy (pain: P =.002; other symptoms: P <.01), pain ( P =.04), health status ( P =.02), and mental well-being ( P =.01). No significant changes were noted in physical activity. While the early cost analysis did not reach statistical significance, it indicated potential cost savings from reduced patient interactions with health care professionals. Sixty-four percent (25/39) of participants had surgery, and this likely contributed in part to improvements in outcomes. System usability was rated above average (mean score 70.1, SD 15.9). Conclusions: The results are promising in relation to participants attending the HOPE program feeling better prepared for surgery. A fully powered efficacy and cost-effectiveness trial is needed to determine the contribution of the HOPE program to outcomes, over and above the contribution of surgery.

Background: Lung cancer is one of the most common types of cancer and the leading cause of cancer death in the United Kingdom. Artificial intelligence-based software has been developed to reduce the number of missed or misdiagnosed lung nodules on computed tomography images. Objective: To assess the accuracy, clinical effectiveness and cost-effectiveness of using software with artificial intelligence-derived algorithms to assist in the detection and analysis of lung nodules in computed tomography scans of the chest compared with unassisted reading. Design: Systematic review and de novo cost-effectiveness analysis. Methods: Searches were undertaken from 2012 to January 2022. Company submissions were accepted until 31 August 2022. Study quality was assessed using the revised tool for the quality assessment of diagnostic accuracy studies (QUADAS-2), the extension to QUADAS-2 for assessing risk of bias in comparative accuracy studies (QUADAS-C) and the COnsensus-based Standards for the selection of health status Measurement INstruments (COSMIN) checklist. Outcomes were synthesised narratively. Two decision trees were used for cost-effectiveness: (1) a simple decision tree for the detection of actionable nodules and (2) a decision tree reflecting the full clinical pathways for people undergoing chest computed tomography scans. Models estimated incremental cost-effectiveness ratios, cost per correct detection of an actionable nodule, and cost per cancer detected and treated. We undertook scenario and sensitivity analyses. Results: Twenty-seven studies were included. All were rated as being at high risk of bias. Twenty-four of the included studies used retrospective data sets. Seventeen compared readers with and without artificial intelligence software. One reported prospective screening experiences before and after artificial intelligence software implementation. The remaining studies either evaluated stand-alone artificial intelligence or provided only non-comparative evidence. (1) Artificial intelligence assistance generally improved the detection of any nodules compared with unaided reading (three studies; average per-person sensitivity 0.43–0.68 for unaided and 0.79–0.99 for artificial intelligence-assisted reading), with similar or lower specificity (three studies; 0.77–1.00 for unaided and 0.81–0.97 for artificial intelligence-assisted reading). Nodule diameters were similar or significantly larger with semiautomatic measurements than with manual measurements. Intra-reader and inter-reader agreement in nodule size measurement and in risk classification generally improved with artificial intelligence assistance or were comparable to those with unaided reading. However, the effect on measurement accuracy is unclear. (2) Radiologist reading time generally decreased with artificial intelligence assistance in research settings. (3) Artificial intelligence assistance tended to increase allocated risk categories as defined by clinical guidelines. (4) No relevant clinical effectiveness and cost-effectiveness studies were identified. (5) The de novo cost-effectiveness analysis suggested that for symptomatic and incidental populations, artificial intelligence-assisted computed tomography image analysis dominated the unaided radiologist in cost per correct detection of an actionable nodule. However, when relevant costs and quality-adjusted life-years from the full clinical pathway were included, artificial intelligence-assisted computed tomography reading was dominated by the unaided reader. For screening, artificial intelligence-assisted computed tomography image analysis was cost-effective in the base case and all sensitivity and scenario analyses. Limitations: Due to the heterogeneity, sparseness, low quality and low applicability of the clinical effectiveness evidence and the major challenges in linking test accuracy evidence to clinical and economic outcomes, the findings presented here are highly uncertain and provide indicators/frameworks for future assessment. Conclusions: Artificial intelligence-assisted analysis of computed tomography scan images may reduce variability of and improve consistency in the measurement and clinical management of lung nodules. Artificial intelligence may increase nodule and cancer detection but may also increase the number of patients undergoing computed tomography surveillance unnecessarily. No direct comparative evidence was found, and nor was any direct evidence found on clinical outcomes and cost-effectiveness. Artificial intelligence-assisted image analysis may be cost-effective in screening for lung cancer but not for symptomatic populations. However, reliable estimates of cost-effectiveness cannot be obtained with current evidence. Study registration: This study is registered as PROSPERO CRD42021298449. Funding: This award was funded by the National Institute for Health and Care Research (NIHR) Evidence Synthesis programme (NIHR award ref: NIHR135325) and is published in full in Health Technology Assessment; Vol. 29, No. 14. See the NIHR Funding and Awards website for further award information.

BACKGROUND The ageing population has resulted in more people living for longer with musculoskeletal conditions who are in need of hip and knee replacement surgery. Lengthening waiting lists are increasingly a challenge for patients and healthcare services. OBJECTIVE This pragmatic study aimed to develop and test a digital self-management intervention (the Hope Programme) to better prepare patients waiting for hip and knee replacement surgery. METHODS The study employed a pragmatic, pre-post with follow-up single-arm design. All intervention and data collection components were delivered online. Following iterative co-development of the intervention, the content was refined and optimised into a final version for testing. The resulting programme was an eight-week intervention delivered via the Hope 4 The Community Interest Company (H4C) digital platform. Data were collected at baseline (pre Hope Programme), 8 weeks (post Hope Programme) and 6 months follow-up. Patient-reported outcome measures related to preparation for surgery, quality of life, physical function, pain, mental wellbeing, self-efficacy and physical activity. Resource utilisation data were collected to calculate the health and social care costs. System Usability Scale data and qualitative satisfaction data were collected post Hope Programme. RESULTS Thirty-nine participants completed ≥5 of the 8 sessions and all surveys. Among the 25 participants who had surgery at 6 months, 92% felt better prepared due to the Hope Programme. Median improvements in most outcomes were observed at 8 weeks, with several continuing to improve at 6 months. The Friedman test showed significant improvements over 6 months in self-efficacy (pain: P=.002, other symptoms: P<.001), pain (P=.037), health status (P=.022), and mental wellbeing (P=.010). No significant changes were noted in physical activity. While the early cost analysis did not reach statistical significance, it indicated potential cost savings from reduced patient interactions with healthcare professionals. Sixty four percent (25/39) of participants had surgery and this likely contributed in part to improvements in outcomes. System usability was rated above average (mean score 70.1), and post-programme feedback was positive, highlighting peer support, coping strategies, and better preparation for surgery. CONCLUSIONS The results are promising in relation to participants attending the Hope Programme feeling better prepared for surgery. A fully powered efficacy and cost effectiveness trial is needed to determine the contribution of the Hope Programme to outcomes, over and above the contribution of surgery.

Objectives To examine the accuracy and impact of artificial intelligence (AI) software assistance in lung cancer screening using CT. Methods A systematic review of CE-marked, AI-based software for automated detection and analysis of nodules in CT lung cancer screening was conducted. Multiple databases including Medline, Embase and Cochrane CENTRAL were searched from 2012 to March 2023. Primary research reporting test accuracy or impact on reading time or clinical management was included. QUADAS-2 and QUADAS-C were used to assess risk of bias. We undertook narrative synthesis. Results Eleven studies evaluating six different AI-based software and reporting on 19 770 patients were eligible. All were at high risk of bias with multiple applicability concerns. Compared with unaided reading, AI-assisted reading was faster and generally improved sensitivity (+5% to +20% for detecting/categorising actionable nodules; +3% to +15% for detecting/categorising malignant nodules), with lower specificity (−7% to −3% for correctly detecting/categorising people without actionable nodules; −8% to −6% for correctly detecting/categorising people without malignant nodules). AI assistance tended to increase the proportion of nodules allocated to higher risk categories. Assuming 0.5% cancer prevalence, these results would translate into additional 150–750 cancers detected per million people attending screening but lead to an additional 59 700 to 79 600 people attending screening without cancer receiving unnecessary CT surveillance. Conclusions AI assistance in lung cancer screening may improve sensitivity but increases the number of false-positive results and unnecessary surveillance. Future research needs to increase the specificity of AI-assisted reading and minimise risk of bias and applicability concerns through improved study design. PROSPERO registration number CRD42021298449.

This study aimed to assess the impact of the COVID-19 pandemic on general practitioner (GP), specialist, and dentist visits among 40 million Iranians covered by the Social Security Organization (SSO). A monthly interrupted time series analysis was conducted over a period of 72 months, including—47 months before the pandemic and 25 months after its onset. The outcomes variables were monthly number of GP, specialist, and dentist visits per 1000 SSO-insured individuals. The analysis was performed by total visits, visits to the SSO direct sector, and visits to the indirect sectors. The study found that in the first month of the pandemic, the number of visits per 1000 insured individuals significantly decreased for visits to GPs (by 51.12, 95% CI −64.42 to −37.88), visits to specialists (by 39.11, 95% CI −51.61 to −26.62), and visits to dentists (by 6.67, 95% CI −8.55 to −4.78). However, during the subsequent months of the pandemic, there was a significant increase in the number of monthly visits for all three categories, with GPs experiencing the highest increase (1.78 visits per 1000 insured), followed by specialists (1.32 visits per 1000 insured), and dentists (0.05 visits per 1000 insured). Furthermore, prior to the pandemic, the number of monthly GP visits per 1000 insured individuals was statistically significantly lower in the indirect sector compared to the direct sector (45.79, 95% CI −52.69 to −38.89). Conversely, the direct sector exhibited lower rates of specialist visits (25.84 visits per 1000 insured individuals, 95% CI 22.87 to 28.82) and dentist visits (0.75 visits per 1000 insured individuals, 95% CI 0.12 to 1.36) compared to the indirect sector. Additionally, the study found that in the first month of the pandemic, the monthly number of GP visits in the indirect sector significantly increased by 34.44 times (95% CI 24.81 to 44.08) compared to the direct sector. For specialist visits and dentist visits, the increase was 3.41 (95% CI −5.87 to 12.69) and 5.01 (95% CI 3.48 to 6.53) per 1000 insured individuals, respectively. Overall, the findings of this study demonstrate statistically significant disruptions in GP, specialist, and dentist visits during the COVID-19 pandemic, although some recovery was observed. Both the direct and indirect sectors experienced decreased visits.

Background: Effects of zinc with and without iron co-supplementation on child development are uncertain therefore the aims of this systematic review were to explore whether supplementation with zinc alone and zinc with iron in children aged 0–5 years old have beneficial or adverse effects on their mental and motor development.Method: We searched MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, CINAHL, Web of Science and Scopus until July 2020 and included randomized controlled trials, which assessed effects of zinc supplementation with and without iron in children less than 5 years old on mental and motor development. Data were pooled by random effects model and the Standardized Mean Differences (SMDs) with 95% confidence interval were estimated. The heterogeneity was assessed by I².Results: Twenty-five studies with 11,559 participants were eligible to be included in this systematic review. Meta-analysis was conducted with eight articles that used Bayley Scales of Infant and Toddler Development II. We concluded that zinc alone and zinc with iron co-supplementation do not have beneficial or adverse effect on child mental and motor development at 6 and 12 months of age with low to moderate quality of the evidence. Furthermore, Zinc supplementation does not have any long term effect on child development in preschool and school age children.Conclusion: Most included studies did not show the efficacy of zinc with and without iron co-supplementation on child mental and motor development up to 9 years old age. Further Randomized Controlled Trials (RCTs) need to be taken into considerations the context-based differences between countries with special focus on socio-economic differences.

Background: The aim of this study was to review the effects of developmental care in neonatal intensive care unit (NICU) setting on mental and motor development of preterm infants.Method: We searched PubMed, EMBASE, CINAHL, Scopus, Web of Science and Cochrane library until October 8th 2017, and included randomized controlled trials that assessed effects of developmental care in NICU on mental and motor development of preterm infants at 12 and 24 months of age, using the Bayley scale of infant development in this systematic review. In addition, data were pooled by random effects model and Standardized Mean Difference (SMD) with 95% confidence intervals (CI), calculated for meta-analysis.Results: Twenty one studies were eligible to be included in this systematic review; however, only thirteen studies had data suitable for meta-analysis. According to statistical analysis, developmental care in NICU improved mental developmental index (MDI) (standardized mean difference [SMD] 0.55, 95% confidence interval [CI] 0.23–0.87;p < 0.05), and psychomotor developmental index (PDI) (SMD 0.33, [CI] 95% CI 0.08–0.57;p < 0.05) of BSID at 12 months of age and PDI at 24 months of age (SMD 0.15, 95% CI -0.02–0.32;p < 0.1) of preterm infants. However, the benefit was not detected at 24 months of age on MDI (SMD 0.15, 95% CI -0.05–0.35;p = 0.15).Conclusion: Current evidence suggests that developmental care in only NICU setting could have significant effect on mental and motor development of preterm infants, especially at 12 months of age. However, because of clinical heterogeneity, more studies are needed to evaluate the effects of developmental NICU care in the development of preterm infants.

Background HIV/AIDS infection is a leading cause of mortality and morbidity in developing and poor countries. It may potentially lead to economic burden on health system. Prevalence of HIV/AIDS infection in is a debatable issue among researchers, and as our best knowledge, there is lack in economic evidence in this regard in Iran. The purpose of this study is calculating the economic burden of HIV/AIDS infection in Iran in 2016. Methods We used a societal perspective to capture the direct and indirect costs attributed to HIV/AIDS infection in until end of 2016. We used a prevalence model based approach to estimate the costs of the infection in Iran. For developing our model, we used the Iran Ministry of Health National Guideline for Managing of the infection of Iran by health and medical practitioners. We analysed the HIV/AIDS Surveillance System in three base case, optimistic and pessimistic approaches as our sensitivity analysis. Results Total direct costs and indirect costs attributed to the HIV/AIDS infection were 7,946,530 and 1,288,586 US$ until end of 2016. In addition, the total cost is 8,785,116. Conclusions Direct costs form approximately 85% of total costs of HIV/AIDS infection in Iran. However, we should consider that these costs are just for about 14.3% of diagnosed people who are under Antiretroviral Therapy, therefor if the government wants to scale up ART to large proportion of the population, it expectedly will raise.

Background: The first step in developing a series of appropriate interventions for controlling the epidemic of smoking is to scrutinize surveillance data to understand any contributing socio-economic factors. The aim of this study is to identify the prevalence rates and the socio-economic factors associated with cigarette smoking among Iranian household heads in 2016. Methods: All data related to cigarette smoking were extracted from the Iranian Households’ Income Expenditure Survey (IHIES) by the Statistical Center of Iran (SCI). The prevalence of current cigarette smoking was calculated by gender, educational level, employment status, age, and marital status. Then, socio-economic factors associated with cigarette smoking were analyzed using a logistic regression. Results: The prevalence of current cigarette smoking was 17.8% for male and 6.6% for female-headed households. Being aged 37─41 years, married, and having a primary school educational level had the highest prevalence for current cigarette smoking in the sample. Being female, unemployed, divorced showed a significant association with the likelihood of current cigarette smoking among household heads. Conclusion: The positive association of current cigarette smoking across all economic levels was an unexpected finding. However, other socio-economic factors including current employment status and educational levels showed expected associations.

Decomposing disparity in adult individual’s mental health in Tehran among lower and higher economic groups; an Oaxaca- Blinder analysis on urban HEART Survey- round 2