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Balochistan PA Speaker calls on Ayaz Sadiq

ISLAMABAD – ISLAMABAD: Speaker Balochistan Assembly Captain (Retd) Abdul Khaliq Khan Achakzai on Monday called on Speaker National Assembly Sardar Ayaz Sadiq.

The meeting focused on fostering inter-parliamentary cooperation as a means to address the country’s key political and economic challenges.

Both dignitaries exchanged views on enhancing institutional collaboration between the National Assembly and the Balochistan Assembly.

The NA Speaker emphasised that sustained coordination among federal and provincial legislatures is essential to strengthening democratic governance and ensuring effective policymaking.

He highlighted Balochistan’s strategic role in Pakistan’s development and reaffirmed that inclusive progress cannot be achieved without the active participation of provincial assemblies.

He assured his Balochistan counterpart of the National Assembly’s full support in reinforcing the institutional effectiveness of the Balochistan Assembly, including initiatives aimed at legislative training, research, and procedural development.

Speaker Balochistan Assembly Captain (Retd) Abdul Khaliq Khan Achakzai underlined the importance of regular interaction between lawmakers across assemblies to share best practices, promote mutual understanding, and enhance legislative performance. He noted that inter-parliamentary engagement is key to building national unity and ensuring a coordinated approach to governance.

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July 1, 2025
Elon Musk’s xAI raises $10 billion in debt and equity

Elon Musk announced his new company xAI, which he says has the goal to understand the true nature of the universe.

Jaap Arriens | Nurphoto | Getty Images

XAI, the artificial intelligence startup run by Elon Musk, raised a combined $10 billion in debt and equity, Morgan Stanley said.

Half of that sum was clinched through secured notes and term loans, while a separate $5 billion was secured through strategic equity investment, the bank said on Monday.

The funding gives xAI more firepower to build out infrastructure and develop its Grok AI chatbot as it looks to compete with bitter rival OpenAI, as well as with a swathe of other players including Amazon-backed Anthropic.

In May, Musk told CNBC that xAI has already installed 200,000 graphics processing units (GPUs) at its Colossus facility in Memphis, Tennessee. Colossus is xAI’s supercomputer that trains the firm’s AI. Musk at the time said that his company will continue buying chips from semiconductor giants Nvidia and AMD and that xAI is planning a 1-million-GPU facility outside of Memphis.

Addressing the latest funds raised by the company, Morgan Stanley that “the proceeds will support xAI’s continued development of cutting-edge AI solutions, including one of the world’s largest data center and its flagship Grok platform.”

xAI continues to release updates to Grok and unveiled the Grok 3 AI model in February. Musk has sought to boost the use of Grok by integrating the AI model with the X social media platform, formerly known as Twitter. In March, xAI acquired X in a deal that valued the site at $33 billion and the AI firm at $80 billion. It’s unclear if the new equity raise has changed that valuation.

xAI was not immediately available for comment.

Last year, xAI raised $6 billion at a valuation of $50 billion, CNBC reported.

Morgan Stanley said the latest debt offering was “oversubscribed and included prominent global debt investors.”

Competition among American AI startups is intensifying, with companies raising huge amounts of funding to buy chips and build infrastructure.

OpenAI in March closed a $40 billion financing round that valued the ChatGPT developer at $300 billion. Its big investors include Microsoft and Japan’s SoftBank.

Anthropic, the developer of the Claude chatbot, closed a funding round in March that valued the firm at $61.5 billion. The company then received a five-year $2.5 billion revolving credit line in May.

Musk has called Grok a “maximally truth-seeking” AI that is also “anti-woke,” in a bid to set it apart from its rivals. But this has not come without its fair share of controversy. Earlier this year, Grok responded to user queries with unrelated comments about the controversial topic of “white genocide” and South Africa.

Musk has also clashed with fellow AI leaders, including OpenAI’s Sam Altman. Most famously, Musk claimed that OpenAI, which he co-founded, has deviated from its original mission of developing AI to benefit humanity as a nonprofit and is instead focused on commercial success. In February, Musk alongside a group of investors, put in a bid of $97.4 billion to buy control of OpenAI. Altman swiftly rejected the offer.

— CNBC’s Lora Kolodny and Jonathan Vanian contributed to this report.

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July 1, 2025
Microsoft Takes Its First Step to Make VS Code an Open-Source AI Editor

Microsoft has taken its first concrete step towards making Visual Studio Code an open-source AI editor by open-sourcing the GitHub Copilot Chat extension under the MIT license. The company announced the milestone on June 30 via the VS Code team’s blog, calling it a move toward transparency, extensibility, and developer-centric AI tooling.

The newly open-sourced code reveals how Copilot Chat handles agent mode, context engineering, and telemetry. According to the blog post, “Everything, from our system prompts, implementation details, to the telemetry we capture, is available in all transparency.” Contributions and feedback from developers are welcome on GitHub, with the long-term goal of integrating this extension into the core VS Code codebase.

The announcement follows Microsoft CEO Satya Nadella’s keynote at Build 2025, where he confirmed the company’s commitment to AI-powered development. “This is a big deal. We will integrate these AI-powered capabilities directly into the core of VS Code, bringing them into the same open source repo that powers the world’s most loved dev tool,” said Nadella.

Erich Gamma, creator of VS Code, reinforced the motivation by highlighting that some organisations really don’t like closed-source IDEs, and for them, VS Code would be a great choice. The company will also open-source its prompt testing infrastructure to support third-party extension developers.

This strategic shift comes amid growing demand for openness in developer tooling. According to Microsoft, the rapid advancement in LLMs and the convergence of best practices across AI coding UIs have reduced the need for proprietary techniques.

While the GitHub Copilot extension for inline completions remains closed, Microsoft plans to bring that functionality into the open-sourced Chat extension in the coming months.

The move invites comparison with AI-first VS Code forks, such as Cursor and Windsurf, both valued in billions. “Is it just me or is it kinda funny that OpenAI bought Windsurf for $3B and then Microsoft just open-sourced Copilot,” quipped a user on X.

Whether it’s community-driven extensibility or agentic DevOps, Microsoft appears ready to reshape how developers interact with AI, on their own terms, and increasingly in the open.

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July 1, 2025
S&P Dow Jones Indices Collaborates with Centrifuge to Bring the S&P 500 Index Onchain, Expanding Access to the World’s Most Widely Recognized Benchmark

S&P Dow Jones Indices Collaborates with Centrifuge to Bring the S&P 500 Index Onchain, Expanding Access to the World’s Most Widely Recognized Benchmark

NEW YORK, July 1, 2025 /PRNewswire/ — S&P Dow Jones Indices (“S&P DJI”), the world’s leading index provider, today announced its plans to collaborate with Centrifuge, a decentralized infrastructure provider specializing in real-world asset (RWA) integration, to enter the fund tokenization space by licensing the S&P 500 Index. This initiative extends the reach of the S&P 500 into onchain investment products and protocols.

For 125 years and counting, S&P DJI’s indices have provided a liquid foundation for increased adoption of index-based investing around the world. By licensing Centrifuge to provide exposure and bring the S&P 500 Index onchain, a blockchain-native investment vehicle will for the first time be built, governed, and accessed directly through Centrifuge’s RWA infrastructure, rather than via traditional brokerages. Anemoy Capital, a web3 native asset manager powered by Centrifuge, has been licensed by S&P DJI along with Janus Henderson, a leading global asset manager, as the sub-advisor for the fund, to offer the Janus Henderson Anemoy S&P 500 Index Fund Segregated Portfolio, which is planned to launch later this year subject to regulatory approval.

“At S&P Dow Jones Indices, our mission is to bring trusted benchmarks to every investor, today and tomorrow. Today’s announcement places The 500™ at the forefront of index tokenization and real-world asset integration and brings the innovation of decentralized infrastructure to the most iconic financial index in the world,” said Cameron Drinkwater, Chief Product Officer at S&P Dow Jones Indices. “Our collaboration with Centrifuge enables investors to gain direct exposure to the S&P 500 Index –– within a blockchain ecosystem that supports liquidity, transparency and interoperability. The potential from here – real-time, programmable, automated and 24/7 indexed portfolio solutions – is incredibly exciting.”

This collaboration combines S&P DJI’s premiere S&P 500 Index and unmatched index data quality with Centrifuge’s blockchain technology to create one of the first digital tokens that represents exposure to the S&P 500 Index. The digital tokens can then be owned, used and transferred through the blockchain, initially providing access to a broader range of market participants. In the future, by licensing S&P DJI’s indices to be embedded directly into DeFi protocols, other tokenized assets and compliant digital investment platforms can provide equal market access and choice to blockchain-native investors and those without access to traditional investment products.

“Bringing the S&P 500 Index onchain is more than a technical milestone, it represents a shift in how institutional portfolios can be constructed and accessed. For the first time, the world’s most trusted benchmark is available through open, transparent, and programmable infrastructure. At Centrifuge and Anemoy, our focus is to establish ‘onchain indices,’ as a core category of onchain asset allocation, bringing institutional-grade products to a decentralized financial system. We are proud to collaborate with S&P Dow Jones Indices and look forward to what this unlocks for the future of onchain finance,” said Anil Sood, Chief Strategy and Growth Officer at Centrifuge and Co-Founder of Anemoy.

The introduction of tokenization further expands S&P DJI’s existing portfolio of digital asset initiatives, which includes its cryptocurrency indices, ranging from single-coin indices to multi-asset solutions. S&P DJI’s diverse offering is designed to provide choice for market participants looking to effectively navigate the growing DeFi and digital asset space.

For more information about S&P Dow Jones Indices, please visit https://www.spglobal.com/spdji/en/.

S&P Global’s digital asset capabilities support transparency and informed decision-making at the intersection of decentralized innovation and traditional finance. To learn more about S&P Global’s DeFi initiatives please click here.

For more information about Centrifuge, please visit https://centrifuge.io/.

ABOUT S&P DOW JONES INDICES

S&P Dow Jones Indices is the largest global resource for essential index-based concepts, data and research, and home to iconic financial market indicators, such as the S&P 500^® and the Dow Jones Industrial Average^®. More assets are invested in products based on our indices than products based on indices from any other provider in the world. Since Charles Dow invented the first index in 1884, S&P DJI has been innovating and developing indices across the spectrum of asset classes helping to define the way investors measure and trade the markets.

S&P Dow Jones Indices is a division of S&P Global (NYSE: SPGI), which provides essential intelligence for individuals, companies, and governments to make decisions with confidence. For more information, visit https://www.spglobal.com/spdji/en/.

The S&P 500 Index is a product of S&P Dow Jones Indices LLC or its affiliates (“S&P DJI), and has been licensed for use by Anemoy Capital Ltd. (“Anemoy”) and k-f dev AG (“Centrifuge”). S&P®, S&P 500®, SPX®, SPY®, US 500™, The 500™, iBoxx®, iTraxx® and CDX® are trademarks of S&P Global, Inc. or its affiliates (“S&P”); Dow Jones® is a registered trademark of Dow Jones Trademark Holdings LLC (“Dow Jones”); and these trademarks have been licensed for use by S&P DJI and sublicensed for certain purposes by Anemoy and Centrifuge. Funds based on the S&P 500 are not sponsored or sold by S&P DJI, Dow Jones, S&P, their respective affiliates and none of such parties make any representation regarding the advisability of investing in such products nor do they have any liability for any errors, omissions, or interruptions of the S&P 500 Index.

FOR MORE INFORMATION:

Silke Mcguinness
Global Head of Communications
(+1) 415 205 8414
silke.mcguinness@spglobal.com

Alyssa Augustyn
Americas Communications
(+1) 773 919 4732
alyssa.augustyn@spglobal.com

Asti Michou
EMEA Communications
+44 (0) 79 70 887 863
asti.michou@spglobal.com

SOURCE S&P Dow Jones Indices

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July 1, 2025
New Quantum Material Could Make Electronics 1000x Faster

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Researchers at Northeastern University have discovered how to change the electronic state of matter on demand, a breakthrough that could make electronics 1,000 times faster and more efficient.

By switching from insulating to conducting and vice versa, the discovery creates the potential to replace silicon components in electronics with exponentially smaller and faster quantum materials.

“Processors work in gigahertz right now,” said Alberto de la Torre, assistant professor of physics and lead author of the research. “The speed of change that this would enable would allow you to go to terahertz.”

Via controlled heating and cooling, a technique they call “thermal quenching,” researchers are able to make a quantum material switch between a metal conductive state and an insulating state. These states can be reversed instantly using the same technique.

Published in the journal Nature Physics, the research findings represent a breakthrough for materials scientists and the future of electronics: instant control over whether a material conducts or insulates electricity.

The effect is like a transistor switching electronic signals. And just as transistors allowed computers to become smaller — from the huge machines the size of rooms to the phone in your pocket — control over quantum materials has the potential to transform electronics, says Gregory Fiete, a professor of physics at Northeastern who worked with de la Torre to interpret the findings.

“Everyone who has ever used a computer encounters a point where they wish something would load faster,” says Fiete. “There’s nothing faster than light, and we’re using light to control material properties at essentially the fastest possible speed that’s allowed by physics.”

By shining light on a quantum material called 1T-TaS₂ at close to room temperature, researchers achieved a “hidden metallic state” that had so far only been stable at cryogenically cold temperatures. Now researchers have created that conductive metallic state at more practical temperatures, says de la Torre. The material maintains its programmed state for months — something that has never been accomplished before.

“One of the grand challenges is, how do you control material properties at will?” says Fiete. “What we’re shooting for is the highest level of control over material properties. We want it to do something very fast, with a very certain outcome, because that’s the sort of thing that can be then exploited in a device.”

So far, electronic devices have needed both conductive and insulating materials, plus a well-engineered interface between the two. This discovery makes it possible to use just one material that can be controlled with light to conduct and then insulate.

“We eliminate one of the engineering challenges by putting it all into one material,” Fiete says. “And we replace the interface with light within a wider range of temperatures.”

The research expands upon previous work that used ultra-fast laser pulses to temporarily change the way materials conduct electricity. But those changes only lasted tiny fractions of a second and usually at extremely cold temperatures.

Stable conductivity switching at higher temperatures is a significant advance for quantum mechanics, Fiete says, and for the long game of supplementing or replacing silicon-based technology. Semiconductors, he says, are so dense with logic components that engineers are now stacking them in three dimensions. But this approach has limitations, he said, which make tiny quantum materials more important for electronics design.

“We’re at a point where in order to get amazing enhancements in information storage or the speed of operation, we need a new paradigm,” Fiete says. “Quantum computing is one route for handling this and another is to innovate in materials. That’s what this work is really about.”

Reference: De La Torre A, Wang Q, Masoumi Y, et al. Dynamic phase transition in 1T-TaS2 via a thermal quench. Nat Phys. 2025. doi: 10.1038/s41567-025-02938-1

This article has been republished from the following materials. Note: material may have been edited for length and content. For further information, please contact the cited source. Our press release publishing policy can be accessed here.

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July 1, 2025
A network analysis of the depression and anxiety comorbidity: a nationwide survey among Chinese adolescents during the normalization phase of COVID-19 pandemic prevention and control | BMC Psychiatry
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Study setting

This study was conducted at four major healthcare facilities in Northwest Ethiopia including Debre Markos Comprehensive Specialized Hospital, Tibebe Gihon Comprehensive Specialized Hospital, University of Gondar Comprehensive Specialized Hospital, and Felege Hiwot Comprehensive Specialized Hospital.

Study period and design

The study was conducted, from January 1, 2023, to May 31, 2023. A retrospective cohort design was employed to assess existing medical records, using a one year dataset.

Source and study population

The study population consisted of patients diagnosed with comorbid mental health and cardiovascular diseases who received care at the participating hospitals. Patients were identified through hospital admission and discharge records, outpatient clinic logs, and electronic health records.

Eligibility criteria

The inclusion criteria for participants were:

A confirmed diagnosis of comorbid mental health and cardiovascular disease in medical records.
Age 18 years or older at the time of diagnosis.
Available medical records for the duration of the study period.

The exclusion criteria for participants were:

Patients with incomplete medical records,
Those who had prior cardiovascular surgeries.
Individuals with terminal illnesses unrelated to comorbid mental health and cardiovascular diseases.

Study variables

The dependent variables are hospital readmission and emergency department visits. The independent variables included mental health treatment, age, sex, and residence.

Sample size determination

This study included all patients who met the eligibility criteria during the study period. As the study design was based on medical record review, no a priori sample size or power calculation was performed. Instead, the full population of eligible patients included to maximize statistical power and ensure generalizability.

A total of 319 patients with comorbid mental health and cardiovascular diseases between January 2018 and December 2022 were identified from four healthcare institutions in Northwest Ethiopia. These institutions were selected in simple random approaching method.

Sampling technique

To ensure the sample was representative of the eligible population across the participating hospitals, a proportional simple random sampling technique was employed. The total number of eligible patients at each hospital during the study period was first identified through a manual review of patient records. Proportional allocation was then used to determine the number of patients to include from each hospital based on its share of the total eligible patient population.

The formula used for proportional allocation was: n_i = (N_i / N) × n.

Where:

n_i = sample size from hospital i.
N_i = number of eligible patients in hospital i.
N = total number of eligible patients across all hospitals.
n = total sample size (319).

Based on estimated eligible patient numbers from hospital records (N = 1,100), the sample was allocated as follows:

Debre Markos Comprehensive Specialized Hospital: n₁ = (360 / 1100) × 319 ≈ 104 patients.
University of Gondar Comprehensive Specialized Hospital: n₂ = (300 / 1100) × 319 ≈ 87 patients.
Felege Hiwot Comprehensive Specialized Hospital: n₃ = (240 / 1100) × 319 ≈ 70 patients.
Tibebe Gihon Comprehensive Specialized Hospital: n₄ = (200 / 1100) × 319 ≈ 58 patients.

After determining the number of participants per hospital, simple random sampling was applied within each hospital. Eligible patient lists were prepared, and random numbers were generated using a computer-based random number generator to select participants independently.

Data collection procedure

This study employed a structured questionnaire, developed after an extensive review of relevant literature. The data collection instrument designed to capture sociodemographic characteristics, clinical parameters, and medication-related variables, with all data extracted from patient medical records. Comorbid conditions including diabetes mellitus, hyperlipidemia, hypertension, and other chronic physical conditions were identified based on clinician-documented diagnoses in the medical charts. Comorbidity was considered present if it was recorded in the patient’s medical history, diagnostic summary, or treatment plan during admission or follow-up visits. These conditions were categorized as binary variables (present or absent), and no additional thresholds related to disease severity, duration, or laboratory values were applied due to variability in documentation across sites.

Multiple methodologies were employed to assess the receipt of mental health treatment. Pharmacy refill records were used to determine whether patients actively received prescribed mental health medications during the study period. The duration of these prescriptions was also assessed as a measure of adherence to treatment regimens. Patient charts were systematically examined for indications of mental health treatment, including therapist notes, treatment plans, and mental health evaluations. Specific diagnosis codes associated with mental health conditions were identified to establish a clear connection between diagnosis and treatment. In addition to assessing the receipt of treatment, clinical outcomes related to mental health treatment were analyzed. Indicators such as psychiatric symptoms, changes in diagnoses, and hospitalization rates for mental health crises were assessed. To examine emergency department visits, patient medical records were reviewed throughout the study period. Details such as the reason for each visit, clinical diagnoses, and related mental health assessments were recorded. Visits were categorized based on their connection to comorbid mental health and cardiovascular diseases, mental health crises, or other health complications. For hospital readmissions, a similar review of patient medical records was conducted to track subsequent admissions within a specified follow-up period after discharge. Diagnosis dates were extracted from electronic health records, inpatient and outpatient medical charts, and physician notes. Diagnosis dates were extracted from electronic health records, inpatient and outpatient medical charts, and physician notes. For psychiatric disorders, clinical evaluations, mental health treatment initiation records, and International Classification of Diseases (ICD-10) codes were reviewed, with specific codes. For cardiovascular conditions, diagnostic imaging reports, laboratory results, and physician-confirmed diagnoses, along with corresponding ICD-10 codes, were examined. When exact diagnosis dates were unavailable, the earliest documented evidence of the condition, based on clinical evaluations or treatment initiation was recorded. Given the study’s focus on comorbid mental health and cardiovascular diseases, special attention was given to cases where the timing of psychiatric and cardiovascular diagnoses differed. For patients with pre-existing psychiatric disorders, the timing of the CVD diagnosis was recorded as the key event indicating the onset of a comorbid mental health and cardiovascular diseases. Conversely, for patients with pre-existing CVD, the timing of the psychiatric disorder diagnosis was recorded as the key event. In instances where both conditions were diagnosed simultaneously (e.g., during a single hospital admission), this date was recorded as the timing for both conditions. For patients with multiple episodes of the same condition, such as recurrent depressive episodes or repeated cardiovascular events, the first documented diagnosis within the study period was used.

Operational definitions

Comorbid mental health and cardiovascular diseases are health conditions that involve both cardiovascular disorders and psychiatric disorders.
Mental health treatment refers to interventions aimed at alleviating symptoms and improving the well-being of patients with diagnosed mental health conditions.
Hospital readmission is defined as any unplanned admission to the hospital. In this study, readmissions included those which are related to comorbid mental health and cardiovascular diseases.
Emergency department visit is any encounter in the emergency department requiring immediate medical attention. In this study, emergency department visits included those related to comorbid mental health and cardiovascular diseases.
Event Occurred: refers patients who experienced the outcome of interest during the study period, including those who had a hospital readmission or an emergency department visit.
Censored: refers patients who did not experience the outcome of interest (hospital readmission or emergency department visit) during the follow-up period. These individuals remained under observation but did not have the event occur before the study’s conclusion or were lost to follow-up.
Survival time (time to event): This is the duration from the start to the event.

Data quality assurance

To ensure the integrity and reliability of the data collected in this study, several quality assurance measures were implemented throughout the data collection process. A structured questionnaire was initially developed based on a comprehensive review of relevant literature, to facilitate standardized data capture across all participating institutions. The questionnaire was pre-tested on a small sample of medical records to identify ambiguities, improve clarity, and refine variable definitions prior to full-scale implementation.

Trained research assistants, all of whom were clinical pharmacists, conducted the data extraction. These data collectors underwent rigorous training on the study protocol, ethical considerations, operational definitions, and standard procedures for interpreting medical records. To assess and enhance inter-rater reliability, a pilot exercise was conducted in which 10% of patient charts were independently reviewed by two data collectors. Discrepancies were discussed and resolved through consensus, leading to adjustments in the protocol where necessary. Throughout the data collection period, Periodic supervisory audits were performed. The principal investigator and hospital-based site coordinators randomly reviewed approximately 10% of extracted data to verify accuracy and adherence to protocol. Any inconsistencies were addressed through targeted feedback and retraining sessions with the data collectors. Throughout the study period to ensure compliance with data collection protocols and to address any potential issues promptly. To minimize information bias, diagnoses were confirmed using multiple sources of documentation. Psychiatric disorders were validated by cross-referencing ICD-10 codes with therapist notes, treatment plans, and prescription records. Cardiovascular diagnoses were corroborated using physician-confirmed diagnoses, laboratory and imaging reports, and treatment documentation. In cases where exact diagnosis dates were missing, the earliest documented clinical evidence such as first mention of symptoms or treatment initiation was used as a proxy. To reduce the impact of missing data, records lacking essential variables (e.g., confirmed diagnoses or outcome data) were excluded from analysis. For less critical variables, a complete-case analysis was performed. Given the low frequency of missing data in those variables, imputation methods were not necessary. When feasible, missing details were recovered through triangulation across multiple record sources. To mitigate selection bias, a total population sampling strategy was used. All eligible patients with coexisting psychiatric and cardiovascular conditions who met the inclusion criteria and received care at any of the four participating hospitals were included. Additionally, all medical records and documentation were verified against the entries in the database to confirm accuracy and completeness.

Data processing and analysis

Data processing and analysis for this study were conducted using statistical software to ensure accurate interpretation of the findings. Following data collection, all questionnaires and medical record entries were reviewed for completeness and consistency. The data were then coded and entered into a secure electronic database to facilitate analysis. Descriptive statistics were generated to summarize the demographic and clinical characteristics of the study population. Categorical variables were described using frequency distributions and percentages, while continuous variables were summarized using means and standard deviations.

To identify factors influencing survival outcomes, Cox proportional hazards regression analysis was performed. The primary outcomes were the time to hospital readmission and the time to the first emergency department visit, both measured in days from the date of discharge or study entry. The follow-up period spanned one year from the date of the first diagnosis or discharge, with censoring applied at the end of the study period or upon loss to follow-up. The assumptions of the Cox proportional hazards model were evaluated using the Schoenfeld residual test. To examine the relationship between baseline variables and patient survival, a two-step approach was employed. Initially, each baseline variable that satisfied the assumptions of the Cox proportional hazards model was analyzed individually using separate Cox regression models. Subsequently, variables with a P-value of less than 0.25 in the bivariate analysis were included in the multivariable analysis. However, final inclusion was not based solely on statistical criteria. We also incorporated variables based on their clinical relevance, biological plausibility, and established evidence from prior studies on mental health and cardiovascular outcomes. The Cox regression model was utilized to identify factors associated with the time to hospital readmission and emergency department visit. The results were reported as crude hazard ratios (CHR) and adjusted hazard ratios (AHR) with corresponding 95% confidence intervals, and statistical significance was determined at a P-value threshold of < 0.05. Additionally, multicollinearity among the independent variables was assessed using the variance inflation factor to detect and eliminate redundant variables that could bias the estimates. The overall mean VIF was calculated to be 1.21, which falls within the acceptable range of 1 to 5. Survival analysis was further conducted using Kaplan-Meier survival curves to illustrate survival functions, and the log-rank test was applied to compare survival distributions between patients who received mental health treatment and those who did not.

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Study period and design

Source and study population

Eligibility criteria

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Sample size determination

Sampling technique

Data collection procedure

Operational definitions

Data quality assurance

Data processing and analysis

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