Diagnosis Of Illness Research Articles

Characteristics of the parent-child relationship in families of children with cerebral palsy and multiple sclerosis (literature review)

Multiple sclerosis (MS) and cerebral palsy (CP) are chronic disabling diseases caused by central nervous system affection. The diagnosis of a chronic illness in a child is a psychotraumatic event that disrupts the structure and functioning of the family system, the psychological well-being and the health of family members. In the process of adapting to the stress factor, both parent-child relationships and marital relationships are deformed. In most families raising disabled children, disharmonious parenting styles prevail. Parents report a decrease in quality of life, dissatisfaction with their role and family relationships, experience anxiety, depression, guilt and helplessness and try to compensate for the psychological distress by focusing on the child's problems, often sacrificing their private life and free time. In addition, parents of children with MS are burdened by a sense of insecurity related to the difficult-to-control and unpredictable course of the disease. The lack of a sufficient number of scientific studies on this topic does not allow us to draw clear conclusions about the influence of the type of child's disease progression (progressive, stationary) on the characteristics of the parent-child relationship. In addition, longitudinal studies are needed to assess the dynamics of parent-child relationships and the effectiveness of psychocorrective interventions in families with children with MS and CP. The review is based on a theoretical study of the issues of parent-child relationships in families raising children with CP and MS and the impact of chronic disabling illness on the family system, which was conducted based on a systematic literature search of PubMed, Google Scholar, eLibrary and DisserCat databases.

An efficient optimized encryption and compression techniques to improve medical image security and transmission in the cloud

As digital technology for illness diagnosis and analysis has advanced, medical images are sent over the Internet. Cloud computing plays a major role for low-cost data storage and data sharing. In the healthcare industry, data security and privacy are key issues with cloud computing. It is imperative that healthcare professionals make sure that patient data is safe against hackers, unauthorized access, and thrift. To secure the confidential data and store the huge amount of data, encryption and compression techniques are used. This paper provides an enhanced optimized encryption and efficient hybrid compression strategies based on cloud environment. The proposed model involves various operations such as generate optimal key, encryption, compression, decompression and decryption. In order to transfer the data using high speed cloud data retrieval, we initially propose the Huffman Fano Hybrid Entropy approach. In the next step, the proposed model performs Elliptic Curve Coding based encryption technique to secure compressed medical image transmission. Here the shared secret keys are generated optimally by dynamic group based cooperative optimization algorithm, which makes use of the encryption quality measures and is called DGBCO–ECC model. On the receiving end, the image is decrypted and decompressed. The proposed model performance is validated by exploiting various parameters namely Mean Square Error, Standard Deviation of the Mean Error, Universal image quality index, Structural Similarity Index, Entropy, Peak Signal to Noise Ratio, Compression Ratio, Data Rate Saving and compression time. When the experimental outcome is contrasted with existing methods, it is found to perform better.

Benefits of Art therapy in Prison Population with a Diagnosis of Mental Illnes: A Systematic Review.

Art therapy is a form of psychotherapy that was introduced in psychiatric hospitals, where psychiatric patients used painting as a way to express themselves. Nowadays, it is used in prison and has positive effects such as stress and anxiety reduction, and decrease in rates of recidivism. The main aim of this study was to analyze the effects of art therapy programs among the prison population with a diagnosis of mental illness. A systematic review was performed and a total of 12 studies were included. Positive results and beneficial effects were observed for this population; the most prominent is the decrease in the rate of recidivism. Art therapy is a form of therapy that is effective for improving socialization and decreasing anxiety and depressive symptoms in patients diagnosed with mental illnesses. It has been proved to decrease recidivism rates in inmates who participate in these programs.

A Novel Rule-based Expert System for Early Diagnosis of Bipolar and Major Depressive Disorder

A confident and timely diagnosis of mental illnesses is one of the primary challenges practitioners repeatedly encounter when they start treating new patients. However, diagnosing can quickly become problematic as the subjects expose comparative symptoms among mental illnesses. Due to influencing a broad populace among mental ailments, an adjusted differentiation between Major Depressive Disorder, Mania Bipolar Disorder, Depressive Bipolar Disorder, and ordinary individuals with mild symptoms is one of the critical subjects for community health. This study responded to the described problem by proposing a novel rule-based Expert System, which evaluates the impact of disorder symptoms on the Certainty Factor concerning each mental status. The semantic rules are developed based on the recommendation of experts, and the implementation is carried out using Prolog and C# languages. Furthermore, an easy-to-use user interface is considered to facilitate the system workflow. The consistency of the developed framework is established by performing rigorous tests by expert psychiatrists as well as 120 clinical samples collected from private samples. Based on the results, the current model classifies mental disorder cases with a success rate of 93.33% using only the 17 symptoms specified in the ontology model. Furthermore, a questionnaire that measures user satisfaction after the test also achieves a mean score of 3.56 out of 4, which indicates a high degree of user acceptance. As a result, it is concluded that the current framework is a reliable tool for achieving a solid diagnosis in a shorter period.

MicroRNA Profiles in Critically Ill Patients.

The use of biomarkers to expedite diagnosis, prognostication, and treatment could significantly improve patient outcomes. The early diagnosis and treatment of critical illnesses can greatly reduce mortality and morbidity. Therefore, there is great interest in the discovery of biomarkers for critical illnesses. Micro-ribonucleic acids (miRNAs) are a highly conserved group of non-coding RNA molecules. They regulate the expression of genes involved in several developmental, physiological, and pathological processes. The characteristics of miRNAs suggest that they could be versatile biomarkers. Assay panels to measure the expression of several miRNAs could facilitate clinical decision-- making for a range of diseases. We have, in this paper, reviewed the current understanding of the role of miRNAs as biomarkers in critically ill patients.

Health, Disability, and Economic Inactivity Following a Diagnosis of a Severe Mental Illness: Cohort Study of Electronic Health Records Linked at the Individual-Level, to Census from England.

The association of social and clinical indicators with employment, disability, and health outcomes among individuals with severe mental illnesses (SMI) remains unclear. Existing evidence primarily comes from smaller cohort studies limited by shorter follow-up and high attrition, or registry-based research, which lacks information on important social determinants. We utilized a novel data linkage consisting of clinical records of individuals diagnosed with schizophrenia-spectrum or bipolar disorders from the South London and Maudsley Mental Health Trust, linked at the individual-level to the 2011 UK Census, a rich source for sociodemographic information. Using logistic regression, we estimated adjusted odds ratios (aORs) and 95% confidence intervals to determine associations between socioeconomic and clinical indicators and economic inactivity, self-rated health, and disability outcomes. The sample comprised 8249 people with SMI diagnoses. Economic inactivity (77.3%), disability (68.3%) and poor health (61.1%) were highly prevalent. Longer duration of illness and comorbid substance misuse were associated with economic inactivity, poorer self-rated health, and disability, with associations noted between living alone and all outcomes (aORs and 95% CI: Economic inactivity: 1.72, 1.45-2.03; disability: 1.48, 1.31-1.68; poor health: 1.32, 1.18-1.49). Relative to the White British group, Black African, South Asian, and Other Black groups were more likely to be economically inactive. Black Caribbean and other groups were less likely to report poorer self-rated health or disability. Our findings highlight considerable disability, poorer health, and economic inactivity experienced by people with SMI. Addressing comorbid substance misuse and social isolation could play a role in improving outcomes.

School well-being in primary school children with chronic illness. A prospective cohort study.

Children with chronic illness perform poorer at school, and school well-being (SWB) may mediate this association. We investigated the association between chronic illness and three domains of SWB in children in first grade. Data from a German population-based prospective cohort study were used. Children with chronic illness were identified via their preschool health examination and follow-up parent surveys during first grade. Children were grouped as either (i) having current special health care needs (SHCN), (ii) having at least one physician diagnosis of a chronic illness but no current SHCN, or (iii) being healthy. SWB was assessed at the end of first grade and measured by the Questionnaire for the Assessment of Emotional and Social School Experiences of First and Second Grade Primary School Children. Based on SWB theory and previous frameworks, the following subscales were used: School-Related Self-Concept, Social Integration, and Joy of Learning. The sum score for each subscale was converted into area-transformed T-values (mean 50 and standard deviation 10). Associations between chronic illness groups and SWB subscales were investigated by multivariable linear regression models. Effect estimates were adjusted for potential confounding variables and standardized mean differences (SMD) were calculated. Of the 1,490 children included, 15% had current SHCN and 37% had a physician diagnosis of a chronic illness but no current SHCN. Compared to healthy children, children with SHCN had lower scores for the School-Related Self-Concept and the Joy of Learning subscale (SMD -0.18 for both) but not for the Social Integration subscale. In contrast, children with a chronic condition but no SHCN had lower scores only for the Social Integration subscale (SMD -0.12). Primary school students with a chronic illness with or without SHCN have lower SWB in some domains compared to their healthy peers. SWB may be a mediator in the association between chronic illness and poor school performance.

Accuracy of Machine Learning in Discriminating Kawasaki Disease and Other Febrile Illnesses: Systematic Review and Meta-Analysis.

Kawasaki disease (KD) is an acute pediatric vasculitis that can lead to coronary artery aneurysms and severe cardiovascular complications, often presenting with obvious fever in the early stages. In current clinical practice, distinguishing KD from other febrile illnesses remains a significant challenge. In recent years, some researchers have explored the potential of machine learning (ML) methods for the differential diagnosis of KD versus other febrile illnesses, as well as for predicting coronary artery lesions (CALs) in people with KD. However, there is still a lack of systematic evidence to validate their effectiveness. Therefore, we have conducted the first systematic review and meta-analysis to evaluate the accuracy of ML in differentiating KD from other febrile illnesses and in predicting CALs in people with KD, so as to provide evidence-based support for the application of ML in the diagnosis and treatment of KD. This study aimed to summarize the accuracy of ML in differentiating KD from other febrile illnesses and predicting CALs in people with KD. PubMed, Cochrane Library, Embase, and Web of Science were systematically searched until September 26, 2023. The risk of bias in the included original studies was appraised using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Stata (version 15.0; StataCorp) was used for the statistical analysis. A total of 29 studies were incorporated. Of them, 20 used ML to differentiate KD from other febrile illnesses. These studies involved a total of 103,882 participants, including 12,541 people with KD. In the validation set, the pooled concordance index, sensitivity, and specificity were 0.898 (95% CI 0.874-0.922), 0.91 (95% CI 0.83-0.95), and 0.86 (95% CI 0.80-0.90), respectively. Meanwhile, 9 studies used ML for early prediction of the risk of CALs in children with KD. These studies involved a total of 6503 people with KD, of whom 986 had CALs. The pooled concordance index in the validation set was 0.787 (95% CI 0.738-0.835). The diagnostic and predictive factors used in the studies we included were primarily derived from common clinical data. The ML models constructed based on these clinical data demonstrated promising effectiveness in differentiating KD from other febrile illnesses and in predicting coronary artery lesions. Therefore, in future research, we can explore the use of ML methods to identify more efficient predictors and develop tools that can be applied on a broader scale for the differentiation of KD and the prediction of CALs.

Does becoming amother make you ill?-Frequency of mental health diagnoses in postpartum mothers compared to childless women based on BARMER billing data

Around 700,000 children are born in Germany every year. Many mothers are under great pressure and strain due to prevailing role models and social expectations. Up to 15% of mothers in Germany are said to be affected by postpartum depression. The available studies, however, are mainly based on surveys, are older and vary widely in their results. This study brings another data source into play and asks how frequently diagnoses of mental illness are found in mothers after childbirth compared to childless women in the billing data of alarge health insurance company. Aretrospective matched cohort study was conducted with health insurance data from 2017-2022, comparing mothers and childless women with the same age and the same medical and socioeconomic characteristics with regard to depression, exhaustion and burnout, and anxiety and stress disorder outcomes. The modelling was carried out using Poisson regression. Mothers were less likely than childless women to receive adiagnosis of amental disorder within five years after childbirth. In the long run, however, the frequency of diagnoses equalised. Becoming amother does not lead to more diagnoses of mental disorders compared to childless women, at least in the first five years after giving birth. However, the interpretation of the data is difficult. Possible influence of external factors on making the diagnosis is not seen in the data, such as the social expectation of a"happy mother" or achange in the utilisation of medical care due to maternity.

Realist assessment of fidelity during the implementation of the PARTNERS collaborative care intervention for people with diagnoses of severe mental illness within a cluster randomised controlled trial

Many with severe mental illnesses are underserved by disjointed service provision. PARTNERS aims to address this via collaborative care with recovery-based coaching. PARTNERS was evaluated in a randomised controlled trial. Understanding how intervention delivery compared to the model, why this was, and under what circumstances, aids interpretation of trial results and optimisation of future implementation. This paper reports the results of a Realist assessment of fidelity, exploring delivery compared to model and refining programme theory. Practitioners, service users, supervisors, primary care representatives, and researchers (n = 39) were interviewed. Additional data included session recordings, follow up interviews, practitioner reflective logs, supervision logs, contact data, service user surveys, and meeting minutes. A framework analysis with evaluative coding was used to assess the extent to which delivery matched the Realist initial programme theory, and how, why and under what circumstances this was the case. Retroductive analysis was used to refine the programme theory. Delivery was good, but varied by practitioner and over time. Delivery improved over time, as practitioner understanding of the intervention increased. Refinements to the programme theory include training leading to practitioners forming collaborative relationships with service users most of the time, but unidentified contextual factors causing variation in consistency. Whether training led to practitioners liaising across different bodies was dependant on the contextual factors of existing relationship skills and previous connections. System-level difficulties in providing consistent supervision made it difficult to assess the impact of this mechanism on delivery. Variation in delivering means caution should be applied when interpreting trial results. Implementation of practitioner-level change without implementing system-level change limits the ability to fully implement the model and to draw conclusions as to effectiveness. Current changes to NHS community mental health care may make this more achievable. Further research is needed to understand the role of supervision and optimal training. Trial registration: This is the realist process evaluation of the cluster randomised controlled trial ISRCTN95702682. REC approval: West Midlands–Edgbaston Research Committee 29/06/2017, ref: 14/WM/0052 (trial registration number ISRCTN95702682).

Depression among Saudi international university students and its associated risk factors

IntroductionDepression disorders are one of the most widespread mental disorders among college students. In university students, depression has been associated with lower academic performance, worse work performance, and increased dropout risk. his study aims to assess the prevalence of depression and highlight risk factors among Saudi international university students (IUS) in the United States (US), the United Kingdom (UK), and Australia.Subject and methodsThis cross-sectional study was conducted among Saudi students who chose to study abroad. A self-administered online questionnaire was sent to the students using an online survey. The questionnaire includes socio-demographic data (e.g., age, gender, specialty, etc.), medical characteristics, social status, and the Patient Health Questionnaire (PHQ-8) to assess the student's depression.ResultsOf the 401 students, 52.4% were younger (age 18 – 25 years), and more than half were males (53.9%). Most of the participants were studying in the US (46.6%), followed by the UK (40.6%) and Australia (12.7%). The rate of major depression among Saudi students studying abroad was 40.4%. In univariate analysis, major depression was influenced by age, marital status, specialty, personal and family history of mental illness, contact with family and friends, and feeling like a stranger. However, after conducting multivariate regression analysis, previous diagnoses of mental illness and feeling a stranger were identified as the significant independent risk factors for major depression.ConclusionThis study provides insights into the prevalence and risk factors associated with depression among Saudi IUS. The prevalence of major depression among Saudi IUS was 40.4%. Previous history of mental illness and feeling like strangers were identified as the most prominent risk factors in this study. Identifying the prevalence and contributing factors of depression can guide academic institutions and governments in the development of effective interventions and support for IUS’s mental well-being. Future research could diversify the sample and employ longitudinal designs.

Investigations of Acute Ischemic Stroke Detection Techniques from CT Images Using Machine Learning Approaches

Stroke is a kind of cerebrovascular accident (CVA) that primarily affects individuals over the age of 50, while it can afflict people of all ages. Stroke patients suffer from a chronic condition that leaves them physically unable till their death. To identify the onset of this illness, numerous studies have been carried out. Common signs of a stroke include stiffness, a shift in posture, and tremors in the arms and legs. Functional and emotional mediation are linked to a significant area of the brain. The symptoms of many brain disorders are similar in the early stages because of disruptions to dopamine and other regulating mechanisms. This is a crucial step in how stroke lesions develop. The CAD system is the most efficient method for identifying a stroke. By speeding up the analysis of the required aberration, the CAD system improves the process of illness diagnosis. The goal of the proposed study is to create a compact system that can lessen processing errors, false positive rates, and complexity—three major problems with the current system. The proposed study uses a machine learning model to interpret and classify a CT brain image that shows an Acute Ischaemic Stroke (AIS) lesion. To distinguish between normal and AIS stroke lesions, the pre-processed image is segmented and classed using a Random Forest classifier. Performance criteria like Peak signal ratio, average gradient, accuracy, specificity, sensitivity, and dice index are used to evaluate the experimental output of the proposed work. The suggested model has a straightforward processing structure and the highest efficiency.

Edge computing-based ensemble learning model for health care decision systems

A growing number of humans have suffered severe chronic illnesses, which has caused a boost in the requirement for diagnostic and medical treatment procedures that are both accurate and fast. Improved patient conditions and enhanced Decision-Making Systems (DMS) for healthcare professionals are the primary objectives of the Clinical Decision Support System (CDSS) recommended in this research article. The main drawback of traditional Machine Learning (ML) techniques is their failure to predict reliably. To solve this problem, the proposed model creates an Ensemble Extreme Learning Machine (EN-ELM) algorithm that combines predictors trained on several different data sets. This lowers the chance of overfitting. The suggested CDSS uses many different data processing methods, including Adaptive Synthetic (ADASYN) and isolation Forest (iForest), which fix problems like outliers and class imbalance. This approach significantly enhances the framework’s classification performance. Also, the CDSS is compatible with an EC model, which enables real-time computation while minimizing the requirement for integrated systems. The recommended CDSS applies iForest and ADASYN to execute large-scale trials validating high standards of accuracy across numerous datasets. Researchers concluded that a suitable ELM classification threshold of 85% is the most effective, which substantially boosts the accuracy of the predictive model. When applied to various medical datasets, such as Hepatocellular Carcinoma (HCC), Cervical Cancer, Chronic Kidney Disease (CKD), Heart Disease, and Arrhythmia, the EN-ELM achieved accuracy rates of 99.36%, 98.15%, 97.85%, 97.06%, and 96.72%, respectively. By measuring this progress, the CDSS could dramatically improve the accuracy of chronic illness diagnosis and treatment, which similarly affects clinicians.

"We Just Brush It Off, God's Got Us . . .": African American Adolescent Girls' Expressions of Spirituality in Response to Stressful Experiences.

The mental health burden is increasing among the young adolescent population worldwide, with more reports of sadness and mental illness diagnoses. This study explored how African American adolescent girls use religious and spiritual practices to cope with stressful experiences. A qualitative descriptive design was used to interview 22 African American adolescent girls. Data were analyzed using content analysis and constant comparison technique. The following themes emerged related to prayer, scripture, and songs: 1) Healing for themselves or others, 2) instructions on how to live, 3) provision from God, 4) protection during dangerous situations, 5) strength to endure, and 6) connectedness to God, self, and others. Adolescent girls used spirituality and religion as coping mechanisms during various life stressors while healing, reframing thoughts to a positive mindset, and seeking direction and protection. Future studies should consider incorporating spiritual and religious coping mechanisms as a mental health intervention, particularly for this population.

Gynecologic oncology patients with severe mental illness diagnosis are more likely to experience adverse outcomes during hospitalizations for cancer-directed treatment

Gynecologic oncology patients with severe mental illness diagnosis are more likely to experience adverse outcomes during hospitalizations for cancer-directed treatment

The effects of the paramedics’ field work on their mental status in Jeddah

ABSTRACT Background: Paramedics are continuously exposed to stressful events, making them liable to mental disorders. This study assesses the health of paramedics in Jeddah, focusing on their mental, emotional, and physical well-being. Methods: This cross-sectional study included fieldwork paramedics in Jeddah through an online survey distributed on social media. With a response rate of 86.09%, 192 complete responses were analyzed. Outcomes included mental health, mood and somatic effect, self-esteem, and sleep. Results: Most participants were males (83.85%), married (50%), non-heavy smokers (51.04%), and non-alcohol drinkers (51.04%). A significant number reported health-related work or daily life difficulties, with 62.5%–67.18% attributing these to physical and emotional issues, respectively. Over half rated their mental health as poor or somewhat poor, impacting their relationships significantly. Additionally, 70.31% frequently felt anger, and many faced limitations in physical activities. About 14.58% had a prior mental illness diagnosis, with nearly a quarter on medication, and 61.98% had recently sought psychiatric help. Sleep deprivation affected 61.98%, with 38.53% reporting poor sleep quality. Conclusions: The findings highlight the negative impact on paramedics’ mental health, showing a substantial diagnosis rate of mental disorders, activity limitations, negative mood, and relationship issues. Despite significant sleep problems, a positive aspect is their proactive psychiatric help-seeking behavior.

Bilateral hip ochronosis, an unsuspected diagnosis in polyarticular pain: A case report

Alkaptonuria is a rare metabolic disorder characterized by the accumulation of homogentisic acid, leading to the deposition of blackish pigment in cartilage and soft tissue, known as ochronosis. This can mimic multiple joint pathologies and is a diagnosis of exclusion. We describe the case of a 52-year-old woman who had a total hip replacement after experiencing left hip pain for which oral medications were unable to provide relief. Unexpectedly, during the procedure, a blackish tissue was noticed. This led to a histological examination and the confirmation of the ochronosis diagnosis. The patient’s medical history revealed a number of malignancies, but no previous metabolic illness diagnoses. Subsequent follow-up revealed rapid-onset pain in other joints, leading to a similar diagnosis of polyarticular ochronosis in the right hip, knees, spine, and shoulders. This highlights how difficult it is to treat this condition. This case underscores the importance of considering ochronosis as a differential diagnosis in patients with chronic polyarticular pain. Current treatment consists of initial non-surgical measures, including physical therapy and analgesics. If there is no improvement, treatment would be joint arthroplasty. Awareness among orthopedic surgeons and histopathological examination of tissues is crucial for the accurate diagnosis and management of ochronosis.

Care Burden and Psychological Resilience of Mothers Accompanying Their Children with Chronic Illnesses Hospitalized in Pediatric Wards

Abstract Background: Psychological resilience plays a pivotal role in managing the caregiving burden experienced by mothers of hospitalized young children with chronic illnesses. The resilience of these mothers is not only critical for their own mental health but also for the well-being and development of their child. Aim: This study aimed to assess the care burden, psychological resilience, and associated factors of mothers with hospitalized children aged 0-2 years who have chronic diseases. Materials and Methods: The participants included 134 mothers of hospitalized children. Data were gathered utilizing a personal information form, the Zarit Burden Interview, and the Brief Resilience Scale. Results: Working mothers with a bachelor’s degree had higher Zarit Burden Interview scores than non-working mothers. Mothers of children with epilepsy and neurological diseases had higher Zarit Burden Interview scores than those with other chronic diseases. Mothers receiving instrumental and spiritual support had significantly higher Brief Resilience Scale scores. Discussion: Among mothers who accompany their children with a chronic illness diagnosis in the hospital, those who are university graduates, employed, or have children diagnosed with epilepsy and neurological diseases are particularly at risk in terms of the burden of care and psychological resilience. Conclusion: Physicians and especially pediatric nurses can enhance the psychological resilience of these mothers identified as a risk group in this study and alleviate their care burden by providing instrumental and moral support in the clinical setting. The support given to these accompanying mothers can contribute more significantly to the child's quality of life and care.

Prevalence of depression, anxiety and post-traumatic stress disorder (PTSD) after acute myocardial infarction: a systematic review and meta-analysis

Abstract Background There is increasing recognition that patients experience a greater risk of mental illnesses after an acute myocardial infarction (AMI), with the former being linked to worse post-AMI outcomes. However, the prevalence of these conditions is largely unknown as most studies focus on depression and few on other illnesses such as anxiety and post-traumatic stress disorder (PTSD). Additionally, existing studies mostly involved diagnoses of mental illnesses through patient-reported questionnaires which may carry subjectivity (1). Therefore, we conducted a systematic review and meta-analysis to estimate the prevalence and risk factors of developing depression, anxiety and PTSD after an AMI with the inclusion of only studies with official diagnoses of the mental illnesses. Methods Searches in MEDLINE, EMBASE, and PsycINFO up to January 23, 2023, identified 25 qualifying studies that examined the risk of depression, anxiety and PTSD after AMI, with case definitions based strictly on psychiatrist-administered structured interviews according to the Diagnostic and Statistical Manual for Mental Disorders (DSM) criteria. Meta-analyses of proportions using random-effects models estimated the pooled prevalence of each outcome at the <3-month and >3-month time-points. Heterogeneity was tested using I-squared statistics, if significant heterogeneity was found, subgroup analyses and meta-regression analyses were performed to identify the source of heterogeneity. Study quality was assessed using the Newcastle-Ottawa Scale (NOS) and Risk of Bias 2 (RoB2) Tool, and publication bias using the funnel plot and verified by the Egger’s and Begg’s tests. Results A total of 25 studies were included in the meta-analysis. The pooled prevalence of depression post-AMI (20 studies) was 16.70% (95% CI: 12.40%; 22.11%), with pooled prevalence <3-month and >3-month post-AMI at 19.46% (95% CI: 15.47%; 24.19%) and 14.87% (95% CI: 9.55%; 22.43%) respectively. Pooled prevalence of anxiety (7 studies) and PTSD (3 studies) were 11.96% (95% CI: 6.15; 21.96%) and 10.26% (95% CI:5.49;18.36%) respectively. Subgroup analysis showed that the pooled prevalence of both depression and anxiety are significantly higher in the female gender, in those with hypertension, diabetes or hyperlipidemia, and in smokers, while the pooled prevalence of depression is higher in unmarried than married individuals and in patients with a history of depression. Meta regression indicates that history of depression is a significant predictor of prevalence of depression (p= 0.0035, regression coefficient 1.54). Conclusion The prevalence of mental illnesses is high after AMI. Risk factors identified included female gender, hypertension, diabetes, hyperlipidemia, smoking, depression history, as well as one’s social set-up. These findings highlight the importance of screening at-risk patients and early intervention to improve long-term outcomes after AMI.Forest plot of depression prevalenceSubgroup analysis for depression

The resurgence of pertussis in Central Serbia in the period from June to December 2023

Abstract Background There was a registered increase in the number of confirmed pertussis cases in Central Serbia between June and December 2023. Among the possible causes of pertussis resurgence are increased awareness of pertussis among healthcare workers, availability of the sensitive RT PCR method in diagnostic, the limited duration of vaccine-induced immunity as well as decline of immunization coverage influenced by the COVID-19 pandemic. Methods The nasopharyngeal samples from clinics and primary care departments were transferred to Institute of Public Health of Serbia. DNA was extracted from each sample using the MagMAX Viral/Pathogen kit. The presence of B. pertussis was determined using two PCR assays, VIASURE Bordetella Real Time PCR Detection Kit and Bosphore B. pertussis/parapertussis Genotyping Kit, Anatolia Geneworks. Results RT-PCR confirmed a total of 338 pertussis cases in Institute of Public Health of Serbia during the observed period. Most of confirmed cases were among children 9-14 years old (49,7%) followed by infants younger than 12 months (21,9%). The more severe cases were among the unvaccinated and uncomplete vaccinated infants, 78,4% of which required hospitalization with two deaths in this age group. Conclusions Based on the analysis of the obtained results, we can highlight the importance of using sensitive methods in pathogen detection. Rapid diagnosis, laboratory confirmation and notification ensure early public health intervention to minimize the spread of disease. Despite long tradition of mandatory immunization against pertussis in the Republic of Serbia, wanning of vaccine induced immunity poses potential risk for the resurgence of pertussis. For pertussis control, in the future, it will be essential to achieve high immunization coverage as well as to introduce a booster dose of pertussis containing vaccine for children aged 14 and to implement vaccination of pregnant women. Key messages • For the better pertussis control vaccination of adolescents and adults, as well as vaccination of pregnant women should be considered in the future vaccination policies. • Clinicians should consider pertussis in the differential diagnosis of persistent cough illness in people of all ages, even those previously immunized.