Future Prevalence Research Articles

Projected number of people in need for long-term care in Germany until 2050

IntroductionCurrent demographic trends predict continuously growing numbers of individuals reliant on care, which has to be accounted for in future planning of long-term care-resources. The projection of developments becomes especially necessary in order to enable healthcare systems to cope with this future burden and to implement suitable strategies to deal with the demand of long-term care. This study aimed to project the prevalence of long-term care and the number of care-dependent people in Germany until 2050.MethodsWe used the illness-death model to project the future prevalence of long-term care in Germany until 2050 considering eight different scenarios. Therefore, transition rates (incidence rate and mortality rates) describing the illness-death model are needed, which have been studied recently. Absolute numbers of people in need for long-term care were calculated based to the 15th population projection of the Federal Statistical Office.ResultsNumbers of people in need for long-term care will increase by at least 12%, namely 5.6 million people, in the period of 2021 until 2050. Assuming an annual incidence-increase of 2% from 2021 to 2050 the number of care-dependent individuals could potentially rise up to 14 million (+180%).ConclusionOur projections indicated a substantial rise in the number of care-dependent individuals. This is expected to lead to raising economic challenges as well as a stronger demand for healthcare and nursing personnel.

Brucellosis Seroprevalence in Cattle in China During 2014–2024: A Systematic Review and Meta-analysis

Brucellosis, caused by several species of Brucella, continues to be a significant illness that poses a global threat to public health. China remains a persistent hotspot for brucellosis, despite the implementation of extensive control measures. This study aims to conduct a systematic review and meta-analysis of the seroprevalence of bovine brucellosis in different breeds and regions of China from 2014-2024, and to provide predictions on the future prevalence patterns of brucellosis in cattle and humans. The analysis comprised a total of 80 research studies, which consisted of 187 datasets and a combined sample size of 3,130,706. We estimated the overall pooled seroprevalence of bovine brucellosis in China to be 1.5% (95% CI: 0.6-2.6%). Subgroup analysis revealed that the seroprevalence in dairy cattle was 3.1%, surpassing the seroprevalence in beef cattle (1.3%) and yak (1.5%). Regions that had authorized vaccination programs exhibited higher seroprevalence (1.8%) compared to regions that did not have vaccination (0.5%). Notably, the study observed a simultaneous rise in both the prevalence of brucellosis in cattle and the number of human brucellosis cases. This suggests that high-quality routine surveillance of brucellosis in cattle will be essential for predicting and responding to cases in humans. Additionally, given the existing prevention and control measures, brucellosis will likely continue to be prevalent in both cattle and people. This systematic review will assist policymakers in adjusting animal surveillance and interregional livestock movement policies, ultimately contributing to the public safety goal of preventing brucellosis in humans by controlling it in animals.

Predicting future hospital antimicrobial resistance prevalence using machine learning

BackgroundPredicting antimicrobial resistance (AMR), a top global health threat, nationwide at an aggregate hospital level could help target interventions. Using machine learning, we exploit historical AMR and antimicrobial usage to predict future AMR.MethodsAntimicrobial use and AMR prevalence in bloodstream infections in hospitals in England were obtained per hospital group (Trust) and financial year (FY, April–March) for 22 pathogen–antibiotic combinations (FY2016-2017 to FY2021-2022). Extreme Gradient Boosting (XGBoost) model predictions were compared to the previous value taken forwards, the difference between the previous two years taken forwards and linear trend forecasting (LTF). XGBoost feature importances were calculated to aid interpretability.ResultsHere we show that XGBoost models achieve the best predictive performance. Relatively limited year-to-year variability in AMR prevalence within Trust–pathogen–antibiotic combinations means previous value taken forwards also achieves a low mean absolute error (MAE), similar to or slightly higher than XGBoost. Using the difference between the previous two years taken forward or LTF performs consistently worse. XGBoost considerably outperforms all other methods in Trusts with a larger change in AMR prevalence from FY2020-2021 (last training year) to FY2021-2022 (held-out test set). Feature importance values indicate that besides historical resistance to the same pathogen–antibiotic combination as the outcome, complex relationships between resistance in different pathogens to the same antibiotic/antibiotic class and usage are exploited for predictions. These are generally among the top ten features ranked according to their mean absolute SHAP values.ConclusionsYear-to-year resistance has generally changed little within Trust–pathogen–antibiotic combinations. In those with larger changes, XGBoost models can improve predictions, enabling informed decisions, efficient resource allocation, and targeted interventions.

Malaria transmission risk is projected to increase in the highlands of Western and Northern Rwanda

Malaria is one of the major health threats in Africa, and the risk of transmission is projected to be exacerbated by global warming. Rwanda experienced an 11-fold increase in malaria incidence from 2011 to 2015 despite extensive funding and implementation of control measures. Here, we focus on Rwanda as a case study and simulate monthly malaria incidence between 2010 and 2015, employing an ensemble learning method. Next, we project future malaria prevalence using shared socio-economic pathways (SSP2-4.5 and SSP5-8.5). We find that the projected increases in temperature and precipitation may shift malaria transmission risk to the highlands of western and northern Rwanda. These two regions that currently experience low malaria transmission. The seasonal effects of malaria incidence may be less apparent from January to June, and the peak season for malaria transmission in the highlands could occur one month earlier. Our findings highlight the impacts of climate change on malaria epidemics in Rwanda, which have implications for other world regions.

Trends in atopic dermatitis prevalence among the Chinese population (1990-2021) with projections for 2022-2030.

Atopic dermatitis (AD) has the highest disease burden among all skin diseases. However, reports on AD prevalence trends in China are limited. This study aimed to investigate the time trends of AD prevalence in China from 1990 to 2021 and to explore the age and sex differences. Data were obtained from the Global Burden of Disease Study, 2021. We analyzed the annual percentage change in the crude prevalence rate and age-standardized prevalence rate (ASPR) of AD from 1990 to 2021 using the Joinpoint model. We predicted the future prevalence of AD from 2022 to 2030 using the Bayesian age-period-cohort model. The total ASPR decreased slightly from 1990 (1357.93 per 100,000) to 2021 (1347.11 per 100,000). The ASPR of females (1457.64 per 100,000) was higher than that of males (1247.04 per 100,000) in 2021. The prevalence rate of AD was highest in the under 5 years old age group (3455.56 per 100,000) and the 5-9 years old age group (3360.31 per 100,000). By 2030, the predicted ASPR of AD will be 1394.36 (per 100,000) in males and 1603.69 in females (per 100,000). The predicted prevalence rate in the under 5 years old (3996.14 per 100,000 in males and 3990.68 per 100,000 in females) and 5-9 years old (3714.61 per 100,000 in males and 3963.96 per 100,000 in females) age groups will be higher than those in other age groups. Despite a slight decrease from 1990 to 2021, there has been an increasing trend since 2015 in the total AD burden. Given the increasing burden of AD on Chinese children and females, healthcare practitioners should enhance community education, improve disease management abilities of patients and caregivers, and reduce the burden and healthcare costs of AD.

Global Trend in Pancreatic Cancer Prevalence Rates Through 2040: An Illness-Death Modeling Study.

Despite remarkable progress in contemporary medical technology and enhanced survival outcomes for various cancer types, pancreatic cancer (PC) continues to stand out as a particularly deadly gastrointestinal malignancy. Given a persistent rise in both incidence and the corresponding mortality rates of PC globally, evaluations of PC burden by sex are of great importance. Here, we used the illness-death multi-state model (IDM) to forecast the prevalence of PC through the year 2040. IDM was established based on obtainable data to predict the future prevalence of PC on global, regional, and national scales from 2019 to 2040. Analyses were also performed regarding sex and 95% confidence intervals (CIs) are presented for all estimates. The projected prevalence rate for 2040 is anticipated to be 6.093 ([95% CI 5.47-6.786] per 100,000) worldwide, indicating a significant increase of 31.45% since 1990, and a 12.29% increase since 2019. The estimated average annual increase since 2020 was 0.5%. Considering sex differences, females are expected to have a steeper slope in prevalence rate than males. Intriguingly, when considering the percentage changes between the periods of 2019-2040 and 1990-2019 for both sexes, females exhibited 29% and 11% increase relative to males (2.6-fold greater increase). By 2040, it is predicted that the prevalence of PC will increase globally, with women being at higher risk of developing the disease. Considering the percentage changes, regions with lower socioeconomic status are anticipated to face a greater risk of experiencing PC compared to other geographical areas.

Enhancing COVID-19 forecasting precision through the integration of compartmental models, machine learning and variants

Predicting epidemic evolution is essential for making informed decisions and guiding the implementation of necessary countermeasures. Computational models are vital tools that provide insights into illness progression and enable early detection, proactive intervention, and targeted preventive measures. This paper introduces Sybil, a framework that integrates machine learning and variant-aware compartmental models, leveraging a fusion of data-centric and analytic methodologies. To validate and evaluate Sybil’s forecasts, we employed COVID-19 data from several European and U.S. states. The dataset included the number of new and recovered cases, fatalities, and variant presence over time. We evaluate the forecasting precision of Sybil in periods in which there is a change in the trend of the pandemic evolution or a new variant appears. Results demonstrate that Sybil outperforms conventional data-centric approaches, being able to forecast accurately the changes in the trend, the magnitude of these changes, and the future prevalence of new variants.

How many people will live and die with serious illness in Ireland to 2040? Estimated needs and costs using microsimulation

As populations age, more people worldwide will live and die with serious illness like cancer, heart disease and dementia. Prior projections of serious illness prevalence and end-of-life care needs have typically used static population-level methods. We estimated future disease prevalence and healthcare costs by applying dynamic microsimulation models to high-quality individual-level panel data on older adults (aged 50 + ) in Ireland. We estimated that the number of people living and dying with serious illness will increase approximately 70 % over 20 years. Per-capita annual costs both at end of life and not at end of life increase substantially due to ageing populations and growing complexity. Total health system expenditures on care for people with serious illness are projected to double before accounting for rising cost of inputs in real terms. Decomposition of these estimates suggests that 39 % of additional costs are accounted for by rising absolute numbers of older people, 37 % by changing age distribution and growing life expectancy, and 23 % due to rising individual complexity including morbidity and functional limitations. Our results and methods will be of interest to other countries planning for the future population health needs, and formidable health system resources associated with these needs, in the coming years.

Past, current, and future trends in the prevalence of primary sclerosing cholangitis and inflammatory bowel disease across England (2015–2027): a nationwide, population-based study

Primary sclerosing cholangitis (PSC) is one of the leading indications for liver transplantation in Europe, and a major risk factor for cancer in inflammatory bowel disease (IBD). However, it is not known how the epidemiology of PSC will change as that of IBD evolves. The aim of this study is to provide nationwide statistics on the past and current prevalence of PSC and IBD across England, and forecast how this is likely to change over time. We accessed and analysed a nationwide population-based administrative healthcare registry, which houses prospectively accrued data since April 1st 2001. In so doing, the past and current prevalence of PSC-IBD and IBD alone was determined among 18-60-year-olds in England, alongside average annual percentage change rates (AAPC), between the 1st of January 2015 and 2020. Past and current prevalence data, alongside trends in incidence and event-free survival rates, were then used to forecast future prevalence between 2021 and2027. In 2015, the prevalence of PSC with prior IBD diagnosis was 5.0 per 100,000 population, rising to 5.7 when including those with IBD diagnosed after PSC. In 2020, prevalence increased to 7.6 (8.6 accounting for IBD developing after PSC), yielding an AAPC of 8.8. In 2027, PSC-IBD prevalence is forecast to be 11.7 (95% prediction interval [PI]: 10.8-12.7), and 13.3 when accounting for IBD developing after PSC (AAPC: 6.4; 95% PI: 5.3-7.5). Comparatively, the prevalence of IBD alone rose among 18-60-year-olds from 384.3 in 2015 to 538.7 in 2020 (AAPC 7.0), and forecast to increase to 742.5 by 2027 (95% PI: 736.4-748.0; AAPC: 4.7, 95% PI: 4.6-4.8). The rate of growth in PSC-IBD is predicted to exceed IBD-alone. Further research is needed to understand changes in disease epidemiology, including aetiological drivers of developing (invariably progressive) liver disease in IBD, and the implications of rising case burden on health care resources. This study was supported by an unrestricted grant provided by Gilead Sciences.

Healthcare-associated infections and antimicrobial use at a major referral hospital in Papua New Guinea: a point prevalence survey

Healthcare-associated infections (HAI) and antimicrobial use (AMU) are drivers for antimicrobial resistance, and robust data are required to inform interventions and track changes. We aimed to estimate the prevalence of HAI and AMU at Port Moresby General Hospital (PMGH), the largest hospital in Papua New Guinea. We did a point prevalence survey (PPS) on HAI and AMU at PMGH in May 2023 using the European Centre for Disease Prevention and Control (ECDC) PPS protocol. We included all critical care patients and randomly sampled half of the patients in other acute-care wards. We calculated weighted HAI and AMU prevalence estimates to account for this sampling strategy. Weighted HAI estimates were also calculated for an expanded definition that included physician diagnosis. Of 361 patients surveyed in 18 wards, the ECDC protocol identified 28 HAIs in 26 patients, resulting in a weighted HAI prevalence of 6.7% (95% CI: 4.6, 9.8). Surgical site infections (9/28, 32%) were the most common HAI. When adding physician diagnosis to the ECDC definitions, more skin and soft tissue, respiratory, and bloodstream HAIs were detected, and the weighted HAI prevalence was 12.4% (95% CI: 9.4, 16.3). The prevalence of AMU was 66.5% (95%CI: 61.3, 71.2), and 73.2% (263/359) of antibiotics were from the World Health Organization Access group. This is the first reported hospital PPS of HAI and AMU in Papua New Guinea. These results can be used to prioritise interventions, and as a baseline against which future point prevalence surveys can be compared. Australian Government Department of Foreign Affairs and Trade and Therapeutic Guidelines Limited Australia.

Predicting the Future Prevalence of Cardiovascular Disease: The Good, the Bad, the Known, and the Unknown.

Predicting the Future Prevalence of Cardiovascular Disease: The Good, the Bad, the Known, and the Unknown.

AI in oncology: Resident perspectives by gender and tech literacy.

9055 Background: The integration of AI and ML in oncology demands that physicians adapt and grasp the basics for responsible use. We evaluated AI knowledge and perspectives among Canadian oncology residents, noting differences by program, gender, and tech literacy, and pinpointing education gaps. Methods: An ethics-approved survey collected anonymous responses from Canadian oncology residents, analyzing gender, program, and tech skills. Descriptive statistics, cross-tabs, and chi-square tests assessed associations; t-tests and Mann-Whitney tests compared groups. Results: A total of 57 (31%) residents and fellows, out of an estimated 182, participated, with representation from each oncology training program in Canada. Most of participants were male (63.2%) and most participants self-identified as white (42.1%) or Asian (22.8%). RO programs were better represented than MO programs (68.5% vs 31.6%) with balanced representation across all years of training. In our survey, women equally favored Medical Oncology (MO) and Radiation Oncology (RO) at 50%, but were more in MO (55.6%) than RO (26.3%). Men preferred RO (77.8%) over MO (22.2%). Tech literacy showed a gender gap, with more men (91.7%) feeling tech-savvy than women (8.3%). Tech-savvy respondents leaned towards RO (84%) and were younger (30 vs 33 years). They also showed more willingness to use AI, with a significant difference in willingness scores (1.52 vs 2.06). Despite gender not influencing AI attitudes significantly, tech literacy correlated with better AI understanding. High awareness of AI in medicine was reported (91.2%), with a strong belief in AI's future prevalence (96%). The majority were willing to use AI (86%) and recognized the need to understand it (74%). RO participants were more inclined towards AI education and usage than MO counterparts. The study highlighted a significant interest in AI learning (73%), with a preference for workshops (79%). Only 29% could describe AI, indicating a gap in AI education, despite 63% acknowledging its importance in training. Formal AI training was rare (12.3%), with a desire for more education, especially among RO residents. All findings had p<0.05. Conclusions: Canadian oncology residents anticipate AI's growing influence in medicine but face educational deficiencies. Gender, program preference, and tech literacy impact attitudes toward AI, highlighting the need for inclusive education to bridge gaps and foster diversity in AI's medical application.

Meta-analysis of the prevalence of restless leg syndrome and associated risk factors in chronic kidney disease patients

Introduction: Restless legs syndrome (RLS) presents as a sleep-related issue in individuals with chronic kidney disease (CKD), contributing to heightened sleep disturbances and diminished quality of life. Consequently, this meta-analysis was conducted to assess the prevalence of RLS among CKD patients. Methods: A comprehensive search for articles published between inception and September 2023 was conducted across CINAHL, Cochrane, Embase, Ovid-MEDLINE, PubMed, Scopus, and Web of Science databases. A Comprehensive Meta-Analysis (CMA) software version 3.0 with random effects models was used for pooled prevalence and prediction interval analysis. Heterogeneity was assessed using the I2 test and Cochran's Q-statistic. The quality of the studies was evaluated using Hoy's risk of bias. Additionally, moderator analyses were performed to investigate variations in the prevalence estimates among the included studies. Results: A total of 97 studies were included in this meta-analysis with 315,875 participants. The pooled prevalence of RLS among CKD patients was 25% (95%CI: 20% to 32%). In terms of the demographic breakdown, the prevalence of RLS was nearly comparable in adults and children/adolescents at 25% and 21%, respectively. The prediction interval indicated a potential future RLS prevalence ranging from 1% to 88%. Notably, iron and phosphorus levels emerged as significant moderating factors influencing the prevalence of RLS. Conclusions: Roughly 25% of individuals with chronic kidney disease (CKD) encounter restless legs syndrome (RLS). It is imperative to foster collaboration among healthcare professionals to facilitate proactive management and early assessment of RLS, thereby enhancing the overall quality of life for CKD patients.

Markov modeling on dynamic state space for genetic disorders and infectious diseases with mutations: Probabilistic framework, parameter estimation, and applications

Abstract The emergence and dynamic prevalence of genetic disorders and infectious diseases with mutations pose significant challenges for public health interventions. This study investigated the parameter estimation approach and the application of the dynamic state-space Markov modeling of these conditions. Using extensive simulations, the model demonstrated robust parameter estimation performance, with biases and mean-squared errors decreasing as sample size increased. Applying the model to COVID-19 data revealed distinct temporal patterns for each variant, highlighting their unique emergence, peak dominance, and decline or persistence trajectories. Despite the absence of clear trends in the data, the model exhibited a remarkable accuracy in predicting future prevalence trends for most variants, showcasing its potential for real-time monitoring and analysis. While some discrepancies were observed for specific variants, these findings suggest the model’s promise as a valuable tool for informing public health strategies. Further validation with larger datasets and exploration of incorporating additional factors hold the potential for enhancing the model’s generalizability and applicability to other evolving diseases.

A case of mistaken identity: a systematic review, meta-analysis, and reinvestigation of hemotropic Mycoplasma spp. infection in Ctenocephalides felis (cat flea)

Background Feline-associated hemotropic Mycoplasma (hemoplasmas) are believed to be transmitted by two primary mechanisms: (1) direct transmission via fighting and (2) vector-borne transmission by the cat flea (Ctenocephalides felis). While the efficiency of transmission by C. felis appears low, most manuscripts focus on the prevalence of hemoplasmas in wild-caught fleas and report either a very low (< 3%) or a high (> 26%) prevalence. Therefore, we aimed to assess the influence of sample processing and PCR methods on C. felis hemoplasma infection prevalence. MethodsA systemic review of PubMed articles identified 13 manuscripts (1,531 fleas/flea pools) that met the inclusion criteria (performed PCR for >1 hemoplasma on C. felis collected from cats). Risk of bias was assessed utilizing the ROBINS-E tool. Meta-analysis performed in R of these manuscripts found that not washing samples and a common set of 16S rRNA primers first published in Jensen et al. 2001 were associated with increased hemoplasma prevalence. To evaluate the influence of washing on newly collected fleas, we assessed the hemoplasma status of 20 pools of 5 C. felis each, half of which were washed and half not washed.ResultsFlea washing did not influence the detection of hemoplasma but instead amplified Spiroplasma. To assess non-specific amplification with the Jensen et al. 2001 primers, 67 C. felis samples (34% previously reported hemoplasma infected) were subject to PCR and sequencing. By this method, hemoplasma was detected in only 3% of samples. In the remaining “hemoplasma infected” fleas, PCR amplified Spiroplasma or other bacteria. ConclusionsTherefore, we concluded that hemoplasma infection in C. felis is rare, and future flea prevalence studies should sequence all positive amplicons to validate PCR specificity. Further investigation of alternative methods of feline-associated hemoplasma transmission and the ability of C. felis to maintain hemoplasma infection is necessary.Graphical

Predicting the prevalence of type 2 diabetes in Brazil: a modeling study.

We adopted a modeling approach to predict the likely future prevalence of type 2 diabetes, taking into account demographic changes and trends in obesity and smoking in Brazil. We then used the model to estimate the likely future impact of different policy scenarios, such as policies to reduce obesity. The IMPACT TYPE 2 DIABETES model uses a Markov approach to integrate population, obesity, and smoking trends to estimate future type 2 diabetes prevalence. We developed a model for the Brazilian population from 2006 to 2036. Data on the Brazilian population in relation to sex and age were collected from the Brazilian Institute of Geography and Statistics, and data on the prevalence of type 2 diabetes, obesity, and smoking were collected from the Surveillance of Risk and Protection Factors for Chronic Diseases by Telephone Survey (VIGITEL). The observed prevalence of type 2 diabetes among Brazilians aged over 25 years was 10.8% (5.2-14.3%) in 2006, increasing to 13.7% (6.9-18.4%) in 2020. Between 2006 and 2020, the observed prevalence in men increased from 11.0 to 19.1% and women from 10.6 to 21.3%. The model forecasts a dramatic rise in prevalence by 2036 (27.0% overall, 17.1% in men and 35.9% in women). However, if obesity prevalence declines by 1% per year from 2020 to 2036 (Scenario 1), the prevalence of diabetes decreases from 26.3 to 23.7, which represents approximately a 10.0% drop in 16 years. If obesity declined by 5% per year in 16 years as an optimistic target (Scenario 2), the prevalence of diabetes decreased from 26.3 to 21.2, representing a 19.4% drop in diabetes prevalence. The model predicts an increase in the prevalence of type 2 diabetes in Brazil. Even with ambitious targets to reduce obesity prevalence, type 2 diabetes in Brazil will continue to have a large impact on Brazilian public health.

Arima Model to Predict the Prevalence of Diabetes Type 1 and Type 2 Patients: A Case Study of Jos University Teaching Hospital

Diabetes Mellitus is a huge burden for human health, increasing number of patient is likely to result in rising demand for the medical emergencies. Due to limited number of hospitals with standard laboratory test kits to differentiate between type 1 and type 2 diabetes it is important to forecast the future incidences and prepare with proper resource planning. The monthly number of Diabetes patients obtained from Jos University Teaching Hospital is fitted by autoregressive integrated moving average (ARIMA) model. Dataset starting from January, 2010 to December,2020. Using ARIMA, several models were evaluated based on the Bayesian Information Criterion (BIC) and Ljung-Box Q statistics. ARIMA(3, 1, 1) is found to be better and used to describe and predict the future trends of Diabetes type 1 and ARIMA(1,1,1) is a better model to predict the future prevalence of diabetes type 2. Therefore, the proposed model will help in the appropriate planning and allocation of resources for emergencies.

Trends in prevalence, mortality, and risk factors of dementia among the oldest-old adults in the United States: the role of the obesity epidemic.

The oldest-old population, those aged ≥ 80years, is the fastest-growing group in the United States (US), grappling with an increasingly heavy burden of dementia. We aimed to dissect the trends in dementia prevalence, mortality, and risk factors, and predict future levels among this demographic. Leveraging data from the Global Burden of Disease Study 2019, we examined the trends in dementia prevalence, mortality, and risk factors (with a particular focus on body mass index, BMI) for US oldest-old adults. Through decomposition analysis, we identified key population-level contributors to these trends. Predictive modeling was employed to estimate future prevalence and mortality levels over the next decade. Between 1990 and 2019, the number of dementia cases and deaths among the oldest-old in the US increased by approximately 1.37 million and 60,000 respectively. The population growth and aging were highlighted as the primary drivers of this increase. High BMI emerged as a growing risk factor. Females showed a disproportionately higher dementia burden, characterized by a unique risk factor profile, including BMI. Predictions for 2030 anticipate nearly 4 million dementia cases and 160,000 related deaths, with a marked increase in prevalence and mortality anticipated among those aged 80-89. The past 30years have witnessed a notable rise in both the prevalence and mortality of dementia among the oldest-old in the US, accompanied by a significant shift in risk factors, with obesity taking a forefront position. Targeted age and sex-specific public health strategies that address obesity control are needed to mitigate the dementia burden effectively.

Study of Relationship between the Knowledge and Attitude of Tuberculosis (TB) Patients about Drug Compliance at the Health Centre

Tuberculosis (TB) is an infectious disease caused by the bacterium Mycobacterium tuberculosis, which most often attacks various organs, especially the lungs. This study aimed to determine the relationship between knowledge and TB patients' attitudes toward compliance with tuberculosis drugs at the Helvetia Medan Health Center. This type of research used an analytical observational method with the cross-sectional approach. The sampling technique used was quota sampling, with 75 respondents. Data collection in this study used to form sheets in questionnaires given to respondents directly and data analysis using the chi-square test. The results showed that respondents with good knowledge were 29 respondents (38.7%), good enough 33 respondents (44%), not good enough 12 respondents (16%), and not good one respondent (1.3%). A good attitude was 23 respondents (30.7%), good enough 37 respondents (49.3%), not good enough 14 respondents (18.7%), and not good one respondent (1.3%). Obedient compliance to TB medication was 25 respondents (33.3%), quite obedient 33 respondents (44%), and less obedient 17 respondents (22.7%). This study concluded that knowledge and attitude had a significant relationship with adherence to tuberculosis medication, with a knowledge p-value of 0.003 &lt;0.05 and an attitude p-value of 0.037 &lt;0.05. Healthcare professionals must employ innovative strategies to enhance knowledge and attitudes, raise public awareness, and ensure full compliance with the TB treatment program. It is crucial to implement this measure to mitigate the future prevalence of antibiotic resistance in treating tuberculosis.

Autosomal recessive non-syndromic hearing loss genes in Pakistan during the previous three decades.

Hearing loss is a clinically and genetically heterogeneous disorder, with over 148 genes and 170 loci associated with its pathogenesis. The spectrum and frequency of causal variants vary across different genetic ancestries and are more prevalent in populations that practice consanguineous marriages. Pakistan has a rich history of autosomal recessive gene discovery related to non-syndromic hearing loss. Since the first linkage analysis with a Pakistani family that led to the mapping of the DFNB1 locus on chromosome 13, 51 genes associated with this disorder have been identified in this population. Among these, 13 of the most prevalent genes, namely CDH23, CIB2, CLDN14, GJB2, HGF, MARVELD2, MYO7A, MYO15A, MSRB3, OTOF, SLC26A4, TMC1 and TMPRSS3, account for more than half of all cases of profound hearing loss, while the prevalence of other genes is less than 2% individually. In this review, we discuss the most common autosomal recessive non-syndromic hearing loss genes in Pakistani individuals as well as the genetic mapping and sequencing approaches used to discover them. Furthermore, we identified enriched gene ontology terms and common pathways involved in these 51 autosomal recessive non-syndromic hearing loss genes to gain a better understanding of the underlying mechanisms. Establishing a molecular understanding of the disorder may aid in reducing its future prevalence by enabling timely diagnostics and genetic counselling, leading to more effective clinical management and treatments of hearing loss.