PAKISTAN JOURNAL OF HEALTH SCIENCES

UNVEILING THE SILENT THREAT: AIR POLLUTION AND SMOG IN RESOURCE-LIMITED SETTINGS

2024-04-05T07:33:42+00:00

Air pollution and smog are pervasive threats that have serious impacts on public health around the world. This editorial delves the scope of this threat and an assessment of the problem, with a focus on resource-limited countries like Pakistan. Health professionals need to be aware of the seriousness of the problem in order to contribute effectively to prevention strategies.

A Complex mixture of particulate matter, nitrogen dioxide (NO2), sulphur dioxide (SO2), ozone (O3), and carbon monoxide (CO) encompasses this variant of pollution. Pollutant levels can be determined using measurement tools like air quality monitoring stations and satellite technology which can provide real time data but in resource scarce country like ours, cost effective monitoring is crucial.

Anthropogenic activities like industrial emissions, vehicular exhaust, and biomass burning are the deep roots of the causative phenomenon. A portmanteau of smoke and fog, the smog is the synergistic product of all these activities particularly in the densely populated regions. The intricate interplay of pollutants poses unique challenges for healthcare professionals in understanding their diverse health implications and making the prevention difficult.

Air pollution and smog have been linked to a myriad of health problems. The spectrum includes respiratory diseases, cardiovascular disorders, and adverse pregnancy outcomes. Children and the elderly are particularly vulnerable population in resource-limited countries like Pakistan. Its impact can be imagined from the fact that air pollution alone is thought to cut the life span of every Pakistani by four years. The icing on top is the strained healthcare infrastructures which has to deal with the burden of preventable diseases associated with air pollution at an alarming level.

Karachi, the hub of Pakistan for its industrial value and Lahore which is no less are both hotspots for this pollution. Peshawar is also included in the top ten most air polluted cities of the world. The smog experienced in this belt is a health challenge persistently contributing to the worsening of the status.
Implementing and enforcing stringent emission standards, promoting public awareness, and investing in green technologies are essential steps when we talk about the solutions for tackling this issue. The realization of this being a national cause, will bring the authoritative bodies to collaborate and brainstorm for the practical solutions and hence the brighter tomorrow.

Government bodies hold a strong ground when it comes to policy formulation and enforcement for social issues. Another stake holder which has to be highlighted is the industrial sector which have to agree to adapt eco-friendly pathways. The health sector would obviously go hands in hands for raising awareness regarding its health implications and preventative strategies. All of us together can ensure public participation for accepting the change and become the strongest link in reducing individual carbon foot prints.Remedial Measures to manage this menace can be stratified as:

1.Regulatory Measures: Stringent regulations on industrial emissions and vehicular standards.

2.Promotion of Green Technologies: Incentivizing and adopting renewable energy sources.

3.Public Awareness Campaigns: Educating the public on the health hazards of this silent killer and preventive measures.

4.Investment in Healthcare Infrastructure: Strengthening healthcare facilities to cope with the rising health burden particularly for the preventable causes.

The battle against air pollution and smog has to be a joint venture of all stakeholders. The role of advocacy, prevention and mitigation has to be played well by the front liner, the health care sectors. For a sustainable future, we need to review our priorities and focus to pave the way for our generations to follow

THE PREVALENCE OF PERIPHERAL DIABETIC NEUROPATHY IN PATIENTS WITH DIABETES MELLITUS

2024-04-05T07:33:43+00:00

Peripheral Diabetic Neuropathy (PDN) is a common complication of Diabetes Mellitus (DM), which can negatively impact a patient's quality of life. The study was aimed at the prevalence of PDN in patients with DM. This cross-sectional study was conducted in Bacha Khan Medical Complex Swabi, with a total of 246 patients having DM over a period of 6 months.

SPSS-20 was used for statistical interpretation. Among the total number of patients, 119 were male and 127 were female. 27 (47.4%) males and 30 (52.6%) females were diagnosed with PDN. The mean age was recorded as 46.43±16.13. It was observed that 77 (66.4%) patients belonged to the age group of 41 to 65 years of age.

The prevalence of PDN was found to be 47.2%. Age was found to be significant, while gender was insignificant. The results of this study indicate a relatively high prevalence of peripheral neuropathy in people with diabetes.

FREQUENCY OF TYPE 2 DIABETES MELLITUS IN PATIENTS WITH HEPATITIS C VIRUS INFECTION

2024-04-05T07:33:43+00:00

PURPOSE: The objective of this study was to determine the frequency of Type 2 Diabetes Mellitus (DM) in patients with Hepatitis C Virus (HCV) infection.
Patients and Methods: This cross-sectional study was conducted at the Bacha Khan Medical Complex in Swabi over a period of 6 months. A total of 300 patients with HCV PCR positive were selected via a non-probability convenient sampling technique. Patients were evaluated for type 2 DM using fasting blood sugar and HbA1c test. The patient's age ranged from 30 to 75 years of age. SPSS-20 was used for statistical analysis.
RESULTS: We found a 28% prevalence of type 2 DM in HCV patients. The mean age of the patients was 44.78±10.7. Among the total number of patients,37.7% were females and 62.7% were males. Patients were divided into two age groups: (1) 30 to 40 and (2) patients greater than 40 years of age. We found a higher number of HCV patients in age group 2, which was 178 (59.3%). There was no statistical significance of Type 2 DM with gender, while age was found to be significant.
CONCLUSION: We concluded that HCV patients are at a higher risk of having Type 2 DM, and increasing age is a major contributor to Type 2 DM in HCV patients.

EFFICACY OF ULTRASOUND IN DETECTING RENAL CALCULI KEEPING NON-ENHANCED COMPUTED TOMOGRAPHY AS A REFERENCE STANDARD

2024-04-05T07:33:43+00:00

Background: Renal calculi is an emergency condition and must be diagnosed to evaluate the presence and location of renal calculi as early as possible.
Objective: To evaluate the efficacy of ultrasound in detecting renal calculi keeping non-enhanced computed tomography as a reference standard.
Methodology: An analytical cross-sectional study was performed at Gurki Trust Hospital, Lahore. All patients with suspicion of renal calculi were included, while patients with polycystic disease, dialysis, ureteric stricture, and those who refused to give informed consent were excluded. Ultrasound was performed bilaterally, focusing on ureters. 16 slice Computer tomography scanner was used to perform computer tomography scan.
Results: Out of 121 patients, 46 (38%) females and 75 (62%) males were examined, on ultrasound, 74 (61.2%) patients had no evidence of renal calculi, while 47 (38.8%) were reported with presence of renal calculi, on contrary, CT scan was performed on same patients showing, 108 (89.3%) with renal calculi and 13 (10.7%) with no evidence of renal calculi. The transabdominal ultrasonography showed an accuracy 83.47%, sensitivity 74.6%, specificity 69.6%, PPV (positive predictive value) 97.8% and NPV (negative predictive value) 37.9%.
Conclusion: Efficacy of ultrasound is comparable to CT, therefore can be considered as an alternative method when CT scan is unavailable or cannot be performed, i.e.; pregnant female. Moreover, ultrasound is cost effective, easily available modality and without radiation exposure, hence making it more appropriate for both initial and follow-up evaluations.

PREVALENCE OF ANTIBIOTIC-ASSOCIATED DIARRHEA

2024-04-05T07:33:43+00:00

Antibiotic Associated Diarrhea is a common complication of antibiotics treatment. The aim of this study was to find the prevalence of Antibiotic Associated Diarrhea. During the study of 90 days duration, 134 patients admitted in Medical Ward BKMC (Bacha Khan Medical Complex Swabi) were randomly selected for the study. 102 (76.1%) were males and 32 (23.9%) were females. The mean age of the patients was 42.46 years and SD was 13.37.
A prevalence of 29.1% of AAD was found in the patients on the basis of clinical and laboratory findings Majority of the patients belonged to the age group of 45 years to 75 years. AAD is more prevalent in 45 to 75 years age group. AAD is statistically significant with age (p=0.0001) and not statistically significant with gender (p=0.139). Preventive policies must be put in place in all hospitals and healthcare units to prevent the development of AAD in patients receiving antibiotics.

ENHANCING HEART HEALTH: PERFORMANCE ANALYSIS & COMPARISON OF SUPERVISED MACHINE LEARNING ALGORITHMS FOR CARDIOVASCULAR DISEASE PREDICTION

2024-04-05T07:33:43+00:00

Heart disease, commonly known as cardiovascular disease, has become a leading cause of death globally. It includes numerous disorders that affect the heart and has been a major cause of death around the world in the previous few decades [1], about 26 million people effects every year. The prediction and prevention of heart failure is a challenge for cardiologists and cardio-surgeons.

The modern lifestyle, poor diet, lack of exercise, and high stress and depression level also increase the rate of cardiovascular disease. Early detection of cardiovascular disease signs and consistent medical monitoring can help decrease in the number of patients and mortality, but however, with limited medical resources and specialist consultants, it is difficult to continuously observe the patient and provide consultation.

The healthcare industry holds a substantial amount of data, making machine learning algorithms essential for accurately predicting heart diseases and facilitating informed decision- making. Recent studies have explored the integration of these approaches to create hybrid machine learning algorithms. In this research, some of the data pre-processing techniques, such as eliminating noisy data, eliminating missing data, filling in default values where applicable, and splitting attributes into categories for predictions and decision making across different levels, this project suggests the development of a predictive model to determine the likelihood of individuals having a heart disease, aiming to offer both awareness and diagnostic insights.

The accuracy of several techniques, such as Support Vector Machine, Logistic Regression, Random Forest Classifier, Naive Bayes Classifier, and K-Nearest Neighbour, are compared in order to achieve this goal. The goal of this comparison is to determine which model predicts cardiovascular disease the most accurately.