BackgroundIn the United States, more than 600 municipalities have smoke-free parks, and more than 100 have smoke-free beaches. Nevertheless, adoption of outdoor smoke-free policies has been slow in certain regions. Critical to widespread adoption is the sharing of knowledge about the policy development and implementation process. In this article, we describe our experience in making City of Philadelphia recreation centers and playgrounds smoke-free.Community ContextOf the 10 largest US cities, Philadelphia has among the highest rates of adult and youth smoking. Our objectives for an outdoor smoke-free policy included protecting against secondhand smoke, supporting a normative message that smoking is harmful, motivating smokers to quit, and mitigating tobacco-related sanitation costs.MethodsThe Philadelphia Department of Public Health and the Department of Parks and Recreation engaged civic leaders, agency staff, and community stakeholders in the following steps: 1) making the policy case, 2) vetting policy options and engaging stakeholders, and 3) implementing policy. Near-term policy impacts were assessed through available data sources.OutcomeMore than 220 recreation centers, playgrounds, and outdoor pools became smoke-free through a combined mayoral executive order and agency regulation. Support for the policy was high. Estimates suggest a policy reach of 3.6 million annual visitors and almost 850 acres of new smoke-free municipal property.InterpretationLocalities can successfully implement outdoor smoke-free policies with careful planning and execution. Such policies hold great potential for reducing exposure to secondhand smoke, promoting nonsmoking norms, and providing additional motivation for residents to quit smoking.
Background: SARS-CoV-2 principally invades the respiratory system. ACE receptor are also abundant throughout the hepatobiliary system and their increased expression on hepatocyte make patients with NAFLD more vulnerable. Aim: To see outcomes of COVID positive diabetic patients suffering from Nonalcoholic fatty liver disease (NAFLD). Study design: Cross Sectional Study. Methodology: 150 diabetic and COVID PCR positive were recruited from COVID ward of Services Hospital in Lahore. Clinical parameters like BMI, SpO2, Hepatomegaly and lab parameters like HbA1C, AST ALT were noted in spreadsheet. Statistical analysis was done using SPSS v.25. Statistical significance for difference in proportions is calculated using Pearson’s Chi-Squared test. P less than 0.05 was considered statistically significant. Results: Around 84(56%) were males and 66(44%) females, smoked were 27(18%), mean age (years) was 59.7333 ±11.35023, mean BMI (kg/m²) was 30.1425±7.30673, 87(58%) patients had NAFLD, who experienced sever disease (53.2%; x^2=0.010) and more mortalities (60.2%;x^2=0.453) as compared to those who do not had condition. Conclusion: We concluded that NAFLD makes COVID-19 infected patients more fragile. Such patients experienced sever disease and more mortalities however need of mechanical ventilation remains almost equal between those who has NAFLD and those who didn’t had. Keywords: Nonalcoholic fatty liver disease, COVID-19, Diabetes, Mortality and Severity.
The coronavirus has a high basic reproduction number (R0) and has caused the global COVID-19 pandemic. Governments are implementing lockdowns that are leading to economic fallout in many countries. Policy makers can take better decisions if provided with the indicators connected with the disease spread. This study is aimed to cluster the countries using social, economic, health and environmental related metrics affecting the disease spread so as to implement the policies to control the widespread of disease. Thus, countries with similar factors can take proactive steps to fight against the pandemic. The data is acquired for 79 countries and 18 different feature variables (the factors that are associated with COVID-19 spread) are selected. Pearson Product Moment Correlation Analysis is performed between all the feature variables with cumulative death cases and cumulative confirmed cases individually to get an insight of relation of these factors with the spread of COVID-19. Unsupervised k-means algorithm is used and the feature set includes economic, environmental indicators and disease prevalence along with COVID-19 variables. The learning model is able to group the countries into 4 clusters on the basis of relation with all 18 feature variables. We also present an analysis of correlation between the selected feature variables, and COVID-19 confirmed cases and deaths. Prevalence of underlying diseases shows strong correlation with COVID-19 whereas environmental health indicators are weakly correlated with COVID-19.
The diabetic patients are more likely to experience depression. The study's objective was to determine the depression prevalence in newly diagnosed patients with type-I (T1DM) and type-II (T2DM) diabetes, as well as the relationship between these symptoms and several independent clinical and sociodemographic variables. Place and Duration: Study took place in the Department of Endocrinology and metabolism in Services Hospital Lahore, Pakistan for six months of duration from January 2017 to June 2017. Methods: A total of 320 patients included in the study, including two control groups of 180 people without diabetes and 75 patients in each group with T1DM and T2DM. The clinical, biochemical and sociodemographic characteristics of the patients were assessed and Beck Depression Inventory (BDI) was used to assess the symptoms of depression among patients. The association between the scores of BDI and independent factors such age, sex, mean arterial pressure, diabetes duration, BMI, diabetic complications and HbA1c level were assessed in the logistic regression analysis. Results: Compared to controls, females and males with T1DM and T2DM had significantly higher mean BDI scores. Females had depression symptoms more frequently than males diagnosed with diabetes mellitus. Patients with T1DM reported depressive symptoms among 20% of females and 5.7% of males, whereas those with T2DM have depression among 40.6% of women and 23.2% of men. The depression increased with age in patients with T1DM and T2DM, their HbA1c level rises, and they experience more complications. Women with T2DM also have a three-fold high risk of developing depressive symptoms than males. Conclusion: In conclusion, diabetic patients are more likely to experience depression in comparison to non-diabetics. 13.3% of T1DM patients and 30.7% of the T2DM patients having depressive symptoms. HbA1c level, age and the occurrence of comorbidities all raise the risk of depression in both T1DM and T2DM, and in T2DM depression is gender -specific. Keywords: Depressive symptoms, Beck Depression Inventory, type I and type II diabetes mellitus.
Background: Unexpectedly and unfortunately the end of the year 2019 has proved to be the start of a deadliest era of Coronavirus disease 19. Spread of this lethal disease has remained uninhibited so far. How rapidly it has wrapped up the whole world is dangerously alarming. Aim: To determine frequency of Covid outcome in Covid patients with preexisting different co-morbid conditions. Methods: This descriptive study was conducted from July 2020 to January 2021 in two tertiary care hospital i.e. Services hospital, Lahore (Punjab) and hospital, Quetta (Baluchistan). After ethical approval and informed consent from the patients, data from PCR positive patients was recorded. The demographic parameters, travel or exposure history, duration of stay in the hospital and co morbidities including diabetes, hypertension, stroke and ischemic heart diseases of the patients were collected. Results: In our study, total 124 patients including 84(67.7%) male and 40(32.3%) female. The mean ages was 41.29±20.21 years, mean weight and height 83.46±15.1, 174.2±8.31. 82%patients were discharged, and 42%patients suffered death. Among the patients included in this study, 51(%) patients presented with diabetes, 55(%) patients presented with hypertension, 52% had ischemic heart diseases and 1.6% had stroke. Conclusion: The conclusion of this study, there is a significant impact of pre-existing co-morbidities on Covid outcomes. Thus, it can be inferred that by modifying the comorbidities, positive outcome can be observed Keywords: COVID-19, Risk factors, Outcome, Mortality
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