Ragweed Pollen Concentration Predict Seasonal Oculo-rhinitis and Asthma Severity in Subjects Allergic to Ragweed.

Objectives: 1. To investigate the correlation between ragweed pollen concentration and conjunctival, nasal and asthma symptoms severity in patients allergic to ragweed using ambient pollen exposure in the Milan area during the 2014 ragweed season; 2. to calculate the pollen / symptom thresholds and 3. to assess the effectiveness of ragweed Allergen Immuno Therapy (AIT).Patients: 66 subjects allergic to Amb a 1 enrolled in the study and were divided into two cohorts: AIT treated (24) and non-AIT treated (42).Measurements: Pollen counts and daily symptom/medication patient diaries. Autoregressive Distributed Lag Models were used to develop predictive models of daily symptoms and to evaluate the short-term effects of temporal variations in pollen concentration on the onset of symptoms. Results: We found significant correlations between ragweed pollen load and the intensity of symptoms, for all three symptom categories respectively, both in non-AIT treated (𝛕= 0.341, 0.352, 0.721 and ρ = 0.48, 0.432, 0.881, p-value < 0.001) and in AIT treated patients (O= 0.46, 0.610, 0.66 and ρ = 0.692, 0.805, 0.824; p-value < 0.001). In both cohorts, we observed a positive correlation between the number of symptoms reported and drug use. Mean symptom levels were significantly greater in non-AIT treated than in AIT treated patients (p < 0.001) for all symptom categories. Pollen concentration thresholds for three symptom severity levels were calculated.Conclusions: Ragweed pollen concentration is predictive of symptom severity in ragweed (Amb a 1) allergy patients. AIT treated patients had significantly reduced mean symptom levels compared to non-AIT patients.

Short-Term Cumulative Exposure to Ambient Traffic-Related Black Carbon and Blood Pressure: MMDA Traffic Enforcers’ Health Study

Exposure to traffic-related air pollution is linked with acute alterations in blood pressure (BP). We examined the cumulative short-term effect of black carbon (BC) exposure on systolic (SBP) and diastolic (DBP) BP and assessed effect modification by participant characteristics. SBP and DBP were repeatedly measured on 152 traffic enforcers. Using a linear mixed-effects model with random intercepts, quadratic (QCDL) and cubic (CCDL) constrained distributed lag models were fitted to estimate the cumulative effect of BC concentration on SBP and DBP during the 10 hours (daily exposure) and 7 days (weekly exposure) before the BP measurement. Ambient BC was related to increased BP with QCDL models. An interquartile range change in BC cumulative during the 7 days before the BP measurement was associated with increased BP (1.2% change in mean SBP, 95% confidence interval (CI), 0.1 to 2.3; and 0.5% change in mean DBP, 95% CI, −0.8 to 1.7). Moreover, the association between the 10-h cumulative BC exposure and SBP was stronger for female (4.0% change, 95% CI: 2.1–5.9) versus male and for obese (2.9% change, 95% CI: 1.0–4.8) vs. non-obese traffic enforcers. Short-term cumulative exposure to ambient traffic-related BC could bring about cardiovascular diseases through mechanisms involving increased BP.

Modelling Covid-19 Pandemic in Nigeria using Multivariate Autoregressive Distributed Lag-Moving Average Models

The aim of this paper was to study the trend of COVID-19 cases and fit appropriate multivariate time series models as research to complement the clinical and non-clinical measures against the menace. The cases of COVID-19, as reported by the National Centre for Disease Control (NCDC) on a daily and weekly basis, include Total Cases (TC), New Cases (NC), Active Cases (AC), Discharged Cases (DC) and Total Deaths (TD). The three waves of the COVID-19 pandemic are graphically represented in the various time plots, indicating the peaks as (June–August, 2020), (December–February, 2021), and (July–September, 2021). Multivariate Autoregressive Distributed Lag Models (MARDLM) and Multivariate Autoregressive Distributed Lag Moving Average (MARDL-MA) models have been found to be suitable for fitting different categories of the COVID-19 pandemic in Nigeria. The graphical representation and estimates have shown a gradual decline in the reported cases after the peak in September 2021. So far, the introduction of vaccines and non-pharmaceutical measures by relevant organisations are yielding plausible results, as evident in the recent decrease in New Cases, Active Cases and an increasing number of Discharged Cases, with fewer deaths. This paper advocates consistency in all clinical and non-clinical measures as a way towards the extinction of the dreaded COVID-19 pandemic in Nigeria and the world.

Short-Term Cumulative Exposure to Ambient Traffic-Related Black Carbon and Blood Pressure: MMDA Traffic Enforcers’ Health Study

Exposure to traffic-related air pollution is linked with acute alterations in blood pressure (BP). We examined the cumulative short-term effect of black carbon (BC) exposure on systolic (SBP) and diastolic (DBP) BP and assessed effect modification by participant characteristics. SBP and DBP were repeatedly measured on 152 traffic enforcers. Using a linear mixed-effects model with random intercepts, quadratic (QCDL) and cubic (CCDL) constrained distributed lag models were fitted to estimate the cumulative effect of BC concentration on SBP and DBP during the 10-hours (daily exposure) and 7-days (weekly exposure) before the BP measurement. Ambient BC was related to increased BP with QCDL models. An interquartile range change in BC cumulative during the 7-days before the BP measurement was associated with increased BP [1.2% change in mean SBP, 95% confidence interval (CI), 0.1 to 2.3; and 0.5% change in mean DBP, 95% CI, &ndash;0.8 to 1.7]. Moreover, the association between the 10-hours cumulative BC exposure and SBP was stronger for females (4.0% change, 95% CI: 2.1&ndash;5.9) versus males, and for obese (2.9% change, 95% CI: 1.0&ndash;4.8) vs. non-obese traffic enforcers. Short-term cumulative exposure to ambient traffic-related BC could bring about cardiovascular diseases through mechanisms involving increased BP.

Associations between ambient air pollutants and hospital admissions: more needs to be done

Deaths from air pollution in the UK are higher by a factor of 10 than from car crashes, 7 for drug-related deaths and 52 for murders, and yet awareness seems to be lacking in local government. We conducted an 18-year retrospective cohort study using routinely collected health care records from Ninewells Hospital, Dundee, and Perth Royal Infirmary, in Tayside, Scotland, UK, from 2000 to 2017. Hospitalisation events and deaths were linked to daily nitric oxides (NOX, NO, NO2), and particulate matter 10 (PM10) levels extracted from publicly available data over this same time period. Distributed lag models were used to estimate risk ratios for hospitalisation and mortality, adjusting for temperature, humidity, day of the week, month and public holiday. Nitric oxides and PM10 were associated with an increased risk of all hospital admissions and cardiovascular (CV) admissions on day of exposure to pollutant. This study shows a significant increase in all cause and CV hospital admissions, on high pollution days in Tayside, Scotland.

Structural Change, Balance of Payments Constraint, and Economic Growth: Evidence from Vietnam

The impact of demand-side factors and rapid structural changes have largely been ignored in explaining the economic growth of Vietnam. This paper employs the multisectoral balance-of-payments constrained economic growth model to capture the influence of structural changes on the exports and economic growth of Vietnam over the period 1997–2016. Based on the estimates for the sectoral income elasticities of demand for exports and imports obtained from autoregressive distributed lag models, this paper argues that it is not relative prices but income that has played a significant role in Vietnam’s economic growth, the income elasticities of demand for exports have grown faster than those of demand for imports, and the weight of exports has significantly moved from primary to high-technology products. JEL Classification: E12, F43, O53

The Effect of High Temperatures on Risk of Hospitalization in Northern Vietnam

The evidence on the temperature and morbidity relationship is limited, especially from tropical regions including Vietnam. This study’s objective was to examine the high temperature-hospitalisation relationship in northern Vietnam. To assess ambient temperature hospitalizations associations in seven provinces of northern Vietnam Generalized Linear and Distributed Lag Models were used. Overall risk for all causes, and infectious, cardiovascular, and respiratory admissions in study provinces was estimated using a random-effects meta-analysis. The pooled estimates showed a significant effect of high temperature on same day (Lag 0) hospitalizations. A 1°C increase in temperature was significantly associated with 1.1% (95% Confidence Interval- CI, 0.9–1.4%) increase in risk for all-cause, 2.4% (95% CI, 1.9–2.9%) increase in risk for infectious, 1.3% (95% CI, 0.9–1.6%) increase in risk for respiratory, and 0.5% (95% CI, 0.1–0.9%) increase in risk for cardiovascular disease admissions. However, the province specific temperature-hospitalisation effect was variable and mostly inconsistent for cardiovascular diseases. Our research in northern Vietnam adds to the evidence of high temperatures associated with hospitalisations in a sub-tropical climate. Our findings have important implications for promoting appropriate adaptation strategies to reduce climate change associated health impacts in similar settings.

Expectations and exchange rates in a Keynes–Harvey model: an analysis of the Brazilian case from 2002 to 2017

This paper investigates the statistical relationship between the future expectations of the exchange rate and GDP growth and the current nominal exchange rate in Brazil during the period 2002–2017. The theoretical framework on which the paper is based is a decision-making model grounded in Keynes (1921; 1936) and Harvey (2006; 2009a), from which the paper's empirical model emerges. This model is tested empirically with autoregressive distributed lag models to identify short- and long-term statistical relationships in time series. The empirical estimations suggest that expectations of future changes in both the exchange rate and GDP growth have a statistically significant relationship with the current nominal exchange rate in Brazil, just as the Keynes–Harvey model predicts.

Effect of Public Debt on Public Investment in Nigeria: 1985-2018

This study examined the effect of Nigeria public debt on public investment from 1985-2018. Data for the analysis was obtained from Central Bank of Nigeria Statistical bulletin and the study chooses Nigeria as its sample. ARDL Auto-regressive Distributed lag models was used to test the effect of the independent variables (Public Debt, Budget Deficit, Debt Servicing, Public Debt to GDP Ratio) on the dependent variable (Public Investment). The cointegration test found the existence of long-run relationship among the investigated variables. The short run result shows that public debt has insignificant effect on public investment in Nigeria. The study therefore recommends among others that Federal government should be fiscal responsible by channeling borrowed funds to investments that will bring growth in the economy. Government should tackle waste and corruption by making sure that funds borrowed and allocated for investment should be transparently and judiciously utilized in the provision of infrastructure. Debts should be taken only when necessary and should be for investment and not for payment of salaries.