Daily Death Counts Research Articles

An assessment of excess mortality during the COVID-19 pandemic, a retrospective post-mortem surveillance in 12 districts – Zambia, 2020–2022

BackgroundThe number of COVID-19 deaths reported in Zambia (N = 4069) is most likely an underestimate due to limited testing, incomplete death registration and inability to account for indirect deaths due to socioeconomic disruption during the pandemic. We sought to assess excess mortality during the COVID-19 pandemic in Zambia.MethodsWe conducted a retrospective analysis of monthly-death-counts (2017–2022) and individual-daily-deaths (2020–2022) of all reported health facility and community deaths at district referral health facility mortuaries in 12 districts in Zambia. We defined COVID-19 wave periods based on a sustained nationally reported SARS-CoV-2 test positivity of greater than 5%. Excess mortality was calculated as the difference between observed monthly death counts during the pandemic (2020–2022) and the median monthly death counts from the pre-pandemic period (2017–2019), which served as the expected number of deaths. This calculation was conducted using a Microsoft Excel-based tool. We compared median daily death counts, median age at death, and the proportion of deaths by place of death (health facility vs. community) by wave period using the Mann-Whitney-U test and chi-square test respectively in R.ResultsA total of 112,768 deaths were reported in the 12 districts between 2020 and 2022, of which 17,111 (15.2%) were excess. Wave periods had higher median daily death counts than non-wave periods (median [IQR], 107 [95–126] versus 96 [85–107], p < 0.001). The median age at death during wave periods was older than non-wave periods (44.0 [25.0–67.0] versus 41.0 [22.0–63.0] years, p < 0.001). Approximately half of all reported deaths occurred in the community, with an even greater proportion during wave periods (50.6% versus 53.1%, p < 0.001), respectively.ConclusionThere was excess mortality during the COVID-19 pandemic in Zambia, with more deaths occurring within the community during wave periods. This analysis suggests more COVID-19 deaths likely occurred in Zambia than suggested by officially reported numbers. Mortality surveillance can provide important information to monitor population health and inform public health programming during pandemics.

Spatio-temporal analysis of COVID-19 lockdown effect to survive in the US counties using ANN

This study aims to quantify the effectiveness of lockdown as a non-pharmacological solution for managing the COVID-19 pandemic. Daily COVID-19 death counts were collected for four states: California, Georgia, New Jersey, and South Carolina. The effectiveness of the lockdown was studied and the number of people saved during 7 days was evaluated. Five neural network models (MLP, FFNN, CFNN, ENN, and NARX) were implemented, and the results indicate that FFNN is the best prediction model. Based on this model, the total number of survivors over a 7-day period is 211, 270, 989, and 60 in California, Georgia, New Jersey, and South Carolina, respectively. The coefficients and weights of the FFNN for each state differ due to various factors, including socio-demographic conditions and the behavior of citizens towards lockdown laws. New Jersey and South Carolina have the most lockdowns and the least.

Associations between short-term exposure to airborne carbonaceous particles and mortality: A time-series study in London during 2010–2019

Exposure to ambient particulate matter (PM) has been identified as a major global health concern; however, the importance of specific chemical PM components remains uncertain. Recent studies have suggested that carbonaceous aerosols are important detrimental components of the particle mixture. Using time-series methods, we investigated associations between short-term exposure to carbonaceous particles and mortality in London, UK. Daily counts of non-accidental, respiratory, and cardiovascular deaths were obtained between 2010 and 2019. For the same period, daily concentrations of carbonaceous particles: organic (OC), elemental (EC), wood-burning (WC), total carbon (TC) and equivalent black carbon (eBC) were sourced from two centrally located monitoring sites (one urban-traffic and one urban-background). Generalized additive models were used to estimate the percentage change in mortality risk associated with interquartile range increases in particulate concentrations. Lagged effects up to 3 days were examined. Stratified analyses were conducted by age, sex, and season, separate analyses were also performed by site-type. For non-accidental mortality, positive associations were observed for all particle species at lag1, including statistically significant percentage risk changes in WC (0.51% (95%CI: 0.19%, 0.82%) per IQR (0.68 μg/m3)) and OC (0.45% (95%CI: 0.04%, 0.87% per IQR (2.36 μg/m3)). For respiratory deaths, associations were greatest for particulate concentrations averaged over the current and previous 3 days, with increases in risk of 1.70% (95%CI: 0.64%, 2.77%) for WC and 1.31% (95%CI: −0.08%, 2.71%) for OC. No associations were found with cardiovascular mortality. Results were robust to adjustment for particle mass concentrations. Stratified analyses suggested particulate effects were greatest in the summer and respiratory associations more pronounced in females. Our findings are supportive of an association between carbonaceous particles and non-accidental and respiratory mortality. The strongest evidence of an effect was for WC; this is of significance given the rising popularity of wood-burning for residential space heating and energy production across Europe.

The impacts of extreme heat events on non-accidental, cardiovascular, and respiratory mortality: An analysis of 12 Canadian cities from 2000 to 2020.

Extreme heat has significant impacts on mortality. In Canada, past research has analyzed the degree to which non-accidental mortality increases during single extreme heat events; however, few studies have considered multiple causes of death and the impacts of extreme heat events on mortality over longer time periods. Daily death counts attributable to non-accidental, cardiovascular, and respiratory causes were retrieved for the 12 most populous cities in Canada from 2000 to 2020. Generalized additive models were applied to quantify daily mortality risks for people aged younger than 65 years and for those aged 65 years and older in each city and for each cause of death. Model results were used to calculate the change in mortality risks and the number of excess deaths attributable to extreme heat during extreme heat events. Elevated mortality risks were observed during extreme heat events in most cities for non-accidental and respiratory causes. The impacts of extreme heat on non-accidental mortality were typically greater for people aged 65 and older than for those aged younger than 65. Significantly higher non-accidental mortality risks were observed during extreme heat events for people aged 65 and older in Montréal, the city of Québec, Surrey, and Toronto. For cardiovascular and respiratory causes, people aged 65 and older had significantly higher mortality risks during extreme heat events in Montréal, and both Montréal and Toronto, respectively. In the 12 cities, approximately 670 excess non-accidental deaths, 115 excess cardiovascular deaths, and 115 excess respiratory deaths were attributable to extreme heat events during the study period. Mortality risks during extreme heat events were generally higher in cities with larger proportions of renter households and fewer extreme heat events. This study estimates the longer-term impacts of extreme heat events on three mortality outcomes in a set of large Canadian cities. As climate change causes more frequent and intense extreme heat events, and as policy makers aim to reduce the health impacts of heat, it is important to understand how and where extreme heat affects health.

Mortality burden attributable to exceptional PM2.5 air pollution events in Australian cities: A health impact assessment

BackgroundPeople living in Australian cities face increased mortality risks from exposure to extreme air pollution events due to bushfires and dust storms. However, the burden of mortality attributable to exceptional PM2.5 levels has not been well characterised. We assessed the burden of mortality due to PM2.5 pollution events in Australian capital cities between 2001 and 2020. MethodsFor this health impact assessment, we obtained data on daily counts of deaths for all non-accidental causes and ages from the Australian National Vital Statistics Register. Daily concentrations of PM2.5 were estimated at a 5 km grid cell, using a Random Forest statistical model of data from air pollution monitoring sites combined with a range of satellite and land use-related data. We calculated the exceptional PM2.5 levels for each extreme pollution exposure day using the deviation from a seasonal and trend loess decomposition model. The burden of mortality was examined using a relative risk concentration-response function suggested in the literature. FindingsOver the 20-year study period, we estimated 1454 (95 % CI 987, 1920) deaths in the major Australian cities attributable to exceptional PM2.5 exposure levels. The mortality burden due to PM2.5 exposure on extreme pollution days was considerable. Variations were observed across Australia. Despite relatively low daily PM2.5 levels compared to global averages, all Australian cities have extreme pollution exposure days, with PM2.5 concentrations exceeding the World Health Organisation Air Quality Guideline standard for 24-h exposure. Our analysis results indicate that nearly one-third of deaths from extreme air pollution exposure can be prevented with a 5 % reduction in PM2.5 levels on days with exceptional pollution. InterpretationExposure to exceptional PM2.5 events was associated with an increased mortality burden in Australia's cities. Policies and coordinated action are needed to manage the health risks of extreme air pollution events due to bushfires and dust storms under climate change.

The impact of fine particulate matter on chronic obstructive pulmonary disease deaths in Pudong New Area, Shanghai, during a long period of air quality improvement

Chronic obstructive pulmonary disease (COPD) deaths attributed to fine particulate matter (with an aerodynamic equivalent diameter <2.5 μm, PM2.5) exposure are a common global public health concern. Recent improvements in air quality and the corresponding health benefits have received much attention. Thus, we have explored the trends of PM2.5 pollution improvement on COPD deaths during an important period of air pollution control (2008–2017) in Pudong New Area, Shanghai, China. Data, including daily COPD death counts, meteorological variables, and ambient air pollutants, were collected from 2008 to 2017. Generalized additive models were fitted to evaluate the percent change (%) in pollution-related COPD deaths. The results showed that the number of days with daily PM2.5 concentrations <35 μg/m3 increased from 19 days (5.19%) in 2008 to 166 days (45.48%) in 2017, and PM2.5 concentrations >75 μg/m3 decreased from 222 days (60.66%) in 2008 to 33 days (9.04%) in 2017. The associations in the overall period between 2008 and 2017 was significant. In subperiod analysis, each 10 μg/m3 increment in PM2.5 was associated with a percent change (%) of 0.89 (95% confidence interval [CI], 0.37, 1.42) at lag 5 and 0.78 (95% CI, 0.26, 1.30) at lag 6 during 2008–2013. Significant results were also found at lag 0–5 [percent change (%), 1.12 (95% CI, 0.09, 2.17)], lag 0–6 [percent change (%), 1.52 (95% CI, 0.43, 2.62)] and lag 0–7 [percent change (%), 1.72 (95% CI, 0.57, 2.88)] during 2008–2013. By contrast, no significant association was found between 2014 and 2017. In conclusion, the decreased COPD deaths associated with PM2.5 exposure were found, especially after the air quality improvement turning point in 2014.

The effects of diurnal temperature range on mortality and emergency department presentations in Victoria state of Australia: A time-series analysis

State of Victoria, Australia (SVA) has a wide variation of diurnal temperatures (DTR). DTR has been reported to be associated with risk of mortality and morbidity. We examined the association between exposure to DTR and risk of all-cause mortality and emergency department (ED) presentations in the SVA. We obtained data on daily counts of deaths and ED presentations, and weather data from 1 st January 2000─2019. We applied a quasi-Poisson time-series regression analysis to examine the association between daily DTR exposures and risk of mortality and ED presentations. The analyses were queried by age, sex, seasons, ED presentations triages, and departure status. Risk of mortality and ED presentation increased by 0.33% (95% CI: 0.24%–0.43%), and 0.094% (95% CI: 0.077%–0.11%) in relation to one degree increase in the daily DTR. The association between DTR and ED presentations was stronger in children (0–15 years) (0.38% [95% CI: 0.34%–0.42%]) and the elderly (75+ years) (0.34% [95% CI: 0.29%–0.39%]). Resuscitation, which was consistently accounted for the highest vulnerability to DTR variation, increased by 0.79% (95% CI: 0.60%–0.99%). This study suggests that the risk of mortality and ED presentations associates with the increase of DTR. Children, the elderly, and their caregivers need to be made aware of the health risk posed by DTR.

Short-term exposure to ultrafine particles and mortality and hospital admissions due to respiratory and cardiovascular diseases in Copenhagen, Denmark

Ultrafine particles (UFP; particulate matter <0.1 μm in diameter) may be more harmful to human health than larger particles, but epidemiological evidence on their health effects is still limited. In this study, we examined the association between short-term exposure to UFP and mortality and hospital admissions in Copenhagen, Denmark. Daily concentrations of UFP (measured as particle number concentration in a size range 11–700 nm) and meteorological variables were monitored at an urban background station in central Copenhagen during 2002–2018. Daily counts of deaths from all non-accidental causes, as well as deaths and hospital admissions from cardiovascular and respiratory diseases were obtained from Danish registers. Mortality and hospital admissions associated with an interquartile range (IQR) increase in UFP exposure on a concurrent day and up to six preceding days prior to the death or admission were examined in a case-crossover study design. Odds ratios (OR) with 95% confidence intervals (CI) per one IQR increase in UFP were estimated after adjusting for temperature and relative humidity. We observed 140,079 deaths in total, 236,003 respiratory and 342,074 cardiovascular hospital admissions between 2002 and 2018. Hospital admissions due to respiratory and cardiovascular diseases were significantly positively associated with one IQR increase in UFP (OR: 1.04 [95% CI: 1.01, 1.07], lag 0–4, and 1.02 [1.00, 1.04], lag 0–1, respectively). Among the specific causes, the strongest associations were found for chronic obstructive pulmonary disease (COPD) mortality and asthma hospital admissions and two-day means (lag 0–1) of UFP (OR: 1.13 [1.01, 1.26] and 1.08 [1.00, 1.16], respectively, per one IQR increase in UFP). Based on 17 years of UFP monitoring data, we present novel findings showing that short-term exposure to UFP can trigger respiratory and cardiovascular diseases mortality and morbidity in Copenhagen, Denmark. The strongest associations with UFP were observed with COPD mortality and asthma hospital admissions.

Impact of heat waves and cold spells on mortality in cities located in the Russian Arctic macroregion

BACKGROUND: Climate warming in Russia is most pronounced in the Arctic, where it is accompanied by an increase in the frequency and duration of heat waves. This emerging risk factor for the health of the population in major cities has not yet received adequate scientific attention. To effectively plan for adaptation to these phenomena, it is crucial to understand their specific impacts in regions with different climate types. Therefore, comprehensive analysis of the associations between climatic factors and health is needed for informed decision-making and strategic adaptation planning.&#x0D; AIM: To assess the impact of heat waves and cold spells on excess age-specific mortality within the urban populations of the Arctic macroregion. Furthermore, we analyzed the potential differences in the studied outcomes across climate types, ranging from marine to continental.&#x0D; METHODS: We analyzed the daily counts of deaths in Arkhangelsk, Magadan, Murmansk, and Yakutsk from 1999 through 2019. Poisson generalized linear regression models were employed to determine the relative mortality risks during heat waves and cold spells. All models accounted for potential lagged effects, seasonal and weekly patterns, and long-term mortality trends. Pearsons chi-squared tests were used to study the differences between the effects of heat and cold, as well as site-specific and age-specific variations in mortality.&#x0D; RESULTS: Effects of the studied climatic phenomena were more pronounced in continental than in marine climate. The age group 65+ years was more susceptible to the effects of extreme heat and cold than the middle-age group. Cold spells had a greater impact on the health of the residents of Murmansk, Arkhangelsk, and Magadan than heat waves, while the opposite was observed in Yakutsk. Cerebrovascular mortality during heath waves was 1.69 (95% CI: 1.342.13) times as high as during periods with normal temperature in the age-group 65+ in Yakutsk while cold spells were associated with 1.54 (95% CI: 1.182.01) greater risk of death from respiratory causes in the same age-group in Arkhangelsk.&#x0D; CONCLUSION: Our findings can be utilized by public health authorities to effectively prevent further fatalities among subarctic populations during heatwaves and cold spells.

The balanced discrete Burr–Hatke model and mixing INAR(1) process: properties, estimation, forecasting and COVID-19 applications

The main concern of this paper is providing a flexible discrete model that captures every kind of dispersion (equi-, over- and under-dispersion). Based on the balanced discretization method, a new discrete version of Burr–Hatke distribution is introduced with the partial moment-preserving property. Some statistical properties of the new distribution are introduced, and the applicability of proposed model is evaluated by considering counting series. A new integer-valued autoregressive (INAR) process based on the mixing Pegram and binomial thinning operators with discrete Burr–Hatke innovations is introduced, which can model contagious data properly. The different estimation approaches of parameters of the new process are provided and compared through the Monte Carlo simulation scheme. The performance of the proposed process is evaluated by four data sets of the daily death counts of the COVID-19 in Austria, Switzerland, Nigeria and Slovenia in comparison with some competitor INAR(1) models, along with the Pearson residual analysis of the assessing model. The goodness of fit measures affirm the adequacy of the proposed process in modeling all COVID-19 data sets. The fundamental prediction procedures are considered for new process by classic, modified Sieve bootstrap and Bayesian forecasting methods for all COVID-19 data sets, which is concluded that the Bayesian forecasting approach provides more reliable results.

Heat-related cardiorespiratory mortality: Effect modification by air pollution across 482 cities from 24 countries

BackgroundEvidence on the potential interactive effects of heat and ambient air pollution on cause-specific mortality is inconclusive and limited to selected locations. ObjectivesWe investigated the effects of heat on cardiovascular and respiratory mortality and its modification by air pollution during summer months (six consecutive hottest months) in 482 locations across 24 countries. MethodsLocation-specific daily death counts and exposure data (e.g., particulate matter with diameters ≤ 2.5 µm [PM2.5]) were obtained from 2000 to 2018. We used location-specific confounder-adjusted Quasi-Poisson regression with a tensor product between air temperature and the air pollutant. We extracted heat effects at low, medium, and high levels of pollutants, defined as the 5th, 50th, and 95th percentile of the location-specific pollutant concentrations. Country-specific and overall estimates were derived using a random-effects multilevel meta-analytical model. ResultsHeat was associated with increased cardiorespiratory mortality. Moreover, the heat effects were modified by elevated levels of all air pollutants in most locations, with stronger effects for respiratory than cardiovascular mortality. For example, the percent increase in respiratory mortality per increase in the 2-day average summer temperature from the 75th to the 99th percentile was 7.7% (95% Confidence Interval [CI] 7.6–7.7), 11.3% (95%CI 11.2–11.3), and 14.3% (95% CI 14.1–14.5) at low, medium, and high levels of PM2.5, respectively. Similarly, cardiovascular mortality increased by 1.6 (95%CI 1.5–1.6), 5.1 (95%CI 5.1–5.2), and 8.7 (95%CI 8.7–8.8) at low, medium, and high levels of O3, respectively. DiscussionWe observed considerable modification of the heat effects on cardiovascular and respiratory mortality by elevated levels of air pollutants. Therefore, mitigation measures following the new WHO Air Quality Guidelines are crucial to enhance better health and promote sustainable development.

Acute effects of air pollution and extreme temperatures on cause-specific mortality in Italy. Results of the BIGEPI project.

Air pollution and extreme temperatures have been associated with multiple adverse health effects, especially on the cardiovascular and respiratory systems. The evidence of a relationship between daily exposures and mortality from metabolic, nervous and mental causes needs to be strengthened. The aim of this study is to investigate the association between daily exposure to fine particulate matter (PM2.5) and extreme temperatures (heat and cold) with cause-specific mortality in the entire Italian population. The daily counts of deaths from natural, cardiovascular, respiratory, metabolic, diabetes, nervous and mental causes were provided by Istat at the municipal level for the period 2006-2015. Through the application of machine-learning models based on satellite data and spatiotemporal variables, population-weighted exposures to daily mean PM2.5 (2013-2015) and air temperature (2006-2015) were estimated at municipality level. Time-series models adjusted for seasonal and long-term trends were applied, and associations between the above exposures and different causes of death at the national level were estimated. The study found a marked effect of PM2.5 on deaths from nervous causes, with a % increase of risk (IR%) of 6.55% (95% confidence interval: 3.38%-9.81%) per PM2.5 increases of 10 µg/m3. The study also highlighted significant effects of low and high temperatures on all study outcomes. The effects were greater for high temperatures. In particular, the effects of heat, expressed as a % increase in risk per temperature increase from the 75th to the 99th percentile, show the highest associations with mortality from nervous causes (58.3%; 95%CI: 49.7%-67.5%), mental causes (48.4%; 95%CI: 40.4%-56.9%), respiratory causes 45.8%; 95%CI: 39.7%-52.1%) and metabolic causes (36.9%; 95%CI: 30.6%-43.5%). The study showed a strong association between daily exposure to PM2.5 and extreme temperatures, especially heat, with mortality outcomes, especially those under-investigated, such as diabetes, metabolic, nervous and mental causes.

Short-term effects of particulate matter and desert dust on mortality in Southern Europe, the Middle East and Eastern Asia

Background: Desert dust represents a threat to health, but the evidence is limited, as past studies were localised and adopted different exposure metrics and/or statistical approaches. We aim to evaluate the short-term effects of desert dust episodes and particulate matter (PM) on natural mortality in multiple cities from different geo-climatic zones. Methods: Daily death counts were collected for 21 cities in southern Europe (France, Greece, Italy, Portugal, Spain), the Middle East (Iran, Kuwait) and Eastern Asia (China, Japan, South Korea, Taiwan) from 2003-2018. Dust advection days were identified using satellite data, operational models and back-trajectories. Daily mean concentrations of PM10 (PM&amp;#x3c;10µm) and PM2.5 (&amp;#x3c;2.5µm) were available for 21 and 16 cities, respectively. City-specific quasi-Poisson regression models were fitted, adjusted for time trends and meteorology, and city-specific estimates were combined using random-effects meta-analysis. Results: The frequency of dust days ranged from 5% in Northern Italy to 61% in Northern China, with highest levels of PM10 and PM2.5 raised in Kuwait (191 µg/m3) and Xian (182 µg/m3). Overall, dust (yes/no), PM10 and PM25 (10 µg/m3) increased mortality at the same day by 0.52% (95% confidence interval: 0.04, 1.01), 0.14% (0.07,0.21) and 0.16% (0.02, 0.31), respectively. We found differential effects of PM on non-dust versus dust days: on non-dust days 10 µg/m3 increases in PM10 and PM25 increased mortality by 0.20% (0.11, 0.29) and 0.26% (0.05, 0.46) in non-dust days, while in dust days they increased mortality by 0.06% (95% confidence interval: 0.01, 0.10) and 0.05% (-0.04, 0.15), respectively. Conclusions: Desert dust may have independent adverse health effects. Further investigation is needed to disentangle the complex role of dust on air quality and health. On-going analyses on a broader set of cities within the MCC network provide a promising opportunity for this purpose. Key words: Desert dust; particulate matter; short-term effects; mortality

A Mathematical Epidemiological Model (SEQIJRDS) to Recommend Public Health Interventions Related to COVID-19 in Sri Lanka

Coronavirus disease 2019 (COVID-19) has been causing negative impacts on various sectors in Sri Lanka, as a result of the public health interventions that the government had to implement in order to reduce the spread of the disease. Equivalent work carried out in this context is outdated and close to ideal models. This paper presents a mathematical epidemiological model, called SEQIJRDS, having additional compartments for quarantine and infected people divided into two compartments as diagnosed and non diagnosed, compared to the SEIR model. We have presented the rate equations for the model and the basic reproduction number is derived. This model considers the effect of vaccination, the viral load of the variants, mask use, mobility, contact tracing and quarantine, natural immunity development of the infected people, and immunity waning of the recovered group as key developments of the model. The model has been validated for the COVID-19 pandemic in Sri Lanka by parameter derivation using mathematical formulations with the help of the existing data, the literature, and by model fitting for historical data. We present a comparison of the model projections for hospitalized infected people, the cumulative death count, and the daily death count against the ground truth values and projections of the SEIR and SIR models during the model validation. The validation results show that the proposed SEQIJRDS model’s 12-week projection performance is significantly better than both the SEIR and SIR models; the 2-, 6-, 8-, and 10-week projection performance is always better, and the 4-week projection performance is only slightly inferior to other models. Using the proposed SEQIJRDS model, we project mortality under different lockdown procedures, vaccination procedures, quarantine practices, and different mask-use cases. We further project hospital resource usage to understand the best intervention that does not exhaust hospital resources. At the end, based on an understanding of the effect of individual interventions, this work recommends combined public health interventions based on the projections of the proposed model. Specifically, three recommendations—called minimum, sub-optimum, and optimum recommendations—are provided for public health interventions.

Relationship between mobility and road traffic injuries during COVID-19 pandemic-The role of attendant factors.

This study investigates the important role of attendant factors, such as road traffic victims' access to trauma centres, the robustness of health infrastructure, and the responsiveness of police and emergency services in the incidence of Road Traffic Injuries (RTI) during the pandemic-induced COVID-19 lockdowns. The differential effects of the first and second waves of the pandemic concerning perceived health risk and legal restrictions provide us with a natural experiment that helps us differentiate between the impact of attendant factors and the standard relationship between mobility and Road Traffic Injuries. The authors use the auto-regressive recurrent neural network method on two population levels-Tamil Nadu (TN), a predominantly rural state, and Chennai, the most significant metropolitan city of the state, to draw causal inference through counterfactual predictions on daily counts of road traffic deaths and Road Traffic Injuries. During the first wave of the pandemic, which was less severe than the second wave, the traffic flow was correlated to Road Traffic Death/Road Traffic Injury. In the second wave's partial and post lockdown phases, an unprecedented fall of over 70% in Road Traffic Injury-Grievous as against Road Traffic Injury-Minor was recorded. Attendant factors, such as the ability of the victim to approach relief centres, the capability of health and other allied infrastructures, transportation and medical treatment of road traffic crash victims, and minimal access to other emergency services, including police, assumed greater significance than overall traffic flow in the incidence of Road Traffic Injury in the more severe second wave. These findings highlight the significant role these attendant factors play in producing the discrepancy between the actual road traffic incident rate and the officially registered rate. Thus, our study enables practitioners to observe the mobility-adjusted actual incidence rate devoid of factors related to reporting and registration of accidents.

A semi-parametric mixed model for short-term projection of daily COVID-19 incidence in Canada

During a pandemic, data are very “noisy” with enormous amounts of local variation in daily counts, compared with any rapid changes in trend. Accurately characterizing the trends and reliable predictions on future trajectories are important for planning and public situation awareness. We describe a semi-parametric statistical model that is used for short-term predictions of daily counts of cases and deaths due to COVID-19 in Canada, which are routinely disseminated to the public by Public Health Agency of Canada. The main focus of the paper is the presentation of the model. Performance indicators of our model are defined and then evaluated through extensive sensitivity analyses. We also compare our model with other commonly used models such as generalizations of logistic models for similar purposes. The proposed model is shown to describe the historical trend very well with excellent ability to predict the short-term trajectory.

A common concentration-response function based on the results applying lags

Abstract Introduction. Estimating the impact of short-term exposure on health outcomes needs knowledge of both the profile and magnitude of the relative risks. This motivates constructions of practical and reliable concentration-response functions (C-RFs). Aim. To define a practical method of finding concentration-response parametric function whose adjustable parameters can be tuned by data-driven well established routines. Material and methods. Mortality data for the period from 1987 to 2015 (10,592 consecutive days) in Montreal, Canada, are used for illustrative purposes. Exposure to ambient ozone measured by its concentration levels is considered health risk. Concentration-response function is built using statistical modelling, conditional Poisson regression, natural spline technique, and a rudimentary hierarchical data clustering. The case-crossover design is applied to fit the model of C-RF to the mortality data consisting of daily counts of non-accidental deaths. Results. Log-linear models of the concentration-response functions were computed for the concentrations and cofactors data lagged by 0 to 7 days; the results were statistically significant within this range of lags. The effectiveness of fitting was confirmed by reliable statistical tests. Digital routines were created to perform all computational tasks; software codes (written for R software platform) are included. The C-RF specifying the current responses to the cumulative exposure in several previous days can be obtained from the responses to lagged exposures. Conclusions. The proposed method of concentration-response function estimation appears practical and effective in producing reliable results. The constructed function is a parametric and monotonic non-decreasing.

Time series analysis of short-term effects of particulate matter pollution on the circulatory system disease mortality risk in Lishui District, China.

Epidemiological evidence has shown a significant association between short-term exposure to air pollution and mortality risk for circulatory system diseases (CSD). However, informative insights on the significance and magnitude of its relationship in the process of government interventions on abating air pollution are still lacking, particularly in a burgeoning Chinese city. In this study, we conducted a time series study in Lishui District, Nanjing, to examine the effect of ambient particulate matter (PM), e.g., PM2.5 and PM10, on daily death counts of CSD which included cardiovascular disease (CVD), cerebrovascular disease (CEVD), and arteriosclerotic heart disease (ASHD) mortality from January 1, 2015, to December 31, 2019. The results revealed that each 10μg/m3 increase in PM2.5 and PM10 concentration at lag0 day was associated with an increase of 1.33% (95% confidence interval, 0.08%, 2.60%) and 1.12% (0.43%, 1.82%) in CSD mortality; 2.42% (0.44%, 4.43%) and 1.43% (0.32%, 2.55%) in CVD mortality; 1.20% (- 0.31%, 2.73%) and 1.21% (0.38%, 2.05%) in CEVD mortality; and 2.78% (0.00%, 5.62%) and 1.66% (0.14%, 3.21%) in ASHD mortality, respectively. For cumulative risk, the corresponding increase in daily mortality for the same change in PM2.5 concentration at lag03 day was significantly associated with 1.94% (0.23%, 3.68%), 3.17% (0.58%, 5.84%), 2.38% (0.17%, 4.63%), and 4.92% (1.18%, 8.81%) for CSD, CVD, CEVD, and ASHD, respectively. The exposure-response curves were approximately nonlinear over the entire exposure range of the PM concentrations. We also analyzed the effect modifications by season (warm or cold), age group (0-64years, 65-74years, or ≥ 75years), and sex (male or female). Although not statistically significant, stratified analysis showed greater vulnerability to PM exposure for cold season, population over 65years of age, and female group.

Forecasting waved daily COVID-19 death count series with a novel combination of segmented Poisson model and ARIMA models

Autoregressive Integrated Moving Average (ARIMA) models have been widely used to forecast and model the development of various infectious diseases including COVID-19 outbreaks; however, such use of ARIMA models does not respect the count nature of the pandemic development data. For example, the daily COVID-19 death count series data for Canada and the United States (USA) are generally skewed with lots of low counts. In addition, there are generally waved patterns with turning points influenced by government major interventions against the spread of COVID-19 during different periods and seasons. In this study, we propose a novel combination of the segmented Poisson model and ARIMA models to handle these features and correlation structures in a two-stage process. The first stage of this process is a generalization of trend analysis of time series data. Our approach is illustrated with forecasting and modeling of daily COVID-19 death count series data for Canada and the USA.

COVID-19\u2019s U.S. Temperature Response Profile

We estimate the U.S. temperature response profile (TRP) for COVID-19 and show it is highly sensitive to temperature variation. Replacing the erratic daily death counts U.S. states initially reported with counts based on death certificate date, we build a week-ahead statistical forecasting model that explains most of their daily variation (R2 = 0.97) and isolates COVID-19’s TRP (p < 0.001). These counts, normalized at 31 °C (U.S. mid-summer average), scale up to 160% at 5 °C in the static case where the infection pool is held constant. Positive case counts are substantially more temperature sensitive. When temperatures are declining, dynamic feedback through a growing infection pool can substantially amplify these temperature effects. Our estimated TRP can be incorporated into COVID-related planning exercises and used as an input to SEIR models employed for longer run forecasting. For the former, we show how our TRP is predictive of the realized pattern of growth rates in per capita positive cases across states five months after the end of our sample period. For the latter, we show the variation in herd immunity levels implied by temperature-driven, time-varying R0 series for the Alpha and Delta variants of COVID-19 for several representative states.