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56 results for “life expectancy”
General practice characteristics associated with life expectancy of practice populations: a cross-sectional study
<p>The dataset was used to investgate features of general practice associated with life expectancy of general practice populations in England for the period 2015-2019.</p>
Life table data for "Bounce backs amid continued losses: Life expectancy changes since COVID-19"
<p><strong>Life table data for "Bounce backs amid continued losses: Life expectancy changes since COVID-19"</strong></p> <p><em>cc-by Jonas Schöley, José Manuel Aburto, Ilya Kashnitsky, Maxi S. Kniffka, Luyin Zhang, Hannaliis Jaadla, Jennifer B. Dowd, and Ridhi Kashyap. "Bounce backs amid continued losses: Life expectancy changes since COVID-19".</em></p> <p>These are CSV files of life tables over the years 2015 through 2021 across 29 countries analyzed in the paper "Bounce backs amid continued losses: Life expectancy changes since COVID-19".</p> <p><strong>40-lifetables.csv</strong></p> <p>Life table statistics 2015 through 2021 by sex, region and quarter with uncertainty quantiles based on Poisson replication of death counts. Actual life tables and expected life tables (under the assumption of pre-COVID mortality trend continuation) are provided.</p> <p><strong>30-lt_input.csv</strong></p> <p>Life table input data.</p> <ul> <li>`id`: unique row identifier</li> <li>`region_iso`: iso3166-2 region codes</li> <li>`sex`: Male, Female, Total</li> <li>`year`: iso year</li> <li>`age_start`: start of age group</li> <li>`age_width`: width of age group, Inf for age_start 100, otherwise 1</li> <li>`nweeks_year`: number of weeks in that year, 52 or 53</li> <li>`death_total`: number of deaths by any cause</li> <li>`population_py`: person-years of exposure (adjusted for leap-weeks and missing weeks in input data on all cause deaths)</li> <li>`death_total_nweeksmiss`: number of weeks in the raw input data with at least one missing death count for this region-sex-year stratum. missings are counted when the week is implicitly missing from the input data or if any NAs are encounted in this week or if age groups are implicitly missing for this week in the input data (e.g. 40-45, 50-55)</li> <li>`death_total_minnageraw`: the minimum number of age-groups in the raw input data within this region-sex-year stratum</li> <li>`death_total_maxnageraw`: the maximum number of age-groups in the raw input data within this region-sex-year stratum</li> <li>`death_total_minopenageraw`: the minimum age at the start of the open age group in the raw input data within this region-sex-year stratum</li> <li>`death_total_maxopenageraw`: the maximum age at the start of the open age group in the raw input data within this region-sex-year stratum</li> <li>`death_total_source`: source of the all-cause death data</li> <li> <p>`death_total_prop_q1`: observed proportion of deaths in first quarter of year</p> </li> <li> <p>`death_total_prop_q2`: observed proportion of deaths in second quarter of year</p> </li> <li> <p>`death_total_prop_q3`: observed proportion of deaths in third quarter of year</p> </li> <li> <p>`death_total_prop_q4`: observed proportion of deaths in fourth quarter of year</p> </li> <li> <p>`death_expected_prop_q1`: expected proportion of deaths in first quarter of year</p> </li> <li> <p>`death_expected_prop_q2`: expected proportion of deaths in second quarter of year</p> </li> <li> <p>`death_expected_prop_q3`: expected proportion of deaths in third quarter of year</p> </li> <li> <p>`death_expected_prop_q4`: expected proportion of deaths in fourth quarter of year</p> </li> <li>`population_midyear`: midyear population (July 1st)</li> <li>`population_source`: source of the population count/exposure data</li> <li>`death_covid`: number of deaths due to covid</li> <li>`death_covid_date`: number of deaths due to covid as of <date></li> <li>`death_covid_nageraw`: the number of age groups in the covid input data</li> <li>`ex_wpp_estimate`: life expectancy estimates from the World Population prospects for a five year period, merged at the midpoint year</li> <li>`ex_hmd_estimate`: life expectancy estimates from the Human Mortality Database</li> <li>`nmx_hmd_estimate`: death rate estimates from the Human Mortality Database</li> <li>`nmx_cntfc`: Lee-Carter death rate projections based on trend in the years 2015 through 2019</li> </ul> <p><em>Deaths</em></p> <ul> <li>source: <ul> <li>STMF input data series (https://www.mortality.org/Public/STMF/Outputs/stmf.csv)</li> <li>ONS for GB-EAW pre 2020</li> <li>CDC for US pre 2020</li> </ul> </li> <li>STMF: <ul> <li>harmonized to single ages via pclm</li> <li>pclm iterates over country, sex, year, and within-year age grouping pattern and converts irregular age groupings, which may vary by country, year and week into a regular age grouping of 0:110</li> <li>smoothing parameters estimated via BIC grid search seperately for every pclm iteration</li> <li>last age group set to [110,111)</li> <li>ages 100:110+ are then summed into 100+ to be consistent with mid-year population information</li> <li>deaths in unknown weeks are considered; deaths in unknown ages are not considered</li> </ul> </li> <li>ONS: <ul> <li>data already in single ages</li> <li>ages 100:105+ are summed into 100+ to be consistent with mid-year population information</li> <li>PCLM smoothing applied to for consistency reasons</li> </ul> </li> <li>CDC: <ul> <li>The CDC data comes in single ages 0:100 for the US. For 2020 we only have the STMF data in a much coarser age grouping, i.e. (0, 1, 5, 15, 25, 35, 45, 55, 65, 75, 85+). In order to calculate life-tables in a manner consistent with 2020, we summarise the pre 2020 US death counts into the 2020 age grouping and then apply the pclm ungrouping into single year ages, mirroring the approach to the 2020 data</li> </ul> </li> </ul> <p><em>Population</em></p> <ul> <li>source: <ul> <li>for years 2000 to 2019: World Population Prospects 2019 single year-age population estimates 1950-2019</li> <li>for year 2020: World Population Prospects 2019 single year-age population projections 2020-2100</li> </ul> </li> <li>mid-year population <ul> <li>mid-year population translated into exposures: <ul> <li>if a region reports annual deaths using the Gregorian calendar definition of a year (365 or 366 days long) set exposures equal to mid year population estimates</li> <li>if a region reports annual deaths using the iso-week-year definition of a year (364 or 371 days long), and if there is a leap-week in that year, set exposures equal to 371/364\*mid_year_population to account for the longer reporting period. in years without leap-weeks set exposures equal to mid year population estimates. further multiply by fraction of observed weeks on all weeks in a year.</li> </ul> </li> </ul> </li> </ul> <p><em>COVID deaths</em></p> <ul> <li>source: COVerAGE-DB (https://osf.io/mpwjq/)</li> <li>the data base reports cumulative numbers of COVID deaths over days of a year, we extract the most up to date yearly total</li> </ul> <p><em>External life expectancy estimates</em></p> <ul> <li>source: <ul> <li>World Population Prospects (https://population.un.org/wpp/Download/Files/1_Indicators%20(Standard)/CSV_FILES/WPP2019_Life_Table_Medium.csv), estimates for the five year period 2015-2019</li> <li>Human Mortality Database (https://mortality.org/), single year and age tables</li> </ul> </li> </ul>
Data for Figures and Tables in "Bounce backs amid continued losses: Life expectancy changes since COVID-19"
<p><strong>Data for Figures and Tables in "Bounce backs amid continued losses: Life expectancy changes since COVID-19"</strong></p> <p><em>cc-by Jonas Schöley, José Manuel Aburto, Ilya Kashnitsky, Maxi S. Kniffka, Luyin Zhang, Hannaliis Jaadla, Jennifer B. Dowd, and Ridhi Kashyap. "Bounce backs amid continued losses: Life expectancy changes since COVID-19".</em></p> <p>These are CSV files of data in the figures and tables published in the paper "Bounce backs amid continued losses: Life expectancy changes since COVID-19".</p> <p><strong>50-e0diffT.csv</strong></p> <p>Figure 1: Life expectancy changes 2019/20 and 2020/21 across countries. The countries are ordered by increasing cumulative life expectancy losses since 2019. Grey dots indicate the average annual LE changes over the years 2015 through 2019.</p> <p><strong>51-arriagaT.csv</strong></p> <p>Figure 2: Age contributions to life expectancy changes since 2019 separated for 2020 and 2021. The position of the arrowhead indicates the total contribution of mortality changes in a given age group to the change in life expectancy at birth since 2019. The discontinuity in the arrow indicates those contributions separately for the years 2020 and 2021. Annual contributions can compound or reverse. The total life expectancy change from 2019 to 2021 in a given country is the sum of the arrowhead positions across age.</p> <p><strong>52-sexdiff.csv</strong></p> <p>Figure 3: Change in the female life expectancy advantage from 2019 through 2021. Blue colors indicate an increase and red colors a decrease in the female life expectancy advantage. Muted colors indicate non-significant changes.</p> <p><strong>53-e0diffcodT.csv</strong></p> <p>Figure 4: Life expectancy deficit in 2021 decomposed into contributions by age and cause of death. LE deficit is defined as observed minus expected life expectancy had pre-pandemic mortality trends continued.</p> <p><strong>55-vaxe0.csv</strong></p> <p>Figure 5: Years of life expectancy deficit during October through December 2021 contributed by ages <60 and 60+ against % of population twice vaccinated by October 1st in the respective age groups. LE deficit is defined as the counterfactual LE from a Lee-Carter mortality forecast based on death rates for the fourth quarter of the years 2015 to 2019 minus observed LE.</p> <p><strong>54-tab_arriaga.csv</strong></p> <p>Table 1: Months of life expectancy (LE) changes and deficits (labelled ES) since the start of the pandemic attributed to age-specific mortality changes (labelled AT). LE deficit is defined as observed minus expected life expectancy had pre-pandemic mortality trends continued.</p>
Figure 5 in How long do dolphins live? Survival rates and life expectancies for bottlenose dolphins in zoological facilities ťs. wild populations
Figure 5. Kaplan-Meier survival curves depicting the proportion of bottlenose dolphins in zoological care surviving to each age (calculated in days, then transformed to years) during four time periods.
Figure 2 in How long do dolphins live? Survival rates and life expectancies for bottlenose dolphins in zoological facilities ťs. wild populations
Figure 2. ASR (95% confidence intervals) of bottlenose dolphin calves <1 yr old in zoological care across historical time periods.
Figure 4 in How long do dolphins live? Survival rates and life expectancies for bottlenose dolphins in zoological facilities ťs. wild populations
Figure 4. The population age structure for bottlenose dolphins in zoological care on the last day of each time period.
Figure 3 in How long do dolphins live? Survival rates and life expectancies for bottlenose dolphins in zoological facilities ťs. wild populations
Figure 3. Survivorship to each age as calculated for age-at-death data for modern-day dolphins in zoological care and two wild populations.
Figure 1 in How long do dolphins live? Survival rates and life expectancies for bottlenose dolphins in zoological facilities ťs. wild populations
Figure 1. ASR (95% confidence intervals) of bottlenose dolphins>1 yr old in zoological care across historical time periods.
Coevolution of relative brain size and life expectancy in parrots
<p><span><span><span><span>Previous studies have demonstrated a correlation between longevity and brain size in a variety of taxa. Little research has been devoted to understanding this link in parrots; yet parrots are well-known for both their exceptionally long lives and cognitive complexity. We employed a large-scale comparative analysis that investigated the influence of brain size and life history variables on longevity in parrots. Specifically, we addressed two hypotheses for evolutionary drivers of longevity: the <em>Cognitive Buffer Hypothesis</em>, which proposes that increased cognitive abilities enable longer life spans, and the <em>Expensive Brain Hypothesis</em>, which holds that increases in life span are caused by prolonged developmental time of, and increased parental investment in, large-brained offspring<em>. </em>We estimated life expectancy from detailed zoo records for 133,818 individuals across 244 parrot species. Using a principled Bayesian approach that addresses data uncertainty and imputation of missing values, we found a consistent correlation between relative brain size and life expectancy in parrots. This correlation was best explained by a direct effect of relative brain size. Notably, we found no effects of developmental time, clutch size, or age at first reproduction. Our results suggest that selection for enhanced cognitive abilities in parrots have in turn promoted longer lifespans.</span></span></span></span></p>
Crude vital rates and indirect estimates of life expectancy at birth for the Nordic countries, 18th and 19th centuries
<p>This file provides the necessary input data (crude vital rates) and shows the calculations for the indirect estimation of life expectancy at birth (e0) for males and females combined, using the method developed in McCann, J. 1976. 'A Technique for Estimating Life Expectancy with Crude Vital Rates', Demography, 13(2): pp. 259-272.</p> <p>Coverage: Sweden (1736-1750), Norway (1735-1845), Denmark (1800-1834), Iceland (1735-1837), and Finland (1751-1877).</p> <p>The annual estimates end in the year before estimates in the Human Mortality Database become available.</p> <p>For a detailed description see Torres, C. and Oeppen, J. 2019. The Health Transition in the Nordic Countries (Working paper, available upon request: ctorres@sdu.dk). </p>
Table 1 in How long do dolphins live? Survival rates and life expectancies for bottlenose dolphins in zoological facilities ťs. wild populations
<p><i>Table 1.</i> Mean and median life expectancies (in years, with 95% confidence intervals) for bottlenose dolphins in zoological care as calculated by Kaplan-Meier analyses.</p><table><tbody><tr><th>Time period</th><th>Median LE (CI)</th><th>Mean LE (CI)</th></tr></tbody><tbody><tr><th>1974–1982</th><td>9.0 (5.9–11.4)</td><td>10.6 (8.8–12.5)</td></tr><tr><th>1983–1992</th><td>15.3 (12.5–17.1)</td><td>17.3 (15.2–19.4)</td></tr><tr><th>1993–2002</th><td>18.2 (14.1–20.3)</td><td>20.3 (18.0–22.5)</td></tr><tr><th>2003–2012</th><td>29.2 (25.0–32.9)</td><td>28.2 (25.3–31.0)</td></tr></tbody></table>
FIGURE 5 in Effects of six greenhouse cucumber cultivars on reproductive performance and life expectancy of Tetranychus turkestani (Acari: Tetranychidae)
FIGURE 5: Ward's dendrogram of six greenhouse cucumber cultivars based on reproductive parameters of Tetranychus turkestani on six greenhouses cucumber cultivars. A- favorite host plant for the reproduction of the strawberry spider mite, B- partially unpleasant group for the reproduction of this mite, B1- comparatively semi-resistant group and B2 - partly more resistant compared to cultivars in the B1 cluster.
FIGURE 3 in Effects of six greenhouse cucumber cultivars on reproductive performance and life expectancy of Tetranychus turkestani (Acari: Tetranychidae)
FIGURE 3: Mean (±SE) hatching rate of Tetranychus turkestani reared on six greenhouse cucumber cultivars. Means followed by same letters in each column are not significantly different (P <0.05, LSD).
FIGURE 2 in Effects of six greenhouse cucumber cultivars on reproductive performance and life expectancy of Tetranychus turkestani (Acari: Tetranychidae)
FIGURE 2: Mean (±SE) oviposition rate Tetranychus turkestani reared on six greenhouse cucumber cultivars. Means followed by same letters in each column are not significantly different (P <0.05, LSD).
FIGURE 4 in Effects of six greenhouse cucumber cultivars on reproductive performance and life expectancy of Tetranychus turkestani (Acari: Tetranychidae)
FIGURE 4: Age – stage reproductive value (vxj) of Tetranychus turkestani fed on six greenhouse cucumber cultivars. Age reproductive value of Tetranychus turkestani in each stage was shown by solid black circle (egg), solid white square (larva), solid black triangle up (nymph 1), solid white diamond (nymph 2), solid white circle (female) on six greenhouse cucumber cultivars. a- cultivar Puia, bcultivar Hedieh, c- cultivar Milad Jadid, d- cultivar Milad Ghadim, e- Khasib, f- Negin.
FIGURE 1 in Effects of six greenhouse cucumber cultivars on reproductive performance and life expectancy of Tetranychus turkestani (Acari: Tetranychidae)
FIGURE 1: Age-stage life expectancy (exj) of Tetranychus turkestani fed on six greenhouse cucumber cultivars. Age life expectancy of T. turkestani in each stage was shown by solid black circle (egg), solid white circle (larva), solid white triangle up (nymph 1), solid black triangle up (nymph 2), simple line (female) and long dash line (male) on six greenhouse cucumber cultivars. a- cultivar Puia, bcultivar Hedieh, c- cultivar Milad Jadid, d- cultivar Milad Ghadim, e- Khasib, f- Negin.
A machine learning based prediction model for life expectancy
<p>The social and financial systems of many nations throughout the world are significantly impacted by life expectancy (LE) models. Numerous studies have pointed out the crucial effects that life expectancy projections will have on societal issues and the administration of the global healthcare system. The computation of life expectancy has primarily entailed building an ordinary life table. However, the life table is limited by its long duration, the assumption of homogeneity of cohorts and censoring. As a result, a robust and more accurate approach is inevitable. In this study, a supervised machine learning model for estimating life expectancy rates is developed. The model takes into consideration health, socioeconomic, and behavioral characteristics by using the eXtreme Gradient Boosting (XGBoost) algorithm to data from 193 UN member states. The effectiveness of the model's prediction is compared to that of the Random Forest (RF) and Artificial Neural Network (ANN) regressors utilized in earlier research. XGBoost attains an MAE and an RMSE of 1.554 and 2.402, respectively outperforming the RF and ANN models that achieved MAE and RMSE values of 7.938 and 11.304, and 3.86 and 5.002, respectively. The overall results of this study support XGBoost as a reliable and efficient model for estimating life expectancy.</p>
Coevolution of relative brain size and life expectancy in parrots
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Data from: Life expectancy in ants explains variation in helpfulness, regardless of phylogenetic relatedness
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A machine learning based prediction model for life expectancy
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