dataesr/scientific-paragraphs-categorization huggingface dataset:
The categorical attribution of causes of death also diff ers from excess mortality in people with a disease followed up over time in a cohort study or through linkage of a disease registry to vital registration data. The excess mortality in such studies might contain deaths that are assigned as the underlying cause, those that are causally related to the disease, and those that are due to confounding, such as by a common underlying risk that predisposes to the disease but where there are also additional pathways to death. These counterfactual and excess mortality relationships are important and need to be quantifi ed by considering the underlying risk, such as elevated fasting plasma glucose. Figure  2  shows the steps in the estimation of causes of death, which are divided into seven categories: cause of death database development (fi gure 2A), Cause of Death Ensemble modelling (CODEm), negative binomial models for rare causes, natural history models, subcause proportion models, prevalence-based models, and CodCorrect (fi gure 2B).
Cause of death database development Figure  2A  shows the detailed steps from data inputs and processing to the fi nalisation of the cause of death database. The methods appendix (pp 52-70) includes details on each step. Cause of death data collected through vital registration systems are available from governments and coded to diff erent variants of the ICD including various national ICD variants. Multiple sources were used in addition to vital registration data, including verbal autopsy data, cancer registries, maternal mortality surveillance, census and survey data on maternal death, census and survey data on selected injuries, and police records for some injuries. Figure  2A  shows how each type of data were processed to deal with the challenges of diff erent coding schemes, diff erent age group reporting, variation in certifi cation, misclassifi cation of HIV/AIDS deaths, misclassifi cation of maternal HIV/AIDS deaths, and incorporation of population-based cancer registry data.
The fi rst and second steps in the cause of death database development were standardisation of multiple data formats to a single GBD standard, then the mapping of each ICD or verbal autopsy variant to the GBD cause map. Figure  7A  shows the number of deaths captured for each year in the GBD causes of death database by coding version. In step 3, we split a small subset of data reported in non-GBD-standard age formats into GBD age categories using the global relative age pattern of mortality for each cause as estimated from the pooled data that provide full age detail. In step 4, based on expert judgment, some causes were not allowed for certain agesex groups, for example, male uterine cancer. In step 5, deaths assigned to causes that cannot be underlying causes of death (ie, garbage coded) were reassigned to their likely underlying cause of death.
14 Step 8 ensured that the process of redistributing garbage codes or identifying misclassifi ed HIV/AIDS deaths would not assign deaths to causes in an age-sex-country-year that violated age-sex or other restrictions. Step 9 excluded vital registration sources that were less than 50% complete in a given geography from the database, because of the potential for selection bias in highly incomplete sources. Sources estimated to be 50-70% complete were identifi ed as non-representative, which was information that we used in the building of the cause of death statistical model to increase the estimated data variance for these datapoints. All included sources were corrected to be 100% complete by multiplying the cause fraction in a source for a countryage-sex-year by the estimate of all-cause mortality for that country-age-sex-year.
Cause-years are defi ned as the number of years for each cause for which data are available. GBD=Global Burden of Disease. ICD=International Classifi cation of Diseases. BTL=basic tabulation list. VA=verbal autopsy. VR=vital registration. ATG=Antigua and Barbuda. VCT=Saint Vincent and the Grenadines. LCA=Saint Lucia. TTO=Trinidad and Tobago. TLS=Timor-Leste. FSM=Federated States of Micronesia. the methods appendix (pp 318-401). To facilitate understanding of the range of quality and availability of data for each geography, we classifi ed geographies into six categories: extensive complete representative vital registration (vital registration data that are 95% complete and cover more than 25 years); moderate data (vital registration data that are 95% complete but cover fewer than 25 years); incomplete representative vital registration (all other geographies with some representative vital registration data); extensive verbal autopsy and other sources (covering more than 20% of cause-years); limited verbal autopsy or other data (all others with some data available); and no data for any cause (methods appendix pp 691-710).
Out-of-sample predictive validity testing was also used to select the psi parameter that determines the number of models and their weight in the fi nal ensemble (fi gure 8). For each cause of death, we ran independent CODEm models by sex and for countries with extensive complete vital registration representation and all other countries. We included all datapoints for the other categories of geographies, whereas for countries with extensive complete vital registration representation, we included only datapoints from those countries, so that heterogeneous data from other countries did not infl ate the uncertainty interval.
Natural history models For some causes, deaths are rarely recorded in either vital registration data or verbal autopsy data. Partly, this is because of the geographical location of the deaths or because of the potential for systematic bias in vital registration data or verbal autopsy data. For 14 causes, we have developed natural history models in which incidence and case-fatality rates are modelled separately and combined to yield estimates of cause-specifi c mortality. We developed natural history models for typhoid fever, paratyphoid fever, whooping cough, measles, visceral leishmaniasis, African trypanosomiasis, yellow fever, syphilis (congenital), and acute hepatitis A, B, C, and E. Additionally, for malaria in sub-Saharan Africa, we have used a natural history model based on the incidence estimated by the Malaria Atlas Project and age-sex-specifi c case-fatality rates estimated from available data.
Subcause proportion models For meningitis, maternal disorders, liver cancer, cirrhosis, and chronic kidney disease, we estimated detailed causes for each of these cause groupings by modelling the proportion of the cause grouping (parent cause) due to each of the component causes. We used this approach because the available data on the specifi c causes can come from sources other than vital registration, such as endstage renal disease registries, or from too few places to model the death rates directly. For these causes, the parent cause was fi rst estimated with CODEm and the fraction of the parent due to each component cause for  each age-sex-geography-year was generally estimated with DisMod-MR 2. 1, a Bayesian meta-regression method developed for the GBD studies. 42, 43 Details for each cluster of causes analysed in this way are shown in the methods appendix (pp 233-52; subcause proportion models).
Prevalence-based models For Alzheimer's disease and other dementias and atrial fi brillation and fl utter, there is evidence of marked changes over time in the propensity of individuals who completed death certifi cates to list these causes as underlying causes of death. 44, 45 These changes created increases in the reported death rates. Conversely, prevalence surveys do not show a matching increase in age-specifi c disease prevalence. Garbage code redistribution algorithms used in the development of the cause of death database have so far not accurately captured this shift over time in the certifi cation of underlying causes of death. For these two causes, we based our estimates on prevalence surveys and estimates of excess mortality based on deaths certifi ed in countries with the greatest proportion of deaths allocated to the correct underlying cause of death in recent years.
CodCorrect Depending on the specifi c data availability and details of individual causes, we adopted diff erent modelling strategies for each cause. We generated a set of underlying cause of death estimates, with uncertainty intervals, that equalled all-cause mortality, with uncertainty intervals, for each age-sex-year-geography and cause and all-cause mortality at the individual draw level. 24 In CodCorrect, for each draw from the posterior distribution of each cause, the sum of cause-specifi c estimates is rescaled to equal the draw from the all-cause distribution (methods appendix p 285).
We corrected the estimated prevalence for each pathogen on the basis of conventional laboratory techniques, such as bacterial culture or enzyme-linked immunosorbent assay (ELISA), to be consistent with the new qPCR method. Cholera mortality was estimated by modelling the under-reporting to the WHO cholera case notifi cation system and applying this correction factor to estimate the number of cholera cases and deaths (methods appendix p 281). The incidence and mortality of Clostridium diffi cile was modelled with natural history and incidence data in DisMod-MR 2. 1. We estimated attributable mortality due to respiratory syncytial virus and infl uenza with a similar approach to that for diarrhoea. We used a counterfactual approach whereby the prevalence in patients with lower respiratory infection was extracted from a systematic literature review and modelled with DisMod-MR 2.
The change in death numbers and population rates with the epidemiological transition was driven by both expected changes in age-specifi c rates and the highly regular shifts in population age structure that occur with rising SDI. Previous attempts to characterise the shifts in disease pattern with development  [56] [57] [58]  have not been as comprehensive and have not been based on a comprehensive database such as that provided by GBD. Norway IHD (1•45) Lung C (0•98) Stroke (0•84) Alzheimer's (1•09) COPD (2•32) Colorect C (1•01) Self-harm (0•65) LRI (0•73) Drugs (3•44) Breast C (1•03) Portugal Stroke (0•56) IHD (0•35) Lung C (0•77) LRI (0•73) Colorect C (0•97) Alzheimer's (0•88) COPD (0•7) Stomach C (0•69) Diabetes (0•79) Self-harm (0•64) Spain IHD (0•48) Lung C (0•87) Stroke (0•37) Alzheimer's (1•13) COPD (1•08) Colorect C (0•81) Breast C (0•89) LRI (0•34) Self-harm (0•37) Road injuries (0•42) Sweden IHD (1•29) Stroke (0•69) Lung C (0•68) Alzheimer's (0•98) Self-harm (0•75) Colorect C (0•82) COPD (1•08) Breast C (1•07) Prostate C (1•92) LRI (0•44) Switzerland IHD (1•17) Lung C (0•9) Alzheimer's (1•07) Self-harm (0•76) Stroke (0•55) Colorect C (0•67) Breast C (1•18) COPD (1•22) Pancreas C (0•78) Falls (1•61) UK IHD (1•1) Lung C (1•08) Stroke (0•69) COPD (2•04) LRI (0•91) Alzheimer's (0•99) Colorect C (0•78) Breast C (1•35) Self-harm (0•49) Other cardio (1•56) England IHD (1•05) Lung C (1•03) Stroke (0•66) COPD (1•98) LRI (0•89) Alzheimer's (0•97) Colorect C (0•75) Breast C (1•34) Self-harm (0•46) Other cardio (1•58) Northern Ireland IHD (0•83) Lung C (1•1) Stroke (0•51) COPD (1•56) LRI (0•91) Alzheimer's (1•05) Colorect C (0•85) Self-harm (0•59) Breast C (1•26) Congenital (0•86) Scotland IHD (1•73) Lung C (1•53) Stroke (1•09) COPD (2•91) LRI (1•05) Alzheimer's (1•0) Colorect C (0•94) Self-harm (0•75) Breast C (1•43) Drugs (5•08) Wales IHD (1•25) Lung C (1•13) Stroke (0•74) COPD (2•15) Alzheimer's (1•14) LRI (0•97) Colorect C (0•88) Breast C (1•4) Self-harm (0•55) Other cardio (1•74) Southern Latin America IHD (0•76) Stroke (0•6) LRI (1•36) Road injuries (0•83) Congenital (1•1) Self-harm (0•83) Lung C (0•79) COPD (1•33) CKD (1•33) Diabetes (1•33) Argentina IHD (0•85) LRI (1•66) Stroke (0•58) Road injuries (0•83) Congenital (1•11) Lung C (0•87) COPD (1•48) Self-harm (0•83) NN preterm (1•32) CKD (1•41) Chile IHD (0•55) Stroke (0•65) Self-harm (0•78) Road injuries (0•85) Stomach C (1•02) LRI (0•71) Congenital (1•12) Lung C (0•52) CKD (1•31) COPD (0•94) Uruguay IHD (0•56) Stroke (0•65) Lung C (1•24) COPD (1•26) Self-harm (1•21) LRI (0•83) Road injuries (0•75) Colorect C (1•23) Congenital (0•88) Breast C (1•39).
We estimate that 17•8% (13•6-20•6) of these HIV/AIDS deaths in 2015 were due to tuberculosis in HIV-positive people, but little data exist for the immediate causes of other HIV/AIDS deaths. Continued high death rates in sub-Saharan Africa highlight the critical importance of improved quality of care and early initiation of therapy, irrespective of disease progression. 69 1] [72] [73]  Stagnating development assistance for health for HIV/AIDS programmes amplifi es the challenge of reducing HIV/AIDS mortality. 74, 75 ur results suggest a notable decrease in malaria mortality in sub-Saharan Africa since 2003, consistent with documented reductions in incidence and prevalence, as well as observed increases in eff ective treatment and preventive interventions. 76 We found that malaria mortality peaked in 2003, which is slightly earlier than reported in previous GBD estimates.
Our estimation of the fraction of deaths due to lower respiratory infection attributable to pneumococcal pneumonia relies on data from vaccine effi cacy studies that show a decrease in all pneumonia and among invasive (bacteraemic) pneumococcal disease in children and adults. However, at least one study that used a urine antigen test in elderly adults found that the relative vaccine effi cacy of pneumococcal conjugate vaccine against invasive pneumococcal disease was 66% greater than against pneumococcal pneumonia. 49 For GBD 2015, we corrected the estimated pneumococcal conjugate vaccine effi cacy against invasive pneumococcal disease by this ratio in both child and adult age groups. This is a change in our GBD 2015 methods, although no studies have used this diagnostic test to detect non-invasive pneumococcal pneumonia in children.
While mortality rates from most infectious diseases fell, dengue emerged as a notable counterexample. The combination of new data and an improved dengue trend covariate (methods appendix p 82) enabled our model to better capture these trends. Compared with GBD 2013 estimates, our GBD 2015 dengue mortality estimates are higher for the most recent decade, yielding a pronounced upward trend that is more consistent with case reports, GBD 2013 incidence estimates, and expert consensus. 79 he increasing geographic range of dengue and, in some areas (eg, Latin America) increasing transmission intensity, contribute to growing concerns about other viruses that are transmitted by Aedes mosquitoes, including the chikungunya and Zika viruses. 80 Progress in dengue vaccine development, including the licensure of the CYD-TDV vaccine in 2015, 81 is promising, but the eventual impact of such vaccines remains unclear.
A similar development for liver cancer due to hepatitis C can be achieved in the coming decades if hepatitis C treatment, which currently has high costs per patient,  100  becomes accessible in high prevalence populations. Data newly available to GBD 2015 improved our understanding of global patterns of cardiovascular disease. High proportions of death due to cardiovascular disease have been observed in Oceania. Previously, very few data sources were available from Kiribati and Fiji,  101  but with additional data from Tonga, Guam, American Samoa, and the Northern Mariana Islands, it seems that 27-30% of deaths in this region were due to cardiovascular disease causes. Newly available data from India show that, similar to surveillance data previously available from Bangladesh, cardiovascular disease accounts for a large and increasing proportion of deaths.
Updated data from China now show a clear trend toward decreasing risk of cardiovascular disease death in all age groups since 2010, when comprehensive vital registration became available. In GBD 2015 we examined agestandardised YLL rates by level of SDI. YLL rates for ischaemic heart disease were lowest in countries with the lowest SDI when country populations were examined by SDI, rising steadily at higher levels of SDI, and only reduced for populations in the quarter of countries with the highest SDI. This pattern was more pronounced among males than females and diff ers from that seen for stroke, where YLL rates decrease gradually at higher levels of SDI and then fall steeply for the highest SDI populations. The result is that for overall cardiovascular disease, YLL rates were lowest in both the lowest and highest sociodemographic groups with an increase for those in the middle of the sociodemographic rankings.
Although the reductions in rates are welcome news, the substantial increase in the number of deaths from Alzheimer's disease and other dementias and the associated increase in prevalence present challenges for health systems and social support systems that need to address the needs of these patients and their families. We estimate small numbers of death due to mental disorders as the underlying cause. We include schizophrenia as a cause of death because high-quality vital registration data every year show a stable, small number of deaths from this cause. Many more deaths occur in people with schizophrenia in excess of what would be expected on the basis of general population mortality rates. 0] [111]  Most deaths from self-harm can be attributed to underlying mental and substance disorders such as depression, anxiety disorders, schizophrenia, bipolar disorder, and alcohol and drug use disorders, as has been quantifi ed based on GBD 2010 estimates.
119 These variations in death rates could be related to variation in cause of death certifi cation, with some medical communities more likely than others to list diabetes as the underlying cause of death, or they might be due to treatment eff ects. Given the importance of diabetes as a cause of death already, and the likely global rise in prevalence of diabetes, more research is needed to understand the determinants of variation in diabetes death rates. 119 n this analysis we have estimated that there were more deaths due to chronic kidney disease than in previous analyses because of improved estimates within countries with large populations such as China, India, and Russia. We have also improved our chronic kidney disease subtype estimation strategy by implementing consistent data source inclusion, as well as estimation strategy across the four subtypes.
Globally, drowning is a leading cause of death in children younger than 5 years. 5] [146]  However, change in access to open water with increasing urbanisation might be a more important factor driving down drowning deaths than specifi c prevention measures. For GBD 2015 we systematically collected data on major transport accidents, natural and man-made disasters, wildfi res, pandemics, and wars. Although the accuracy of the death numbers due to some of these causes vary by country (ie, they are usually more accurate in stable or higher income countries), they tend to be more reliable than are the estimates for wars. 8] [149] [150]  The challenge is due to scarcity of vital statistics during wars, the increase in refugee populations who are displaced internally or externally, and the fact that surveys can only capture mortality rates among those who have remained in their households during the time of interviews.
Measurement challenges and opportunities The assessment of all-cause mortality in GBD 2015 includes important innovations that have substantially increased uncertainty for adult mortality in some countries. We believe this is a much more accurate refl ection of our knowledge of age-specifi c mortality in countries without vital registration or sample registration systems. The most important changes included processing of sibling history data using single years rather than pooling data for 5-year intervals, and the propagation of uncertainty in HIV/AIDS crude death rates used in the fi rst stage of the spatiotemporal Gaussian process regression model into the estimation of 45q15. In addition to these changes, we have greatly improved our parameter selection process for both 5q0 and 45q15 Gaussian process regression to refl ect data density and quality of data partly represented by the data variance.
Combined, these changes led to an increased uncertainty interval for 45q15. In many countries in Africa, the width of the uncertainty interval for 45q15 more than doubled. The increase in the uncertainty interval for 45q15 directly leads to higher uncertainty intervals for our age-specifi c mortality rate estimates. This partly helped with the removal of an arbitrary matching algorithm from GBD 2013, which picked the pairs of draws of all-cause mortality and HIVspecifi c mortality estimates that were consistent. With the widened uncertainty interval in all-cause mortality estimates, we are able to apply the ensemble model to combine draw-level HIV/AIDS mortality estimates from the demographic estimation process and the epidemiological model of EPP-Spectrum for all 1000 draws. Cause of death data for low SDI countries in sub-Saharan Africa were limited to a small number of mostly local verbal autopsy data often with small sample size.
5 This potentially important resource for global health estimation is being underused because of the limitations of current versions of the INTERVA tool for cause ascertainment. Given that injury death assignment might (although there are no objective validation data on this issue) be more plausibly established from INTERVA, we have chosen to use the INTERVA results from INDEPTH for eight types of injuries: transport injuries; falls; drowning; fi re, heat, and hot substances; poisonings; venomous animal contact; selfharm; and interpersonal violence. Hopefully, a large number of deaths from INDEPTH verbal autopsies will be assessed using physician-certifi ed verbal autopsy or more robust computer algorithms in the future. In GBD 2013, for select causes, we developed separate CODEm models for countries with long series of complete vital registration data in order that UI estimation in these settings was not aff ected by UI in settings with either less complete or lower quality data.
For GBD 2015, we standardised this approach. We defi ned countries with extensive complete vital registration representation as those with vital registration equal to or more than 95% complete for more than 25 years. We also modifi ed the ranges of psi used in testing diff erent ensemble models to be higher, eff ectively allowing CODEm to evaluate outof-sample ensembles made up of fewer models. In countries with nearly complete high-quality time series, smaller numbers of models in the ensemble allow the models to follow the data more closely with narrower UIs for many but not all causes. It is extremely diffi cult to properly inform national and global policy responses to reduce mortality when available cause of death information is very sparse, outdated, or unreliable. Fortunately, there is now evidence of increased investment and technical support being off ered to countries to improve their vital registration systems.
2 Although the initial focus of these eff orts is on improving data systems, a substantial impact on data quality and availability can be reasonably expected within the next 3-5 years. 159 This will not only improve the evidence base for guiding local policy decisions, but will also lead to reduced uncertainty in global comparative mortality assessments, such as the GBD study. WHO led the process of developing new guidelines for reporting on global health estimation. 20 For GBD 2015, we invested substantial resources to ensure that the GBD 2015 studies, including this Article, are compliant with the GATHER recommendations. Figures  1  and 2  provide detailed workfl ow diagrams. In appendices and the Global Health Data Exchange, we also provide detailed documentation of all sources used in the analysis.