Effectiveness of Action in India to Reduce Exposure of Gyps Vultures to the Toxic Veterinary Drug Diclofenac

Contamination of their carrion food supply with the non-steroidal anti-inflammatory drug diclofenac has caused rapid population declines across the Indian subcontinent of three species of Gyps vultures endemic to South Asia. The governments of India, Pakistan and Nepal took action in 2006 to prevent the veterinary use of diclofenac on domesticated livestock, the route by which contamination occurs. We analyse data from three surveys of the prevalence and concentration of diclofenac residues in carcasses of domesticated ungulates in India, carried out before and after the implementation of a ban on veterinary use. There was little change in the prevalence and concentration of diclofenac between a survey before the ban and one conducted soon after its implementation, with the percentage of carcasses containing diclofenac in these surveys estimated at 10.8 and 10.7%, respectively. However, both the prevalence and concentration of diclofenac had fallen markedly 7–31 months after the implementation of the ban, with the true prevalence in this third survey estimated at 6.5%. Modelling of the impact of this reduction in diclofenac on the expected rate of decline of the oriental white-backed vulture (Gyps bengalensis) in India indicates that the decline rate has decreased to 40% of the rate before the ban, but is still likely to be rapid (about 18% year−1). Hence, further efforts to remove diclofenac from vulture food are still needed if the future recovery or successful reintroduction of vultures is to be feasible

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Estimates of changes between three surveys of ungulate carcasses (T1, T2, T3) in the true prevalence <i>f</i> of diclofenac, the arithmetic mean concentration of diclofenac in livers of animals in which it was present (ppm wet weight), the estimated mean percentage of vultures killed by a meal of mixed tissues, and the annual percentage rate of decline of the vulture population.

<p>The interval between meals <i>F</i> was assumed to be two or three days and annual adult survival in the absence of diclofenac <i>S₀</i> was assumed to be either 0.90 or 0.97. Ratios of parameter estimates and their bootstrap 95% confidence limits are shown for each pairwise comparison of surveys.</p

Comparison of probability density functions of diclofenac dose per unit vulture body weight from ungulate tissue before and after the ban on the veterinary use of diclofenac.

<p>Probability density functions are shown of estimated diclofenac dose (mg kg⁻¹ wet weight) per meal for birds eating a mixture of all edible ungulate tissues and feeding at intervals of three days. Results are shown for three surveys: red = T1, pre-ban, dark green = T2, soon after the ban, dark blue = T3, 7–31 months after the ban. The proportion of vultures expected to be killed by a given dose of diclofenac is shown by the dose-response curve (black, with right-hand y axis). The products of the dose probability density functions and the dose-response curve are shown by the orange, light green and light blue curves for surveys T1, T2 and T3 respectively. The areas under these curves give the estimated proportion of vultures killed per meal.</p

Comparisons between the residual deviance and Akaike Information Criterion (AIC) of various logistic regression models of the variation among site clusters (S) and survey time periods (T) in the apparent prevalence of diclofenac (the proportion of liver samples with detectable levels of the drug).

<p>A null model in which the proportion was assumed to be constant (C) across site clusters and time periods was compared with models in which the odds of a sample having detectable diclofenac varied either among site clusters or time periods or was given by the product of a site-cluster effect and a time-period effect (denoted S+T). A full model with proportions specific to each site-time combination is denoted S.T.</p

Estimates of the parameters of a model which describes the true prevalence <i>f</i> of diclofenac in liver samples taken during three surveys of ungulate carcasses (T1, T2, T3) and the scale <i>a</i> and shape <i>b</i> parameters of the Weibull distribution of diclofenac concentrations (ppm wet weight).

<p>The value <i>b</i> is assumed to be common to all three surveys. Also shown is the arithmetic mean concentration of diclofenac (ppm wet weight) for those samples which contained the compound, calculated from <i>a</i> and <i>b</i>. Parameter estimates and their bootstrap 95% confidence limits are shown for each of three surveys.</p

Comparison of probability density functions of diclofenac concentrations in ungulate liver before and after the ban on the veterinary use of diclofenac.

<p>Fitted probability density functions are shown of diclofenac concentration (ppm wet weight) in ungulate liver samples from three surveys: red = T1, pre-ban, dark green = T2, soon after the ban, dark blue = T3, 7–31 months after the ban. The curves are derived from a Weibull model in which both the true prevalence of diclofenac <i>f</i> (including those with concentrations < LOQ) and the scale parameter <i>a</i> of the Weibull distribution of concentrations of diclofenac in those samples are determined by a site-cluster effect and a survey period effect. The shape parameter <i>b</i> of the Weibull distribution is assumed not to vary with site-cluster or survey period. Values of <i>f</i> and <i>a</i> in all three surveys were adjusted so that the results simulate those expected if the 21 site-clusters covered by the T1 (pre-ban) survey had been covered at the same sampling intensity in the second T2 and third T3 surveys.</p

Comparisons between the residual deviance and Akaike Information Criterion (AIC) of various Weibull models of the variation among site clusters (S) and survey time periods (T) in the concentration of diclofenac in liver samples with detectable levels.

<p>A null model in which the scale and shape parameters <i>a</i> and <i>b</i> of the Weibull distribution of concentrations of diclofenac were assumed to be constant (C) across sites and time periods is compared with models in which the scale parameter <i>a</i> and/or the shape parameter <i>b</i> varied with site cluster or time period or were given by the product of S and T effects (denoted by S+T). The full model with parameters specific to each site cluster and time combination is denoted by S.T.</p

Numbers of ungulate liver samples collected in each of 21 site clusters in three survey periods: T1 = May 2004–July 2005, T2 = April–December 2006, T3 = January 2007–December 2008.

<p>Also shown are the total numbers of samples taken, the number and proportion of them in which diclofenac was detected, the arithmetic mean concentration of diclofenac (ppm wet weight) in the samples in which the compound was detected and the species composition of the ungulates from which liver tissue was sampled.</p