Superbug Risk Rises as Big Pharma Fails to Disclose Antibiotic Waste Leaked From Factories
By Madlen Davies and Sam Loewenberg
Many of the world's leading drug manufacturers may be leaking antibiotics from their factories into the environment, according to a new report from a drug industry watchdog. This risks creating more superbugs.
The report surveyed household-name pharmaceutical giants like GSK, Novartis and Roche as well as generic companies which make non-branded products for the NHS and other health systems.
None of the 18 companies polled would reveal how much antibiotic discharge they release into the environment, according to the independent report from the not-for-profit body, the Access to Medicine Foundation. Only eight said they set limits for how much could be released in wastewater.
Only one disclosed the name of its suppliers—a move which is seen as important as it would make companies accountable for their environmental practices.
Commenting on the report, Dr. Mark Holmes, a veterinary scientist at the University of Cambridge, said, "Antibiotic resistance is complex but if we are to deal with this challenge every sector must do their bit. The pharmaceutical industry has been a key player in improving public health but a failure to address environmental impacts of antibiotic pollution could undo much of their good work."
Changing Markets, an NGO which has campaigned on the issue of pharmaceutical waste, said, "Pharmaceutical companies have a clear responsibility to tackle pollution in their supply chains, not least because of the considerable human health impacts associated with untreated waste from pharma manufacturing, prime among the creation of drug-resistant bacteria. From our own research in India and China, where most of the world's generic drugs are made, we know this is an ongoing problem and that very little progress is happening on the ground.
"As the report also highlights, there is a crying lack of transparency about pharmaceutical supply chains which means that we know practically nothing about where our drugs are made. This is a scandal and pharmaceutical companies will face increasing calls to do something about it."
Antibiotic waste from pharmaceutical manufacturing leaking into the environment is a neglected driver of antimicrobial resistance—or AMR—according to a global report published in 2016 by ex-finance minister Lord Jim O'Neill. This is because residues of antibiotics in the environment expose bacteria to levels of the drugs that fuel the emergence of resistance. The "superbugs" that form as a result can spread all over the world. To tackle the problem, Lord O'Neill called for regulators to set minimum standards around the release of waste and for manufacturers to drive higher standards through their supply chains.
AMR has been described as one of the greatest health problems facing the world. Without effective antibiotics, infections become more difficult to treat and common medical procedures like joint replacements, C sections and chemotherapy care for cancer—which rely on the drugs to kill infection—could become too risky to carry out.
Last year the Bureau of Investigative Journalism reported on a study which revealed "excessively high" levels of antimicrobial drugs—as well as superbugs—in wastewater from a major drug production hub in the Indian city of Hyderabad. The quantities found were strong enough to treat patients, scientists said. This followed an earlier report of resistant bacteria in the wastewater of a factory there which supplies the NHS with antibiotics.
The Antimicrobial Resistant Benchmark 2018 report—released Wednesday at the World Economic Forum conference in DAVOS—evaluated how a cross-section of the pharmaceutical industry are responding to the threat of AMR.
It found none disclosed their actual discharge levels—information the authors said is "valuable and vital" as it could allow governments and researchers to understand the relationship between discharge and the development of superbugs.
Three generic drug companies—Cipla, Lupin and Sun Pharma—did not show any evidence of a strategy to minimize the impact of their antibiotic manufacturing on the environment, the report found, although Cipla promised to develop one this year.
Of particular concern were external companies that work for the main drug companies. Third-party companies manufacture and supply most drug firms with the key components of antibiotics, known as active pharmaceutical ingredients (APIs); and external waste treatment plants, which many drug companies use to process their discharge from antibiotic manufacturing. Some companies have on-site wastewater treatment.
Only eight companies set discharge limits for antibiotic waste, and for half the companies these limits only apply to their own sites, rather than their suppliers' too. Only two companies—GSK and Novartis—require their external waste treatment plants to follow their limits. Sanofi and Roche, for example, do not monitor the discharge made by their external waste treatment plants, the report noted.
The Medicines Company was the only one willing to identify its third-party manufacturers, a move the report said would enable governments and researchers to assess the impact of individual manufacturers on antibiotic resistance. The report noted that pharmaceutical companies that sell antibiotics "may be able to exert considerable influence over the environmental risk management of their suppliers."
The large pharmaceuticals polled were GSK, Johnson and Johnson, Merck & Co, Novartis, Pfizer, Roche, Sanofi and Shinogi. The generic companies were Aspen, Aurobindo, Cipla, Dr Reddy's, Fresenius Kabi, Lupin, Macleods, Mylan, Sun Pharma and Teva.
Access to Medicine is an Amsterdam-based NGO that receives funding from the UK Government, the Bill & Melinda Gates Foundation and the Dutch Ministry of Foreign Affairs.
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By Eric Tate and Christopher Emrich
Disasters stemming from hazards like floods, wildfires, and disease often garner attention because of their extreme conditions and heavy societal impacts. Although the nature of the damage may vary, major disasters are alike in that socially vulnerable populations often experience the worst repercussions. For example, we saw this following Hurricanes Katrina and Harvey, each of which generated widespread physical damage and outsized impacts to low-income and minority survivors.
Mapping Social Vulnerability<p>Figure 1a is a typical map of social vulnerability across the United States at the census tract level based on the Social Vulnerability Index (SoVI) algorithm of <a href="https://onlinelibrary.wiley.com/doi/abs/10.1111/1540-6237.8402002" target="_blank"><em>Cutter et al.</em></a> . Spatial representation of the index depicts high social vulnerability regionally in the Southwest, upper Great Plains, eastern Oklahoma, southern Texas, and southern Appalachia, among other places. With such a map, users can focus attention on select places and identify population characteristics associated with elevated vulnerabilities.</p>
Fig. 1. (a) Social vulnerability across the United States at the census tract scale is mapped here following the Social Vulnerability Index (SoVI). Red and pink hues indicate high social vulnerability. (b) This bivariate map depicts social vulnerability (blue hues) and annualized per capita hazard losses (pink hues) for U.S. counties from 2010 to 2019.<p>Many current indexes in the United States and abroad are direct or conceptual offshoots of SoVI, which has been widely replicated [e.g., <a href="https://link.springer.com/article/10.1007/s13753-016-0090-9" target="_blank"><em>de Loyola Hummell et al.</em></a>, 2016]. The U.S. Centers for Disease Control and Prevention (CDC) <a href="https://www.atsdr.cdc.gov/placeandhealth/svi/index.html" target="_blank">has also developed</a> a commonly used social vulnerability index intended to help local officials identify communities that may need support before, during, and after disasters.</p><p>The first modeling and mapping efforts, starting around the mid-2000s, largely focused on describing spatial distributions of social vulnerability at varying geographic scales. Over time, research in this area came to emphasize spatial comparisons between social vulnerability and physical hazards [<a href="https://doi.org/10.1007/s11069-009-9376-1" target="_blank"><em>Wood et al.</em></a>, 2010], modeling population dynamics following disasters [<a href="https://link.springer.com/article/10.1007%2Fs11111-008-0072-y" target="_blank" rel="noopener noreferrer"><em>Myers et al.</em></a>, 2008], and quantifying the robustness of social vulnerability measures [<a href="https://doi.org/10.1007/s11069-012-0152-2" target="_blank" rel="noopener noreferrer"><em>Tate</em></a>, 2012].</p><p>More recent work is beginning to dissolve barriers between social vulnerability and environmental justice scholarship [<a href="https://doi.org/10.2105/AJPH.2018.304846" target="_blank" rel="noopener noreferrer"><em>Chakraborty et al.</em></a>, 2019], which has traditionally focused on root causes of exposure to pollution hazards. Another prominent new research direction involves deeper interrogation of social vulnerability drivers in specific hazard contexts and disaster phases (e.g., before, during, after). Such work has revealed that interactions among drivers are important, but existing case studies are ill suited to guiding development of new indicators [<a href="https://doi.org/10.1016/j.ijdrr.2015.09.013" target="_blank" rel="noopener noreferrer"><em>Rufat et al.</em></a>, 2015].</p><p>Advances in geostatistical analyses have enabled researchers to characterize interactions more accurately among social vulnerability and hazard outcomes. Figure 1b depicts social vulnerability and annualized per capita hazard losses for U.S. counties from 2010 to 2019, facilitating visualization of the spatial coincidence of pre‑event susceptibilities and hazard impacts. Places ranked high in both dimensions may be priority locations for management interventions. Further, such analysis provides invaluable comparisons between places as well as information summarizing state and regional conditions.</p><p>In Figure 2, we take the analysis of interactions a step further, dividing counties into two categories: those experiencing annual per capita losses above or below the national average from 2010 to 2019. The differences among individual race, ethnicity, and poverty variables between the two county groups are small. But expressing race together with poverty (poverty attenuated by race) produces quite different results: Counties with high hazard losses have higher percentages of both impoverished Black populations and impoverished white populations than counties with low hazard losses. These county differences are most pronounced for impoverished Black populations.</p>
Fig. 2. Differences in population percentages between counties experiencing annual per capita losses above or below the national average from 2010 to 2019 for individual and compound social vulnerability indicators (race and poverty).<p>Our current work focuses on social vulnerability to floods using geostatistical modeling and mapping. The research directions are twofold. The first is to develop hazard-specific indicators of social vulnerability to aid in mitigation planning [<a href="https://doi.org/10.1007/s11069-020-04470-2" target="_blank" rel="noopener noreferrer"><em>Tate et al.</em></a>, 2021]. Because natural hazards differ in their innate characteristics (e.g., rate of onset, spatial extent), causal processes (e.g., urbanization, meteorology), and programmatic responses by government, manifestations of social vulnerability vary across hazards.</p><p>The second is to assess the degree to which socially vulnerable populations benefit from the leading disaster recovery programs [<a href="https://doi.org/10.1080/17477891.2019.1675578" target="_blank" rel="noopener noreferrer"><em>Emrich et al.</em></a>, 2020], such as the Federal Emergency Management Agency's (FEMA) <a href="https://www.fema.gov/individual-disaster-assistance" target="_blank" rel="noopener noreferrer">Individual Assistance</a> program and the U.S. Department of Housing and Urban Development's Community Development Block Grant (CDBG) <a href="https://www.hudexchange.info/programs/cdbg-dr/" target="_blank" rel="noopener noreferrer">Disaster Recovery</a> program. Both research directions posit social vulnerability indicators as potential measures of social equity.</p>
Social Vulnerability as a Measure of Equity<p>Given their focus on social marginalization and economic barriers, social vulnerability indicators are attracting growing scientific interest as measures of inequity resulting from disasters. Indeed, social vulnerability and inequity are related concepts. Social vulnerability research explores the differential susceptibilities and capacities of disaster-affected populations, whereas social equity analyses tend to focus on population disparities in the allocation of resources for hazard mitigation and disaster recovery. Interventions with an equity focus emphasize full and equal resource access for all people with unmet disaster needs.</p><p>Yet newer studies of inequity in disaster programs have documented troubling disparities in income, race, and home ownership among those who <a href="https://eos.org/articles/equity-concerns-raised-in-federal-flood-property-buyouts" target="_blank">participate in flood buyout programs</a>, are <a href="https://www.eenews.net/stories/1063477407" target="_blank" rel="noopener noreferrer">eligible for postdisaster loans</a>, receive short-term recovery assistance [<a href="https://doi.org/10.1016/j.ijdrr.2020.102010" target="_blank" rel="noopener noreferrer"><em>Drakes et al.</em></a>, 2021], and have <a href="https://www.texastribune.org/2020/08/25/texas-natural-disasters--mental-health/" target="_blank" rel="noopener noreferrer">access to mental health services</a>. For example, a recent analysis of federal flood buyouts found racial privilege to be infused at multiple program stages and geographic scales, resulting in resources that disproportionately benefit whiter and more urban counties and neighborhoods [<a href="https://doi.org/10.1177/2378023120905439" target="_blank" rel="noopener noreferrer"><em>Elliott et al.</em></a>, 2020].</p><p>Investments in disaster risk reduction are largely prioritized on the basis of hazard modeling, historical impacts, and economic risk. Social equity, meanwhile, has been far less integrated into the considerations of public agencies for hazard and disaster management. But this situation may be beginning to shift. Following the adage of "what gets measured gets managed," social equity metrics are increasingly being inserted into disaster management.</p><p>At the national level, FEMA has <a href="https://www.fema.gov/news-release/20200220/fema-releases-affordability-framework-national-flood-insurance-program" target="_blank">developed options</a> to increase the affordability of flood insurance [Federal Emergency Management Agency, 2018]. At the subnational scale, Puerto Rico has integrated social vulnerability into its CDBG Mitigation Action Plan, expanding its considerations of risk beyond only economic factors. At the local level, Harris County, Texas, has begun using social vulnerability indicators alongside traditional measures of flood risk to introduce equity into the prioritization of flood mitigation projects [<a href="https://www.hcfcd.org/Portals/62/Resilience/Bond-Program/Prioritization-Framework/final_prioritization-framework-report_20190827.pdf?ver=2019-09-19-092535-743" target="_blank" rel="noopener noreferrer"><em>Harris County Flood Control District</em></a>, 2019].</p><p>Unfortunately, many existing measures of disaster equity fall short. They may be unidimensional, using single indicators such as income in places where underlying vulnerability processes suggest that a multidimensional measure like racialized poverty (Figure 2) would be more valid. And criteria presumed to be objective and neutral for determining resource allocation, such as economic loss and cost-benefit ratios, prioritize asset value over social equity. For example, following the <a href="http://www.cedar-rapids.org/discover_cedar_rapids/flood_of_2008/2008_flood_facts.php" target="_blank" rel="noopener noreferrer">2008 flooding</a> in Cedar Rapids, Iowa, cost-benefit criteria supported new flood protections for the city's central business district on the east side of the Cedar River but not for vulnerable populations and workforce housing on the west side.</p><p>Furthermore, many equity measures are aspatial or ahistorical, even though the roots of marginalization may lie in systemic and spatially explicit processes that originated long ago like redlining and urban renewal. More research is thus needed to understand which measures are most suitable for which social equity analyses.</p>
Challenges for Disaster Equity Analysis<p>Across studies that quantify, map, and analyze social vulnerability to natural hazards, modelers have faced recurrent measurement challenges, many of which also apply in measuring disaster equity (Table 1). The first is clearly establishing the purpose of an equity analysis by defining characteristics such as the end user and intended use, the type of hazard, and the disaster stage (i.e., mitigation, response, or recovery). Analyses using generalized indicators like the CDC Social Vulnerability Index may be appropriate for identifying broad areas of concern, whereas more detailed analyses are ideal for high-stakes decisions about budget allocations and project prioritization.</p>
By Jessica Corbett
Sen. Bernie Sanders on Tuesday was the lone progressive to vote against Tom Vilsack reprising his role as secretary of agriculture, citing concerns that progressive advocacy groups have been raising since even before President Joe Biden officially nominated the former Obama administration appointee.