The Evil of Unregistered Clinical Trials in Europe

This article adds to debate on the heavy lack all over the European Union of the required diligence and transparency regarding the registration in publicly available databases of research clinical trials and their results. This gap undermines patients’ right to an equitable access to health while jeopardizing financial resources for research

By  Daniele Dionisio

PEAH – Policies for Equitable Access to Health

The Evil of Unregistered Clinical Trials in Europe

 

On April 30th, 2019, Transparimed, BUKO Pharma-Kampagne, Test-Aankoop and Health Action International-HAI released the joint report  Clinical Trial Transparency at European Universities: Mapping Unreported Drug Trials.

The report points the finger at documented non-fulfilment all over the European Union (EU), including Italy, of due diligence and transparency regarding the registration in publicly available databases of research clinical trials and their results. Non-fulfilment here runs contrary to the human right to equitable access to health and puts financial resources for research at risk.

Admittedly, trials results are instrumental to allow doctors, patients and policy leaders to make informed choices including relevant to the safety and appropriateness of therapeutic interventions. As such, sharing methods and results of all clinical investigations does represent a scientific and moral duty.

How to contextualize the reported gap inside the EU regulatory system? In a landscape characterized by various and complex local/national situations, the European Medicines Agency – EMA runs the EU Clinical Trials Register–EUCTR  where clinical trials, once notified by the sponsors as completed, are put in following input by national (AIFA in Italy) regulatory agencies.

As an integral part of – and parallel with – EUCTR, EMA also runs the EudraCT database open to insertion, by the sponsors and national regulatory agencies, of updated data relevant to in progress clinical trials. Once ratified by the sponsors and completed, trials enter inclusion route in EUCTR.

Unfortunately, these rules suffer from disregard in Europe, as the just released joint report tells us. For example, as regards Italy’s low ranking in the report, we know that once a trial is approved, notification to AIFA is required for insertion in AIFA’s national register of clinical studies. As such, and consistently with the EU system, it should be incumbent to AIFA register and sponsors  to pour registration and results of Italian clinical trials directly into EudraCT. With how much completeness and sense of timing has this being happened so far? This is a well-grounded question in the light of the joint report results.

In any case, the gap of trials registration in EUCTR, far from being circumscribed to the present time or a few countries, actually strikes transversely with the majority of accountable national bodies in Europe being defaulting.

In spite of non-stop blaming by several studies in recent years (including the one by Christine Schmucker et al. published on PLoS One in 2014), the problem has long been overlooked by the media, nor has it raised enough attention by the European Commission and national governments as well.

Last year, finally, an exhaustive analysis by Ben Goldacre et al. on the British Medical Journal achieved (also thanks to a tireless pressure on the media by Till Bruckner, TranspariMED Director) widespread international success, and paved the way for a seemingly just in motion reawakening of policy makers’ consciences from inaction.

In a wider perspective, and now that a silver lining looks like it would emerge in Europe, can long-term forecasts be possible? Unfortunately, no happy ending can be counted on at a time when governments must keep on being strictly pro-active and alert so that all involved parties comply with their duty to make trials data fully accessible to citizens, health professionals and researchers.

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A Closer Look at The Toronto Star’s Stats on Vaccine Coverage in Ontario

...despite decades of underfunding and a “motley of surveillance systems” (as characterized by The Star), public health units and healthcare providers have still managed, by and large, to protect Ontarians against vaccine preventable diseases through promoting and tracking high rates of compliance and coverage...

By Dr. Lawrence C. Loh

 Adjunct Professor, Dalla Lana School of Public Health, University of Toronto

 The opinions he expresses here are his own and do not represent any other institution with which he is presently or has previously affiliated

Stats, Data and the Popular Media: a Closer Look at The Toronto Star’s Stats on Vaccine Coverage in Ontario

 

There is a part of the world where measles outbreaks are incredibly common, owing to a relatively lower measles-vaccine coverage rate of 65%. Between 2017 and the middle of 2018 there were nearly 1000 cases of measles reported, with just under a hundred cases being reported monthly and a major outbreak to kick off 2017 with nearly 500 cases seen between January and February.

A recent article in the Toronto Star, Canada’s largest daily circulating newspaper, has reported a similarly low “coverage rate” in Toronto and other health units in Ontario, which might have you imagining that this is the case right here at home.

But it’s not. In fact, the jurisdiction in question (with the mid-sixties measles coverage rate) is actually Guinea, in West Africa – also infamous for being the epicenter of the 2014 Ebola outbreak.

The flawed statistics and analyses are presented in the Toronto Star article, along with a suggestion by an academic at the University of California, Berkley that the “coverage rates” presented are “nowhere near the level required for herd immunity.”

If true, however, why isn’t Ontario and the Greater Toronto Area seeing endemic transmission, perennial outbreaks, and hundreds of cases being reported every single month?

The answer lies in in echoes of a similar misstep by the newspaper around a since-retracted article on the safety of the quadrivalent-HPV vaccine.

How vaccine records and coverage rates are determined in Ontario

It’s important to understand a few basics: firstly, how vaccine coverage data is collected in Ontario, and secondly, how coverage rates are calculated.

In Ontario, vaccinations are delivered by a variety of providers, most typically physicians, and students are typically screened annually throughout the school year for compliance with the Immunization for School Pupils Act, which requires up to date records of vaccination or a filed exemption for school entry.

This data is constantly evolving as the school year goes on, and can’t necessarily be calculated in real-time, since it relies on the health unit obtaining records from parents that they themselves have obtained from their provider.

To that end, parents are typically sent several requests, followed by warnings, before being issued with a notice of suspension, all with the goal of obtaining either up to date records, or a filed exemption (either medical or philosophic.)

Once that data is obtained, it can be used to calculate coverage rates, which are specific to disease, not to “vaccines given”. Someone up to date for ‘pentacel’, for example, is up to date for five diseases and shores up overall coverage rates for diphtheria, pertussis, polio, tetanus and haemophilus influenzae B.

So does The Star’s metric actually represent “coverage rates?”

Where did it go wrong with the statistic used by the Star? In its investigative article, presented as “coverage rates” is the summary measure: “all seven-year olds who are up to date for all antigens (diseases) required by law for school entry as of September 2018.”

While one can agree that being up-to-date with vaccines is a great individual health asset for anyone, there are several reasons why using this statistic is a significant misinterpretation:

Coverage rates are disease specific

The Star’s metric does not represent a coverage rate; coverage rates are calculated specific to disease. There is no disease called “up to date for all vaccines.”

Take, for example, a student who received every vaccine except their Men-C. They would in fact be covered for every disease save for Men-C but would still show up as “not covered” in the Toronto Star’s summary measure.

Coverage rates should answer the question: “How well protected are we against (disease x)?” That means digging into the disease specific coverage rates for the population in question.

On this, incidentally, appropriate coverage data do exist and are released annually by Public Health Ontario at the close of the enforcement year. Using measles as an example, coverage rates in 2016-17 among 7-year old students are closer to the 85-90% level across the province and much closer to required levels for community immunity. This is why Ontario’s measles picture does not look like Guinea, despite cases of measles passing through our communities with remarkable regularity.

The metric is drawn at the wrong point in time

Hockey fans know that a winning percentage at the start of the season changes dramatically as the season goes on. Similarly, the data reported by the Star was pulled at the beginning of the school year, before enforcement activities began. As work progresses over the school year, more data on the vaccination status of children (reflected as well in higher coverage rates!) becomes available, meaning The Star’s numbers largely reflect a reporting delay rather than a doomsday scenario.

Compliance rates matter more and 7-year olds are only part of the population

While 7-year olds are the age used for provincial coverage reporting, public health units also enforce legislative compliance for all students of all ages; “compliance” means having either updated records or an exemption filed. This is most useful in outbreaks, and where The Star’s metric falls short yet again – because a summary measure says nothing about which students have filed records or exemptions for a specific disease. When pertussis comes to call, 98-99% compliance rates in most health units allow determination around which students are vaccinated and which students should be excluded until the outbreak is over.

Different health units, different realities

Given thirty-five different health units in Ontario with vastly different socioeconomic and demographic realities, one can imagine that some health units will have not that many students, while others contend with a large, diverse, and growing number. Despite some things being easier in rural areas than in the metropolis, though, health units still manage to get the data they need over the course of a year – to ensure that coverage rates don’t resemble that of Guinea.

What does it really mean?

The simplest test of whether The Star’s numbers hold muster is thus in the overall infectious disease reality observed in Ontario. Our picture is not the picture of Guinea, or many other settings worldwide, where many vaccine-preventable diseases remain an ever-present threat.

While they did get the metric wrong, The Star in an accompanying editorial used their findings to helpfully highlight the potential perils posed by vaccine hesitancy. Certainly, more could be done to reassure parents that vaccinations remain one of the best ways to protect the health of their children.

More likely, however, the numbers reflected in their summary measure from the start of the school year simply reflect how data moves in a system where vaccines are delivered by one stakeholder, held by another, and tracked by a third; they also do not reflect what is commonly understood as “coverage rates.”

While real-time coverage rate tracking would be the holy-grail, such work would require providers to be on board with the mandatory reporting of the vaccines they give and significant additional investment in electronic data capture, which looms heavy against a backdrop of significant proposed funding cuts to public health in Ontario.

Perhaps the real story here is thus a foreshadowing: despite decades of underfunding and a “motley of surveillance systems” (as characterized by The Star), public health units and healthcare providers have still managed, by and large, to protect Ontarians against vaccine preventable diseases through promoting and tracking high rates of compliance and coverage. Where might further cuts lead?

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Ethical Challenges In Big Data In The Developing World

Third party sale of big data for commercial reason is more common than actually believed and many times without patient’s knowledge and consent. These challenges are compounded when the literacy rates are suboptimal in LMICs where questions of understanding informed consent have risen. There is an ongoing debate in the developed world on how to establish national and international standards and policies. Lacks of uniform definition of big data along with its blurred geographical boundaries thus have a destabilizing effect on existing bioethical norms

By Nighat Khan

Fertility and Gynecology Center, Karachi Pakistan

nighat.khan.13@ucl.ac.uk

Ethical Challenges In Big Data In The Developing World 

 

Introduction

Exponential global growth of information and communication technology has been witnessed recently in every day and in health care. Global cellular penetration is approaching 96% (Lower middle income countries -LMICs’ penetration 89%), and mobile users have reached over 7 billion according to International Telecommunications Union (ITU)  and at the end of 2018, 51.2 per cent of the global population, or 3.9 billion people, were using the Internet (1) [Fig 1].

Health care challenges like resource shortages and patient safety concerns demand a quick access and exchange of medical information amongst the practitioners with an increasing involvement of patients empowering them with a more dominant role in their care (2). In the developed world, the technology-driven exchange of information has diverse and ubiquitous sources ranging from health care industry generated like electronic medical records to patient generated personal health information through social media domains. These massive data sets or big data have the potential to improve quality of healthcare delivery in a cost effective manner by supporting wide spread health benefits such as disease surveillance, decision support and public health management (3).

Fig 1: Global information and communication technology (ICT) developments according to a recent ITU report (1)

 

Big data

Microsoft defines it as “Big data is the term increasingly used to describe the process of applying serious computing power – the latest in machine learning and artificial intelligence – to seriously massive and of- ten highly complex sets of information” (4). Other definitions describe big data as a term describing the storage and analysis of large and or complex data sets using a series of techniques including, but not limited to: NoSQL, Map Reduce and machine learning (5).

Although there is a lack of uniformity in defining big data, its attributes throw some light on its meaningful interpretation. These characteristics (6 Vs) are: Volume: the data comes in large amounts.  Variety: the data (structured and unstructured) have different sources. Velocity: the data has a real-time and continuous nature. Veracity: the data can be triangulated from multiple sources. Validity: the data reflects primary sources of collection, and, Volatility: the data is available over time.  Another important attribute, the seventh V concerns data’s value (6). A McKinsey report has predicted that big data, fueled by “disruptive” technologies (e.g., mobile Internet, Internet of things, the cloud, advanced robotics, genomics, and social media, will significantly increase by 2025 (7).

Sources of big data

Big data in healthcare is overwhelming not only because of its volume but also because of the diversity of data types and the speed at which it must be managed (8). It includes clinical data, such as from physician’s written notes, prescriptions, medical imaging, laboratory, pharmacy, insurance, and other administrative data); patient data in electronic patient records; machine generated/sensor data, such as from monitoring vital signs; social media posts, including Twitter feeds, blogs, LinkedIn status updates on Facebook and other platforms, and health related web pages (Facebook 2.07 billion  and Twitter: 330 million monthly active users); patient-specific information, including emergency care data, news feeds, and articles in medical journals; patient generated (Fitness app, social media Smart watch/fitness apps (Fitbit 23.6 million unique U.S. users) (9); big transaction data: health care claims and other billing records increasingly available in semi-structured and unstructured formats; biometric data, such as finger prints, genetics, handwriting, retinal scans, x-ray and other medical images, blood pressure, pulse and pulse-oximetry readings, and other similar types of data (10).

Data generated by US health systems alone has reached 150 exabytes a decade ago and may reach the zettabyte scale. Kaiser Permanente with over 9 million members has over 40 petabytes of data from electronic health records (11).

Opportunities of big data

In the developed world the data scientists and analysts view existence of these data sets as huge opportunity. By analysing  associations, patterns and trends there is a potential to improve quality of care in a cost effective manner. These patterns provide insights to disease epidemiology, better diagnosis and treatment as well as predict disease outcomes. They proactively identify patients who can benefit from preventive strategies by influencing the care provider behaviour such as diabetes, hypertension and other lifestyle diseases (12). Using these data sets trends in treatment can be computed and predicted. This can be achieved by combing and analysing a variety of structured and unstructured data from: electronic health records (EHR) or electronic medical records (EMR); financial and operational data; and more recently genomic data to match treatments with outcomes. Thus enabling the data scientists to predict patients at risk for disease or readmission and provide more efficient care (13).

McKinsey estimates that big data analytics can enable more than $300 billion in savings per year in U.S. healthcare, two thirds of that through reductions of approximately 8% in national healthcare expenditures. McKinsey believes big data could help reduce waste and inefficiency in many areas such as (14):

Clinical operations:  By determining more clinically relevant and cost-effective ways to diagnose and treat patients with the help of available data.

Research and development:  By predictive modeling to lower attrition and produce a leaner, faster, more targeted R&D pipeline in drugs and devices; statistical tools and algorithms to improve clinical trial design and patient recruitment to better match treatments to individual patients. This can lead to reduction in trial failures and might speed up new treatments to market. Analysis of clinical trials and patient data can identify follow on indications and discover adverse effects before products reach the market.  These two areas alone can potentially save $165 billion and $108 billion in waste respectively.

Public health: By analyzing disease patterns and tracking disease outbreaks and transmission to improve public health surveillance and speed response; Faster development of more accurately targeted vaccines, e.g., choosing the annual influenza strains; and, turning large amounts of data into actionable information that can be used to identify needs, provide services, and predict and prevent crises, especially for the benefit of populations.

Genomic analytics: By executing gene sequencing more efficiently and cost effectively and make genomic analysis a part of the regular medical care decision process and the growing patient medical record.

In public health challenges in LMICs arising from epidemics (e.g., Ebola, Zika), natural disasters (e.g., earthquakes, storms), and humanitarian crises (e.g., migration, conflict, security) ICTs will be a key resource, and big data undoubtedly comes with huge potential to aid in this quest (15).

Ethical issues in big data

Compared to traditional methods of healthcare, digital data has no geopolitical boundaries; hence clients can seek services or sell services across international and local borders. Laws on medical licences, client privacy, advertisement & marketing of services, vary in different places, therefore a law that is internationally applicable is a subject of debate and deliberation. Third party sale of big data for commercial reason is more common than actually believed and many times without patient’s knowledge and consent. These challenges are compounded when the literacy rates are suboptimal in LMICs where questions of understanding informed consent have risen. There is an ongoing debate in the developed world on how to establish national and international standards and policies. Lacks of uniform definition of big data along with its blurred geographical boundaries thus have a destabilizing effect on existing bioethical norms (16, 17).

Keys areas of ethical concern in big data exchange are

  • Data privacy and security issues by hackers and malware
  • Respect for patient privacy
  • Third party use of data
  • Quality of patient generated data
  • Data ownership
  • Respect of patient dignity in terms of continuous home monitoring
  • Genomic data access by employers and health insurance
  • Digital divide and aging population
  • Global transfer of medical information and interoperability issues
  • Informed consent or lack of informed consent in LMICs
  • Data colonization specially with data generated from LMICs
  • Lack of uniformed standards and policies across the globe
  • Difficulty in contextualization of data from the perspective of LMICs.

Global laws on data protection

The last five points of above paragraph are in context with the regions where data protection acts are weak or nonexistent. Figure 2 provides a bird’s eye view of data protection laws across the globe (18). There are diverse types of data protection laws. On 25 May 2018, the European Union (EU) regulation 2016/679 on data protection, also known as the General Data Protection Regulation (GDPR) took an effect. The GDPR, repealed previous European legislation on data protection (Directive 95/46/EC) (1), is bound to have major effects on biomedical research and digital health technologies, in Europe and beyond, given the global reach of EU-based research and the prominence of international research networks requiring interoperability of standards (19). The European Commission has so far recognized AndorraArgentinaCanada (commercial organisations), Faroe IslandsGuernseyIsraelIsle of ManJapanJerseyNew ZealandSwitzerlandUruguay and the United States of America (limited to the Privacy Shield framework) as providing adequate protection. Adequacy talks are ongoing with South Korea (19).

Fig.2: Global data protection (18)

 

Data protection laws in the developing countries

A recent UN conference on trade and development (UNCTD, 2016) summarized that the number of national data protection laws has grown rapidly, but major gaps persist. Some countries have no laws in this area, some have partial laws, and some have laws that are outdated and require amendments (20). The present regulatory environment on protection of data is far from ideal. In fact, some countries do not have rules at all. In other cases, the various pieces of legislation introduced are incompatible with each other. Increased reliance on cloud-computing solutions also raise questions about what jurisdictions apply in specific cases. Such lack of clarity creates uncertainty for consumers and businesses, limits the scope for cross-border exchange and stifles growth (UNCTD).

South East Asian countries like Pakistan have a vibrant and robust information technology (IT) workforce, however data protection and privacy laws have yet to be enacted. Data Protection Act (DPA) was drafted more than a decade ago in 2005, however it has not been promulgated into law.  Despite the availability of cheaper smart phones and tablets the government has failed to implement the DPA-2005 (21, 22). This data protection act is similar to UK Data Protection act in legal terms, however in absence of its enactment it remains a document.  Moreover, broadband internet services such as 3G and 4G are provided low cost package by mobile network operators (MNOs) thus further popularizing its use among lower to middle income masses.

Massive amounts of personal consumer information is collected, exchanged and stored without the consent and knowledge.  In absence of any legal protection this equates to data theft. This is violation of privacy under the Article 12 of UN International declaration of Human Rights 1948. Telecom operators collect vital information like address, telephone numbers and the National Identity Cards (NICs) along with the biometrics. Concerns are raised about third party sharing of this data. Digital scientists in Pakistan have been voicing their concerns at the lethargic approach to enactment of DPA and since this draft formation 12 years ago. Moreover, the technology has progressed and many clauses may need revision and updating.

 

References

  1. https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx (Accessed 10th April, 2019).
  2. Haluza, D. and Jungwirth, D., 2015. ICT and the future of health care: aspects of health promotion. International journal of medical informatics84(1), pp.48-57.
  3. pdf (Accessed 10th April, 2019).
  4. Big Data Definition – MIKE2.0, the open source methodology for Information Development. http://mike2.openmethodology.org/wiki/Big Data Definition
  5. Ward, J.S. and Barker, A., 2013. Undefined by data: a survey of big data definitions. arXiv preprint arXiv:1309.5821.
  6. Gandomi, A. and Haider, M., 2015. Beyond the hype: Big data concepts, methods, and analytics. International journal of information management, 35(2), pp.137-144.
  7. Groves, P., Kayyali, B., Knott, D. and Kuiken, S.V., 2016. The ‘big data ‘revolution in healthcare: Accelerating value and innovation.
  8. Manyika J M, Chui B, Bughin R, Dobbs C, Roxburgh A. 2011. Big data. The next frontier for innovation, competition and productivity. Washington DC; McKinsey Global institute.
  9. Raghupathi and Raghupathi Health Information Science and Systems 2014, 2:3 http://www.hissjournal.com/content/2/1/3 (Accessed 10th April, 2019).
  10. Paul, A., Ahmad, A., Rathore, M.M. and Jabbar, S., 2016. Smart buddy: defining human behaviors using big data analytics in social internet of things. IEEE Wireless communications23(5), pp.68-74.
  11. Ratha, N.K., Connell, J.H. and Pankanti, S., 2015. Big Data approach to biometric-based identity analytics. IBM Journal of Research and Development59(2/3), pp.4-1.
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  13. Hemingway, H., Asselbergs, F.W., Danesh, J., Dobson, R., Maniadakis, N., Maggioni, A., Van Thiel, G.J., Cronin, M., Brobert, G., Vardas, P. and Anker, S.D., 2017. Big data from electronic health records for early and late translational cardiovascular research: challenges and potential. European heart journal39(16), pp.1481-1495.
  14. Pramanik, M.I., Lau, R.Y., Demirkan, H. and Azad, M.A.K., 2017. Smart health: Big data enabled health paradigm within smart cities. Expert Systems with Applications87, pp.370-383.
  15. Sahay, S., 2016. Big Data and Public Health: Challenges and Opportunities for Low and Middle Income Countries. CAIS39, p.20.
  16. Taylor, L., 2016. No place to hide? The ethics and analytics of tracking mobility using mobile phone data. Environment and Planning D: Society and Space, 34(2), pp.319-336.
  17. Nickel, P.J., 2019. The ethics of uncertainty for data subjects. In The Ethics of Medical Data Donation(pp. 55-74). Springer, Cham.
  18. https://idpc.org.mt/en/Pages/dp/transfers/internationaladequacy.aspx (Accessed 7th April, 2019).
  19. https://ec.europa.eu/info/law/law-topic/data-protection/data-transfers-outside-eu/adequacy-protection-personal-data-non-eu-countries_en (Accessed 7th April, 2019).
  20. https://unctad.org/en/PublicationsLibrary/dtlstict2016d1_en.pdf (Accessed 7th April, 2019).
  21. https://privacyinternational.org/state-privacy/1008/state-privacy-pakistan (Access 8th April, 2019).
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Why Public Health Care is Better

Viva Salud is a Belgian NGO that supports movements for health in the Philippines, Palestine and DR Congo. Recently, they launched the paper Why Public Health Care is Better  with the aim to debunk long lasting myths concerning the commercialisation of health care

 By Julie Steendam

Policy Officer on Health, Viva Salud

Brussels, Belgium

 Why Public Health Care is Better

 

This article is a summary of the paper
 Why Public Health Care is Better
 We hope that this paper can be a support for those social movements standing up for social justice

 

 The majority of countries in the world agreed to take all possible measures to fulfil the right to accessible and qualitative health care for their population. However, year 2019, this is far from achieved. Even worse, many national governments and international institutions direct health policies along a market-led approach. Privatisations have been brought up as the solution to national health systems’ funding shortages. But numerous case studies and comprehensive research show that health outcomes get worse when the pursuit of profit comes in:

Privatisation triggers higher inequality in access to care
Private hospitals have to make a profit, so they focus mainly on people who can afford it. This creates the risk of a health system at two speeds. On the one hand, high-tech and specialised care for the rich and, on the other hand, simple public health care for the less well-off. This despite the fact that it is the duty of public service providers to provide care to everyone, without distinction.

Privatisation is often more expensive in the long run
Unexpected costs, such as rising interest rates or expensive energy prices, are usually passed on to the government or the patient in private hospitals. An Oxfam study calculated that a public-private hospital in Lesotho costs the government three times more than the public hospital it replaced. Under some contracts, the company can even sue the state for costs related to protests of employees.

Privatisation isn’t more efficient
Public systems are more efficient because they ensure economies of scale in the purchasing, supply and distribution of drugs and equipment.
By contrast, in the United Kingdom, the number of managers in the National Health Service tripled since the introduction of a market logic. In the Netherlands, private health insurers spend 500 million euros per year on advertising campaigns.

Privatisation doesn’t lead to better quality
In today’s market logic, private institutions will focus on the treatments that are financially interesting, instead of those that benefit the patient the most. In Peru, private hospitals are much more likely to choose a more risky caesarean section than a natural birth, because the doctor can charge more.

Privatisation leads to less public control
Negotiations between private players often take place under strict rules of confidentiality. Public control is therefore very difficult, which makes the risk of corruption increase. Engaging funds from the private sector opens the way for corporate involvement in policy shaping.

Privatisation leads to a lower availability of health workers and deteriorating working conditions for them
Commercial companies take the scarce resources, such as health workers, away from the public sector. This so-called “brain drain” leads to shortages in the public sector and in more remote areas. Moreover, the drive for ever higher profit margins often leads to poorer working conditions, unpaid overtime and higher work pressure. Burn-out and stress symptoms are very common in the health sector.

The alternative exists

This paper starts from the positive side. All over the world, social movements and governments make efforts to change their health care system for the benefit of the population.

Universal access to quality health care is a feasible political choice

Countries that prioritise people’s well-being and choose to invest in making health care accessible to all achieve better health outcomes. Even countries with low expenditure on health have been able to build strong health systems.

Need, not wealth

The only proven route to realise this is cancelling all fees for health care and essential medicines, and increasing public investments in well-trained staff, nearby health facilities, and prevention and health promotion programs. A unified public system does not have the disadvantageous contradictions brought by the fragmentation and competition that characterises mixed private-public health systems.

What the international community can do

Reinforcing countries financial capacities to cope with a potential budget increase should also be a focus of the international community and international institutions, for example by

– stepping away from imposed budget restrictions in public services

– regulating pharmaceutical companies’ monopoly positions

– cancelling debt

– fighting large-scale tax evasion

– excluding health services from trade and investment agreements.

Due to globalisation, the vast majority of people in the world are subjected to very similar economic realities, forces and dynamics: environmental degradation, the global competition of workers, attacks to and exclusion from social protection schemes and a growing inequality between social classes to name but a few.[i]

This global emergency situation represents an unprecedented challenge for humanity. Since health and other societal challenges are very interconnected, the struggle for health can function as a major unifying factor in the mobilisations required to address these issues.

——————————

[i] The Struggle for Health, 2018, p.2

Health Breaking News 329

Health Breaking News Links, as part of the research project PEAH (Policies for Equitable Access to Health), aim to focus on the latest challenges by trade and governments rules to equitable access to health in resource-limited settings

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Development-bank climate funds seek new dollars, as competition heats up 

 

 

 

 

 

 

 

 

 

 

Health Breaking News 328

Health Breaking News Links, as part of the research project PEAH (Policies for Equitable Access to Health), aim to focus on the latest challenges by trade and governments rules to equitable access to health in resource-limited settings

Health Breaking News 328

 

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