Midwives Can Significantly Reduce Maternal Mortality, But They Need Support

first_img ShareEmailPrint To learn more, read: Posted on May 5, 2016May 4, 2018By: Kayla McGowan, Project Coordinator, Women and Health Initiative, Harvard T.H. Chan School of Public HealthClick to share on Facebook (Opens in new window)Click to share on Twitter (Opens in new window)Click to share on LinkedIn (Opens in new window)Click to share on Reddit (Opens in new window)Click to email this to a friend (Opens in new window)Click to print (Opens in new window)In honor of International Day of the Midwife, I sat down with Rima Jolivet, our Maternal Health Technical Director, for insight into her experience as a certified nurse-midwife as well as her thoughts on the current and future landscape of midwifery.How did you first become interested in the field of maternal health and midwifery?RJ: I got interested in midwifery because my son was born in the U.K., in the National Health Service system, where midwifery is standard of care for low-risk women. I arrived there about seven and a half months pregnant and was funneled into midwifery care because I was young and low-risk. And I thought it was amazing. I thought the system was organized in a very different way from our [U.S.] system. It seemed to me that it was organized around the needs and preferences of women and families for the most part. Although it wasn’t fancy, it was very much oriented toward being people-centered. I had a great experience and it made me want to make that kind of care available to more women.What is it like being a midwife? Could you tell me a bit about the challenges as well as some of the rewarding aspects of the career?RJ: What I did not expect about being a midwife is that in the U.S., midwifery is not only a profession, but also sort of a political struggle, because the midwifery model of care is not the standard of care. Part of being a midwife in this country is being a political activist for the midwifery model of care, which I didn’t expect. The midwifery model of care is evidence-based, centered on respectful care, and yet, there’s a lot of struggle to implement it because of structural and ideological differences. There are certain interventions or choices – for example, vaginal birth after Cesarean section or vaginal breech delivery when the circumstances are appropriate, or delivery outside of the hospital – that are very much supported in other systems, and all cadres work together to make those as safe and effective as possible. That’s not the case here. There’s a lot of battling about whose evidence is better, and who’s right, as opposed to working together to make choices that women want available, safe, and effective. Midwives attend about 12% of all vaginal births and 8% of total births in the U.S., so it’s not the mainstream approach, although it’s very much supported by evidence.What are some of the barriers midwives face in providing quality care? RJ:  There has been a series of midwifery strategy meetings going on recently, co-led by WHO, the International Confederation of Midwives (ICM), White Ribbon Alliance, and USAID, and the WHO presented a body of research on barriers and challenges for midwives to practice quality midwifery care. The research describes challenges in three domains: economic, professional, and social. It lays out patterns of economic discrimination that mirror gender inequality in the workplace; the professional area has to do with lack of professional legitimacy, authority and respect; and the social domain has to do with how midwives are perceived, their social status, as compared to other providers.RJ: One challenging issue is that there are many different definitions of midwives, and even the idea that other types of health workers can provide “midwifery care.”  The ICM has been promoting a standard definition of the midwife. Professional groups in countries are working to meet this definition. For example, in the U.S., there’s been a big push by direct entry (or “lay”) midwives, who are not nurse midwives, to meet ICM and other criteria for a qualified maternal health provider and to obtain legal licensure in all the jurisdictions of this country. In the last decade, there’s been a lot of progress for certified professional midwives. There have also been great strides in the attempts to bring all of the kinds of midwives together under one big tent, to work together in coalition.What would you say is the biggest misconception regarding the field of midwifery, either here in the U.S. on in low-income settings?RJ: In the U.S., there are a lot of misconceptions about midwives and their scope of practice, for example the idea that midwives only attend out-of-hospital births or that you can’t have an epidural with a midwife or that midwives aren’t well trained. A lot of people don’t know that midwives in the U.S. provide the full scope of women’s health care, including contraception and family planning, and routine gynecology. Similar misconceptions may also exist in low- and middle-income countries where midwives are sometimes perceived as unqualified.According to the Lancet series on midwifery, universal coverage of essential interventions that fall within the scope of midwifery practice (including pre-pregnancy, antenatal, labor, birth, and postpartum care and family planning) could prevent 83% of all maternal deaths, stillbirths, and neonatal deaths. This is monumental! Could you explain this a bit? What are some of the most significant ways midwives can save the lives of moms and babies? RJ: Nearly all essential life-saving interventions are within the scope of practice of midwives, and if there were enough midwives to ensure universal coverage of those interventions, that’s how the lives would be saved. It’s a high value, relatively low-cost investment in midwifery. It’s a cost-effective solution if it were implemented and scaled up.Could you tell me about the role of midwives in achieving the Sustainable Development Goals (SDGs) and meeting the strategies toward ending preventable maternal mortality (EPMM) targets?RJ: EPMM takes a very broad approach to maternal health and survival, which includes a focus on health system strengthening to ensure equitable access to high quality care for every woman and baby. As we just discussed, achieving this means addressing the workforce issues in every setting to be sure there are enough qualified midwives, themselves a high-quality, cost-effective solution, working in functional, well-equipped care settings. Effectively scaling up midwifery care will help achieve the SDG/EPMM targets. Midwives have the capacity and the potential to really contribute to mortality reduction. But they need the support of the enabling environment to do that: more midwives need to be trained so there are sufficient numbers, and attention is needed to ensure the availability of sufficient, functional facilities, with good referral systems and essential commodities. In lots of low- and middle-income countries, midwives are the tip of the spear, and all of the burden is on them without the enabling environments they need to ensure quality of care. The studies commissioned by WHO highlight the gender dimension of midwifery. Midwives and nurses tend to be women, and as frontline workers they do not receive as much respect as physicians, so their status within health systems is perceived as lower and they are poorly represented in leadership and decision-making positions. They bear an inordinate burden of deficient health systems without the agency to effect change. They are at the front line without the necessary materials and protections: commodities, infrastructure, support, and respect.According to the UNFPA, of the 73 countries that make up 92% of all maternal and newborn deaths in the world, only 4 have a midwifery workforce sufficient to meet the universal needs for reproductive, sexual, maternal, and newborn health. How do you think the global health community can scale up midwifery programs to ensure adequate human resources and quality care?RJ: There are issues of education and training of midwives, as well as their distribution in the workforce (recruiting, retention, and getting midwives where they’re most needed in sufficient numbers). But if the working conditions are as difficult as they are in many high burden countries, how do you sell that? It can be a hard sell.The ICM has a new resource called the Midwifery Services Framework (MSF), which helps countries interested in strengthening midwifery go through a process to look at their need, starting with the question, “What is the essential service package that all women should get?” From there, the framework walks decision makers through a process to identify how many of those interventions could be delivered by midwives, how many midwives would be needed to provide coverage of those interventions to all of the women in the country, and then what that implies in terms of need to scale up education programs and training for midwives. The MSF walks stakeholders through that entire workforce development cascade based on a rational decision-making process to analyze the need.The theme of IDM 2016 is “Women and Newborns: The Heart of Midwifery,” and the International Confederation of Midwives is asking midwives to share their stories on social media. How would you complete this sentence, “I am a midwife, this is what I do…”?RJ: For me, this goes back to the reason I became a midwife. What midwives do (and as a midwife, this is what I try to do) is to put women and families at the center of all we do…including health care planning, implementation, and continuous improvement to better reflect the needs, values, and preferences of the people the health system is for.Where do you see the future of midwifery?RJ: I see midwifery in the future respected and acknowledged as the global standard of care, with all of the support and the enabling factors needed to bear that standard in the areas of education and training, workforce protections, and adequate infrastructure, commodities, team-based professional support and functional referral systems.—To learn more about the state of midwifery around the globe, read our roundup: #IDM2016: Key Resources for Midwifery!Join us in celebrating International Day of the Midwife! Follow along on Twitter by using #IDM2016Share this:last_img read more

Just as Google offers one place to search for all

first_imgJust as Google offers one place to search for all online data, Alation is hoping to offer a single place for enterprises to search their data. Alation, launched officially this morning, is a startup with software that ties into a company’s data stores, and then gives users a natural language search interface for querying that information.Satyen Sangani, CEO and cofounder of Alation, said the company has been working on this technology for two years. “We partnered with massive companies that have lots of data; our installation gets us up and running in days or hours, in some cases,” he said.“We’re able to parse through query logs from 20-odd database systems, and all that happens pretty seamlessly. You point us to the underlying data storage, either through URI or some underlying data connection.” Making natural language queries available on enterprise data sources allows everyone to have access to the data, not just scientists, said Sangani. “A query could look like, ‘Tell me how many doctors there are in Nebraska.’ It does translate natural language search, giving you the context of the data. We tell you not just the table, but also that Jim has used this table 400 times in the last month. We give you a whole bunch of context that’s not a part of the data itself,” he said.Alation is also a collaborative platform, said Sangani. “There’s a collaborative layer. We give you a StackOverflow-like interface on top of the data, so people can start talking about how they handle the data. Now that you can find the data, you can find what people have done with it,” he said.Sangani said that Alation’s approach is the opposite of traditional data storage thinking. “The historic approach has been, ‘Let’s go ahead and document the data through some massive human-based effort,’ ” he said. “That’s like painting the Golden Gate Bridge: By the time you get to one end, you have to start over and repaint it again.“We think of this kind of as a search problem. Every time someone creates a data model, they’re creating a new bit of information. The minimum viable product for search is an extremely high bar. People have tried to document data for many decades. We’re aware of the many failures before us.”last_img read more

Knowledge is the foundation of intelligence— wheth

first_imgKnowledge is the foundation of intelligence— whether artificial intelligence or conventional human intellect. The understanding implicit in intelligence, its application towards business problems or personal ones, requires knowledge of these problems (and potential solutions) to effectively overcome them.The knowledge underpinning AI has traditionally come from two distinct methods: statistical reasoning, or machine learning, and symbolic reasoning based on rules and logic. The former approach learns by correlating inputs with outputs for increasingly progressive pattern identification; the latter approach uses expert, human-crafted rules to apply to particular real-world domains.RELATED CONTENT: Ethical design — What is it and why developers should careTrue or practical AI relies on both approaches. They supplement one another for increasingly higher intelligence and performance levels. Enterprise knowledge graphs— domain knowledge repositories containing ideal machine learning training data—furnish the knowledge base for maximum productivity of total AI. Symbolic reasoningKnowledge graphs are at the basis of symbolic reasoning systems using expert rules for real-life business problems. Regardless of the particular domain, data source, data format, or use case, they seamlessly align data of any variation according to uniform standards focused on relationships between nodes. Semantic rules and inferencing create new types of understanding about business knowledge that machine learning couldn’t generate at all. Examples include optimizing the array of sensor data found in smart cities for event planning based on factors such as traffic patterns, weather conditions, previous event outcomes, and preferences of the hosts and their constituencies. Symbolic reasoning also has the advantage that in the end, one still can explain how and why certain new knowledge and new suggestions were generated.Statistical feedback loopDespite what has been said in the previous paragraphs, knowledge graphs can greatly benefit from machine learning and add value to the symbolic rules-based systems. When modeling car driving behavior, for example, modern image recognition systems (relying on deep learning) can produce more realistic models when deployed in conjunction with rules. Nonetheless, the general paradigm by which machine learning complements rules-based AI is by creating a feedback mechanism for improving the latter’s outcomes—and enhancing the knowledge of semantic graphs.In the preceding smart city use case organizations can deploy machine learning to the outcome of rules-based systems, especially when those outcomes are measured in terms of KPIs. These metrics can assess, for example, the success of the event as measured by the enjoyment of the attendees, the subjective costs and the real costs to the municipality, these costs for the organizations involved in the event, the rate of attendance, etc. Machine learning algorithms can analyze those KPIs for predictions to improve future events.Horizontal applicabilityThe interplay of the knowledge graph foundation with both the statistical and symbolic reasoning form of AI is critical for several reasons. Firstly, they all augment each other. The graphs provide the knowledge for rules-based systems and optimize machine learning training data. The machine learning feedback mechanism improves the graph’s knowledge and the rules, while the output of rules-based systems provides knowledge upon which to run machine learning. Secondly, this process is applicable to any number of horizontal use cases across industries. Most of all, however, there are amazingly advanced applications of AI empowered by this combination, the likes of which makes simple automation seem mundane.There’s risk management use cases in law enforcement and national security in which one can observe terrorists, for example, integrate that information and create hypothetical events or scenarios based on probability (determined by machine learning). Rules-based systems for security measures, then, are transformed into probabilistic rules-based systems that unveil the likelihood of events occurring and how best to mitigate them. Similar processes apply to many other instances of risk management.last_img read more

Unless nations act air pollution deaths will double by 2050 study concludes

first_img Sign up for our daily newsletter Get more great content like this delivered right to you! Country Click to view the privacy policy. Required fields are indicated by an asterisk (*) To get a clearer picture, researchers led by Jos Lelieveld of the Germany-based Max Planck Institute decided to take a global look at outdoor air pollution, which the World Health Organization (WHO) estimates is responsible for almost 3.5 million premature deaths annually. (WHO estimates indoor air pollution accounts for an additional 3.5 million.)Using a computer model that fused air pollution and atmospheric chemistry data, they estimated what annual average levels of ozone (a key smog ingredient) and fine particulates smaller than 2.5 microns (PM2.5) were in 2010 within 100-km-by-100-km grid squares across the world. Then they forecast what the levels of both pollutants would be in 2050, assuming policymakers implemented no new controls.Next, the researchers estimated how many premature deaths the pollution caused in each square. To do that, they used a set of equations—recently updated based on the most recent epidemiological research—describing how exposure to air pollution affects a person’s risk of dying from various diseases. These “exposure response relationship” equations enabled the researchers to calculate how fine particles and smog would affect the risk of a range of diseases, including heart attacks, strokes, lung cancer, and pulmonary disorders.In a final step, they estimated the fraction of deaths in each square attributable to a specific pollution source, including automobiles, power plants, in-home energy generation, and farm activities such as burning crop residues.Overall, the researchers concluded that, in 2010, 3.3 million people died prematurely from outdoor PM2.5 and ozone pollution. That number echoes recent WHO estimates. But the more troubling finding, the researchers say, is that the annual death toll would rise to 6.6 million by 2050 without new controls.The deadliest outdoor pollution source—accounting for 31%, or about 1 million, of premature deaths in 2010—is residential energy use, such as furnaces. And the bulk of these deaths would occur in Asian countries such as India and China, the researchers concluded, where households often use soot-emitting stoves and furnaces powered by wood. These emissions could be tricky to clamp down on; for instance, persuading residents of India to adopt cleaner technologies has proven difficult, Lelieveld says, in no small part because of cultural and family traditions.The second deadliest source of pollution in 2010 was agriculture, accounting for about 20%, or more than 600,000, of the premature deaths in 2010, the researchers say.“I was surprised” by that result, Lelieveld said. “What you tend to think is that [air pollution comes from] mostly traffic, and maybe industry.” But agricultural activities such as animal husbandry and fertilizer use generate ammonia, which can be converted to fine particles in the air, he explained. Agriculture is the leading source of outdoor pollution–related premature mortality in the eastern United States, Europe, and in countries such as Russia, Japan, and Turkey, the researchers found.Other pollution sources, including the power sector, industry, biomass burning, and vehicle traffic, each made smaller contributions to the death total, the study concluded.Lelieveld cautions that the findings depend on a number of assumptions. One is that all forms of PM2.5 have the same toxicity. But the particles can differ in chemical composition, he notes, and thus could differ in toxicity, based on location or source type. For instance, a limited body of research suggests that carbon-rich particles from residential energy and biomass are more toxic than particles from agriculture and other sources, Lelieveld says. If that’s true—though Cohen argues that this is still an area of unsettled science—the fraction of outdoor pollution–related deaths from residential energy and biomass burning could be higher than the study found, whereas the fraction from the other sources would go down, the researchers say.The mortality numbers also depend to some degree on the accuracy of assumptions about how exposure to different levels of pollution affects disease risk. For example, in the case of deaths due to cardiovascular disease related to PM 2.5 exposure, research now suggests that adding even small amounts of pollution to relatively clean air boosts disease risks more than adding the same amount of pollution to relatively dirty air. The researchers incorporated that research in modeling how PM2.5 levels related to risk of death. That carries a big policy implication, Cohen says: It not only “makes both public health and economic sense to clean up dirty places,” but also means there could be significant health benefits from reducing air pollution even in areas that already have relatively tight controls.“Even in countries with good air quality such as Australia, there is still a health gain to be made by reducing fine particle pollution,” noted health researchers Christine Cowie and Bin Jalaludin the University of New South Wales, Kensington, in Australia, in a statement released by the Science Media Centre.Cohen notes a limitation to the study. The authors assumed that death rates from cardiovascular disease would be constant over time, he says, even though populations in countries like China and India are steadily aging—potentially boosting such death rates. To offset that demographic impact, China and India may have to make even deeper pollution cuts in order to cut death rates, Cohen and other researchers noted earlier this year in a study published in Environmental Science & Technology. Still, Cohen lauds the new work. “It’s important,” he says, “because actions taken to improve air quality, and to improve public health, have to focus on [controlling emissions from] major sources of air pollution.” Emailcenter_img The annual death toll from outdoor air pollution could double to 6.6 million globally by 2050 without new antipollution measures, a new study suggests. But policymakers seeking to reduce the death toll will need to clamp down on a wide array of potentially hard to control pollution sources—including household furnaces and agricultural activities—that are expected to play a growing role, researchers report today in Nature.The study marks a solid step toward clarifying exactly how major sources of air pollution contribute to premature death around the world, says Aaron Cohen, an epidemiologist at the Health Effects Institute, a nonprofit research organization in Boston, who wasn’t involved in the study. That information will prove useful to policymakers, he suggests.Existing estimates have been hampered by gaps in air pollution data, particularly in the developing world, and a lack of knowledge about how specific air pollution sources contribute to the risk of disease and death.  Country * Afghanistan Aland Islands Albania Algeria Andorra Angola Anguilla Antarctica Antigua and Barbuda Argentina Armenia Aruba Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bermuda Bhutan Bolivia, Plurinational State of Bonaire, Sint Eustatius and Saba Bosnia and Herzegovina Botswana Bouvet Island Brazil British Indian Ocean Territory Brunei Darussalam Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Cayman Islands Central African Republic Chad Chile China Christmas Island Cocos (Keeling) Islands Colombia Comoros Congo Congo, the Democratic Republic of the Cook Islands Costa Rica Cote d’Ivoire Croatia Cuba Curaçao Cyprus Czech Republic Denmark Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Eritrea Estonia Ethiopia Falkland Islands (Malvinas) Faroe Islands Fiji Finland France French Guiana French Polynesia French Southern Territories Gabon Gambia Georgia Germany Ghana Gibraltar Greece Greenland Grenada Guadeloupe Guatemala Guernsey Guinea Guinea-Bissau Guyana Haiti Heard Island and McDonald Islands Holy See (Vatican City State) Honduras Hungary Iceland India Indonesia Iran, Islamic Republic of Iraq Ireland Isle of Man Israel Italy Jamaica Japan Jersey Jordan Kazakhstan Kenya Kiribati Korea, Democratic People’s Republic of Korea, Republic of Kuwait Kyrgyzstan Lao People’s Democratic Republic Latvia Lebanon Lesotho Liberia Libyan Arab Jamahiriya Liechtenstein Lithuania Luxembourg Macao Macedonia, the former Yugoslav Republic of Madagascar Malawi Malaysia Maldives Mali Malta Martinique Mauritania Mauritius Mayotte Mexico Moldova, Republic of Monaco Mongolia Montenegro Montserrat Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands New Caledonia New Zealand Nicaragua Niger Nigeria Niue Norfolk Island Norway Oman Pakistan Palestine Panama Papua New Guinea Paraguay Peru Philippines Pitcairn Poland Portugal Qatar Reunion Romania Russian Federation Rwanda Saint Barthélemy Saint Helena, Ascension and Tristan da Cunha Saint Kitts and Nevis Saint Lucia Saint Martin (French part) Saint Pierre and Miquelon Saint Vincent and the Grenadines Samoa San Marino Sao Tome and Principe Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Sint Maarten (Dutch part) Slovakia Slovenia Solomon Islands Somalia South Africa South Georgia and the South Sandwich Islands South Sudan Spain Sri Lanka Sudan Suriname Svalbard and Jan Mayen Swaziland Sweden Switzerland Syrian Arab Republic Taiwan Tajikistan Tanzania, United Republic of Thailand Timor-Leste Togo Tokelau Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Turks and Caicos Islands Tuvalu Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Vanuatu Venezuela, Bolivarian Republic of Vietnam Virgin Islands, British Wallis and Futuna Western Sahara Yemen Zambia Zimbabwelast_img read more