Tag Archives: Data

From the HIP Blog

Making a List, Checking it Twice

Although Health Impact Assessments are great tools for analyzing the health impacts of development and other urban planning initiatives, they can be long and resource-intensive. HIA is not always the best tool, especially when project proponents and public health practitioners participate early on or arrive very late in the planning process. So among planning departments there has been a lot of recent interest in healthy development checklists as alternative approaches to data collection and analysis to ensure health and equity are considered in decision-making.

A healthy development checklist includes a list of indicators of health and well-being tied to development, and a set of associated criteria meant to evaluate proposed policies, plans, and projects. Many jurisdictions have created indicator systems – measures that can be used to capture the status of social and environmental conditions – but not all of these have criteria against which specific development proposals can be evaluated. So a checklist is an indicator system, but not all indicator systems are checklists.

The San Francisco Department of Public Health has applied a healthy development checklist to planning activities such as public housing redevelopment, pedestrian and bicycle planning, and several specific area plans. Examples of outcomes of these checklist applications include greater community involvement in plan development, potential mitigations and design strategies, and policy and implementation recommendations to better account for health.

Before jumping in, jurisdictions considering developing a checklist should also consider the process, benefits, and challenges of creating an indicator system. HIP produced this resource for the San Diego Association of Governments. It provides a review of several jurisdictions’ experiences with indicator systems and offers some approaches that may prove useful for those considering developing a healthy development checklist.

There are, however, additional considerations that checklist developers and users need to be aware of. In theory, a checklist can be a useful collaboration tool for public health and planning practitioners to ensure health goals are included in development, but keep the following questions in mind:

  • Who develops the checklist? Is the process collaborative? Which priorities are reflected in the checklist? The development of a checklist involves selecting domains of interest, ways of measuring these domains via indicators, determining the health and equity objectives that the indicators reflect, and criteria to gauge whether an indicator will meet stated objectives. Who is involved in the checklist decision-making process will influence the objectives and criteria expressed by the checklist – and ultimately, what value they have to the larger community.
  • Which domains and indicators should be included? To be inclusive, a range of perspectives should be sought. But ultimately, the priorities should reflect human needs – an underlying set of values determined by collaborators. Resist the urge to include the easiest indicators, or all indicators you can think of, in the checklist. Some of the most important things to include relate to what people need to live and be productive members of society – a living wage, education, and freedom from injustice and violence.
  • Will data be available for all the important indicators? There is a good chance that for at least some indicators, data will be hard to come by, which will affect your budget, process, and analysis or interpretation. A collaborative process can help to overcome this challenge because affected communities can be included in data collection and interpretation. Be creative and, wherever possible, make plans to accommodate additional data collection efforts for hard-to-reach but important indicators.
  • What is the process for applying the checklist to proposals? Who will be included? Will the community have input into in the process of interpreting the data, deciding whether criteria and objectives are met, and what should be done if they are not? Make these decisions up front and include them in instructions that accompany the checklist – otherwise, its value as a tool will be limited.

Most importantly, uphold the values of HIA – equity, democracy, sustainable development, ethical use of evidence, and a comprehensive approach to health – in developing and applying a healthy development checklist. Using these values will help ensure that the checklist and its application advance not just the technical goal of considering health, but the ethical and just goal of creating healthy communities.

From the HIP Blog

Two Essentials: Community and Communication

As part of my HIP fellowship, I get to interview leading practitioners and partners to learn more about the fascinating field of Health Impact Assessment. Two things stand out from my conversations as most important: community and communication. 

Tia Henderson, research manager at Upstream Public Health, says that the more she does HIAs, the more she is convinced that that the process must be owned by the community. Without community participation, she says, we can only speculate about health impacts, but integrating community members in the process bolsters the research and findings.

Sandra Witt, director of Healthy Communities (North Region) at the California Endowment, agreed, but added that community participation is not just about getting the best data, it’s also about equity. “The people most affected need to be present at the table,” and that people working in public health “need to be rooted in social justice.” This is especially of high importance because often times the field of public health can be disconnected from social issues that affect health.

Steve White, a project manager at the Oregon Public Health Institute, emphasized that HIAs are not only vehicles for research but for communication, so the assessment, recommendations, and reporting steps should carry substantial weight. Part of the responsibility to communicate, said Aaron Wernham, director of the Health Impact Project, includes forthrightly addressing opposing arguments because it helps build a more robust HIA. “Don’t work on an HIA where the holes are not addressed,” he said.

Everyone has provided insightful and useful recommendations. The responses are fascinating because they demonstrate the structured, yet fluid composition of HIAs. Interviewing HIA stakeholders has been especially helpful in deciphering whether I’m heading in the right direction with my own HIA project (which focuses on wage theft).

Finally, it reflects the strong interconnection that exists among various HIA organizations across the nation.

From the HIP Blog

What Data Tells us About the Farm Bill

In the documentary, “A Place at the Table,” a brave woman from the Witnesses to Hunger program Barbie Izquiriedo asked policy makers “Do you see me? My name is Barbie and I exist.”  Barbie and her two small children regularly experience food insecurity, and she was questioning policy makers because she believes that if they really saw her that they would not continue to jeopardize critical food programs like the Supplemental Nutrition Assistance Program (SNAP) or “food stamps.”

As a pediatrician at a safety net hospital, it isn’t hard for me to see Barbie. Every day, I treat patients and their families who experience real hunger. I see what it does to their health and to their well-being. Congress will reconsider the Farm Bill next month, and while I hope that they will be persuaded by the Barbies of the world to protect SNAP and programs like it, they can also rely on the data.

The Health Impact Project undertook a health impact assessment (HIA) of the original House and Senate proposed Farm Bills, focusing on the policies related to SNAP, analyzing the new deduction requirements and differing decreased access of the food benefit. The HIA had a few important pieces of data that cannot be overemphasized. Access to enough healthy food has a direct impact on the maintenance of Diabetes. The changes made to SNAP in the original House Bill, which “saved” $20 billion over 10 years, has the potential to cost $15 billion over the same time period in increased diabetes-related healthcare costs alone. That says two things to me: we won’t save money and Diabetes patients will get sicker.

The HIA also found it would cost MORE to administer the SNAP program under the new bill, and the proposed cuts to benefits would affect over 5 million people, many of these families trying to feed their children. Colleagues of mine at Children’s HealthWatch and I recently published a commentary in Lancet entitled “SNAP cuts will harm US children,” not because we are zealots for SNAP, but because THAT IS WHAT THE DATA SHOWS.

The data is clear that SNAP works to end hunger. With healthcare costs rising and 50 million people in poverty hungry, we cannot afford to cut it. I hope the policymakers in Congress deciding on the Farm Bill see this data because it shows that cutting SNAP will have a negative impact on health and increase healthcare costs. If they want to make an evidenced-based decision, the evidence is there. They just have to be willing to “see” the data, just like they have to want to see Barbie.

From the HIP Blog

HIA Research: What’s the Right Approach for Your Question?

[As research director at Human Impact Partners, Holly Avey spends a lot of time not just looking at our findings but thinking about how we conduct and use research. This is one in a series of blogs about the role of research in HIA.]

Last week I discussed philosophies of research, and how different people might see the same information as either an appropriate source of data or a source of bias. This week, let’s think about different approaches to answering research questions. While your philosophy influences how you think about research, the questions you ask influence how you collect and analyze your data.

A document from the National Institutes of Health (NIH), explains the difference between quantitative and qualitative approaches to research. When people have strong reactions about the pros and cons of these, I believe it stems from a difference in their underlying philosophy of research.

Quantitative research uses numeric data that can be analyzed statistically to assess relationships among variables and understand cause and effect

Qualitative research uses interviews, observations, and reviews of documents (among other methods) to understand the context and meaning of the situation

So which is right for HIA? Our personal philosophy of research will guide how we think about this initially, but the next question should be what kinds of questions do we want our research to answer?

First, what is the purpose of HIA? In 2001, the Merseyside Guidelines for Health Impact Assessment, were published for HIA practitioners in the UK. They state that the aims of HIA are:

  • “to assess the potential health impacts, both positive and negative, of projects, programmes and policies
  • to improve the quality of public policy decision making through recommendations to enhance predicted positive health impacts and minimise negative ones”

Based on this thinking, your overarching research questions might be:  “What are the relationships between the pending decision and any potential health impacts?” “Is the pending decision likely to cause any health effects?” The quantitative approach is good for assessing relationships among variables and cause-and-effect, so you should use a quantitative approach, right? But what happens when you don’t have the quantitative data to answer those questions? Often there are times when HIAs are focused on neighborhood or local-level decisions, with significant limitations on the available quantitative data. In these cases, a combination of methods may be the best bet.

Let’s look back at that NIH document, which defines this combination of methods in this way:

Mixed methods research “involves the intentional collection of both quantitative and qualitative data and the combination of the strengths of each to answer research questions.” (p. 4-5).

One example of combining quantitative and qualitative data is a story that is often told by Aaron Wernham, of the Health Impact Project. Wernham tells about a small community where a natural resource extraction processing facility was operating. Quantitative air quality data for the area did not show any significant violations of air quality standards after the facility began operating. Asthma rates tracked by the state also didn’t show an increase. But community members consistently reported that they perceived asthma rates to be higher. During the HIA, community members offered testimony at public meetings, which was tracked by the HIA team. During the testimony, one of the community members specified that the asthma rates got worse for people when certain conditions aligned – when the facility flared gas under certain weather conditions, with the wind directed toward the village. Community members also testified that the air quality data would not be likely to detect emissions under these conditions because of the location of the air quality monitor for the area.

In this case, quantitative data was available but limited to one monitor, which provided a limited perspective on conditions for the area. Qualitative data from initial discussions with community was also limited, as it provided general perceptions without specificity. Additional qualitative data from the testimony provided the specific context that allowed the HIA team to interpret some of the quantitative data from a new perspective, and understand the discrepancies between the two types of data in other cases. The combination of these two approaches allowed the HIA team to explore a new causal pathway for the HIA to investigate potential health impacts. Thus, combining the two approaches provided the opportunity for the HIA researchers to explore a more complete and accurate picture, and identified data gaps that were limiting the ability to address community concerns. Ultimately, this contributed to a recommendation that was adopted by the decision-makers as a formal requirement for more specific air quality modeling and modeling near potentially affected communities.

How far should we go with qualitative research in HIA? Is it just used when we don’t have enough quantitative data to answer our research question, or are there other reasons to consider incorporating qualitative research into your HIA work? That’s the next research blog topic.

From the HIP Blog

Reflections on the National HIA Meeting

Two weeks ago I took a trip to a fun city, reconnected with old friends and made some new ones, and came back rejuvenated, inspired, and ready to get back to work. But I wasn’t on vacation. I was at a conference, and definitely not the boring, stuffy event you might think of. This year’s National HIA Meeting, Sept. 26-28 in Washington, D.C., was the second time practitioners from all over the country have gathered to discuss our work. It was my first, and to me it felt like a lovefest. Here are three reasons why:

Keynote Address by Councilman Joe Cimperman of Cleveland. Cimperman’s moving and inspiring address made me want to jump out of my seat and move to Cleveland right then and there. He discussed the importance of health and equity in his city, and the success of HIA in bringing health into decision-making and generating recommendations for improving health. He also talked about HIA as a tool for creating grassroots support and building relationships. Here is a great interview with Cimperman.

As a participant, my favorite breakout session was “Achieving Health and Equity in Education HIAs.” To my knowledge, the HIAs discussed in this panel are the only education HIAs ever completed in the US. Panelists included Phyllis Hill from ISAIAH in Minnesota, Susana Morales-Konishi and youth researcher Asha Simpson from Youth UpRising in Oakland, and Maisie Chin from CADRE in Los Angeles. These inspiring women represent community-based organizations that prioritize health and equity in their work. Community organizations are a growing group of HIA practitioners, but were under-represented at the conference, so these women brought fresh voices. Asha Simpson and her young colleagues, who were also in the room during the session, are the first youth team to conduct an HIA.

In the final minute of this session, an audience member asked a provocative question: “What about the fact that qualitative, community-generated data is not legitimate?” We didn’t have time to tackle it from the podium, but later talked privately and decided the real question should be: “Has the community legitimized your data?” Many HIAs are conducted without taking into account community knowledge and lived experience, and panelists agreed that practicing HIAs like this raises the red flag of illegitimacy more than the opposite approach. This episode and subsequent discussion really illuminated for me the value that community organizations bring, not just to an HIA but also to conferences like this one. This conversation should definitely be continued at the next national meeting.

My very favorite highlight was the people who came together from around the country and the world to make the conference happen. I never stopped running into old friends I’ve gotten to know over the last five years of doing HIAs. Just as often, I met new people and heard new stories about fascinating HIA projects and other health and equity work. (I guess you call this networking, but that word is too boring for describing the passion people brought to these conversations.)

Now I’m back home and ready to apply my renewed enthusiasm to a couple of new projects. But also excited for the next opportunity to meet with the 450-strong (and counting) national HIA community. The 3rd National HIA meeting is tentatively scheduled for Spring 2015 in Washington, DC.

From the HIP Blog

How Does HIA Bring Change?

There is a dirty little secret among HIA practitioners: We don’t all agree about what makes the work we are doing effective and about how doing HIA will lead to change. This became clear to me during conversations that started during the “Advocacy and Objectivity in HIA” panel at HIA of the Americas earlier this year. But these differences crystalized for me flying home last week from the National HIA Meeting.

The terms advocacy, bias and subjective have been thrown around a lot lately in the HIA field – terms that reveal deep differences among practitioners. I think there are at least three distinct theories of change held among our community.

1. Data alone.  Subscribers to this theory of change believe all HIA practitioners need to do is to provide decision makers with data about health and health disparities. Armed with that data, decision makers will make better decisions.

2. Data and consensus. Subscribers to this theory believe that the best way to make change is to reach out to stakeholders with diverse views, which usually include community members and, depending on the HIA, could include people from different agencies, project proponents, and decision makers from across the political spectrum. With data and good facilitation, consensus can be reached regarding the impacts, recommendations, and report. That process and the findings will lead to decision makers making better decisions.

3. Data and Power. Subscribers to this theory believe that change is most likely to come from strong data combined with an HIA process that is used to build power in disenfranchised communities that face inequities. With this increased power and strong data, the voices of those most impacted will be heard and decision makers will make better decisions.

Each of these theories has its merit and each may have its time and place. Each has examples it can hold up that show that it leads to decisions that improve health.

But, in our experience, if HIA is really a tool to achieve health and reduce inequities, combining data and power is the most effective way of getting there. History shows that the other two are challenging ways to truly change policies, plans, and projects that create inequities, especially if those in power don’t have the will to do so or if there is ideological tension around the proposal my ding. Those in power, in favor of a status quo that benefits them and is harming the disenfranchised, are simply not willing to yield power in the face of mere data.  And the compromises that result from consensus building between those who have power and those who do not usually support at best a middle ground that does not significantly benefit those most harmfully effected by decisions.

This is why at Human Impact Partners we do our HIAs in partnership with community organizing groups whose focus is building leadership in low-income communities and communities of color, lifting the voices of populations left out of decision-making discourse, and building the power of those communities.

We know the data and power theory works. With our partners, we’ve used it over the last couple of years to win over $40 million in affordable housing in South Los Angeles (our USC Specific Plan HIA and Farmers Field HIAs), substantial increases in funds for alternatives to incarceration in Republican-controlled Wisconsin, and better policies for racial integration of schools in Minnesota. We’ve used it to raise awareness about the harmful impacts of detentions and deportations on immigrant children and families. And, through those processes, we’ve left behind not just awareness and better policies, but more importantly, a community that is more engaged in our democracy and more empowered to fight on their own behalf in the future.

In Closing the Gap in a Generation, the World Health Organization Commission on the Social Determinants of Health declared: “Any serious effort to reduce health inequities will involve changing the distribution of power within society and global regions, empowering individuals and groups to represent strongly and effectively their needs and interests and, in so doing, to challenge and change the unfair and steeply graded distribution of social resources (the conditions for health) to which all, as citizens, have claims and rights.” The great Brazilian philosopher and educator Paulo Freire said it more simply: “Washing one’s hands of the conflict between the powerful and the powerless means to side with the powerful, not to be neutral.”

From the HIP Blog

Closing the Gap Between Data Science and Community Empowerment

I’ve been thinking a lot about the role of data in our work. I’ve sometimes been conflicted about my desire to do innovative quantitative work – because it’s fun and challenging – while making it useable to the communities we work with. Our ultimate goal must be empowering communities to harness the power of data and wield it on their own terms. The challenge lies in opening up the research process to communities in a way that is rigorous but also accessible to and trusted by community members.

Basic statistical analysis is not always going to yield the right knowledge from data. Yet so many otherwise successful projects rely exclusively on basic statistics without looking at more innovative methods that may lead to deeper insights about the problem we’re studying.

I’m inspired by this TEDx Montreal talk by DataMind co-founder and executive director Jake Porway. It got me thinking of the implicit value of quantitative data, and how the burgeoning field of data science should be leveraged as a force for good.

Some of you may be hesitant, in light of the revelations about NSA surveillance, companies tracking you from your phone signal, or how Facebook creepily knows when to start showing you engagement ring ads. But to Porway, those are just symptoms of data scientists using their skills to solve their own narrow range of problems. He argues that when you put data, and the skills necessary to extract information from that data, into the hands of folks working to direct that information into positive action for the social good, powerful change is possible. To do that, we have to use data in a way that is transparent, progressive, local, and accessible.

When we use data transparently we simultaneously demystify it and give it greater power. When we use data progressively, we ensure that it is used to benefit everyone and not just those that already speak its language. When we make data local and accessible, we free it to speak for us.

The public health movement is in a prime position to lead the transformation of data science. The time is right, the need to address the problems threatening our society is urgent, and an increasing number of public health professionals have the technical skills to make it happen. All that is missing is to form the right connections.

There are already a lot of great examples of putting data to work for the collective good:

Porway says that while “we live in a top-down world” when it comes to how we generally intake and interpret information, we’re “starting to see a rise of the bottom-up approach” to data. Instead of it being a barrier and a black box, let’s make data a tool our partners can wield to make real changes in their communities.