Its always a thing to celebrate, getting these new papers out. This one covers a topic close to home. After years of doing global health work, I never thought I’d be doing domestic health and even less certain that I’d be covering topics just down the road from me.
Together with partners from Wayne State University (Health Urban Waters), UM-Dearborn and the University of Michigan Ann Arbor, we characterized the state of recurrent flooding in Detroit, MI and explore possible public health impacts. The article appears in the International Journal of Environmental Research in Public Health. This was extremely rewarding work.
Article is open access.
Household flooding has wide ranging social, economic and public health impacts particularly for people in resource poor communities. The determinants and public health outcomes of recurrent home flooding in urban contexts, however, are not well understood. A household survey was used to assess neighborhood and household level determinants of recurrent home flooding in Detroit, MI. Survey activities were conducted from 2012 to 2020. Researchers collected information on past flooding, housing conditions and public health outcomes. Using the locations of homes, a “hot spot” analysis of flooding was performed to find areas of high and low risk. Survey data were linked to environmental and neighborhood data and associations were tested using regression methods. 4803 households participated in the survey. Flooding information was available for 3842 homes. Among these, 2085 (54.26%) reported experiencing pluvial flooding. Rental occupied units were more likely to report flooding than owner occupied homes (Odd ratio (OR) 1.72 [95% Confidence interval (CI) 1.49, 1.98]). Housing conditions such as poor roof quality and cracks in basement walls influenced home flooding risk. Homes located in census tracts with increased percentages of owner occupied units (vs. rentals) had a lower odds of flooding (OR 0.92 [95% (CI) 0.86, 0.98]). Household factors were found the be more predictive of flooding than neighborhood factors in both univariate and multivariate analyses. Flooding and housing conditions associated with home flooding were associated with asthma cases. Recurrent home flooding is far more prevalent than previously thought. Programs that support recovery and which focus on home improvement to prevent flooding, particularly by landlords, might benefit the public health. These results draw awareness and urgency to problems of urban flooding and public health in other areas of the country confronting the compounding challenges of aging infrastructure, disinvestment and climate change.
What are we talking about when we discuss socio-economic position and health in developing countries?
A wide body of literature has found that socio-economic position (SEP) has profound impacts on the health status of individuals. Poor people are sicker than rich people. We find this relationship all over the world and in countries like the United States, it couldn’t be more apparent.
Poor people, particularly poor minorities, are more likely to see their children die, are more likely to be obese, have worse cardiac outcomes, develop cancer more often, are disproportionately afflicted by infectious diseases and die earlier than people who are not poor. There is ample evidence to support this.
However, the exact factors which lead to this disparity are up to debate. Some focus on issues of lifestyle, diet, neighborhood effects and access to health care. Poor people, particularly minorities, live hard, eat worse, live in dangerous or toxic environments and have low access to quality care all contributing to a perfect storm of dangerous health risks.
However, even when controlling for all or any of these factors, we still find that poor people, and particularly African-Americans, still get sick more often, get sicker and die earlier. This leads us to speculate that health disparities are not simply a matter of access to material goods which promote good health, but are tightly related to something less tangible, such as social marginalization and racism, which are both incredibly difficult to measure. Though difficult to quantify, however, we do have plenty of well documented qualitative and historical data which indicate that these relationships are entirely plausible.
The awful history of slavery and apartheid, however, is somewhat (but not completely) unique to the United States. Further, our ideas of class come from another Western idea, the Marxist concept of one privileged group exploiting the weak for their own financial gain, particularly in the context of manufacturing.
Yet, though these ways of conceiving of race and class are so specific to the West, they are applied liberally to analyses of developing country health, with little consideration of their validity.
It is not uncommon to see studies of socio-economic status and health. The typical method of measuring socio-economic status in developing countries is to examine the collection of household assets such as TVs, radios, bicycles, etc. and, using statistically derived weights, sum up all of the things a household owns and call that sum a total measure of wealth. The collection of total measures for each household are then divided into categories, with the implication that they roughly approximate our conception of class.
Not surprisingly, it is usually found that people who don’t own much are, compared with people who do, at higher risk for malaria, TB, diarrheal disease, infant and maternal mortality and a host of other things that one wouldn’t wish on anyone.
But this measure is problematic. First, there is often little care taken to parse out which items are related to the disease of interest. For example, we would expect that better housing conditions are associated with a decreased risk for malaria, since mosquitoes aren’t able to enter a house at night. We would also expect that people with access to clean water would be more likely to not get cholera. If we find relationships of SEP with malaria or diarrheal disease which include these items, these associations should be treated with suspicion.
Second, if we do find a relationship of “class” with health, can we view it in the same way in which we might view this relationship in the United States? A Marxist approach, with a few exploiting the many for profit, in sub-Saharan Africa doesn’t make a whole lot of sense. The manufacturing capacity of African countries is tiny, and most people are sole entrepreneurs operating in an economy that hasn’t changed appreciably from pre-colonial times. Stripping away any requirements of legal protection of property rights, Africa looks incredibly libertarian.
Further, the elite in Africa hardly profit financially from the poor, receiving their cash flows mainly from abroad in the form of foreign aid or bribery and foreign activity is mostly limited to resource exploitation, which doesn’t make a dent into Africa’s vast levels of unemployment. While the West is certainly complicit is Africa’s economic woes, post slavery, the West rarely engages Africans themselves.
So, is it valid to attempt to apply the same ideas of class to African health problems? Is there a way to attribute health disparities to class in societies with limited economic capacity and where the “citizenry” is only marginally engaged and groups suffer mainly from a reluctance to cooperate and engage people of other tribes or neighboring countries?
Certainly, the causes of poverty and marginalization in Africa need to be examined, but I don’t think that we can approach them in the same way we do in the States.
I was part of a short, but interesting discussion last night regarding this very good article on the political implications of data analysis. The argument made (assuming I understood it correctly) was simply that statistical measures are inherently ideological since they impose a particular view of the world from one social group (us, the elite) on another (the non-elite). She takes this further, stating that though the voice of the elite can be heard through anecdotes (and opinionated blog posts), the experience of the non-elite relies on statistics and numbers. Statistics, then, is the language of power.
The conversation went further to discuss the implications of statistical methods themselves, particularly the measures of central tendency: the mean, median and mode. With perfectly symmetrical data, these measures are all the same, but, of course, no set of data is perfectly symmetrical, so that the application of each will produce different results. Though any responsible statistician would make statements of assumptions, limitations and appropriateness, with politics, these statements are overlooked and the method chosen is often that which best supports one’s political position, asking for trouble.
Moreover, the measure of central tendency itself in inherently flawed since it concentrates on the center and silences the extremes, supporting the status quo, or so it was argued. The choice of measure, I would argue, depends on the goals of the particular study. For example, a study which sought to determine if average graduation rates lower for blacks than whites would necessarily use a measure of central tendency, while a study on which students in a particular school are the least likely to graduate might look at outliers and extremes.
Either way, I agreed with the writer that, no matter what, we are influenced by our ideology. However, there is a difference between performing a study which seeks to maintain impartiality for the greater good and one which seeks to deceive in order to merely win a political battle, particularly among those who benefit from marginalizing, for example, the poor and disenfranchised.
However, I found this passage quite interesting and it can be applied to a post on this blog regarding what we do and don’t know about the poor:
Perhaps statistics should be considered a technology of mistrust—statistics are used when personal experience is in doubt because the analyst has no intimate knowledge of it. Statistics are consistently used as a technology of the educated elite to discuss the lower classes and subaltern populations, those individuals that are considered unknowable and untrustworthy of delivering their own accounts of their daily life. A demand for statistical proof is blatant distrust of someone’s lived experience. The very demand for statistical proof is otherizing because it defines the subject as an outsider, not worthy of the benefit of the doubt.
Part of my academic work focuses on the refinement of measurements of poverty. I am keenly aware of the “othering” of this process and how these measurements use a language of the educated elite (me) to speak for the daily experiences of people not like me.
This “othering” is not limited to statistics at all. Even merely referring to “the poor” is a condescending labeling of a group of people who are mostly powerless to speak for themselves within global power structures. Moreover, “the poor” ignores the diverse and varied experiences of most of humanity.
When I first entered the School of Public Health at UM, I was extremely uncomfortable with the language used in studies of ethnicity and public health in the United States. Studies would simply throw people into simplistic categories of black, white, hispanic, asian and “other” (whatever that is), ignoring the great diversity of people within, for example, urban slums. The method of categorization seemed to be a horrible anachronism and bought back awful memories of Mississippi. Simply putting people into neat categories risked continuing an already divisive view of the world.
However, the more I thought about it, the method is justified since we are looking at the effects of a racist view of the world on the very people who are the most burdened by it. Certainly, there are better ways of viewing the world, but when criticizing social power structures, it can be advantageous to speak its language. I still don’t like it, but I’m at least more understanding of it.
It’s a fine thread to walk. On the one hand, as advocates for “the poor,” we have to work within the very structures which oppress, exploit and ignore them. To succeed, however uncomfortable it may be, we may be required to adopt the language of those structures. On the other, we must remain aware of the potentially dire implications of the ways in which we describe those we advocate for and how they can be misused.
I really have no clue. I think I’m too distracted by the utter awfulness of this musical crime against humanity. Can we really give Lavigne that much credit?
Is this racist? Somewhat odd given the themes of the song (submissive Japanese women ready to commit to her man “unconditionally”), but at least a step above the first clip.
Is this racist? I’m willing to say probably. Japanese girls bowing down to the white lady at the beginning kind of throws me over the edge. At least some locals got a paycheck….
Is this racist? Though I have to credit Styx with teaching me the first Japanese I ever learned, watching this video now does give me the shivers. Japanese people as army of mindless, though secretly cunning robots (with big teeth a la Breakfast at Tiffany’s) ready to infiltrate and destroy America’s sacred classic rock world.
Is this racist? Kobota Toshinobu and EXILE in blackface. I’m pretty sure Kobota and EXILE are both great fans of American soul and plenty of Japanese stars have tried to look like white people in the past so I’m hesitant to call this racist, but painful, nonetheless.
I’ll leave it up to the reader to discuss, but THIS is DEFINITELY racist:
“I want to tell you one more thing I know about the Negro,” he said. Mr. Bundy recalled driving past a public-housing project in North Las Vegas, “and in front of that government house the door was usually open and the older people and the kids — and there is always at least a half a dozen people sitting on the porch — they didn’t have nothing to do. They didn’t have nothing for their kids to do. They didn’t have nothing for their young girls to do.
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“And because they were basically on government subsidy, so now what do they do?” he asked. “They abort their young children, they put their young men in jail, because they never learned how to pick cotton. And I’ve often wondered, are they better off as slaves, picking cotton and having a family life and doing things, or are they better off under government subsidy? They didn’t get no more freedom. They got less freedom.”