Thursday, August 30, 2007

Parables of the River - The end of Type 2 Diabetes?


The end of Type 2 Diabetes?

I REALLY like this story forwarded by LG (below the video). Here is ideo (Pablo & Bruno).


Parables of the River
I have been working on introductions to learning modules for a community organizing course I will be teaching online in the Fall. One of the things I wanted to include was what some community organizers call the “Parable of the River” (or sometimes a waterfall) that is often attributed to Saul Alinsky. I was searching across the Internet to find a good representation of the parable and found a wide range of different versions. (To avoid writing introductions, I seem to have ended up writing this post . . . .) Interestingly, it seems like there are versions of this parable with a different perspective than that used by community organizers. And this different version seems somewhat more prevalent among those oriented towards more traditional social service.

First an example of a “community organizing” version of the parable:
    Once upon a time there was a small village on the edge of a river. The people there were good and life in the village was good. One day a villager noticed a baby floating down the river. The villager quickly swam out to save the baby from drowning. The next day this same villager noticed two babies in the river. He called for help, and both babies were rescued from the swift waters. And the following day four babies were seen caught in the turbulent current. And then eight, then more, and still more!
    The villagers organized themselves quickly, setting up watchtowers and training teams of swimmers who could resist the swift waters and rescue babies. Rescue squads were soon working 24 hours a day. And each day the number of helpless babies floating down the river increased. The villagers organized themselves efficiently. The rescue squads were now snatching many children each day. While not all the babies, now very numerous, could be saved, the villagers felt they were doing well to save as many as they could each day. Indeed, the village priest blessed them in their good work. And life in the village continued on that basis.

    One day, however, someone raised the question, "But where are all these babies coming from? Let’s organize a team to head upstream to find out who’s throwing all of these babies into the river in the first place!"
Now a different version of this parable:
    While walking along the banks of a river, a passerby notices that someone in the water is drowning. After pulling the person ashore, the rescuer notices another person in the river in need of help. Before long, the river is filled with drowning people, and more rescuers are required to assist the initial rescuer. Unfortunately, some people are not saved, and some victims fall back into the river after they have been pulled ashore. At this time, one of the rescuers starts walking upstream

    “Where are you going?” the other rescuers ask, disconcerted. The upstream rescuer replies, “I’m going upstream to see why so many people keep falling into the river.” As it turns out, the bridge leading across the river up- stream has a hole through which people are falling. The upstream rescuer realizes that fixing the hole in the bridge will prevent many people from ever falling into the river in the first place.

In both parables, the key issue is that those trying to rescue the drowning people are making an error by focusing on the current emergency rather than on what is causing the emergency. As a result, they have no hope of actually solving the problem.

A key distinction between them is that in the first parable an agent is assumed to be causing the babies to fall in the river. In the second the problem is simply technical, with no agent attached. The bridge “has” a hole (note the passive voice).

This tendency to obscure the agents behind oppression and social harm may be a key difference between what I would term a “community organizing” approach and more familiar “social service” and “social science” approaches. From social service and social science perspectives there simply are these problems that need to be solved. The highest level of action is identifying and addressing the (usually impersonal) causes of shared problems.
Importantly, this approach generally obscures the activity of the agents who are perpetuating social challenges through their action or inaction.

Perhaps some of the tendency to avoid seeking out responsible agents is a result of the enormous challenges involved in identifying someone or some institution that one can definitively say is causing a particular problem. But maybe part of the problem is this focus on “causes” in the first place. In fact, the “cause” question can become a pretty complex, ultimately unsolveable existential challenge with no clear solution. Is the cause of pollution from a coal plant the owners of the plant, or bad government standards, or perverse incentives that make clean production unprofitable, or any of an innumerable set of other influences? What is the “cause” of the fact that so many poor kids have difficulty reading?
In my experience, as social scientists, most educational scholars tend to draw from the second version of this parable rather than the first. There “are” problems and we need technical solutions to solve them. In fact, to the many scholars who tend to avoid thinking about “causes,” even the limited insights of the second parable seem like a revelation.
In contrast, when organizers are looking for targets (see earlier post) they aren’t really worried about who or what is the “cause” of a problem. Instead, they try to figure out who can or should be made responsible for the problem now that we have it. In other words, the challenge for a community organizer is to identify the agent that can be induced to solve the problem, regardless of the vast chain of influences that produced it. The aim is to build a coherent link between specific agents and a specific social problem, and the substance of such a link can vary widely.

From an organizing perspective, many people and institutions have resources that are not fairly shared, and the aim is to find ways to force some subset of these agents to use their resources in more equitable ways.

To simplify the distinction I am making, here, one might say that social scientists and social service people tend to focus on “what” caused a problem and “how” to solve the problem, while organizers focus on “who” can solve the problem. And in many cases, answering “what” and “how” questions seem like pre-organizing issues. Sometimes, of course, getting people to figure out the answers to these questions themselves in a collective manner can be tools for engaging, educating, and organizing them, but often this does not seem to be the case.

One limitation of a focus on causes and solutions without focusing on agents is that each agent will be linked to different resources and different possible actions. In other words, different agents imply different solutions. Perhaps more problematically, failing to focus on the identification of realistic agents of change often creates an enormous unbridgeable gulf between theoretical solutions and actual solutions.

Here is a somewhat relevant example that indicates some of the differences between the social science approach and the organizing approach: We have been working on the beginnings of an effort to transform dental care for low-income urban children. For a range of reasons, we want to fight for a school-based dental treatment program. And we have identified an agent and avenue of change—the state health department and the state health insurance program. But there is no clear established “blue chip” model or “solution” to fight for. So we have stepped back, and I have been working with the state dental school and local district officials to get a pilot school-based services project funded. A local “proof of concept” effort would provide the basis for a program blueprint that we could then fight for on a state level. To a large extent, however, this social science investigation work is “pre-organizing.”

Two final observations:
First, there is a key problem with this parable in both of its versions. It represents those who are harmed as powerless victims, often babies. But people are rarely entirely powerless, and organizers never approach people as if they were powerless or babies. It seems odd that this central parable used by many organizers contains such a disempowering metaphor at its core.


Second, it is interesting to note that in a version that Stanley Cohen says he got from Alinsky, “a fisherman is rescuing drowning people from a river. Finally, he leaves the next body to float by while he sets off upstream ‘to find out who the hell is pushing these poor folks into the water.’ According to Cohen, Alinsky used this story to make a further ethical point: ‘While the fisherman was so busy running along the bank to find the ultimate source of the problem, who was going to help those poor wretches who continued to float down the river?’”


Thursday, August 16, 2007

Monday, August 13, 2007

Endocrine Regulation of Energy Metabolism by the Skeleton Cell -- Lee et al.

Endocrine Regulation of Energy Metabolism by the Skeleton

http://www.cell.com/content/article/fulltext?uid=PIIS0092867407007015

The regulation of bone remodeling by an adipocyte-derived hormone implies that bone may exert a feedback control of energy homeostasis. To test this hypothesis we looked for genes expressed in osteoblasts, encoding signaling molecules and affecting energy metabolism. We show here that mice lacking the protein tyrosine phosphatase OST-PTP are hypoglycemic and are protected from obesity and glucose intolerance because of an increase in β-cell proliferation, insulin secretion, and insulin sensitivity. In contrast, mice lacking the osteoblast-secreted molecule osteocalcin display decreased β-cell proliferation, glucose intolerance, and insulin resistance. Removing one Osteocalcin allele from OST-PTP-deficient mice corrects their metabolic phenotype. Ex vivo, osteocalcin can stimulate CyclinD1 and Insulin expression in β-cells and Adiponectin, an insulin-sensitizing adipokine, in adipocytes; in vivo osteocalcin can improve glucose tolerance. By revealing that the skeleton exerts an endocrine regulation of sugar homeostasis this study expands the biological importance of this organ and our understanding of energy metabolism.

Monday, August 06, 2007

INDIANAPOLIS – All healthy adults ages 18 to 65 years need moderate-intensity aerobic physical activity for at least 30 minutes on five days each week or vigorous-intensity aerobic physical activity for at least 20 minutes on three days each week, according to updated physical activity guidelines released today by the American College of Sports Medicine (ACSM) and the American Heart Association (AHA).

Further, adults will benefit from performing activities that maintain or increase muscular strength and endurance for at least two days each week. It is recommended that 8-10 exercises using the major muscle groups be performed on two non-consecutive days. To maximize strength development, a resistance (weight) should be used for 8-12 repetitions of each exercise resulting in willful fatigue.

1. Moderate-intensity physical activity has been clarified.

2. Vigorous-intensity physical activity has been explicitly incorporated into the recommendation.

3. Specified: Moderate- and vigorous-intensity activities are complementary in producing health benefits, and a variety of activities can be combined to meet the recommendation.

4. Specified: Aerobic activity is needed in addition to routine activities of daily life.

5. More is better.

6. Short bouts of exercise are OK.

7. A muscle-strengthening recommendation is now included.

8. Wording has been clarified.

For detail click here

Friday, August 03, 2007

How to test whether the change among surveys in one group is equal to the change in the other groups?

1. Scenery

We have a table below:

NHES

(S1)

N I

(S2)

N II

(S3)

N III

(S4)

N IV

(S5)

Change

(C)

BMI Group

High Cholesterol (%> 240 mg/dl)

< style=""> (G1)

27.1

22.3

22.1

13.8

15.2

-11.9 (C1)

25.0 – (G2)

39.2

33.1

31.2

23.3

18.7

-20.5 (C2)

> 30 (G3)

38.9

33.1

31.5

23.0

17.9

-21.0 (C3)

Age and sex-adjusted trends in CVD risk factors, by level of obesity and survey year in the

We want to know: Ho: C1 = C3.

2. Algorithm Solution

Recall:

Y= α*(S5, G1) + β1*(S1) + β2*(S2) + β3*(S3) + β4*(S4) + β5*(G2) + β6*(G3)

+ β7*(S1, G2) + β8* (S1, G3) + β9*(S2, G2) + β10*(S2, G3) + β11*(S3, G2)

+ β12*(S3, G3) + β13*(S3, G2) + β14*(S3, G3)

And: Prevalence of high cholesterol in NHES (S1) among persons with normal BMI (G1)

[S1-G1] = α + β1 = 27.1, and

[S5-G1] = α = 15.2, then

C1 = [S5-G1] - [S1-G1] = 15.2 – 27.1 = -11.9

Also:

[S1-G3] = α + β1 + β6 + β8 = 38.9, and

[S5-G3] = α + β6 = 17.9, then

C3 = [S5-G3] - [S1-G3] = -21.0

So:

When we test whether C1 = C3, we are going to test:

α - (α + β1) = (α + β6) – (α + β1 + β6 + β8),

i.e. β8 = 0

Same for others:

To test C1 = C2, we are going to test, β7 = 0

3. Implementation

Using PROC RLOGIST:

proc rlogist data= all;

  nest survey3 strata3 psu3/psulev=3 MISSUNIT;

  weight mecwgt3;

  subpopn age >19;

  subgroup bmigrp agegrp sex survey3 WHITE BMIADHOC;

  levels 3 3 2 5 2 2;

  model High_chol = bmigrp agegrp sex survey3 bmigrp*survey3;

  reflevel survey3 =5 agegrp =1 bmigrp=1;

  pred_eff bmigrp=(1,0,0)*survey3=(1,0,0,0,-1) /name="Survey: first versus last in BMI<25";

  pred_eff bmigrp=(0,1,0)*survey3=(1,0,0,0,-1)/name="Survey: first versus last in 25<BMI<30";

  pred_eff bmigrp=(0,0,1)*survey3=(1,0,0,0,-1)/name="Survey: first versus last in BMI>30";

  PREDMARG BMIGRP SURVEY3 BMIGRP*SURVEY3;

  PRINT BETA P_BETA PREDMRG SEPRDMRG P_PMCON PRMGCON SEPMCON

  /PREDMRGFMT=f7.3 PRMGCONFMT=F7.3 SEPMCONFMT=F7.3;

RUN;

SUDAAN will give us these beta and p value for beta:

-------------------------------

variable beta p value

-------------------------------

BMIGRP,

BY NHES & NHANES

1, 1 0.00 .

1, 2 0.00 .

1, 3 0.00 .

1, 4 0.00 .

1, 5 0.00 .

2, 1 0.33 0.0483 <- b=""> ß7

2, 2 0.32 0.0414

2, 3 0.24 0.1284

2, 4 0.41 0.0130

2, 5 0.00 .

3, 1 0.37 0.0181 <- b=""> ß8

3, 2 0.38 0.0156

3, 3 0.30 0.0350

3, 4 0.44 0.0021

3, 5 0.00 .

----------------------------------


Life course epidemiology
by Yoav Ben-Shlomo

This edition of the International Journal of Epidemiology has four papers and accompanying commentaries that can be conveniently clustered under the heading of life course epidemiology. In the concluding chapter of ‘A life course approach to chronic disease epidemiology’, Diana Kuh and I raised several emerging and common themes that we felt needed to be addressed by future research. These were (i) understanding heterogeneity, (ii) going beyond repeat measures to understand trajectories, (iii) the role of accelerated postnatal weight and height gain and (iv) the use of life cohort cohorts and less conventional designs. All of these topics are addressed to some degree by these publications. ...
for full text article click here