Posted in COVID19 Risk communication

When and how to wear a face mask

Even almost a year after the covid19 pandemic, reminding ourselves when and how to wear a face mask continues to become a life-saving activity.

Wearing a face mask alone is inadequate to combat this pandemic.

However, wearing a face mask is essential to combat this pandemic. This post reminds us of the basic rules of when and how to wear a face mask as recommended by the World Health Organization.

How to wear a medical or surgical mask

  • Wash hands before touching the mask
  • cover nose, mouth, and chin (my emphasis: at all times; it cannot be below the nose! not an easy task)
  • Wash hands after taking off the mask

What type of a mask?

Medical or surgical masks, if you are,

  • over 60
  • have a medical condition
  • feeling unwell
  • looking after an ill family member

Who should wear medical or surgical mask, when and where?

Otherwise, you can wear a fabric mask.

How to wear a fabric mask

How to wear a fabric mask (source: World Health Organization)

What type of fabric?

When and how children should wear a mask

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Posted in COVID19publichealthresearch

Face mask compliance: An observational study

This face mask compliance observational study attracted my attention because of its simplistic nature and reader-friendly presentation. I must thank Assistant Professor Susan Parham and Dr. Matthew Hardy for publishing the study on The Centre for Evidence-Based Medicine website.

About the location

Researchers have chosen a small tourist city in Paris for this face mask compliance study. Their reason for choosing this place is because of its popularity among both tourists and the local population. Wearing of face coverings became mandatory in this place by the time they conducted the observation.

Location selection

Professor Susan Parham and Dr. Matthew Hardy have chosen their place for observation off the main street. They cite two reasons for the selection: 1) It was a busy place so that they can count people and observe accurately gather more information; 2) They expected people to behave more naturally due to less expectation of official surveillance of face covering.

The observation method

They have observed the people for 30 minutes at lunchtime on two separate days at the same place: One weekday and one weekend day. On the first day, they have observed adults and children and on the second day, they have observed adult men and women separately.

Day 1mask wearing behavior of adults and children
Day 2mask-wearing behavior of male and female

Data collection tool

What attracted me about their presentation of this study was that they have pictured their actual data documentation tool as follows;

Source: The Center for Evidence-based Medicine, University of Oxford

How they tabulated findings:

Data tabulations

I created two blank tables that mimic their actual tables. Those interested can read their actual data through the link I have cited at the end of this post.

Day 1 data tabulation table

maskedunmaskedsemi-maskedmasked childunmasked child
total (273)
%
% of adults
Adapted from The Center for Evidence-based Medicine, University of Oxford

Data 2 data tabulation table

masked male masked femaleunmasked maleunmasked male semi-masked malesemi-asked female
Total
%of all adults
% of each sex

Findings

Day 1:
  • Of the total of 272 adults observed, 82 percent were adhering to face mask compliance as recommended.
  • Of the rest who were not complying, 12.5 percent were unmasked 8.5 percent were semi-masked (mask-wearing under the nose).
Day 2:
  • Of the 218 adults observed, 74.3 percent were wearing masks with 26.7 percent were either unmasked (16 percent) or semi-masked (9.7 percent).
  • Of them, 76.9 percent of women and 71.3 percent of men were wearing masks.

The authors of this face mask compliance observational study bring forward an interesting discussion about social norms conformity using the findings that were really critical in creating messages and social marketing campaigns.

Those interested can read the full paper through this link; https://www.cebm.net/2020/10/face-coverings-self-surveillance-and-social-conformity-in-a-time-of-covid-19/?unapproved=319632&moderation-hash=ea7fb58dd08238c803648b8afee163c2#comment-319632

Posted in message framing storytelling in science

The “Plastic Bag”; a short film by Ramin Bahrani

“They told me it’s out there: The Pacific Vortex. Paradise”; The “Plastic bag” anticipates his destiny through Werner Herzog’s voice.

“No one needs me here anymore, Not even my Maker”; the “Plastic bag” laments while observing the sunset on the beach. “He” is about to dive into the deep ocean heading for “paradise”: the gigantic plastic garbage dump that sits deep Pacific Ocean Vortex.

Ramin Bahrani opens his “Plastic bag” (2010), an 18 minutes long film with the above narration. It premiered at the Venice Film Festival and later screened at the New York Film Festival.

Ramin is an acclaimed Iranian-American filmmaker.

The technique of anthropomorphism

Ramin anthropomorphizes a plastic bag into a human; rather the bag thinks and feels like a human. This technique is called anthropomorphism, an excellent creative writing technique. It “transports” us into an imaginary world for a short period. After the visit, we return to the world where we lived prior to the journey with changed beliefs and attitudes. It helps to retain the message in our minds for a long time. We can find the same technique in Franz Kafka’s “metamorphosis”, Lewis Carrol’s “Alice in Wonderland”, and George Orwell’s “Animal Farm” and Markus Zusak’s “The Book Thief”.

The long journey begins

Soon after the opening scene at the beach, Ramin takes us to a place where the plastic bag begins his long journey to the Deep Pacific ocean vortex. The starting place is the store’s cashier countertop from where his Maker, a young woman, places her bought- stuff into the bag. Besides carrying stuff to the woman’s home, the bag carries various kinds of duties for the woman from going with her to the tennis court, helping her with ice to ease her ankle pain, and even bagging her pet dog’s shit. However, it feels abandoned when the woman dumps him and ends up at a large garbage site.

From this garbage site until he sets into the deep Pacific ocean vortex, Ramin makes the bag traveling through the land, sky, forests, buildings, houses, and the ocean. Throughout this journey, the bag meets animals, fish, and even a “girlfriend” bag.

Human emotions

The bag voices its human feelings through Werner Herzog’s voice: love, hope, loss, frustration, and the yearning for reaching the ultimate destiny.

Intimate moments with the young woman

He feels happy for being “part of her life” and joy when he intimates with the lady’s skin to ease her ankle pain with ice.

“I made her happy and she made me happy”, the bag thinks. And, he yearns to be with her, “we would be together forever”.

Then, he feels despair when she abandons her and finds himself at a dumping site.

At the large garbage site

“Nothing could destroy me”, boasts the bag even after a large garbage truck running over it several times. Then, the wind takes him out of the site and the destiny.

Meeting his “girlfriend”

During his resumed journey, he meets his red-colored “girlfriend” plastic bag. He moves and dances gracefully with her.

You can watch the full movie here.

“Plastic bag” short film by Ramin Bahrani

Ramin’s laser-beam focus on the main narrative does not deviate even for a second to bring forward the bigger picture of the plastic pollution crisis. He is strictly disciplined about it. The focus directs at the plastic bag’s goal: Re-uniting his Maker or else finding its ultimate destiny – the 100 million tons gigantic garbage dump deep inside the Pacific Ocean vortex.

Iranian-American filmmaker, Ramin Bahrani (Source:https://en.wikipedia.org/wiki/Ramin_Bahrani)
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Posted in how they did it spin in science writing

Cannabis use and psychosis research paper: A re-reading

This post is a follow-up to my previous post on the same topic, which was inspired by a journal club podcast that dealt with a research paper on Cannabis use and psychosis.

The podcast was presented by Matt. Chris, and Don at the Population Health Exchange of the Boston University School Health Public Health.

Delving into the subject, in this post, I am deconstructing the paper with an emphasis on “spin in science writing”.

This will be the seventh addition to the series of “spin in science writing”.

About the study

This is a case-control study conducted in several countries and was published in The Lancet Psychiatry in 2019. Its title is “the contribution of cannabis use to variation in the incidence of psychotic disorder across Europe (EU-GEI): a multicenter case-control study”: You can access the full paper by clicking this link.

About the study design

In the abstract’s methods section, the authors write: “We included patients aged 18–64 years who presented to psychiatric services in 11 sites across Europe and Brazil with first-episode psychosis and recruited controls representative of the local populations.”

My comment: The cases were the first-episode psychosis. They are the ideal choice to explore incident (new) cases. However, another research reveals that the median time between the first appearing of symptoms and the diagnosis of first-episode psychosis is 2-2.5 years and the median age at diagnosis is 30 years in the UK. Further research reveals that as many as 64 percent who first experienced the first-episode psychosis have used cannabis and 30 percent of them had a cannabis use disorder. These facts resonate with one of the inherent problems in the retrospective case-control design: The problem of recall bias. It is troubling because the relationship between cannabis use and first-episode psychosis could be bi-directional.

Moreover, as the journal club podcast presenters point out cannabis may be just unmasking psychosis among those who are genetically predisposed.

Because of these reasons, the temporality – whether cannabis use precedes the first-episode psychosis – is very difficult to adduce.

Causality assumption:

In the abstract’s methods section, the authors write: “Assuming causality, we calculated the population attributable fractions (PAFs) for the patterns of cannabis use associated with the highest odds of psychosis and the correlation between such patterns and the incidence rates for psychotic disorder across the study sites.”

Assuming causality: The authors “assume causality” in their study based on findings from previous studies. As podcast presenters highlight, this assumption is a huge part because case-control study design, by definition, does not allow a causality assumption. After assuming causality, they have gone further and calculated population attribution fraction (PAF). Read the following sentence;

Causality language

Case-control study designs are observational by nature. They allow us to conclude associations, certainly not causations. In other words, we cannot use words or phrases that allude to causality; which means no declarative verbs. Instead, we should ideally use “descriptive” verbs to describe associations.

You can find more details about what declarative and descriptive verbs mean by reading this post.

In this post, I search and highlight the sections, sentences, and phrases that I consider containing declarative and descriptive words and verbs.

However, I request readers to contribute to this post and am willing to correct myself if my facts and arguments are incorrect as per your opinion.

In the study’s title:

The authors write: “The contribution of cannabis use to variation in the incidence of psychotic disorder across Europe (EU-GEI): a multicenter case-control study”.

My comment: Case-control study designs may prove association but not the causation. The phrases – “contribution” of cannabis use to the “incidence” of psychotic disorder – allude to a causative relationship. However, the word, “contribution” may mean an association also.

In the abstract:

Findings section: The authors write: “Daily cannabis use was associated with increased odds of the psychotic disorder compared with never users (adjusted odds ratio [OR] 3·2, 95% CI 2·2–4·1)”.

My comment: This is a correct characterization of the study findings because the case-control study designs warrant using the phrase, “associated with”.

Prevention?:

Findings section: The authors write: “The PAFs calculated indicated that if high-potency cannabis were no longer available, 12·2% (95% CI 3·0–16·1) of cases of first-episode psychosis could be prevented across the 11 sites, rising to 30·3% (15·2–40·0) in London and 50·3% (27·4–66·0) in Amsterdam.”

My comment: They claim that by removing high-potency cannabis in these 11 sites 12.2 percent cases of first-episode psychosis could be prevented. And, in some sites – London and Amsterdam – it could be as much as 30.3 percent and 50.3 percent. This claim goes way beyond the study design warrants because it is based on causality.

Interpretation section: The authors write: “Differences in frequency of daily cannabis use and in use of high-potency cannabis contributed to the striking variation in the incidence of psychotic disorder across the 11 studied sites. Given the increasing availability of high-potency cannabis, this has important implications for public health.”

My comment: Is it possible to use the phrase, “contributed to the incidence of psychotic disorder” because it alludes to, again the causality?

In the main text:

Results section: The authors write: “The use of high-potency cannabis (THC ≥10%) modestly increased the odds of a psychotic disorder compared with never use.”

My comment: In this sentence, the use of the word, “increased” suggests causality.

The authors write: “Adjusted logistic regression indicated that daily use of high-potency cannabis carried more than a four-times increase in the risk of psychotic disorder (OR 4·8, 95% CI 2·5–6·3) compared with never having used cannabis.”

My comment: The phrase: ” four-times increase in the risk of psychotic disorder” suggests causality.

Discussion section: The authors write: “The strongest independent predictors of whether any given individual would have a psychotic disorder or not were daily use of cannabis and use of high-potency cannabis.”

My comment: The word, “predictor” suggests causality.

The authors write: “The odds of the psychotic disorder among daily cannabis users were 3·2 times higher than for never users.”

My comment: I believe that this is a correct characterization of the findings.

The main focus of this post is to highlight reporting practices of a case-control study with special reference to the causality language. However, there are several other areas that we can discuss here; one such issue is the recruitment of controls and their comparability.

The problem of recruiting controls:

Ideally, the recruited controls should be “potential cases”. In other words, should they experience first-episode psychosis, they are expected to be included in the study as cases. Therefore, both groups’ basic characteristics should be more or less similar. However, according to Table 1, we find statistically significant differences between cases and controls in age, ethnicity, education status; the cases were younger, lower educational status, and non-whites. That means the control group did not represent the local population from where the potential cases of first-episode psychosis should originate.

I found another excellent account based on this paper written by Suzanne H. Gage to The Lancet. It also enriches this discussion. And, I found another excellent write up by Kristen Monaco written to MedPage TODAY.

Posted in how they did it spin in science writing

Cannabis use and psychosis research paper: A re-reading

This post is a follow-up to my previous post on the same topic, which was inspired by a journal club podcast that dealt with a research paper on Cannabis use and psychosis.

The podcast was presented by Matt. Chris, and Don at the Population Health Exchange of the Boston University School Health Public Health.

Delving into the subject, in this post, I am deconstructing the paper with an emphasis on “spin in science writing”.

This will be the seventh addition to the series of “spin in science writing”.

About the study

This is a case-control study conducted in several countries and was published in The Lancet Psychiatry in 2019. Its title is “the contribution of cannabis use to variation in the incidence of psychotic disorder across Europe (EU-GEI): a multicenter case-control study”: You can access the full paper by clicking this link.

About the study design

In the abstract’s methods section, the authors write: “We included patients aged 18–64 years who presented to psychiatric services in 11 sites across Europe and Brazil with first-episode psychosis and recruited controls representative of the local populations.”

My comment: The cases were the first-episode psychosis. They are the ideal choice to explore incident (new) cases. However, another research reveals that the median time between the first appearing of symptoms and the diagnosis of first-episode psychosis is 2-2.5 years and the median age at diagnosis is 30 years in the UK. Further research reveals that as many as 64 percent who first experienced the first-episode psychosis have used cannabis and 30 percent of them had a cannabis use disorder. These facts resonate with one of the inherent problems in the retrospective case-control design: The problem of recall bias. It is troubling because the relationship between cannabis use and first-episode psychosis could be bi-directional.

Moreover, as the journal club podcast presenters point out cannabis may be just unmasking psychosis among those who are genetically predisposed.

Because of these reasons, the temporality – whether cannabis use precedes the first-episode psychosis – is very difficult to adduce.

Causality assumption:

In the abstract’s methods section, the authors write: “Assuming causality, we calculated the population attributable fractions (PAFs) for the patterns of cannabis use associated with the highest odds of psychosis and the correlation between such patterns and the incidence rates for psychotic disorder across the study sites.”

Assuming causality: The authors “assume causality” in their study based on findings from previous studies. As podcast presenters highlight, this assumption is a huge part because case-control study design, by definition, does not allow a causality assumption. After assuming causality, they have gone further and calculated population attribution fraction (PAF). Read the following sentence;

Causality language

Case-control study designs are observational by nature. They allow us to conclude associations, certainly not causations. In other words, we cannot use words or phrases that allude to causality; which means no declarative verbs. Instead, we should ideally use “descriptive” verbs to describe associations.

You can find more details about what declarative and descriptive verbs mean by reading this post.

In this post, I search and highlight the sections, sentences, and phrases that I consider containing declarative and descriptive words and verbs.

However, I request readers to contribute to this post and am willing to correct myself if my facts and arguments are incorrect as per your opinion.

In the study’s title:

The authors write: “The contribution of cannabis use to variation in the incidence of psychotic disorder across Europe (EU-GEI): a multicenter case-control study”.

My comment: Case-control study designs may prove association but not the causation. The phrases – “contribution” of cannabis use to the “incidence” of psychotic disorder – allude to a causative relationship. However, the word, “contribution” may mean an association also.

In the abstract:

Findings section: The authors write: “Daily cannabis use was associated with increased odds of the psychotic disorder compared with never users (adjusted odds ratio [OR] 3·2, 95% CI 2·2–4·1)”.

My comment: This is a correct characterization of the study findings because the case-control study designs warrant using the phrase, “associated with”.

Prevention?:

Findings section: The authors write: “The PAFs calculated indicated that if high-potency cannabis were no longer available, 12·2% (95% CI 3·0–16·1) of cases of first-episode psychosis could be prevented across the 11 sites, rising to 30·3% (15·2–40·0) in London and 50·3% (27·4–66·0) in Amsterdam.”

My comment: They claim that by removing high-potency cannabis in these 11 sites 12.2 percent cases of first-episode psychosis could be prevented. And, in some sites – London and Amsterdam – it could be as much as 30.3 percent and 50.3 percent. This claim goes way beyond the study design warrants because it is based on causality.

Interpretation section: The authors write: “Differences in frequency of daily cannabis use and in use of high-potency cannabis contributed to the striking variation in the incidence of psychotic disorder across the 11 studied sites. Given the increasing availability of high-potency cannabis, this has important implications for public health.”

My comment: Is it possible to use the phrase, “contributed to the incidence of psychotic disorder” because it alludes to, again the causality?

In the main text:

Results section: The authors write: “The use of high-potency cannabis (THC ≥10%) modestly increased the odds of a psychotic disorder compared with never use.”

My comment: In this sentence, the use of the word, “increased” suggests causality.

The authors write: “Adjusted logistic regression indicated that daily use of high-potency cannabis carried more than a four-times increase in the risk of psychotic disorder (OR 4·8, 95% CI 2·5–6·3) compared with never having used cannabis.”

My comment: The phrase: ” four-times increase in the risk of psychotic disorder” suggests causality.

Discussion section: The authors write: “The strongest independent predictors of whether any given individual would have a psychotic disorder or not were daily use of cannabis and use of high-potency cannabis.”

My comment: The word, “predictor” suggests causality.

The authors write: “The odds of the psychotic disorder among daily cannabis users were 3·2 times higher than for never users.”

My comment: I believe that this is a correct characterization of the findings.

The main focus of this post is to highlight reporting practices of a case-control study with special reference to the causality language. However, there are several other areas that we can discuss here; one such issue is the recruitment of controls and their comparability.

The problem of recruiting controls:

Ideally, the recruited controls should be “potential cases”. In other words, should they experience first-episode psychosis, they are expected to be included in the study as cases. Therefore, both groups’ basic characteristics should be more or less similar. However, according to Table 1, we find statistically significant differences between cases and controls in age, ethnicity, education status; the cases were younger, lower educational status, and non-whites. That means the control group did not represent the local population from where the potential cases of first-episode psychosis should originate.

I found another excellent account based on this paper written by Suzanne H. Gage to The Lancet. It also enriches this discussion. And, I found another excellent write up by Kristen Monaco written to MedPage TODAY.

Posted in how they did it spin in science writing

Cannabis use and Psychosis: A reading of a case-control study

This post is based on my learning experience by listening to a journal club podcast from the Population Heath Exchange website managed by the School of Public Health, Boston University.

Cannabis use and psychosis case-control study

The podcast presenters discuss this multi-center case-control study on cannabis use and psychosis published in The Lancet Psychiatry On May 1, 2019. The discussion helped me to refresh my knowledge about critical analysis of retrospective case-control studies with special relevance to today’s context.

In addition to highlighting common biases inherent to retrospective case-control designs like recall bias, social desirability bias, as well as vague operational definitions of variables, the presenters bring our attention to some “spin” methods used when writing a research paper: Use of causal language in observational study designs.

In the second half of the podcast, they discuss a recent phenomenon called “ethics dumping” in which the researchers from countries with strict regulations collaborate with researchers from other countries where the conditions are not so strict. One such example is collaborating with Chinese counterparts on studies of editing embryos. They briefly discuss an Indian study about pap smears.

You can listen to the podcast through this link;https://populationhealthexchange.org/fa-episode-047/.

Posted in how they did it spin in science writing

Cannabis use and Psychosis: A reading of a case-control study

This post is based on my learning experience by listening to a journal club podcast from the Population Heath Exchange website managed by the School of Public Health, Boston University.

Cannabis use and psychosis case-control study

The podcast presenters discuss this multi-center case-control study on cannabis use and psychosis published in The Lancet Psychiatry On May 1, 2019. The discussion helped me to refresh my knowledge about critical analysis of retrospective case-control studies with special relevance to today’s context.

In addition to highlighting common biases inherent to retrospective case-control designs like recall bias, social desirability bias, as well as vague operational definitions of variables, the presenters bring our attention to some “spin” methods used when writing a research paper: Use of causal language in observational study designs.

In the second half of the podcast, they discuss a recent phenomenon called “ethics dumping” in which the researchers from countries with strict regulations collaborate with researchers from other countries where the conditions are not so strict. One such example is collaborating with Chinese counterparts on studies of editing embryos. They briefly discuss an Indian study about pap smears.

You can listen to the podcast through this link;https://populationhealthexchange.org/fa-episode-047/.

Extended Parallel Process Model
Posted in message framing

Extended Parallel Process Model (EPPM)

Whenever we craft a message we need to have a clear idea of how we are going to evaluate the efficacy after releasing the message. The Extended Parallel Process Model (EPPM) provides a useful model exactly for that.

Let first us see what it is and then how it helps us.

We need to keep in mind that this model particularly applies to fear- arousing messages.

What is EPPM?

The EPPM forces us to look at it from the message recipient’s point of view. According to the model, the message recipients process a message in two stages. in the first stage, the message recipients appraise the threat level; based on this initial appraisal, they proceed into stage two: To take action; it could either the danger control (appropriate/adaptive) or fear control (inappropriate/maladaptive).

Stage 1: Appraising the threat

First, as soon as we receive a fear-arousing message, we appraise its “perceived” threat level. Here the keyword is “perceived”. What matters most is the message recipient’s perceived threat level, not the message sender’s perceived threat level. Often, message senders who tend to be more knowledgeable than the message recipients become disappointed because the message senders think the recipients do not perceive to the level that the senders perceive. Being at a higher position in the social ladder, message framers put the blame on the message recipients.

What does the “perceived threat” refer to?

Although we employ here the fear appeal as the strategy, according to Kim Witte the threat differs from fear;

Fear is an emotion; the threat is a cognition

According to the, two criteria should fulfill to perceived the threat; the message recipients should perceive severity and susceptibility.

What does it mean by perceived severity?

Think of COVID 19 virus. First, the recipients assess how severe the problem is. In the first wave, our perceived severity became very much higher than now. Isn’t it? In other words, the perceived severity can vary with time.

What does it mean by perceived susceptibility?

Next, the recipients assess how much they are vulnerable in contracting the COVID 19 virus.

The perceived threat consists of the perceived severity and perceived susceptibility to the problem.

If the recipients perceive the problem is not severe enough and they are not vulnerable, they are very unlikely to do something about it. In contrast, if they perceive the problem is severe enough and they are susceptible, they feel they are under threat and think of doing something about it.

According to the EPPM, they switch into the next stage: Action. Here, the message recipients evaluate the the actions they can resort to: Appraising the efficacy of the proposed action/s.

Stage 2: Appraising the efficacy

In this stage, they evaluate two dimensions of the proposed action/s.

Perceived self-efficacy

First, they evaluate whether the proposed action is doable. For example, in regards to COVID 19 pandemic response, the key messages we receive are “stay at home”, “wear a face mask”, “wash hands”, and “keep the distance”.

Perceived response efficacy

This is the final element; perceived response efficacy. Here, the recipients evaluate whether the proposed actions do really work for them.

Our message satisfies all the above-mentioned four criteria, according to the EPPM, the message recipients are highly likely to engage in the recommended behavior change. Obviously, we cannot expect one message can satisfy all these criteria for all the people. That is why we first have to define the target audience and study their socio-demographic and psychographic characteristics before crafting the message.

Danger control versus fear control

Whenever our message meets the four perceptions of the target audience, they delve into “danger control”. This is what we want them to do. The experts view this as a cognitive process.

However, there are many situations that our messages do not meet all the above criteria.

What will happen if some members of the target audience perceive a higher threat level with a lower level of perception that they are not capable of engaging in the suggested action?

According to Michael Basil and Kim Witte, in such situations message recipients will resort to “fear control” methods; this is an emotion control process. Here, they will ignore the message and somehow find reasons to justify their course of inaction.

What arguments they are likely put forward? According to Michael Basil and Kim Witte, those are;

  • They may the risk is overstated unnecessarily.
  • They may the threat is not that severe.
  • They may, Whatever happens, may happen; we cannot do anything; this is life.
  • They may this is a deliberate attempt to limit their freedom.

What we have to do in message framing is to promote message recipients to adopt danger control actions not fear control ones.

EPPM elements against control strategies

This is only an introduction to the EPPM. There is much more to it. And, researchers have addressed the limitations of the model too. For example, this model only deals with the process of dealing with fear. However, fear is not the only emotion messages invoke. They can invoke anger and frustration too.

Researchers firmly advocate the self-efficacy and response efficacy perception levels should be higher than the problem severity and susceptibility perception levels for the message recipients to resort to danger control behaviors. If the reverse takes effect, they will resort to maladaptive fear control behaviors.

I will discuss its applications in another post.

References
Posted in Risk communication

Avoid traps in risk communication

As an individual who closely follow the pandemic communication, I have been observing some communication “traps” that the communicators fall into. During my research on this, I found excellent advice from the US CDC website with regard to this topic. I am sharing relevant pieces from that post here.

This post details out dos and don’ts when we communicate events related to an outbreak.

DosDo not s
define technical terms in plain languageUse language that even a small section cannot understand
Ask whether you have made the information clear.Do not assume that everything is clear.
use examples or analogues to explain a complex topicDo not assume they understand everything.
Focus on facts at handDo not speculate
Promise only what you can deliver Do not make promises you cannot deliver
Take responsibility of your share of the problem; use empathyDo not blame or shame others
Simpson's paradox
Posted in Paradoxes

Simpson’s paradox in action

In 1934, two researchers – Morris Cohen and Ernst Nagel – tabulated death rates due to pulmonary tuberculosis in two cities – New York and Richmond. They found higher death rates in Richmond City than in New York City; 226 per 100,000 population versus 187 per 100,000 population.

However, they disaggregated the rates by ethnic groups; Caucasians versus African Americans. Interestingly, their previous finding was reversed. It was the complete opposite: for each ethnic group, the death rates became higher in New York than in Richmond (Look at the table below).

Death rates due to pulmonary tuberculosis

EthnicityNew YorkRichmond
Caucasian179/100,000162/100,000
African American560/100,000332/100,000
Total187/100,000226/100,000
The detailed data set is available through this source: https://plato.stanford.edu/entries/paradox-simpson/notes.html#note-1

This conundrum is known as “Simpson’s paradox”.

How did this happen?

When we disaggregate data into two different sub-groups, the real situation appears; in extreme instances, it becomes to reverse.

Those situations are identified as “Simpson’s paradox” because Edward Simpson explained the phenomenon using hypothetical data as far back as in 1951. However, prior to him, Yule demonstrated the bias again using hypothetical data set much earlier: in 1903.

According to statisticians, Simpson’s paradox, by definition, is not a true paradox; rather, it is a statistical illusion and could also be called aggregate bias. It is also a manifestation of confounding effect.

Its practical implications could well be devastating, particularly when we make decisions based on aggregate data.

In the above example, if the decision-makers were not aware of this, they would have allocated resources erroneously to Richmond instead of New York City to reduce the death rate due to pulmonary tuberculosis.

This bias seems to have been occurring much commoner than earlier thought.

Here are few more examples;

Hospital admissions of men with psychiatric illnesses over the years; gone up or down?

I created the following table using data that appeared in a short paper in the British Medical Journal.

According to the first table, the admission rates of men with psychiatric illnesses out of all admissions with such illnesses have declined slightly from 1970 to 1975.

19701975
Admission rate46.4% (343/739)46.2% (238/515)

Now, look at this following dis-aggregated data by age. The pattern reversed; the male admission rates have gone up.

19701975
Those aged <=6559.4% (255/429)60.5% (156/258)
Those >6528.4% (88/310)31.9% (82/257)
Overall46.4% (343/739)46.2% (238/515)

Another example from a hospital setting

The data that appears below is from a paper published based on a study about the use of prophylactic antibiotics in eight hospitals in the Netherlands. According to the first table, it seems better prophylactic use of antibiotics because the urinary tract infection rate is lower when using it rather than not using it.

Prophylactic antibioticsNo prophylactic antibiotics
Urinary tract infection rate (UTI)3.3% (42/1279)4.6% (104/2240)

Since the researchers were skeptical about the finding, they dis-aggregated data by grouping hospitals based on UTI infection rates; low-incident and high-incident hospitals using 2.5% as the artificial cut-off rate. Now, the first observation was reversed; the rates were higher when prophylactic antibiotics were used.

UTI ratesProphylactic antibioticsNo prophylactic antibiotics
Low incident (<=2.5%) hospitals1.8% (20/1113)0.7% (5/720)
High-incidence (>2.5%) hospitals 13.2% (22/166)6.5% (99/1520)
Overall UTI rate3.3% (42/1279)4.6% (104/2240)

The above study appeared on the Royal Statistical Society website when it discusses the Simpson’s paradox.