Scholars gauged energy inequality among Eurasian Economic union member states

Russia has the lowest energy efficiency, while Armenia leads for renewable energy development

Peer-Reviewed Publication

NATIONAL RESEARCH UNIVERSITY HIGHER SCHOOL OF ECONOMICS

The amount of energy consumption depends on various parameters: GDP, population, income disparities, etc. Using these indicators, it is possible to measure energy inequality both within individual countries and between whole groups of countries on a regional scale. HSE University and University of Genoa researchers Liliana Proskuryakova https://www.hse.ru/staff/proskuryakova, Alyona Starodubtseva and Vincenzo Bianco focused on the Eurasian Economic Union (EAEU) member states of Armenia, Belarus, Kazakhstan, Kyrgyzstan and Russia. Although together, they represent one of the largest energy markets in the world, the question of regional energy inequality remains largely unexamined. The collaboration between Russian and Italian scholars has helped fill that gap. They published the results of their research in the journal Renewable and Sustainable Energy Reviews https://www.sciencedirect.com/science/article/abs/pii/S1364032121004445.

On What Was the Study Based?

Researchers based their work on data from 2000-2017 and employed several methods. First, they analysed scientific research on the EAEU energy sector. It turned out that no more than 20 such studies were available on the ScienceDirect and Web of Science databases.

Second, they studied EAEU sustainable development goal documents concerning both the Union as a whole and its member states. These included programmes, declarations, press releases and official reports. Researchers also used international and national statistical data to verify the information contained in political statements.

Third, the researchers used mathematical methods of analysis. Two of those methods, suitable for understanding trends in energy consumption, were used to measure the level of energy inequality: the decomposition of energy consumption factors using the logarithmic mean Divisia index (LMDI) and the estimation of inequality using the Theil index. 

The decomposition analysis yielded an ‘ideal decomposition of energy consumption’ — that is, an understanding of the contribution of each factor and each country to consumption trends in the EAEU region.

The Theil index, calculated based on annual energy consumption, GDP variables, population, etc. provided estimates of energy inequality according to two components: ‘inside’ (the degree of inequality for reasons internal to the country) and ‘between’ (the contribution to inequality of the region in which the country is located).

 

What Was the Result?

 

Electricity consumption in the EAEU increased by 153% from 2000 to 2017, with a cumulative annual growth rate of 5.3%. The study found three contributing factors for this:

- economic activity: the increase in energy consumption as a result of GDP growth in the region;

- the structural effect — that is, the influence that an individual country’s economic activities have on the region as a whole;

- intensity — considers the influence that changes in the energy intensity of EAEU member states have on energy consumption in individual states and on the region as a whole.

 

The growth of economic activity in 2000-2017 had the greatest influence at the regional level, contributing to a 67% increase in energy consumption. Energy intensity had the second greatest influence at 32%. The structural effect accounted for the rest.

The structural factor played a minor role because the entire region is largely under the clear and ongoing influence of one country — Russia.

On the other hand, the structural component has a positive value in all EAEU member states except Russia. ‘It follows from this that, compared to Russia, the relative economic weight of Armenia, Belarus, Kazakhstan and Kyrgyzstan has increased and was responsible for the increase in energy consumption’, the researchers said.

 

The increase in energy intensity is explained by the rise in energy consumption in Kazakhstan and the 38% increase in Russia. Rich in fossil fuels, these countries have fewer incentives to increase the energy efficiency of their economies.

The reverse process is occurring in Armenia and Belarus, where intensity is decreasing, probably due to a decline in fossil fuel consumption and a more dynamic development of ‘green’ energy. Kyrgyzstan is following a path typical of developing countries by focusing on economic growth at the expense of wise energy use.

At the level of the EAEU, energy intensity significantly drove the growth of electricity consumption in 2006-2011. After that, the positive and negative influence alternated depending on the situation in Russia: as the latter consumed significantly more energy, it affected the entire region adversely.

The economic activity (GDP growth) component positively influenced energy consumption, except during the years 2008-2009 and 2014-2015. ‘During these periods’, the researchers explained, ‘several countries, including Russia, saw GDP decline, which reduced energy consumption’.

‘In the EAEU states, GDP and energy consumption are interrelated, which is consistent with the literature on developing countries’, they concluded. However, ensuring energy sustainability in the region requires something completely different.

It is necessary to decouple energy consumption from economic growth, in part by increasing the share of renewable energy.

In addition to GDP, the degree of energy inequality can depend on other factors such as population size, income distribution, the standard of living, etc. Researchers used the Theil index to determine their significance and found that this indicator continuously declined and remained low from 2008 onward.

They also used the index to ‘decompose’ factors between and within two groups of countries — the energy-rich and energy-poor. At first, until 2008, most inequalities resulted from differences between the two groups. ‘The rich could benefit from their dominant position in international energy markets, and this explains the higher level of inequality compared to the poor, that took some time to reach a comparable level’, the researchers noted.

 

Then, the situation changed. ‘In 2008-2017, inequality declined and the contribution within the groups prevailed: economic growth levelled the standard of living in the region by reducing the difference between countries’.

 

How Is this Useful?

 

The results of this work help not only assess energy inequality — which, because it examined the EAEU for the first time, is valuable in itself — but also to determine actions that can reduce this inequality. At the level of the Eurasian Economic Union, this primarily requires the cooperation of all the member countries.

The researchers have shown that the states should expand their cooperation to include such areas as, for example, energy efficiency and alternative energy. The necessary conditions for this exist, but approaches to energy policy would have to change, ranging from amendments to the EAEU Treaty to stimulating joint scientific research.

Combined efforts are also needed to break the established link between economic growth and energy consumption. ‘Decoupling GDP growth from energy consumption is not an easy task and might seem impossible given the necessity of ensuring energy access to everyone’ the authors of the study said. One of the most likely solutions is for ‘EAEU members to share the burden, making it possible to achieve economies of scale, specialisation and the hedging of risk’.

The creation of common energy markets is a step in this direction. The process is underway, but it involves fossil fuel markets, which are inconsistent with Goal 7 targets calling for a transition to ‘green’ energy. However, the researchers are certain that these integration goals can be adjusted if the political will exists.

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JOURNAL

Renewable and Sustainable Energy Reviews

DOI

10.1016/j.rser.2021.111155

SUBJECT OF RESEARCH

Not applicable

ARTICLE TITLE

Energy inequality in the Eurasian Economic Union

ARTICLE PUBLICATION DATE

13-May-2021

 

Study shows how US immigration policy can have domestic health effects

The Trump administration's "Muslim ban" negatively affected the health of people from targeted nations living in the US

Peer-Reviewed Publication

BROWN UNIVERSITY

PROVIDENCE, R.I. [Brown University] — After a controversial federal order suspending travel to the U.S. from seven Muslim-majority countries was signed in 2017, the number of visits to emergency departments by Minneapolis-St. Paul area residents from those nations increased significantly. And that development followed an already marked increase in primary care visits by members of the same population, which began in November 2016 following an election season characterized by significant anti-immigrant rhetoric.

That’s according to a new JAMA Network Open study led by a Brown University health services researcher in collaboration with a group of public health and health services researchers from across the country. Those changes in health care utilization likely reflected elevated cumulative stress due to an increasingly hostile climate toward Muslims in the U.S., the authors say.

“It’s clear that U.S. immigration policies can have significant effects on the health of people living here in the U.S.,” said Dr. Elizabeth Samuels, corresponding author of the study and an assistant professor of emergency medicine at Brown University’s Warren Alpert Medical School. “In this case, we saw a rise in emergency department visits among people from nations targeted in the ban as well as a rise in missed appointments from people from Muslim majority countries not named in the ban. I think that that's indicative of the kind of rippling health effects these types of policies can have.”

On Jan. 27, 2017, one week after taking office, President Donald Trump issued Executive Order 13769, “Protecting the Nation from Foreign Terrorist Entry into the United States.” Samuels said that when the order took effect, she and other medical colleagues wondered how the immigration policy might affect the health of people from Iran, Iraq, Libya, Somalia, Sudan, Syria and Yemen (the included countries) living in the U.S.

Similar policies have exacerbated levels of discrimination, hostility and “othering” that Muslims in the U.S. experience, she said, and research has shown that increases in hate crimes and hostility directed toward Muslims negatively impacts their health. For example, Samuels notes, after the Sept. 11 attacks, rates of anxiety, depression and low birth weights increased among Arab Americans.

However, because of the way demographic and health data is collected in the U.S., Samuels wasn’t sure if the changing health behaviors of Muslims in the U.S. was even something that could be measured, because health care administrative databases are not mandated to collect information on religious affiliation.

“One of the biggest challenges for those of us who work in Muslim communities is to try to figure out how to find Muslim Americans within U.S. health care databases, because unlike race, ethnicity or even sexual or gender identity, religious identity is not routinely captured or recorded,” said Dr. Aasim I. Padela, a study author and professor of emergency medicine, bioethics, and the medical humanities at the Medical College of Wisconsin. “And in an emotionally-charged post-9-11 environment in which Muslims are often stigmatized, there’s actually a disincentive to offer up this kind of information.”

The result is that while there are smaller studies involving these communities, there is a lack of population-level data. But in researching potential study populations, Samuels discovered that health care provider and insurance company HealthPartners collected country of origin data on patients visiting clinics and hospitals in the Minneapolis-St. Paul area, home to the largest Somali Muslim community in the U.S.

In conducting the study, the researchers analyzed the HealthPartners database and grouped 252,594 patients receiving care between January 2016 and December 2017 into three groups: adults born in one of the nations included in the executive order; adults born in Muslim-majority nations not listed in the order; and U.S.-born non-Latinx adults. They compared changes in primary care and emergency department visits, missed scheduled clinic appointments, and visits they categorized as “stress-responsive,” among individuals from nations included in the executive order from one year before to one year after it was issued.

They found that after the order was issued, there was an immediate increase in emergency department visits among people from the included countries. The study estimates that 232 additional emergency department visits were made by people from Muslim ban-targeted nations in the 360 days after the Muslim ban was issued beyond what would have been estimated if emergency department utilization had followed a trend similar to that seen by U.S.-born non-Latinx adults. This was especially pronounced in the first 30 to 60 days after the ban was issued.

Study results suggested that adults born in Muslim-majority nations not listed in the order missed approximately 101 additional primary care appointments during the time period beyond what they would have expected to miss if following the trend of non-Latinx U.S.-born people.

Some forms of health care utilization were also noted to change even before adoption of the ban. Clinic visits and stress-related diagnoses increased before the executive order was issued, most notably after the 2016 presidential election.

Despite the statistically significant increase in emergency visits, Samuels says she was surprised not to see larger overall health effects, especially related to stress, in the wake of the order. In the study, the researchers discuss why this might be, and how potential changes in health care utilization after the order may have been attenuated by factors specific to Minneapolis-St. Paul. The very factor that made it possible to focus on this population in the study — a concentrated, civically engaged community of Somali-Americans and Muslims — may have also offered protection against political stressors, they note, as the ability to secure benefits through social structures, like community associations or civic organizations, may attenuate the negative health impacts of discrimination.

Padela said that while discrimination is known to impact health care behaviors, measuring the health effects of discrimination in an aggregate fashion, especially for a population that isn’t quantified by health care groups, is tricky.

“This study was able to not only identify a Muslim community within the health care system, but also to analyze their health care behaviors before and after a policy-level decision widely recognized as discriminatory,” he said.

Additional study authors included Dr. Pooja Agrawal, Gregg Gonsalves, Lilla Orr, Dennis Wang and Elizabeth B. White from Yale University; Dr. Altaf Saadi from Massachusetts General Hospital; Dr. Michael Westerhaus from HealthPartners Center for International Health; and Dr. Aarti D. Bhatt from the University of Minnesota.

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JOURNAL

JAMA Network Open

DOI

10.1001/jamanetworkopen.2021.18216

METHOD OF RESEARCH

Observational study

SUBJECT OF RESEARCH

People

ARTICLE TITLE

Health Care Utilization Before and After the “Muslim Ban” Executive Order Among People Born in Muslim-Majority Countries and Living in the US

ARTICLE PUBLICATION DATE

30-Jul-2021

Trump’s ‘Muslim ban’ harmed health of Muslim Americans, study finds

Peer-Reviewed Publication

YALE UNIVERSITY

New Haven, Conn. —  When former President Donald J. Trump issued an executive order in 2017 banning Muslims from select countries from traveling to the United States, the sweeping decree quickly rippled down to affect health outcomes for Muslim-Americans, Yale researchers say.

A new study by the Yale School of Public Health and partner institutions found that a significant number of people in the Muslim community in the Minneapolis-St. Paul area skipped their primary care appointments after the ban, and there was also an increase in their visits to the emergency department.

The findings, published July 30 in the journal JAMA Network Open, provides evidence that an abrupt change in federal immigration policy can directly affect health outcomes among people residing in the United States legally. The study is one of the first to measure the causal impact of how policy changes such as these may affect Muslim American immigrant and refugee communities.

Before the ban, primary care visits and diagnoses of stress for individuals from Muslim-majority nations were on the rise, the researchers said. In the year following the ban, however, there were approximately 101 missed primary care appointments beyond what would have been expected among people from Muslim majority countries not named in the ban. There were also approximately 232 more emergency department visits by individuals from nations targeted by the ban than would have been predicted.

“This offers support to the thesis that the Islamophobia fostered by former President Trump affected the health of Muslim-Americans in the United States and that immigration policies can have indirect and unexpected consequences for those targeted by such actions,” said Yale School of Public Health Associate Professor Gregg Gonsalves, the study’s senior author.

Trump issued Executive Order 13769, “Protecting the Nation from Foreign Terrorist Entry into the United States,” shortly after his inauguration to limit the travel of refugees from Iran, Iraq, Libya, Syria, Sudan, and Yemen. The ban was upheld by the U.S. Supreme Court in 2018, only to be revoked by President Joe Biden in January 2021.

For the new study, the researchers examined more than 250,000 adult patients treated at a Minneapolis-St. Paul HealthPartners primary care clinic or in emergency departments in 2016 and 2017. These patients belonged to one of the following three groups: 1) born in a Muslim nation targeted by the ban, 2) born in a Muslim-majority nation not listed in the ban, and 3) non-Hispanic and born in the United States.

The study was co-authored by Pooja Agrawal, assistant professor at Yale School of Medicine, and Elizabeth White, a doctoral student at the Yale School of Public Health, along with researchers and health practitioners from Brown University, Massachusetts General Hospital, Medical College of Wisconsin, HealthPartners Center for International Health, University of Minnesota, Harvard University, and the Yale National Clinician Scholars Program.

JOURNAL

JAMA Network Open

METHOD OF RESEARCH

Data/statistical analysis

SUBJECT OF RESEARCH

People

ARTICLE PUBLICATION DATE

30-Jul-2021

Original Investigation 

Public Health

July 30, 2021

Health Care Utilization Before and After the “Muslim Ban” Executive Order Among People Born in Muslim-Majority Countries and Living in the US

Elizabeth A. Samuels, MD, MPH, MHS¹; Lilla Orr, MA²; Elizabeth B. White, BA³; et alAltaf Saadi, MD, MSc⁴; Aasim I. Padela, MD, MSc⁵; Michael Westerhaus, MD, MA⁶; Aarti D. Bhatt, MD⁷; Pooja Agrawal, MD, MPH⁸; Dennis Wang, MD, MPH⁹; Gregg Gonsalves, PhD³

Author Affiliations Article Information

JAMA Netw Open. 2021;4(7):e2118216. doi:10.1001/jamanetworkopen.2021.18216

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Key Points

Question  Was the 2017 “Muslim ban” executive order associated with changes in health care utilization by people born in Muslim-majority countries living in Minneapolis-St. Paul, Minnesota?

Findings  This cohort study of 252 594 patients found that after the executive order was issued, there was an increase in missed primary care appointments and increased emergency department visits among people from Muslim-majority countries living in Minneapolis-St. Paul.

Meaning  Changes in health care utilization among people from Muslim-majority countries after the Muslim ban may reflect changes in population health influenced by federal immigration policy.

Abstract

Importance  The health effects of restrictive immigration and refugee policies targeting individuals from Muslim-majority countries are largely unknown.

Objective  To analyze whether President Trump’s 2017 executive order 13769, “Protecting the Nation from Foreign Terrorist Entry into the United States” (known as the “Muslim ban” executive order) was associated with changes in health care utilization by people born in targeted nations living in the US.

Design, Setting, and Participants  This retrospective cohort study included adult patients treated at Minneapolis-St. Paul HealthPartners primary care clinics or emergency departments (EDs) between January 1, 2016, and December 31, 2017. Patients were categorized as (1) born in Muslim ban–targeted nations, (2) born in Muslim-majority nations not listed in the executive order, or (3) non–Latinx and born in the US. Data were analyzed from October 1, 2019, to May 12, 2021.

Exposures  Executive order 13769, “Protecting the Nation from Foreign Terrorist Entry into the United States.”

Main Outcomes and Measures  Primary outcomes included the number of (1) primary care clinic visits, (2) missed primary care appointments, (3) primary care stress-responsive diagnoses, (4) ED visits, and (5) ED stress-responsive diagnoses. Visit trends were evaluated before and after the Muslim ban issuance using linear regression, and differences between the study groups after the executive order issuance were evaluated using difference-in-difference analyses.

Results  A total of 252 594 patients were included in the analysis: 5667 in group 1 (3367 women [59.4%]; 5233 Black individuals [92.3%]), 1254 in group 2 (627 women [50%]; 391 White individuals [31.2%]), and 245 673 in group 3 (133 882 women [54.5%]; 203 342 White individuals [82.8%]). Group 1 was predominantly born in Somalia (5231 of 5667 [92.3%]) and insured by Medicare or Medicaid (4428 [78.1%]). Before the Muslim ban, primary care visits and stress-responsive diagnoses were increasing for individuals from Muslim-majority nations (groups 1 and 2). In the year after the ban, there were approximately 101 additional missed primary care appointments among people from Muslim-majority countries not named in the ban (point estimate [SE], 6.73 [2.90]; P = .02) and approximately 232 additional ED visits by individuals from Muslim ban–targeted nations (point estimate [SE], 3.41 [1.53]; P = .03).

Conclusions and Relevance  Results of this cohort study suggest that after issuance of the Muslim ban executive order, missed primary care appointments and ED visits increased among people from Muslim-majority countries living in Minneapolis-St. Paul.

Introduction

The 2016 US presidential election was marked by anti-Muslim and anti-immigrant rhetoric, and the subsequent Trump administration introduced multiple restrictive immigration policies targeting individuals from Muslim-majority and Latin American countries.¹ On January 27, 2017, President Trump issued executive order 13769, “Protecting the Nation from Foreign Terrorist Entry into the United States,”² commonly referred to as the “Muslim ban.” The first iteration of the Muslim ban suspended the US Refugee Resettlement Program and prevented citizens from 7 Muslim-majority countries (Iraq, Syria, Iran, Libya, Somalia, Sudan, and Yemen) from traveling or immigrating to the US. The ban underwent multiple legal challenges but was upheld by the US Supreme Court in 2018.³ On January 20, 2021, President Biden repealed the Muslim ban by executive order.⁴

Policies like the Muslim ban exacerbate heightened levels of discrimination, hostility, and “othering” that US Muslims experience.⁵ Over the past 2 decades, there has been an increase in hate crimes⁶ and social hostility^7,8 directed toward US Muslims—experiences that negatively affect health. Following the September 11 attacks, Arab Americans, including Muslim Arab Americans, experienced increased rates of anxiety, depression, and low birth weights.^5,9-12 Islamophobia and restrictive entry policies have been associated with worse health outcomes among migrants, placing Muslim American immigrants and refugees at increased vulnerability as they are additionally targeted by immigration and refugee policy changes, above and beyond discrimination faced by US Muslims more broadly.^13-15 Recent research has shown increased incidence of preterm birth among people from Muslim-majority countries after the Muslim ban was issued.¹⁶ However, it is largely unknown and can be difficult to measure how health and health care utilization in Muslim American immigrant and refugee communities changes in response to shifting sociopolitical climates due to several methodological challenges. National health and health care surveys, as well as administrative data sets, do not routinely capture religious affiliation, and naming and country-of-origin algorithms are not precise enough to distinguish people who are Muslim.^17,18

To describe changes in health care utilization of people from Muslim-majority countries after enactment of the Muslim ban, we examined changes in primary care and emergency department (ED) utilization by people from Muslim ban–targeted nations living in the Minneapolis-St. Paul, Minnesota, metropolitan area, which is home to the largest Somali Muslim community in the US.¹⁹

Methods

Study Design

We conducted a retrospective, cohort study documenting trends in primary care and ED utilization, missed scheduled appointments, and stress-responsive conditions among individuals from Muslim ban–targeted nations living in Minneapolis-St. Paul 1 year before (January 1, 2016) to 1 year after (December 31, 2017) issuance of the ban. For outcomes with similar visit and diagnosis trends before the ban was issued, we used a difference-in-difference analysis to compare differences in utilization trends between people born in Muslim ban–targeted nations and non–Latinx US-born citizens. Supplementary analyses compared trends among people born in a Muslim-majority nation not listed in the Muslim ban to non–Latinx US-born citizens. Patient demographic, visit, and diagnosis data were extracted from the HealthPartners electronic health record (EHR) by a HealthPartners data analyst. HealthPartners and the institutional review board of Yale University approved this study and waived the need for patient informed consent. All records were deidentified and assigned a unique study identification number before secure filing from HealthPartners to the Yale analytic team. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.²⁰

Study Setting and Population

The Minneapolis-St. Paul metropolitan area has 3.63 million residents and the largest Somali Muslim population in the US. In 2017, approximately 252 000 area residents (59.3%) were White non-Latinx individuals, 81 900 (19.3%) were Black non-Latinx individuals, 26 800 (6.3%) were Asian non-Latinx individuals, and 40 900 (9.6%) were Latinx individuals.²¹ In 2016, there were approximately 17 889 people born in Muslim ban–targeted nations living in Minneapolis-St. Paul, of whom 15 808 (88.4%) were born in Somalia.²¹

We analyzed EHR data from HealthPartners, one of Minneapolis-St. Paul’s largest health care and insurance organizations, serving over 1.2 million patients at 55 primary care centers, 22 acute care centers, and 8 hospitals. Although religion is not recorded in the EHR, the HealthPartners EHR is unique in that it includes nation of origin information. This allowed us to characterize patients receiving care from January 1, 2016, to December 31, 2017, into 3 groups: (1) adults born in a country listed in the executive order (group 1) (Table 1; eTable 1 in the Supplement); (2) adults born in Muslim-majority nations not listed in the executive order (group 2) (Table 1, eTable 2 in the Supplement); and (3) US-born non–Latinx adults (Group 3) (Table 1). We excluded US-born Latinx patients (persons who identify as Latino, Latina, or Hispanic, or having lineage from Mexico or any nation in Central or South America) as they have been subject to distinct anti-immigrant rhetoric and policies that have important effects on their health and health care utilization.^22,23

Outcomes

We hypothesized that after issuance of the Muslim ban, we would observe increased health care visits for stress-responsive diagnoses, increased primary care missed appointments, and increased ED visits. Primary outcomes included the number of (1) primary care visits, (2) missed primary care appointments, (3) primary care stress-responsive diagnoses, (4) ED visits, and (5) ED stress-responsive diagnoses.

Primary Care Utilization

Primary care visits, missed appointments, and stress-responsive diagnoses were analyzed as counts per 1000 people. Kept and missed visit trends were examined overall, regardless of diagnosis, and stress-responsive diagnoses were analyzed separately. We identified stress-responsive diagnoses through literature review and opinion by study team members with expertise in Muslim, immigrant, and refugee health and who provide primary care to people from Muslim-majority countries living in Minneapolis-St.Paul.^9,23-30 Diagnoses considered to be stress responsive were agreed upon by consensus (eTable 3 in the Supplement) and included 138 International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10), codes in 6 categories: mental health, sleep disorders, gastrointestinal symptoms, neurologic symptoms, food-related disorders, and pain syndromes.

Emergency Department Utilization

Overall ED visits and ED stress-responsive diagnoses were also analyzed as counts per 1000 people. As previously described, ED stress-responsive diagnoses were identified through literature review and expert opinion, were agreed upon by consensus, and included 27 ICD-10 codes for acute coronary syndrome, assault, suicide attempt, and syncope (eTable 4 in the Supplement)^9,23-32 as well as ambulatory sensitive conditions. Ambulatory sensitive conditions are conditions for which an ED visit or hospitalization is considered preventable through outpatient interventions and can be exacerbated by social stressors and inequalities.^31,33 Ambulatory sensitive diagnoses included 21 ICD-10 codes for angina, asthma, congestive heart failure, chronic obstructive pulmonary disease, diabetes complications, and hypertension (eTable 4 in the Supplement).^31,33

Statistical Analyses

We used local linear regression to characterize visit and diagnosis trends in 2016 and 2017 and fit separate models for all outcomes: primary care visits, missed primary care appointments, primary care stress-responsive diagnoses, ED visits, and ED stress-responsive diagnoses. After documenting changes over time, we identified outcomes for which trends appeared to be similar among non–Latinx US-born individuals and individuals from Muslim ban–targeted nations in the preintervention period by examining the interaction between study group and time (30-day periods) in linear regression models. For outcomes that followed approximately parallel trends before the ban was issued, we conducted difference-in-difference analyses.

The primary difference-in-difference analyses estimated the change in outcomes between pre- and post-ban periods among individuals from Muslim ban–targeted nations above and beyond the change observed among non–Latinx US-born individuals. For the difference-in-difference analyses, we fit the linear regression model described in equation 1:

Y = β₀ + (β₁Muslim ban targeted) + (β₂Muslim ban targeted × post-Muslim ban) + (β_t period) + ε.

We compared the 360 days before and after the ban was issued, divided into 24 distinct, 30-day periods. Each outcome Y is a count per person per 30-day time period. The designations Muslim ban targeted and Muslim ban targeted × post-Muslim ban represent being from a Muslim ban–targeted nation and being from a Muslim ban–targeted nation after the ban was issued, respectively. For each model, we estimated the average difference in differences (β₂) over increasing time intervals centered on the ban issuance date, beginning with 30 days before and after the ban was issued and increasing by 30-day increments to 360 days before and after issuance.

Demographic details were not included in the primary analysis because they do not vary over time. However, it is important to account for demographic details, because there are important differences between study groups that could influence health care utilization. In order to account for different characteristics between study groups and assess robustness, we compared changes in health care utilization by individuals from Muslim ban–targeted nations to 2 subgroups of non–Latinx US-born individuals demographically similar to individuals in group 1 (Muslim ban-targeted): a matched comparison group and a synthetic control. First, we matched on age, sex, race/ethnicity, and insurance type (Medicaid or Medicare, or commercial) to reweight members of group 3 (non–Latinx, US born) and identify a subset of group 3 with similar characteristics as group 1 (Muslim ban–targeted). We used the R package MatchIt, version 3.6.1, (R Core Team) to identify this reference group, then fit a weighted version of the model described in equation 1 (eTable 10 in the Supplement).³⁴ Second, we used a generalized synthetic control method to reweight members of group 3 to produce a reference group with demographic characteristics and pre–Muslim ban outcomes more similar to those observed in group 1. Models were fit using the R package gsynth with parametric bootstrap standard errors.³⁵ These robustness checks are reported in eTables 8-10 and in the eFigure in the Supplement. All P values were 2-sided, and P < .05 was considered significant. Data were analyzed from October 1, 2019, to May 12, 2021.

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Health Care Utilization Before and After the “Muslim Ban” Executive Order Among People Born in Muslim-Majority Countries and Living in the US | Emergency Medicine | JAMA Network Open | JAMA Network

 

Personally addressed emails designed with behavioral science can increase COVID-19 vaccine registration among vaccine-hesitant healthcare workers

Behavioral scientists at Geisinger’s Steele Institute for Health Innovation report findings from randomized trial

Peer-Reviewed Publication

GEISINGER HEALTH SYSTEM

DANVILLE, Pa. – A research letter published today in JAMA Network Open reports that individually addressed email reminders designed with behavioral science increased registration for a COVID-19 vaccination more than two-fold among healthcare workers who had not received a vaccine, compared with those who did not receive an email.

In one of the first real-world tests of an intervention to increase COVID-19 vaccination, members of the Behavioral Insights Team at Geisinger’s Steele Institute for Health Innovation used a combination of behavioral science principles, such as highlighting how many people have already been vaccinated, comparing the risk of a vaccine to that of COVID-19, and embedding a link to the registration portal, to develop the email messages.

The rate of COVID-19 vaccination is slowing in the United States, with some states having vaccination rates far below the target of 70%. Meanwhile, the increasing prevalence of the Delta variant, the threat of other COVID-19 variants, and the possibility that some or all vaccinated people will need to return for a booster shot highlight the importance of testing ways to increase vaccination.

“The evidence for easily scalable interventions for COVID-19 vaccination, like a well-designed email, has largely been limited to laboratory experiments that only measure intentions for getting a vaccine, which may or may not translate into actual behavior,” said Henri Santos, Ph.D., staff scientist in Geisinger’s Behavioral Insights Team and first author of the paper. “Although we focused on encouraging vaccination among healthcare workers—one of the first groups to gain access to COVID-19 vaccines—these messages could be adapted by other organizations and sent to other kinds of employees, students, or patients.”

Employees of Geisinger, a large, integrated health system in central and northeast Pennsylvania, began to access COVID-19 vaccines in mid-December. Between then and when the project was conducted in mid-January, the health system sent at least 36 COVID-19 vaccine-related emails to employees. At that point, employees were randomly assigned to one of three groups: one receiving an individually addressed “social norms” email, another receiving an individually addressed “reframing risks” email, or a delayed control group who received one of the two emails three days later. Those who received the social norms email were told that millions of U.S. residents and most of their fellow employees had been vaccinated or had scheduled a vaccination. Those who received the reframing risks email were presented with a comparison of the side effects of a vaccine against the much more serious known and unknown complications of COVID-19. Both emails were sent from the chief of infectious diseases and requested that the employee make a yes-or-no decision about receiving a vaccine.

Both the social norms and reframing risks emails led to more than twice as many registrations within three days (6.5% and 6.9% of recipients, respectively) compared with the delayed control group who had not yet received an email (3.2%)—an average absolute increase in vaccination registration of 3.5 percentage points. However, there was no significant difference between those who received the social norms and reframing risks emails.

“Like many lightweight behavioral interventions—sometimes called ‘nudges’—tested in the real world, ours had a modest impact, increasing the vaccination rate in our sample by 3.5 percentage points,” said Michelle Meyer, faculty co-director of the Behavioral Insights Team, assistant professor in Geisinger’s Research Institute, and the senior author of the paper. “On the other hand, these emails can be quickly sent to large numbers of people for free, and in some places, 3.5 percentage points might be the difference between reaching and failing to reach herd immunity. Our results suggest that some people who have been hesitant about the vaccine, even after a great deal of prior communication, can still be reached, at virtually no cost.”

About Geisinger
Geisinger is committed to making better health easier for the more than 1 million people it serves. Founded more than 100 years ago by Abigail Geisinger, the system now includes nine hospital campuses, a health plan with more than half a million members, a Research Institute, and the Geisinger Commonwealth School of Medicine. With nearly 24,000 employees and more than 1,600 employed physicians, Geisinger boosts its hometown economies in Pennsylvania by billions of dollars annually. Learn more at geisinger.org or connect with us on Facebook, Instagram, LinkedIn and Twitter.

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JOURNAL

JAMA Network Open

DOI

10.1001/jamanetworkopen.2021.18702

METHOD OF RESEARCH

Randomized controlled/clinical trial

SUBJECT OF RESEARCH

People

ARTICLE TITLE

Effect of Targeted Behavioral Science Messages on COVID-19 Vaccination Registration Among Employees of a Large Health System

ARTICLE PUBLICATION DATE

28-Jul-2021