Providing laypeople with results from dynamic infectious disease modelling studies affects their allocation preference for scarce medical resources—a factorial experiment

doi:10.21203/rs.3.rs-495548/v1

Download PDF

Research Article

Providing laypeople with results from dynamic infectious disease modelling studies affects their allocation preference for scarce medical resources—a factorial experiment

https://doi.org/10.21203/rs.3.rs-495548/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background

Allocation of scarce medical resources can be based on different principles. It has not yet been investigated which allocation schemes are preferred by medical laypeople in a particular situation of medical scarcity like an emerging infectious disease and how the choices are affected by providing information about expected population-level effects of the allocation scheme based on modelling studies.

Methods

In a two-way factorial experiment (n = 878 participants), we investigated if prognosis of the disease or information about expected effects on mortality at population-level (based on dynamic infectious disease modelling studies) influenced the choice of preferred allocation schemes for prevention and treatment of an unspecified sexually transmitted infection. A qualitative analysis of the reasons for choosing specific allocation schemes supplements our results.

Results

Presence of the factor “information about the population-level effects of the allocation scheme” substantially increased the probability of choosing a resource allocation system that minimized overall harm among the population, while prognosis did not affect allocation choices. The main reasons for choosing an allocation scheme differed among schemes, but did not differ among those who received additional model-based information on expected population-level effects and those who did not.

Conclusions

Providing information on the expected population-level effects from dynamic infectious disease modelling studies resulted in a substantially different choice of allocation schemes. This finding supports the importance of incorporating model-based information in decision-making processes and communication strategies.

Infectious Diseases

Allocation strategies

factorial design

STI

Medical resources can be scarce because of restricted supply chains (e.g., vaccines) or high costs (e.g., antiretroviral therapy). Even in high-income countries, generally available resources can become scarce during emergencies like a pandemic. Persad et al. (1,2) proposed guiding principles for the allocation of scarce resources: equal treatment (random selection, waiting list), utilitarianism (prognosis, number of lives saved), prioritarianism (sickest first, youngest first), and instrumental value (rewarding social usefulness in the past or in the future). Yousef et al. (3) added the two principles monetary contribution to the costs of one’s own medical treatment and individual behaviour (not engaging in risky behaviours that caused one’s medical condition) to this list.

The allocation of scarce treatment and prevention measures against infectious diseases is particularly challenging since the prevention or reduction of infectiousness directly affects the risk of infection for other individuals in the population. Dynamic infectious disease modelling studies can be used to assess the effects of different allocation schemes on a population level and to provide evidence for public health decision-making (4,5). Nevertheless, it is not clear if people consider results from modelling studies as an important source of information. Furthermore, information from modelling studies underscores the utilitarian perspective, i.e., looking for the allocation scheme that saves most human lives or maximises (on average) a favourable outcome, while possibly ignoring the individual suffering. Zhang et al. (6) discuss this dilemma in a commentary about the consequences of redistributing antiretroviral therapy in low-resource settings to sicker HIV/AIDS patients, and not to patients immediately upon diagnosis, which would minimize overall harm among the population (1). In addition, utilitarian allocation schemes regarding sexually transmitted infections (STI) often favour those individuals with the riskiest behaviour.

When confronted with hypothetical situations of scarcity, health professionals may choose different allocation schemes compared with laypeople (7). Some authors have asked experts and laypeople to rank allocation schemes (3,8), but it has not yet been investigated which allocation schemes are chosen by laypeople in the context of infectious disease prevention and how the choices are affected by providing information about expected results based on modelling studies. This is relevant for communication of decisions regarding allocation: If the allocation scheme by the authorities is not in line with the choices of the general population, this decreases the acceptance of their implementation (9). By surveying a sample of inhabitants of Lower Saxony, Germany, we investigated the effect of providing information about expected population-level outcomes (based on dynamic infectious disease modelling studies) on the choice of allocation schemes. In addition, we investigated if prognosis of disease (time until death in case of infection) plays a role in choosing an allocation scheme.

Sample

We implemented a factorial experiment within HaBIDS, an online panel to assess preventive behaviour regarding infectious diseases (10,11). In brief, 26,895 individuals (15–69 years old) from four districts in Lower Saxony, Germany were invited, of which 9% participated in the panel. In February 2016, the questionnaire about allocation of scarce medical resources (Additional File 1) was activated; 1,037 participants were still enrolled in HaBIDS at that time.

Of those, 878 individuals participated in this factorial experiment. Compared with the sampling frame (the four districts in Lower Saxony), the participants of this study were older (median age group 50–54 years vs. 40–44 years in the sampling frame), more likely to be female (59.9% vs. 50.0%), more likely to have a university degree (41.8% vs. 13.5%), and more likely to be married (59.0% vs. 46.7%).

Factorial experiment

Participants were presented with a hypothetical resource allocation problem concerning an STI that is spreading in a city. Participants were 1:1 randomly assigned to a scenario where either prevention (vaccination) or treatment (cure) should be distributed.

The description stated that inhabitants differ in how often they change sex partners and how often they have several sex partners at the same time (information corresponding to results from the Natsal study (12)). Participants were randomly assigned to one of 6 combinations, based on a 2 × 3 factorial design (Figure 1): time until death as indicator of severity of the disease (5 years vs. 15 years) × model-based information on expected population-level effects of each allocation scheme (number of the avoided deaths in two versions as described below vs. no information) The two factors were randomized independently from each other.

Participants were asked to choose among the following options for allocation: “random allocation” (i.e., equal treatment), “young individuals first” (i.e., prioritarianism), “promiscuous individuals first” (i.e., utilitarianism in the case of this STI), “individuals with long-lasting partnerships first” (i.e., individual behaviour), or “undecided”. In the scenario “treatment”, an additional option “first come, first served” was given because the waiting-list principle is often applied to (expensive) treatments, but usually not to (non-expensive) vaccinations. We excluded allocation schemes based on instrumental value or monetary contribution because these are not applicable to STI.

For the model-based information on expected population-level effects, there were three options: no information, or one of two versions of information on the effects of the various allocation schemes. The information consisted of a bar chart (Additional File 1): The top bar showed the expected number of deaths in the absence of treatment. Then, for each allocation scheme, a bar showed the expected number of deaths if the treatment was distributed according to this scheme. The expected numbers of deaths were based on the results of a simple compartmental model of the disease dynamics. The two versions of the information differed only in the top bars of the bar chart: One version showed that in the absence of treatment, 10,000 inhabitants would die while the other stated that 20,000 inhabitants would die (Additional File 1). All other numbers of deaths (i.e., all other bars) were equal for both versions, so that in the version with 10,000 deaths the relative differences between various allocation schemes appeared substantially larger, while in the version with 20,000 deaths the various allocation schemes appeared more similar to each other (but more different from the scenario with no treatment).

Sample size

We aimed to investigate any difference in the distribution of the choices of allocation schemes dependent on the randomization factors. We estimated that 500 participants per scenario (i.e., 1,000 participants in total, corresponding to the size of the HaBIDS panel), would allow us to achieve at least 95% power in a chi-squared test if there was a medium or large effect (Cohen’s effect size index (13) w = 0.3 or w = 0.5, respectively).

The 1:1 randomization resulted in 441 participants in the scenario “prevention” (Additional File 2) and 437 participants in the scenario “treatment” (Additional File 3).

Statistical analysis

The influence of each factor on the choice of allocation scheme was investigated with Pearson's chi-squared tests. The factor “model-based information on expected population-level effects” was entered as no information vs. any additional information for the primary analysis. To assess if there was any interaction between the two factors, we investigated the influence of the factor “time until death” on the choice of allocation schemes separately in the subgroups “No info” and “Additional info”.

To investigate if the number of deaths in the event of no treatment influences the choice of allocation scheme by making the differences between them appear larger or smaller, the two versions of the factor “model-based information on expected population-level effects” were analysed with chi-squared tests among the participants who had received any additional information. All analyses were performed with R (14) version 4.0.4.

Qualitative analysis

In a free text field, participants were also asked to give the reason for their choice of allocation scheme within the factorial experiment. These responses were evaluated with a modified and extended structured content analysis according to Mayring (15). They were presented to three independent researchers who developed a category system with subcategories, which was further elaborated with the help of an external researcher. Three researchers, one of whom was not involved in the previous process, applied the category system independently. Intercoder reliability was calculated by using Krippendorff's alpha (16), which indicates the overall match of the three encoders (0 = no match, 1 = perfect match). A consensus was found for any nonmatching categorizations according to pre-established rules by two researchers to determine the final classification. The structured content analysis revealed four categories with a total of eight subcategories (Additional File 4). Krippendorff's alpha among the three encoders was lowest for “condemnation of a particular lifestyle” (0.51) and highest for “minimize risks/number of deaths” (0.83).

When comparing the two scenarios “treatment” versus “prevention,” the frequency of undecided participants was higher in the scenario “treatment” (28.4%) than in the scenario “prevention” (16.6%), while the frequency of choosing the utilitarian allocation scheme was lower (24.3% vs. 46.0% in the scenario “treatment” vs. “prevention,” respectively).

Irrespective of the randomization group, the utilitarian allocation scheme was most often chosen in the scenario “prevention”. There was neither evidence for an effect of the factor “time until death” on choosing an allocation scheme in the scenario “prevention” (e.g., utilitarian scheme: 45.4% for 5 years until death vs. 46.7% for 15 years until death, p-value of chi-squared test across all categories = 0.77; Table 1) nor in the scenario “treatment” (e.g., utilitarian scheme: 26.0% vs. 22.3%, p = 0.82).

In contrast, chi-squared tests indicated an effect of the factor “model-based information on expected population-level effects” on the choice of an allocation scheme (Table 1). The frequency of choosing the utilitarian allocation scheme was higher among participants who received information about expected effects of the various choices based on modelling (52.1% vs. 34.2%, p<0.001, in the scenario “prevention” and 30.0% vs. 12.5%, p = 0.004, in the scenario “treatment”). The frequencies of choosing random allocation or “undecided” were not affected by providing additional information. The higher frequency of choosing the utilitarian allocation scheme in the scenario “prevention” was accompanied by lower frequencies of choosing “young individuals first” and “long-lasting partnerships first”. This shift was not that pronounced in the scenario “treatment”.

There was no evidence that both randomization factors interacted with each other.

Differences in the population-level effects (resulting from the number of deaths in the absence of treatment) played no role for the choice of the utilitarian allocation scheme among participants who had received model-based information on expected population-level effects; in the scenario “prevention”, 54.9% chose the utilitarian allocation scheme among those presented with 10,000 deaths in the absence of treatment compared with 49.3% among those presented with 20,000 deaths (Additional File 5). In the scenario “treatment”, 28.1% vs. 32.0% chose the utilitarian allocation scheme among those presented with 10,000 vs. 20,000 deaths in the absence of treatment (Additional File 5).

Table 1: Relative frequencies (%) of choice of allocation scheme by randomization factors

	Time until death				Model-based information on expected population-level effects
	Prevention (p_{chi-squared test} = 0.77)		Treatment (p_{chi-squared test} = 0.82)		Prevention (p_{chi-squared test}<0.001)		Treatment (p_{chi-squared test} = 0.004)
Death within 5 years (n = 227)	Death within 15 years (n = 214)	Death within 5 years (n = 231)	Death within 15 years (n = 206)	No info (n = 149)	Additional info (n = 292)	No info (n = 144)	Additional info (n = 293)
Random allocation	12.8	11.7	6.1	7.3	12.8	12.0	8.3	5.8
First come, first served	NA	NA	19.9	24.3	NA	NA	27.1	19.5
Young individuals first	13.7	11.2	10.0	8.3	22.1	7.5	11.1	8.2
Promiscuous individuals first	45.4	46.7	26.0	22.3	34.2	52.1	12.5	30.0
Long-lasting partnerships first	11.0	14.5	10.0	9.2	16.8	10.6	9.7	9.6
Undecided	17.2	15.9	28.1	28.6	14.1	17.8	31.2	27.0

Qualitative analysis

Eighty-three percent of the participants (n = 728) entered reasons for their choice of an allocation scheme. Utilitarian choices were provided by 63.4% of these participants in the scenario “prevention” and 47.8% in the scenario “treatment” (Additional File 4). The main reasons for choosing an allocation scheme differed among all the schemes (Figure 2). The percentage of participants who chose “promiscuous individuals first” to “minimize risks/number of deaths” was above 90%. This did not differ among those who received additional model-based information on expected population-level effects and those who received no additional information.

We show that providing information about expected population-level outcomes (based on dynamic infectious disease modelling studies) affects the choice of preferred allocation schemes among medical laypeople.

If prevention measures had to be distributed, individuals predominantly chose the allocation scheme that minimized the number of deaths, followed by allocation schemes that correspond to the “need” principle (17). This trend towards the utilitarian allocation scheme accentuated if additional information about the population-level effect was provided. If treatment had to be distributed, individuals selected the “first come, first served” or “undecided” options with nearly equal frequency. If additional information about the population-level effect was provided, the frequency of choosing the utilitarian allocation scheme doubled so that one-third of all participants chose to prioritize promiscuous individuals.

Our results show that individuals differentiate between prevention and treatment; without additional information, prevention is allocated to the group with risky behaviour, but treatment is not. There seems to be a sound understanding of the transmission of infections in the population, and individuals are willing to allocate vaccinations to the people that contribute most to transmission, irrespective of their lifestyle. This is not surprising as this option shows the best effect at a societal level.

If, however, people have been infected because of their lifestyle, only a small proportion of individuals would allocate treatment to these people. Additional information about the expected number of deaths could persuade individuals to allocate treatment to infected promiscuous people. The qualitative analysis revealed that individuals attribute the number of deaths to the whole population in the scenario “prevention”, but only to the group of promiscuous people in the scenario “treatment.” This can be interpreted as retaliation of those self-responsible for their medical emergency. It is known from earlier studies that people tend to withdraw aid from individuals being self-responsible for their precarious situation (18) even if it is at costs of their benefits (19).

Limitations

There may be implicit and explicit content overlaps among the studied allocation schemes. It was stated in the survey that younger inhabitants change their sex partners more often than older ones. This could have created logical overlaps between the options “young individuals first” and “promiscuous individuals first,” but such overlaps cannot be avoided in a real-life scenario. While we could not assess whether our sample was representative for Germany concerning attitudes towards STI and promiscuity, our study population contained people with more than average education indicating that the results are probably not generalizable to the general population.

Providing information on the expected population-level effects from dynamic infectious disease modelling studies resulted in a substantially different choice of preferred allocation schemes. This finding supports the importance of incorporating model-based information in the communication and societal discussion about infection control measures.

Ethics approval and consent to participate

The HaBIDS study was approved by the Ethics Committee of Hannover Medical School (No. 2021-2013) and by the Federal Commissioner for Data Protection and Freedom of Information in Germany. All participants provided written informed consent before entering the study.

Consent for publication

Not applicable.

Availability of data and materials

The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

Internal funding of the Helmholtz Centre for Infection Research.

Authors' contributions

NR, SC, PK, TS, and RM developed the study design and the questionnaires. NR managed the acquisition of data. NR, SC, CJK-T, and TS developed the category system for the structured content analysis. NR, CJK-T, and JO applied the category system for the structured content analysis. NR and BGV conducted the statistical analysis. NR wrote the initial version of the manuscript. NR, BGV, SC, CJK-T, JO, PK, TS, RM, and AK contributed to the interpretation of the results, to writing and to revising the manuscript.

Acknowledgements

We express our thanks to the whole ESME team and especially Amelie Schaible, Anna-Sophie Peleganski, and Lea-Marie Meyer for their help in conducting the study. We also thank all participants of the study.

Hygiene and Behaviour Infectious Diseases Study (HaBIDS)

Sexually transmitted infection (STI)

Persad G, Wertheimer A, Emanuel EJ. Principles for allocation of scarce medical interventions. Lancet. 2009 Jan;373(9661):423–31.
Emanuel EJ, Persad G, Upshur R, Thome B, Parker M, Glickman A, et al. Fair allocation of scarce medical resources in the time of Covid-19. N Engl J Med. 2020 Mar 23;NEJMsb2005114.
Yousef MH, Alhalaseh YN, Mansour R, Sultan H, Alnadi N, Maswadeh A, et al. The fair allocation of scarce medical resources: a comparative study from Jordan. Front Med. 2021;7(January):1–9.
Grassly NC, Fraser C. Mathematical models of infectious disease transmission. Nat Rev Microbiol. 2008;6(6):477–87.
Boily MC, Mâsse B. Mathematical models of disease transmission: A precious tool for the study of sexually transmitted diseases. Can J Public Heal. 1997;88(4):255–65.
Zhang Y, Bärnighausen T, Eyal N. When global ART budgets cannot cover all patients, who should be eligible? JAIDS J Acquir Immune Defic Syndr. 2019 Feb;1.
Krütli P, Rosemann T, Törnblom KY, Smieszek T. How to to fairly allocate scarce medical resources: ethical argumentation under scrutiny by health professionals and lay people. PLoS One. 2016 Jul 27;11(7):e0159086.
Grover S, McClelland A, Furnham A. Preferences for scarce medical resource allocation: Differences between experts and the general public and implications for the COVID-19 pandemic. Br J Health Psychol. 2020;25(4):889–901.
Suchman MC. Managing legitimacy: strategic and institutional approaches. Acad Manag Rev. 1995 Jul;20(3):571.
Rübsamen N, Akmatov MK, Castell S, Karch A, Mikolajczyk RT. Comparison of response patterns in different survey designs: a longitudinal panel with mixed-mode and online-only design. Emerg Themes Epidemiol. 2017 Dec 21;14(1):4.
Rübsamen N, Akmatov MK, Castell S, Karch A, Mikolajczyk RT. Factors associated with attrition in a longitudinal online study: results from the HaBIDS panel. BMC Med Res Methodol. 2017 Dec 31;17(1):132.
Mercer CH, Tanton C, Prah P, Erens B, Sonnenberg P, Clifton S, et al. Changes in sexual attitudes and lifestyles in Britain through the life course and over time: findings from the National Surveys of Sexual Attitudes and Lifestyles (Natsal). Lancet. 2013 Nov;382(9907):1781–94.
Cohen J. The effect size index: w. In: Statistical power analysis for the behavioral sciences. 2nd ed. Hillsdale, NJ: Lawrence Erlbaum Associates; 1988. p. 224–7.
R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria; 2019.
Mayring P. Combination and integration of qualitative and quantitative analysis. Qual Quant Res Conjunctions Divergences. 2001 Feb;2(1).
Zapf A, Castell S, Morawietz L, Karch A. Measuring inter-rater reliability for nominal data – which coefficients and confidence intervals are appropriate? BMC Med Res Methodol. 2016 Dec 5;16(1):93.
Deutsch M. Equity, equality, and need: What determines which value will be used as the basis of distributive justice? J Soc Issues. 1975 Jul;31(3):137–49.
Skitka LJ, Tetlock PE. Allocating scarce resources: A contingency model of distributive justice. J Exp Soc Psychol. 1992 Nov;28(6):491–522.
Fehr E, Gächter S. Altruistic punishment in humans. Nature. 2002 Jan;415(6868):137–40.

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Providing laypeople with results from dynamic infectious disease modelling studies affects their allocation preference for scarce medical resources—a factorial experiment

Status:

Version 1

Abstract

Figures

Background

Methods

Sample

Factorial experiment

Sample size

Statistical analysis

Qualitative analysis

Results

Qualitative analysis

Discussion

Limitations

Conclusions

Declarations

Ethics approval and consent to participate

Consent for publication

Availability of data and materials

Competing interests

Funding

Authors' contributions

Acknowledgements

Abbreviations

References

Additional Declarations

Supplementary Files

Status:

Version 1