| Literature DB >> 35599194 |
Émilien Schultz1, Léo Mignot2, Jeremy K Ward3, Daniela Boaventura Bomfim4, Christian Chabannon5, Julien Mancini6.
Abstract
OBJECTIVES: During the coronavirus disease 2019 (COVID-19) pandemic, public debates overtly addressed the promises of new innovative drugs. Many of these debates pitted those who advocated for the development of new drugs by pharmaceutical companies against those who favored the repositioning of existing drugs. Our study explored perceptions of the association between drug novelty and effectiveness as well as perceptions of the role of the pharmaceutical industry in drug development.Entities:
Keywords: COVID-19; Drug innovation; Public attitude; Social pharmacology; Sociology
Year: 2022 PMID: 35599194 PMCID: PMC9077798 DOI: 10.1016/j.therap.2022.05.001
Source DB: PubMed Journal: Therapie ISSN: 0040-5957 Impact factor: 3.367
Distribution of the association between novelty/effectiveness and the role of pharmaceutical companies regarding independant variables.
| Dependent variable (or Variable) | Association novelty/effectiveness | Driven by the pharmaceutical industry | Total | ||||
|---|---|---|---|---|---|---|---|
| Distribution | 1 – Disagree | 2 – Agree | 1 – Agree | 2 – Disagree | Total | ||
| Variable | Modalities | ||||||
| Age | [0–25[ | 13.0% | 87.6 (67.4%) | 42.4 (32.6%) | 57.2 (44.0%) | 72.8 (56.0%) | 130.0 (100%) |
| [25–45[ | 36.5% | 259.6 (71.1%) | 105.4 (28.9%) | 165.7 (45.4%) | 199.3 (54.6%) | 365.0 (100%) | |
| [45–65[ | 37.7% | 299.8 (79.5%) | 77.2 (20.5%) | 147.3 (39.1%) | 229.7 (60.9%) | 377.0 (100%) | |
| [65+ | 12.8% | 99.5 (77.7%) | 28.5 (22.3%) | 62.3 (48.7%) | 65.7 (51.3%) | 128.0 (100%) | |
| Sex | Female | 51.4% | 400.6 (77.9%) | 113.4 (22.1%) | 206.1 (40.1%) | 307.9 (59.9%) | 514.0 (100%) |
| Male | 48.6% | 346.0 (71.2%) | 140.0 (28.8%) | 226.4 (46.6%) | 259.6 (53.4%) | 486.0 (100%) | |
| Education | Below BD | 18.5% | 136.5 (73.9%) | 48.1 (26.1%) | 93.9 (50.9%) | 90.6 (49.1%) | 184.6 (100%) |
| BD | 19.7% | 140.2 (71.2%) | 56.6 (28.8%) | 86.1 (43.8%) | 110.6 (56.2%) | 196.7 (100%) | |
| Above BD | 61.9% | 469.9 (75.9%) | 148.8 (24.1%) | 252.4 (40.8%) | 366.3 (59.2%) | 618.7 (100%) | |
| Incomes | less than 1000 | 5.3% | 34.9 (66.2%) | 17.8 (33.8%) | 26.0 (49.3%) | 26.7 (50.7%) | 52.7 (100%) |
| 1000 to 2000 | 23.4% | 177.1 (75.6%) | 57.3 (24.4%) | 95.7 (40.8%) | 138.7 (59.2%) | 234.4 (100%) | |
| 2000 to 3000 | 28.4% | 220.4 (77.7%) | 63.1 (22.3%) | 112.3 (39.6%) | 171.2 (60.4%) | 283.5 (100%) | |
| 3000 to 5000 | 24.8% | 176.2 (70.9%) | 72.4 (29.1%) | 115.1 (46.3%) | 133.4 (53.7%) | 248.5 (100%) | |
| 5000 and more | 6.8% | 42.4 (62.8%) | 25.1 (37.2%) | 40.3 (59.7%) | 27.2 (40.3%) | 67.6 (100%) | |
| NA | 11.3% | 95.6 (84.4%) | 17.7 (15.6%) | 43.0 (38.0%) | 70.3 (62.0%) | 113.3 (100%) | |
| Health condition | Good | 63.7% | 468.4 (73.5%) | 168.6 (26.5%) | 305.7 (48.0%) | 331.3 (52.0%) | 637.0 (100%) |
| Average | 29.0% | 217.7 (75.1%) | 72.2 (24.9%) | 102.0 (35.2%) | 187.9 (64.8%) | 289.9 (100%) | |
| Bad | 7.3% | 60.4 (82.7%) | 12.6 (17.3%) | 24.8 (33.9%) | 48.3 (66.1%) | 73.1 (100%) | |
| Trust in scientists | Yes | 87.0% | 650.6 (74.8%) | 219.6 (25.2%) | 383.4 (44.1%) | 486.7 (55.9%) | 870.2 (100%) |
| No | 13.0% | 95.9 (73.9%) | 33.9 (26.1%) | 49.0 (37.8%) | 80.8 (62.2%) | 129.8 (100%) | |
| Trust in doctors | Yes | 92.1% | 688.3 (74.7%) | 232.7 (25.3%) | 406.4 (44.1%) | 514.5 (55.9%) | 920.9 (100%) |
| No | 7.9% | 58.3 (73.7%) | 20.8 (26.3%) | 26.1 (33.0%) | 53.0 (67.0%) | 79.1 (100%) | |
| Trust in politics | Yes | 15.2% | 83.4 (54.8%) | 68.7 (45.2%) | 100.7 (66.2%) | 51.4 (33.8%) | 152.1 (100%) |
| No | 84.8% | 663.1 (78.2%) | 184.7 (21.8%) | 331.7 (39.1%) | 516.1 (60.9%) | 847.9 (100%) | |
| Trust in industrials | Yes | 42.2% | 276.8 (65.6%) | 145.2 (34.4%) | 253.1 (60.0%) | 168.9 (40.0%) | 422.0 (100%) |
| No | 57.8% | 469.7 (81.3%) | 108.3 (18.7%) | 179.3 (31.0%) | 398.6 (69.0%) | 578.0 (100%) | |
| Information seeking | Yes | 84.0% | 642.5 (76.5%) | 197.7 (23.5%) | 346.9 (41.3%) | 493.3 (58.7%) | 840.2 (100%) |
| No | 16.0% | 104.0 (65.1%) | 55.8 (34.9%) | 85.6 (53.6%) | 74.2 (46.4%) | 159.8 (100%) | |
| Concerns about COVID-19 | Yes | 31.6% | 221.1 (70.0%) | 94.6 (30.0%) | 149.2 (47.3%) | 166.5 (52.7%) | 315.8 (100%) |
| Some | 62.3% | 474.0 (76.1%) | 148.9 (23.9%) | 255.3 (41.0%) | 367.6 (59.0%) | 622.9 (100%) | |
| No | 6.1% | 51.4 (83.7%) | 10.0 (16.3%) | 28.0 (45.6%) | 33.4 (54.4%) | 61.4 (100%) | |
| NHL | Q1-Low | 27.1% | 228.2 (84.2%) | 42.7 (15.8%) | 84.4 (31.2%) | 186.4 (68.8%) | 270.9 (100%) |
| Q2 | 25.0% | 217.3 (86.7%) | 33.2 (13.3%) | 89.4 (35.7%) | 161.1 (64.3%) | 250.5 (100%) | |
| Q3 | 25.9% | 180.3 (69.5%) | 79.0 (30.5%) | 111.4 (43.0%) | 147.9 (57.0%) | 259.3 (100%) | |
| Q4-High | 21.9% | 120.7 (55.0%) | 98.6 (45.0%) | 147.2 (67.1%) | 72.1 (32.9%) | 219.3 (100%) | |
| Total | 99.9% | 746.5 (74.6%) | 253.5 (25.4%) | 432.5 (43.2%) | 567.5 (56.8%) | 1000.0 (100%) | |
BD: Bachelor degree; NA: no answer; NHL: navigating health literacy; Q1: quartile 1.
Logistic regression of the association novelty/effectiveness.
| Variable | Modality | OR – CI 95% | |
|---|---|---|---|
| Intercept | 0.62 [0.32–1.23] | 0.171 | |
| Age | [0–25[ | ||
| [25–45[ | 0.78 [0.49–1.25] | 0.3 | |
| [45–65[ | 0.54 [0.34–0.88] | 0.013* | |
| [65+ | 0.57 [0.31–1.03] | 0.061 | |
| Sex | Female | ||
| Male | 1.36 [1.00–1.85] | 0.053 | |
| NHL | Q1-Low | ||
| Q2 | 0.80 [0.49–1.32] | 0.379 | |
| Q3 | 2.01 [1.30–3.10] | 0.002** | |
| Q4-High | 3.34 [2.13–5.24] | 0.0*** | |
| Trust in politicians | Yes | ||
| No | 0.63 [0.42–0.95] | 0.028* | |
| Trust in industrials | Yes | ||
| No | 0.68 [0.49–0.94] | 0.021* | |
| Information-seeking behaviour | Yes | ||
| No | 1.47 [0.99–2.19] | 0.057 | |
| Concerns about COVID-19 | Yes | ||
| Some | 0.62 [0.45–0.87] | 0.005** | |
| No | 0.38 [0.18–0.82] | 0.014* |
COVID-19: coronavirus disease 2019; NHL: navigating health literacy; Q1: quartile 1; *:p-value < 0.05; **:p-value < 0.01;***:p-value<0.001.
Logistic regression for agreement that pharmaceutical companies should drive drug development.
| Variable | Modality | OR – CI 95% | |
|---|---|---|---|
| Intercept | 0.83 [0.58–1.18] | 0.304 | |
| NHL | Q1-Low | ||
| Q2 | 1.20 [0.81–1.77] | 0.355 | |
| Q3 | 1.12 [0.76–1.66] | 0.555 | |
| Q4-High | 2.62 [1.73–3.97] | 0.0*** | |
| Coupling efficiency/novelty | Disagree | ||
| Agree | 3.85 [2.76–5.39] | 0.0*** | |
| Health condition | Good | ||
| Average | 0.60 [0.44–0.82] | 0.002** | |
| Bad | 0.77 [0.44–1.34] | 0.351 | |
| Trust in industrials | Yes | ||
| No | 0.38 [0.28–0.50] | 0.0*** |
NHL: navigating health literacy; Q1: quartile 1; *:p-value < 0.05; **:p-value < 0.01;***:p-value<0.001.