| Literature DB >> 20439253 |
Lana Ivanitskaya1, Jodi Brookins-Fisher, Irene O Boyle, Danielle Vibbert, Dmitry Erofeev, Lawrence Fulton.
Abstract
BACKGROUND: Websites of many rogue sellers of medications are accessible through links in email spam messages or via web search engines. This study examined how well students enrolled in a U.S. higher education institution could identify clearly unsafe pharmacies.Entities:
Mesh:
Substances:
Year: 2010 PMID: 20439253 PMCID: PMC2885783 DOI: 10.2196/jmir.1520
Source DB: PubMed Journal: J Med Internet Res ISSN: 1438-8871 Impact factor: 5.428
Features of online pharmacies used in this study
| Feature | Pharmacy A (URL extension: .net) | Pharmacy B (URL extension: .com) |
| Advertising claims (as they appeared in the source) | “Beozine—US $37.99—now available in a gel!” | “Beozine retails for US $200, we sell for $59.50!” |
| Prescription process | Fill out and submit an online questionnaire. No prescription is required. | Submit a valid prescription by FAX or email (with a scanned prescription attached) or request an updated prescription. |
| Contact options | Pharmacy’s physical address (outside the US), online contact form, and email address. | Pharmacy’s physical address (outside the US), toll-free FAX, online contact form, and email address. |
| Information requested from customers | Name, date of birth, email address, mailing address, detailed insurance information, specific medical problems, all past surgeries, conditions treated with each surgery, all medications they plan to take, and all current medical conditions. | All over-the-counter and prescription medications they are currently taking, the length of time for each, and medications they plan to take in the near future. |
| Promises and disclaimers | “Any information provided by our customers is never shared, sold, or released to any third party outside of our network of doctors, who need to view the information in order to write and fill a prescription and our network of partners.” Customers must agree with a responsibility statement: “All questions asked of me during the medication request have been answered truthfully and completely.” | “By requesting this medication the requestor confirms the release of pharmacy and all of its employees and contractors, including doctors, from ANY and ALL liability whatsoever associated or connected with the request for and use of medication. The statements have not been evaluated by the FDA. No advice or product listed here is intended to diagnose, treat, cure, or prevent any disease.” |
| Statements to reassure customers | “Our organization is committed to meeting and exceeding current regulations. We utilize licensed doctors. Our pharmacies are licensed to ship medication worldwide and employ licensed pharmacists to provide you with the highest standards of pharmaceutical care.” “Online consultations are the latest concept in health care.” | “Rest assured you are receiving the same medication as you would at your neighborhood pharmacy.” “As a marketing group primarily involved in membership-based ordering service promotion, we established relationships with the largest pharmaceutical wholesalers. We don't sell any type of medications, we are here just to help members get cheap medications.” |
| Customer testimonials, examples | No testimonials. | “I tried your pharmacy after I read a testimony of a customer who got a new prescription in 15 minutes. I am so happy I did not have to go see an expensive doctor...” |
Figure 1Scatter plot of respondents’ ratings of Pharmacies A and B (n = 1914)
Joint and marginal probabilities for respondents’ ratings of online pharmacies (n = 1914)
| Pharmacy B | Total | ||||
| Rating range | 0 to 3.3 | 3.3 to 6.7 | 6.7 to 10 | ||
| Pharmacy A | 0 to 3.3 | 31.0% | 3.8% | 2.2% | 37.0% |
| 3.3 to 6.7 | 10.4% | 17.5% | 4.1% | 32.0% | |
| 6.7 to 10 | 7.4% | 8.9% | 14.7% | 31.0% | |
| Total | 48.9% | 30.1% | 21.0% | 100.0% | |
Conditional probabilities for respondents’ ratings of online pharmacies (n = 1914)
| Pharmacy B | Total | ||||
| Rating Range | 0 to 3.3 | 3.3 to 6.7 | 6.7 to 10 | ||
| Pharmacy A | 0 to 3.3 | 83.8% | 10.3% | 5.9% | 100.0% |
| 3.3 to 6.7 | 32.5% | 54.6% | 12.9% | 100.0% | |
| 6.7 to 10 | 23.9% | 28.7% | 47.4% | 100.0% | |
Distributions for respondents’ ratings of online pharmacies (n = 1914)
| Ratinga | Cumulative Percent of Respondents | |
| Pharmacy A | Pharmacy B | |
| 0 up to 1.0 | 17.7 | 25.0 |
| 1.0 up to 2.0 | 25.7 | 35.4 |
| 2.0 up to 3.0 | 32.7 | 44.7 |
| 3.0 up to 4.0 | 41.0 | 52.4 |
| 4.0 up to 5.0 | 50.2 | 62.7 |
| 5.0 up to 6.0 | 63.5 | 73.8 |
| 6.0 up to 7.0 | 70.3 | 80.7 |
| 7.0 up to 8.0 | 78.3 | 86.8 |
| 8.0 up to 9.0 | 86.1 | 93.5 |
| 9.0 up to 10.0 | 100.0 | 100.0 |
aRatings were made on a 0 to 10 electronic visual analog scale with a .025 increment and end points marked as “0 = Very bad” and “10 = Very good.”
Respondents’ explanations for low cost of Beozine sold by Pharmacy B
| Reasons | Percent of Respondentsa | |
| Few regulations: pharmacy B may follow fewer operational guidelines or service standards than neighborhood pharmacies | 60.0 | |
| Low quality of drugs: pharmacy B may not meet the standards of drug quality that neighborhood pharmacies must meet | 47.5 | |
| Selling customer information: revenue from information sold to others may be used to lower prices in Pharmacy B | 30.0 | |
| Low operation costs: it may cost less to operate Pharmacy B (eg, because customers type their own information) | 56.7 | |
| Advertising: revenue from online ads may be used to lower prices in Pharmacy B | 37.1 | |
| Comparison shopping: the customers of Pharmacy B may compare prices, demand free shipping, discounts, coupons or other incentives | 34.6 | |
| High sales volume: more people may buy drugs online than in neighborhood pharmacies, which lowers prices in Pharmacy B | 30.3 | |
| None of the above | 7.5 | |
an = 1914. The sum of percentages exceeds 100% because the respondents could choose more than one reason.
Summary of hierarchical regression analysis for variables predicting a pharmacy evaluation index (n = 1914)
| Model 1 | Model 2 | |||||
| Variable | Ba | SEbB | Betac | Ba | SEbB | Betac |
| Age | 0.01 | 0.00 | 0.09d | 0.01 | 0.00 | 0.10d |
| Gender | 0.02 | 0.01 | 0.03 | 0.01 | 0.01 | 0.02 |
| College credits earned | 0.04 | 0.01 | 0.09d | 0.03 | 0.01 | 0.07d |
| Health major | 0.02 | 0.00 | 0.13d | 0.01 | 0.00 | 0.10d |
| Self-reported health | -0.01 | 0.00 | -0.08d | -0.01 | 0.00 | -0.08d |
| Belief in the high quality of Internet health information | -0.03 | 0.01 | -0.06d | |||
| Made health decisionse | -0.02 | 0.00 | -0.17d | |||
| R2 | .05 | .08 | ||||
| F change for R2 | 19.56d | 34.66d | ||||
aUnstandardized regression coefficient (uses units unique to each variable)
bStandard error of B
cStandardized regression coefficient (uses the same units for all variables in the equation)
dSignificant at the .01 level
eWhether an individual used information from general Internet searches for health decision making, for self, or to help others