Health care lobbying in the United States

Landers, Steven H.; Sehgal, Ashwini R.

doi:10.1016/j.amjmed.2003.10.037

Cited by 42 publications

(15 citation statements)

References 13 publications

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“…Pharmaceutical Lobby. Pharmaceutical lobbying is another controversial issue that affects many parts of the world [22,23,24,25]. Among its many aspects, we focus on the marketing practices of large pharmaceutical companies which manipulate the opinions of doctors, health care providers and law-makers by providing biased information and through other tactics [26,27].…”

Section: Search Engine Biasmentioning

confidence: 99%

Detecting Sponsored Recommendations

Krishnasamy

Sen

et al. 2015

Proceedings of the 2015 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems

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Personalized recommender systems provide great opportunities for targeted advertisements, by displaying ads alongside genuine recommendations. We consider a biased recommendation system where such ads are displayed without any tags (disguised as genuine recommendations), rendering them indistinguishable to users. We consider the problem of detecting such a bias and propose an algorithm that uses statistical analysis based on binary feedback data from a subset of users. We prove that the proposed algorithm detects bias with high probability for a broad class of recommendation systems with sufficient number of feedback samples.

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Section: Search Engine Biasmentioning

confidence: 99%

Detecting Sponsored Recommendations

Krishnasamy

Sen

et al. 2015

Proceedings of the 2015 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems

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“…Conflict over the distributional effects of policies that affect health and health care, for example, can be expensive. 3,4 Efforts to address obesity through structural intervention are likely to be similarly expensive. 5 Thus, although the direct costs of policy change may amount to very little (as Katz correctly notes), the social costs can be quite substantial.…”

Section: Indirect Costs Of Structural Interventionsmentioning

confidence: 99%

“…3 In the United States, women of lower socioeconomic status or low educational level, ethnic minorities, and immigrants are significantly more likely to both develop and die of cervical cancer. 4 More than differences in individual risk, these disparities arise from the systematic inability of underserved groups to access health care services, such as regular screening and prompt treatment of premalignant cervical lesions. 5 Despite its limitations, the HPV vaccine has the potential to reduce the existing disparity in cervical cancerrelated morbidity and mortality.…”

mentioning

confidence: 99%

Marketing and the HPV Vaccine

Betensky¹

2009

JAMA

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“…Pharmaceutical lobbying is another controversial issue that affects many parts of the world [22,23,24,25]. Among its many aspects, we focus on the marketing practices of large pharmaceutical companies which manipulate the opinions of doctors, health care providers and law-makers by providing biased information and through other tactics [26,27].…”

Section: Applicationsmentioning

confidence: 99%

Detecting Sponsored Recommendations

Krishnasamy

Sen

et al. 2015

SIGMETRICS Perform. Eval. Rev.

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With a vast number of items, web-pages, and news to choose from, online services and the customers both benefit tremendously from personalized recommender systems. Such systems however provide great opportunities for targeted advertisements, by displaying ads alongside genuine recommendations. We consider a biased recommendation system where such ads are displayed without any tags (disguised as genuine recommendations), rendering them indistinguishable to a single user. We ask whether it is possible for a small subset of collaborating users to detect such a bias. We propose an algorithm that can detect such a bias through statistical analysis on the collaborating users' feedback. The algorithm requires only binary information indicating whether a user was satisfied with each of the recommended item or not. This makes the algorithm widely appealing to real world issues such as identification of search engine bias and pharmaceutical lobbying. We prove that the proposed algorithm detects the bias with high probability for a broad class of recommendation systems when sufficient number of users provide feedback on sufficient number of recommendations. We provide extensive simulations with real data sets and practical recommender systems, which confirm the trade offs in the theoretical guarantees.

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Health care lobbying in the United States

Cited by 42 publications

References 13 publications

Detecting Sponsored Recommendations

Detecting Sponsored Recommendations

Marketing and the HPV Vaccine

Detecting Sponsored Recommendations

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