Unbiased Analysis of Today's Healthcare Issues

Drugs from the sea

Written By: Jason Shafrin - Nov• 02•16

My daughter recently went to the Aquarium of the Pacific in Long Beach.  Not only did she enjoy seeing all the marine life, but she learned that the oceans also help humans in many ways.  For instance, many pharmaceutical firms are looking to the ocean to see if there are compounds created by sea plants, animals and other organisms that can be used to improve the health of humans.

A post from NOAA provides some examples of chemicals produced by marine animals that may be useful in treating human diseases:

  • Ecteinascidin: Extracted from tunicates; being tested in humans for treatment of breast and ovarian cancers and other solid tumors
  • Discodermalide: Extracted from deep-sea sponges belonging to the genus Discodermia; anti-tumor agent
  • Bryostatin: Extracted from the bryozoan, Bugula neritina; potential treatment for leukemia and melanoma
  • Pseudopterosins: Extracted from the octocoral (sea whip) Pseudopterogorgia elisabethae; anti-inflammatory and analgesic agents that reduce swelling and skin irritation and accelerate wound healing
  • w-conotoxin MVIIA: Extracted from the cone snail, Conus magnus; potent pain-killer


Patterns of Adherence to Atypical Antipsychotics

Written By: Jason Shafrin - Nov• 01•16

A study titled “Patterns of Adherence to Oral Atypical Antipsychotics Among Patients Diagnosed with Schizophrenia” was just published in the November edition of JMCP.  This is work with co-authors Joanna P. MacEwan, Felicia M. Forma, Jason Shafrin, Ainslie Hatch, Darius N. Lakdawalla, Jean-Pierre Lindenmayer. The abstract is below.  Go check it out!

BACKGROUND: Poor medication adherence contributes to negative treatment response, symptom relapse, and hospitalizations in schizophrenia. Many health plans use claims-based measures like medication possession ratios or proportion of days covered (PDC) to measure patient adherence to antipsychotics. Classifying patients solely on the basis of a single average PDC measure, however, may mask clinically meaningful variations over time in how patients arrive at an average PDC level.

OBJECTIVE: To model patterns of medication adherence evolving over time for patients with schizophrenia who initiated treatment with an oral atypical antipsychotic and, based on these patterns, to identify groups of patients with different adherence behaviors.

METHODS: We analyzed health insurance claims for patients aged ≥ 18 years with schizophrenia and newly prescribed oral atypical antipsychotics in 2007-2013 from 3 U.S. insurance claims databases: Truven MarketScan (Medicaid and commercial) and Humana (Medicare). Group-based trajectory modeling (GBTM) was used to stratify patients into groups with distinct trends in adherence and to estimate trends for each group. The response variable was the probability of adherence (defined as PDC ≥ 80%) in each 30-day period after the patient initiated antipsychotic therapy. GBTM proceeds from the premise that there are multiple distinct adherence groups. Patient demographics, health status characteristics, and health care resource use metrics were used to identify differences in patient populations across adherence trajectory groups.

RESULTS: Among the 29,607 patients who met the inclusion criteria, 6 distinct adherence trajectory groups emerged from the data: adherent (33%); gradual discontinuation after 3 months (15%), 6 months (7%), and 9 months (5%); stop-start after 6 months (15%); and immediate discontinuation (25%). Compared to patients 18-24 years of age in the adherent group, patients displaying a stop-start pattern after 6 months had greater odds of having a history of drug abuse (OR = 1.46; 95% CI = 1.26-1.66; P < 0.001), alcohol abuse (OR = 1.34; 95% CI = 1.14-1.53; P< 0.001), and a codiagnosis of major depressive disorder (OR = 1.24; 95% CI = 1.05-1.44; P< 0.001) and were less likely to be aged 35-54 years (OR = 0.66; 95% CI = 0.46-0.85; P < 0.001).

CONCLUSIONS: Longitudinal medication adherence patterns can be expressed as distinct trajectories associated with specific patient characteristics and health care utilization patterns. We found 6 distinct patterns of adherence to antipsychotics over 12 months. Patients in different groups may warrant different types of clinical interventions to prevent hospitalizations, longer hospital stays, and increased clinical complexity. For example, clinicians may consider regular home visits, assertive community treatment, and other related interventions for patients at high risk of immediate discontinuation. Health plans should consider supplementing claims-based adherence measures with new technologies that are able to track patient adherence patterns over time.

The Healthcare Economist on NPR

Written By: Jason Shafrin - Oct• 31•16

Check out my interview on NPR with Here & Now’s Jeremy Hobson about what recent changes in health insurance premiums changes mean for the future of the Affordable Care Act.


The value of adherence information

Written By: Jason Shafrin - Oct• 30•16

A study titled “Estimating the Value of New Technologies That Provide More Accurate Drug Adherence Information to Providers for Their Patients with Schizophrenia” was just published in the November edition of JMCP.  This is work with co-authors Taylor T. Schwartz, Darius N. Lakdawalla, and Felicia M. Forma.  The abstract is below.  Go check it out!


BACKGROUND: Nonadherence to antipsychotic medication among patients with schizophrenia results in poor symptom management and increased health care and other costs. Despite its health impact, medication adherence remains difficult to accurately assess. New technologies offer the possibility of real-time patient monitoring data on adherence, which may in turn improve clinical decision making. However, the economic benefit of accurate patient drug adherence information (PDAI) has yet to be evaluated.

OBJECTIVE: To quantify how more accurate PDAI can generate value to payers by improving health care provider decision making in the treatment of patients with schizophrenia.

METHODS: A 3-step decision tree modeling framework was used to measure the effect of PDAI on annual costs (2016 U.S. dollars) for patients with schizophrenia who initiated therapy with an atypical antipsychotic. The first step classified patients using 3 attributes: adherence to antipsychotic medication, medication tolerance, and response to therapy conditional on medication adherence. The prevalence of each characteristic was determined from claims database analysis and literature reviews. The second step modeled the effect of PDAI on provider treatment decisions based on health care providers’ survey responses to schizophrenia case vignettes. In the survey, providers were randomized to vignettes with access to PDAI and with no access. In the third step, the economic implications of alternative provider decisions were identified from published peer-reviewed studies. The simulation model calculated the total economic value of PDAI as the difference between expected annual patient total cost corresponding to provider decisions made with or without PDAI.

RESULTS: In claims data, 75.3% of patients with schizophrenia were found to be nonadherent to their antipsychotic medications. Review of the literature revealed that 7% of patients cannot tolerate medication, and 72.9% would respond to antipsychotic medication if adherent. Survey responses by providers (n = 219) showed that access to PDAI would significantly alter treatment decisions for nonadherent or adherent/poorly controlled patients (P < 0.001). Payers can expect to save $3,560 annually per nonadherent patient who would respond to therapy if adherent. Savings increased to $9,107 per nonadherent patient when PDAI was given to providers who frequently augmented therapy for these patients. Among all poorly controlled patients (i.e., the nonadherent or those who were adherent but unresponsive to therapy), access to PDAI decreased annual patient cost by $2,232. Savings for this group increased to $7,124 per patient when PDAI was given to providers who frequently augmented therapy.

CONCLUSIONS: Access to PDAI significantly improved provider decision making, leading to lower annual health care costs for patients who were nonadherent or adherent but poorly controlled. Additional research is warranted to evaluate how new technologies that accurately monitor adherence would affect health and economic outcomes among patients with serious mental illness.

Friday Links

Written By: Jason Shafrin - Oct• 27•16

How are payers tackling value-based drug pricing?

Written By: Jason Shafrin - Oct• 26•16

A growing number of value-based drug pricing arrangements are tying the price of treatments to outcomes. While these payment models are far from prominent now, is it likely that drug makers will have a greater share of the risks and rewards associated with value-based care moving forward?

This is what is written in a feature article at Healthcare Dive titled How payers are tackling value-based drug pricing?  The Healthcare Economist (yours truly) is interviewed and I give my take on these programs.  An excerpt is below.

“Value is not an intrinsic number as value varies depending on the stakeholder of interest,” Shafrin said. For instance, his own research on cancer care has indicated that providers value treatments that offer improvements in average or median survival. Meanwhile, patients value treatments that offer a significant chance at long-term survival even if average or median survival gains are lower. “Value should be defined as ‘value to whom.’”

Go check out the whole article.

Obamacare Death Spiral?

Written By: Jason Shafrin - Oct• 26•16

Health insurance premiums are projected to increase an astronomical 25% for plans in the health insurance exchanges. Some pundits claim that these increases represent that Obamacare is crumbling or in a death spiral. As premiums rise, healthy people flee the market. This leaves insurers with only more sick individuals which leads to premium increases. More increases mean that the moderately sick leave and so on. Eventually, insurers leave the marketplace due to excessive adverse selection.  In fact,  health insurers (e.g., Aetna and UnitedHealth) have already left the marketplace.

Is this what is going on or is there another explanation?

There could be other explanations include:

  • Low introductory offer. 2017 health insurance premiums may be high, but premium hikes in 2015 and 2016 may have been unnaturally low.  Insurers could have kept pricing low to attract new business.  If health plans are “sticky” in that patients are less likely to change plans, pricing low initiatively may be a good approach initially.  There is some evidence that premiums may have been too low in the early Obamacare years.
  • Insurers didn’t know the market.  The individual insurance market differs greatly from the group market.  Health insurers such as Aetna and UnitedHealth that are more used to group plans may have underpriced.  Insurers remaining after their exit may represent a more realistic picture of the true cost of care.
  • Reinsurance goes away.  When Obamacare was set up, the government would subsidize an insurer’s highest cost patients.   Reinsurance funds were set at $10 billion in 2014, $6 billion in 2015, and $4 billion in 2016 (KFF).  However, by 2017, these funds are likely to dry up and insurers will be responsible for covering the full cost of their highest cost patients.
  • Blame the risk corridors.  Risk corridors are a form of market stabilization whereby very profitable plans subsidize less profitable plans.  Specifically, KFF reports: “HHS collects funds from plans with lower than expected claims and makes payments to plans with higher than expected claims. Plans with actual claims less than 97% of target amounts pay into the program and plans with claims greater than 103% of target amounts receive funds.”   Similar to the reinsurance system, the risk corridors are going away in 2017.

In short, the large increase may represent the end of Obamacare, or it could just represent the stabilization of the market as government subsidies dry out and insurers more experienced in the individual market settle in.

Chocolate Bar

Written By: Jason Shafrin - Oct• 24•16

A truly amazing and uplifting story from Kind World from WBUR about a boy with Glycogen Storage Disease Type 1b, his friend and the Chocolate Bar Book.

LANTZ: Dylan is 10 years old now. But this story starts when the boys were in first grade. Dylan’s mom Debra Siegel was driving her son home from Jonah’s house when she told him that Jonah had a rare condition he could die from.

DYLAN SIEGEL: I’m like, oh, my God, I want to help.

LANTZ: That’s Dylan. His mom explained there was no cure and doctors needed money to find one.

DEBRA SIEGEL: And I said, do you want to do a bake sale? Do you want to do a lemonade stand? He’s like, no. He looked at me like I was insane. What horrible ideas.

DYLAN: I’m like, how about I could write a book?

LANTZ: The next day, Dylan took out his markers and made a picture book he dedicated to Jonah. His plan, sell the book to raise money for research.

SIEGEL: He marched in my office and said, here’s my book. Will you go make copies?

DYLAN: Please, please, please, print it, print it, print it, print it.

LANTZ: When he took his books to a school event…

DYLAN: We sold out.

LANTZ: So the next week, Dylan spoke at a PTA meeting.

SIEGEL: Somebody asked him how much money do you want to raise?


DYLAN: A million dollars.

The funny thing is…he did. The Today show reports that he did in fact raise $1m.

More details and Dylan and Jonah’s website is HERE. Please donate.

Extended Cost Effectiveness Analysis

Written By: Jason Shafrin - Oct• 23•16

Cost effectiveness analysis (CEA) examines whether treatment benefits outweight treatment costs on average for a given patient population. A 2016 paper by Verguet, Kim and Jamison examine the concept of extended cost effectiveness analysis (ECEA) which applies cost effectiveness methodologies to health care policies. The policies are evaluated over 4 domains: (1) health gains; (2) financial risk protection (FRP) benefits; (3) the total costs to the policy makers; and (4) the distributional benefits.

Policymakers can use these results to identify preferred policy interventions.

Usually, ECEA displays the three outcomes of health gains, private expenditures crowded out, and FRP, by population stratum…Furthermore, the two major outcomes of ECEA, health gains and financial protection per population stratum, can be scaled with the net cost of the policy to a particular budget constraint or per dollar expenditure…The motivation is to enable the expression of ECEA findings in terms of the ‘efficient purchase’ of financial protection and equity, in addition to the efficient purchase of health gains, as in a traditional CEA.

For instance, one could identify the cost of treatment per life year saved or per poverty case avoided.

The authors sum up the benefit of their framework as follows:

ECEA approach permits the inclusion of non-health benefits and distributional consequences and equity in the economic evaluation of health policies. It enables the consideration of key criteria into the resource allocation problem and into the design of health benefits packages.


Friday Links

Written By: Jason Shafrin - Oct• 20•16