Dual practice regulation

Dual practice regulatory mechanisms in the health sector A systematic review of approaches and implementation 

Dual practice refers to physicians concurrent activity, public and private. The conflicts of incentives arise and some regulatory mechanisms are needed.

The regulatory mechanisms that have been employed across countries can be divided into three categories: those that advocate for total banning of DP, those that allow it with restrictions and those that allow it without restriction. Countries that attempted total banning of dual practice, as in Portugal and Greece, could not easily stamp it out. DP continued to exist on a wide scale in Portugal until the ban was lifted in 1993 (Oliveira and Pinto, 2005). Similarly, the ban in Greece from 1983 to 2002 did not prevent public doctors from practising privately (Mossialos et al., 2005). Efforts to ban dual practice failed because of lack of capacity to enforce it. The resources needed to enforce it may not be commensurate with the benefits a country gets from banning it. Moreover, banning dual practice has in some countries been associated with the migration of health workers, especially specialists, from the public to the private sector as well as an international brain drain (Buchan and Sochalski, 2004; Mossialos et al., 2005). In LMIC settings where health workers are underpaid and members of the general population are willing to pay for more convenient and possibly better services, this option might not be viewed as legitimate or even feasible.

 The second category is allowing dual practice with restrictions. This was the most frequent approach used by countries. Financial and licensure restrictions as well as promotional incentives were employed. Financial restrictions included limiting private sector earnings, providing incentives to limit private sector activities, salary increases for public sector workers and performance-based payments. All financial restrictions intrinsically require well-established and adequate health financing systems to fund and monitor public and private sector activity. A combination of tax-based public financing, mandatory health insurance and private insurance might be necessary to counter the financial resource demands of this approach, while supervision, monitoring systems and transparent bureaucracies would be necessary to ensure that private sector activities and earnings are indeed limited and payments are matched by performance.

 Allowing DP without restrictions was noted in countries like Indonesia and Egypt, where DP is routine and accepted. An interesting point to note is that in both countries, the productivity of physicians far exceeded the capacity of the public sector to employ them. Because of the low salaries offered in the public sector, physicians are allowed to supplement their incomes with private sector earnings. This approach is unlikely to be feasible in countries with health worker shortages. Considering the three options of total ban, allowing dual practice with restrictions and allowing it without restrictions, the most feasible for the LMICs is allowing it with restrictions. With health workers who are underpaid, in short supply and working in areas with a high burden of disease, they will scarcely be able to satisfy the demands of the public or the private sector alone.

Hockney

 

DRGs 101

 DIAGNOSIS-RELATED GROUPS: a question and answer guide on case-based classification and payment systems

WHO has released a report on DRGs that is useful as introduction to the concept and the design of payment systems.

The document consists of four parts:

Part 1 outlines definitions, terminology and the main conceptual aspects related to CBG and DRG.

Part 2 covers the assessment phase and highlights questions and issues that policy-makers should consider before taking the decision to introduce a CBG system.

Part 3 delves into the preparation phase by exploring policy and design aspects once a country has decided to introduce a CBG system.

Part 4 is concerned with the implementation phase and discusses implementation questions, requirements for system adjustments and the need for monitoring and revision in order to identify and address unintended impacts of a CBG system.

 

 

Improving CRISPR, a crowd of proteins

Improving CRISPR from Mammoth Biosciences; 

 Genome editing is the process researchers use to make targeted changes to an organism’s DNA (its genome). Scientists have used a variety of technologies for genome editing (see the history of genome editing here). However, since ~2012, CRISPR has made the genome editing processes much easier. CRISPR associated or “Cas” proteins drive this process. They are relatively easy to target to specific DNA sequences. They also work in many organisms.

Yet, the main Cas protein currently used for CRISPR genome editing, SpCas9, has limitations. In this post, we cover SpCas9’s limitations and how newly discovered Cas protein families, Cas14 and CasΦ, potentially overcome these limitations. We hope Cas14 and CasΦ will enable more efficient genome editing in diverse organisms and tissues.

 

Spillover effects of payment systems

 Randomized trial shows healthcare payment reform has equal-sized spillover effects on patients not targeted by reform

From PNAS: 

Changes in the way health insurers pay healthcare providers may not only directly affect the insurer’s patients but may also affect patients covered by other insurers.

This is the research question. And this is the result:

We use a payment reform in TM, which was randomly applied to some markets but not others, to study spillovers of healthcare payment reform. We find spillovers of the same sign and similar magnitude on privately insured MA patients. Naturally, our findings are specific to our setting; the existence, sign and magnitude of any spillovers may well vary across contexts.

Sounds good. However, there is a previous research question, which is the insurer's market share that allows to have the option to change the payment system. This former question is as relevant as the later one.

 

Hockney

Health impact of social isolation and loneliness

Social Isolation And Health

The lonely century

Social isolation and loneliness are both terms that denote a degree of social disconnection. Social isolation is an objective state marked by few or infrequent social contacts. Loneliness is the subjective and distressing feeling of social isolation, often defined as the discrepancy between actual and desired level of social connection.

Social connection and connectedness encompass a variety of terms used in the scientific literature (for example, social support, social integration, social cohesion) that document the ways that being physically or emotionally connected to others can influence health and well-being. 

In this HA Brief, the author explains that loneliness impact on health may be greater than we think. Rather than being alarming, she shows a description of the situation, the evidence and a proposal for policy agenda.

And if you are not convinced, than I would suggest this book:

An unprecedented discovery

Yesterday was World Crispr Day

 

Mapping genetic regulation

The GTEx Consortium atlas of genetic regulatory effects across human tissues

A great step in research:

The GTEx v8 data release represents a deep survey of both intra- and interindividual transcriptome variation across a large number of tissues. With 838 donors and 15,201 samples—approximately twice the size of the v6 release used in the previous set of GTEx Consortium papers—we have created a comprehensive resource of genetic variants that influence gene expression and splicing in cis. This substantially expands and updates the GTEx catalog of sQTLs, doubles the number of eGenes per tissue, and saturates the discovery of eQTLs with greater than twofold effect sizes in ~40 tissues. The fine-mapping data of GTEx cis-eQTLs provide a set of thousands of likely causal functional variants. While trans-QTL discovery and the characterization of sex- and population-specific genetic effects are still limited by sample size, analyses of the v8 data provide important insights into each.

Fig. 1 Sample and data types in the GTEx v8 study.

(A) Illustration of the 54 tissue types examined (including 11 distinct brain regions and two cell lines), with sample numbers from genotyped donors in parentheses and color coding indicated in the adjacent circles. Tissues with 70 or more samples were included in QTL analyses. (B) Illustration of the core data types used throughout the study. Gene expression and splicing were quantified from bulk RNA-seq of heterogeneous tissue samples, and local and distal genetic effects (cis-QTLs and trans-QTLs, respectively) were quantified across individuals for each tissue.