Valuing the health and wellbeing aspects of Community Empowerment (CE) in an ...cheweb1
Valuing the health and wellbeing aspects of Community Empowerment (CE) in an Urban Regeneration context using economic evaluation techniques. Economic evaluation seminar presented by Camilla Baba, PhD candidate, University of Glasgow 12 May 2016
Ignite your medical funding opportunties emeAlan Scrase
IGNITE your.... medical funding opportunities
Presenter - Dr Lisa Douet, Senior Research Fellow, and Ellie Monks, EME Programme Manager will present about
“Research Funding: The Efficacy and Mechanism Evaluation Programme”
The EME Programme funds clinical efficacy studies. The studies it supports usually test if an intervention works as expected, in a well-defined population or group of patients. The Programmealso also provides an opportunity to use clinical studies to understand disease or treatment mechanisms, which may in turn lead to improvements in health and patient care.
Valuing the health and wellbeing aspects of Community Empowerment (CE) in an ...cheweb1
Valuing the health and wellbeing aspects of Community Empowerment (CE) in an Urban Regeneration context using economic evaluation techniques. Economic evaluation seminar presented by Camilla Baba, PhD candidate, University of Glasgow 12 May 2016
Ignite your medical funding opportunties emeAlan Scrase
IGNITE your.... medical funding opportunities
Presenter - Dr Lisa Douet, Senior Research Fellow, and Ellie Monks, EME Programme Manager will present about
“Research Funding: The Efficacy and Mechanism Evaluation Programme”
The EME Programme funds clinical efficacy studies. The studies it supports usually test if an intervention works as expected, in a well-defined population or group of patients. The Programmealso also provides an opportunity to use clinical studies to understand disease or treatment mechanisms, which may in turn lead to improvements in health and patient care.
Behavioral economics approach to reduce injectable contraceptive discontinuat...JSI
This was presented by Ali Karim as part of a preformed panel at the International Conference on Family Planning (ICFP) in Kigali, Rwanda in November 2018.
Contraceptive prevalence in Ethiopia jumped from 6% in 2000 to 35% in 2016, primarily attributed to the increase in injectable contraceptive method use from 3% in 2000 to 23% in 2016. Nonetheless, discontinuation rate among injectable contraceptive users was 38%.
Given that injectable methods are the preferred method among married women of reproductive age in Ethiopia, the Last Ten Kilometers Project (L10K) of JSI Research & Training Institute, Inc. (JSI) in collaboration with ideas42 worked with Ethiopia’s flagship Health Extension Program to apply behavioral economics (BE) approaches to mitigate discontinuation of injectable contraceptives.
Methods: The project followed a BE methodology to conduct a behavioral diagnosis and design an intervention package, consisting of 1) health worker planner calendar, 2) client care checklist, and 3) client appointment cards.
Conclusion: Discontinuation can be influenced by health systems factors like supply issues. Nonetheless, the use of two BE tools—the appointment card and client care checklist—effectively decreased injectable discontinuation in the presence other health system bottlenecks. BE is an effective approach to enhance family planning programs in Ethiopia and elsewhere.
MicroGuide app, pop up uni, 1pm, 3 september 2015NHS England
Expo is the most significant annual health and social care event in the calendar, uniting more NHS and care leaders, commissioners, clinicians, voluntary sector partners, innovators and media than any other health and care event.
Expo 15 returned to Manchester and was hosted once again by NHS England. Around 5000 people a day from health and care, the voluntary sector, local government, and industry joined together at Manchester Central Convention Centre for two packed days of speakers, workshops, exhibitions and professional development.
This year, Expo was more relevant and engaging than ever before, happening within the first 100 days of the new Government, and almost 12 months after the publication of the NHS Five Year Forward View. It was also a great opportunity to check on and learn from the progress of Greater Manchester as the area prepares to take over a £6 billion devolved health and social care budget, pledging to integrate hospital, community, primary and social care and vastly improve health and well-being.
More information is available online: www.expo.nhs.uk
This research explores the feasibility of introducing an Outcome-Based Payment approach for new cancer drugs in England. A literature review explored the current funding landscape in England, the available evidence on existing OBP schemes internationally, and
which outcomes cancer patients value most. Two focus groups and an online survey with patients and carers, as well as interviews with NHS and government stakeholders, healthcare
professionals, and pharmaceutical industry representatives, provided additional evidence on the feasibility and suitability of OBP schemes
Co-ordinated malaria research for better policy and practice: the role of res...ACT Consortium
Prof. David Schellenberg from the London School of Hygiene & Tropical Medicine presents on behalf of the ACT Consortium at the European Congress on Tropical Medicine and International Health in Basel, Switzerland, 8 September 2015
Matt Sutton: reduced mortality with hospital Pay for Performance in EnglandThe King's Fund
Matt Sutton, Professor of Health Economics at the University of Manchester, explains what the Pay for Performance scheme is and how it has led to a reduction in mortality in the North West of England.
Analysis of cross-country changes in health services IDS
This presentation was given in a session at the Global Symposium on Health Systems Research which was organised by the Future Health Systems Consortium. The author is Toru Matsubayashi from Johns Hopkins Bloomberg School of Public Health
Presented by Nick Baker
General and Community Paediatrician
Executive Clinical Director for Community Based Services, Nelson Marlborough District Health Board.
American Public Health Association- Annual Meeting 2014 Presentation scherala
Title: Using Quantitative Data to focus Medical Home Facilitation Interventions in the Massachusetts Patient Centered Medical Home Initiative (MA PCMHI)
Behavioral economics approach to reduce injectable contraceptive discontinuat...JSI
This was presented by Ali Karim as part of a preformed panel at the International Conference on Family Planning (ICFP) in Kigali, Rwanda in November 2018.
Contraceptive prevalence in Ethiopia jumped from 6% in 2000 to 35% in 2016, primarily attributed to the increase in injectable contraceptive method use from 3% in 2000 to 23% in 2016. Nonetheless, discontinuation rate among injectable contraceptive users was 38%.
Given that injectable methods are the preferred method among married women of reproductive age in Ethiopia, the Last Ten Kilometers Project (L10K) of JSI Research & Training Institute, Inc. (JSI) in collaboration with ideas42 worked with Ethiopia’s flagship Health Extension Program to apply behavioral economics (BE) approaches to mitigate discontinuation of injectable contraceptives.
Methods: The project followed a BE methodology to conduct a behavioral diagnosis and design an intervention package, consisting of 1) health worker planner calendar, 2) client care checklist, and 3) client appointment cards.
Conclusion: Discontinuation can be influenced by health systems factors like supply issues. Nonetheless, the use of two BE tools—the appointment card and client care checklist—effectively decreased injectable discontinuation in the presence other health system bottlenecks. BE is an effective approach to enhance family planning programs in Ethiopia and elsewhere.
MicroGuide app, pop up uni, 1pm, 3 september 2015NHS England
Expo is the most significant annual health and social care event in the calendar, uniting more NHS and care leaders, commissioners, clinicians, voluntary sector partners, innovators and media than any other health and care event.
Expo 15 returned to Manchester and was hosted once again by NHS England. Around 5000 people a day from health and care, the voluntary sector, local government, and industry joined together at Manchester Central Convention Centre for two packed days of speakers, workshops, exhibitions and professional development.
This year, Expo was more relevant and engaging than ever before, happening within the first 100 days of the new Government, and almost 12 months after the publication of the NHS Five Year Forward View. It was also a great opportunity to check on and learn from the progress of Greater Manchester as the area prepares to take over a £6 billion devolved health and social care budget, pledging to integrate hospital, community, primary and social care and vastly improve health and well-being.
More information is available online: www.expo.nhs.uk
This research explores the feasibility of introducing an Outcome-Based Payment approach for new cancer drugs in England. A literature review explored the current funding landscape in England, the available evidence on existing OBP schemes internationally, and
which outcomes cancer patients value most. Two focus groups and an online survey with patients and carers, as well as interviews with NHS and government stakeholders, healthcare
professionals, and pharmaceutical industry representatives, provided additional evidence on the feasibility and suitability of OBP schemes
Co-ordinated malaria research for better policy and practice: the role of res...ACT Consortium
Prof. David Schellenberg from the London School of Hygiene & Tropical Medicine presents on behalf of the ACT Consortium at the European Congress on Tropical Medicine and International Health in Basel, Switzerland, 8 September 2015
Matt Sutton: reduced mortality with hospital Pay for Performance in EnglandThe King's Fund
Matt Sutton, Professor of Health Economics at the University of Manchester, explains what the Pay for Performance scheme is and how it has led to a reduction in mortality in the North West of England.
Analysis of cross-country changes in health services IDS
This presentation was given in a session at the Global Symposium on Health Systems Research which was organised by the Future Health Systems Consortium. The author is Toru Matsubayashi from Johns Hopkins Bloomberg School of Public Health
Presented by Nick Baker
General and Community Paediatrician
Executive Clinical Director for Community Based Services, Nelson Marlborough District Health Board.
American Public Health Association- Annual Meeting 2014 Presentation scherala
Title: Using Quantitative Data to focus Medical Home Facilitation Interventions in the Massachusetts Patient Centered Medical Home Initiative (MA PCMHI)
Poster presented at ECE Maastricht 2015 LBacelar-NicolauLBNicolau
"Screening Policies in Health Impact Assessment: easier decision making through cluster analysis" went very well. Many interesting questions and comments at the end!
Seeking value: Experience from the UK's National Institute for Health and Car...OECD Governance
This presentation was made by Tommy Wilkinson, United-Kingdom, at the 4th meeting of the Joint DELSA/GOV-SBO Network on Fiscal Sustainability of Health Systems, held in Paris on 16-17 February 2015.
Public sector productivity - Peter van de Ven, OECDOECD Governance
This presentation was made by Peter Van de Ven, OECD, at the 12th Annual Meeting on Performance and Results held at the OECD, Paris, on 24-25 November 2016
Developing Networks of Care through Long Term Conditions Year of Care Commissioning & Long Term Conditions Improvement Programmes
Bev Matthews
Programme Lead for Long Term Conditions @Bev_J_Matthews
Presentation from the Tackling Long Term Conditions conference on 29 October 2014
The CMS Innovation Center held the fifth in a series of webinars for potential applicants interested in applying to Health Care Innovation Awards Round Two. The webinar held on Wednesday, June 26, 2013 from 1:00–2:00pm EDT, focused on measuring project success and developing an operational plan.
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CMS Innovations
http://innovations.cms.gov
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Academy Health- Annual Research Meeting - State Policy Interest Groups- 2013scherala
Title: Massachusetts Patient-Centered Medical Home Initiative (MA PCMHI): Impact on Clinical Quality at Midpoint
Authors: Judith Steinberg, Sai Cherala, Christine Johnson, Ann Lawthers.
Research Objective:
To assess the impact on clinical quality of practices’ participation in a Patient-Centered Medical Home (PCMH) demonstration. The MA PCMHI is a statewide, three-year, multi-payer demonstration of PCMH implementation in 45 primary care practices. Practices receive technical assistance including learning collaborative, coaching provided by external facilitators, and feedback of aggregated data, to support their implementation of PCMH processes. This study aims to assess the overall impact of this approach to transformation on a practice’s delivery of selected clinical services, including preventive care, care coordination and care management, and its processes and outcomes of care related to the initiative’s targeted conditions of diabetes and asthma at the midpoint of the initiative.
Do height and BMI affect human capital formation? Natural experimental evidence from DNA. CHE seminar presentation by Neil Davies, University of Bristol 12 June 2020
Healthy Minds: A Randomised Controlled Trial to Evaluate PHSE Curriculum Deve...cheweb1
CHE Seminar presentation 16 January 2020, Alistair McGuire, Department of Health Policy, LSE. Evaluating the Healthy Minds program: The impact on adolescent’s health related quality of life of a change in a school curriculum
Baker what to do when people disagree che york seminar jan 2019 v2cheweb1
Public values, plurality and health care resource allocation: What should we do when people disagree? (..and should economists care about reasons as well as choices?) CHE Seminar 21 January 2019
Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...Oleg Kshivets
RESULTS: Overall life span (LS) was 2252.1±1742.5 days and cumulative 5-year survival (5YS) reached 73.2%, 10 years – 64.8%, 20 years – 42.5%. 513 LCP lived more than 5 years (LS=3124.6±1525.6 days), 148 LCP – more than 10 years (LS=5054.4±1504.1 days).199 LCP died because of LC (LS=562.7±374.5 days). 5YS of LCP after bi/lobectomies was significantly superior in comparison with LCP after pneumonectomies (78.1% vs.63.7%, P=0.00001 by log-rank test). AT significantly improved 5YS (66.3% vs. 34.8%) (P=0.00000 by log-rank test) only for LCP with N1-2. Cox modeling displayed that 5YS of LCP significantly depended on: phase transition (PT) early-invasive LC in terms of synergetics, PT N0—N12, cell ratio factors (ratio between cancer cells- CC and blood cells subpopulations), G1-3, histology, glucose, AT, blood cell circuit, prothrombin index, heparin tolerance, recalcification time (P=0.000-0.038). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and PT early-invasive LC (rank=1), PT N0—N12 (rank=2), thrombocytes/CC (3), erythrocytes/CC (4), eosinophils/CC (5), healthy cells/CC (6), lymphocytes/CC (7), segmented neutrophils/CC (8), stick neutrophils/CC (9), monocytes/CC (10); leucocytes/CC (11). Correct prediction of 5YS was 100% by neural networks computing (area under ROC curve=1.0; error=0.0).
CONCLUSIONS: 5YS of LCP after radical procedures significantly depended on: 1) PT early-invasive cancer; 2) PT N0--N12; 3) cell ratio factors; 4) blood cell circuit; 5) biochemical factors; 6) hemostasis system; 7) AT; 8) LC characteristics; 9) LC cell dynamics; 10) surgery type: lobectomy/pneumonectomy; 11) anthropometric data. Optimal diagnosis and treatment strategies for LC are: 1) screening and early detection of LC; 2) availability of experienced thoracic surgeons because of complexity of radical procedures; 3) aggressive en block surgery and adequate lymph node dissection for completeness; 4) precise prediction; 5) adjuvant chemoimmunoradiotherapy for LCP with unfavorable prognosis.
Flu Vaccine Alert in Bangalore Karnatakaaddon Scans
As flu season approaches, health officials in Bangalore, Karnataka, are urging residents to get their flu vaccinations. The seasonal flu, while common, can lead to severe health complications, particularly for vulnerable populations such as young children, the elderly, and those with underlying health conditions.
Dr. Vidisha Kumari, a leading epidemiologist in Bangalore, emphasizes the importance of getting vaccinated. "The flu vaccine is our best defense against the influenza virus. It not only protects individuals but also helps prevent the spread of the virus in our communities," he says.
This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
To encourage widespread vaccination, the government is also collaborating with local schools, workplaces, and community centers to facilitate vaccination drives. Special attention is being given to ensuring that the vaccine is accessible to all, including marginalized communities who may have limited access to healthcare.
Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
ASA GUIDELINE
NYSORA Guideline
2 Case Reports of Gastric Ultrasound
Ethanol (CH3CH2OH), or beverage alcohol, is a two-carbon alcohol
that is rapidly distributed in the body and brain. Ethanol alters many
neurochemical systems and has rewarding and addictive properties. It
is the oldest recreational drug and likely contributes to more morbidity,
mortality, and public health costs than all illicit drugs combined. The
5th edition of the Diagnostic and Statistical Manual of Mental Disorders
(DSM-5) integrates alcohol abuse and alcohol dependence into a single
disorder called alcohol use disorder (AUD), with mild, moderate,
and severe subclassifications (American Psychiatric Association, 2013).
In the DSM-5, all types of substance abuse and dependence have been
combined into a single substance use disorder (SUD) on a continuum
from mild to severe. A diagnosis of AUD requires that at least two of
the 11 DSM-5 behaviors be present within a 12-month period (mild
AUD: 2–3 criteria; moderate AUD: 4–5 criteria; severe AUD: 6–11 criteria).
The four main behavioral effects of AUD are impaired control over
drinking, negative social consequences, risky use, and altered physiological
effects (tolerance, withdrawal). This chapter presents an overview
of the prevalence and harmful consequences of AUD in the U.S.,
the systemic nature of the disease, neurocircuitry and stages of AUD,
comorbidities, fetal alcohol spectrum disorders, genetic risk factors, and
pharmacotherapies for AUD.
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
Prix Galien International 2024 Forum ProgramLevi Shapiro
June 20, 2024, Prix Galien International and Jerusalem Ethics Forum in ROME. Detailed agenda including panels:
- ADVANCES IN CARDIOLOGY: A NEW PARADIGM IS COMING
- WOMEN’S HEALTH: FERTILITY PRESERVATION
- WHAT’S NEW IN THE TREATMENT OF INFECTIOUS,
ONCOLOGICAL AND INFLAMMATORY SKIN DISEASES?
- ARTIFICIAL INTELLIGENCE AND ETHICS
- GENE THERAPY
- BEYOND BORDERS: GLOBAL INITIATIVES FOR DEMOCRATIZING LIFE SCIENCE TECHNOLOGIES AND PROMOTING ACCESS TO HEALTHCARE
- ETHICAL CHALLENGES IN LIFE SCIENCES
- Prix Galien International Awards Ceremony
Title: Sense of Taste
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
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The hemodynamic and autonomic determinants of elevated blood pressure in obes...
Economic evaluation of changes to the organisation and delivery of health services
1. METHODS FOR THE ECONOMIC EVALUATION
OF CHANGES TO THE ORGANISATION AND
DELIVERY OF HEALTH SERVICES
Rachel Meacock
Centre for Health Economics, University of York
6th July 2017
2. Background
• Established methods exist for the economic evaluation of
new health technologies seeking NHS funding in England
• Treatments go through mandatory NICE appraisal process
• Changes to the organisation and delivery of health services,
including policy changes, (service interventions) are funded
from the same NHS budget, but not covered by this process
• Often rolled out without supportive evidence or evaluation
3. Background
• Inconsistent approaches - differing levels of scrutiny likely to
result in allocative inefficiency in the health system
• Resulted in a lack of methodological development and cost-
effectiveness evidence
• Whilst principle of assessing cost-effectiveness should apply
to all NHS spending, methods will need adapting in places to
enable evaluation of service interventions
4. Aim
To contribute to the development of methods for the economic
evaluation of service interventions
1. Method to quantify effects of service interventions in terms
of QALYs in absence of primary data collection on HRQoL
2. Demonstrate how survival analysis can be used to improve
treatment effect estimates associated with service
interventions (length of life component of QALY)
5. Part 1:
Quantifying the effects of service interventions in terms of
QALYs in the absence of primary data collection on HRQoL
6. Introduction
• Often estimate the impact of service changes on mortality
• Useful indicator of the impact of a programme, but tells us
nothing about the intervention’s value
• To assess cost-effectiveness, we need to estimate the
impact of a programme in terms of QALYs
• Problem = usually evaluate using administrative data which
does not contain information on HRQoL
7. Proposed method
• A QALY ‘tariff’ applied to mortality outcomes to estimate the
QALY gains associated with detected mortality reductions
• Discounted and quality-adjusted life expectancy (DANQALE)
tariff
• Stratified by single year of age (18 - 100) and sex
• Represents the average stream of remaining QALYs for an
individual i in each age-sex group a from general population
8. DANQALE
Two components of the QALY:
• Length of life component: Sex-specific life expectancy
estimates at each single year of age taken from 2008-10
ONS interim life tables
• QoL adjustment: Age-sex specific mean EQ-5D values from
2006 wave of the Health Survey for England
9. We calculate the DANQALE (𝑄𝑖𝑎) for each individual i in each
age-sex group a as:
𝑄𝑖𝑎 = 1 − 𝑚𝑖
𝑘=𝑎
𝐿 𝑎
𝑞 𝑘 1 + 𝑟 − 𝑘−𝑎
• 𝑚𝑖 equals 1 if the individual dies within 30days and 0
otherwise
• k indexes ages from a to the life expectancy of an individual
currently aged a(𝐿 𝑎)
• 𝑞 𝑘 is HRQoL at age k
• r is the discount rate (3.5%)
10. Analysis
• Attach DANQALE to each individual in the data
• Perform analysis as normal, but on DANQALE variable
• Can then compare to costs of the programme, either at the
individual or total programme level
Mortality Discounted QALYs
(DANQALE)
Total QALYs
AQ -0.9** [-1.4, -0.4] 0.07** [0.04, 0.11] 5,227
11. Limitations
• Likely to over-estimate health gains enjoyed by additional
survivors as assumes those surviving past 30days experience:
– Average life expectancy of the general population
– Average QoL of the general population
BUT
• Only captures QALYs gained due to mortality reductions i.e.
deaths averted
• Does not capture pure QoL improvements
12. Potential extensions
• Might want to update QoL values – later waves of HSE, other
data sources (e.g. SF-6D from Understanding Society)
• Could use condition-specific QoL estimates from audits if
available
Reference
Meacock R, Kristensen S, Sutton M. (2014). The cost-
effectiveness of using financial incentives to improve provider
quality: a framework and application. Health Economics, 23, 1-
13.
13. Part 2:
Using survival analysis to improve estimates of life year
gains in policy evaluations
14. Introduction
• Focus on methodology for estimating the impact of service
interventions on length of life
• Length of life is a key outcome in cost-effectiveness analysis:
– Cost per life year gained
– Cost per QALY
• Evaluations attempting to take a lifetime horizon can use
admin data to estimate changes in short-term mortality
• Convert these to projected life year gains using published
estimates of life expectancy from the general population
15. Previous approach
• Estimate the impact of a programme in terms of 30 day
mortality (binary outcome)
• Estimated reductions in this mortality are then translated into
life years gained
– Patients dying within 30 days are attributed no survival days
(effectively assumed to die instantly)
– Patients surviving past 30 days are assigned the remaining
age/gender-specific life expectancy of the general population
16. Limitations to previous approach
• Length of life of patients affected by interventions is likely to
differ from the general population – may lead to incorrect
estimations of the impact on life years gained
• True impact of policies on survival may be more complex,
potentially impacting survival over the whole life course
• Such longer-term effects not captured by evaluations
focusing solely on mortality rates within e.g. 30 days
17. Proposed solution
• Even with minimal data of 1 financial year available in many
administrative data sets, possible to observe most patients
for longer than 30 days
• Prolonged follow-up often ignored in policy evaluations
• Survival analysis is commonly used in clinical trials to
extrapolate gains in life expectancy from observed trial data
• Utilises all available follow-up information on patients rather
than applying an arbitrary cut-off window
18. Aim
• Examine whether the additional information available within
admin data sets on survival beyond usual 30 days
considered, albeit censored, can be used to improve
accuracy of estimated life year gains
• Demonstrate the feasibility and materiality of using
parametric survival models commonly employed in clinical
trials analysis to extrapolate future survival in policy evals
19. Motivating example
• Previous CEA of Advancing Quality (AQ) P4P programme
• Examine pneumonia patients only
• Consider a typical situation – data on dates of admission and
death are available for 1 financial year pre and post AQ
• Parametric survival models to estimate the effect of AQ on
survival over lifetime horizon
• Compare to results obtained using previous method
20. Data
• Hospital Episode Statistics linked to ONS death records
• Patients admitted to hospital in England for pneumonia:
– 2007/08 (pre-AQ)
– 2009/10 (post-AQ)
• Data period: 1st April 2007 – 31st March 2011
• Risk-adjustment: primary & secondary diagnoses (ICD-10),
age, gender, financial quarter of admission, provider,
admitted from own home vs institution, emergency vs
transfer
21. Methods
1. Comparison of methods on a development cohort
• Cohort of patients admitted to any hospital in England prior
to introduction of AQ (2007/08)
• Compare 3 methods for estimating remaining life years using
data from 2007/08 only
• Compare to observed survival of this cohort now available up
to 31st March 2011
22. Purpose of development cohort
• Illustrate difference in magnitude of estimated remaining life
years of a patient population when:
– Additional information available on survival past 30 days is utilised
– Risk of death is taken from the population under investigation rather
than general population figures
• Exercise also used to select the most appropriate functional
form for the survival models later used to evaluate AQ
23. Method i
Simple application of published life expectancy tariffs
• Simplified version of DANQALE applied in original evaluation
(does not incorporate discounting or QoL)
• 30 day mortality assessed as a binary outcome
• Gender-specific general population life expectancy estimates
at each single year of age (18 – 100) attached to patients
surviving beyond 30 days to estimate remaining life
expectancy
24. Method i
Remaining life expectancy:
𝐿𝑖
𝑔𝑎
= 𝑠𝑖
30
∙ 𝐿 𝑔𝑎
𝐿 𝑔𝑎 is the life expectancy of an individual of gender g who is
currently aged a
𝑠𝑖
30
equals 1 if individual i survives more than 30 days and 0
otherwise
25. Method i
Implicitly assumes that individuals surviving beyond 30 days
after admission survive, on average, the life expectancy of the
general population
Will produce an inaccurate estimate of the actual life
expectancy:
a) Period of survival within 30 days is not incorporated
b) Assumes life expectancy of individuals surviving beyond 30 days
after admission will be equal to that of the general population of their
age and gender
Ignores information on observed survival available in data
26. Method ii
Short-term observed survival plus application of published life
expectancy estimates
• Extend method i to utilise all information on mortality available
within year of data (2007/08)
• Can follow patients for between 1 – 365 days depending on
admission date
• For those that died during the period, number of days survived
between admission and death are counted
• Life expectancy again applied to those surviving past the end of
the financial year
27. Method ii
Improves on method i by:
• Eliminating problem a) period of survival within 30 days is not
incorporated
• Reducing, but not eliminating, inaccuracies due to problem
b) assuming life expectancy of those surviving beyond 30
days after admission will be equal to that of the general
population of their age and gender
28. Method iii
Extrapolation using survival models
• Improve on method used for extrapolation by estimating
parametric survival models on the observed year of data
• Predict lifetime survival based on mortality rates of the
population of interest
• Considered six standard parametric models (exponential,
Weibull, Gompertz, log-logistic, log-normal, generalised
gamma)
29. Method iii
Model fit assessed using:
• AIC
• Tests of whether restrictions on the parameters in the
generalised gamma model suggest it could be reduced to the
simpler models it nests
• Examination of residual plots
External validity of extrapolations assessed by comparing
proportion of the cohort predicted to be alive at annual intervals
to the observed survival now available to 31st March 2011
30. Method iii
• In our case, while standard parametric models were able to
fit the observed data well, the tails of these distributions did
not correctly represent the pattern of future mortality
• Hazard rates experienced by our patient cohort changed
over time – extremely high-risk period shortly after
emergency hospital admission not representative of lifetime
risk of those surviving past this period
31. Method iii
Solution = estimate survival in 2 separate models:
• Short-term survival during the first year estimated on the
observed 1 year of data
• Extrapolation of long-term survival (1 year + after admission)
based on a model estimated on data excluding first 30 days
following admission
Long-term models represent hazards experienced by our
patient cohort after the initial high-risk period following
emergency admission – still much higher than general
population but significantly lower than when first admitted
32. Allowed us to estimate the effect of covariates on survival in both the observed
and extrapolated period
33. Method iii
Improves on method i by:
• Again eliminating problem a) period of survival within 30
days is not incorporated
• Using information on mortality risk from the patient
population under investigation rather than general population
estimates
We compare results at each stage as assumptions are dropped
– illustrates materiality of these developments
34. Application
2. Application to the evaluation of AQ
• Stage 1 demonstrates the materiality of the difference
survival analysis makes to the estimated life years remaining
of our patient cohort
• Stage 2 illustrates how these models can be used in an
applied programme evaluation
35. Dichotomous difference-in-differences (DiD) design:
𝐿𝑖𝑗𝑡 = 𝑓(𝑎 + 𝑋′ 𝑏 + 𝑢𝑗 + 𝑣 𝑡 + 𝛿𝐷𝑗
1
∙ 𝐷𝑡
2
+ 𝜀𝑖𝑗𝑡)
• 𝐿𝑖𝑗𝑡 is the life expectancy of individual i treated in hospital j at
time t
• f(∙) is the link function
• X is the vector of case-mix covariates
• 𝑢𝑗 are provider fixed effects
• 𝑣 𝑡 are time fixed effects
• 𝐷𝑗
1
is a dummy = 1 for hospitals that become part of AQ
• 𝐷𝑡
2
is a dummy = 1 in the periods after the introduction of AQ
• 𝜀𝑖𝑗𝑡 is an individual-specific error terms
• 𝛿 is the DiD term, which is our coefficient of interest
36. Application
First, consider situation where data on admissions and deaths
are available for 1 financial year pre and post AQ
• Pre AQ (2007/08)
• Post AQ (2009/10)
BUT, survival analysis can utilise additional follow-up on the
pre-intervention group collected during same period as initial
follow-up of the post-intervention group
• Examine how life expectancy estimates are affected when including
additional follow-up available (2008/09 – 2009/10) on pre-AQ group –
should improve accuracy of estimates
37. Application
Use average partial effects to calculate the effect of AQ on life
expectancy
Estimate life expectancy for individuals admitted to AQ
hospitals in the post-AQ period under 2 scenarios:
– DiD term set = 0 (absence of AQ)
– DiD term set = 1 (presence of AQ)
Compare results to those obtained using methods i and ii
(linear regression on gen pop life expectancy estimates)
39. Annual mortality rates for females
Age General
population
Patients admitted for pneumonia
2007/08
years % % (n deaths)
20 0.02 6.12 (98)
30 0.04 4.71 (191)
40 0.10 11.33 (309)
50 0.24 17.53 (291)
60 0.56 27.23 (584)
70 1.46 42.78 (783)
80 4.52 59.63 (1,469)
90 14.60 77.50 (1,142)
100 39.19 89.90 (109)
• Highlights importance of using information on the risk of death from the patient
cohort under investigation rather than general population figures when
estimating remaining life years
• Using gen pop figures would underestimate the annual mortality rate by a factor
of between 2 (age > 100 years) and over 300 (age 20)
40. Exponential Weibull Gompertz Log-
normal
Log-
logistic
Generalised
gamma
Internal
validity:
AIC 326,943 288,141 291,563 283,531 285,139 283,386
External
validity:
Time point Predicted survival, % Observed
survival, %
31/03/08 56.76 60.02 60.02 60.10 59.78 60.21 61.05
31/03/09 36.02 46.51 52.87 49.08 47.63 48.96 49.73
31/03/10 25.93 39.26 52.37 43.92 41.77 43.67 43.86
31/03/11 20.04 34.30 52.24 40.49 37.92 40.14 39.31
Internal and external validity of different parametric survival functions
• Lowest AIC
• Wald test confirmed does not reduce to the log-normal
• Best performance on external validity – predicted proportion of cohort alive to
within 1% of observed survival at each of 4 annual time points available
41. Method Assessment period Extrapolation
method
Those alive at end
of assessment
period, n (%)
Estimated life years
remaining, mean
i Admission to 30 days Gen pop life
expectancy
82,208 (72.56%) 13.15
ii Admission to end of
financial year
Gen pop life
expectancy
69,158 (61.05%) 11.98
iii Admission to end of
financial year
Parametric survival
models
69,158 (61.05%) 9.19
Comparison of estimates of remaining life years for patients admitted
for pneumonia 2007/08 (n = 113,289)
• Taking account of additional information on survival past 30 days reduced the
estimate of average remaining life years by 9% (method ii)
• Using survival models to extrapolate future survival reduced original estimate by
30% (method iii)
43. Patients admitted in 2007/08 Patients admitted in 2009/10
North West Rest of England North West Rest of England
n 17,993 95,296 19,946 106,365
Age at admission 71.7 72.2 71.9 72.8
Female, % 49.8% 48.7% 50.3% 49.1%
Comorbidities, n 1.79 1.65 1.99 1.92
Unadjusted
mortality within 30
days
28.4% 27.3% 25.6% 26.0%
Dead by end of
the financial year
40.7% 38.6% 37.3% 37.3%
Descriptive statistics for patients admitted for pneumonia, by region
and time period
• Pre-AQ the unadjusted mortality rate was higher in the North West within 30
days of admission and persisted in the longer-term to end of financial year
• Unadjusted mortality rates decreased in both regions, with a greater reduction in
the North West – positive effect of AQ on mortality previously detected
44. Estimated effect of AQ on the remaining life expectancy of
patients admitted to hospitals in the North West in 2009/10
45. Method i Method ii Method iii
Source of life expectancy
estimates
Gen pop life
tables
Gen pop
life tables
Survival analysis using 1 financial year of
follow-up
Short-term model:
Entry time =
admission
Long-term model:
Entry time = 31 days
post admission
Estimation method OLS OLS Generalised gamma Generalised gamma
DiD coefficient
(robust t stat)
0.154
(2.39)
0.221
(3.04)
0.103
(2.64)
0.089
(1.71)
Observations 239,600 239,600 239,600 156,860
Deaths, n 63,845 91,272 91,272 26,785
Life expectancy for patients
admitted in North West
2009/10
13.218 11.982 9.041
Counterfactual estimate, life
expectancy for patients in
North West in absence of AQ
13.064 11.761 8.730
Effect of AQ on life
expectancy for those
admitted in North West
0.154 0.221 0.311
46. Interpretation
• Lower absolute estimates of life expectancy both in the
presence and absence of AQ were expected from methods ii
and iii – additional deaths were observed and risk taken from
patient cohort under investigation
• Despite lower absolute estimates of life expectancy, estimate
of the effect of AQ increased – indicates that AQ impacted on
survival beyond 30 day post-admission window
• Generalised gamma parameterized in the AFT metric –
coefficients < 1 indicate time passes more slowly – failure
(death) expected to occur later as a result of AQ
47. Method iii
Source of life
expectancy estimates
Survival analysis using 1 financial
year of follow-up
Survival analysis using all available
follow-up ( to 31/03/10)
Short-term
model:
Entry time =
admission
Long-term model:
Entry time = 31
days post
admission
Short-term
model:
Entry time =
admission
Long-term model:
Entry time = 31
days post
admission
DiD coefficient
(robust t stat)
0.103
(2.64)
0.089
(1.71)
0.142
(3.62)
0.101
(2.26)
Observations 239,600 156,860 239,600 164,438
Deaths, n 91,272 26,785 110,747 45,290
Life expectancy for
patients admitted in
North West 2009/10
9.041 8.439
Counterfactual estimate,
life expectancy for
patients in North West in
absence of AQ
8.730 8.059
Effect of AQ on life
expectancy for those
admitted in North West
0.311 0.380
48. Interpretation
Utilising additional follow-up data available on pre-AQ group:
• Increased precision of estimates
• Slightly decreased estimated remaining life expectancy for
the cohort both in the presence and absence of AQ
• Further increased estimated treatment effect of AQ
49. Discussion
Demonstrated:
– Feasibility of using parametric survival models to extrapolate future
survival in policy evaluations
– Materiality of the impact this has on estimates of remaining life years of a
patient cohort and a policy treatment effect
Detected impact of AQ beyond 30 day window usually assessed
shows advantage of survival analysis – ability to capture effects of
policies over the whole life course
In pre- and post- evaluation design, survival models can be
developed on the pre-intervention population and predictive
performance evaluated against observed follow-up available during
post-intervention period – external validity
50. Future work
• For estimates of life years gained to be used in CEA, the
stream of remaining life years need to be adjusted for QoL
and discounted – quite simple extensions
• More sophisticated survival models
Reference
Meacock, Sutton, Kristensen, Harrison. (2017). Using survival
analysis to improve estimates of life year gains in policy
evaluations. Medical Decision Making, 37, 415-426.
51. Overall conclusions
• Development of methods and applications of economic
evaluation of service interventions has the potential to
improve allocative efficiency
• Still a LONG way to go, but (hopefully) offered some useful
approaches
• Both strands of health economics have made impressive
methodological progress in different aspects of evaluation –
could learn a lot from each other
55. Method i
Remaining life expectancy:
𝐿𝑖
𝑔𝑎
= 𝑠𝑖
30
∙ 𝐿 𝑔𝑎
𝐿 𝑔𝑎 is the life expectancy of an individual of gender g who is
currently aged a
𝑠𝑖
30
equals 1 if individual i survives more than 30 days and 0
otherwise
56. Method ii
𝐿𝑖
𝑔𝑎
= 𝑠𝑖
𝑡∗
∙ 𝐿 𝑔𝑎
+ (1 - 𝑠𝑖
𝑡∗
) ∙ (𝑡𝑖
ϯ
- 𝑡𝑖
0
)
Where 𝑠𝑖
𝑡∗
is a binary indicator equal to 1 if individual i survives
to the end of the observation period 𝑡∗
𝑡𝑖
ϯ
is the date of death for individuals who die before the end of
the observation period
𝑡𝑖
0
is the date of admission
58. Following estimation of survival models, created additional rows
of data for each individual for each possible future year up to
age 100
Estimated the probability of surviving to that year, allowing for
the progression of time and increments in age – analogous to
estimating transition probabilities in a Markov model:
𝑚𝑖
𝑡
(𝑎𝑖0, 𝑥𝑖) =
𝑠 𝑖𝑡 (𝑎 𝑖𝑡,𝑥 𝑖)
𝑠 𝑖,𝑡−1 (𝑎 𝑖𝑡,𝑥 𝑖)
– 1
𝑚𝑖
𝑡
is the probability that individual i will die by time t, given that
they have survived to time t-1
𝑠𝑖𝑡 is the probability that individual i will survive to time t, given
the values of their covariates x and their age 𝑎𝑖 at the time of
their admission
59. Then calculated the individual’s life expectancy using the sum of
the probability of surviving to the end of the first year and the
survival rates for each subsequent year, to a max age of 100:
𝐿𝑖
= 1 − 𝑚𝑖
1
∙ 𝑡∗
− 𝑡𝑖
0
+
𝑗= 𝑎 𝑖0+1
𝐴
𝑠𝑖,𝑗−𝑎 𝑖0
𝑎𝑖𝑗, 𝑥𝑖 ∙ (1 −
1
𝑚𝑖
𝑗+1− 𝑎 𝑖0
)
𝐿𝑖 is the life expectancy of individual i
𝑚𝑖
1
is the probability that individual i will die by the end of the
first year
𝑡∗ - 𝑡𝑖
0
is the length of time between the individual’s admission
date and the end of the first year