Regional variation in health service use at the Regionale … · 2018-08-14 · 28 • Higher...

Regional variation in health service use at the end of life

Regionale Unterschiede medizinischer Behandlungen am Lebensende» Ergebnisse aus dem Nationalen Forschungsprogramm NFP 67

Marcel ZwahlenInstitut für Sozial- und PräventivmedizinUniversität Bern

Definitions of EOLC

> “The period between the last 12 month of life of a person with a chronic and progressive diseases including last days to last hours of life” (Liverpool Care Pathway, UK)

> “The period of time when health care provider expects that the death is likely to occur within 6 months” (American Psychological Association)

> “Care provided to patients with incurable and progressive diseases to live as well as possible until they die” (The National Council for Palliative Care, UK)

Focus on individual and regional factors

- associated with costs in the last 12 months of life in Switzerland

- associated with dying in hospital versus in nursing homes in Switzerland

Causes of regional variation in EOLC

Accepted warrented variation (individual characteristics of patients)

Unwarranted variation (supply-sensitive care)

Data Sources

> Six health insurance providers and the BFS

> Study population: Swiss residents 19 years or older deceased 2008-2010

• Health insurance claim data of 52,126 males & 59,167 females from 6 large health insurers

• In total 111,293 which are app. 60.2% of deceased

564 MedStat regionsin71 Hospital service areas (HSA)

Boundary of hospital service area (HSA)

564 MedStat regionsin71 Hospital service areas (HSA)

Boundary of hospital service area (HSA)

White lines are boundaries of MedStat regions

Panczak et al 2012. JECH;66:1129

Statistical Analysis

> Multi-level linear regression models on ln(cost)

> Sequence of models gradually more complex— 1 ) only HSA (hospital service area) and medstat region— 2) plus individual factors : age, cause of death, civil status,

nationality, religion— 3) plus regional factors: urbanicity, language region, SwissSEP— 4) plus supply measures: density of physicians, of nursing home

beds, of hospital beds

Females

15Cost Ratio

16Cost Ratio

Females

Cost flow according to age, providers & cause of death

Distribution according to hospital service area

Adjusted cost ratios of inpatient hospital care

Adjusted cost ratios of outpatient hospital care

Adjusted cost ratios of GP care

Adjusted cost ratios of specialist care

Aims of the second study

> To describe regional differences in the proportion of deaths in hospitals versus in nursing homes

> To identify patient (e.g. age, gender), regional (e.g. urbanicity, language region, Swiss-SEP) and supply of health care (e.g. density of hospitals beds, nursing homes) associated with place of death

Study Population

A total of 52,037 deaths occurred in 2010 among patients aged 66 or older

41, 275 people died in institutions (79 % of total deaths in 2010)

54% of deaths occurred in nursing homes

46% in hospital setting

57% females

Crude regional variation in the ratio* of dying in hospital versus in nursing homes

26* Acutally deviation on the ln(ratio) is shown

Age and sex-adjusted regional variation in the ratio of dying in hospital versus in nursing homes

Taken from Luta et al 2016. Dying among the elderly in Switzerland: who dies in hospital, who dies in a nursing home? BMC Palliative Care

• Higher probability of nursing home deaths in rural areas.

• Higher probability of hospital death in French and Italian speakingregion.

• No association with Swiss SEP.

• Density of physicians, and hospitals beds were not associated with dying in an institution.

• Density of ambulatory care physicians and nursing homes beds negatively associated with death in hospital.

66 - 7071 - 7576 - 8081 - 8586 - 90

MaleFemale

UrbanPeri-urban

GermanFrenchItalian

1st (lowest)2nd

3rd (highest)

1st (lowest)2nd tertile

3rd (highest)

1st(lowest)2nd tertile

3rd (highest)

Age group

Urbanicity

Language region

Swiss SEP

Physicians per 10k

Hospital beds per 10k

Acute beds per 10k

N.homes beds per 10k

Amb. physicans 10k

.5 1 1.5 2 2.53OR of dying in hospital vs n.homes

Adjusted for individual, regional & supply characteristics

Hospital vs Nursing homes

Conclusions

> Basic health insurance EOL care cost depend mainly on the cause of death and age at death.

> Supply measures weakly associated with costs and somewhat associated with dying in hospital versus nursing homes.

> The observed geographical variation might indicate that care processes at the end of life are organized differently between (linguistic) regions

Limitations

> Our study is based on retrospective data collected for administrative purposes.

> Study reflects situation of the years 2008-2010> Data on patient and caregiver preference was not available.> Costs recorded in health insurance do not cover all costs (not

the full hospital costs, nursing home costs & Spitex costs incompletely captured)

Strengths

> The first studies in Switzerland which examine regional variation across HSAs and Medstat regrions

> Inclusion of individual and ecological-level charateristics

EOLC or care in the last year of live involves more than costs and where persons die

•Respect preferences of the dying patients/families

•Praying•Talking with someonefrom relegiouscommunity

•Resolving unsettledissues with others

•Anxiety•Depression•Fear of death•Emotional distress

•Pain management•Releif of symptoms•Avoidinginappropriateprolongation of dying

Comfortcare

Mental healthneeds

PreferencesSpiritual needs

Acknowledgments

> Radek Panczak, Xhyljeta Luta, Maud Maessen, Kerri Clough-Gorr, Matthias Egger at ISPM Bern

> Oliver Reich (Helsana) & Viktor von Wyl (CSS) for helpful advice

> The 6 collaborating health insurance companies and the Federal Statistical Office for providing and explaining data

> Funding from Swiss National Science Foundation and FMH, KKA Konferenz der Kantonalen Ärztegesellschaften & NewIndex AG

Regional variation in health service use at the Regionale … · 2018-08-14 · 28 • Higher...

Documents

Mathematics Higher level - aptutorgroup.com · Math higher level paper 3 statistics and probability specimen question paper Math higher level paper 3 statistics and probability specimen

Dynamic Pricing and Yield Managementdspace.mit.edu/.../0/l22ymlecture2.pdfProvide higher LOS to those who pay a lot by giving them more frequent service, higher probability of service,

The New Practitioners Guide to Dispensing Opioids During ... · • Opioid deaths were five times higher in 2016 than in 1999 • Deaths from synthetic opioids doubled between 2015

Left‐handed musicians show a higher probability of ...diposit.ub.edu/dspace/bitstream/2445/164177/1/700452.pdf · RESEARCH ARTICLE Left-handed musicians show a higher probability

· Web viewOf the 312 infant deaths registered in 2007, 166 were neonatal deaths (134 early neonatal deaths and 32 late neonatal deaths) and 146 were post-neonatal deaths. The infant

HAMILTON COUNTY · The IMR for January 2017 was 6.7 infant deaths per 1,000 live births (Figure 1). The January IMR is higher than the Healthy People 2020 goal of 6.0 infant deaths

Empowering Central Bank Asset Purchases: The Role of ......more lending at lower borrowing costs, banks compensate lower margins with higher leverage, at the cost of higher probability

CASE STUDY: TERESINA, BRAZIL...TERESINA 32 cases 559cases 2718 cases 9899 cases 14433 cases 2 deaths 14 deaths 105 deaths 424 deaths 742 deaths AZIL 201 deaths 5091 deaths 29314 deaths

Statistics and Probability (Calculator) Higher Tier

Care Home Data Pack · 2020-03-25 · home deaths in SEL (28 deaths for every 100 beds) was higher than rates recorded across London (25 for every 100 beds) and England (26 for every

Preventable Deaths 2016 - National Council for ... · PREVENTABLE DEATHS 2016 1 ... who was on assignment from Professional ... those working at a height of six feet or higher on

Unit 1: Statistics and Probability (calculator) Higher Tier ... 1/1H...Edexcel GCSE Mathematics B Unit 1: Statistics and Probability (calculator) Higher Tier Practice paper Time: 1

1918: 40-50 million deaths 1957: 2 million deaths 1968: 1 million deaths Pandemic Stats {

Expert Panel Questions and Answersdownloadcenter.sdl.com/tridion/S1000D/SDL_Askan... · probability that S1000D is in your future. Today, the probability is higher for new systems

28_6_2018_11_18_13_RomaniaRegionalReferralNetworksfinal ... · Web viewFigure 1. Deaths from heart disease are higher in Romania than elsewhere in Europe5 Figure 2. Health spending

Normal.dot - health.gov.auFile/wSept-2017-IMD-Surveillanc… · Web viewST 11 is associated with a higher risk of invasive disease and a higher case fatality rate. Nine deaths have

2000Ethiopia - The DHS ProgramPathway to Death Malaria deaths ARI deaths Neonatal deaths Under-five mortality Diarrhea deaths Measles deaths Deaths from other causes Severe malnutrition

HIGHER STATISTICS AND PROBABILITY (Predicted topics) Q1

Students explore content such as higher- order polynomial functions, trigonometry, inequalities, logarithms, conic sections, probability and statistics,

ISSUE BRIEF Health Disparities Related to Opioid Misuse in … · Several factors contributing to higher rates of opioid misuse and overdose deaths converge in Appalachia. Higher