Variation In All-cause Mortality Research Articles

Variations in all-cause mortality, premature mortality and cause-specific mortality among persons with diabetes in Ontario, Canada

IntroductionPatients with diabetes have a higher risk of mortality compared with the general population. Large population-based studies that quantify variations in mortality risk for patients with diabetes among subgroups in...

Co-Inheritance of Variation in All-Cause Mortality and Biochemical Risk Factors.

Biomarkers may be useful endophenotypes for genetic studies if they share genetic sources of variation with the outcome, for example, with all-cause mortality. Australian adult study participants who had reported their parental survival information were included in the study: 14,169 participants had polygenic risk scores (PRS) from genotyping and up to 13,365 had biomarker results. We assessed associations between participants' biomarker results and parental survival, and between biomarker results and eight parental survival PRS at varying p-value cut-offs. Survival in parents was associated with participants' serum bilirubin, C-reactive protein, HDL cholesterol, triglycerides and uric acid, and with LDL cholesterol for participants' fathers but not for their mothers. PRS for all-cause mortality were associated with liver function tests (alkaline phosphatase, butyrylcholinesterase, gamma-glutamyl transferase), metabolic tests (LDL and HDL cholesterol, triglycerides, uric acid), and acute-phase reactants (C-reactive protein, globulins). Association between offspring biomarker results and parental survival demonstrates the existence of familial effects common to both, while associations between biomarker results and PRS for mortality favor at least a partial genetic cause of this covariation. Identification of genetic loci affecting mortality-associated biomarkers offers a route to the identification of additional loci affecting mortality.

All-cause mortality after antiretroviral therapy initiation in HIV-positive women from Europe, Sub-Saharan Africa and the Americas.

Women account for over half of persons living with HIV/AIDS globally. We examined geographic variation in all-cause mortality after antiretroviral therapy (ART) for women living with HIV (WLWH) worldwide. We pooled data from WLWH at least 18 years initiating ART 2000-2014 within COHERE (Europe) and IeDEA regions (East Africa, West Africa, South Africa, North America, Latin America/Caribbean). Mortality rates were calculated at 0-3, 3-6, 6-12, 12-24 and 24-48 months after ART, and mortality rate ratios were compared with European rates with piecewise exponential parametric survival models based on Poisson regression. One hundred ninety thousand, one hundred and seventy-five WLWH (16% Europe, 47% East Africa, 13% West Africa, 19% South Africa, 1% South America, 3% North America and 2% Central America/Caribbean) were included. The highest death rates occurred 0-3 months after ART [1.51 (95% CI 1.25-1.82) per 100 person-years in Europe, 12.45 (11.30-13.73), 14.03 (13.12-15.02) and 9.44 (8.80-10.11) in East, West and South Africa, and 1.53 (0.97-2.43), 7.83 (5.44-11.27) and 17.02 (14.62-19.81) in North, South America and Central America/Caribbean, respectively] and declined thereafter. Mortality in Europe was the lowest, with regional differences greatest in the first 3 months and smaller at longer ART durations [adjusted rate ratios 24-48 months after ART: 3.63 (95% CI 3.04-4.33), 5.61 (4.84-6.51) and 3.47 (2.97-4.06) for East, West and South Africa; 2.86 (2.26-3.62), 2.42 (1.65-3.55) and 2.50 (1.92-3.26) for North, South America and Central America/Caribbean, respectively]. Global variations in short-term and long-term mortality among WLWH initiating ART may inform context-specific interventions.

Mortality advantage among migrants according to duration of stay in France, 2004\u20132014

BackgroundThe migrant mortality advantage is generally interpreted as reflecting the selection of atypically healthy individuals from the country of origin followed by the wearing off of selection effects over time, a process theorised to be accelerated by progressive and negative acculturation in the host country. However, studies examining how migrant mortality evolves over duration of stay, which could provide insight into these two processes, are relatively scarce. Additionally, they have paid little attention to gender-specific patterns and the confounding effect of age. In this study, we analyze all-cause mortality according to duration of stay among male and female migrants in France, with a particular focus on the role of age in explaining duration of stay effects.MethodsWe use the Échantillon Démographique Permanent (Permanent Demographic Sample; EDP), France’s largest socio-demographic panel and a representative 1% sample of its population. Mortality was followed-up from 2004 to 2014, and parametric survival models were fitted for males and females to study variation in all-cause mortality among migrants over duration of stay. Estimates were adjusted for age, duration of stay, year, education level and marital status. Duration of stay patterns were examined for both open-ended and fixed age groups.ResultsWe observe a migrant mortality advantage, which is most pronounced among recent arrivals and converges towards the mortality level of natives with duration of stay. We show this pattern to be robust to the confounding effect of age and find the pattern to be consistent among males and females.ConclusionsOur novel findings show an intrinsic pattern of convergence of migrant mortality towards native-born mortality over time spent in France, independent from the ages at which mortality is measured. The consistent pattern in both genders suggests that males and females experience the same processes associated with generating the migrant mortality advantage. These patterns adhere to the selection-acculturation hypothesis and raise serious concerns about the erosion of migrant health capital with increasing exposure to conditions in France.

Excess Deaths Attributable to Influenza-Like Illness in the ESRD Population

Morbidity and mortality vary seasonally. Timing and severity of influenza seasons contribute to those patterns, especially among vulnerable populations such as patients with ESRD. However, the extent to which influenza-like illness (ILI), a syndrome comprising a range of potentially serious respiratory tract infections, contributes to mortality in patients with ESRD has not been quantified. We used data from the Centers for Disease Control and Prevention (CDC) Outpatient Influenza-like Illness Surveillance Network and Centers for Medicare and Medicaid Services ESRD death data from 2000 to 2013. After addressing the increasing trend in deaths due to the growing prevalent ESRD population, we calculated quarterly relative mortality compared with average third-quarter (summer) death counts. We used linear regression models to assess the relationship between ILI data and mortality, separately for quarters 4 and 1 for each influenza season, and model parameter estimates to predict seasonal mortality counts and calculate excess ILI-associated deaths. An estimated 1% absolute increase in quarterly ILI was associated with a 1.5% increase in relative mortality for quarter 4 and a 2.0% increase for quarter 1. The average number of annual deaths potentially attributable to ILI was substantial, about 1100 deaths per year. We found an association between community ILI activity and seasonal variation in all-cause mortality in patients with ESRD, with ILI likely contributing to >1000 deaths annually. Surveillance efforts, such as timely reporting to the CDC of ILI activity within dialysis units during influenza season, may help focus attention on high-risk periods for this vulnerable population.

Characteristics Associated With Decreased or Increased Mortality Risk From Glycemic Therapy Among Patients With Type 2 Diabetes and High Cardiovascular Risk: Machine Learning Analysis of the ACCORD Trial.

OBJECTIVEIdentifying patients who may experience decreased or increased mortality risk from intensive glycemic therapy for type 2 diabetes remains an important clinical challenge. We sought to identify characteristics of patients at high cardiovascular risk with decreased or increased mortality risk from glycemic therapy for type 2 diabetes using new methods to identify complex combinations of treatment effect modifiers.RESEARCH DESIGN AND METHODSThe machine learning method of gradient forest analysis was applied to understand the variation in all-cause mortality within the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial (N = 10,251), whose participants were 40–79 years old with type 2 diabetes, hemoglobin A1c (HbA1c) ≥7.5% (58 mmol/mol), cardiovascular disease (CVD) or multiple CVD risk factors, and randomized to target HbA1c <6.0% (42 mmol/mol; intensive) or 7.0–7.9% (53–63 mmol/mol; standard). Covariates included demographics, BMI, hemoglobin glycosylation index (HGI; observed minus expected HbA1c derived from prerandomization fasting plasma glucose), other biomarkers, history, and medications.RESULTSThe analysis identified four groups defined by age, BMI, and HGI with varied risk for mortality under intensive glycemic therapy. The lowest risk group (HGI <0.44, BMI <30 kg/m2, age <61 years) had an absolute mortality risk decrease of 2.3% attributable to intensive therapy (95% CI 0.2 to 4.5, P = 0.038; number needed to treat: 43), whereas the highest risk group (HGI ≥0.44) had an absolute mortality risk increase of 3.7% attributable to intensive therapy (95% CI 1.5 to 6.0; P < 0.001; number needed to harm: 27).CONCLUSIONSAge, BMI, and HGI may help individualize prediction of the benefit and harm from intensive glycemic therapy.

Summer Temperature and Spatial Variability of all-Cause Mortality in Surat City, India.

Background:Ample information is available on extreme heat associated mortality for few Indian cities, but scant literature is available on effect of temperature on spatial variability of all-cause mortality for coastal cities.Objective:To assess the effect of daily maximum temperature, relative humidity and heat index on spatial variability of all-cause mortality for summer months (March to May) from 2014 to 2015 for the urban population of Surat (coastal) city.Materials and Methods:Retrospective analysis of the all-cause mortality data with temperature and humidity was performed on a total of 9,237 deaths for 184 summer days (2014-2015). Climatic and all-cause mortality data were obtained through Tutiempo website and Surat Municipal Corporation respectively. Bivariate analysis performed through SPSS.Observations:Mean daily mortality was estimated at 50.2 ± 8.5 for the study period with a rise of 20% all-cause mortality at temperature ≥ 40°C and rise of 10% deaths per day during extreme danger level (HI: > 54°C) days. Spatial (Zone wise) analysis revealed rise of 61% all-cause mortality for Southeast and 30% for East zones at temperature ≥ 40°C.Conclusions:All-cause mortality increased on high summer temperature days. Presence of spatial variation in all-cause mortality provided the evidence for high risk zones. Findings may be helpful in designing the interventions at micro level.

Trends in crude death rates in English hospitals

British Journal of Healthcare ManagementVol. 22, No. 12 Money MattersTrends in crude death rates in English hospitalsRod JonesRod JonesSearch for more papers by this authorRod JonesPublished Online:19 Dec 2016https://doi.org/10.12968/bjhc.2016.22.12.616AboutSectionsView articleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareShare onFacebookTwitterLinked InEmail View article References Jones R (2013) Analysing excess winter mortality: 2012/13. BJHCM 19(12): 601–605 Google ScholarJones R (2015a) Simulated rectangular wave infectious-like events replicate the diversity of time-profiles observed in real-world running 12 month totals of admissions or deaths. FGNAMB 1(3): 78–79. doi: https://doi.org/10.15761/FGNAMB.1000114 Google ScholarJones R (2015b) A ‘fatal’ flaw in hospital mortality models: How spatiotemporal variation in all-cause mortality invalidates hidden assumptions in the models. FGNAMB 1(3): 82–96. doi: https://doi.org/10.15761/FGNAMB.1000116 Google ScholarJones R (2015c) Recurring outbreaks of an infection apparently targeting immune function, and consequent unprecedented growth in medical admission and costs in the United Kingdom: a review. Br J Med Med Res 6(8): 735–70. doi: https://doi.org/10.9734/BJMMR/2015/14845 Crossref, Google ScholarJones R (2015d) A new type of infectious outbreak? SMU Medical Journal 2(1): 19–25 Google ScholarJones R (2015e) Unexpected and disruptive changes in admissions associated with an infectious-like event experienced at a hospital in Berkshire, England around May of 2012. Br J Med Med Res 6(1): 56–76. doi: https://doi.org/10.9734/BJMMR/2015/13938 Crossref, Google ScholarJones R (2015f) Small area spread and step-like changes in emergency medical admissions in response to an apparently new type of infectious event. FGNAMB 1(2): 42–54. doi: https://doi.org/10.15761/FGNAMB.1000110 Google ScholarJones R, Beauchant S (2015) Spread of a new type of infectious condition across Berkshire in England between June 2011 and March 2013: Effect on medical emergency admissions. Br J Med Med Res 6(1): 126–148. doi: https://doi.org/10.9734/BJMMR/2015/14223 Crossref, Google ScholarJones R (2016a) Bed occupancy and hospital mortality. BJHCM 22(7): 380–81 Google ScholarJones R (2016b) Is cytomegalovirus involved in recurring periods of higher than expected death and medical admissions, occurring as clustered outbreaks in the northern and southern hemispheres? Br J Med Med Res 11(2): 1–31. doi: https://doi.org/10.9734/BJMMR/2016/20062 Crossref, Google ScholarJones R (2016c) The real reason for the huge NHS overspend? BJHCM 22(1): 40–42 Google ScholarJones R (2016d) Deaths in English lower super output areas (LSOA) show patterns of very large shifts indicative of a novel recurring infectious event. SMU Medical Journal 3(2): 23–36 Google ScholarJones R (2016e) The unprecedented growth in medical admissions in the UK: the ageing population or a possible infectious/immune aetiology? Epidemiology (Sunnyvale) 6(1): 1000219 Crossref, Google ScholarJones R (2016f) Rising emergency admissions in the UK and the elephant in the room. Epidemiology (Sunnyvale): Open Access 6(4): 1000261 Google ScholarJones R (2016g) A presumed infectious event in England and Wales during 2014 and 2015 leading to higher deaths in those with neurological and other disorders. Journal of Neuroinfectious Diseases 7(1): 1000213 doi: https://doi.org/10.4172/2314-7326.1000213 Google Scholar FiguresReferencesRelatedDetails 2 December 2016Volume 22Issue 12ISSN (print): 1358-0574ISSN (online): 1759-7382 Metrics History Published online 19 December 2016 Published in print 2 December 2016 Information© MA Healthcare LimitedPDF download

Trend in proportion of deaths occurring in hospital

British Journal of Healthcare ManagementVol. 22, No. 11 Money MattersTrend in proportion of deaths occurring in hospitalRod JonesRod JonesSearch for more papers by this authorRod JonesPublished Online:18 Nov 2016https://doi.org/10.12968/bjhc.2016.22.11.572AboutSectionsView articleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareShare onFacebookTwitterLinked InEmail View article References Beeknoo N, Jones R (2016) The demography myth-how demographic forecasting vastly underestimates hospital admissions, and creates the illusion that fewer hospital beds or community-based bed equivalents will be required in the future. Brit J Med Medical Res (in press) Google ScholarCohen J, Bilsen J, Addington-Hall J et al. (2008) Population-based study of dying in hospital in six European countries. Palliat Med 22(6): 702–10. doi: https://doi.org/10.1177/0269216308092285 Crossref, Google ScholarFoote M, Bowen T, Forte P (2008) Identifying alternatives to hospital for people at the end of life. Available at: https://www.nao.org.uk/wp-content/uploads/2008/11/07081043_alternatives.pdf (accessed 5 November 2016) Google ScholarGomes B, Higginson I (2008) Where people die (1974-2030). Past trends, future projections and implications for care. Palliat Med 22(1): 33–41. doi: https://doi.org/10.1177/0269216307084606 Crossref, Google ScholarHanlon P, Walsh D, Whyte B et al. (1998) Hospital use by an ageing cohort: an investigation into the association between biological, behavioural and social risk markers and subsequent hospital utilization. J Public Health Med 20(4): 467–76 Crossref, Google ScholarJones R (2015a) Simulated rectangular wave infectious-like events replicate the diversity of time-profiles observed in real-world running 12 month totals of admissions or deaths. FGNAMB 1(3): 78–9. doi: https://doi.org/10.15761/FGNAMB.1000114 Google ScholarJones R (2015b) A ‘fatal’ flaw in hospital mortality models: How spatiotemporal variation in all-cause mortality invalidates hidden assumptions in the models. FGNAMB 1(3): 82–96. doi: https://doi.org/10.15761/FGNAMB.1000116 Google ScholarJones R (2015c) Recurring outbreaks of an infection apparently targeting immune function, and consequent unprecedented growth in medical admission and costs in the United Kingdom: A review. Brit J Med Medical Res 6(8): 735-770. doi: https://doi.org/10.9734/BJMMR/2015/14845 Crossref, Google ScholarJones R (2016a) Is cytomegalovirus involved in recurring periods of higher than expected death and medical admissions, occurring as clustered outbreaks in the northern and southern hemispheres? Brit J Med Medical Res 11(2): 1–31. doi: https://doi.org/10.9734/BJMMR/2016/20062 Crossref, Google ScholarJones R (2016b) Deaths in English Lower Super Output Areas (LSOA) show patterns of very large shifts indicative of a novel recurring infectious event. SMU Medical Journal 3(2): 23–36 Google ScholarJones R (2016c) The unprecedented growth in medical admissions in the UK: the ageing population or a possible infectious/immune aetiology? Epidemiology (Sunnyvale) 6(1): 1000219 Crossref, Google ScholarJones R (2016d) A presumed infectious event in England and Wales during 2014 and 2015 leading to higher deaths in those with neurological and other disorders. J Neuroinfectious Diseases 7(1): 1000213. doi: https://doi.org/10.4172/2314-7326.1000213 Google ScholarJones R (2016e) Rising emergency admissions in the UK and the elephant in the room. Epidemiology (Sunnyvale): Open Access 6(4): 1000261. doi: https://doi.org/10.4172/2161-1165.1000261 Google ScholarJones R, Sleet G, Pearce O, Wetherill M (2016) Is the weekend effect in hospital mortality real, or is a fundamental weekly cycle in adult blood biochemistry, health and death a contributory factor? Brit J Med Medical Res (in press) Google ScholarLeadbeater C, Garber J (2010) Dying for change. Available at: http://www.demos.co.uk/files/Dying_for_change_-_web_-_final_1_.pdf (accessed 5 November 2016) Google ScholarRosenwax L, McNamara B, Murray K, et al. (2011) Hospital and emergency department use in the last year of life: a baseline for future modifications of end-of-life care. Med J Aust 194(11): 570–73 Google ScholarTolle S, Rosenfeld A, Tilden V, Park Y (1999) Oregon's low in-hospital death rates: what determines where people die and satisfaction with decisions of place of death? Ann Intern Med 130(8):681–5 Crossref, Google ScholarWennberg J, Fisher E, Stukel T et al. (2004) Use of hospitals, physician visits, and hospice care during the last six months of life among cohorts loyal to highly respected hospitals in the United States. BMJ 328: 607. doi: https://doi.org/10.1136/bmj.328.7440.607 Crossref, Google Scholar FiguresReferencesRelatedDetails 2 November 2016Volume 22Issue 11ISSN (print): 1358-0574ISSN (online): 1759-7382 Metrics History Published online 18 November 2016 Published in print 2 November 2016 Information© MA Healthcare LimitedPDF download

Unusual trends in NHS staff sickness absence

British Journal of Healthcare ManagementVol. 22, No. 4 Money MattersUnusual trends in NHS staff sickness absenceRod JonesRod JonesSearch for more papers by this authorRod JonesPublished Online:20 Apr 2016https://doi.org/10.12968/bjhc.2016.22.4.239AboutSectionsView articleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareShare onFacebookTwitterLinked InEmail View article References Explorable (2016) Discovery of Helicobacter pylori. Available at: https://explorable.com/helicobacter-pylori (accessed 1 April 2016) Google ScholarJones R (2015a) Recurring outbreaks of an infection apparently targeting immune function, and consequent unprecedented growth in medical admission and costs in the United Kingdom: a review. British Journal of Medicine and Medical Research 6(8): 735–70 Crossref, Google ScholarJones R (2015b) Are emergency admissions contagious? BJHCM 21(5): 227–35 Google ScholarJones R (2015c) Small area spread and step-like changes in emergency medical admissions in response to an apparently new type of infectious event. Fractal Geom Nonlinear Anal Med Biol 1(2): 42–54 Google ScholarJones R (2015d) Deaths and international health care expenditure. BJHCM 21(10): 491-493. Google ScholarJones R (2015e) Links between bed occupancy, deaths and costs. BJHCM 21(11): 544–45 Google ScholarJones R (2015f) Influenza-like-illness, deaths and health care costs. BJHCM 21(12): 587–89 Google ScholarJones R (2015g) Simulated rectangular wave infectious-like events replicate the diversity of time-profiles observed in real-world running 12 month totals of admissions or deaths. Fractal Geom Nonlinear Anal Med Biol 1(3) (in press) Google ScholarJones R (2015h) A ‘fatal’ flaw in hospital mortality models: How spatiotemporal variation in all-cause mortality invalidates hidden assumptions in the models. Fractal Geom Nonlinear Anal Med Biol 1(3): (in press) Google ScholarJones R (2015i) Infectious-like spread of an agent leading to increased medical hospital admission in the North East Essex area of the East of England. Fractal Geom Nonlinear Anal Med Biol 1(3): (in press) Google ScholarJones R (2016a) Is cytomegalovirus involved in recurring periods of higher than expected death and medical admissions, occurring as clustered outbreaks in the northern and southern hemispheres? Brit J Med Medical Res 11(2): 1–31 Crossref, Google ScholarJones R (2016b) The real reason for the huge NHS overspend? BJHCM 22(1): 40–42 Google ScholarJones R (2016c) The unprecedented growth in medical admissions in the UK: the ageing population or a possible infectious/immune aetiology? Epidemiology: Open access 6(1): 1000219 Crossref, Google ScholarJones R (2016d) A fatal flaw in national mortality-based disease surveillance. BJHCM 22(3): (in press) Google ScholarTrueman C (2015) Edward Jenner. Available at: http://www.historylearningsite.co.uk/a-history-of-medicine/edward-jenner/ (accessed 1 April 2016) Google Scholar FiguresReferencesRelatedDetailsCited byNHS sickness absence in England: hidden patternsRodney P Jones14 April 2020 | British Journal of Healthcare Management, Vol. 26, No. 4Have Outbreaks of the World Health Organisation’s Disease X Been Identified?SSRN Electronic JournalUnexpected trends in hospital standardized mortality indicate a novel causeEuropean Journal of Internal Medicine, Vol. 52Outbreaks of a Presumed Infectious Pathogen Creating On/Off Switching in DeathsSSRN Electronic JournalHospital bed occupancy and deaths (all-cause mortality) in 2015Rod Jones16 May 2016 | British Journal of Healthcare Management, Vol. 22, No. 5 2 April 2016Volume 22Issue 4ISSN (print): 1358-0574ISSN (online): 1759-7382 Metrics History Published online 20 April 2016 Published in print 2 April 2016 Information© MA Healthcare LimitedPDF download

A fatal flaw in mortality based disease surveillance

British Journal of Healthcare ManagementVol. 22, No. 3 Money MattersA fatal flaw in mortality based disease surveillanceRod JonesRod JonesSearch for more papers by this authorRod JonesPublished Online:31 Mar 2016https://doi.org/10.12968/bjhc.2016.22.3.143AboutSectionsView articleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareShare onFacebookTwitterLinked InEmail View article References Jones R (2013) Analysing excess winter mortality: 2012/13. BJHCM 19(12): 601–605 Google ScholarJones R (2015a) Recent trends in life expectancy at older ages. Available at: http://www.hcaf.biz/2015/PHE_Life_Expectancy.pdf Google ScholarJones R (2015b) Recurring Outbreaks of an Infection Apparently Targeting Immune Function, and Consequent Unprecedented Growth in Medical Admission and Costs in the United Kingdom: A Review. Brit J Med Medical Res 6(8): 735–70. doi: https://doi.org/10.9734/BJMMR/2015/14845 Crossref, Google ScholarJones R (2015c) Are emergency admissions contagious? BJHCM 21(5): 227–35 Google ScholarJones R (2015d) Unexpected Increase in Deaths from Alzheimer's, Dementia and Other Neurological Disorders in England and Wales during 2012 and 2013. J Neuroinfectious Dis 6: 172. doi: https://doi.org/10.4172/2314-7326.1000172 Crossref, Google ScholarJones R (2015e) Small area spread and step-like changes in emergency medical admissions in response to an apparently new type of infectious event. FGNAMB 1(2): 42–54. doi: https://doi.org/10.15761/FGNAMB.1000110 Google ScholarJones R (2015f) Deaths and international health care expenditure. BJHCM 21(10): 491–3 Google ScholarJones R (2015g) Links between bed occupancy, deaths and costs. BJHCM 21(11): 544–45 Google ScholarJones R (2015h) Simulated rectangular wave infectious-like events replicate the diversity of time-profiles observed in real-world running 12 month totals of admissions or deaths. FGNAMB 1(3): in press Google ScholarJones R (2015i) A ‘fatal’ flaw in hospital mortality models: How spatiotemporal variation in all-cause mortality invalidates hidden assumptions in the models. FGNAMB 1(3): in press Google ScholarJones R (2015j) Infectious-like spread of an agent leading to increased medical hospital admission in the North East Essex area of the East of England. FGNAMB 1(3): in press Google ScholarJones R (2015k) Influenza-like-illness, deaths and health care costs. BJHCM 21(12): 587–9 Google ScholarJones R (2016a) Is cytomegalovirus involved in recurring periods of higher than expected death and medical admissions, occurring as clustered outbreaks in the northern and southern hemispheres? Brit J Med Medical Res 11(2): 1–31. doi: https://doi.org/10.9734/BJMMR/2016/20062 Crossref, Google ScholarJones R (2016b) The real reason for the huge NHS overspend? BJHCM 22(1): 40–42 Google ScholarJones R (2016c) The unprecedented growth in medical admissions in the UK: the ageing population or a possible infectious/immune aetiology? Epidemiology: Open access (in press) Crossref, Google ScholarWoolhouse M, Gowtage-Sequeria S. (2005) Host range and emerging and reemerging pathogens. Emerg Infect Dis 11(12): 1842–7 Crossref, Google ScholarXu G, Kula T, Xu Q, Li M et al. (2015) Comprehensive serological profiling of human populations using a synthetic human virome. Science 348(6239): aaa0698 Crossref, Google Scholar FiguresReferencesRelatedDetailsCited byHave Outbreaks of the World Health Organisation’s Disease X Been Identified?SSRN Electronic Journal 2 March 2016Volume 22Issue 3ISSN (print): 1358-0574ISSN (online): 1759-7382 Metrics History Published online 31 March 2016 Published in print 2 March 2016 Information© MA Healthcare LimitedPDF download

A ‘fatal’ flaw in hospital mortality models: How spatiotemporal variation in all-cause mortality invalidates hidden assumptions in the models

Both policy makers and health care regulators require measures to reveal instances of poor care. Both in-hospital mortality and mortality within 30 days of admission are commonly used measures to reveal the number of ‘excess deaths’. However, all models contain hidden assumptions which can invalidate the model. In this instance, all current approaches to hospital mortality make the assumption that there are no large spatiotemporal events capable of creating disparate effects in time and place in both all-cause mortality in general, and hospital mortality in specific. The study of international trends in deaths, does however, reveal one such large an unexplained effect against deaths, which also appears to simultaneously affect medical admissions, i.e. both the numerator and denominator in the models are subject to unexplained forces. The action of these forces upon all-cause mortality in England is demonstrated, along with evidence for spatiotemporal variation in age-standardised mortality within 30 days of admission for myocardial infarction across various locations and organisations in England. Cyclic and parallel movement in both all-cause mortality and SHMI are demonstrated in Milton Keynes, Ipswich and Northampton hospitals. An identical situation is demonstrated at the Wigan Royal Infirmary for HSMR. Finally the potential contribution of these events to the ‘excess’ deaths at the Mid Staffordshire hospital are investigated. It would appear that hospital mortality models do indeed contain a ‘fatal’ flaw which severely limits their ability to detect true instances of excess hospital mortality. Correspondence to: Rodney P Jones (PhD, ACMA, CGMA), Healthcare Analysis and Forecasting, Camberley, UK, Tel: +44(0)1276 21061; E-mail: hcaf_rod@yahoo.co.uk

Links between bed occupancy, deaths and costs

British Journal of Healthcare ManagementVol. 21, No. 11 Money MattersLinks between bed occupancy, deaths and costsRod JonesRod JonesSearch for more papers by this authorRod JonesPublished Online:14 Nov 2015https://doi.org/10.12968/bjhc.2015.21.11.544AboutSectionsView articleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareShare onFacebookTwitterLinked InEmail View article References Ekamper P, Van Poppel F, Van Duin C, Garssen J (2009) 150 years of temperature-related excess mortality in the Netherlands. Demographic Research 21: 385–426. Crossref, Google ScholarJones R (2013) Do recurring outbreaks of a type of infectious immune impairment trigger cyclic changes in the gender ratio at birth? Biomedicine International 4(1): 26–39 Google ScholarJones R (2014a) Unexpected single-year-of-age changes in the elderly mortality rate in 2012 in England and Wales. Brit J Med Medical Res 4(16): 3196–207. doi: https://doi.org/10.9734/BJMMR/2014/9072 Crossref, Google ScholarJones R (2014b) Infectious-like spread of an agent leading to increased medical admissions and deaths in Wigan (England), during 2011 and 2012. Brit J Med Medical Res 4(28): 4723–41. doi: https://doi.org/10.9734/BJMMR/2014/10807 Crossref, Google ScholarJones R, Beauchant S (2015) Spread of a new type of infectious condition across Berkshire in England between June 2011 and March 2013: Effect on medical emergency admissions. Brit J Med Medical Res 6(1): 126–48 Crossref, Google ScholarJones R (2015a) A previously uncharacterized infectious-like event leading to spatial spread of deaths across England and Wales: Characteristics of the most recent event and a time series for past events. Brit J Med Medical Res 5(11): 1361–80 Crossref, Google ScholarJones R (2015b) Unexpected and disruptive changes in admissions associated with an infectious-like event experienced at a hospital in Berkshire, England around May of 2012. Brit J Med Medical Res 6(1): 56–76. doi: https://doi.org/10.9734/BJMMR/2015/13938 Crossref, Google ScholarJones R (2015c) Recurring outbreaks of an infection apparently targeting immune function, and consequent unprecedented growth in medical admission and costs in the United Kingdom: a review. Brit J Med Medical Res 6(8): 735–70. doi: https://doi.org/10.9734/BJMMR/2015/14845 Crossref, Google ScholarJones R (2015d) Are emergency admissions contagious? BJHCM 21(5): 227–35 Google ScholarJones R (2015e) Unexpected increase in deaths from Alzheimer's, dementia and other neurological disorders in England and Wales during 2012 and 2013. J Neuroinfectious Dis 6: 172. doi: https://doi.org/10.4172/2314-7326.1000172 Crossref, Google ScholarJones R (2015f) A time series of infectious-like events in Australia between 2000 and 2013 leading to extended periods of increased deaths (all-cause mortality) with possible links to increased hospital medical admissions. Int J Epidemiologic Res 2(2): 53–67 Google ScholarJones R (2015g) Small area spread and step-like changes in emergency medical admissions in response to an apparently new type of infectious event. FGNAMB 1(2): 42–54. doi: https://doi.org/10.15761/FGNAMB.1000110 Google ScholarJones R (2015h) Deaths and international health care expenditure. BJHCM 21(10): 491–93 Google ScholarJones R (2015i) Simulated rectangular wave infectious-like events replicate the diversity of time-profiles observed in real-world running 12 month totals of admissions or deaths. FGNAMB 1(3) (in press) Google ScholarJones R (2015j) A ‘fatal’ flaw in hospital mortality models: How spatiotemporal variation in all-cause mortality invalidates hidden assumptions in the models. FGNAMB 1: (in press) Google ScholarJones R (2015k) Infectious-like spread of an agent leading to increased medical hospital admission in the North East Essex area of the East of England. FGNAMB 1 (in press) Google ScholarJones R (2015l) Trends in demand for urgent care. J Paramed Pract 7(10) (in press) Link, Google ScholarJones R (2016) Is cytomegalovirus involved in recurring periods of higher than expected death and medical admissions, occurring as clustered outbreaks in the northern and southern hemispheres? Brit J Med Medical Res 11(2): 1–31. doi: https://doi.org/10.9734/BJMMR/2016/20062 Crossref, Google ScholarOliver D (2014) Preventing hospital admission: we need evidence based policy rather than ‘policy based evidence’. BMJ 349: g5538. https://doi.org/http://dx.doi.org/10.1136/bmj.g5538 Crossref, Google Scholar FiguresReferencesRelatedDetailsCited byA Machine Learning Solution for Bed Occupancy Issue for Smart Healthcare Sector13 January 2022 | Automatic Control and Computer Sciences, Vol. 55, No. 6 2 November 2015Volume 21Issue 11ISSN (print): 1358-0574ISSN (online): 1759-7382 Metrics History Published online 14 November 2015 Published in print 2 November 2015 Information© MA Healthcare LimitedPDF download

A multilevel analysis of mortality following acute myocardial infarction in Norway: do municipal health services make a difference?

ObjectivesStudies link area features such as neighbourhood socioeconomic deprivation to poor health outcomes. However, there is a paucity of research based on representative data investigating the effects of area-level health...

Cause-specific mortality in Africa and Asia: evidence from INDEPTH health and demographic surveillance system sites

Background Because most deaths in Africa and Asia are not well documented, estimates of mortality are often made using scanty data. The INDEPTH Network works to alleviate this problem by collating detailed individual data from defined Health and Demographic Surveillance sites. By registering all deaths over time and carrying out verbal autopsies to determine cause of death across many such sites, using standardised methods, the Network seeks to generate population-based mortality statistics that are not otherwise available. Objective To build a large standardised mortality database from African and Asian sites, detailing the relevant methods, and use it to describe cause-specific mortality patterns. Design Individual demographic and verbal autopsy (VA) data from 22 INDEPTH sites were collated into a standardised database. The INDEPTH 2013 population was used for standardisation. The WHO 2012 VA standard and the InterVA-4 model were used for assigning cause of death. Results A total of 111,910 deaths occurring over 12,204,043 person-years (accumulated between 1992 and 2012) were registered across the 22 sites, and for 98,429 of these deaths (88.0%) verbal autopsies were successfully completed. There was considerable variation in all-cause mortality between sites, with most of the differences being accounted for by variations in infectious causes as a proportion of all deaths. Conclusions This dataset documents individual deaths across Africa and Asia in a standardised way, and on an unprecedented scale. While INDEPTH sites are not constructed to constitute a representative sample, and VA may not be the ideal method of determining cause of death, nevertheless these findings represent detailed mortality patterns for parts of the world that are severely under-served in terms of measuring mortality. Further papers explore details of mortality patterns among children and specifically for NCDs, external causes, pregnancy-related mortality, malaria, and HIV/AIDS. Comparisons will also be made where possible with other findings on mortality in the same regions. Findings presented here and in accompanying papers support the need for continued work towards much wider implementation of universal civil registration of deaths by cause on a worldwide basis.

Role of smoking in regional variation in mortality in Poland.

We assess the effect of smoking on regional disparities in mortality in Poland and its contribution to the change in regional disparities during the last two decades. We used population-level mortality data from the population registry for 379 Nomenclature of Territorial Units for Statistics (NUTS)-4 Polish regions for 1991-93 and 2008-10. The importance of smoking was assessed by smoking-attributable mortality (SAM) derived using a simplified indirect Peto-Lopez method. Regional differences in age-standardized all-cause, smoking- and non-smoking-attributable mortality (NSAM) rates at ages 35 years and over were mapped, and spatial clustering (Moran's I) and coefficients of variation (CV) were estimated. The contribution of SAM to variation in all-cause mortality was assessed by variance decomposition and compared over time. In 2008-10, all-cause and SAM rates were characterized by a similar pattern of spatial clustering (Moran's I > 0.44, P < 0.0001). For NSAM, a more random pattern with less regional clustering showed (Moran's I = 0.34, P < 0.0001). The contribution of smoking to regional variation was substantial [54%, 95% confidence interval (CI) = 44.9, 62.5 among men; 24.9%, 95% CI = 20.9, 29.1 among women], and compared with 1991-93, 27.5 percentage points lower for men and 6.3 percentage points higher for women. Smoking contributed to the divergence between the regions in all-cause mortality between 1991-93 and 2008-10 for men [increase in CV of SAM by 2% (0, 4%)], but not for women [decrease in CV of SAM by 15% (22, 10%)]. Differences in past smoking behaviour may largely explain the regional differences in all-cause mortality existing in 2008-10 in Poland, and its trends since 1991-1993.

Global Inequalities in Youth Mortality, 2007-2012

There is limited cross-national research on youth mortality. We examined age-and gender-variations in all-cause mortality among youth aged 15-34 years across 52 countries. Using the 2014 WHO mortality database, mortality rates for all countries were computed for the latest available year between 2007 and 2012. Rates, rate ratios, and ordinary least squares (OLS) and Poisson regression were used to analyze international variation in mortality. Mortality rates among youth aged 15-34 years varied from a low of 28.4 deaths per 100,000 population for Hong Kong to a high of 250.6 for Russia and 619.1 for South Africa. For men aged 15-34, Singapore and Hong Kong had the lowest mortality rates (≈40 per 100,000), compared with South Africa and Russia with rates of 589.7 and 383.3, respectively. Global patterns in mortality among women were similar. Youth aged 15-24 in South Africa had 14 times higher mortality and those in the Philippines, Mexico, Russia, Colombia, and Brazil had 5-7 times higher mortality than those in Hong Kong. Youth aged 25-34 in Russia and South Africa had, respectively, 10 and 29 times higher mortality than their counterparts in Hong Kong. United States (US) had the 12th highest mortality rate among youth aged 15-24 and the 13th highest rate among youth aged 25-34. Overall, the US youth had 2-3 times higher rates of mortality than their counterparts in many industrialized countries including Hong Kong, Singapore, Netherlands, Switzerland, Germany, Norway, and Sweden. Income inequality, unemployment rate, and human development explained 50-66% of the global variance in youth mortality. Compared to the countries with low unemployment and income inequality and high human development levels, countries with high unemployment and income inequality and low human development had, respectively, 343%, 213%, and 205% higher risks of youth mortality. Marked international disparities in youth all-cause mortality largely reflect differences in violence and injury deaths and in such risk factors as unemployment, income inequality, human development, and alcohol consumption. The US ranks in the upper quartile of all-cause mortality, with youth in Canada and many western industrialized countries showing signifi cantly lower mortality risks than the US youth.

A socioeconomic deprivation index for small areas in Denmark

To describe the development of a deprivation index for Danish parishes and to investigate its association with all-cause mortality compared with the Townsend index and individual-level factors. Nine socioeconomic factors were aggregated to the parish level from individual-level register data comprising the entire Danish population in 2005. A principal component analysis was conducted to reduce the number of factors and to apply weights. An ecological analysis investigated the association between the Danish Deprivation Index (DANDEX) and standardised mortality ratios in Danish parishes. Results were compared with the Townsend index and a possible modifying effect of population density was investigated. Sensitivity of the index was investigated with multilevel survival analyses evaluating the association between all-cause mortality and DANDEX, the Townsend Index, individual-level socioeconomic factors, and population density. DANDEX consists of two components measuring socioeconomic properties of all 2119 Danish parishes. The first component measures deprivation related to housing (house and car ownership) while the second component is related to classical measures of socioeconomic status (education, income, occupation). A clear gradient in standardised mortality ratios was seen across quintiles of both index components. Modifying effects were seen when stratifying the index components by population density. In a frailty model, DANDEX accounted for 76% of the between-parish variation in all-cause mortality, while the Townsend index and individual socioeconomic factors accounted for 71 and 76%, respectively. The index can be used to identify Danish parishes by their levels of deprivation and it provides municipalities with a tool to allocate resources to the geographic areas where they are most needed.

Abstract P105: Women with Atrial Fibrillation have Increased Risk of Incident Stroke: Insights from a Propensity-Matched Study of the AFFIRM Trial

Background: Women with atrial fibrillation (AF) have been reported to have poor outcomes. It remains unclear if this association is intrinsic or mediated by the higher comorbidity burden of female AF patients. Therefore, we examined the association between sex and outcomes in a balanced cohort of propensity-matched AF patients who participated in the Atrial Fibrillation Follow-up Investigation of Rhythm Management (AFFIRM) trial. Methods: Of the 4060 AFFIRM patients, 1594 (39%) were women. Propensity scores for female sex were calculated for each of the 4060 patients, and were used to assemble a cohort of 1097 pairs of men and women who were balanced on 51 baseline characteristics, including major cardiovascular (CV) risk factors and medication use, including warfarin. Matched Cox regression models were used to estimate the association between female sex and outcomes during 6 years of follow-up. Results: Patients (n=4060) had a mean (±SD) age of 70 (±8) years and 13% were African American. All-cause mortality occurred in 19% and 15% of matched men and women, respectively (matched HR when women were compared to men, 0.88; 95% CI, 0.69-1.11; p=0.279). All-cause hospitalization occurred in 61% of both men and women (matched HR for women, 1.06; 95% CI, 0.93-1.21; p=0.372). Sex was not associated with CV hospitalization (matched HR for women, 1.13; 95% CI, 0.97-1.32; p=0.111). Ischemic stroke occurred in 3% and 5% of matched men and women, respectively (OR when women were compared to men, 2.02; 95% CI, 1.28-3.18; p=0.002). There was no sex-related difference in major bleeding (7% each). Conclusion: In a cohort of AF patient in which men and women were well-balanced on 51 baseline characteristics including warfarin use, women had increased risk of stroke, but there was no sex-related variation in all-cause mortality or CV hospitalization.

The Relationship Between the Supply of Fast-food Chains and Cardiovascular Outcomes

To examine the extent to which inter-regional differences in fast-food concentrations account for variations in all-cause mortality and acute coronary syndromes throughout Ontario, Canada. Nine distinct fast-food chains were selected based on top sales data in 2001. The per capita rate of fast-food outlets per region was calculated for each of 380 regions throughout Ontario. Outcome measures, obtained using 2001 vital statistics data and hospital discharge abstracts, included regional per capita mortality rates and acute coronary syndrome hospitalization rates; head trauma served as a comparator. All regional outcomes were adjusted for age, gender, and socio-economic status, and were analyzed as continuous and rank-ordered variables as compared with the provincial average. Mortality and admissions for acute coronary syndromes were higher in regions with greater numbers of fast-food services after adjustment for risk. Risk-adjusted outcomes among regions intensive in fast-food services were more likely to be high outliers for both mortality (Adjusted Odds Ratio (OR): 2.52, 95% confidence intervals (CI): 1.54 - 4.13, p < 0.001) and acute coronary hospitalizations (Adjusted OR: 2.62, 95% CI 1.42 - 3.59, p < 0.001) compared to regions with low fast-food service intensity. There was no relationship between the concentration of fast-food outlets and risk-adjusted head-trauma hospitalization rates. Inter-regional cardiac outcome disparities throughout Ontario were partially explained by fast-food service intensity. Such findings emphasize the need to target health promotion and prevention initiatives to highest-risk communities.