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Our aim was to estimate the effects of smoking and high systolic blood pressure (SBP), fasting plasma glucose (FPG), total cholesterol (TC), and high body mass index (BMI) on mortality and life expectancy, nationally and subnationally, using representative data and comparable methods. We used data from the Non-Communicable Disease Surveillance Survey to estimate means and standard deviations for the metabolic risk factors, nationally and by region. Lung cancer mortality was used to measure cumulative exposure to smoking. We used data from the death registration system to estimate age-, sex-, and disease-specific numbers of deaths in 2005, adjusted for incompleteness using demographic methods. We used systematic reviews and meta-analyses of epidemiologic studies to obtain the effect of risk factors on disease-specific mortality. We estimated deaths and life expectancy loss attributable to risk factors using the comparative risk assessment framework. In 2005, high SBP was responsible for 41,000 (95% uncertainty interval: 38,000, 44,000) deaths in men and 39,000 (36,000, 42,000) deaths in women in Iran. High FPG, BMI, and TC were responsible for about one-third to one-half of deaths attributable to SBP in men and\u002For women. Smoking was responsible for 9,000 deaths among men and 2,000 among women. If SBP were reduced to optimal levels, life expectancy at birth would increase by 3.2 years (2.6, 3.9) and 4.1 years (3.2, 4.9) in men and women, respectively; the life expectancy gains ranged from 1.1 to 1.8 years for TC, BMI, and FPG. SBP was also responsible for the largest number of deaths in every region, with age-standardized attributable mortality ranging from 257 to 333 deaths per 100,000 adults in different regions. Management of blood pressure through diet, lifestyle, and pharmacological interventions should be a priority in Iran. Interventions for other metabolic risk factors and smoking can also improve population health.",{"EN":125},"National and subnational mortality effects of metabolic risk factors and smoking in Iran: a comparative risk assessment",{"VOID":127},"[\"4636500857358763246\"]",{"VOID":129},"10.1186\u002F1478-7954-9-55","PUBLICATION","VERIFIED","2024-04-30T05:08:06.020+00:00","Auto 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Lancet 1992,339(8804):1268-78. 10.1016\u002F0140-6736(92)91600-D","https:\u002F\u002Fdoi.org\u002F10.1016\u002F0140-6736(92)91600-d",{"mag":389,"openalex":390,"pm":391,"doi":392},"2114616454","W2114616454","1349675","10.1016\u002F0140-6736(92)91600-d",{"id":340,"text":394,"url":342,"identifiers":395},"Danaei G, Ding EL, Mozaffarian D, Taylor B, Rehm J, Murray CJ, et al.: The preventable causes of death in the United States: comparative risk assessment of dietary, lifestyle, and metabolic risk factors. PLoS medicine 2009,6(4):e1000058. 10.1371\u002Fjournal.pmed.1000058",{"doi":344},{"id":23,"text":397,"url":398,"identifiers":399},"Ezzati M, Lopez AD: Measuring the accumulated hazards of smoking: global and regional estimates for 2000. Tob Control 2003,12(1):79-85. 10.1136\u002Ftc.12.1.79","https:\u002F\u002Fdoi.org\u002F10.1136\u002Ftc.12.1.79",{"mag":400,"pmc":401,"openalex":402,"pm":403,"doi":404},"2135351591","1759096","W2135351591","12612368","10.1136\u002Ftc.12.1.79",{"id":340,"text":406,"url":342,"identifiers":407},"Lawes CM, Parag V, Bennett DA, Suh I, Lam TH, Whitlock G, et al.: Blood glucose and risk of cardiovascular disease in the Asia Pacific region. Diabetes Care 2004,27(12):2836-42.",{"doi":344},{"id":23,"text":409,"url":410,"identifiers":411},"Law MR, Wald NJ, Wu T, Hackshaw A, Bailey A: Systematic underestimation of association between serum cholesterol concentration and ischaemic heart disease in observational studies: data from the BUPA study. 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J Am Coll Cardiol 2010,55(21):2390-8. 10.1016\u002Fj.jacc.2009.12.053","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0735109710010570",{"doi":436},"10.1016\u002Fj.jacc.2009.12.053",{"id":23,"text":438,"url":23,"identifiers":439},"Jafari N, Kabir MJ, Motlagh ME: Death Registration System in I.R.Iran. Iranian J Publ Health 2009,38(Suppl 1):3.",{},{"id":441,"text":442,"url":443,"identifiers":444},"903ec525-315f-4f52-9ac7-b1a09a888d5b","Hill K, Lopez AD, Shibuya K, Jha P: Interim measures for meeting needs for health sector data: births, deaths, and causes of death. Lancet 2007, 26.","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0140673607613099",{"doi":445},"10.1016\u002Fs0140-6736(07)61309-9",{"id":23,"text":447,"url":448,"identifiers":449},"Rajaratnam JK, Marcus JR, Flaxman AD, Wang H, Levin-Rector A, Dwyer L, et al.: Neonatal, postneonatal, childhood, and under-5 mortality for 187 countries, 1970-2010: a systematic analysis of progress towards Millennium Development Goal 4. Lancet 2010,375(9730):1988-2008. 10.1016\u002FS0140-6736(10)60703-9","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fs0140-6736(10)60703-9",{"mag":450,"openalex":451,"pm":452,"doi":453},"2126861008","W2126861008","20546887","10.1016\u002Fs0140-6736(10)60703-9",{"id":23,"text":455,"url":456,"identifiers":457},"Khosravi A, Rao C, Naghavi M, Taylor R, Jafari N, Lopez AD: Impact of misclassification on measures of cardiovascular disease mortality in the Islamic Republic of Iran: a cross-sectional study. Bull World Health Organ 2008,86(9):688-96. 10.2471\u002FBLT.07.046532","https:\u002F\u002Fdoi.org\u002F10.2471\u002Fblt.07.046532",{"mag":458,"pmc":459,"openalex":460,"pm":461,"doi":462},"2107404370","2649498","W2107404370","18797644","10.2471\u002Fblt.07.046532",{"id":340,"text":464,"url":342,"identifiers":465},"King G, Honaker J, Joseph A, Scheve K: Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation. American Political Science Review 2001,95(No 1):21.",{"doi":344},{"id":467,"text":468,"url":469,"identifiers":470},"d0f8920e-08db-447f-ab4e-3e8787649f19","Ezzati M, Lopez AD, Rodgers A, Vander Hoorn S, Murray CJ: Selected major risk factors and global and regional burden of disease. Lancet 2002,360(9343):1347-60. 10.1016\u002FS0140-6736(02)11403-6","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0140673602114036",{"doi":471},"10.1016\u002Fs0140-6736(02)11403-6",{"id":340,"text":473,"url":342,"identifiers":474},"Coale A, Guo G: Revised regional model life tables at very low levels of mortality. Population Index 1989, 55: 31.",{"doi":344},{"id":23,"text":476,"url":23,"identifiers":477},"Preston SH, Heuveline P, Guillot M: Demography: Measuring and Modeling Population Processes. First edition. Oxford UK: Blackwell; 2001.",{},{"id":479,"text":480,"url":481,"identifiers":482},"2668ad1e-9162-410d-9f4a-25865b9cfca6","Filmer D, Pritchett LH: Estimating wealth effects without expenditure data-or tears: An application to educational enrollments in states of India. Demography 2001,38(1):18.","https:\u002F\u002Fread.dukeupress.edu\u002Fdemography\u002Farticle\u002F38\u002F1\u002F115\u002F170414\u002FEstimating-wealth-effects-without-expenditure-data",{"doi":483},"10.1353\u002Fdem.2001.0003",{"id":23,"text":485,"url":486,"identifiers":487},"Farzadfar F, Finucane MM, Danaei G, Pelizzari PM, Cowan MJ, Paciorek CJ, et al.: National, regional, and global trends in serum total cholesterol since 1980: systematic analysis of health examination surveys and epidemiological studies with 321 country-years and 3.0 million participants. Lancet 2011,377(9765):578-86. 10.1016\u002FS0140-6736(10)62038-7","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fs0140-6736(10)62038-7",{"mag":488,"openalex":489,"pm":490,"doi":491},"2156317766","W2156317766","21295847","10.1016\u002Fs0140-6736(10)62038-7",{"id":23,"text":493,"url":494,"identifiers":495},"Finucane MM, Stevens GA, Cowan MJ, Danaei G, Lin JK, Paciorek CJ, et al.: National, regional, and global trends in body-mass index since 1980: systematic analysis of health examination surveys and epidemiological studies with 960 country-years and 9.1 million participants. Lancet 2011,377(9765):557-67. 10.1016\u002FS0140-6736(10)62037-5","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fs0140-6736(10)62037-5",{"mag":496,"pmc":497,"openalex":498,"pm":499,"doi":500},"2158569927","4472365","W2158569927","21993114","10.1016\u002Fs0140-6736(10)62037-5",{"id":23,"text":502,"url":503,"identifiers":504},"Danaei G, Finucane MM, Lin JK, Singh GM, Paciorek CJ, Cowan MJ, et al.: National, regional, and global trends in systolic blood pressure since 1980: systematic analysis of health examination surveys and epidemiological studies with 786 country-years and 5.4 million participants. Lancet 2011,377(9765):568-77. 10.1016\u002FS0140-6736(10)62036-3","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fs0140-6736(10)62036-3",{"mag":505,"openalex":506,"pm":507,"doi":508},"2149284219","W2149284219","21295844","10.1016\u002Fs0140-6736(10)62036-3",{"id":340,"text":510,"url":342,"identifiers":511},"Begg S, Vos T, Barker B, Stevenson C, Stanley L, Lopez AD: The burden of disease and injury in Australia 2003. In PHE 82. Canberra: AIHW; 2007.",{"doi":344},{"id":340,"text":513,"url":342,"identifiers":514},"Norman R, Bradshaw D, Schneider M, Joubert J, Groenewald P, Lewin S, et al.: A comparative risk assessment for South Africa in 2000: Towards promoting health and preventing disease. SAMJ 2007,97(7):5.",{"doi":344},{"id":340,"text":516,"url":342,"identifiers":517},"Stevens GA, Dias RH, Thomas KJA, Rivera JA, Carvalho N: Characterizing the epidemiological transition in Mexico: National and subnational burden of diseases, injuries, and risk factors. PLoS medicine 2008,5(6):.",{"doi":344},{"id":340,"text":519,"url":342,"identifiers":520},"Zhang X, Patel A, Horibe H, Wu Z, Barzi F, Rodgers A, et al.: Cholesterol, coronary heart disease, and stroke in the Asia Pacific region. Int J Epidemiol 2003,32(4):563-72.",{"doi":344},{"id":23,"text":522,"url":523,"identifiers":524},"Yusuf S, Hawken S, Ounpuu S, Dans T, Avezum A, Lanas F, et al.: Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. Lancet 2004,364(9438):937-52. 10.1016\u002FS0140-6736(04)17018-9","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fs0140-6736(04)17018-9",{"mag":525,"openalex":526,"pm":527,"doi":528},"2155121555","W2155121555","15364185","10.1016\u002Fs0140-6736(04)17018-9",{"id":340,"text":530,"url":342,"identifiers":531},"Danaei G, Finucane MM, Lu Y, Singh GM, Cowan MJ, Paciorek CJ, et al.: National, regional, and global trends in fasting plasma glucose and diabetes prevalence since 1980: systematic analysis of health examination surveys and epidemiological studies with 370 country-years and 2.7 million participants. Lancet 2011, in press.",{"doi":344},{"id":23,"text":533,"url":23,"identifiers":534},"Samavat T: National health program for control and prevention of hypertension. Tehran: Ministry of Health; 2003.",{},{"id":23,"text":536,"url":537,"identifiers":538},"Ikeda N, Gakidou E, Hasegawa T, Murray CJ: Understanding the decline of mean systolic blood pressure in Japan: an analysis of pooled data from the National Nutrition Survey, 1986-2002. Bull World Health Organ 2008,86(12):978-88. 10.2471\u002FBLT.07.050195","https:\u002F\u002Fdoi.org\u002F10.2471\u002Fblt.07.050195",{"mag":539,"pmc":540,"openalex":541,"pm":542,"doi":543},"2153238187","2649578","W2153238187","19142299","10.2471\u002Fblt.07.050195",{"id":23,"text":545,"url":546,"identifiers":547},"He FJ, MacGregor GA: Effect of modest salt reduction on blood pressure: a meta-analysis of randomized trials. Implications for public health. J Hum Hypertens 2002,16(11):761-70. 10.1038\u002Fsj.jhh.1001459","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fsj.jhh.1001459",{"mag":548,"openalex":549,"pm":550,"doi":551},"2056013702","W2056013702","12444537","10.1038\u002Fsj.jhh.1001459",{"id":340,"text":553,"url":342,"identifiers":554},"Ghassemi H, Harrison G, Mohammad K: An accelerated nutrition transition in Iran. Public Health Nutr 2002,5(1A):149-55.",{"doi":344},{"id":23,"text":556,"url":23,"identifiers":557},"Farzadfar F, Murray CJ, Gakidou E, Bossert T, Namdari H, Alikhani S, et al.: Can rural primary healthcare manage noncommunicable diseases and risk factors? Evaluation of the effect of Behvarz system on diabetes and hypertension management in Iran. Submitted manuscript 2011.",{},{"id":23,"text":559,"url":560,"identifiers":561},"Romon M, Lommez A, Tafflet M, Basdevant A, Oppert JM, Bresson JL, et al.: Downward trends in the prevalence of childhood overweight in the setting of 12-year school- and community-based programmes. Public Health Nutr 2009,12(10):1735-42. 10.1017\u002FS1368980008004278","https:\u002F\u002Fdoi.org\u002F10.1017\u002Fs1368980008004278",{"mag":562,"openalex":563,"pm":564,"doi":565},"1969868489","W1969868489","19102807","10.1017\u002Fs1368980008004278",false,{"id":568,"createTime":569,"updateTime":570,"relativeEntities":571,"slug":572,"properties":573,"entityType":130,"verifyStatus":131,"verifyTime":584,"verifyNote":133,"languages":23,"translateLanguages":23,"viewCount":24,"primaryUrl":585,"fullTextUrl":23,"authors":586,"publicationType":279,"publisherRelationship":621,"citationCount":672,"citationInfo":673,"publishDate":676,"publishYear":334,"citationAnalyzeStatus":677,"lastCitationAnalyze":570,"indexDatabases":678,"openAccess":23,"references":23,"isForceReanalyzing":566},"79cf7a19-b240-4984-8155-b496b4797e88","2023-11-25T14:33:03.230+00:00","2026-07-18T18:13:38.380+00:00",[],"Using-funnel-plots-in-public-health-surveillance",{"abstract":574,"title":576,"gsPaper":578,"references":580,"doi":582},{"EN":575},"Public health surveillance is often concerned with the analysis of health outcomes over small areas. Funnel plots have been proposed as a useful tool for assessing and visualizing surveillance data, but their full utility has not been appreciated (for example, in the incorporation and interpretation of risk factors). We investigate a way to simultaneously focus funnel plot analyses on direct policy implications while visually incorporating model fit and the effects of risk factors. Health survey data representing modifiable and nonmodifiable risk factors are used in an analysis of 2007 small area motor vehicle mortality rates in Alberta, Canada. Small area variations in motor vehicle mortality in Alberta were well explained by the suite of modifiable and nonmodifiable risk factors. Funnel plots of raw rates and of risk adjusted rates lead to different conclusions; the analysis process highlights opportunities for intervention as risk factors are incorporated into the model. Maps based on funnel plot methods identify areas worthy of further investigation. Funnel plots provide a useful tool to explore small area data and to routinely incorporate covariate relationships in surveillance analyses. The exploratory process has at each step a direct and useful policy-related result. Dealing thoughtfully with statistical overdispersion is a cornerstone to fully understanding funnel plots.",{"EN":577},"Using funnel plots in public health surveillance",{"VOID":579},"[\"4139107040663046458\"]",{"VOID":581},"CDC: Guidelines for evaluating surveillance systems. MMWR 1988.,37(S-5):\nWoodall DH: The Use of Control Charts in Health-Care and Public-Health Surveillance. J Qual Technol 2006,38(2):89-104.\nRogerson P, Yamada I: Statistical detection and surveillance of geographic clusters. Hoboken, NJ, Taylor & Francis; 2008.\nMarshall T, Mohamnmed MA, Rouse A: A randomized controlled trial of league tables and control charts as aids to health service decision-making. Int J Qual Health Care 2004,16(4):309-315. 10.1093\u002Fintqhc\u002Fmzh054\nMarshall CE, Spiegelhalter DJ: Reliability of league tables of in vitro fertilisation clinics: retrospective analysis of live birth rates. BMJ 1998, 316: 1701-1705. 10.1136\u002Fbmj.316.7146.1701\nShen W, Louis TA: Triple-goal estimates for disease mapping. Statistics in Medicine 2000, 19: 2295-2308. 10.1002\u002F1097-0258(20000915\u002F30)19:17\u002F18\u003C2295::AID-SIM570>3.0.CO;2-Q\nSui DZ, Holt JB: Visualizing and Analysing Public-Health Data Using Value-by-Area Cartograms: Toward a New Synthetic Framework. Cartographica 2008,43(1):3-20. 10.3138\u002Fcarto.43.1.3\nLight RJ, Pillemer DB: Summing up: the science of reviewing research. Cambridge, Mass., Harvard University Press; 1984.\nSterne JAC, Egger M, Smith GD: Investigating and dealing with publication and other biases in meta-analysis. BMJ 2001, 323: 101-105. 10.1136\u002Fbmj.323.7304.101\nBenneyan JC, Lloyd RC, Plsek PE: Statistical process control as a tool for research and healthcare improvement. Qual Saf Health Care 2003, 12: 458-464. 10.1136\u002Fqhc.12.6.458\nSpiegelhalter DJ: Funnel plots for comparing institutional performance. Statistics in Medicine 2005, 24: 1185-2102. 10.1002\u002Fsim.1970\nIezzoni LI, (ed): Risk Adjustment for Measuring Health Care Outcomes. 3rd edition. Chicago, IL, Health Administration Press; 2003.\nAlberta Health and Wellness:Calculating Small Area Analysis: Definition of Sub-regional Geographic Units in Alberta. Alberta. 2003. [http:\u002F\u002Fwww.health.alberta.ca\u002Fdocuments\u002FGeo-Calculating-Small-Area-2003.pdf]\nStatistics Canada:Canadian Community Health Survey (CCHS). [http:\u002F\u002Fwww.statcan.gc.ca\u002Fcgi-bin\u002Fimdb\u002Fp2SV.pl?Function=getSurvey&SurvId=3226&SurvVer=0&InstaId=15282&InstaVer=4&SDDS=3226&lang=en&db=IMDB&dbg=f&adm=8&dis=2]\nKorn EL, Graubard BI: Analysis of Health Surveys. New York, NY, Wiley; 1999.\nRogerson P, Yamada I: Statistical Detection and Surveillance of Geographic Clusters. Boca Raton, FL, Chapman and Hall; 2008.\nBarss P, Smith GS, Baker SP, Mohan D: Injury prevention: an international perspective epidemiology, surveillance, and policy. New York, Oxford University Press; 1998.\nMinistry of Transportation, Government of Canada:Vision 2010 - Making Canada's Roads the Safest in the World. 2002. [http:\u002F\u002Fwww.ccmta.ca\u002Fenglish\u002Fpdf\u002Frsv_report_02_e.pdf]\nBirkmeyer JD: Primer on Geographic Variation in Health Care. Effective Clinical Practice 2001,4(5):232-233.\nTerrin N, Schmid CJ, Lau J: In an empirical evaluation of the funnel plot, researchers could not visually identify publication bias. Journal of Clinical Epidemiology 2005, 58: 894-901. 10.1016\u002Fj.jclinepi.2005.01.006\nSpiegelhalter DJ: Handling over-dispersion of performance indicators. Qual Saf Health Care 2005, 14: 347-351. 10.1136\u002Fqshc.2005.013755\nKim H, Kriebel D: Regression models for public health surveillance data: a simulation study. Occup Environ Med 2009, 66: 733-739. 10.1136\u002Foem.2008.042887\nOhlssen DI, Sharples LD, Spiegelhalter DJ: A hierarchical modelling framework for identifying unusual performance in health care providers. J R Statist Soc A 2007, 170: 865-890. 10.1111\u002Fj.1467-985X.2007.00487.x\nBerk R, MacDonald J: Overdispersion and Poisson Regression. Journal of Quantitative Criminology 2008, 24: 269-284. 10.1007\u002Fs10940-008-9048-4\nPires AN, Amado C: Interval estimators for a binomial proportion: comparison of twenty methods. Revstat 2008,6(2):165-197.\nRothman KJ, Greenland S, Lash TL: Modern Epidemiology. 3rd edition. Philadelphia, PA Lippincott Williams & Wilkins; 2008.",{"VOID":583},"10.1186\u002F1478-7954-9-58","2024-06-25T03:31:58.049+00:00","https:\u002F\u002Fpophealthmetrics.biomedcentral.com\u002Farticles\u002F10.1186\u002F1478-7954-9-58",[587,604],{"id":588,"sortIndex":91,"researcher":23,"roles":589,"affiliations":590,"properties":599,"displayName":601,"givenName":23,"familyName":23},"682c3983-d61d-4402-8143-04e615bd0349",[139],[591],{"id":592,"sortIndex":91,"affiliation":593,"properties":23},"f629b131-5f43-4635-95f3-40fd32cdb7b4",{"id":592,"createTime":23,"updateTime":23,"relativeEntities":594,"slug":23,"properties":595,"entityType":23,"verifyStatus":23,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":23,"url":23,"parentIds":598,"statistic":23},[],{"title":596},{"EN":597},"Alberta Health and Wellness, Edmonton, Canada",[],{"title":600,"gsAuthor":602},{"VI":601},"Douglas C Dover",{"VOID":603},"[\"uyb0PucAAAAJ\"]",{"id":605,"sortIndex":24,"researcher":23,"roles":606,"affiliations":607,"properties":616,"displayName":618,"givenName":23,"familyName":23},"9310f67a-216a-4206-b3fb-04349f75debe",[139],[608],{"id":609,"sortIndex":91,"affiliation":610,"properties":23},"38cc0521-68e9-4849-b79e-47b17162482d",{"id":609,"createTime":23,"updateTime":23,"relativeEntities":611,"slug":23,"properties":612,"entityType":23,"verifyStatus":23,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":23,"url":23,"parentIds":615,"statistic":23},[],{"title":613},{"VI":614},"School of Public Health, University of Alberta, Edmonton, Canada",[],{"title":617,"gsAuthor":619},{"VI":618},"Donald P Schopflocher",{"VOID":620},"[\"IYDAgJEAAAAJ\"]",{"url":585,"publisher":622,"properties":669},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":623,"slug":10,"properties":624,"entityType":21,"verifyStatus":22,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":24,"subjectFields":629,"manageAffiliations":638,"indexDatabases":649,"url":89,"thumbnailPath":23,"statistic":664,"gsStatistic":23,"type":23,"analyzePriority":23},[],{"country":625,"eissn":626,"issn":627,"title":628},{"VOID":13},{"VOID":15},{"VOID":15},{"EN":18},[630,634],{"id":27,"createTime":23,"updateTime":23,"relativeEntities":631,"label":632,"description":633,"parentId":23,"standard":23,"scholarHubFieldId":23},[],{"EN":30},{},{"id":33,"createTime":23,"updateTime":23,"relativeEntities":635,"label":636,"description":637,"parentId":23,"standard":23,"scholarHubFieldId":23},[],{"EN":36},{},[639,644],{"id":40,"createTime":23,"updateTime":23,"relativeEntities":640,"slug":23,"properties":641,"entityType":23,"verifyStatus":23,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":23,"url":23,"parentIds":643,"statistic":23},[],{"title":642},{"EN":44},[],{"id":47,"createTime":23,"updateTime":23,"relativeEntities":645,"slug":23,"properties":646,"entityType":23,"verifyStatus":23,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":23,"url":23,"parentIds":648,"statistic":23},[],{"title":647},{"EN":51},[],[650,657],{"id":55,"indexDatabase":651,"url":66,"indexYears":67,"academicFieldIds":656,"indexDatabaseRanking":71},{"id":57,"createTime":23,"updateTime":23,"relativeEntities":652,"label":653,"description":654,"key":63,"publicationTags":655,"standard":23},[],{"EN":60,"VI":60},{"EN":60,"VI":62},[65],[69,70],{"id":73,"indexDatabase":658,"url":86,"indexYears":23,"academicFieldIds":663,"indexDatabaseRanking":23},{"id":75,"createTime":23,"updateTime":23,"relativeEntities":659,"label":660,"description":661,"key":82,"publicationTags":662,"standard":23},[],{"EN":78,"VI":78},{"EN":80,"VI":81},[84,85],[88],{"impactFactor":91,"impactFactorByYear":665,"i10Index":91,"i10IndexLast5Year":91,"totalPublication":93,"totalPublicationByYear":666,"totalCitation":91,"totalCitationByYear":667,"totalCitationPerPublication":91,"totalCitationPerPublicationByYear":668,"hindexLast5Year":91,"hindex":91},{},{"2003":95,"2004":96,"2005":95,"2006":97,"2007":98,"2008":95,"2009":99,"2010":100,"2011":101,"2012":102,"2013":103,"2014":99,"2015":104,"2016":105,"2017":106,"2018":107,"2019":103,"2020":105,"2021":108,"2022":104,"2023":102},{},{},{"pages":670,"volume":671},{"VOID":330},{"VOID":332},61,{"total":672,"publishYear":334,"statisticByYear":674},{"2012":24,"2013":96,"2014":96,"2015":202,"2016":24,"2017":97,"2018":675,"2019":97,"2020":96,"2021":186,"2022":202,"2023":186,"2024":24,"2025":202,"2026":186},14,"2011-11-10","DONE_ANALYZE_CITATION",[84,71],{"id":680,"createTime":681,"updateTime":682,"relativeEntities":683,"slug":684,"properties":685,"entityType":130,"verifyStatus":131,"verifyTime":696,"verifyNote":133,"languages":23,"translateLanguages":23,"viewCount":91,"primaryUrl":697,"fullTextUrl":23,"authors":698,"publicationType":279,"publisherRelationship":746,"citationCount":23,"citationInfo":23,"publishDate":799,"publishYear":800,"citationAnalyzeStatus":22,"lastCitationAnalyze":682,"indexDatabases":801,"openAccess":23,"references":23,"isForceReanalyzing":566},"adc128ff-53ca-42ac-8161-687e542f9e7a","2024-01-15T19:28:45.789+00:00","2026-07-17T13:58:39.212+00:00",[],"Describing-the-longitudinal-course-of-major-depression-using-Markov-models-Data-integration-across-three-national-surveys",{"abstract":686,"title":688,"gsPaper":690,"references":692,"doi":694},{"EN":687},"Most epidemiological studies of major depression report period prevalence estimates. These are of limited utility in characterizing the longitudinal epidemiology of this condition. Markov models provide a methodological framework for increasing the utility of epidemiological data. Markov models relating incidence and recovery to major depression prevalence have been described in a series of prior papers. In this paper, the models are extended to describe the longitudinal course of the disorder. Data from three national surveys conducted by the Canadian national statistical agency (Statistics Canada) were used in this analysis. These data were integrated using a Markov model. Incidence, recurrence and recovery were represented as weekly transition probabilities. Model parameters were calibrated to the survey estimates. The population was divided into three categories: low, moderate and high recurrence groups. The size of each category was approximated using lifetime data from a study using the WHO Mental Health Composite International Diagnostic Interview (WMH-CIDI). Consistent with previous work, transition probabilities reflecting recovery were high in the initial weeks of the episodes, and declined by a fixed proportion with each passing week. Markov models provide a framework for integrating psychiatric epidemiological data. Previous studies have illustrated the utility of Markov models for decomposing prevalence into its various determinants: incidence, recovery and mortality. This study extends the Markov approach by distinguishing several recurrence categories.",{"EN":689},"Describing the longitudinal course of major depression using Markov models: Data integration across three national surveys",{"VOID":691},"[\"15902808060557351218\"]",{"VOID":693},"Patten SB, Lee RC: Refining Estimates of Major Depression Incidence and Episode Duration in Canada using a Monte Carlo Markov Model. Med Decis Making 2004, 24: 351-358. 10.1177\u002F0272989X04267008\nPatten SB, Lee RC: Towards a dynamic model of major depression epidemiology. Epidemiologia e Psychiatria Sociale 2004, 13: 21-28.\nPatten SB, Lee RC: Epidemiological theory, decision theory and health services research. Soc Psychiatry Psychiatr Epidemiol 2004, 39: 893-898. 10.1007\u002Fs00127-004-0872-z\nPatten SB: Markov models of major depression for linking psychiatric epidemiology to clinical practice. Clinical Practice & Epidemiology in Mental Health 2005, 1: 2. 10.1186\u002F1745-0179-1-2\nPatten SB: An analysis of data from two general health surveys found that increased incidence and duration contributed to elevated prevalence of major depression in person with chronic medical conditions. J Clin Epidemiol 2005, 58: 184-189. 10.1016\u002Fj.jclinepi.2004.06.006\nKruijshaar ME, Barendregt J, Vos T, de Graaf R, Spijker J, Andrews G: Lifetime prevalence estimates of major depression: An indirect estimation method and a quantification of recall bias. Eur J Epidemiol 2005, 20: 103-111. 10.1007\u002Fs10654-004-1009-0\nBijl RV, van Zessen G, Ravelli A, de Rijk C, Langendoen Y: The Netherlands Mental Health Survey and Incidence Study (NEMESIS): objectives and design. Soc Psychiatry Psychiatr Epidemiol 1998, 33: 581-586. 10.1007\u002Fs001270050097\nAndrews G, Henderson S, Hall W: Prevalence, comorbidity, disability and service utilisation. Overview of the Australian National Mental Health Survey. Br J Psychiatry 2001, 178: 145-153. 10.1192\u002Fbjp.178.2.145\nSonnenberg FA, Beck JR: Markov models in medical decision making: a practical guide. Med Decis Making 1993, 13: 322-338.\nKessler RC, Andrews G, Colpe LJ, E. H, Mroczek DK, Normand SL: Short screening scales to monitor population prevalences and trends in non-specific psychological distress. Psychol Med 2002, 32: 959-976. 10.1017\u002FS0033291702006074\nAssociation AP: Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR). Washington, American Psychiatric Association; 2000.\nKessler RC, Ustun TB: The World Mental Health (WMH) Survey Initiative Version of the World Health Organization (WHO) Composite International Diagnostic Interview (CIDI). Int J Methods Psychiatr Res 2004, 13: 83-121.\nRothman KJ, Greenland S: Measures of Disease Frequency. In Modern Epidemiology. Volume 3. 2nd edition. Edited by: Rothman KJ and Greenland S. Philadelphia, Lippincott-Raven; 1998:29-64.\nInvestigators ESEMDMHEDEA: Prevalence of mental disorders in Europe: results from the European Study of the Epidemiology of Mental Disorders (ESEMeD) project. Acta Psychiatr Scand 2004, 109(Suppl. 420): 21-27.\nKessler RC, Berglund P, Demler O, Jin R, Koretz D, Merikangas KR, Rush JA, Waters EE, Wang PS: The epidemiology of major depressive disorder: results from the National Comorbidity Survey Replication (NCS-R). JAMA 2003, 289: 3095-3105. 10.1001\u002Fjama.289.23.3095\nWaraich PS, Goldner EM, Somers JM, Hsu L: Prevalence and incidence studies of mood disorders: a systematic review of the literature. Can J Psychiatry 2004, 49: 124-138.\nÜstün TB, Kessler RC: Global burden of depressive disorders: the issue of duration. Br J Psychiatry 2002, 181: 181-183. 10.1192\u002Fbjp.181.3.181\nEaton WW, Anthony JC, Gallo J, Cai G, Tien A, Romanoski A, Lyketsos C: Natural history of diagnostic interview schedule\u002FDSM-IV major depression. The Baltimore Epidemiological Catchment Area follow-up. Arch Gen Psychiatry 1997, 54: 993-999.\nPatten SB, Brandon-Christie J, Devji J, Sedmak B: Performance of the Composite International Diagnostic Interview Short Form for Major Depression in a Community Sample. Chron Dis Can 2000, 21: 68-72.\nAndrews G, Anstey K, Brodaty H, Issakidis C, Luscombe G: Recall of depressive episode 25 years previously. Psychol Med 1999, 29: 787-791. 10.1017\u002FS0033291799008648\nEaton WW, Anthony JC, Gallo J, Cai G, Tien A, Romanoski A, Lyketsos C: Natural history of diagnostic interview schedule\u002FDSM-IV major depression. The Baltimore epidemiological catchment area follow-up. Arch Gen Psychiatry 1997, 54: 993-999.\nNewman SC, Bland RC: Incidence of mental disorders in Edmonton: estimates of rates and methodological issues. J Psychiatr Res 1998, 32: 273-282. 10.1016\u002FS0022-3956(98)00011-9\nRegier DA, Kaelber CT, Rae DS, Farmer ME, Knauper B, Kessler RC, Norquist GS: Limitations of diagnostic criteria and assessment instruments for mental disorders. Arch Gen Psychiatry 1998, 55: 109-115. 10.1001\u002Farchpsyc.55.2.109\nSorensen J, Kind P: Modelling cost-effectiveness issues in the treatment of clinical depression. IMA J Math Appl Med Biol 1995, 12: 369-385.\nVos T, Haby MM, Berendregt JJ, Kruijshaar M, Corry J, Andrews G: The burden of major depression avoidable by longer-term treatment strategies. Arch Gen Psychiatry 2004, 61: 1097-1103. 10.1001\u002Farchpsyc.61.11.1097\nAndrews G, Issakidis C, Sanderson K, Corry J, Lapsley H: Utilising survey data to inform public policy: comparison of the cost-effectiveness of treatment of ten mental disorders. Br J Psychiatry 2005, 184: 526-533. 10.1192\u002Fbjp.184.6.526",{"VOID":695},"10.1186\u002F1478-7954-3-11","2024-06-26T12:41:07.485+00:00","https:\u002F\u002Fpophealthmetrics.biomedcentral.com\u002Farticles\u002F10.1186\u002F1478-7954-3-11",[699,724],{"id":700,"sortIndex":91,"researcher":23,"roles":701,"affiliations":702,"properties":719,"displayName":721,"givenName":23,"familyName":23},"9107459f-2b5c-41a4-965c-a085d6dfea09",[139],[703,711],{"id":704,"sortIndex":91,"affiliation":705,"properties":23},"19b0be90-5642-4fd3-be20-68ec86b058e4",{"id":704,"createTime":23,"updateTime":23,"relativeEntities":706,"slug":23,"properties":707,"entityType":23,"verifyStatus":23,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":23,"url":23,"parentIds":710,"statistic":23},[],{"title":708},{"VI":709},"Department of Community Health Sciences, University of Calgary, Calgary, Canada",[],{"id":712,"sortIndex":24,"affiliation":713,"properties":23},"26d5e2d2-4ac4-4885-8342-7df99f48fb1b",{"id":712,"createTime":23,"updateTime":23,"relativeEntities":714,"slug":23,"properties":715,"entityType":23,"verifyStatus":23,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":23,"url":23,"parentIds":718,"statistic":23},[],{"title":716},{"VI":717},"Department of Psychiatry, University of Calgary, Calgary, Canada",[],{"title":720,"gsAuthor":722},{"VI":721},"Scott B Patten",{"VOID":723},"[\"LLAet2EAAAAJ\"]",{"id":725,"sortIndex":24,"researcher":23,"roles":726,"affiliations":727,"properties":743,"displayName":745,"givenName":23,"familyName":23},"65edb209-7137-4663-b0c4-28fb626ceb35",[139],[728,736],{"id":729,"sortIndex":91,"affiliation":730,"properties":23},"fafe7943-ace1-438b-849b-10222ec0c478",{"id":729,"createTime":23,"updateTime":23,"relativeEntities":731,"slug":23,"properties":732,"entityType":23,"verifyStatus":23,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":23,"url":23,"parentIds":735,"statistic":23},[],{"title":733},{"VI":734},"Health Technology Implementation Unit, Calgary Health Region. Foothills Medical Centre, Calgary, Canada",[],{"id":704,"sortIndex":24,"affiliation":737,"properties":742},{"id":704,"createTime":23,"updateTime":23,"relativeEntities":738,"slug":23,"properties":739,"entityType":23,"verifyStatus":23,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":23,"url":23,"parentIds":741,"statistic":23},[],{"title":740},{"VI":709},[],{},{"title":744},{"VI":745},"Robert C Lee",{"url":697,"publisher":747,"properties":794},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":748,"slug":10,"properties":749,"entityType":21,"verifyStatus":22,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":24,"subjectFields":754,"manageAffiliations":763,"indexDatabases":774,"url":89,"thumbnailPath":23,"statistic":789,"gsStatistic":23,"type":23,"analyzePriority":23},[],{"country":750,"eissn":751,"issn":752,"title":753},{"VOID":13},{"VOID":15},{"VOID":15},{"EN":18},[755,759],{"id":27,"createTime":23,"updateTime":23,"relativeEntities":756,"label":757,"description":758,"parentId":23,"standard":23,"scholarHubFieldId":23},[],{"EN":30},{},{"id":33,"createTime":23,"updateTime":23,"relativeEntities":760,"label":761,"description":762,"parentId":23,"standard":23,"scholarHubFieldId":23},[],{"EN":36},{},[764,769],{"id":40,"createTime":23,"updateTime":23,"relativeEntities":765,"slug":23,"properties":766,"entityType":23,"verifyStatus":23,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":23,"url":23,"parentIds":768,"statistic":23},[],{"title":767},{"EN":44},[],{"id":47,"createTime":23,"updateTime":23,"relativeEntities":770,"slug":23,"properties":771,"entityType":23,"verifyStatus":23,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":23,"url":23,"parentIds":773,"statistic":23},[],{"title":772},{"EN":51},[],[775,782],{"id":55,"indexDatabase":776,"url":66,"indexYears":67,"academicFieldIds":781,"indexDatabaseRanking":71},{"id":57,"createTime":23,"updateTime":23,"relativeEntities":777,"label":778,"description":779,"key":63,"publicationTags":780,"standard":23},[],{"EN":60,"VI":60},{"EN":60,"VI":62},[65],[69,70],{"id":73,"indexDatabase":783,"url":86,"indexYears":23,"academicFieldIds":788,"indexDatabaseRanking":23},{"id":75,"createTime":23,"updateTime":23,"relativeEntities":784,"label":785,"description":786,"key":82,"publicationTags":787,"standard":23},[],{"EN":78,"VI":78},{"EN":80,"VI":81},[84,85],[88],{"impactFactor":91,"impactFactorByYear":790,"i10Index":91,"i10IndexLast5Year":91,"totalPublication":93,"totalPublicationByYear":791,"totalCitation":91,"totalCitationByYear":792,"totalCitationPerPublication":91,"totalCitationPerPublicationByYear":793,"hindexLast5Year":91,"hindex":91},{},{"2003":95,"2004":96,"2005":95,"2006":97,"2007":98,"2008":95,"2009":99,"2010":100,"2011":101,"2012":102,"2013":103,"2014":99,"2015":104,"2016":105,"2017":106,"2018":107,"2019":103,"2020":105,"2021":108,"2022":104,"2023":102},{},{},{"pages":795,"volume":797},{"VOID":796},"1-9",{"VOID":798},"3","2005-11-15",2005,[84,71],{"id":803,"createTime":804,"updateTime":805,"relativeEntities":806,"slug":807,"properties":808,"entityType":130,"verifyStatus":131,"verifyTime":819,"verifyNote":133,"languages":23,"translateLanguages":23,"viewCount":91,"primaryUrl":820,"fullTextUrl":23,"authors":821,"publicationType":279,"publisherRelationship":898,"citationCount":23,"citationInfo":23,"publishDate":951,"publishYear":952,"citationAnalyzeStatus":953,"lastCitationAnalyze":954,"indexDatabases":955,"openAccess":23,"references":23,"isForceReanalyzing":566},"20db9f97-548e-442a-8129-93ca3322c577","2023-12-18T05:31:10.180+00:00","2026-06-07T12:43:02.277+00:00",[],"The-components-of-self-rated-health-among-adults-in-Ouagadougou-Burkina-Faso",{"abstract":809,"title":811,"gsPaper":813,"references":815,"doi":817},{"EN":810},"Although the relationship between self-rated health (SRH) and physical and mental health is well documented in developed countries, very few studies have analyzed this association in the developing world, particularly in Africa. In this study, we examine the associations of SRH with measures of physical and mental health (chronic diseases, functional limitations, and depression) among adults in Ouagadougou, Burkina Faso, and how these associations vary by sex, age, and education level. This study was based on 2195 individuals aged 15 years or older who participated in a cross-sectional interviewer-administered health survey conducted in 2010 in areas of the Ouagadougou Health and Demographic Surveillance System. Logistic regression models were used to analyze the associations of poor SRH with chronic diseases, functional limitations, and depression, first in the whole sample and then stratified by sex, age, and education level. Poor SRH was strongly correlated with chronic diseases and functional limitations, but not with depression, suggesting that in this context, physical health probably makes up most of people’s perceptions of their health status. The effect of functional limitations on poor SRH increased with age, probably because the ability to circumvent or compensate for a disability diminishes with age. The effect of functional limitations was also stronger among the least educated, probably because physical integrity is more important for people who depend on it for their livelihood. In contrast, the effect of chronic diseases appeared to decrease with age. No variation by sex was observed in the associations of SRH with chronic diseases, functional limitations, or depression. Our findings suggest that different subpopulations delineated by age and education level weight the components of health differently in their self-rated health in Ouagadougou, Burkina Faso. In-depth studies are needed to understand why and how these groups do so.",{"EN":812},"The components of self-rated health among adults in Ouagadougou, Burkina Faso",{"VOID":814},"[]",{"VOID":816},"Salomon JA, Nordhagen S, Oza S, Murray CJL: Are Americans feeling less healthy? The puzzle of trends in self-rated health. Am J Epidemiol 2009, 170: 343-351. 10.1093\u002Faje\u002Fkwp144\nDeSalvo KB, Bloser N, Reynolds K, He J, Muntner P: Mortality prediction with a single general self-rated health question: a meta-analysis. J Gen Intern Med 2006, 21: 267-275. 10.1111\u002Fj.1525-1497.2005.00291.x\nIdler EL, Benyamini Y: Self-rated health and mortality: a review of twenty-seven community studies. J Health Soc Behav 1997, 38: 21-37. 10.2307\u002F2955359\nIdler EL, Kasl SV: Self-ratings of health: do they also predict change in functional ability? J Gerontology: Soc Sci 1995, 50: S344-S353.\nIdler EL, Russell LB, Davis D: Survival, functional limitations, and self-rated health in the NHANES I epidemiologic follow-up study, 1992. Am J Epidemiol 2000, 152: 874-883. 10.1093\u002Faje\u002F152.9.874\nFerraro KF, Farmer MM, Wybraniec JA: Health trajectories: long-term dynamics among black and white adults. J Health Soc Behav 1997, 38: 38-54. 10.2307\u002F2955360\nMøller L, Kristensen TS, Hollnagel H: Self-rated health as a predictor of coronary heart disease in Copenhagen, Denmark. J Epidemiol Community Health 1996, 50: 423-428. 10.1136\u002Fjech.50.4.423\nJylhä M: What is self-rated health and why does it predict mortality? Towards a unified conceptual model. Soc Sci Med 2009, 69: 307-316. 10.1016\u002Fj.socscimed.2009.05.013\nMavaddat N, Kinmonth A, Sanderson S, Surtees P, Bingham S, Khaw KT: What determines Self-Rated Health (SRH)? A cross-sectionnal study of SF-36 health domains in the EPIC-Norfolk cohort. J Epidemiol Community Health 2011, 65: 800-806. 10.1136\u002Fjech.2009.090845\nMolarius A, Janson S: Self-rated health, chronic diseases, and symptoms among middle-aged and elderly men and women. J Clin Epidemiol 2002, 55: 364-370. 10.1016\u002FS0895-4356(01)00491-7\nSchnittker J: When mental health becomes health: age and the shifting meaning of self-evaluations of general health. Milbank Q 2005, 83: 397-423. 10.1111\u002Fj.1468-0009.2005.00407.x\nSingh-Manoux A, Martikainen P, Ferrie J, Zins M, Marmot M, Goldberg M: What does self-rated health measure? Results from the British Whitehall II and French Gazel cohort studies. J Epidemiol Community Health 2006, 60: 364-372. 10.1136\u002Fjech.2005.039883\nSmith P, Glazier R, Sibley L: The predictors of self-rated health and the relationship between self-rated health and health service needs are similar across socioeconomic groups in Canada. J Clin Epidemiol 2010, 63: 412-421. 10.1016\u002Fj.jclinepi.2009.08.015\nIdler EL, Hudson SV, Leventhal H: The meanings of self-ratings of health: a qualitative and quantitative approach. Res Aging 1999, 21: 458-476. 10.1177\u002F0164027599213006\nKrause NM, Jay GM: What do global self-rated health items measure? Med Care 1994, 32: 930-942. 10.1097\u002F00005650-199409000-00004\nManderbacka K: Examining what self-rated health question is understood to mean by respondents. Scand J Soc Med 1998, 26: 145-153.\nSimon JG, De Boer JB, Joung IMA, Bosma H, Mackenbach JP: How is your health in general? A qualitative study on self-assessed health. Eur J Public Health 2005, 15: 200-208. 10.1093\u002Feurpub\u002Fcki102\nRahman MO, Barsky AJ: Self-reported health among older Bangladeshis: how good a health indicator is it? Gerontologist 2003, 43: 856-863. 10.1093\u002Fgeront\u002F43.6.856\nZimmer Z, Natividad J, Lin HS, Chayovan N: A cross-national examination of the determinants of self-assessed health. J Health Soc Behav 2000, 41: 465-481. 10.2307\u002F2676298\nCharasse-Pouélé C, Fournier M: Health disparities between racial groups in South Africa: a decomposition analysis. Soc Sci Med 2006, 62: 2897-2914. 10.1016\u002Fj.socscimed.2005.11.020\nChin B: Income, health, and well-being in rural Malawi. Demogr Res 2010, 23: 997-1030.\nDebpuur C, Welaga P, Wak G, Hodgson A: Self-reported health and functional limitations among older people in the Kassena-Nankana District, Ghana. Global Health Action 2010, Supplement 2: 54-63.\nGilbert L, Soskolne V: Self-assessed health - a case study of social differentials in Soweto, South Africa. Health Place 2003, 9: 193-205. 10.1016\u002FS1353-8292(02)00039-4\nKuate-Defo B: Facteurs associés à la santé perçue et à la capacité fonctionnelle des personnes âgées dans la préfecture de Bandjoun au Cameroun. Cahiers québécois de démographie 2005, 34: 1-46.\nObare F: Self-rated health status and morbidity experiences of teenagers in Nairobi’s low income settings. Afr Popul Stud 2007, 22: 57-74.\nSpiers N, Jagger C, Clarke M, Arthur A: Are gender differences in the relationship between self-rated health and mortality enduring? Results from three birth cohorts in Melton Mowbray, United Kingdom. Gerontologist 2003, 43: 406-411. 10.1093\u002Fgeront\u002F43.3.406\nIburg KM, Salomon JA, Tandon A, Murray CJL: Global programme on evidence for health policy discussion paper no 14. In Cross-population comparability of self-reported and physician-assessed mobility levels: evidence from the third national health and nutrition examination survey. Geneva: World Health Organization; 2001.\nCase A, Paxson C: Sex differences in morbidity and mortality. Demography 2005, 42: 189-214. 10.1353\u002Fdem.2005.0011\nSingh-Manoux A, Guéguen A, Martikainen P, Ferrie J, Marmot M, Shipley M: Self-rated health and mortality: short- and long-term associations in the Whitehall II study. Psychosom Med 2007, 69: 138-143. 10.1097\u002FPSY.0b013e318030483a\nDelpierre C, Datta GD, Kelly-Irving M, Lauwers-Cances V, Berkman L, Lang T: What role does socio-economic position play in the link between functional limitations and self-rated health: France vs. USA? Eur J Public Health 2011, 22: 317-321.\nDelpierre C, Kelly-Irving M, Munch-Petersen M, Lauwers-Cances V, Datta GD, Lepage B, Lang T: SRH and HrQOL: does social position impact differently on their link with health status? BMC Publ Health 2012, 12: 19. 10.1186\u002F1471-2458-12-19\nDelpierre C, Lauwers-Cances V, Datta GD, Lang T, Berkman L: Using self-rated health for analysing social inequalities in health: a risk for underestimating the gap between socioeconomic groups? J Epidemiol Community Health 2009, 63: 426-432. 10.1136\u002Fjech.2008.080085\nDesesquelles AF, Egidi V, Salvatore MA: Why do Italian people rate their health worse than French people do? An exploration of cross-country differentials of self-rated health. Soc Sci Med 2009, 68: 1124-1128. 10.1016\u002Fj.socscimed.2008.12.037\nOuédraogo MM, Ripama MT: Rapport d’analyse. In Recensement général de la population et de l’habitation de 2006 (RGPH-2006): État et structure de la population. Burkina Faso: Ouagadougou: Ministère de l’Économie et des Finances; 2009.\nOuattara A, Somé L: Rapport d’analyse. In Recensement général de la population et de l’habitation de 2006 (RGPH-2006): Thème 09 : la croissance urbaine au Burkina Faso. Burkina Faso: Ouagadougou: Ministère de l’Économie et des Finances; 2009.\nInstitut National de la Statistique et de la Démographie (INSD), ICF International: Enquête démographique et de santé et à indicateurs multiples du Burkina Faso 2010. Calverton, MD, USA: INSD & ICF International; 2012.\nInstitut National de la Statistique et de la Démographie (INSD), Macro International Inc: Enquête démographique et de santé du Burkina Faso 1998–1999. Calverton, MD, USA: INSD & Macro International Inc; 2000.\nBaya B, Bonkoungou Z, Zida\u002FBangré H: Rapport d’analyse. In Recensement général de la population et de l’habitation de 2006 (RGPH-2006): thème 7 : la mortalité au Burkina Faso. Burkina Faso: Ouagadougou: Ministère de l’Économie et des Finances; 2009.\nKobiané JF, Bougma M: Rapport d’analyse. In Recensement général de la population et de l’habitation de 2006 (RGPH-2006): thème 4 : education: instruction, alphabétisation, scolarisation. Burkina Faso: Ouagadougou: Ministère de l’Économie et des Finances; 2009.\nBoyer F, Delaunay D: “OUAGA 2009”: peuplement de Ouagadougou et développement urbain: rapport provisoire. Ouagadougou: IRD; 2009.\nRossier C, Soura A, Baya B, Compaoré G, Dabiré B, Dos Santos S, Duthé G, Gnoumou B, Kobiané JF, Kouanda S, et al.: Profile: the Ouagadougou health and demographic surveillance system. Int J Epidemiol 2012, 41: 658-666. 10.1093\u002Fije\u002Fdys090\nRossier C, Ducarroz L: La pauvreté dans les quartiers de l’OPO: une approche qualitative. Ouagadougou: ISSP, Université de Ouagadougou; 2012.\nSteyn K, Gaziano TA, Bradshaw D, Laubscher R, Fourie J, South African Demographic and Health Collaborating Team: Hypertension in South African adults: results from the demographic and health survey, 1998. J Hypertens 2001, 19: 1717-1725. 10.1097\u002F00004872-200110000-00004\nFreeman EE, Zunzunegui MV, Kouanda S, Aubin MJ, Popescu ML, Miszkurka M, Cojocaru D, Haddad S: Prevalence and risk factors for near and far visual difficulty in Burkina Faso. Ophthalmic Epidemiol 2010, 17: 301-306. 10.3109\u002F09286586.2010.508354\nAbubakari AR, Lauder W, Agyemang C, Jones M, Kirk A, Bhopal RS: Prevalence and time trends in obesity among adult West African populations: a meta-analysis. Obesity Review 2008, 9: 297-311. 10.1111\u002Fj.1467-789X.2007.00462.x\nRossier C, Soura A, the OPO Group: Conference of the union for African population studies. In Poverty and health at the periphery of Ouagadougou. Accra, Ghana: Union for African Population Studies (UAPS); 2011.\nZeba AN, Delisle HF, Renier G, Savadogo B, Baya B: The double burden of malnutrition and cardiometabolic risk widens the gender and socio-economic health gap: a study among adults in Burkina Faso (West Africa). Public Health Nutr 2012, 15: 2210-2219. 10.1017\u002FS1368980012000729\nNikiema A, Rossier C, Millogo R, Ridde V: Conference of the union for African population studies. In Inégalités de l’accès aux soins en milieu urbain africain: Le cas de la périphérie nord de Ouagadougou. Accra, Ghana: Union for African Population Studies (UAPS); 2011.\nRaghunathan TE, Lepkowski JM, Van Hoewyk J, Solenberger P: A multivariate technique for multiply imputing missing values using a sequence of regression models. Survey Methodology 2001, 27: 85-95.\nVan Buuren S, Boshuizen HC, Knook DL: Multiple imputation of missing blood pressure covariates in survival analysis. Stat Med 1999, 18: 681-694. 10.1002\u002F(SICI)1097-0258(19990330)18:6\u003C681::AID-SIM71>3.0.CO;2-R\nStataCorp: Stata multiple-imputation reference manual: release 12. TX: Stata Press: College Station; 2011a.\nWashington Group on Disability Statistics (WG): Development of an internationally comparable disability measure for censuses. Hyattsville, MD: Washington Group; 2008.\nWorld Health Organization: International classification of functioning, disability and health. Geneva: WHO; 2001.\nSheehan DV, Lecrubier Y, Sheehan KH, Amorim P, Janavs J, Weiller E, Hergueta T, Baker R, Dunbar GC: The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. Journal of Clinical Psychiatry 1998, 59: 22-57.\nAmerican Psychiatric Association: Diagnostic and statistical manual of mental disorders(DSM-IV). 4th edition. Washington DC: APA; 1994.\nBabor TF, Higgins-Biddle JC, Saunders JB, Monteiro MG: AUDIT: the alcohol use disorders identification test: guidelines for use in primary care. Geneva, Switzerland: World Health Organization; 2001.\nStataCorp: Stata survey data reference manual: release 12. TX: Stata Press: College Station; 2011b.\nRahman MO, Liu JH: Gender differences in functioning for older adults in rural Bangladesh: the impact of differential reporting? Journal of Gerontology: MEDICAL SCIENCES 2000, 55A: M28-M33.\nMäntyselkä PT, Turunen JHO, Ahonen RS, Kumpusalo EA: Chronic pain and poor self-rated health. JAMA 2003, 290: 2435-2442. 10.1001\u002Fjama.290.18.2435",{"VOID":818},"10.1186\u002F1478-7954-11-15","2024-05-17T07:10:44.841+00:00","https:\u002F\u002Fpophealthmetrics.biomedcentral.com\u002Farticles\u002F10.1186\u002F1478-7954-11-15",[822,837,850,874],{"id":823,"sortIndex":91,"researcher":23,"roles":824,"affiliations":825,"properties":834,"displayName":836,"givenName":23,"familyName":23},"1112886f-9257-4ce7-95a5-16672f287d0f",[139],[826],{"id":827,"sortIndex":91,"affiliation":828,"properties":23},"9b3ed856-c532-4fec-b3c3-3fd623038145",{"id":827,"createTime":23,"updateTime":23,"relativeEntities":829,"slug":23,"properties":830,"entityType":23,"verifyStatus":23,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":23,"url":23,"parentIds":833,"statistic":23},[],{"title":831},{"VI":832},"Département de démographie, Université de Montréal, Montréal (Québec), Canada",[],{"title":835},{"VI":836},"Yentéma Onadja",{"id":838,"sortIndex":24,"researcher":23,"roles":839,"affiliations":840,"properties":847,"displayName":849,"givenName":23,"familyName":23},"4a507ab9-b47e-43bd-9182-196acb6f6c2f",[139],[841],{"id":827,"sortIndex":91,"affiliation":842,"properties":23},{"id":827,"createTime":23,"updateTime":23,"relativeEntities":843,"slug":23,"properties":844,"entityType":23,"verifyStatus":23,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":23,"url":23,"parentIds":846,"statistic":23},[],{"title":845},{"VI":832},[],{"title":848},{"VI":849},"Simona 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Canada",[],{"id":887,"sortIndex":24,"affiliation":888,"properties":894},"f8b6ae61-a2bd-4886-b559-be71c584ed36",{"id":887,"createTime":23,"updateTime":23,"relativeEntities":889,"slug":23,"properties":890,"entityType":23,"verifyStatus":23,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":23,"url":23,"parentIds":893,"statistic":23},[],{"title":891},{"VI":892},"Centre de Recherche du Centre Hospitalier de l’Université de Montréal (CRCHUM), Université de Montréal, Montréal(Québec), Canada",[],{},{"title":896},{"VI":897},"Maria-Victoria Zunzunegui",{"url":820,"publisher":899,"properties":946},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":900,"slug":10,"properties":901,"entityType":21,"verifyStatus":22,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":24,"subjectFields":906,"manageAffiliations":915,"indexDatabases":926,"url":89,"thumbnailPath":23,"statistic":941,"gsStatistic":23,"type":23,"analyzePriority":23},[],{"country":902,"eissn":903,"issn":904,"title":905},{"VOID":13},{"VOID":15},{"VOID":15},{"EN":18},[907,911],{"id":27,"createTime":23,"updateTime":23,"relativeEntities":908,"label":909,"description":910,"parentId":23,"standard":23,"scholarHubFieldId":23},[],{"EN":30},{},{"id":33,"createTime":23,"updateTime":23,"relativeEntities":912,"label":913,"description":914,"parentId":23,"standard":23,"scholarHubFieldId":23},[],{"EN":36},{},[916,921],{"id":40,"createTime":23,"updateTime":23,"relativeEntities":917,"slug":23,"properties":918,"entityType":23,"verifyStatus":23,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":23,"url":23,"parentIds":920,"statistic":23},[],{"title":919},{"EN":44},[],{"id":47,"createTime":23,"updateTime":23,"relativeEntities":922,"slug":23,"properties":923,"entityType":23,"verifyStatus":23,"verifyTime":23,"verifyNote":23,"languages":23,"translateLanguages":23,"viewCount":23,"url":23,"parentIds":925,"statistic":23},[],{"title":924},{"EN":51},[],[927,934],{"id":55,"indexDatabase":928,"url":66,"indexYears":67,"academicFieldIds":933,"indexDatabaseRanking":71},{"id":57,"createTime":23,"updateTime":23,"relativeEntities":929,"label":930,"description":931,"key":63,"publicationTags":932,"standard":23},[],{"EN":60,"VI":60},{"EN":60,"VI":62},[65],[69,70],{"id":73,"indexDatabase":935,"url":86,"indexYears":23,"academicFieldIds":940,"indexDatabaseRanking":23},{"id":75,"createTime":23,"updateTime":23,"relativeEntities":936,"label":937,"description":938,"key":82,"publicationTags":939,"standard":23},[],{"EN":78,"VI":78},{"EN":80,"VI":81},[84,85],[88],{"impactFactor":91,"impactFactorByYear":942,"i10Index":91,"i10IndexLast5Year":91,"totalPublication":93,"totalPublicationByYear":943,"totalCitation":91,"totalCitationByYear":944,"totalCitationPerPublication":91,"totalCitationPerPublicationByYear":945,"hindexLast5Year":91,"hindex":91},{},{"2003":95,"2004":96,"2005":95,"2006":97,"2007":98,"2008":95,"2009":99,"2010":100,"2011":101,"2012":102,"2013":103,"2014":99,"2015":104,"2016":105,"2017":106,"2018":107,"2019":103,"2020":105,"2021":108,"2022":104,"2023":102},{},{},{"pages":947,"volume":949},{"VOID":948},"1-12",{"VOID":950},"11","2013-08-08",2013,"ERROR_IN_GET_PLATFORM_ID","2026-06-07T12:43:02.276+00:00",[84,71],{"id":957,"createTime":958,"updateTime":959,"relativeEntities":960,"slug":961,"properties":962,"entityType":130,"verifyStatus":131,"verifyTime":975,"verifyNote":133,"languages":23,"translateLanguages":23,"viewCount":91,"primaryUrl":976,"fullTextUrl":23,"authors":977,"publicationType":279,"publisherRelationship":1064,"citationCount":1112,"citationInfo":1113,"publishDate":1117,"publishYear":1114,"citationAnalyzeStatus":22,"lastCitationAnalyze":1118,"indexDatabases":1119,"openAccess":23,"references":23,"isForceReanalyzing":566},"f13dc457-1595-4897-80e9-06b0998aab61","2024-04-05T17:12:26.234+00:00","2026-05-21T16:22:13.587+00:00",[],"Measuring-causes-of-death-in-populations-a-new-metric-that-corrects-cause-specific-mortality-fractions-for-chance",{"abstract":963,"title":965,"gsPaper":967,"keywords":969,"references":971,"doi":973},{"EN":964},"Verbal autopsy is gaining increasing acceptance as a method for determining the underlying cause of death when the cause of death given on death certificates is unavailable or unreliable, and there are now a number of alternative approaches for mapping from verbal autopsy interviews to the underlying cause of death. For public health applications, the population-level aggregates of the underlying causes are of primary interest, expressed as the cause-specific mortality fractions (CSMFs) for a mutually exclusive, collectively exhaustive cause list. Until now, CSMF Accuracy is the primary metric that has been used for measuring the quality of CSMF estimation methods. Although it allows for relative comparisons of alternative methods, CSMF Accuracy provides misleading numbers in absolute terms, because even random allocation of underlying causes yields relatively high CSMF accuracy. Therefore, the objective of this study was to develop and test a measure of CSMF that corrects this problem. We developed a baseline approach of random allocation and measured its performance analytically and through Monte Carlo simulation. We used this to develop a new metric of population-level estimation accuracy, the Chance Corrected CSMF Accuracy (CCCSMF Accuracy), which has value near zero for random guessing, and negative quality values for estimation methods that are worse than random at the population level. The CCCSMF Accuracy formula was found to be CCSMF Accuracy = (CSMF Accuracy - 0.632) \u002F (1 - 0.632), which indicates that, at the population-level, some existing and commonly used VA methods perform worse than random guessing. CCCSMF Accuracy should be used instead of CSMF Accuracy when assessing VA estimation methods because it provides a more easily interpreted measure of the quality of population-level estimates.",{"EN":966},"Measuring causes of death in populations: a new metric that corrects cause-specific mortality fractions for chance",{"VOID":968},"[\"14998929046370459844\"]",{"EN":970},"",{"VOID":972},"Mathers CD, Ma Fat D, Inoue M, Rao C, Lopez AD. Counting the dead and what they died from: an assessment of the global status of cause of death data. Bull World Health Organ. 2005;83:171–7.\nAbouZahr C, Boerma T. Health information systems: the foundations of public health. Bull World Health Organ. 2005;83:578–83.\nMahapatra P, Shibuya K, Lopez AD, Coullare F, Notzon FC, Rao C, et al. Civil registration systems and vital statistics: successes and missed opportunities. Lancet. 2007;370:1653–63.\nPhillips DE, Lozano R, Naghavi M, Atkinson C, Gonzalez-Medina D, Mikkelsen L, et al. A composite metric for assessing data on mortality and causes of death: the vital statistics performance index. Popul Health Metr. 2014;12:14.\nSetel PW, Sankoh O, Rao C, Velkoff VA, Mathers C, Gonghuan Y, et al. Sample registration of vital events with verbal autopsy: a renewed commitment to measuring and monitoring vital statistics. Bull World Health Organ. 2005;83:611–7.\nSetel PW, Macfarlane SB, Szreter S, Mikkelsen L, Jha P, Stout S, et al. A scandal of invisibility: making everyone count by counting everyone. Lancet. 2007;370:1569–77.\nMikkelsen L, Phillips DE, AbouZahr C, Setel PW, de Savigny D, Lozano R, et al. A global assessment of civil registration and vital statistics systems: monitoring data quality and progress. Lancet. 2015. doi:10.1016\u002FS0140-6736(15)60171-4.\nYang G, Hu J, Rao KQ, Ma J, Rao C, Lopez AD. Mortality registration and surveillance in China: History, current situation and challenges. Popul Health Metr. 2005;3:3.\nJha P, Gajalakshmi V, Gupta PC, Kumar R, Mony P, Dhingra N, et al. Prospective Study of One Million Deaths in India: Rationale, Design, and Validation Results. PLoS Med. 2005;3, e18.\nSankoh O, Byass P. Cause-specific mortality at INDEPTH Health and Demographic Surveillance System Sites in Africa and Asia: concluding synthesis. Glob Health Action. 2014;7.\nLopez AD, Setel PW. Better health intelligence: a new era for civil registration and vital statistics? BMC Med. 2015;13:73.\nMurray CJ, Lozano R, Flaxman AD, Serina P, Phillips D, Stewart A, et al. Using verbal autopsy to measure causes of death: the comparative performance of existing methods. BMC Med. 2014;12:5.\nMurray CJ, Lozano R, Flaxman AD, Vahdatpour A, Lopez AD. Robust metrics for assessing the performance of different verbal autopsy cause assignment methods in validation studies. Popul Health Metr. 2011;9:28.\nBoulle A, Chandramohan D, Weller P. A case study of using artificial neural networks for classifying cause of death from verbal autopsy. Int J Epidemiol. 2001;30:515–20.\nReeves BC, Quigley M. A review of data-derived methods for assigning causes of death from verbal autopsy data. Int J Epidemiol. 1997;26:1080–9.\nFlaxman AD, Vahdatpour A, Green S, James SL, Murray CJ. Random forests for verbal autopsy analysis: multisite validation study using clinical diagnostic gold standards. Popul Health Metr. 2011;9:29.\nMurray CJ, Lopez AD, Black R, Ahuja R, Ali SM, Baqui A, et al. Population Health Metrics Research Consortium gold standard verbal autopsy validation study: design, implementation, and development of analysis datasets. Popul Health Metr. 2011;9:27.\nLozano R, Lopez AD, Atkinson C, Naghavi M, Flaxman AD, Murray CJ, et al. Performance of physician-certified verbal autopsies: multisite validation study using clinical diagnostic gold standards. Popul Health Metr. 2011;9:32.\nJames SL, Flaxman AD, Murray CJ. Performance of the Tariff Method: validation of a simple additive algorithm for analysis of verbal autopsies. Popul Health Metr. 2011;9:31.\nMurray CJ, James SL, Birnbaum JK, Freeman MK, Lozano R, Lopez AD, et al. Simplified Symptom Pattern Method for verbal autopsy analysis: multisite validation study using clinical diagnostic gold standards. Popul Health Metr. 2011;9:30.\nFlaxman AD, Vahdatpour A, James SL, Birnbaum JK, Murray CJ. Direct estimation of cause-specific mortality fractions from verbal autopsies: multisite validation study using clinical diagnostic gold standards. Popul Health Metr. 2011;9:35.\nLozano R, Freeman MK, James SL, Campbell B, Lopez AD, Flaxman AD, et al. Performance of InterVA for assigning causes of death to verbal autopsies: multisite validation study using clinical diagnostic gold standards. Popul Health Metr. 2011;9:50.\nDesai N, Aleksandrowicz L, Miasnikof P, Lu Y, Leitao J, Byass P, et al. Performance of four computer-coded verbal autopsy methods for cause of death assignment compared with physician coding on 24,000 deaths in low- and middle-income countries. BMC Med. 2014;12:20.\nKotz S, Balakrishnan N, Johnson NL. Continuous Multivariate Distributions, Volume 1, Models and Applications. Wiley; 2000.\nDorn HF, Moriyama IM. Uses and Significance of Multiple Cause Tabulations for Mortality Statistics. Am J Public Health Nations Health. 1964;54:400–6.\nDésesquelles AF, Salvatore MA, Pappagallo M, Frova L, Pace M, Meslé F, et al. Analysing Multiple Causes of Death: Which Methods For Which Data? An Application to the Cancer-Related Mortality in France and Italy. 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J Occup Med 1986, 28: 425-433. 10.1097\u002F00043764-198606000-00009\nKang HK, Bullman TA: Mortality among U.S. veterans of the Persian Gulf War. N Engl J Med 1996, 335: 1498-1504. 10.1056\u002FNEJM199611143352006\nGuest CS, Venn AJ: Mortality of former prisoners of war and other Australian veterans. Med J Aust 1992, 157: 132-135.\nBross ID, Bross NS: Do Atomic Veterans have excess cancer? New results correcting for the healthy soldier bias. Am J Epidemiol 1987, 126: 1042-1050.\nMcLaughlin R, Nielsen L, Waller M: An evaluation of the effect of military service on mortality: quantifying the healthy soldier effect. Ann Epidemiol 2008, 18: 928-936. 10.1016\u002Fj.annepidem.2008.09.002\nMcMichael AJ, Haynes SG, Tyroler HA: Observations on the Evaluation of Occupational Mortality Data. J Occup Med 1975, 17: 128-131. 10.1097\u002F00043764-197502000-00019\nGroves FD, Page WF, Gridley G, Lisimaque L, Stewart PA, Tarone RE, Gail MH, Boice JD, Beebe GW: Cancer in Korean war navy technicians: mortality survey after 40 years. Am J Epidemiol 2002, 155: 810-818. 10.1093\u002Faje\u002F155.9.810\nMacIntyre NR, Mitchell RE, Oberman A, Harlan WR, Graybiel A, Johnson E: Longevity in military pilots: 37-year followup of the Navy's \"1000 aviators\". Aviat Space Environ Med 1978, 49: 1120-1122.\nFett MJ, Dunn M, Adena MA: The mortality report Part I: A retrospective cohort study of mortality among Australian National Servicemen of the Vietnam Conflict era, and an executive summary of the mortality report. Canberra: AGPS. 1984.\nHarrex WK, Horsley KW, Jelfs P, van der Hoek R, Wilson EJ: Mortality of Korean War veterans: the veteran cohort study. A report of the 2002 retrospective cohort study of Australian veterans of the Korean War. Canberra: Department of Veterans' Affairs 2003.\nWilson EJ, Horsley KW, van der Hoek R: Australian Vietnam Veterans Mortality Study 2005. Canberra: Department of Veterans' Affairs 2005.\nWilson EJ, Horsley KW, van der Hoek R: Australian National Service Vietnam Veterans Mortality and Cancer Incidence Study 2005. Canberra: Department of Veterans' Affairs 2005.\nAustralian Institute of Health and Welfare (AIHW) 2008. GRIM (General Record of Incidence of Mortality) Books AIHW: Canberra\nStataCorp. 2007. Stata Statistical Software: Release 10 College Station, TX: StataCorp LP\nRothberg JM, Bartone PT, Holloway HC, Marlowe DH: Life and death in the US Army. In Corpore sano. JAMA 1990, 264: 2241-2244. 10.1001\u002Fjama.264.17.2241\nTsai SP, Wen CP: A review of methodological issues of the standardized mortality ratio (SMR) in occupational cohort studies. Int J Epidemiol 1986, 15: 8-21. 10.1093\u002Fije\u002F15.1.8\nAustralian Bureau of Statistics, 4102.0 - Australian Social Trends. 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NEJM 2007, 356: 2388-2398. 10.1056\u002FNEJMsa053935",{"doi":1471},"10.1056\u002FNEJMsa053935",{"id":1473,"createTime":1474,"updateTime":1475,"relativeEntities":1476,"slug":1477,"properties":1478,"entityType":130,"verifyStatus":131,"verifyTime":1487,"verifyNote":133,"languages":23,"translateLanguages":23,"viewCount":91,"primaryUrl":1488,"fullTextUrl":23,"authors":1489,"publicationType":279,"publisherRelationship":1535,"citationCount":103,"citationInfo":1586,"publishDate":1588,"publishYear":952,"citationAnalyzeStatus":22,"lastCitationAnalyze":1589,"indexDatabases":1590,"openAccess":23,"references":1591,"isForceReanalyzing":566},"c27cb4c7-e4d8-406c-8011-fa367fd48baf","2023-12-10T02:53:14.181+00:00","2025-07-24T00:39:05.973+00:00",[],"Developing-the-design-of-a-continuous-national-health-survey-for-New-Zealand",{"abstract":1479,"title":1481,"gsPaper":1483,"doi":1485},{"EN":1480},"A continuously operating survey can yield advantages in survey management, field operations, and the provision of timely information for policymakers and researchers. We describe the key features of the sample design of the New Zealand (NZ) Health Survey, which has been conducted on a continuous basis since mid-2011, and compare to a number of other national population health surveys. A number of strategies to improve the NZ Health Survey are described: implementation of a targeted dual-frame sample design for better Māori, Pacific, and Asian statistics; movement from periodic to continuous operation; use of core questions with rotating topic modules to improve flexibility in survey content; and opportunities for ongoing improvements and efficiencies, including linkage to administrative datasets. The use of disproportionate area sampling and a dual frame design resulted in reductions of approximately 19%, 26%, and 4% to variances of Māori, Pacific and Asian statistics respectively, but at the cost of a 17% increase to all-ethnicity variances. These were broadly in line with the survey’s priorities. Respondents provided a high degree of cooperation in the first year, with an adult response rate of 79% and consent rates for data linkage above 90%. A combination of strategies tailored to local conditions gives the best results for national health surveys. In the NZ context, data from the NZ Census of Population and Dwellings and the Electoral Roll can be used to improve the sample design. A continuously operating survey provides both administrative and statistical advantages.",{"EN":1482},"Developing the design of a continuous national health survey for New 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RG, Templeton R To appear as chapter 22 of Hard to Survey population s. In Sampling the Māori population using proxy screening, the electoral roll and disproportionate sampling in the New Zealand Health Survey. Cambridge University Press; 2014. Pre-final version of chapter available as Centre for Statistical and Survey Methodology Working Paper 10-12. http:\u002F\u002Fro.uow.edu.au\u002Fcssmwp\u002F99\u002F","http:\u002F\u002Fro.uow.edu.au\u002Fcssmwp\u002F99\u002F",{},{"id":23,"text":1597,"url":1598,"identifiers":1599},"Ministry of Health (MOH): The New Zealand Health Survey: objectives and topic areas. 2010. http:\u002F\u002Fwww.health.govt.nz\u002Fpublication\u002Fnew-zealand-health-survey-objectives-and-topic-areas-August-2010","http:\u002F\u002Fwww.health.govt.nz\u002Fpublication\u002Fnew-zealand-health-survey-objectives-and-topic-areas",{},{"id":23,"text":1601,"url":1602,"identifiers":1603},"Ministry of Health (MOH): The New Zealand Health Survey: sample design years1–3(2011-2013). Wellington: Ministry of Health; 2011. http:\u002F\u002Fwww.health.govt.nz\u002Fpublication\u002Fnew-zealand-health-survey-sample-design-years-1-3-2011-2013","http:\u002F\u002Fwww.health.govt.nz\u002Fpublication\u002Fnew-zealand-health-survey-sample-design-years-1-3-2011-2013",{},{"id":23,"text":1605,"url":23,"identifiers":1606},"Mohadjer L, Curtin LR: Balancing sample design goals for the national health and nutrition survey. Survey Methodol 2008,34(1):119-126.",{},{"id":23,"text":1608,"url":1609,"identifiers":1610},"Katki HA, Sanders CL, Graubard BI, Bergen W: Using DNA fingerprints to infer familial relationships within NHANES III households. J Am Stat Assoc 2010,105(490):552-563. 10.1198\u002Fjasa.2010.ap09258","https:\u002F\u002Fdoi.org\u002F10.1198\u002Fjasa.2010.ap09258",{"mag":1611,"pmc":1612,"openalex":1613,"pm":1614,"doi":1615},"2020460924","2909633","W2020460924","20664713","10.1198\u002Fjasa.2010.ap09258",{"id":23,"text":1617,"url":1618,"identifiers":1619},"Health Survey for England (HSE): Health Survey for England 2010 volume 2: methods and documentation. 2010. http:\u002F\u002Fwww.ic.nhs.uk\u002Fpubs\u002Fhse10report","http:\u002F\u002Fwww.ic.nhs.uk\u002Fpubs\u002Fhse10report",{},{"id":23,"text":1621,"url":1622,"identifiers":1623},"Australian Bureau of Statistics (ABS): Australian Health Survey: users’ guide, 2011-2013. 2012. http:\u002F\u002Fwww.abs.gov.au\u002FAUSSTATS\u002Fabs@.nsf\u002Fmf\u002F4363.0.55.001","http:\u002F\u002Fwww.abs.gov.au\u002FAUSSTATS\u002Fabs@.nsf\u002Fmf\u002F4363.0.55.001",{},{"id":23,"text":1625,"url":1626,"identifiers":1627},"Béland Y, Dale V, Dufour J, Hamel M Proceedings of the American Statistical Association joint statistical meeting, section on survey research methods, August, 2005. In The Canadian Community Health Survey: building on the success from the past. American Statistical Association; 2005. https:\u002F\u002Fwww.amstat.org\u002Fsections\u002Fsrms\u002FProceedings\u002Fy2005\u002FFiles\u002FJSM2005-000494.pdf","https:\u002F\u002Fwww.amstat.org\u002Fsections\u002Fsrms\u002FProceedings\u002Fy2005\u002FFiles\u002FJSM2005-000494.pdf",{},{"id":340,"text":1629,"url":342,"identifiers":1630},"Bankier MD: Power allocations: determining sample sizes for subnational areas. Am Stat 1988,42(3):174-177.",{"doi":344},{"id":23,"text":1632,"url":1633,"identifiers":1634},"Kalton G, Anderson DW: Sampling rare populations. J Royal Stat Soc Series A 1986,149(1):65-82. 10.2307\u002F2981886","https:\u002F\u002Fdoi.org\u002F10.2307\u002F2981886",{"mag":1635,"openalex":1636,"doi":1637},"2461825338","W2461825338","10.2307\u002F2981886",{"id":23,"text":1639,"url":1640,"identifiers":1641},"Clark RG: Sampling of subpopulations in two-stage surveys. Stat Med 2009,28(28):3697-3717. 10.1002\u002Fsim.3723","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fsim.3723",{"mag":1642,"openalex":1643,"pm":1644,"doi":1645},"2102318905","W2102318905","19739232","10.1002\u002Fsim.3723",{"id":23,"text":1647,"url":1648,"identifiers":1649},"Clark RG: Sample design using imperfect design data. J Surv Stat Methodol 2013,1(1):6-12. 10.1093\u002Fjssam\u002Fsmt002 10.1093\u002Fjssam\u002Fsmt002","https:\u002F\u002Fdoi.org\u002F10.1093\u002Fjssam\u002Fsmt002",{"mag":1650,"openalex":1651,"doi":1652},"1970100300","W1970100300","10.1093\u002Fjssam\u002Fsmt002",{"id":23,"text":1654,"url":23,"identifiers":1655},"Lohr SL: Sampling: Design and Analysis. Boston: Duxbury Press; 1999.",{},{"id":23,"text":1657,"url":1658,"identifiers":1659},"Reeder AI, Waa A, Scragg R: New Zealand Youth Tobacco Survey: youth smoking surveillance: the report of the scientific advisory committee, New Zealand ministry of health. 2000. Available from http:\u002F\u002Fdnmeds.otago.ac.nz\u002Fdepartments\u002Fpsm\u002Fresearch\u002Fsbru\u002Fpdf\u002Ftobacco_dec00.pdf","http:\u002F\u002Fdnmeds.otago.ac.nz\u002Fdepartments\u002Fpsm\u002Fresearch\u002Fsbru\u002Fpdf\u002Ftobacco_dec00.pdf",{},{"id":23,"text":1661,"url":1662,"identifiers":1663},"Gray A Proceedings of the international association for official statistics meeting. In Strategies for New Zealand household surveys which oversample Māori and Pasifika. Wellington New Zealand; 2005. 2005; paper 16.2. Available from: http:\u002F\u002Fisi.cbs.nl\u002Fiaos\u002F 2005; paper 16.2. Available from:","http:\u002F\u002Fisi.cbs.nl\u002Fiaos\u002F",{},{"id":23,"text":1665,"url":23,"identifiers":1666},"Box GEP, Jenkins GM: Time Series Analysis: Forecasting and Control. New Jersey: Prentice-Hall; 1976.",{},{"id":23,"text":1668,"url":23,"identifiers":1669},"Steel DG, McLaren C: Chapter 33: design and analysis of surveys repeated over time. In Handbook of Statistics. Volume 29, Part B. Edited by: Rao CR. Amsterdam: Elsevier; 2009:289-313.",{},{"id":23,"text":1671,"url":1672,"identifiers":1673},"The American Association for Public Opinion Research (AAPOR): Standard definitions: final dispositions of case codes and outcome rates for surveys. 7th edition. 2011. http:\u002F\u002Fwww.aapor.org\u002FResources.htm","http:\u002F\u002Fwww.aapor.org\u002FResources.htm",{},{"id":23,"text":1675,"url":1676,"identifiers":1677},"Ministry of Health (MOH): New Zealand health survey methodology report. Wellington: Ministry of Health; 2012. Available from http:\u002F\u002Fwww.health.govt.nz\u002Fpublication\u002Fnew-zealand-health-survey-methodology-report","http:\u002F\u002Fwww.health.govt.nz\u002Fpublication\u002Fnew-zealand-health-survey-methodology-report",{},{"id":23,"text":1679,"url":23,"identifiers":1680},"Kish L: Survey Sampling. New York: Wiley; 1965.",{},{"id":23,"text":1682,"url":1683,"identifiers":1684},"United Nations Statistical Division (UNSD) Studies in methods no. 98, series F. Department of economic and social affairs, statistics division, united nations. Designing household survey samples: practical guidelines 2005. Available from http:\u002F\u002Funstats.un.org\u002Funsd\u002Fdemographic\u002Fsources\u002Fsurveys\u002FHandbook23June05.pdf","http:\u002F\u002Funstats.un.org\u002Funsd\u002Fdemographic\u002Fsources\u002Fsurveys\u002FHandbook23June05.pdf",{},{"id":23,"text":1686,"url":23,"identifiers":1687},"Kish L: Weighting in Deft2. The Survey Statistician; 1987. June",{},{"id":1689,"createTime":1690,"updateTime":1691,"relativeEntities":1692,"slug":1693,"properties":1694,"entityType":130,"verifyStatus":131,"verifyTime":1691,"verifyNote":133,"languages":23,"translateLanguages":23,"viewCount":91,"primaryUrl":1703,"fullTextUrl":23,"authors":1704,"publicationType":279,"publisherRelationship":1872,"citationCount":23,"citationInfo":23,"publishDate":1925,"publishYear":1926,"citationAnalyzeStatus":22,"lastCitationAnalyze":23,"indexDatabases":1927,"openAccess":23,"references":23,"isForceReanalyzing":566},"bcc6cfd7-ae45-4f5d-b53c-291dcb0169c8","2023-12-06T05:00:51.775+00:00","2025-02-26T21:37:56.502+00:00",[],"Monitoring-the-progress-of-health-related-sustainable-development-goals-SDGs-in-Brazilian-states-using-the-Global-Burden-of-Disease-indicators",{"abstract":1695,"title":1697,"references":1699,"doi":1701},{"EN":1696},"Measuring the Global Burden of Disease (GBD) has been the key to verifying the evolution of health indicators worldwide. We analyse subnational GBD data for Brazil in order to monitor the performance of the Brazilian states in the last 28 years on their progress towards meeting the health-related SDGs. As part of the GBD study, we assessed the 41 health-related indicators from the SDGs in Brazil at the subnational level for all the 26 Brazilian states and the Federal District from 1990 to 2017. The GBD group has rescaled all worldwide indicators from 0 to 100, assuming that for each one of them, the worst value among all countries and overtime is 0, and the best is 100. They also estimate the overall health-related SDG index as a function of all previously estimated health indicators and the SDI index (Socio-Demographic Index) as a function of per capita income, average schooling in the population aged 15 years or over, and total fertility rate under the age of 25 (TFU25). From 1990 to 2017, most subnational health-related SDGs, the SDG and SDI indexes improved considerable in most Brazilian states. The observed differences in SDG indicators within Brazilian states, including HIV incidence and health worker density, increased over time. In 2017, health-related indicators that achieved good results globally included the prevalence of child wasting, NTD, household air pollution, conflict mortality, skilled birth attendance, use of modern contraceptive methods, vaccine coverage, and health worker density, but poor results were observed for child overweight and homicide rates. The high rates of overweight, alcohol consumption, and smoking prevalence found in the historically richest regions (i.e., the South and Southeast), contrast with the high rates of tuberculosis, maternal, neonatal, and under-5 mortality and WASH-related mortality found in the poorer regions (i.e., the North and Northeast). The majority of Brazil’s health-related SDG indicators have substantially improved over the past 28 years. However, inequalities in health among the Brazilian states and regions remain noticeable negatively affecting the Brazilian population, which can contribute to Brazil not achieving the SDG 2030 targets.",{"EN":1698},"Monitoring the progress of health-related sustainable development goals (SDGs) in Brazilian states using the Global Burden of Disease indicators",{"VOID":1700},"Hosseinpoor AR, Bergen N, Magar V. Monitoring inequality: an emerging priority for health post-2015: SciELO Public Health; 2015.\nWHO. Health in 2015: from MDGs, millennium development goals to SDGs, sustainable development goals. Geneva: World Health Organization; 2015.\nUNESCO. La UNESCO avanza la Agenda 2030 para el Desarrollo Sostenible. Paris: Unesco; 2017.\nWHO. Handbook on health inequality monitoring: with a special focus on low-and middle-income countries: World Health Organization; 2013.\nWorld Bank. GINI index: The World Bank; 2017. Available from: http:\u002F\u002Fdata.worldbank.org\u002Findicator\u002FSI.POV.GINI\u002Fcountries\u002F%201W?display. .\nWorld Bank. Gross domestic product (GDP) ranking table based on purchasing power parity (PPP): The World Bank; 2017. Available from: https:\u002F\u002Fdatacatalog.worldbank.org\u002Fdataset\u002Fgdp-ranking-ppp-based. .\nAzzoni CR, Haddad EA. Regional Disparities. In: Edmund Amann E, Azzoni C, Baer W, (Org.), editors. Oxford Handbook of the Brazilian Economy. 1. England: Oxford University Press; 2018. p. 422-445.\nMalta DC, Felisbino-Mendes MS, Machado ÍE, Passos VMA, Abreu DMX, Ishitani LH, et al. Fatores de risco relacionados à carga global de doença do Brasil e Unidades Federadas, 2015. Revista Brasileira de Epidemiologia. 2017;20:217–32.\nMarinho F, de Azeredo Passos VM, Malta DC, França EB, Abreu DM, Araújo VE, et al. Burden of disease in Brazil, 1990-2016: a systematic subnational analysis for the Global Burden of Disease Study 2016. 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Revista Brasileira de Epidemiologia. 2016;19(3):484–93.\nChongsuvivatwong V, Bachtiar H, Chowdhury ME, Fernando S, Suwanrath C, Kor-anantakul O, et al. Maternal and fetal mortality and complications associated with cesarean section deliveries in teaching hospitals in Asia. Journal of Obstetrics and Gynaecology Research. 2010;36(1):45–51.\nLiu S, Liston RM, Joseph K, Heaman M, Sauve R, Kramer MS. Maternal mortality and severe morbidity associated with low-risk planned cesarean delivery versus planned vaginal delivery at term. Cmaj. 2007;176(4):455–60.\nVillar J, Carroli G, Zavaleta N, Donner A, Wojdyla D, Faundes A, et al. Maternal and neonatal individual risks and benefits associated with caesarean delivery: multicentre prospective study. Bmj. 2007;335(7628):1025.\nVillar J, Valladares E, Wojdyla D, Zavaleta N, Carroli G, Velazco A, et al. Caesarean delivery rates and pregnancy outcomes: the 2005 WHO global survey on maternal and perinatal health in Latin America. 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Effect of city-wide sanitation programme on reduction in rate of childhood diarrhoea in northeast Brazil: assessment by two cohort studies. The Lancet. 2007;370(9599):1622–8.\nMurray J, de Castro Cerqueira DR, Kahn T. Crime and violence in Brazil: systematic review of time trends, prevalence rates and risk factors. Aggression and violent behavior. 2013;18(5):471–83.\nHsieh C-C, Pugh MD. Poverty, income inequality, and violent crime: a meta-analysis of recent aggregate data studies. Criminal justice review. 1993;18(2):182–202.\nNaghavi M, Marczak LB, Kutz M, Shackelford KA, Arora M, Miller-Petrie M, et al. Global mortality from firearms, 1990-2016. Jama. 2018;320(8):792–814.\nGawryszewski VP, Costa LS. Social inequality and homicide rates in Sao Paulo City, Brazil. Revista de saude publica. 2005;39(2):191–7.\nAraújo EM, MdCN C, Oliveira NF, FdS S, Barreto ML. Hogan V, et al. Spatial distribution of mortality by homicide and social inequalities according to race\u002Fskin color in an intra-urban Brazilian space. Revista Brasileira de Epidemiologia. 2010;13:549–60.\nPereira DV, Mota CM, Andresen MA. Social disorganization and homicide in Recife, Brazil. International journal of offender therapy and comparative criminology. 2017;61(14):1570–92.\nMachado DB, Rodrigues LC, Rasella D, Barreto ML, Araya R. Conditional cash transfer programme: Impact on homicide rates and hospitalisations from violence in Brazil. PloS one. 2018;13(12):e0208925.\nSchmidt MI, Duncan BB. e Silva GA, Menezes AM, Monteiro CA, Barreto SM, et al. Chronic non-communicable diseases in Brazil: burden and current challenges. The Lancet. 2011;377(9781):1949–61.\nRasella D, Harhay MO, Pamponet ML, Aquino R, Barreto ML. Impact of primary health care on mortality from heart and cerebrovascular diseases in Brazil: a nationwide analysis of longitudinal data. Bmj. 2014;349:g4014.\nMalta DC, Silva MMAd. As doenças e agravos não transmissíveis, o desafio contemporâneo na Saúde Pública. SciELO Public Health; 2018.\nBrasil. Boletim Epidemiológico - Hepatites virais. Brasilia: Secretaria de Vigilância em Saúde − Ministério da Saúde; 2018.\nBrasil. Casos de malária por UF de notificação de 2015 a 2019* \u002F Malaria cases by State of registry: 2015-2019* Brasilia: SIVEP-MALÁRIA\u002FSVS - Ministério da Saúde. 2019; Available from: https:\u002F\u002Fpublic.tableau.com\u002Fprofile\u002Fmal.ria.brasil#!\u002Fvizhome\u002FMiniSivep1519_2019_Cidado\u002FOrientaes. Accessed 29 Apr 2019.\nSouto FJD. Distribution of hepatitis B infection in Brazil: the epidemiological situation at the beginning of the 21 st century. Revista da Sociedade Brasileira de Medicina Tropical. 2016;49(1):11–23.\nRasella D, Basu S, Hone T, Paes-Sousa R, Ocké-Reis CO, Millett C. Child morbidity and mortality associated with alternative policy responses to the economic crisis in Brazil: a nationwide microsimulation study. PLos Med. 2018;15(5):e1002570.\nMassuda A, Hone T, Leles FAG, de Castro MC, Atun R. The Brazilian health system at crossroads: progress, crisis and resilience. BMJ global health. 2018;3(4):e000829.\nAndrade RG, Pereira RA, Sichieri R. Food intake in overweight and normal-weight adolescents in the city of Rio de Janeiro. Cadernos de saude publica. 2003;19(5):1485–95.\nMonteiro CA, Benicio MHDA, Conde WL, Konno S, Lovadino AL, Barros AJ, et al. Narrowing socioeconomic inequality in child stunting: the Brazilian experience, 1974-2007. Bulletin of the World Health Organization. 2010;88:305–11.\nSichieri R, Allam V. Assessment of the nutritional status of Brazilian adolescents by body mass index. Jornal de pediatria. 1996;72(2):80–4.\nVictora CG, Aquino EM. do Carmo Leal M, Monteiro CA, Barros FC, Szwarcwald CL. Maternal and child health in Brazil: progress and challenges. 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In practice most interventions to curb early-life mortality target births based on a single risk factor, such as poverty. However, most premature deaths are not from the targeted group. Thus interventions target many births that are at not at high risk and miss many births at high risk. Using data from the second wave of Demographic and Health Surveys from India and a hierarchical Bayesian model, we estimate infant mortality risk for 73.320 infants in India as a function of 4 risk factors. We show how this information can be used to improve program targeting. We compare our novel approach against common programs that target groups based on a single risk factor. A conventional approach that targets mothers in the lowest quintile of income correctly identifies only 30% of infant deaths. By contrast, using four risk factors simultaneously we identify a group of births of the same size that includes 57% of all deaths. Using the 2012 census to translate these percentages into numbers, there were 25.642.200 births in 2012 and 4.4% died before the age of one. Our approach correctly identifies 643.106 of 1.128.257 infant deaths while poverty only identifies 338.477 infant deaths. Our approach considerably improves program targeting by identifying more infant deaths than the usual approach that targets births based on a single risk factor. This leads to more efficient program targeting. This is particularly useful in developing countries, where resources are lacking and needs are high.",{"EN":1938},"Improving program targeting to combat early-life mortality by identifying high-risk births: an application to India",{"VOID":1940},"Moser A, Leon D, Gwatkin D. How does progress towards the child mortality millennium development goal affect inequalities between the poorest and least poor? Analysis of Demographic and Health Survey data. BMJ: Br Med J. 2005;331:1180–3.\nStuckler D, Basu S, Mckee M. Drivers of inequality in Millennium Development Goal progress: a statistical analysis. PLoS Medicine. 2010;7(3):e1000241.\nMolyneux M, Molyneux E. Reaching Millennium Development Goal 4. The Lancet Global Health. 2016;4:146–7.\nVictora C, Requejo JH, Barros AJ, Berman P, Bhutta Z, Boema T, et al. Countdown to 2015: a decade of tracking progress for maternal, newborn, and child survival. The Lancet. 2016;387(10032):2049–59.\nNIMS, ICMR and UNICEF. Infant and Child Mortality in India: Levels, Trends and Determinants. New Delhi: National Institute of Medical Statistics (NIMS), Indian Council of Medical Research (ICMR), and UNICEF India Country Office; 2012.\nHouweling TA, Kunst AE. Socio-economic inequalities in childhood mortality in low- and middle-income countries: a review of the international evidence. Br Med Bull. 2010;93:7–26.\nGwatkin D. How much would the poor gain from faster progress towards the Millennium Development Goals for health? The Lancet. 2005;365:813–7.\nVictora C, Wagstaff A, Schellenberg J, Gwatkin D, Claeson M, Habicht J. Applying an equity lens to child health and mortality: more of the same is not enough. The Lancet. 2003;362(9379):233–41.\nSastry N. Trends in socioeconomic inequalities in mortality in developing countries: the case of child survival in Sao Paulo, Brazil. Demography. 2004;41(3):443–64.\nWagstaff A. Socioeconomic inequalities in child mortality: comparisons across nine developing countries. Bulletin of The World Health Organization. 2000;78(1):19–29.\nBrockerhoff M, Hewett P. Inequality of child mortality among ethnic groups in sub-Saharan Africa. Bulletin of The World Health Organization. 2000;78(1):30–41.\nAntai D. Regional inequalities in under-5 mortality in Nigeria: a population-based analysis of individual- and community-level determinants. Population Health Metrics. 2011;9(1):1–10.\nJankowska MM, Benza M, Weeks JR. Estimating spatial inequalities of urban child mortality. Demographic Research. 2013;28(2):33–62.\nGwatkin D, Bhuiya A, Victora C. Making Health Systems more equitable. The Lancet. 2004 October;364:1273–80.\nBlack R, Morris S, Bryce J, Venis S. Where and why are 10 mil- lion children dying every year? Commentary. The Lancet. 2003;361:2226–34.\nBraveman P, Starfield B, Geiger J. World Health Report 2000: how it removes equity from the agenda for public health monitoring and policy. Br Med J. 2001;323:678–81.\nBryce J, el Arifeen S, Pariyo G, Lanata C, Gwatkin D, Habicht J. Reducing child mortality: can public health deliver? The Lancet. 2003;362(9378):159–64.\nJones G, Steketee R, Black R, Bhutta Z, Morris S. How many child deaths can we prevent this year? The Lancet. 2003;362:65–71.\nGlassman A, Duran D, Fleisher L, Singer D, Sturke R, Angeles G, Charles J, et al. Impact of Conditional Cash Transfers on Maternal and Newborn Health. J Health, Popul Nutr. 2013;31.4(Suppl 2):S48–66.\nBasset L. Can Conditional Cash Transfer Programs Play a Greater Role in Reducing Child Undernutrition? World Bank; 2008.\nAkresh R, de Walque D, Kazianga H. Alternative Cash Transfer Delivery Mechanism: Impacts on Routine Preventive Health Clinic Visits in Burkina Faso. Natl Bur Econ Res. 2015;17785\nBanerjee A, Duflo E, Glennerster R, Kothari D. Improving immunization coverage in rural India: clustered randomized controlled evaluation of immunization campaigns with and without incentives. Br Med J. 2010;340:c2220.\nHuicho L, Segura ER, Huayanay-Espinoza CA, Guzman JN, Restrepo-Mendez MC, Tam Y, et al. Child health and nutrition in Peru within an antipoverty political agenda: a Countdown to 2015 country case study. The Lancet Global Health. 2016;4(6):e414–26.\nGakidou E, Oza S, Fuertes CV, Li AY, Lee DK, Sousa A, et al. Improving Child Survival Through Environmental and Nutritional Interventions The Importance of Targeting Interventions Toward the Poor. J Am Med Assoc. 2007;298(16):1876–87.\nGakidou E, King G. Measuring total health inequality: adding individual variation to group-level differences. Int J Equity in Health. 2002;1(1):3–3.\nGakidou E, King G. Determinants of inequality of child survival: results from 39 countries. In: Murray CJL, editor. Health Systems Performance Assessment: Debates, Methods and Empiricism. World Health Organiza- tion; 2003. p. 194–216.\nWorld Health Organization. Health systems: improving performance. World Health Organization; 2000.\nPandey A, Choe MK, Luther NY, Chand J. National Family Health Survey Subject Report No 11. International Institute for Population Sciences, Mumbai, India East-West Center Program on Population, Honolulu, Hawaii, USA; 1998.\nPandey A, Bhattacharya BN, Sahu D, Sultana R. Are too early, too quickly and too many births the high risk births: An analysis of infant mortality in India using National Family Health Survey. Demography India. 2004;33(2):127–56.\nR Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria\nHadfield JD. MCMC Methods for Multi-Response Generalized Linear Mixed Models: The MCMCglmm R Package. J Stat Softw. 2010;33(2):1–22.\nMarmot M. Health in an unequal world. The Lancet. 2006;368:2081–94.\nHastie T, Tibshirani R, Friedman J. Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. Springer; 2009.\nGelman A, Carlin J, Stern HS, Dubson DB, Vehtari A, Rubin D. Bayesian Data Analysis. 3rd ed. New York: Chapman & Hall\u002FCRC Press; 2013.\nWHO, UNICEF, UNFPA ,The World Bank, and United Nations Population Division. Trends in maternal mortality 1990-2013. Geneva: WHO; 2014.\nBustreo F, Say L, Koblinsky M, Pullum T, Temmerman M. Ending preventable maternal deaths: the time is now. The Lancet Global Health. 2013;1:176–7.\nMurray C. Commentary: comprehensive approaches are needed for full understanding. Br Med J. 2001;323(7314):680–1.\nBlack R, Cousens S, Johnson H, Lawn J, Rudan I, Bassani D, et al. Global, regional, and national causes of child mortality in 2008: a systematic analysis. 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