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@Article{alho1992,
  author =    {Juha M. Alho},
  title =     {On Prevalence, Incidence, and Duration in General Stable Populations},
  journal =   {Biometrics},
  year =      {1992},
  volume =    {48},
  number =    {2},
  pages =     {587-592},
  abstract =  {The relationship between prevalence, incidence, and duration of disease is studied in exponentially growing/declining stable populations. Prevalence odds is shown to be a weighted average of age-specific products between incidence and discounted disease duration. If and only if the covariance between incidence and duration is zero, does prevalence odds equal the product of average incidence and average duration. The product of averages is shown typically to overestimate prevalence in epidemiologic applications. Ignoring population growth also tends to lead to overestimation of prevalence.},
  issn =      {0006341X, 15410420},
  publisher = {[Wiley, International Biometric Society]},
  url =       {http://www.jstor.org/stable/2532312}
}

@Article{berthelot07,
  author =    {Philippe Berthelot, Maud Garnier, Pascal Fascia, Stéphane Guyomarch, Richard Jospé, Frédéric Lucht, Fabrice Zéni, Christian Auboyer, Bruno Pozzetto},
  title =     {Conversion of Prevalence Survey Data on Nosocomial Infections to Incidence Estimates: A Simplified Tool for Surveillance?},
  journal =   {Infection Control and Hospital Epidemiology},
  year =      {2007},
  volume =    {28},
  number =    {5},
  pages =     {633-636},
  abstract =  {We calculated the incidence of nosocomial infection in 2 intensive care units (ICUs) on the basis of prevalence data recorded from 1997 through 2002 and compared these estimates to cumulative incidences measured in the 2 ICUs during the same period to investigate the feasibility and the reliability of converting prevalence data to incidence estimates. Decreases in the calculated and measured incidences over time in the ICUs were found to be statistically significantly related.},
  issn =      {0899823X, 15596834},
  publisher = {[Cambridge University Press, Society for Healthcare Epidemiology of America]},
  url =       {http://www.jstor.org/stable/10.1086/513536}
}

@Article{bonferroniholm,
  author =    {Sture Holm},
  title =     {A Simple Sequentially Rejective Multiple Test Procedure},
  journal =   {Scandinavian Journal of Statistics},
  year =      {1979},
  volume =    {6},
  number =    {2},
  pages =     {65-70},
  abstract =  {This paper presents a simple and widely applicable multiple test procedure of the sequentially rejective type, i.e. hypotheses are rejected one at a time until no further rejections can be done. It is shown that the test has a prescribed level of significance protection against error of the first kind for any combination of true hypotheses. The power properties of the test and a number of possible applications are also discussed.},
  issn =      {03036898, 14679469},
  publisher = {[Board of the Foundation of the Scandinavian Journal of Statistics, Wiley]},
  url =       {http://www.jstor.org/stable/4615733}
}

@Article{freeman1980,
  author =   {Freeman, Jonathan and Hutchison, George B.},
  title =    {Prevalence, Incidence and duration},
  journal =  {American Journal of Epidemiology},
  year =     {1980},
  volume =   {112},
  number =   {5},
  pages =    {707-723},
  abstract = {Prevalence, incidence and duration of a condition or illness in a steady-state population are interrelated in such a way that two of these quantities may be used to obtain the third. Data may be collected in the most expedient manner, either as prevalence or incidence series of cases, and the results expressed as both incidence and prevalence. Information about the distribution of total durations of the condition in an incidence series of cases, or about the distribution of total durations or durations-to-date in a prevalence series is necessary in order to use these relations between prevalence and incidence of a condition or illness. The duration of a condition may be taken as one measure of the effect of the condition or illness on a population, and a “treatment effect” measured by comparing two populations may be termed the etiologic duration. Exact methods are presented for interconverting the distribution of total durations of condition among prevalence and incidence series of cases arising from the same steady-state population. Both prevalence and incidence series of cases may naturally arise in the same epidemiologic study, such as the initiation and conduct of a periodic screening program, and under certain conditions the size and even the direction of the etiologic duration may differ as measured in corresponding prevalence and incidence series of cases.},
  eprint =   {http://aje.oxfordjournals.org/content/112/5/707.full.pdf+html},
  url =      {http://aje.oxfordjournals.org/content/112/5/707.abstract}
}

@Article{freemanhalton,
  author =    {G. H. Freeman, J. H. Halton},
  title =     {Note on an Exact Treatment of Contingency, Goodness of Fit and Other Problems of Significance},
  journal =   {Biometrika},
  year =      {1951},
  volume =    {38},
  number =    {1/2},
  pages =     {141-149},
  issn =      {00063444},
  publisher = {[Oxford University Press, Biometrika Trust]},
  url =       {http://www.jstor.org/stable/2332323}
}

@Article{gastmeier01,
  author =    {Petra Gastmeier, Helga Bräuer, Dorit Sohr, Christine Geffers, Dietmar H. Forster, Franz Daschner, Henning Rüden},
  title =     {Converting Incidence and Prevalence Data of Nosocomial Infections: Results From Eight Hospitals},
  journal =   {Infection Control and Hospital Epidemiology},
  year =      {2001},
  volume =    {22},
  number =    {1},
  pages =     {31-34},
  abstract =  {OBJECTIVE.  To investigate the use of the formula of Rhame and Sudderth for the interconversion of prevalence and incidence data on the frequency of nosocomial infections. DESIGN.  Comparison of observed and calculated incidence data and prevalence data. SETTING.  One 8‐week incidence investigation in the surgical and intensive care units of eight medium‐sized hospitals; three separate point‐prevalence studies in the same units. RESULTS.  The overall prevalence observed after the three prevalence studies in 2,169 patients was 6.8% (95% confidence interval [CI95], 5.7‐8.0). In 2,882 discharged patients observed during the incidence study, the mean hospitalization was 9.8 days; patients with one or more nosocomial infection had a mean hospitalization time of 22.3 days and a mean interval of 8.2 days from admission to the first day of infection. Based on these data, the overall calculated incidence was 4.7%, whereas the observed incidence was 4.3% (CI95, 3.6‐5.2). Vice versa, an overall prevalence of 6.2% was found when calculated from the observed incidence data. The incidence data calculated from prevalence data also were within the confidence interval of the incidences observed for urinary tract infections and surgical‐site infections. (However, it was not possible to convert the data for two of the eight hospitals.) CONCLUSION.  The approximate mathematical relationship between the prevalence and incidence data of nosocomial infection is confirmed by this study. However, although it is theoretically possible, we would not recommend the conversion of prevalence into incidence data or vice versa.},
  issn =      {0899823X, 15596834},
  publisher = {[Cambridge University Press, Society for Healthcare Epidemiology of America]},
  url =       {http://www.jstor.org/stable/10.1086/501821}
}

@Article{haore05,
  author =    {Houssein Gbaguidi Haore, Arno Muller, Daniel Talon, Xavier Bertrand},
  title =     {Estimation of the Cumulative Incidence of Hospital‐Acquired Bacteremia From Prevalence Data: A Formula},
  journal =   {Infection Control and Hospital Epidemiology},
  year =      {2005},
  volume =    {26},
  number =    {4},
  pages =     {415-417},
  abstract =  {ABSTRACT We evaluated the validity of the formula of Rhame and Sudderth to estimate the cumulative incidence of nosocomial bacteremia from prevalence studies. The observed cumulative incidence was threefold higher than the calculated cumulative incidence. We do not recommend converting prevalence into incidence data for nosocomial bloodstream infections.},
  issn =      {0899823X, 15596834},
  publisher = {[Cambridge University Press, Society for Healthcare Epidemiology of America]},
  url =       {http://www.jstor.org/stable/10.1086/502560}
}

@Article{jankowski2009,
  author =    {Jankowski, Hanna K. and Wellner, Jon A.},
  title =     {Estimation of a discrete monotone distribution},
  journal =   {Electron. J. Statist.},
  year =      {2009},
  volume =    {3},
  pages =     {1567--1605},
  doi =       {10.1214/09-EJS526},
  fjournal =  {Electronic Journal of Statistics},
  publisher = {The Institute of Mathematical Statistics and the Bernoulli Society},
  url =       {http://dx.doi.org/10.1214/09-EJS526}
}

@Article{keiding1991,
  author =    {Niels Keiding},
  title =     {Age-Specific Incidence and Prevalence: A Statistical Perspective},
  journal =   {Journal of the Royal Statistical Society. Series A (Statistics in Society)},
  year =      {1991},
  volume =    {154},
  number =    {3},
  pages =     {371-412},
  abstract =  {In epidemiology incidence denotes the rate of occurrence of new cases (of disease), while prevalence is the frequency in the population (of diseased people). From a statistical point of view it is useful to understand incidence and prevalence in the parameter space, incidence as intensity (hazard) and prevalence as probability, and to relate observable quantities to these via a statistical model. In this paper such a framework is based on modelling each individual's dynamics in the Lexis diagram by a simple three-state stochastic process in the age direction and recruiting individuals from a Poisson process in the time direction. The resulting distributions in the cross-sectional population allow a rigorous discussion of the interplay between age-specific incidence and prevalence as well as of the statistical analysis of epidemiological cross-sectional data. For the latter, this paper focuses on methods from modern nonparametric continuous time survival analysis, including random censoring and truncation models and estimation under monotonicity constraints. The exposition is illustrated by examples, primarily from the author's epidemiological experience.},
  issn =      {09641998, 1467985X},
  publisher = {[Wiley, Royal Statistical Society]},
  url =       {http://www.jstor.org/stable/2983150}
}

@Article{king14,
  author =    {Carina King, Paul Aylin, Alison Holmes},
  title =     {Converting Incidence and Prevalence Data: An Update to the Rule},
  journal =   {Infection Control and Hospital Epidemiology},
  year =      {2014},
  volume =    {35},
  number =    {11},
  pages =     {1432-1433},
  issn =      {0899823X, 15596834},
  publisher = {[Cambridge University Press, Society for Healthcare Epidemiology of America]},
  url =       {http://www.jstor.org/stable/10.1086/678435}
}

@Article{magill14,
  author =  {Magill, Shelley S. and Edwards, Jonathan R. and Bamberg, Wendy and Beldavs, Zintars G. and Dumyati, Ghinwa and Kainer, Marion A. and Lynfield, Ruth and Maloney, Meghan and McAllister-Hollod, Laura and Nadle, Joelle and Ray, Susan M. and Thompson, Deborah L. and Wilson, Lucy E. and Fridkin, Scott K.},
  title =   {Multistate Point-Prevalence Survey of Health Care–Associated Infections},
  journal = {New England Journal of Medicine},
  year =    {2014},
  volume =  {370},
  number =  {13},
  pages =   {1198-1208},
  note =    {PMID: 24670166},
  doi =     {10.1056/NEJMoa1306801},
  eprint =  { http://dx.doi.org/10.1056/NEJMoa1306801 },
  url =     { 
        http://dx.doi.org/10.1056/NEJMoa1306801
    
}
}

@Book{nidep,
  title     = {Nosokomiale Infektionen in Deutschland - Erfassung und Prävention (NIDEP-Studie) - Teil 2:Studie zur Einführung eines Qualitätsmanagementprogrammes},
  publisher = {Nomos Verlagsgesellschaft},
  year      = {2000},
  author    = {NIDEP},
}

@Article{PhilippeBerthelot2007,
  author =    {Philippe Berthelot, Maud Garnier, Pascal Fascia, Stéphane Guyomarch, Richard Jospé, Frédéric Lucht, Fabrice Zéni, Christian Auboyer, Bruno Pozzetto},
  title =     {Conversion of Prevalence Survey Data on Nosocomial Infections to Incidence Estimates: A Simplified Tool for Surveillance?},
  journal =   {Infection Control and Hospital Epidemiology},
  year =      {2007},
  volume =    {28},
  number =    {5},
  pages =     {633-636},
  abstract =  {We calculated the incidence of nosocomial infection in 2 intensive care units (ICUs) on the basis of prevalence data recorded from 1997 through 2002 and compared these estimates to cumulative incidences measured in the 2 ICUs during the same period to investigate the feasibility and the reliability of converting prevalence data to incidence estimates. Decreases in the calculated and measured incidences over time in the ICUs were found to be statistically significantly related.},
  issn =      {0899823X, 15596834},
  publisher = {[Cambridge University Press, Society for Healthcare Epidemiology of America]},
  url =       {http://www.jstor.org/stable/10.1086/513536}
}

@Article{rhame,
  author =  {Rhame, Frank S. and Sudderth, William D.},
  title =   {Incidence and Prevalence as used in the analysis of the occurence of nosocomial infections},
  journal = {American Journal of Epidemiology},
  year =    {1981},
  volume =  {113},
  number =  {1},
  pages =   {1-11},
  eprint =  {http://aje.oxfordjournals.org/content/113/1/1.full.pdf+html},
  url =     {http://aje.oxfordjournals.org/content/113/1/1.short}
}

@Article{SuChen2012,
  author =        {Su, Tung-Hung and Chen, Pei-Jer},
  title =         {Emerging hepatitis B virus infection in vaccinated populations: a rising concern?},
  journal =       {Emerg Microbes Infect},
  year =          {2012},
  volume =        {1},
  pages =         {e27--},
  month =         sep,
  __markedentry = {[willrichn:6]},
  publisher =     {Shanghai Shangyixun Cultural Communication Co., Ltd},
  url =           {http://dx.doi.org/10.1038/emi.2012.28}
}

@TechReport{surveyecdc,
  author =        {ECDC-Survey},
  title =         {Point prevalence survey of healthcare-associated infections and antimicrobial use in European acute care hospitals 2011-2012},
  institution =   {Stockholm: ECDC},
  year =          {2013},
  __markedentry = {[willrichn:]},
  keywords =      {PREVALENCE, ANTIBIOTIQUE, CONSOMMATION, ANTIBIORESISTANCE, STAPHYLOCOCCUS AUREUS, ENTEROCOCCUS , ENTEROBACTERIE, CARBAPENEME, CEPHALOSPORINE},
  language =      {Eng},
  pages =         {5 p.}
}

@Article{tess93,
  author =   {Tess, B. H. and Glenister, H. M. and Rodrigues, L. C. and Wagner, M. B.},
  title =    {Incidence of hospital-acquired infection and length of hospital stay},
  journal =  {European Journal of Clinical Microbiology and Infectious Diseases},
  volume =   {12},
  number =   {2},
  pages =    {81--86},
  abstract = {Two different measures of hospital-acquired infection (HAI), risk per discharge and incidence rate, were used to analyse the incidence of 225 primary HAIs detected in 3,090 patients in an 11-month survey. Longer hospital stay was associated with a greater risk of developing HAI, but the strength of the association was different for the two measures used. Day-specific incidence rates were found to vary, with a peak between the 14th and 19th days of hospitalisation. Similar patterns were observed when the data were stratified by age, sex and operation. Methods for calculating HAI should control for the length of hospital stay. Further studies are required to clarify the mechanisms that affect the temporal pattern of incidence of HAI observed with length of hospitalisation.},
  doi =      {10.1007/BF01967579},
  issn =     {1435-4373},
  url =      {http://dx.doi.org/10.1007/BF01967579}
}

@Article{sizebiasforall,
  author        = {{Arratia}, R. and {Goldstein}, L. and {Kochman}, F.},
  title         = {{Size bias for one and all}},
  journal       = {ArXiv e-prints},
  year          = {2013},
  month         = aug,
  adsnote       = {Provided by the SAO/NASA Astrophysics Data System},
  adsurl        = {http://adsabs.harvard.edu/abs/2013arXiv1308.2729A},
  archiveprefix = {arXiv},
  eprint        = {1308.2729},
  keywords      = {Mathematics - Probability, 60-02},
  primaryclass  = {math.PR},
}

@Article{meijs2017incidence,
  author    = {Meijs, AP and Ferreira, JA and de Greeff, Sabine C and Vos, Margreet C and Koek, MBG},
  title     = {Incidence of surgical site infections cannot be derived reliably from point prevalence survey data in Dutch hospitals},
  journal   = {Epidemiology \& Infection},
  year      = {2017},
  volume    = {145},
  number    = {5},
  pages     = {970--980},
  publisher = {Cambridge University Press},
}

@Book{haviv2013queues,
  title     = {Queues},
  publisher = {Springer},
  year      = {2013},
  author    = {Haviv, Moshe},
}

@Manual{prevtoinc,
  title  = {prevtoinc: Package for prevalence to incidence calculations in a nosocomial setting (WIP)},
  author = {Niklas Willrich},
  note   = {R package version 0.0.0.9000},
}

@Manual{R,
  title        = {R: A Language and Environment for Statistical Computing},
  author       = {{R Core Team}},
  organization = {R Foundation for Statistical Computing},
  address      = {Vienna, Austria},
  year         = {2016},
  url          = {https://www.R-project.org/},
}

@TechReport{icumanual,
  author      = {ECDC},
  title       = {European surveillance of healthcare- associated infections in intensive care units – HAI-Net ICU protocol, version 1.02. Stockholm: ECDC; 2015},
  institution = {European Centre for Disease Prevention and Control.},
  year        = {2015},
}

@Article{graves03,
  author    = {Graves, Nicholas and Nicholls, Tanya M and Wong, Christopher GS and Morris, Arthur J},
  title     = {The prevalence and estimates of the cumulative incidence of hospital-acquired infections among patients admitted to Auckland District Health Board Hospitals in New Zealand},
  journal   = {Infection Control \& Hospital Epidemiology},
  year      = {2003},
  volume    = {24},
  number    = {1},
  pages     = {56--61},
  publisher = {Cambridge University Press},
}

@Article{kanerva09,
  author    = {Kanerva, Mari and Ollgren, Jukka and Virtanen, Mikko J and Lyytik{\"a}inen, Outi and Prevalence Survey Study Group and others},
  title     = {Estimating the annual burden of health care--associated infections in Finnish adult acute care hospitals},
  journal   = {American journal of Infection Control},
  year      = {2009},
  volume    = {37},
  number    = {3},
  pages     = {227--230},
  publisher = {Elsevier},
}

@Article{cassini2016burden,
  author    = {Cassini, Alessandro and Plachouras, Diamantis and Eckmanns, Tim and Sin, Muna Abu and Blank, Hans-Peter and Ducomble, Tanja and Haller, Sebastian and Harder, Thomas and Klingeberg, Anja and Sixtensson, Madlen and others},
  title     = {Burden of six Healthcare-associated infections on european population health: estimating incidence-based disability-adjusted life years through a population prevalence-based modelling study},
  journal   = {PLoS medicine},
  year      = {2016},
  volume    = {13},
  number    = {10},
  pages     = {e1002150},
  publisher = {Public Library of Science},
}

@Book{beicheltfatti,
  title     = {Stochastic Processes and their Applications},
  publisher = {CRC Press},
  year      = {2002},
  author    = {Frank Beichelt and Paul Fatti},
}

@Book{rothmanme,
  title     = {Modern Epidemiology},
  publisher = {Wolters Kluwer Health/Lippincott Williams \& Wilkins},
  year      = {2008},
  author    = {Rothman, K.J. and Greenland, S. and Lash, T.L.},
  edition   = {3rd},
  isbn      = {9780781755641},
  lccn      = {2007036316},
}

@Article{urgell2005,
  author    = {Rossello-Urgell, Jose and Rodriguez-Pla, Alicia},
  title     = {Behavior of Cross-Sectional Surveys in the Hospital Setting: A Simulation Model},
  journal   = {Infection Control and Hospital Epidemiology},
  year      = {2005},
  volume    = {26},
  number    = {4},
  pages     = {362–368},
  doi       = {10.1086/502553},
  publisher = {Cambridge University Press},
}

@Article{freemanmcgowan,
  author  = {Freeman, Jonathan and McGowan Jr. , John E.},
  title   = {Day-specific incidence of nosocomial infection estimated from a prevalence survey},
  journal = {American Journal of Epidemiology},
  year    = {1981},
  volume  = {114},
  number  = {6},
  pages   = {888-901},
  doi     = {10.1093/oxfordjournals.aje.a113259},
  eprint  = {/oup/backfile/content_public/journal/aje/114/6/10.1093/oxfordjournals.aje.a113259/2/114-6-888.pdf},
  url     = {http://dx.doi.org/10.1093/oxfordjournals.aje.a113259},
}

@Article{niklas,
  author  = {Niklas Willrich and Sebastian Haller and Tim Eckmanns and Benedikt Zacher and Diamantis Plachouras and Carl Suetens and Michael Behnke and Petra Gastmeier and Jan Walter},
  title   = {From prevalence to incidence - a new approach in the nosocomial setting},
  journal = {Manuscript to be submitted},
  year    = {2018},
}

@Comment{jabref-meta: databaseType:bibtex;}
