PEER REVIEW HISTORY ARTICLE DETAILS VERSION 1 - REVIEW. Elizabeth Comino Centre fo Primary Health Care and Equity 12-Aug-2015

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1 PEER REVIEW HISTORY BMJ Open publishes all reviews undertaken for accepted manuscripts. Reviewers are asked to complete a checklist review form ( and are provided with free text boxes to elaborate on their assessment. These free text comments are reproduced below. ARTICLE DETAILS TITLE (PROVISIONAL) AUTHORS Predicting admissions and time spent in hospital over a decade in a population based record linkage study: the EPIC-Norfolk cohort Luben, Robert; Hayat, Shabina; Wareham, Nicholas; Khaw, Kay-Tee VERSION 1 - REVIEW REVIEWER REVIEW RETURNED Elizabeth Comino Centre fo Primary Health Care and Equity 12-Aug-2015 GENERAL COMMENTS This study describes risk factors for hospital admission over 10 years for a community base cohort. A simple algorithm is used to calculate risk of admission. This is a well prepared paper. This reviewer has a few questions of a minor nature. Introduction, line5: did the authors mean risk of disease or risk of admission? Methods Study design did the different time periods for recruitment (1993-8) and hospital data ( ) affect the prevalence of demographic and lifestyle variables? Outcome measures are hospitalisation, number of days in hospital, and number of hospitalisations. Transfers and immediate readmissions were identified and included as a single admission. Were outpatient visits excluded from the study? Page 6, line 4: could the authors provide a bit more explanation of one plus presumably discharge minus admission date = zero if same day admission and discharge and plus one means the duration is counted as one. Statistical analysis, line 22: were demographic and lifestyle factors taken at baseline? Line31: NR is for Norfolk? This may need explaining for non-uk resident. The results are useful and of value to readers. The risk score based on commonly collected measures seems to have merit. Discussion: it would be useful for some comment on the implications of these findings for health care providers and planners. REVIEWER REVIEW RETURNED Francesca Ieva Università degli Studi di Milano, Milano (Italy) 29-Sep-2015 GENERAL COMMENTS Please consider the comments I provide in the report below. I suggest a maior revision, focused on exploiting and refining the statistical methods adopted.

2 The paper aims at estimating the absolute rates of hospital use (in terms of number of accesses and days spent in hospital) in a general community (Norfolk cohort) based on a 10 years perspective study. The idea of using administrative datasets, linking available information across the National health systems is crucial. In fact, this kind of studies may be inserted within the trend observed nowadays in many developed countries with advanced healthcare systems. In fact, the use of administrative data for clinical purposes and for optimizing the healthcare management is more and more common and accepted also in clinical practice. From a medical and statistical perspective, the topic is extremely of interest and perfectly suitable for BMJopen, but in my opinion the paper is far from being ready for publication. Therefore I suggest that a major revision should be undertaken by the author. My maior concerns regard the following issues: The paper lacks in explaining and exploiting the statistical methods adopted for getting results. In particular, since no innovation is proposed in the methods, I expect the authors address at least the following issues: (number of admission, days spent in hospital, mortality), but they are likely to be strongly correlated. Did the author verified that? Did they accounted for that? hospitalizations or days spent in hospital, but these variables rise as continuous (discrete) variables. Instead of several different logistic regression, I would expect a Poisson regression on the entire outcome. The authors, if decide not to pursue this way, should at least discuss the reasons for that. score is useful for. Do the authors use it for prediction? If yes, which are the main conclusions it allows for? r. At line 43, page 7, the authors claim We were able to estimate the probability of hospital admissions and total bed days over 10 year. It would be nice if some example of such computations, profiled by risks and patients characteristics, would be provided. stationary trend is evincible from Tables reported in the paper, are they claiming about any statistical evidence? I would appreciate to see the p-values of statistical tests on proportions, or simple linear regression coefficients (for example, fo the number of days spent in hospital again risk) to quantify these claims. causes of admission and did not execute any kind of survival analysis stratified for risk factors and so on. I think that such kind of analyses would be more appropriate than the ones actually proposed, in order to address the questions the authors pretend to answer. ay The differences by sex and BMI we observed were independent of social class and education. The procedure for assessing this claim should be made explicit. Analysis stratified by risk would be better.

3 conclude. No evidence for trend appears, ad no test are carried out neither. Moreover, I would report percentage and not absolute values of counting. Minor points: descriptive data? -27: please rephrase. : which is the sense of reporting the second ad first row, respectively? Please avoid. Male sex. Did the authors choose this for maintaining the OR positive? If so, I think it would be easier to maintain the same reference class in all the cases. VERSION 1 AUTHOR RESPONSE Reviewer: 1 Reviewer Name: Elizabeth Comino Institution and Country: Centre for Primary Health Care and Equity Please state any competing interests or state 'None declared': no competing interests Please leave your comments for the authors below This study describes risk factors for hospital admission over 10 years for a community base cohort. A simple algorithm is used to calculate risk of admission. This is a well prepared paper. This reviewer has a few questions of a minor nature. Introduction, line5: did the authors mean 'risk of disease' or 'risk of admission'? Response: We agree with the reviewer's comment and have changed the wording in the manuscript to say 'risk of admission' Methods Study design - did the different time periods for recruitment (1993-8) and hospital data ( ) affect the prevalence of demographic and lifestyle variables? Response: The study design is similar in most ways to a standard prospective analysis with demographic and lifestyle variables measured at baseline used to identify groups at high risk. Outcome measures are hospitalisation, number of days in hospital, and number of hospitalisations. Transfers and immediate re-admissions were identified and included as a single admission. Were outpatient visits excluded from the study? Response: Outpatient visits although available to us were not considered in this analysis. While the number of outpatient visits was large, they were more heterogeneous in nature. We chose to focus on those needing admission to hospital since this was more likely to capture people with the most serious conditions. Page 6, line 4: could the authors provide a bit more explanation of 'one plus' - presumably discharge

4 minus admission date = zero if same day admission and discharge and 'plus one' means the duration is counted as 'one'. Response: Your interpretation is correct. If we had used the simplest formula ( discharge minus admission), those people admitted and discharged on the same day would be counted as zero and their time in hospital and number of admissions not considered. Instead our formula used one plus ( discharge minus admission). We have improved the wording in the manuscript to clarify this. Statistical analysis, line 22: were factors taken at baseline? Response: Yes, demographic and lifestyle were from baseline. We have changed the text in the statistical analysis section to include the word baseline ("defined as the sum of five baseline risk factors") Line31: NR is for Norfolk? This may need explaining for non-uk resident. Response: Thank you for this observation. You are right that a non-uk resident might not understand this abbreviation and we clarified this in the manuscript. The results are useful and of value to readers. The risk score based on commonly collected measures seems to have merit. Response: Thank you for these positive comments. Discussion: it would be useful for some comment on the implications of these findings for health care providers and planners. Response: We have added a sentence to the conclusions discussing the implications of the findings. Reviewer: 2 Reviewer Name: Francesca Ieva Institution and Country: Universit degli Studi di Milano, Milano (Italy) Please state any competing interests or state 'None declared': Non declared Please leave your comments for the authors below Please consider the comments I provide in the report attached. I suggest a maior revision, focused on exploiting and refining the statistical methods adopted. Response: The purpose of our paper was to examine basic commonly collected demographic and lifestyle factors such as those that might be recorded on a visit to a GP and their impact on hospital usage. It was not our intention to present results based on complex statistical modelling as we wanted the data to be presented in a format that was clear and transparent to the reader and as close to its original form as possible. The statistical technique we used was logistic regression which is an extremely common approach used in many epidemiological studies. While the further exploitation of statistical methods in the context of hospital usage analysis may be of interest, it is a different type of paper and not something that we wanted to focus on here. The paper aims at estimating the absolute rates of hospital use (in terms of number of accesses and days spent in hospital) in a general community (Norfolk cohort) based on a 10 years perspective study. The idea of using administrative datasets, linking available information across the National health systems is crucial. In fact, this kind of studies may be inserted within the trend observed

5 nowadays in many developed countries with advanced healthcare systems. In fact, the use of administrative data for clinical purposes and for optimizing the healthcare management is more and more common and accepted also in clinical practice. From a medical and statistical perspective, the topic is extremely of interest and perfectly suitable for BMJopen, but in my opinion the paper is far from being ready for publication. Therefore I suggest that a major revision should be undertaken by the author. My maior concerns regard the following issues: The paper lacks in explaining and exploiting the statistical methods adopted for getting results. In particular, since no innovation is proposed in the methods, I expect the authors address at least the following issues: Response: We would agree that this area is of considerable interest and suitable for publication and thank the reviewer for the positive comments made, however we disagree with the ascension that major changes are necessary along the lines suggested since this would alter the nature and focus of the work. We have clearly explained the methods used (Statistical Analyses, page 6) but it was not our intention to include innovative statistical methods to describe the data we present. We have instead shown a detailed and comprehensive description of the data in our tables and used standard commonly used statistical methods. The analysis is conducted separately on the different outcomes (number of admission, days spent in hospital, mortality), but they are likely to be strongly correlated. Did the author verified that? Did they accounted for that? Response: It is self evident that the two measures of hospital usage are strongly correlated since having many hospital admissions almost always implies spending a lot of time in hospital. We are simply looking at the most important ways of assessing hospital use which are clinically relevant. Using two different but related methods enable us to measure something broadly similar while capturing different dimensions. I can see the reason why the authors categorize the number of hospitalizations or days spent in hospital, but these variables rise as continuous (discrete) variables. Instead of several different logistic regression, I would expect a Poisson regression on the entire outcome. The authors, if decide not to pursue this way, should at least discuss the reasons for that. Response: Categorical variables are simpler to interpret for the reader. We felt that most readers would find it harder to interpret our models if we were to have used the approach suggested by the reviewer. Instead we chose to dichotomise the outcomes in Table 3 using "no hospital admissions", "seven or more hospital admissions" and " twenty or more hospital nights" and present odds ratios relative to those categories. However, we have shown the continuous relationship elsewhere (in table 2a and 2b). Page 6, line 19: it is not clear from the description what the risk score is useful for. Do the authors use it for prediction? If yes, which are the main conclusions it allows for? Response: We describe the use of the risk score in the Discussion section (Page 7, lines 11 to 27) No models are cited nor reported in the paper. At line 43, page 7, the authors claim "We were able to estimate the probability of hospital admissions and total bed days over 10 year...". It would be nice if some example of such computations, profiled by risks and patients characteristics, would be provided. Each time the authors say that an increasing or decreasing or stationary trend is evincible from

6 Tables reported in the paper, are they claiming about any statistical evidence? I would appreciate to see the p-values of statistical tests on proportions, or simple linear regression coefficients (for example, fo the number of days spent in hospital again risk) to quantify these claims. Response: We have added a column showing p-values to table 2a and table 2b and to sensitivity table 2a and 2b At line 59, page 7, the authors say they did stratified over the causes of admission and did not execute any kind of survival analysis stratified for risk factors and so on. I think that such kind of analyses would be more appropriate than the ones actually proposed, in order to address the questions the authors pretend to answer. Response: We were not entirely clear about what the reviewer meant here and somewhat concerned about the use of the phrase "the authors pretend to answer" which is not an appropriate description of our work. Survival analysis techniques such as Cox regression were not used in this paper since we did not want to censor participants who died. We are trying to assess the impact of hospital usage and do not make a distinction between non-attendance due to good health and non-attendance because of death. In this important respect, the paper differs from a standard prospective analysis. We have now clarified this point in the statistical methods section. At line 52, page 7, the authors say "The differences by sex and BMI we observed were independent of social class and education". The procedure for assessing this claim should be made explicit. Response: All covariates in the three models presented in table 3 are mutually adjusted and hence all the associations shown are independent. A footnote has been added to table 3 to explain this. Table 5 does not make much sense to me. I think that a Survival Analysis stratified by risk would be better. Response: Table 5 is explained on page 7 lines 1 to 5 and lines on page 6. Sensitivity Table 4: It is not clear to me what this Table allows to conclude. No evidence for trend appears, ad no test are carried out neither. Moreover, I would report percentage and not absolute values of counting. Response: The rationale for the sensitivity analysis is explained on page 17 lines 3 to 13. Sensitivity Table 4 is identical in form and structure to Table 4 but is restricted to a subset of EPIC-Norfolk participants who were living in the Norfolk postcode area between 1999 and Minor points: Page 6, line 16: what do the authors mean with "baseline descriptive data"? Response: We describe EPIC Norfolk baseline on Page 5 line 22 ("Study Design") Page 6, line 48: what does "independent relationship" mean? Response: This means that all covariates appear in the same model and are adjusted for one another. Page 7, lines 25-27: please rephrase.

7 Response: The wording has been altered. Tables 2a and 2b: which is the sense of reporting the second ad first row, respectively? Please avoid. Table 3: In the first block there is "Female sex", but in the ollowing "Male sex". Did the authors choose this for maintaining the OR positive? If so, I think it would be easier to maintain the same reference class in all the cases. Response: In table 3, the first block shows the positive outcome of "No hospital admissions" while the second and third blocks show negative outcomes. All the categorical measures shown in the first block (that is all variables except age) are the inverse of those shown in the subsequent two blocks. We would prefer to keep this format as we consider it more straightforward for the reader to interpret. VERSION 2 REVIEW REVIEWER REVIEW RETURNED Elizabeth Comino Centre for Primary Health Care and Equity, University of NSW, Australia 23-Nov-2015 GENERAL COMMENTS This was a resubmitted paper. the authors have addressed the issues that were previously raised by two reviewers. The only area of concern is the work on sensitivity analysis. The sensitivity analysis includes a number of tables that essentially repeat the information that is included in the main tables (a lot of work) and a brief explanation although no results and conclusion. The conclusion is mentioned on page 7, paragraph 9 and indicates that the results did not change. One queries if these add to the paper. Perhaps a comment to the effect that excluding patients with an Ipswich code did not change the results.