Is the number of previous hospitalizations associated with increased in-hospital mortality after hip fracture in a developing country?

PURPOSE: We aimed to examine whether the number of previous hospitalizations and the main diagnoses of those hospitalizations are associated with increased in-hospital hip fracture mortality for older people. That assessment is relevant because if those variables are shown to be associated with increased mortality, that finding could support their use as proxies for comorbidity burden for case-mix adjustment in statistical models seeking to compare the performance of hospitals regarding hip fracture mortality in settings with limited hospital information systems.
METHODS: In this retrospective cohort study of all public hospital admissions for older adults with hip fractures in the city of Rio de Janeiro between 2010 and 2011, we used data from the Hospital Admission Information System database to examine the association between in-hospital mortality and the number of hospitalizations in the previous two years and their main diagnoses through logistic regression.
RESULTS: Among 1938 patients included in the study there were 103 (5.3%) in-hospital deaths. Although the presence of hospitalization episodes within the two years preceding the index hip fracture was associated with increased mortality (OR: 1.78, 95%CI: 1.07 to 2.97) we did not find evidence of a gradient of increased mortality with a growing number of previous hospitalizations. Additionally, several diseases recorded as main diagnoses of previous hospitalizations were not associated with increased mortality rates, as was expected based on existing knowledge on risk factors for decreased survival in older adults with hip fractures.
CONCLUSIONS: Our results suggest that, in settings where local hospital information systems have limited access to secondary diagnoses, the use of the number of previous hospitalizations or the main diagnoses associated with those hospitalizations as proxies for the profile of comorbidities of older adults with hip fractures may not be an effective way to adjust for case-mix when comparing in-hospital mortality rates among hospitals.

Introduction

The quality of health services must be constantly evaluated and monitored to optimize their impact on population health. Health service quality encompasses aspects such as access to services, equity, technical adequacy, effectiveness, costs, safety, and patient satisfaction [1]. Performance indicators are useful when evaluating health service quality because they gauge the care process as well as its favorable or unfavorable impact on the health of individuals [1]. Performance indicators are instruments that seek to monitor the quality of health care services and work processes to identify the need for more specific evaluation when potential problems are identified [2].

In-hospital mortality after a hip fracture is an important indicator of the quality of care for a common condition that affects older adults around the world. It is considered a performance indicator and may be used to evaluate a single hospital’s quality over time or to compare several hospitals against each other or a certain benchmark [2-4]. Indeed, in-hospital mortality after a hip fracture is among the 26 quality indicators that the Agency for Healthcare Research and Quality determined for the assessment of care in American hospitals [5].

Reviews of predictive factors for death following a hip fracture point to a strong relationship between death and factors such as male sex, advanced age, number of comorbidities, and the presence of cognitive deficit [6-9]. Comorbidities most closely associated with death in these studies were pulmonary diseases, heart diseases, kidney diseases, diabetes, and stroke [6-8]. Previous hospitalizations have also been considered to be associated with increased mortality following a hip fracture [10, 11].

For in-hospital mortality after hip fracture in older adults to be used as a quality indicator to compare the performance of hospitals, there are important factors that must be taken into account so that differences in findings do not primarily reflect differences in case-mix among hospitals, but rather the quality of care provided in those institutions [11-14]. Risk adjustment aims to reduce the confounding role that some variables related to case-mix, such as patients’ burden of comorbidities, functional and socioeconomic status, may play on health outcomes that are used as markers of quality of care among institutions [14-18].

In Brazil, hospitalization data from the Brazilian National Health System are recorded in an administrative database, the Hospital Admission Information System, which is the most important national source of information for planning and monitoring hospital care in the country. Additionally, the Hospital Admission Information System may serve as an instrument for the assessment of the quality of inpatient care. However, up to this moment, the Brazilian Hospital Admission Information System still allows the recording of only one comorbidity and this information has been historically poorly recorded [14]. This is an important limitation of the Brazilian Hospital Admission Information System, which is shared by information systems from several other developing countries [19]. A possible strategy to attempt to overcome that important shortcoming for analyses related to the quality of healthcare is to use data from previous hospitalizations routinely recorded in Hospital Admission Information Systems, such as the number of previous hospitalizations and the main diagnosis for each hospitalization episode, as a tool for risk adjustment in statistical models.

This study aims to analyze the association between previous hospital admissions and in-hospital mortality in older adults who underwent surgical repair of a hip fracture. It also seeks to consider the association of main diagnoses of previous hospitalizations and in-hospital death following a hip fracture. The presence of significant associations between previous hospitalizations and/or their main diagnoses and in-hospital mortality after a hip fracture could represent evidence favoring the use of those variables for risk adjustment using administrative data in scenarios typical of several developing countries, where hospital databases suffer from major shortcomings regarding the registry of comorbidities and secondary diagnoses.

Methods

This is a retrospective cohort study of a population of elderly patients hospitalized for hip fractures in the city of Rio de Janeiro, Brazil, between January 1, 2010, and December 31, 2011. We analyzed data from the Hospital Admission Information System for all patients aged 62 years and older, whose main diagnostic codes for hospital admission according to 10th revision of the International Classification of Diseases (ICD10) were fracture of the neck of femur (S72.0), pertrochantheric fracture (S72.1), or subtrochanteric fracture (S72.2), and who underwent surgical treatment for the fracture. We excluded patients that did not undergo surgical treatment for the hip fracture and patients whose hip fracture resulted from multiple high-intensity trauma (e.g. car accident).

Causes of previous hospitalizations were identified through the main diagnosis recorded in the Hospital Admission Information System for hospitalizations that took place up to two years before the hospitalization for the index hip fracture. We recovered this information through probabilistic record linkage of the Hospital Admission Information System data from patients aged 62 years and older hospitalized for hip fractures in 2010 and 2011 and the same database for patients aged 60 years and older hospitalized between 2008 and 2011 for any cause.

We used a five-stage probabilistic record linkage technique, following a strategy recommended by Camargo Jr. and Coeli [20, 21] and using the OpenReclink software (version: 3.1) (http://reclink.sourceforge.net/). Previous research in a similar setting showed 99.4% specificity, 85.5% sensibility, 98.1% positive predictive value, and 94.9% negative predictive value for correct matching of records using this methodological approach [22].

We described categorical data as absolute numbers and proportions. Continuous data were described as mean and standard deviation (SD) when their distribution was approximately normal, or otherwise as median and interquartile ranges (IQR). We assessed distributions of continuous data for normality by inspecting their histograms.

We analyzed the association between in-hospital mortality after a hip fracture and previous hospitalizations based on three ways of classifying the latter: according to the existence of previous hospitalizations (yes or no), according to the number of previous hospitalizations (categorized as 0, 1, and 2 or more), and according to the main diagnosis of previous hospitalizations. For the last classification, we grouped the main diagnoses based on the scientific literature on risk factors for mortality following a hip fracture [6–8, 18].

We used Pearson’s chi-square test to evaluate the association between the dependent variable (in-hospital mortality) and the following variables in simple analyses: presence and number of previous hospitalizations within the two years before the index fracture, sex, age (classified in age groups from 60 to 69 years, 70 to 79 years, 80 to 89 years and 90 years or older) and type of hip fracture (classified as femoral neck fracture, pertrochantheric fracture, and subtrochanteric fracture). We calculated the odds ratio of in-hospital death associated with previous hospitalizations through two multivariable logistic regression models [23], adjusted for sex, age, and type of fracture. In one model the occurrence of previous hospitalizations over the last two years was coded as a dichotomous variable and in the other model as the total number of previous hospitalizations within that same time frame. We did not adjust those regression models for the number or type of main diagnoses from previous hospitalizations because too much collinearity would be expected with the variable encoding the number of previous hospitalizations.

We also evaluated the association between in-hospital mortality after a hip fracture and the different causes of previous hospitalizations using simple and multivariable logistic regressions. For the multivariable regression models, all groups of causes of previous hospitalizations were included and adjusted for patients’ sex, age, and type of fracture.

We assessed the possibility of sparse data bias in our analyses by examining the frequency of outcome events per each category of each variable used in our models and by comparing the results of our logistic regressions with the results from penalized logistic regressions performed using the data augmentation method recommended by Greenland, Mansournia and Altman [24]. That method involved the use of a conservative F-distribution prior with a 95% odds ratio interval equivalent to 1/39 to 39, which reflects the fact that such a range encompasses most associations observed in epidemiologic studies. Whenever we found evidence of sparse data bias for any given variable, we reported the odds ratio estimates and confidence intervals from logistic regressions that penalized those variables, as described above.

We used Stata9® software to perform all analyses. We used a two-tailed alpha value of 0.05 to define statistical significance.

Ethical approval

Access to the databases used in this study was granted by the Rio de Janeiro Municipal Health Secretary, after approval by the Ethics Review Committees from the Public Health Institute and the Municipal Health Secretary, under processes 44114515.7.0000.5286 and 15903313.0.3001.5279, respectively.

Following Brazilian regulation for ethics in research, the ethics committees waived the requirement for informed consent because obtaining informed consent would have been impossible or impracticable since this was an observational retrospective study based on secondary data from patients who were no longer under follow-up at the evaluated hospitals. The probabilistic record linkage process was carried out using identified databases because that method [20, 21] required the names of patients as an important source of information for the linkage between databases. Information was anonymized and de-identified before analysis. Only the research team had access to those databases, which were stored in a secure server at the Public Health Institute.

Results

There were 2046 records of patients aged 62 years and older admitted to hospitals of the Brazilian Public Health System because of a hip fracture between 2010 and 2011 in the city of Rio de Janeiro. We excluded 94 (4.59%) patients because they had not been submitted to surgical repair of the fracture and 14 (0.68%) patients because their hip fracture resulted from multiple high-intensity trauma. The mean age of the remaining 1938 patients included was 79.4 years (SD: 8.3). The median length of stay of patients was 15 days (IQR: 10 to 22 days). There were 103 episodes of in-hospital death corresponding to a 5.3% mortality rate. There were no missing data regarding the variables used in our analyses. Table 1 displays information regarding the distribution of patients regarding sex, age groups, type of fracture, and number of hospitalizations both in overall terms and concerning the occurrence of in-hospital death. Table 2 shows the frequency of the main diagnoses recorded for previous hospitalizations episodes. Table 3 displays the results of the two multivariable logistic regression models examining the association between in-hospital mortality and previous hospitalizations within the last two years. Table 4 shows the results of simple and multivariable logistic regression models examining the association between main diagnoses from previous hospitalizations within the last two years and in-hospital death after a hip fracture.

Table 1

Comparison of patient characteristics according to the occurrence of in-hospital mortality after a hip fracture.

Variable	Alive		Death		P*
	N	%	N	%
Sex					0.65
Female	1,334	94.5	77	5.5
Male	501	95.1	26	4.9
Age (years)					< 0.01
60–69	265	98.1	5	1.9
70–79	638	97.0	20	3.0
80–89	742	94.2	46	5.8
≥ 90	190	85.6	32	14.4
Fracture Type					0.04
Cervical	1027	93.5	71	6.5
Pertrochantheric	596	96.3	23	3.7
Subtrochanteric	212	95.9	9	4.1
Previous Hospitalization					0.08
No	1575	95.0	82	5.0
Yes	260	92.5	21	7.2
Number of Previous Hospitalizations					0.27
0	1566	95.0	82	5.0
1	198	92.5	16	7.5
≥ 2	71	93.4	5	6.6
Total	1835	94.7	103	5.3

* χ2 test.

Table 2

Frequency of main diagnoses recorded for hospitalization episodes within the two years preceding the index hip fracture.

Diagnoses	Alive		Dead
	N	%	N	%
Fractures
Hip fracture	21	100.0	0	0.0
Other fractures and lesions	74	91.4	7	8.6
Cardiovascular
Cerebrovascular disease	13	92.9	1	7.1
Ischemic heart diseases	4	80.0	1	20.0
Peripheral Vascular Disease	9	100.0	0	0.0
Cardiac valve disease and congestive heart failure	8	88.9	1	11.1
Neoplasms
Lymphoma and leukemia	10	100.0	0	0.0
Malignant neoplasms	26	92.9	2	7.1
Urinary System
Severe and moderate kidney disease	3	60.0	2	40.0
Urinary tract infections	17	85.0	3	15.0
Respiratory System
Pneumonia	14	82.4	3	17.6
Tuberculosis	3	100.0	0	0.0
Chronic obstructive pulmonary disease	4	100.0	0	0.0
Metabolic, Nutritional and Hematologic Diseases
Anemias	8	100.0	0	0.0
Diabetes mellitus	6	100.0	0	0.0
Other metabolic, nutritional and hematologic diseases	6	75.00	2	25.0
Other
Chronic liver disease	0	0.0	1	100.0
Connective tissue disease	8	100.0	0	0.0
Osteoarthritis	10	100.0	0	0.0
Infectious, gastrointestinal and other diseases	49	98.0	1	2.0
Schizophrenia	1	100.0	0	0.0
Vision disorders	16	100.0	0	0.0
Ulcers	2	100.0	0	0.0

Table 3

Simple and multivariable logistic regression models assessing the in-hospital mortality after hip fracture according to the occurrence of previous hospitalizations or the number of previous hospitalizations within the two years preceding the index hip fracture.

Variables	Crude OR (95% CI)	Adjusted OR (95% CI)	Adjusted OR (95% CI)
Sex
Male	0.90 (0.57 to 1.42)	1.05 (0.66 to 1.68)	1.07 (0.67 to 1.71)
Female	-	-	-
Age (years)
60–69	-	-	-
70–79	1.30 (0.56 to 3.00)	1.34 (0.58 to 3.09)	1.75 (0.65 to 9.49)
80–89	2.56 (1.18 to 5.59)	2.82 (1. 29 to 6.18)	3.70 (1.44 to 9.31)
≥ 90	6.84 (3.03 to 15.44)	8.21 (3.60 to 18.77)	10.96 (4.13 to 29.07)
Fracture Type
Femoral neck	-	-	-
Pertrochanteric	0.57 (0.36 to 0.91)	0.47 (0.29 to 0.77)	0.47 (0.29 to 0.77)
Subtrochanteric	0.64(0.32 to 1.26)	0.58 (0.28 to 1.18)	0.57 (0.28 to 1.18)
Previous Hospitalization*
Yes	1.49 (0.91 to 2.45)	1.78 (1.07 to 2.97)
No	-	-
Number of Previous Hospitalizations*
0	-		-
1	1.54 (0.89 to 2.69)		1.69 (0.95 to 2.99)
≥ 2	1.34 (0.53 to 3.42)		1.82 (0.70 to 4.74)

OR: odds ratio; CI: confidence interval.

Table 4

Simple and multivariable logistic regression models for in-hospital mortality after hip fracture, according to the main causes of hospitalizations within the two years preceding the index hip fracture.

Variable	Crude OR (95% CI)	Adjusted* OR (95% CI)
Cerebrovascular diseases	1.22 (0.22 to 6.70)	1.84 (0.28 to 12.00)
Severe and moderate kidney disease	6.18 (1.02 to 37.51)	9.05 (1.29 to 63.21)
Cardiac valve disease and congestive heart failure	1.64 (0.27 to 10.13)	1.50 (0.24 to 9.48)
Urinary tract infections	2.64 (0.78 to 8.97)	3.05 (0.86 to 10.83)
Infectious and gastrointestinal diseases	0.49 (0.11 to 2.16)	0.55 (0.12 to 2.49)
Ischemic heart diseases	2.39 (0.31 to 18.18)	3.18 (0.35 to 29.00)
Metabolic, nutritional and hematologic diseases	3.86 (0.77 to 19.36)	3.27 (0.62 to 17.19)
Other fractures and lesions	1.66 (0.76 to 3.64)	1.86 (0.82 to 4.21)
Neoplasms	1.28 (0.34 to 4.80)	1.06 (0.27 to 4.12)
Pneumonia	3.10 (0.78 to 16.71)	2.8 (0.75 to 10.50)

OR: odds ratio; CI: confidence interval.

* The Multivariable logistic regression model was adjusted for sex, age, and type of fracture.

Note: Due to a lack of variability, we were not able to estimate the OR for the following causes: anemias, arrhythmias, and conduction disorders, diabetes mellitus, chronic liver disease, chronic obstructive pulmonary disease, peripheral vascular disease, connective tissue disease, schizophrenia, hip fracture, lymphoma and leukemia, tuberculosis, ulcers, prostatic hyperplasia, and vision disorders.

Discussion

This is one of the largest epidemiological studies examining the in-hospital hip fracture mortality in Brazil [25-32]. Our study aimed to analyze the association between previous hospitalizations and their main diagnoses with in-hospital mortality after hip fracture surgery in older adults. Our main finding was that the occurrence of previous hospitalizations within the last two years was significantly associated with in-hospital mortality but paradoxically the number of hospitalizations in that period was not associated with a gradient of increased in-hospital mortality. On the one hand, the relatively low number of individuals who had been hospitalized two or more times in our cohort could explain this phenomenon because of a lack of statistical power. On the other hand, the low number of individuals in that category might also be pointing towards the presence of selection bias, where individuals with worse health status are denied surgical treatment or even hospitalization for hip fracture, as has been discussed by others [33, 34]. The latter hypothesis is especially plausible given the fact that about 5% of the original sample were excluded because patients did not receive surgical treatment. Hence, our results suggest that the number of hospitalizations may not be a good proxy of the health status of older patients undergoing hip fracture surgery for risk adjustment in statistical models in contexts prone to the possibility of selection bias. Therefore, previous hospitalizations should be used with caution for risk adjustment of in-hospital mortality after hip fracture surgery in settings of Hospital Admission Information Systems with limited availability of data regarding comorbidities and secondary diagnoses.

To some extent, our findings disagree with those of other studies [10, 11]. For instance, Meyer et al. from Oslo, Norway [10], found that among 248 older adults hospitalized due to a hip fracture, patients who had two or more hospitalizations in the two years preceding the hip fracture were four times more likely to die within one year than patients who had not been previously hospitalized. However, that study identified a clear dose-response gradient with higher odds of mortality for those with two or more hospitalizations than for that one episode of hospital admission. Aigner et al. [11] from Germany also observed that the odds of dying within six months and one year after a hip fracture was approximately two times higher in patients who had been hospitalized within the three months preceding the index hip fracture when compared with patients who had not had a similar experience. However, that study did not assess for the presence of a dose-response gradient regarding mortality and their analyses did not show statistically significant differences regarding in-hospital mortality between those who had been hospitalized within three months before fracture and those who did not.

Regarding our assessment of causes of previous hospitalizations as a proxy for patients’ comorbidities, our results disclosed significant associations with in-hospital mortality only for one disease group, namely severe and moderate kidney disease. Although that association is in agreement with the medical literature concerning risk factors for mortality after a hip fracture, the finding of no association between other equally relevant disease groups and mortality (e.g. dementia) is not [6–8, 18, 35–37], and signals the inherent limitations of using the main diagnoses of previous hospitalizations as a proxy and single source of information about the burden of comorbidities of patients. Such a phenomenon is likely to have occurred because the process of ascertaining the main diagnosis for an episode of hospitalization usually favors acute diagnoses over more chronic baseline comorbidities. For instance, an older adult suffering from dementia who was hospitalized because of aspiration pneumonia will usually have the latter diagnosis recorded as the main diagnosis for hospital admission instead of dementia.

Our study has several limitations. Our analyses were restricted to hospitalizations funded by the Brazilian National Health System and we were not able to access data from privately funded hospitalizations. However, recent estimates suggest that only 7.5% of older adults in Brazil have any form of private health insurance [38]. Therefore, our data is probably consistent with the health care provided to the majority of the older people in the city of Rio de Janeiro. Additionally, our limited database did not allow for a comparison between statistical models using previous hospitalizations and their main diagnoses as proxies for the burden of comorbidity of patients with models using more complete data about secondary diagnoses of patients. Finally, we were not able to analyze long term mortality outcomes beyond the hospitalization period because this would require having access to the Mortality Information System database, which was beyond the scope of this manuscript.

In conclusion, our results suggest that, in settings where local hospital information systems have limited access to secondary diagnoses, the use of the number of previous hospitalizations or the main diagnoses associated with those hospitalizations as proxies for the profile of comorbidities of older adults with hip fractures may not be an effective way to adjust for case-mix when comparing in-hospital mortality rates among hospitals.

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5 Sep 2019

PONE-D-19-23508

Is the number of previous hospitalizations associated with increased in-hospital mortality after hip fracture in a developing country?

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beautifully written. please attach the ethics comittee approval.

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7 Oct 2019

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We revised the manuscript to comply with PLOS ONE's style requirements including those for file naming.

2. In ethics statement in the manuscript and in the online submission form, please provide additional information about the patient records used in your retrospective study. Specifically, please ensure that you have discussed whether all data were fully anonymized before you accessed them and/or whether the IRB or ethics committee waived the requirement for informed consent. If patients provided informed written consent to have data from their medical records used in research, please include this information.

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In the revised version of our manuscript we have provide more detailed information regarding the patients’ records used in our study. The new section of our manuscript dedicated to the ethical approval of our investigation reads as follows:

“The Ethics Review Committees of the Public Health Institute and the Municipal Health Secretary approved this study under the processes 44114515.7.0000.5286 and 15903313.0.3001.5279, respectively. Following Brazilian regulation for ethics in research the ethics committees waived the requirement for informed consent because obtaining informed consent would have been impossible or impracticable since this was an observational retrospective study based on secondary data from patients who were no longer under follow-up at the evaluated hospital units. The probabilistic record linkage process was carried out using identified databases because that method [20-21] required the names of patients as an important source of information for the linkage between databases. Information was anonymized and de-identified prior to analysis. Only the research team had access to those databases, which were stored in a secure server at the Public Health Institute.”

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beautifully written. please attach the ethics comittee approval.

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Thank you for your generous comments. We have uploaded the reports with the approval of our study by both Ethics Review Committees to the Editorial Manager Submission System.

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P.9, line 110 of the attached file: “Please attach this approval”

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We have uploaded the reports with the approval of our study by both Ethics Review Committees to the Editorial Manager Submission System. Please note that we have also corrected the identification numbers of both processes at each Ethics Review Committees.

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17 Jan 2020

PONE-D-19-23508R1

Is the number of previous hospitalizations associated with increased in-hospital mortality after hip fracture in a developing country?

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: General comments:

It’s interesting to study the leading factors of mortality among hip fracture patients in addition to medical and public health importance, but this study idea titled “Is the number of previous hospitalizations associated with increased in-hospital mortality after hip fracture in a developing country?” raises some concerns.

Unfortunately, the hypothesis and research question about the relation between one main diagnosis "what was found in records" and mortality in those patients is not based in scientific basis that we couldn't find in the introduction or discussion sections. Hip fracture is a condition mainly related to elderly, the mean age of patients in this study is 79.4 years (SD: 8.3) with expected complicated medical conditions in the past two years from the index hip fracture. With no doubt before conduction of this study, one reported diagnosis of previous hospitalizations is not the only related factor to the mortality among those patients with more than one morbidity condition in this study

In addition, there are major defects in the manuscript and not well written especially in methods, data analysis and results sections. However, I have provided some remarks below.

Abstract:

In conclusion section: the word correlation is not correct to be written all over the manuscript, better to say associated factors or correlates.

The sentences in lines 20 to 24 are not clear and not focused on the study aims, as “ evaluate , performance of hospitals, limited information system.

“Correlates”: is mentioned in the title and didn’t mentioned after words anywhere all over the manuscript.

In methods section: no data were mentioned about data collection

The logistic regression analysis was not mentioned and its results “ which is not clear in analysis section after that”

Introduction

• Title and aim of the study are not matched with the introduction

• First two paragraphs are not related to the title nor the aim of the study

• Page 4, line 62: this study was not done to evaluate the quality of health care in hip fracture patients.

• No need to write about evaluation of health care in hp fracture patients, this could be mentioned in one sentence.

• Page 5, line 72 to 75: “Additionally, the national Hospital Admission Information System may serve as an instrument for the assessment of the quality of inpatient care. However, up to this moment the Brazilian Hospital Admission Information System still allows the recording of only one comorbidity and this information has been historically recorded poorly”….. this sentence about the poor data source is talking about a deficient tool to do this study,

Methods:

• Non concurrent: corrected to be retrospective cohort study

• Between 2010 and 2011: could be corrected to be “ from the start of 2010 to the end of 2011” or whatever the included months.

• Mention the level of significance of p value

• Please mention the details of sensitivity analysis that was referred to in results section.

Results:

- Median of hospital stay duration: is this variable is non parametric?? Please clarify!!

- Presentation of results is not well written regarding tables 2,3 and 4, the titles only were mentioned

- Page 9, line 66 sensitivity analysis is firstly to be mentioned here. This analysis was not mentioned in methods section and no details here were presented.

- N in tables: better to be corrected to “no.”

- Table (1):

• Total column could be moved after p value column for better understanding.

• No. of previous hospitalization: is better to be grouped to 3 groups: “0, 1 and 2 and more”. Qui square test could be done correctly without “0” in any cell.

• Write the test of significance as a footnote under the table.

- Table (2): add number of mortalities in each diagnosis.

- Table 3 and 4:

- In general, the number of observations is lower than needed to carry logistic regression as in case of number of previous hospitalizations and ischemic heart disease. Number of mortalities in each diagnosis is not clear while number of cases was only mentioned. In general, regression analysis models were not done on statistical basis.

• In the title “Multiple logistic regression models were adjusted for sex, age and type of fracture” please write this sentence as a footnote

Discussion:

• No discussion of the mortality incidence was found with other studies

• Discussion should be rewritten after corrections in results section to reevaluate the significant relations

Reviewer #2: Very interesting article. The major problem is the English language; abstract, introduction and methods are not clear. Seems like they have been written from a different person than the other parts.

Abstract: I would change the phrase "That assessment.....systems" is too long, and in "restrospective...2011" there is no verb

Introduction: You have analized too much what performance indicators are (I suggest to cancel 42-44 and 57-60 for example)

Methods: ethical approval is repeated: 106-107, 132-134

**********

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Reviewer #1: Yes: Dalia G Mahran

Reviewer #2: No

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20 Aug 2020

Reviewer’s Comments

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: (No Response)

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: (No Response)

Reviewer #2: Yes

Authors' response: We are grateful for the reviewer #2 assessment.

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: (No Response)

Reviewer #2: I Don't Know

Authors' response: We revised the statistical methods of our manuscript and corrected our previous analyses to avoid the occurrence of sparse data bias, as described in detail in our answers to the comments of reviewer #1.

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: (No Response)

Reviewer #2: Yes

Authors' response: Please note that in the previous version of our manuscript we had added a data availability statement to the end of the manuscript explaining that the authors do not have the permission to deposit the datasets used in this study into the public domain because even the de-identified dataset derived from the probabilistic record linkage process has the potential to be re-identified through the combination of several databases. If other researchers wish to have access to the de-identified dataset, they should contact the authors who will file an authorization request to the Ethics Review Committees that authorized this study. Once that permission is granted by both Ethics Research Committees, the authors shall be able to share the study dataset with requesting researchers. We understand that doing so is consistent with PLOS ONE policy on data availability, which recognizes that "in some instances, authors may not be able to make their underlying data set publicly available for legal or ethical reasons”.

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: (No Response)

Reviewer #2: No

Authors' response: An experienced English teacher revised our manuscript. Additionally, we asked for other colleagues to read our manuscript and to confirm whether it was sufficiently clear.

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: General comments:

It’s interesting to study the leading factors of mortality among hip fracture patients in addition to medical and public health importance, but this study idea titled “Is the number of previous hospitalizations associated with increased in-hospital mortality after hip fracture in a developing country?” raises some concerns.

Unfortunately, the hypothesis and research question about the relation between one main diagnosis "what was found in records" and mortality in those patients is not based in scientific basis that we couldn't find in the introduction or discussion sections. Hip fracture is a condition mainly related to elderly, the mean age of patients in this study is 79.4 years (SD: 8.3) with expected complicated medical conditions in the past two years from the index hip fracture. With no doubt before conduction of this study, one reported diagnosis of previous hospitalizations is not the only related factor to the mortality among those patients with more than one morbidity condition in this study

In addition, there are major defects in the manuscript and not well written especially in methods, data analysis and results sections. However, I have provided some remarks below.

Authors' response: We have read all the comments of reviewer #1 with great attention and we are grateful for the time they dedicated to evaluating our manuscript. We revised our manuscript carefully and provide below point-by-point responses to their comments.

The reviewer stated that “the hypothesis and research question about the relation between one main diagnosis "what was found in records" and mortality in those patients is not based in scientific basis that we couldn't find in the introduction or discussion sections." Respectfully, we disagree with the reviewer's remark and could not find in our manuscript any evidence corroborating their argument. Our introduction section was divided into 6 paragraphs that outlined the scientific rationale underlying our study. In the first paragraph, we explained why health service quality is an important field of research in public health and why performance indicators play an important role in the assessment of health service quality. In the second paragraph, we argued that in-hospital mortality after a hip fracture in older adults is an important indicator of the performance of care and that it is listed among the 26 quality indicators that the Agency for Healthcare Research and Quality determined for the assessment of the quality of care in American hospitals. In the third paragraph, we explored briefly some known risk factors for mortality after a hip fracture and provided references to previous studies that had found an association between previous hospitalizations and hip fracture mortality. In the fourth paragraph, we explained that "for in-hospital mortality after hip fracture in older adults to be used as a quality indicator to compare the performance of hospitals there are important factors that must be taken into account so that differences in findings do not primarily reflect differences in case-mix among hospitals, but rather the quality of care provided in those institutions". In the fifth paragraph, we introduced the reader to the Brazilian Hospital Admission Information System and to its limitation regarding the possibility to record only one comorbidity for each hospitalization episode, which is shared by information systems from other developing countries. In that paragraph, we also outlined the reasoning behind a strategy to overcome that limitation by using previous hospitalizations and the main diagnosis of each hospitalization episode as tools for risk adjustment in statistical models aiming to assess the quality of hospital health services for older adults with a hip fracture. Finally, in the sixth paragraph, we explained that in this study we aimed to evaluate the association between in-hospital mortality in older people who underwent surgical repair of a hip fracture and previous hospital admissions and the main diagnosis recorded for each hospitalization because if such associations were found they could be construed as evidence favoring the use of those variables for risk adjustment in health care quality comparisons among hospitals in developing countries whose administrative databases share similar limitations as the Brazilian Hospital Admission Information System. Hence, we believe that our introduction section thoroughly outlined the scientific basis and relevance of our research question.

We also carefully reexamined our discussion section and found that it was consistent with the arguments laid out in our introduction section as can be seen in the following excerpts of our manuscript:

“Hence, our results suggest that the number of hospitalizations may not be a good proxy of the health status of older patients undergoing hip fracture surgery for risk adjustment in statistical models in contexts prone to the possibility of selection bias. Therefore, previous hospitalizations should be used with caution for risk adjustment of in-hospital mortality after hip fracture surgery in settings of Hospital Admission Information Systems with limited availability of data regarding comorbidities and secondary diagnoses.” (first paragraph of the discussion section)

“In conclusion, our results suggest that, in settings where local hospital information systems have limited access to secondary diagnoses, the use of the number of previous hospitalizations or the main diagnoses associated with those hospitalizations as proxies for the profile of comorbidities of older adults with hip fractures may not be an effective way to adjust for case-mix when comparing in-hospital mortality rates among hospitals.” (last paragraph of the discussion section)

Still, regarding the reviewer’s first comment we feel concerned about the misuse of supposed quoted citations from our text. We thoroughly searched our text and could not find the reviewer’s quote “what was found in records".

The reviewer also stated that “With no doubt before conduction of this study, one reported diagnosis of previous hospitalizations is not the only related factor to the mortality among those patients with more than one morbidity condition in this study”. Respectfully, we never claimed that previous hospitalizations were the only risk factors for hip fracture mortality in any context. As can be seen in the third paragraph of our introduction section, we are aware of the most common risk factors for hip fracture mortality. Our focus on the associations between in-hospital mortality after a hip fracture and previous hospital admissions and the main diagnoses associated with those hospitalization episodes is explained by our intent to assess whether those variables could be useful risk adjustment tools in the context of health service quality research. Unfortunately, we are deeply concerned that the reviewer's comments above might indicate that they did not understand the scientific context and rationale of our investigation.

Reviewer #1:

Abstract:

In conclusion section: the word correlation is not correct to be written all over the manuscript, better to say associated factors or correlates.

Authors' response: Respectfully, we feel puzzled and deeply concerned about the reviewer’s comment lacking any plausible relationship with the previous version of our manuscript that we had submitted to PLOS ONE. The word “correlation” was not used anywhere in our abstract or in our main text.

Reviewer #1:

The sentences in lines 20 to 24 are not clear and not focused on the study aims, as “ evaluate , performance of hospitals, limited information system.

Authors' response: Respectfully, we disagree with the reviewer’s assessment. Please find below the whole “Purpose” section of the previous version of our abstract.

“Purpose: We aimed to examine whether the number of previous hospitalizations and the main diagnoses of those hospitalizations are associated with increased in-hospital hip fracture mortality for older people. That assessment is relevant because if those variables are shown to be associated with increased mortality, that finding could support their use as proxies for comorbidity burden for case mix adjustment in statistical models seeking to compare the performance of hospitals regarding hip fracture mortality in settings with limited hospital information systems.”

The reviewer argued that the last sentence of that section was not clear and was unrelated to the study’s aim. Our whole manuscript was revised by an experienced English teacher, who assured us that that sentence was already clear in the last version of our manuscript. The only correction that was added to that fragment involved substituting "case-mix" for "case mix". Additionally, we believe that that sentence is essential to convey the rationale underpinning the aim of our research question.

Reviewer #1: "Correlates”: is mentioned in the title and didn’t mentioned after words anywhere all over the manuscript.

Authors' response: Respectfully, the title of our manuscript never included the word “correlates”. Our title was and still is “Is the number of previous hospitalizations associated with increased in-hospital mortality after hip fracture in a developing country?” Once again, we cannot fathom how the reviewer was able to make such a statement with no relationship with the content of our submission.

Reviewer #1: In methods section: no data were mentioned about data collection

Authors' response: We understand the reviewer's comment and recognize that the construction of clear, informative, and succinct abstracts is a balancing act, where researchers must weight which pieces of information are essential to convey the main message of their study, and which are not. In an attempt to comply with the reviewer's request for more information regarding data extraction and with PLOS ONE's 300-word limit for abstracts, we added information about the database that we used in this study to the methods section of our abstract but we were not able to include details about the variables that were extracted from that database. The new methods section of our abstract reads as follows:

“Methods: In this retrospective cohort study of all public hospital admissions for older adults with hip fractures in the city of Rio de Janeiro between 2010 and 2011, we used data from the Hospital Admission Information System database to examine the association between in-hospital mortality and the number of hospitalizations in the previous two years and their main diagnoses through logistic regression.”

Reviewer #1:

The logistic regression analysis was not mentioned and its results “ which is not clear in analysis section after that”

Authors' response: Respectfully, we disagree with the reviewer's comment. We reported in the results section of our abstract results from our logistic regression analyses, which were represented by the odds ratio (OR: 1.78, 95%CI: 1.07 to 2.97). Because of the lack of space, we were not able to present other quantitative results from our logistic regression analyses, but we believe that our main results were communicated effectively to readers. The revised results section of our abstract reads as follows.

“Results: Among 1938 patients included in the study there were 103 (5.3%) in-hospital deaths. Although the presence of hospitalization episodes within the two years preceding the index hip fracture was associated with increased mortality (OR: 1.78, 95%CI: 1.07 to 2.97) we did not find evidence of a gradient of increased mortality with a growing number of previous hospitalizations. Additionally, several diseases recorded as main diagnoses of previous hospitalizations were not associated with increased mortality rates, as was expected based on existing knowledge on risk factors for decreased survival in older adults with hip fractures.”

Reviewer #1: Introduction

• Title and aim of the study are not matched with the introduction

Authors' response: Respectfully, we disagree with the reviewer’s comment. The title of our article is “Is the number of previous hospitalizations associated with increased in-hospital mortality after hip fracture in a developing country?”. The aim of our article in our introduction section was described as “This study aims to analyze the association between previous hospital admissions and in-hospital mortality in older adults who underwent surgical repair of a hip fracture.” (Lines 78-79). Hence, we argue that our title and aim are perfectly in line with each other.

Reviewer #1:

• First two paragraphs are not related to the title nor the aim of the study

Authors' response: Respectfully, we do not understand the basis for the reviewer’s expectation that there should be a perfect relationship between the first two paragraphs of our introduction section and the title of our manuscript. We have examined the STROBE statement and its checklist and could not find any recommendation that the aim of the study or its title should be outlined in the first two paragraphs of the introduction section. On the other hand, we firmly believe that our introduction section did comply with the STROBE Statement requirement that the background and rationale of observational studies be explained in the introduction section of manuscripts, as described in detail in our answer to the reviewer's first comment above.

Reviewer #1:

• Page 4, line 62: this study was not done to evaluate the quality of health care in hip fracture patients.

Authors' response: We agree with the reviewer's comment

. Indeed, we never stated that our study aimed to assess the quality of healthcare for patients with hip fractures. The paragraph that began in line 62 of the previous version (with track changes) of our manuscript stated the following:

"For in-hospital mortality after hip fracture in older adults to be used as a quality indicator to compare the performance of hospitals, there are important factors that must be taken into account so that differences in findings do not primarily reflect differences in case-mix among hospitals, but rather the quality of care provided in those institutions [11-14]. Risk adjustment aims to reduce the confounding role that some variables related to case-mix, such as patients' burden of comorbidities, functional and socioeconomic status, may play on health outcomes that are used as markers of quality of care among institutions [14-18]"

That paragraph was never meant to describe the aim of our study but simply to describe the rationale behind our aim.

Reviewer #1:

• No need to write about evaluation of health care in hp fracture patients, this could be mentioned in one sentence.

Authors' response: Respectfully, we disagree with the reviewer's comment. As described in our introduction section, the explanations about the quality of hospital health services, and performance indicators in the context of hip fractures in older adults represent essential information without which the background and rationale of our study cannot be conveyed appropriately to the reader.

Reviewer #1: • Page 5, line 72 to 75: "Additionally, the national Hospital Admission Information System may serve as an instrument for the assessment of the quality of inpatient care. However, up to this moment the Brazilian Hospital Admission Information System still allows the recording of only one comorbidity and this information has been historically recorded poorly”….. this sentence about the poor data source is talking about a deficient tool to do this study,

Authors' response: Respectfully, we disagree with the reviewer’s comment. Once again, we have the impression that the reviewer did not understand the basic aspects of the context and rationale of our study. It suffices to read the whole paragraph from which the reviewer extracted the text fragment above and its following paragraph, to understand that the rationale of our study took into account the limitations of the Brazilian Hospital Admission Information System and sought to assess whether, in the context of those limitations, previous hospitalizations were associated with in-hospital mortality. We copy the content of those two paragraphs below.

“In Brazil, hospitalization data from the Brazilian National Health System are recorded in an administrative database, the Hospital Admission Information System, which is the most important national source of information for planning and monitoring hospital care in the country. Additionally, the Hospital Admission Information System may serve as an instrument for the assessment of the quality of inpatient care. However, up to this moment, the Brazilian Hospital Admission Information System still allows the recording of only one comorbidity and this information has been historically poorly recorded [14]. This is an important limitation of the Brazilian Hospital Admission Information System, which is shared by information systems from several other developing countries [19]. A possible strategy to attempt to overcome that important shortcoming for analyses related to the quality of healthcare is to use data from previous hospitalizations routinely recorded in Hospital Admission Information Systems, such as the number of previous hospitalizations and the main diagnosis for each hospitalization episode, as a tool for risk adjustment in statistical models.

This study aims to analyze the association between previous hospital admissions and in-hospital mortality in older adults who underwent surgical repair of a hip fracture. It also seeks to consider the association of main diagnoses of previous hospitalizations and in-hospital death following a hip fracture. The presence of significant associations between previous hospitalizations and/or their main diagnoses and in-hospital mortality after a hip fracture could represent evidence favoring the use of those variables for risk adjustment using administrative data in scenarios typical of several developing countries, where hospital databases suffer from major shortcomings regarding the registry of comorbidities and secondary diagnoses.”

Reviewer #1:

Methods:

• Non concurrent: corrected to be retrospective cohort study

Authors' response: We would like to point out that the term "non-concurrent cohort study" is an accepted nomenclature for the classification of our study as explained in the following text on the taxonomy of study designs: https://www.ncbi.nlm.nih.gov/books/NBK154468/ .

Nevertheless, we followed the reviewer's recommendation and changed our text using the more commonly used wording: "retrospective cohort study".

Reviewer #1:

• Between 2010 and 2011: could be corrected to be “ from the start of 2010 to the end of 2011” or whatever the included months.

Authors' response: We followed the reviewer’s recommendation and modified our text, which now reads as follows:

“This is a retrospective cohort study of a population of elderly patients hospitalized for hip fractures in the city of Rio de Janeiro, Brazil, between January 1, 2010, and December 31, 2011.”

Reviewer #1:

• Mention the level of significance of p value

Authors' response: In compliance with the reviewer's request, we added the following text to our methods section:

“We used a two-tailed alpha value of 0.05 to define statistical significance.” (last paragraph of the Methods section)

Reviewer #1:

• Please mention the details of sensitivity analysis that was referred to in results section.

Authors' response: We followed one of the reviewer's recommendations made during their comments regarding our results section below and recategorized the number of previous hospitalizations variable in table 1 into 0, 1, and 2 or more hospitalization episodes, as requested. Aiming to attain consistency in the presentation of our results, we also used that categorization scheme for the regression analyses involving that variable. Hence, there was no need to perform our previous sensitivity analysis anymore because that analysis involved exactly that recategorization.

Reviewer #1: Results:

- Median of hospital stay duration: is this variable is non parametric?? Please clarify!!

Authors' response: We have added the following text to our methods section:

“We described categorical data as absolute numbers and proportions. Continuous data were described as mean and standard deviation (SD) when their distribution was approximately normal, or otherwise as median and interquartile ranges (IQR) [20]. We assessed distributions of continuous data for normality by inspecting their histograms.”

Thereby, we believe that the reader will understand that when we described a continuous variable using median and IQR, that variable did not follow the normal distribution.

Reviewer #1:

- Presentation of results is not well written regarding tables 2,3 and 4, the titles only were mentioned

Authors' response: We have made changes to the titles of tables 2, 3, and 4. The new titles are as follows:

Table 2. Frequency of main diagnoses recorded for hospitalization episodes within the two years preceding the index hip fracture.

Table 3. Simple and multivariable logistic regression models assessing the in-hospital mortality after hip fracture according to the occurrence of previous hospitalizations or the number of previous hospitalizations within the two years preceding the index hip fracture.

Table 4. Simple and multivariable logistic regression models for in-hospital mortality after hip fracture, according to the main causes of hospitalizations within the two years preceding the index hip fracture.

Please, note that we reframed table 3 to make it clear that we conducted two separate multivariable regressions, one in which previous hospitalizations were treated as a dichotomous variable and another where the number of previous hospitalizations was categorized as 0, 1, and 2 or more.

Reviewer #1:

- Page 9, line 66 sensitivity analysis is firstly to be mentioned here. This analysis was not mentioned in methods section and no details here were presented.

Authors' response: We addressed the issue of the sensitivity analysis in a previous answer.

Reviewer #1:

- N in tables: better to be corrected to "no."

Authors' response: It is common practice in scientific journals to report the number of individuals using the letter “N” and we could not find any recommendation in PLOS ONE’s guidelines for authors to avoid that standard. Nevertheless, if PLOS ONE’s editors believe we should follow another standard, we will be happy to comply.

Reviewer #1:

- Table (1):

• Total column could be moved after p value column for better understanding.

Authors' response: We have substituted the column with the totals by a column with the number of alive individuals. We believe that this change made it easier for readers to compare the different frequencies of in-hospital death according to the categories of each variable.

Reviewer #1:

• No. of previous hospitalization: is better to be grouped to 3 groups: "0, 1 and 2 and more". Qui square test could be done correctly without "0" in any cell.

Authors' response: We followed the reviewer's recommendation and recategorized that variable into 0, 1, and 2 or more hospitalization episodes, as described in a previous answer.

Reviewer #1: • Write the test of significance as a footnote under the table.

Authors' response: The Chi-square test of significance had already been added as a footnote to table 1 in the previous version of our manuscript.

Reviewer #1:

- Table (2): add number of mortalities in each diagnosis.

Authors' response: We followed the reviewer’s request and formatted table 2 according to the same standard used in table 1.

Reviewer #1:

- Table 3 and 4:

- In general, the number of observations is lower than needed to carry logistic regression as in case of number of previous hospitalizations and ischemic heart disease. Number of mortalities in each diagnosis is not clear while number of cases was only mentioned. In general, regression analysis models were not done on statistical basis.

Authors' response: We are very grateful for the reviewer's comment that allowed us to recognize that our previous analyses had incurred in the problem of sparse data bias (see Greenland et al 2016 https://www.bmj.com/content/352/bmj.i1981). We followed Greenland's et al recommendation and assessed the presence of sparse data bias in our results by examining the frequencies of outcome events per variable and by comparing the results of our logistic regressions with the results from penalized logistic regressions performed using the data augmentation method proposed by those authors using an F-distribution prior with a 95% odds ratio interval equivalent to 1/39 to 39. Whenever we found evidence of sparse data bias for any given variable, we reported the odds ratio estimates and confidence intervals from logistic regressions that penalized those variables using the approach recommended by Greenland et al 2016. That approach was described in the methods section of the new version of our manuscript.

• In the title “Multiple logistic regression models were adjusted for sex, age and type of fracture” please write this sentence as a footnote

Authors' response: In the previous clean version (i.e. without track changes) of our manuscript, we had already reported that information as a footnote for tables 3 and 4. However, we apologize for our mistake that this change was not apparent in the previous version of our manuscript with track changes, which must be the one that the reviewer assessed.

Reviewer #1:

Discussion:

• No discussion of the mortality incidence was found with other studies

Authors' response: The in-hospital mortality rate of our study was 5.3%, whereas the in-hospital mortality reported by other Brazilian studies of hip fracture in older adults ranged between 3.8% and 14.6% (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3971362/ ; https://pubmed.ncbi.nlm.nih.gov/16871434/ ; https://www.scielosp.org/article/rsp/2015.v49/12/ ). The in-hospital mortality in our study was similar to that reported in southern Ontario, Canada (5.0%) (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2947119/) and higher than that reported for intertrochanteric fractures in the US (1.7%) (https://pubmed.ncbi.nlm.nih.gov/28255840/). However, we felt that it would not be appropriate to deviate the focus of our discussion beyond the aims of our study. We believe that if we deviated our discussion from the main objectives of our study to address the possible reasons behind the different mortality rates among epidemiological studies of hip fracture in Brazil and elsewhere, our discussion section would become much lengthier but would not be substantially improved to justify such a decision. Furthermore, we recognize that although PLOS ONE does not impose any length limits on the articles that it publishes, it does recommend that study findings be presented and discussed concisely (https://journals.plos.org/plosone/s/submission-guidelines). Furthermore, the STROBE statement recommends that the discussion section summarizes "key results with reference to study objectives", which we did.

Reviewer #1: • Discussion should be rewritten after corrections in results section to reevaluate the significant relations

Authors' response: The changes that our results underwent after the consideration of the presence of sparse data bias and the implementation of the penalized logistic regression methods were incorporated into our discussion section bud did not change the overall interpretation of our findings and did not require major changes to our discussion.

Reviewer #2: Very interesting article.

Authors' response: We are grateful for the reviewer’s generous comment.

Reviewer #2:

The major problem is the English language; abstract, introduction and methods are not clear. Seems like they have been written from a different person than the other parts.

Authors' response: An experienced English teacher revised our manuscript. Additionally, we asked for other colleagues to read our manuscript and to confirm whether it was sufficiently clear.

Reviewer #2:

Abstract: I would change the phrase "That assessment.....systems" is too long, and in "restrospective...2011" there is no verb

Authors' response: We understand that writing an informative and yet concise abstract is always challenging. We agree that that is a long sentence. However, we have confirmed that it is sufficiently clear and we firmly believe that it conveys an essential piece of information regarding the rationale underpinning our study.

Reviewer #2:

Introduction: You have analized too much what performance indicators are (I suggest to cancel 42-44 and 57-60 for example)

Authors' response: We firmly believe that those sentences are essential to convey important background information about the relevance and the rationale of our study. Deleting those sentences would jeopardize the understanding of our investigation aims.

Reviewer #2: Methods: ethical approval is repeated: 106-107, 132-134

Authors' response: We followed the reviewer's recommendation and restricted the information about the ethics review committee's approval to a single section of our manuscript.

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

23 Sep 2020

Is the number of previous hospitalizations associated with increased in-hospital mortality after hip fracture in a developing country?

PONE-D-19-23508R2

Dear Dr. Pinheiro,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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Reviewer #2: Yes

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Reviewer #2: I Don't Know

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Reviewer #1: (No Response)

Reviewer #2: Yes

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Reviewer #2: Yes

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Reviewer #1: (No Response)

Reviewer #2: Dear Dr. Pinheiro,

I am sorry about your loss.

Your article is now sufficiently clear. I do appreciate your work.

For the future, it would be interesting to analyze the association between comorbidity and fragility through mono- o multi-dimentional scales (like CIRS or MPI etc) and in-hospital mortality after a hip fracture, in this category of patients. Moreover, the adoption of the multidimensional scales of valuation during the hospitalization permit to get information even if the local hospital information systems have limited access to secondary diagnoses. It could be also important to evaluate if the patients undergo surgical treatment within 48 hours; this can be an important confounding factor.

Best Regards

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Reviewer #1: No

Reviewer #2: No

30 Sep 2020

PONE-D-19-23508R2

Is the number of previous hospitalizations associated with increased in-hospital mortality after hip fracture in a developing country?

Dear Dr. Pinheiro:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

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