The epidemiology of falls in Portugal: An analysis of hospital admission data.

BACKGROUND: Falls are a common cause of injury and pose an increased risk of morbidity, mortality, and lifelong disability. Falls encompass a troublesome definition and can pose challenges in epidemiological studies. Data on fall-related hospital admissions in Portugal remain unpublished. This study aimed to examine the epidemiology of fall-related hospital admissions in the Portuguese population between 2010 and 2018. It also aimed to examine annual rates of fall-related hospital admissions using three methodological approaches.
METHODS: The Portuguese Hospital Morbidity Database was used to identify all cases resulting in one or more inpatient admission in public hospitals related to falls from 2010 to 2018. Fall-related hospital admissions were described by age groups, sex, geographical area of residence, and type of fall. Annual rates were computed using three approaches: i) based on the number of inpatient admissions with an ICD code of fall, ii) based on the number of patients admitted to inpatient care with an ICD code of fall, and iii) based on the number of inpatient admissions with a principal diagnosis of injury.
RESULTS: Between 2010 and 2018, 383,016 fall-related admissions occurred in 344,728 patients, corresponding to 2.1% of the total number of hospitalizations during the same period. Higher rates were seen among the younger (20-25) and the oldest age groups (+85), males until the age of 60, females from the age of 60, and areas of residence with a higher aging index. An overall rate of falls per 100,000 population was estimated at 414 (based on number of admissions), 373 (based on number of patients) and 353 (based on number of admissions with a principal diagnosis of injury).
CONCLUSIONS: This study provides an overall picture of the landscape of falls in a scarcely explored setting. The results aim to contribute to identifying appropriate preventive interventions and policies for these populations.

Introduction

Falls are one of the most common causes of injury and pose an increased risk of morbidity, mortality, and lifelong disability, particularly in the older age groups [1].

The latest Global Burden of Diseases, Injuries, and Risk Factors Study (GBD, 2019) showed falls ranked as the 21st leading cause of disability-adjusted life years in 2019 [2]. In the oldest age group (75+ years) falls ranked 8th, outranking conditions such as chronic kidney disease, prostate cancer, or road injuries. Because of this, most research on the risk factors for falls and fall-related injuries is focused on older adults [3].

Regardless of age, most falls result from the interplay between predisposing factors (intrinsic or extrinsic) and precipitating factors in a person’s environment [4].

The definition of falls in the literature has lacked standardisation, due to the methods utilized for identifying when a fall has occurred, methods of analysis, and details reported. This heterogeneity makes it difficult to aggregate or compare data from different studies. Moreover, falls can have a variety of outcomes ranging from no injury to life-threatening consequences. Nevertheless, several studies have contributed to the knowledge of the epidemiology of falls in Portugal. The GBD 2019 estimates the age-standardized incidence rate of falls that require medical care at 3,112 (95% uncertainty interval 2,695 to 3,627) per 100,000 [1].

In Portugal, the proportion of a self-reported fall in the previous 12 months has been previously estimated at 24.1% in community-dwelling adults in the EpiReumaPt study [5]. Results from this population-based cross-sectional study show an increasing association between age and falls, with people aged 75 and older having greater odds of falling (OR = 1.86; 95% CI 1.49–2.31). Neurologic and rheumatic diseases were also significantly associated with falls.

National data on emergency hospital admissions due to falls are also available through EVITA’s injury surveillance system. Injuries caused by falls at home were the most frequently recorded, with young (0 to 14 years) and older (65+) patients being the most affected [6]. In 2018, 30,196 falls among people aged 65 and older were recorded in the EVITA system. The majority of falls occurred at home (64.4%), followed by public areas (9.7%) and outdoor spaces (9.0%); these differences were statistically significant (p<0.01). The type of lesion most frequently observed was bruise (64.1%) and open wound (14.9%) [7].

Falls encompass a troublesome definition and can pose challenges in epidemiological studies. Data on fall-related hospital admissions in Portugal remain unpublished to the best of our knowledge. This study aimed to examine the epidemiology of fall-related hospital admissions in the Portuguese population between 2010 and 2018. It also aimed to examine the rate of fall-related hospital admissions, using three methodological approaches.

By exploring the landscape of fall-related admissions over the years, we seek to identify trends and possible unmet needs, thus tackling the existing knowledge gap in this matter, contributing to deploying appropriate cost-effective preventive interventions and policies for these populations.

Methods

Data sources and case identification

This study used a descriptive design to examine the epidemiology of fall-related hospital admissions in the Portuguese population, between 2010 and 2018. The Portuguese Hospital Morbidity Database was used for this research. The Hospital Morbidity Database is a national registry of all public hospital-related care, both inpatient and ambulatory care, centrally held by the Central Administration of the Health System (ACSS). Around 70% of all inpatient hospital admissions occur in public hospitals [8]. In 2017, Portugal registered a population of 10,291,027 inhabitants, 53% of these women [9]. The Portuguese health system includes three overlapping systems. All residents have access to the National Health Service (NHS), which is universal and almost free. The NHS is centrally financed by the Ministry of Health, funded mainly through general taxation. In addition, special health insurance schemes cover specific sectors or professions, and can be either public or private. Private Voluntary Health Insurance is supplementary and speeds up access to elective hospital treatment and ambulatory consultations; it also increases the choice of provider. Between one-fifth and one-fourth of the population has a second health insurance coverage through special schemes or private insurance. Health care is delivered by both public and private providers, with public provision being predominant in primary care and hospital care [10]. Being primarily created for administrative purposes, the Hospital Morbidity Database comprises data on patient demographics, admission and discharge information (including responsible hospital, date and time), in-hospital mortality, International Classification of Diseases (ICD) diagnosis codes, and diagnostic related grouping codes (for cost information). Therefore, clinical data not coded through ICD is absent. All records for individuals are linked utilizing an encrypted patient identification code.

This study included all inpatient admissions related to falls registered between 2010 and 2018. Inpatient admissions related to falls were defined as inpatient episodes with a diagnosis of falls. Falls were defined following the ICD 9th Revision, Clinical Modification (ICD-9-CM) codes E-880-888 and E829.3 and the 10th revision, Clinical Modification/Procedure Coding System (ICD-10-CM/PCS) codes W00-W19. Further analyses considered inpatient episodes with a diagnosis of falls and injury. Injuries were defined following the ICD 9th Revision (ICD-9) codes 800–848, 850–854, 860–887, 890–897, 900–959, and the 10th revision (ICD-10) codes S00-S99, T07 and T14, T15-28, T30-34, and T36-78. For each episode, data related to the type of falls or further characterization was only possible trough coded ICD-9-CM or ICD-10-CM.

Statistical analyses

This study was exploratory and aimed at investigating the distribution of falls in the whole inpatient population. Therefore, we had no a priori hypothesis to test, as this requires previous knowledge of the phenomenon in the Portuguese setting.

The total number of inpatient admissions related to falls was summarized by age groups, sex, geographical area of residence, and type of fall.

Rates of fall-related hospital admissions were computed using three different approaches: i) based on the total number of inpatient admissions with an ICD code of fall, ii) based on the total number of patients admitted to inpatient care with an ICD code of fall, and iii) based on the total number of inpatient admissions with a principal diagnosis of injury. We estimated annual rates, between 2010 and 2018, for each approach.

Data were analyzed using R version 3.6.3 and R studio version 1.2.5001 [11].

Results

Total inpatient admissions related to falls

Between 2010 and 2018, 383,016 inpatient admissions related to falls occurred in 344,728 patients. This number corresponded to 2.1% of the total number of admissions during the same period. In Table 1, the annual total number of patients admitted to inpatient care related to falls, as well as total and mean number of admissions for the same period are reported. The mean annual number of admissions related to falls over this period remained somewhat stable and ranged between 1.06 and 1.19 per patient.

Table 1

Number of admissions related to falls, between 2010 and 2018, in the Portuguese population.

	2010	2011	2012	2013	2014	2015	2016	2017	2018	Total
Patients	33,835	36,514	36,742	39,456	39,359	41,044	43,658	39,270	34,850	344,728*
Admissions	38,288	43,592	41,578	44,173	43,788	45,117	47,193	42,042	37,245	383,016
Mean number of admissions	1.13	1.19	1.13	1.12	1.11	1.09	1.08	1.07	1.06	1.11

*The total does not correspond to the sum of patients over the full period, as some patients appear more than once, and in more than one year.

Fig 1 shows the distribution of the mean number of admissions per patient over the analysis period. The mean number of fall-related admissions appears higher among the younger age groups (20–25) and the oldest age groups (+85).

Fig 1

Mean number of admissions per patient related to falls, between 2010 and 2018, in the Portuguese population.

Rates of fall-related hospital admissions

The estimated overall rate of falls, per 100,000 population, for the analysis period was 414 (based on the total number of episodes), 373 (based on the total number of individuals) and 353 (based on the total number of episodes with a principal diagnosis of injury). Fig 2 shows an overall steady increase in the rates of falls, from 2010 to 2016, wherefrom a sharp decline can be observed, for both males and females, both when taking into account episodes and patients.

Fig 2

Rates of falls (per 100,000 population at risk) by sex, based on admissions and based on patients, between 2010 and 2018, in the Portuguese population.

Admissions related to falls by age and sex

Table 2 shows the total number and rates of inpatient admissions related to falls by age and sex, between 2010 and 2018. In both scenarios, males show higher rates of fall-related admissions than females until the age of 60. However, from the age of 60 years, females represent the largest proportion of fall-related admissions. Annual rates of fall-related admissions by sex are presented in S1 Table, and the number of patients and admissions related to falls by age group are presented in S2 Table.

Table 2

Distribution of rates of falls (per 100,000 population at risk) based on patients and admissions related to falls, between 2010 and 2018, in the Portuguese population.

Age groups (years)	Population		Number of admissions			Rate (based on number of admissions)*			Number of patients			Rate (based on number of patients)*
	M	F	M	F	Total	M	F	Total	M	F	Total	M	F	Total
0–5	2,099,393	2,004,989	3394	2211	5605	161.7	110.3	136.6	2948	1949	4897	140.4	97.2	119.3
5–10	2,308,309	2,202,375	4712	2604	7316	204.1	118.2	162.2	4260	2350	6610	184.6	106.7	146.5
10–15	2,483,506	2,366,507	5264	1933	7197	212.0	81.7	148.4	4763	1756	6519	191.8	74.2	134.4
15–20	2,555,769	2,448,497	4271	1146	5417	167.1	46.8	108.2	3806	1022	4828	148.9	41.7	96.5
20–25	2,531,072	2,481,552	3728	1097	4825	147.3	44.2	96.3	3194	951	4145	126.2	38.3	82.7
25–30	2,622,834	2,642,593	4027	1295	5322	153.5	49.0	101.1	3475	1125	4600	132.5	42.6	87.4
30–35	2,942,058	3,087,519	4884	1708	6592	166.0	55.3	109.3	4177	1508	5685	142.0	48.8	94.3
35–40	3,364,706	3,603,756	6223	2419	8642	184.9	67.1	124.0	5330	2191	7521	158.4	60.8	107.9
40–45	3,449,588	3,710,444	7194	3209	10,403	208.5	86.5	145.3	6165	2868	9033	178.7	77.3	126.2
45–50	3,316,279	3,595,950	8354	4365	12,719	251.9	121.4	184.0	7163	3886	11,049	216.0	108.1	159.8
50–55	3,210,570	3,517,355	9234	6653	15,887	287.6	189.1	236.1	7972	5934	13,906	248.3	168.7	206.7
55–60	2,975,582	3,309,496	9925	9155	19,080	333.5	276.6	303.6	8534	8073	16,607	286.8	243.9	264.2
60–65	2,724,730	3,101,002	10,300	11,905	22,205	378.0	383.9	381.2	8815	10,363	19,178	323.5	334.2	329.2
65–70	2,392,718	2,852,586	11,186	15,784	26,970	467.5	553.3	514.2	9465	13,684	23,149	395.6	479.7	441.3
70–75	1,971,213	2,518,396	12,614	21,713	34,327	639.9	862.2	764.6	10,743	18,834	29,577	545.0	747.9	658.8
75–80	1,620,271	2,264,592	15,729	32,277	48,006	970.8	1425.3	1235.7	13,497	27,806	41,303	833.0	1227.9	1063.2
80–85	1,135,065	1,823,388	17,057	41,700	58,757	1502.7	2287.0	1986.1	14,794	35,928	50,722	1303.4	1970.4	1714.5
≥85	761,723	1,626,695	21,517	62,225	83,742	2824.8	3825.2	3506.2	18,512	52,864	71,376	2430.3	3249.8	2988.4

F: Females; M: Males.

*Rate per 100,000 population at risk (estimated resident population for Portugal).

The distribution of fall-related admission rates by geographical area of residence is shown in Table 3. The Centre region of Portugal registered the highest rate of fall-related admissions over the years, with an overall rate ranging between 411.6 (based on the number of patients) and 487.31 (based on the number of admissions) per 100,000 population. The islands of Madeira and Azores (not displayed in Table 3) registered rates between 87.8 (based on the number of patients) and 179.19 (based on the number of admissions), and between 82.83 (based on the number of admissions) and 92.7 (based on the number of patients), respectively. Areas of residence with a higher aging index presented the highest rates.

Table 3

Distribution of rates of falls (per 100,000 population at risk) by geographical area of residence, between 2010 and 2018, in the Portuguese population.

Area of residence	Admissions	Population	Rate (based on number of admissions)	Aging index 2015	Patients	Rate (based on number of patients)
Alentejo	29,735	6,598,373	450.64	191.6	26,541	402.2
Algarve	16,013	3,987,539	401.58	138.4	14,019	351.6
Lisbon	96,738	25,398,930	380.87	131.7	79,317	312.3
Centre	99,607	20,440,118	487.31	183.3	84,137	411.6
North	130,948	32,650,164	401.06	139.5	113,745	348.4

Number of admissions related to falls by type of fall

Table 4 describes the number of inpatient admissions related to falls, by type of fall. Across all years, the most common type of fall was "Other and unspecified fall" (ranging between 71.51% and 80.22%) followed by "Accidental fall on same level from slipping, tripping or stumbling" (ranging between 5.16% and 10.01%).

Table 4

Number of fall-related admissions by type of fall, in the Portuguese population.

	2010	2011	2012	2013	2014	2015	2016	2017	2018
Admissions	38,288	43,592	41,578	44,173	43,788	45,117	47,193	42,042	37,245
Type of falls	n (%)	n (%)	n (%)	n (%)	n (%)	n (%)	n (%)	n (%)	n (%)
Accidental fall on or from stairs or steps	1088 (2.84)	1181 (2.71)	1109 (2.67)	1378 (3.12)	1354 (3.09)	1460 (3.24)	1452 (3.08)	1571 (3.74)	1318 (3.54)
Accidental fall on or from ladders or scaffolding	144 (0.38)	185 (0.42)	150 (0.36)	266 (0.60)	297 (0.68)	310 (0.69)	324 (0.69)	375 (0.89)	390 (1.05)
Accidental fall from or out of building or other structure	580 (1.51)	596 (1.37)	567 (1.36)	503 (1.14)	468 (1.07)	409 (0.91)	413 (0.88)	435 (1.03)	381 (1.02)
Accidental fall into hole or other opening in surface	71 (0.19)	52 (0.12)	64 (0.15)	88 (0.20)	54 (0.12)	66 (0.15)	82 (0.17)	100 (0.24)	103 (0.28)
Other accidental falls from one level to another	2701 (7.05)	2689 (6.17)	2368 (5.70)	2797 (6.33)	2707 (6.18)	2936 (6.51)	2851 (6.04)	3053 (7.26)	2756 (7.40)
Accidental fall on same level from slipping tripping or stumbling	3832 (10.01)	3596 (8.25)	3038 (7.31)	2564 (5.80)	2468 (5.64)	2326 (5.16)	2690 (5.70)	3426 (8.15)	2121 (5.69)
Fall on same level from collision, pushing, or shoving, by or with other person	474 (1.24)	859 (1.97)	953 (2.29)	1006 (2.28)	682 (1.56)	368 (0.82)	325 (0.69)	96 (0.23)*	-
Fracture, cause unspecified	1550 (4.05)	1574 (3.61)	1385 (3.33)	1521 (3.44)	2000 (4.57)	2635 (5.84)	3586 (7.60)	15 (0.04)*	-
Other and unspecified fall	27,381 (71.51)	32,141 (73.73)	31,417 (75.56)	33,636 (76.15)	33,411 (76.30)	34,333 (76.10)	35,235 (74.66)	32,797 (78.01)	29,879 (80.22)
Late effects of accidental fall	541 (1.41)	802 (1.84)	592 (1.42)	471 (1.07)	405 (0.92)	365 (0.81)	327 (0.69)	251 (0.60)	268 (0.72)

*Type of fall only included in ICD-9, and no matching code in ICD-10.

Discussion

This study aimed to examine the epidemiology of fall-related hospital admissions in the Portuguese population, from 2010 to 2018. It also aimed to estimate the rate of fall-related admissions, using three methodological approaches.

The analysis of the Hospital Morbidity Database found that, between 2010 and 2018, 383,016 inpatient admissions related to falls occurred in 344,728 patients. Males registered higher rates of fall-related admissions than females until the age of 60. From the age of 60, females represented the largest proportion of fall-related admissions. Previous observational studies found that older women have a higher likelihood of falls than older men [12-14]. Gale et al. [15] sought to investigate sex-specific associations between potential risk factors and the likelihood of falling in patients aged 60 and older, in England. This cross-sectional study suggested that there were differences between the sexes in some risk factors for falls, such as incontinence and frailty in women, and older age, higher symptoms of depression and being unable to perform a standing balance test in men. In a subsequent longitudinal study, Gale et al. [16] examined the relationship between a wide range of factors and the risk of incident falls. The results suggest that the association between pain, balance and comorbidity and incident falls risk varied by sex. A longitudinal study by Ek et al. [17] also suggests that men and women have different fall risk profiles.

Areas of residence with a higher aging index presented the highest rates of fall-related admissions, with the Centre region of Portugal registering the highest rates over the years. This difference highlights the importance of age as a risk factor for falls. In a recent study by Chang et al., [18] falls were considered as one of the 92 age-related diseases (diseases with incidence rates among the adult population increasing quadratically with age).

Across all ages, the most common type of fall was "Other and unspecified fall" followed by "Accidental fall on same level from slipping tripping or stumbling". Patients aged 65 and older represented over 60% of the total sample. Kusljic et al. [19] examined the types of falls in a sample of 250 patients above the age of 65. They reported the most common type of falls being a fall on the same level related to slipping, tripping, or losing balance (35% of the sample), followed by unspecified fall on the same level (31%) and other types of fall (19%). Paul et al. [20] examined ambulance records and hospital admission data in New South Wales, Australia, among individuals aged 65 years and older and found that slips and trips were the most common mechanism of falls requiring hospitalization in this population (52%).

Different approaches to estimating fall-related hospital admissions rates resulted in estimates between 353 and 414 per 100,000 population. Identifying fall-related admissions based on episodes provides an overall picture of the landscape of falls in a scarcely explored setting in Portugal. However, such an approach poses limitations as to the uncertainty surrounding the true number of falls. This is because one event of fall may generate several hospital admissions. The approach based on the number of patients may underestimate the true number of falls because several events may be accrued to the same individual. Yet, identifying fall-related admissions based on episodes with a principal diagnosis of injury may increase the likelihood of selecting episodes of falls occurring outside the hospital setting and indicating that the injury was the chief reason for admission. A large proportion of fall-related admissions had a principal diagnosis of injury (about 85%), which may indicate that the fall was in fact the cause for hospital admission. The choice of which approach to take may depend on the aim of the analysis.

Adequately identifying what population groups may be at higher risk of falls and related morbidity is key to devising appropriate public health policies and programmes. The finding that higher rates of falls occur at different ages among men and women highlight the importance of gender-adequate preventive programmes, as also stressed by Wei et al. [12] and Ek et al. [17]. The analysis of type of fall suggest that further investigations are warranted to try to disentangle the aspects behind the classification of “Other and unspecified fall”, which represented the largest proportion of fall-related admissions.Slipping, tripping, or stumbling as mechanisms of falls were the next most common type of fall (although relatively small proportion), which supports the importance of community-based intervention focusing on fall prevention. Further studies should examine sex differences underlying different mechanisms of falls.

The most recent GBD study [2] points out the complex multifactorial context of falls in the elderly and its most often iatrogenic underpinnings stemming from incorrect diagnosis and treatments. The study highlights the link between falls and psychotropic and cardiovascular medications, cognitive impairment, depression, and general frailty. Extensive evidence exists on the effectiveness of multifactorial interventions combining education, exercise, and interventions targeting home safety modification [21], and international guidelines exist for the assessment and prevention of falls in the elderly [22].

The global report on falls prevention, issued by the World Health Organisation in 2007 [23], discussed the importance of population ageing in the epidemiology and burden of falls. The report highlights the need for knowledge and preparedness of primary health care and social service providers on the complexity of factors that predispose falls in the older age. This is critical to ensure appropriate treatment and management of falls within primary care, as well as access to falls prevention programmes. At the national level, a report issued by the Portuguese Directorate-General of Health [24] drafted a national strategy for healthy ageing and reiterates the importance of investing in the creation of safe physical environments for the prevention of falls and related injuries. Other international agencies [25] followed suit and have stated the importance of investment in healthy ageing and building inclusive societies recognising its importance for overall population wellbeing and economic growth.

This study provides an overall picture of the landscape of falls in a scarcely explored setting in Portugal. Additionaly, the database used to describe the distribution of falls covers about 70% of all inpatient hospital admissions to public hospitals in the country. There are, however, a few limitations to take into account. Our study only accounts for one of the dimensions included in the injury pyramid for falls [26, 27]. This pyramid considers four levels of injury including general practitioner registry, emergency department registers, hospital discharge and mortality data. The effect of falls in general practitioner or emergency department visits (without inpatient admission) and deaths (which occurred outside the hospital setting) is not included in our estimates as the data pertains only to the hospital setting.

The transition from ICD-9-CM to ICD-10-CM/PCS during the study period (from 2016 onwards) may have had an impact on the coding of external causes of injury. It is not possible to ascertain the impact of such transition in our results.

The Hospital Morbidity Database used in this study to describe the landscape of fall-related admissions in Portugal has some limitations. This database was created to monitor hospital productivity for mainland Portugal within the publicly financed NHS. The islands of Madeira and Azores have their own health subsystem and reporting, therefore they are underrepresented within the database. The rates reported may therefore better represent the setting of mainland Portugal.

Conclusions

This study reveals that fall-related hospital admissions are higher among the younger and the oldest age groups, among males until the age of 60, females from the age of 60, and in areas of residence with a higher aging index. Annual rates of falls differed based on the methodological approach chosen, which warrants further study. Further research should be conducted to better understand the risk profiles of these population groups in the Portuguese setting, and help design and implement appropriate policy for prevention of falls in at risk groups.

Distribution of rates of falls based on patients and admissions related to falls, between 2010 and 2018, in the Portuguese population.

(DOCX)

Click here for additional data file.

Distribution of number of patients and inpatient admissions related to falls, between 2010 and 2018, in the Portuguese population.

(DOCX)

Click here for additional data file.

6 Sep 2021

PONE-D-21-21269The epidemiology of falls in Portugal: an analysis of hospital admission dataPLOS ONE

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We will update your Data Availability statement to reflect the information you provide in your cover letter.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. 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: Partly

**********

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

Reviewer #1: No

**********

3. 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

**********

4. 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

**********

5. 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:

The study idea about the epidemiology of falls in the Portuguese population is interesting with this large sample size in nine years and is of great research importance.

But unfortunately, this work is not presented in a way that can deal with rich data. However, I have provided some remarks below.

Title:

“Epidemiology” is a wider term than what was studied and presented in the results.

Abstract:

The abstract all over its section should be corrected accordingly after rewriting of the manuscript.

Introduction

- Is well written

Methods:

- The study design was not included in this section

- The sentence in lines 112 and 113 is not related to the methodology, please delete.

- In page 12: lines 114 to 128: write the idea in two sentences only. Go direct to the data source and case identification, this section is informative and needed

- The available data related to falls in the “ Hospital Morbidity Database “ were not discussed in this section to be used in analysis.

- In statistical analysis section: there are no details about the analysis, The used variables should be written and use statistical significance tests

Results:

- In general:

• include “ in Portuguese population” to the titles of the tables and figures

• Overall, there is no tests of significance were used in analysis and to make comparisons within the groups in each table and to compare with the results of other studies in the discussion section. It’s important to do further analysis

• Classify the age groups in tables and figures in “ten years classes” of age in each group, and not by 5 years in this wide range of age., the last one could be 70+

• In tables including rates, add per 100,000 in the title

- In table (1):

• the numbers are written in two lines, this not an acceptable way to present numbers.

• add the “%” and “minimum -maximum” for presentation of data to be clearer.

• add the “SD” to mean number and do comparisons by the T test

- Figure (1):

• write (0-10) and not (0,10) in classes of age

- Page 15, line 183: correct to Figure (2)

- The distribution of falls according to demographics is not restricted to age and sex

- Table (2):

• add the “%” presentation of data to be clearer.

• replace “MF” by total

• aggregate the age classes as mentioned before

Discussion:

In general:

- The discussion is not well structured and not well written

- There are multiple undefined ideas

- There are redundancy with unimportant multiple sentences

- There are no significance in the presented data to compare the results with other studies or to compare within the same table e.g between age categories or between males and females “ on what basis???”

- Delete the title “ summary of main findings” and delete all this section to be rewritten.

- Limitations: shorten to 2-3 sentences and put them at the end of the discussion

- Methodology limitation: to not include mortality data and other data due to “the nature of the study” unclear??

- Add strength points

- “Policy implications and directions for future research”: this is not unclear section, is it a discussion or a conclusion, writing about the prevention as a recommendation with the conclusion in one sentence is better

- Conclusion:

Conclusion:

- shorten it and write about the main findings

- “This study provides…..” in the conclusion section, this could be added to the strength points not a conclusion.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #1: Yes: Dalia G Mahran

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25 Oct 2021

Response to journal requirements and reviewers’ comments

We would like to thank the editor and the reviewer for their comments, which have significantly contributed to the paper. We have made changes in the paper according to their suggestions. All changes are highlighted by using the track changes mode in the main manuscript and presented under each comment in blue.

Journal requirements:

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We have made sure the manuscript meets PLOS ONE´s requirements, including those for file naming.

2. Thank you for stating the following in the Acknowledgments Section of your manuscript: “ This work was produced with the computational support of INCD – Instituto Nacional de Computação Distribuida – which is funded by FCT and FEDER under the project 01/SAICT/2016 No. 022153”. We note that you have provided additional information within the Acknowledgements Section that is not currently declared in your Funding Statement. Please note that funding information should not appear in the Acknowledgments section or other areas of your manuscript. We will only publish funding information present in the Funding Statement section of the online submission form. Please remove any funding-related text from the manuscript and let us know how you would like to update your Funding Statement. Currently, your Funding Statement reads as follows: “This publication was supported by Fundação para a Ciência e Tecnologia (FCT) under the references UIDB/04295/2020 and UIDP/04295/2020.” Please include your amended statements within your cover letter; we will change the online submission form on your behalf.

3. Thank you for stating the following financial disclosure: “This publication was supported by Fundação para a Ciência e Tecnologia (FCT) under the references UIDB/04295/2020 and UIDP/04295/2020.” Please state what role the funders took in the study. If the funders had no role, please state: "The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript." If this statement is not correct you must amend it as needed. Please include this amended Role of Funder statement in your cover letter; we will change the online submission form on your behalf.

According to the editor´s comments 2 and 3 above, we have removed any funding information from the manuscript. The acknowledgment section was also removed. We would like to delete the current funding statement (“This publication was supported by Fundação para a Ciência e Tecnologia (FCT) under the references UIDB/04295/2020 and UIDP/04295/2020.”) since we did not receive funding from this source during the course of this study. This source of funding was initially included as the authors considered them for funding the open access at Plos One. However, the affiliation of the first and corresponding author, Filipa Sampaio, from Uppsala University Sweden, has an agreement for open access. Hence, naming “This publication was supported by Fundação para a Ciência e Tecnologia (FCT) under the references UIDB/04295/2020 and UIDP/04295/2020.” as source of funding is not anymore relevant.

We would like to replace the old funding statement with the following:

“The author Paulo Nogueira received computational support of INCD – Instituto Nacional de Computação Distribuida – which is funded by FCT and FEDER under the project 01/SAICT/2016 No. 022153. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.”

4. In your Data Availability statement, you have not specified where the minimal data set underlying the results described in your manuscript can be found. PLOS defines a study's minimal data set as the underlying data used to reach the conclusions drawn in the manuscript and any additional data required to replicate the reported study findings in their entirety. All PLOS journals require that the minimal data set be made fully available. For more information about our data policy, please see http://journals.plos.org/plosone/s/data-availability. Upon re-submitting your revised manuscript, please upload your study’s minimal underlying data set as either Supporting Information files or to a stable, public repository and include the relevant URLs, DOIs, or accession numbers within your revised cover letter. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories. Any potentially identifying patient information must be fully anonymized. Important: If there are ethical or legal restrictions to sharing your data publicly, please explain these restrictions in detail. Please see our guidelines for more information on what we consider unacceptable restrictions to publicly sharing data: http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions. Note that it is not acceptable for the authors to be the sole named individuals responsible for ensuring data access. We will update your Data Availability statement to reflect the information you provide in your cover letter.

We would like to update the data availability statement as follows:

“Data availability statement

All available data are within the manuscript and its supporting information file. This study was based on the analysis of proprietary data, owned by the Portuguese Central Administration of the Health System, I.P. (ACSS). Aggregated data can be accessed via the Transparency Portal held by the Portuguese Ministry of Health, at the following link: https://transparencia.sns.gov.pt/explore/?sort=title&q=morbilidade+hospital.”

Reviewers' comments to author:

Reviewer #1

The study idea about the epidemiology of falls in the Portuguese population is interesting with this large sample size in nine years and is of great research importance. But unfortunately, this work is not presented in a way that can deal with rich data. However, I have provided some remarks below.

We would like to thank the reviewer for the valuable comments provided. We have, to the best of our knowledge, tried to incorporate all suggestions provided. We hope the manuscript is now at a state deemed acceptable for publication.

Title:

“Epidemiology” is a wider term than what was studied and presented in the results.

Thank you for the comment regarding the relevance of the title for the scope of the paper. We understand the concept of epidemiology is quite broad. MacMahon and Pugh definition, updated by Greenland and Rothman (2008) (MacMahon and Pugh 1970 modified by Greenland and Rothman Chapter 3 Measures of Occurrence page 32 in Modern Epidemiology 3rd Edition K.Rothman, S. Greenland and T. Lash 2008 Lippincott Williams & Wilkins), presented epidemiology as the study of the distribution and determinants of disease frequency in human populations. We have in our paper attempted to describe the distribution of falls in the Portuguese population, using national population data from a national registry of inpatient and ambulatory care within the public sector. This database covers about 70% of all inpatient hospital admissions in the country. We have clarified in the title that the scope of the epidemiologic analysis pertains to hospital related data.

Abstract:

The abstract all over its section should be corrected accordingly after rewriting of the manuscript.

We have adjusted the abstract according to the changes incorporated in the manuscript.

Introduction:

Is well written

Methods:

- The study design was not included in this section

The study design has been included at the beginning of the method section. Please see page 6, lines 135-136:

“This study used a descriptive design to examine the epidemiology of fall-related hospital admissions in the Portuguese population between 2010 and 2018.”

- The sentence in lines 112 and 113 is not related to the methodology, please delete.

The sentence has been deleted.

- In page 12: lines 114 to 128: write the idea in two sentences only. Go direct to the data source and case identification, this section is informative and needed.

The section on study setting has been deleted and some of its information shortened and integrated in the section on “Data sources and case identification”. Please see pages 5 – 6, lines 115-152 for the new restructured section.

- The available data related to falls in the “ Hospital Morbidity Database “ were not discussed in this section to be used in analysis.

Thank you for pointing this out. We have below attempted at clarifying this matter, as well as have included in the manuscript more information under the Methods section on “Data sources and case identification”. Please see pages 7-8, lines 153-170.

Being primarily created for administrative purposes, the Hospital Morbidity Database comprises data on patient demographics, admission, and discharge information (including responsible hospital, date, and time), in-hospital mortality, International Classification of Diseases (ICD) diagnosis codes, and diagnostic related grouping codes (for cost information).

The Hospital Morbidity Database is owned by the Portuguese Central Administration of the Health System, I.P. (ACSS) and its access is possible upon request for investigational purposes.

By using the Hospital Morbidity Database, we are limited to data collected at a national level, primarily for administrative purposes, where clinical information is coded by trained physicians (ICD). ICD-codes for external causes were used to identify the occurrence of fall and to categorize it according to the type of fall. ICD-10-CM provides further detail and granularity than ICD-9-CM and it was necessary to crosswalk between the two to present and interpret data.

- In statistical analysis section: there are no details about the analysis, The used variables should be written and use statistical significance tests.

The Hospital Morbidity Database is a national database created for administrative purposes; therefore, all variables are routinely collected, independently from our study. This means that we were limited to the variables included in this database, as described in the previous comment.

Thank you for your comment regarding the use of statistical tests. We would like to clarify our reasoning behind not using inferential statistics and thereby significance estimates. The database used in this study is a national database, used primarily for administrative purposes, which routinely collects data from the hospitals from the National Health Service. This data collection is done independently from our study. This means that this database contains inpatient and selected outpatient information for the Portuguese mainland population, which amounts to about 10 million people. Given these circumstances, this database provided us with the ideal setting to explore the landscape of falls, which had not yet been done for the Portuguese setting. Being aware of the many dimensions of falls, we were able to explore one of its dimensions pertaining to hospital admission-related falls. This was study was, hence, exploratory and aimed at investigating the distribution of falls in the whole inpatient population. Therefore, we had no a priori hypothesis to test, as this requires previous knowledge of the phenomenon, which we did not have. We would like to thank you for giving us the opportunity to clarify this matter in this reply, as well as in the manuscript. We have added a short sentence in the methods section, under “Statistical analyses”, on page 8, lines 173-175.

Other authors have done similar work of describing the distribution of events in different populations, without the use of inferential statistics, when on a population level. For instance, James et al (2017) with the Global Burden of Disease Collaboration has estimated the burden of falls in different countries (reference below).

James SL, Lucchesi LR, Bisignano C, et al The global burden of falls: global, regional and national estimates of morbidity and mortality from the Global Burden of Disease Study 2017

Injury Prevention 2020;26:i3-i11.

Results:

- In general:

• include “ in Portuguese population” to the titles of the tables and figures

This has now been included in all titles of tables and figures.

• Overall, there is no tests of significance were used in analysis and to make comparisons within the groups in each table and to compare with the results of other studies in the discussion section. It’s important to do further analysis

Thank you for your comment. Please refer to the previous comment on our reasoning behind the absence of inferential statistics and statistical tests.

• Classify the age groups in tables and figures in “ten years classes” of age in each group, and not by 5 years in this wide range of age., the last one could be 70+

Thank you for your comment. We would like to clarify that the age group intervals used in our study are in agreement with the official age group intervals used by Statistics Portugal (https://www.ine.pt/xportal/xmain?xpid=INE&xpgid=ine_main), which is the national statistics authority. With this in mind, these age group intervals also allow our data and findings to be used by other researchers in the field who would like to have detailed information on the phenomenon of falls in Portugal. This is in line with the goal of the paper, that is to provide a detailed (as detailed as possible) overview of fall-related hospital admissions.

• In tables including rates, add per 100,000 in the title

This has been added to all tables reporting rates.

- In table (1):

• the numbers are written in two lines, this not an acceptable way to present numbers.

• add the “%” and “minimum -maximum” for presentation of data to be clearer.

• add the “SD” to mean number and do comparisons by the T test

Table 1 has been corrected. Please refer to the previous comment on our reasoning behind the absence of inferential statistics and statistical tests.

- Figure (1):

• write (0-10) and not (0,10) in classes of age

Figure 1 has been corrected.

- Page 15, line 183: correct to Figure (2)

This has been corrected and the reference to Figure 1 has been replaced by Figure 2.

- The distribution of falls according to demographics is not restricted to age and sex

Thank you for your comment. We have corrected this section of results so that it reads as “Admissions related to falls by age and sex” (page 12, line 224)

- Table (2):

• add the “%” presentation of data to be clearer.

Table 2 has been corrected for ease of understanding. We have used a thousand separators for numbers related to the population. All rates are presented as rates per 100,000 population at risk.

• replace “MF” by total

This has been replaced by “Total”.

• aggregate the age classes as mentioned before

Thank you for your comment. We would like to clarify that the age group intervals used in our study are in agreement with the official age group intervals used by Statistics Portugal (https://www.ine.pt/xportal/xmain?xpid=INE&xpgid=ine_main), which is the national statistics authority. With this in mind, these age group intervals also allow our data and findings to be used by other researchers in the field who would like to have detailed information on the phenomenon of falls in Portugal. This is in line with the goal of the paper, that is to provide a detailed (as detailed as possible) overview of fall-related hospital admissions.

Discussion:

In general:

- The discussion is not well structured and not well written

- There are multiple undefined ideas

- There are redundancy with unimportant multiple sentences

- There are no significance in the presented data to compare the results with other studies or to compare within the same table e.g between age categories or between males and females “ on what basis???”

- Delete the title “ summary of main findings” and delete all this section to be rewritten.

- Limitations: shorten to 2-3 sentences and put them at the end of the discussion

Thank you for your comments on the discussion section of the paper. We have restructured and rewritten the discussion so it reads better and presents ideas in a more clear way. We have also shortened the text on limitations. Please see the full discussion section on pages 16-21.

- Methodology limitation: to not include mortality data and other data due to “the nature of the study” unclear??

Thank you for your question. The hospital morbidity database used in this paper includes data on ambulatory and inpatient care for Portuguese public hospitals (about 70% of all inpatient hospital admissions to public hospitals in the country). When studying falls, we are limited to hospital related data, hence we do not have access to the falls that occurred and were limited to community/primary care. Additionally, we do not have data on deaths due to falls in the community, nor does our data allow us to know if deaths occurring within the hospital setting are due to a fall. We have attempted to clarify this aspect in the text.

- Add strength points

A couple of strengths point have been added. Please see page 19, lines 84-88.

- “Policy implications and directions for future research”: this is not unclear section, is it a discussion or a conclusion, writing about the prevention as a recommendation with the conclusion in one sentence is better

Thank you for your comment. We have restructured this section and the discussion as a whole so that it flows better. We have also added a short note about prevention in the conclusion as suggested. Please see the full discussion section on pages 16-21 and the conclusion on page 21-22, lines 143-150.

Conclusion:

- shorten it and write about the main findings

- “This study provides…..” in the conclusion section, this could be added to the strength points not a conclusion.

Thank you for your suggestions. We have restructured the conclusion accordingly. Please see the conclusion on page 21-22, lines 143-150.

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

3 Dec 2021

The epidemiology of falls in Portugal: an analysis of hospital admission data

PONE-D-21-21269R1

Dear Dr. Sampaio,

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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Kind regards,

Osama Farouk

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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: All comments have been addressed

**********

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: Yes

**********

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

Reviewer #1: Yes

**********

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: Yes

**********

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: Yes

**********

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: (No Response)

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Dalia G Mahran

13 Dec 2021

PONE-D-21-21269R1

The epidemiology of falls in Portugal: an analysis of hospital admission data

Dear Dr. Sampaio:

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.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

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on behalf of

Dr. Osama Farouk

Academic Editor

PLOS ONE