Exploring seasonality and hospital differentiation as factors in hospital mortality among Community-Acquired Pneumonia patients in Portugal: Beyond traditional factors

doi:10.21203/rs.3.rs-4808646/v1

Download PDF

Research Article

Exploring seasonality and hospital differentiation as factors in hospital mortality among Community-Acquired Pneumonia patients in Portugal: Beyond traditional factors

https://doi.org/10.21203/rs.3.rs-4808646/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Background

Community-Acquired Pneumonia (CAP) is regarded as a substantial part of the global burden of disease and a public health priority. Besides host factors, such as sociodemographic characteristics, comorbidities and clinical severity, CAP morbidity and mortality may also be related to other factors like socioeconomic background, seasonality and level of health care. This study aims to analyze trends in hospital admissions and in-hospital mortality due to CAP in National Health Service hospitals in mainland Portugal from 2010 to 2018. Additionally, it seeks to assess the impact of various host and external factors on in-hospital mortality attributed to CAP.

Methods

A retrospective cross-sectional study was conducted on 391,732 CAP hospitalizations. Multivariable Logistic Regression analysis was used to estimate the probability of in-hospital death, considering various host and environmental factors.

Results

A decrease in the number of admissions and mortality rate over time was observed. The regression model identified advanced age, male gender, clinical severity, comorbidities, summer season, early school leaving rate, unemployment rate, and lower hospital differentiation as factors increasing the probability of death (p < 0.001).

Conclusions

Throughout the nine-year span, a consistent decline in in-hospital mortality rates was observed. Mortality exhibited a dual influence, being shaped by host factors (such as age, sex, clinical severity, and comorbidities) as well as non-traditional factors, such as socioeconomic vulnerability, the summer season, and the level of hospital differentiation. Therefore, effectively reducing CAP mortality necessitates comprehensive policies that target at-risk groups and address a broad range of risk factors. These policies should prioritize enhancing vaccination coverage, improving healthcare accessibility, and ameliorating thermal housing conditions, especially for socially vulnerable individuals.

Community-Acquired Pneumonia

hospital mortality

risk factors

low socioeconomic status

weather

age factors

sex

comorbidity

Community-acquired pneumonia (CAP) continues to be a predominant infectious disease leading to global mortality, particularly in elderly and individuals with underlying comorbidities (1–6). CAP is responsible for high hospitalizations and mortality rates, resulting in significant premature deaths and placing a substantial clinical and economic burden on healthcare systems in Europe (5–6). In Portugal, although there have been improvements in recent years (7–10), statistics remain a matter of concern compared to the rest of Europe (11, 12).

The COVID-19 pandemic has highlighted the profound influence of various factors on global health, underlining the importance of adopting an environmental health approach.

This approach extends beyond traditional boundaries by considering a wide range of determinants within a broader environmental context, encompassing all external factors affecting the host (13).

These determinants include psychosocial, economic, and social factors as well as other factors associated with natural (e.g., weather changes) and man-made environments (e.g., healthcare infrastructure and resources) (14, 15).

Therefore, when developing policies to promote health and well-being it is crucial to consider not only the host factors but also the external ones as part of the broader environmental context of health and disease (13).

The aims of this study are to analyze trends in hospital admissions and in-hospital mortality, as well as the contribution of various host and environmental factors to in-hospital mortality from CAP in National Health Service (NHS) hospitals in mainland Portugal between 2010 and 2018.

This study employs a retrospective cross-sectional design to analyze hospitalizations due to community-acquired pneumonia (CAP) in mainland Portugal from 2010 to 2018. Data were sourced from the National Hospital Discharge Database, which provides comprehensive information on hospital admissions, including patient demographics, clinical characteristics, and outcomes.

Data Collection

Data for this study were extracted from the National Hospital Discharge Database, covering all hospitalizations due to CAP in mainland Portugal’s National Health Service (NHS) hospitals between 2010 and 2018. The database includes detailed information on patient demographics, primary and secondary diagnoses, procedures performed, and outcomes at discharge.

Inclusion Criteria

The study included all hospital admissions with a primary diagnosis of CAP, identified using the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) and 10th Revision (ICD-10-CM) codes. Patients of all ages were included to capture the full spectrum of CAP hospitalizations.

Conceptual Model Development

To provide a comprehensive analysis of in-hospital mortality among CAP patients, we developed a conceptual model that integrates both host and environmental factors. The conceptual model included:

Host factors: Age, gender, comorbidities (e.g., malignant neoplasm, hypertension, obesity, COPD, stroke, renal insufficiency, acute respiratory failure, asthma, dementia, malignant neoplasm of bronchus or lung, hepatic disease, ischemic heart disease, heart failure, diabetes mellitus), and clinical severity (e.g., need for mechanical ventilation, non-invasive ventilation, hemodialysis).
External/Environmental factors: Seasonality (captured as trimester), socioeconomic status (early school leaving rate, unemployment rate), and hospital differentiation (level of care provided by the hospital).

The rationale for including this model is to capture the complex interplay between these variables, allowing for a more nuanced understanding of the determinants of mortality. Figure 1 illustrates this conceptual model, showing the interaction between host and environmental (external) factors and their impact on in-hospital mortality.

Step-by-Step Inclusion of Predictor Variables in the Logistic Regression Model

The logistic regression model included predictor variables in a step-by-step manner based on their expected impact and importance on in-hospital mortality. The order of inclusion was as follows:

Age Group: Age was categorized into groups (<1, 1-4, 5-14, 15-24, 25-44, 45-64, 65-74, 75-84, 85-94, ≥95), with <1 as the reference category.
Mechanical Ventilation
Cancer
Hypertensive Disease
Overweight/Obesity
Non-Invasive Ventilation
Chronic Obstructive Pulmonary Disease
Cerebrovascular Disease
Hospital Differentiation: Categorized as Level III (reference category), Level II, Level I.
Chronic Renal Failure
Acute Respiratory Failure
Asthma
Gender: Male (1) and female (0).
Dementia
Seasonality: Trimesters with April-June as the reference category.
Lung cancer
Liver disease/viral hepatitis
Hemodialysis
Hospitalizations per Triennium: 2010-2012 as the reference category.
Early School Leaving Rate
Unemployment Rate
Ischemic Heart Disease
Heart Failure
Diabetes Mellitus

This structured inclusion ensures that the model first captures the most immediate and significant predictors of mortality, followed by external/environmental factors to refine the analysis.

Statistical Analysis

Descriptive statistics were used to summarize the characteristics of the study population. Continuous variables were expressed as means and standard deviations, while categorical variables were presented as frequencies and percentages. The chi-square test and Student's t-test were used to compare categorical and continuous variables, respectively.

Multivariable logistic regression analysis was performed to identify factors associated with in-hospital mortality. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were calculated for each predictor variable. The area under the receiver operating characteristic (ROC) curve was used to assess the model's discriminative ability. Statistical significance was set at p < 0.05.

All statistical analyses were conducted using SPSS software, version 28 (IBM Corp., Armonk, NY, USA).

From 2010 to 2018, a total of 378,449 episodes of CAP hospitalizations were registered in mainland Portugal’s NHS hospitals, of which 75,845 resulted in patient deaths with a mortality rate of 20%. A notable trend was observed: hospitalizations decreased in all age groups except for those aged ≥ 95 and 85–94 years old, where an increase was recorded until 2015 (Fig. 2 - A). Simultaneously, in-hospital deaths decreased across all age groups apart from individuals aged ≥ 95 years (Fig. 2 - B). The highest number of hospital deaths occurred in the age group of 85 to 94 years. Of the CAP hospitalization episodes, 53.7% involved male patients, and approximately two-thirds occurred in individuals aged over 75 years. The winter season accounted for one-third of these hospitalizations, and over half took place in level I hospitals, indicative of the burden of CAP during colder months. Most prevalent comorbidities are also described in Table 1.

Since 2010, comparing successive three-year periods, there has been a decline in both hospitalizations and mortality (p < 0.002) (Table 1). The analysis of host factors revealed a higher percentage of deaths among males and an increased risk with advancing age, especially above 75 years. Notable comorbidities contributing to higher mortality included cancer, heart failure, ischemic heart disease, cerebrovascular disease, dementia, renal failure, and acute respiratory failure. Patients requiring non-invasive ventilation, mechanical ventilation, or hemodialysis exhibited higher mortality (Table 1).

Table 1

Distribution of CAP hospitalization episodes (n = 378,449) and percentage of in-hospital mortality, according to host and environmental factors between 2010 and 2018.
	CAP Hospital ADMISSION	CAP IN-H. MORTALITY
	% distribution (n) or Mean (± SD) / median (IQR)	% of Death (n) or Mean (± SD) / median (IQR)	p-value*
HOST FACTORS
Gender
Male	53.7% (203100)	20.6% (41851)	0.000^a
Female	46.3% (175349)	19.4% (33994)	0.000^a
Age Group
<1	1.2% (4609)	0.6% (27)	0.000 ^b
1–4	3.6% (13743)	0.2% (25)
5–14	2.0% (7585)	0.6% (45)
15–24	0.8% (3162)	3.4% (107)
25–44	3.7% (14040)	4.7% (659)
45–64	11.7% (44269)	10.1% (4468)
65–74	14.0% (52794)	15.5% (8181)
75–84	31.3% (118609)	21.9% (25999)
85–94	28.4% (107651)	29.5% (31704)
≥95	3.2% (11987)	38.6% (4630)
Comorbidities
Cancer	9.0% (34110)	33.7% (11506)	0.000^a
Lung Cancer	1.4% (5310)	34.1% (1810)	0.000^a
Acute Respiratory Failure	21.6% (81641)	25.6% (20934)	0.000^a
COPD	14.5% (54986)	16,4% (9031)	0.000^a
Asthma	2.6% (10028)	7.5% (756)	0.000^a
Diabetes Mellitus	24.6% (93012)	20.0% (18636)	0.485^a
Hypertensive Disease	46.4% (175430)	18,8% (32989)	0.000^a
Heart Failure	22.9% (86568)	22.9% (19831)	0.000^a
Ischemic Heart Diseases	13% (49067)	23.7% (11620)	0.000^a
Cerebrovascular Disease	13.7% (52002)	27.4% (14259)	0.000^a
Dementia	10.3% (38903)	28.1% (10929)	0.000^a
Chronic Renal Failure	18.5% (69892)	27.4% (19116)	0.000^a
Liver Disease/Viral Hepatitis	3.4% (12721)	20.3% (2581)	0.242^a
Overweight/Obesity	7.1% (26800)	9.7% (2610)	0.000^a
Procedures performed
Non-invasive Ventilation	4.8% (18051)	29.8% (5371)	0.000^a
Mechanical Ventilation	2.2% (8179)	43.9% (1338)	0.000^a
Hemodialysis	1.5% (5787)	31.0% (1795)	0.000^a
ENVIRONMENTAL FACTORS
Early leavers from education and training rate (n = 335657)	1.57 (± 2.02)/1.28 (2)	1.61 (± 2.03)/1.32 (2)	0.000^c
Unemployment rate (n = 335657)	12.73 (± 4.36)/12.21 (5)	12.73(± 4.27)/12.20 (5)	0.215 ^c
Hospitalizations per triennium
2010 − 1012	35.0% (132391)	20.2% (26800)	0.0015^b
2013–2015	33.6% (127293)	20.1% (25552)
2016–2018	31.4% (118765)	19.8% (23493)
Hospitalizations by quarter
Jan-mar	34.7% (131191)	19.1% (25023)	0,000^b
April-jun	22.6% (85615)	19.2% (16463)
Jul-sept	17.7% (67098)	22.4% (15059)
Oct-dec	25.0% (94545)	20.4% (19300)
Hospital service differentiation
Level III	21.7% (81992)	16.4% (13435)	0.000^b
Level II	21.6% (81706)	21.2% (17301)
Level I	56.0% (211810)	21.0% (44562)
Post-discharge destination - Death	20.0% (75845)

^* p value refers to comparation of in-hospital mortality proportions/measures of central tendency

^a Fisher exact test

^b Chi-square test

^c U Mann-Whitney test

Adjusted Odds Ratio (aOR) analysis demonstrated that the highest probability of hospital death from CAP was in males (14.8% increased risk) and increased with age, notably in the ≥ 95 years age group, which had a death probability 140 times higher than those under 1 year old. Specific comorbidities further augmented mortality risk, as did the need for mechanical ventilation, non-invasive ventilation, or hemodialysis (Table 2; Fig. 3 – A/B). Interestingly, some comorbidities such as diabetes, heart failure, COPD, hypertensive disease, obesity, and asthma showed a reduced risk of mortality.

External factors, broadly understood as environmental influences, played a significant role in mortality rates.

Hospitalizations during summer and in less differentiated hospitals (levels II and I) also showed higher mortality rates (Table 2; Fig. 3 – A). A one percentage point increase in early school leaving and unemployment rates corresponded to a 0.6% and 0.5% increase in mortality risk, respectively (Table 2).

Table 2

Adjusted odds ratio for “death” event according to host and environmental factors between 2010 and 2018 (p value < 0.05)
CAP IN-H. MORTALITY
	OR (95%CI)			P value	Adjusted OR (95%CI)			P value
HOST FACTORS
Gender
Male	1.079	1.062	1.097	< 0.001	1.148	1.126	1.170	< 0.001
Age group
<1								0.000
1–4	0.309	0.179	0.533	0.000	0.262	0.136	0.506	< 0.001
5–14	1.013	0.628	1.634	0.958	1.184	0.698	2.006	0.531
15–24	5.944	3.887	9.088	0.000	6.298	3.933	10.085	< 0.001
25–44	8.358	5.680	12.299	0.000	8.177	5.325	12.557	< 0.001
45–64	19.051	13.034	27.845	0.000	18.493	12.131	28.190	< 0.001
65–74	31.120	21.302	45.462	0.000	33.622	22.066	51.228	< 0.001
75–84	47.642	32.628	69.566	0.000	57.138	37.520	87.014	< 0.001
85–94	70.843	48.518	103.440	0.000	89.030	58.462	135.581	< 0.001
≥ 95	106.800	73.030	156.186	0.000	140.464	92.077	214.280	< 0.001
Comorbidities mentioned in the hospital record
Cancer	2.215	2.163	2.269	0.000	2.286	2.217	2.356	0.000
Lung Cancer	2.089	1.973	2.213	0.000	1.455	1.354	1.564	< 0.001
Cerebrovascular Disease	1.625	1.591	1.660	< 0.001	1.389	1.356	1.423	< 0.001
Chronic Renal Failure	1.671	1.640	1.703	< 0.001	1.332	1.301	1.362	< 0.001
Acute Respiratory Failure	1,519	1,492	1,547	0.000	1.330	1.302	1.360	0.000
Liver Disease/Viral Hepatitis	1.016	0.972	1.062	0.477	1.300	1.234	1.369	< 0.001
Dementia	1.653	1.614	1.692	< 0.001	1.174	1.142	1.206	< 0.001
Ischemic Heart Disease	1.281	1.253	1.310	< 0.001	1.068	1.040	1.096	< 0.001
Diabetes Mellitus	1.000	0.981	1.018	0.966	0.970	0.949	0.991	0.005
Heart Failure	1.251	1.229	1.274	< 0.001	0.962	0.941	0.983	0.001
COPD	0.755	0.737	0.773	0.000	0.666	0.647	0.685	< 0.001
Hypertensive Disease	0.866	0.852	0.880	< 0.001	0.615	0.603	0.627	0.000
Overweight/Obesity	0.410	0.394	0.427	< 0.001	0.499	0.476	0.524	< 0.001
Asthma	0.319	0.296	0.343	0.000	0.484	0.444	0.527	< 0.001
Procedures performed
Mechanical Ventilation	3.233	3.093	3.380	0.000	4.532	4.282	4.795	0.000
Non-invasive Ventilation	1.743	1.686	1.801	< 0.001	1.984	1.906	2.065	< 0.001
Hemodialysis	1.813	1.714	1.918	< 0.001	1.395	1.300	1.497	< 0.001
ENVIRONMENTAL FACTORS
Early leavers from education and training rate	1.013	1.009	1.017	0.000	1.006	1.002	1.011	0.004
Unemployment rate	1.000	0.998	1.002	0.770	1.005	1.003	1.007	< 0.001
Hospitalizations per triennium
2010–2012				0.015				< 0.001
2013–2015	0.990	0.971	1.009	0.281	0.944	0.924	0.965	< 0.001
2016–2018	0.972	0.953	0.991	0.004	0.908	.887	0.930	< 0.001
Hospitalizations by quarter
April-jun				0.000				< 0.001
Jan-mar	0.990	0.969	1.012	0.369	1.020	0.995	1.046	0.126
July-sept	1.216	1.186	1.246	0.000	1.147	1.114	1.180	< 0.001
Oct-dec	1.077	1.053	1.103	0.000	1.076	1.048	1.105	< 0.001
Hospital service differentiation
Level III				0.000				< 0.001
Level lI	1.371	1.337	1.405	0.000	1.475	1.433	1.519	< 0.001
Level I	1.360	1.331	1.389	0.000	1.525	1.487	1.564	< 0.001

The determination of the area under the ROC curve resulted in a value of 73.5%, indicating a good model fit for predicting in-hospital mortality risk factors in CAP (19).

This comprehensive retrospective analysis underscores the sustained decrease in CAP-related hospitalizations and in-hospital mortality over the period in analysis. Mortality risk was influenced by a combination of host factors like age and gender, comorbidities, clinical severity, and external/environmental factors, including seasonal variations, socioeconomic conditions, and hospital differentiation.

This study’s strength lies in its extensive population base, covering all NHS CAP admissions across different age groups, throughout a nine-year period (2010–2018), offering robust insights into trends of CAP-hospitalizations and its mortality factors.

The consistent decrease in both in-hospital mortality rates and hospitalizations aligns with other Portuguese studies (10) and may be partially attributed to effective health policies, such as the existence of a National Vaccination Program and, since 2012, a National Program for Respiratory Diseases with specific measures for at risk groups (20–26).

Age emerged as a primary host factor influencing mortality risk, particularly among older adults, reflecting the combined impact of age-related immunological changes and chronic conditions (6, 9, 10, 27–29). Gender also played a significant role, with males exhibiting higher mortality rates potentially due to behavioural and healthcare utilization patterns (30). Clinical severity, as reflected by the need for mechanical ventilation, non-invasive ventilation, or haemodialysis, also influenced the mortality. Comorbidities like cancer, cerebrovascular disease, renal failure, acute respiratory failure, and dementia significantly increased mortality risk, while diabetes, heart failure and COPD showed a protective effect in the Portuguese context. This is in line with another national study (9), where diabetes and COPD were protective concerning the risk of death, contrary to what is described in the international literature (6). One plausible explanation could be the impact of national policies implemented as part of Portuguese health priority programs specifically targeted towards diabetes, cardiac diseases, and respiratory diseases.

Our findings also highlight the "obesity survival paradox," where obese patients showed a reduced risk of pneumonia mortality, explained eventually by a lower pneumonia severity (31–38).

External factors, including seasonal variations and social determinants such as education and unemployment rates, were critical in mortality risk. Despite the increase in hospitalizations occurred during the winter season, summer season related to hot weather showed a strong association with increased mortality, emphasizing the impact of climate on health outcomes, as described in some studies (39–42). Additionally, the findings of our study are supported by recent research on the impact of environmental factors on health outcomes. A study by Alho et al. (2024) demonstrated a significant increase in daily hospital admissions during heatwave days in Portugal, with notable impacts across all age groups and major disease categories (43). This study highlights the broader context of environmental stressors on health, emphasizing the importance of including environmental variables in health outcome analyses. Their findings of increased hospitalizations due to heatwaves underscore the critical need for adaptive healthcare strategies to mitigate the effects of climate change, a factor that could potentially exacerbate the burden of CAP during extreme weather events.

We found that individuals living in parishes characterized by high rates of early school leaving were at an increased risk of mortality. Several studies have demonstrated an inverse association between mortality risk in adulthood and educational level (44–46). The same finding occurred within high unemployment parishes. Both early schools leaving, and unemployment rates were used as proxies of social vulnerability. Education and economic status have long been recognized as a primary cause of health inequalities and constitute adverse environmental markers associated with morbidity and mortality patterns, both in children and adults with pneumonia (10, 47–49).

Social vulnerability encompasses factors like poverty, limited access to healthcare, and inadequate housing which contribute to heightened risks of adverse health outcomes and ultimately lead to higher mortality rates. Thus, a

ddressing social vulnerability becomes crucial in reducing mortality associated with CAP and health policies should involve increasing accessibility to health care, reducing poverty, and improving thermal housing conditions.

Hospital differentiation appears to play a role in influencing mortality rates, as indicated in existing literature (50). Hospitals with greater resources tend to demonstrate improved outcomes, underscoring the significance of quality care and resource availability in CAP treatment. Nevertheless, this underscores the necessity for additional research in these contexts to further explore these dynamics.

Although our study provides valuable insights, its exploratory and retrospective nature, based on existing data, introduces limitations, as it may not address all relevant host and environmental factors.

In addition, the omission of certain environmental elements, such as atmospheric pollution and above all ambient temperature, represents another limitation and merits future research. The study's scope is further restricted to NHS hospitals in mainland Portugal, potentially impeding the generalizability of findings to settings or countries with distinct healthcare systems and environmental conditions. Lastly, the study does not account for the COVID-19 pandemic era, highlighting the necessity for future investigations to include a separate analysis of this period for a more comprehensive understanding of CAP mortality risk factors.

This comprehensive study underscores that in-hospital mortality due to CAP in Portugal is multifaceted, influenced by a combination of host factors, and environmental conditions. Key findings indicate the following in-hospital mortality main risk factors:

Age and Gender: Mortality risk significantly increases with age, especially in patients aged 95 years and above. Additionally, males have a higher mortality risk.Comorbidities: The presence of certain comorbidities markedly heightens the risk of mortality, underscoring the importance of integrated care for patients with chronic conditions.Social Vulnerability: Higher mortality was associated with place of residence, unemployment, and early school leaving rates, suggesting that broader social determinants of health significantly impact patient outcomes.Seasonal Variation: Hospitalization during summer periods was linked to an increased risk of mortality, indicating that environmental factors such as high ambient temperatures influence health outcomes.Hospital Differentiation: Lower hospital differentiation was associated with higher mortality rates, indicating that the availability and quality of resources influence patient care.

We conclude that addressing CAP mortality extends beyond medical treatment to encompass wider health measures, including socio-economic improvements and environmental health strategies. Policymakers and healthcare providers should consider these multifactorial influences in their efforts to reduce CAP-related mortality. Comprehensive policies should prioritize increasing vaccination and access to health care and improving thermal housing conditions, particularly for socially vulnerable individuals.

Conflicts of Interest

None.

Funding

Funded by FCT, reference UIDB/04295/2020 and UIDP/04295/2020.

Author Contribution

EP and CB wrote the main manuscript text.Study design (EP, PN), data acquisition (EP, PN), data analysis (EP, PN), manuscript drafting (EP, CB, AC, PN), and critical manuscript revision (CB, AC, PN). PN prepared figures 1-3. All authors read and approved the final manuscript.

Acknowledgement

The data that support the findings of this study are available from [ACSS] but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of [ACSS].

GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1204–22. 10.1016/S0140-6736(20)30925-9.
Global Burden of Disease Study 2016. Estimates of the global, regional, and national morbidity, mortality, and aetiologies of lower respiratory infections in 195 countries, 1990–2016: a systematic analysis for the. Lancet Infect Dis. 2018;18(11):1191–210. 10.1016/S1473-3099(18)30310-4. Epub 2018 Sep 19. PMID: 30243584; PMCID: PMC6202443.
Ferreira-Coimbra J, Sarda C, Rello J. Burden of Community-Acquired Pneumonia and Unmet Clinical Needs. Adv Ther. 2020;37(4):1302–18. 10.1007/s12325-020-01248-7. Epub 2020 Feb 18. PMID: 32072494; PMCID: PMC7140754.
Torres A, Cilloniz C, Niederman MS, Menéndez R, Chalmers JD, Wunderink RG, van der Poll T, Pneumonia. Nat Rev Dis Primers. 2021;7(1):25. 10.1038/s41572-021-00259-0. PMID: 33833230.
Torres A, Cillóniz C, Blasi F, Chalmers JD, Gaillat J, Dartois N, Schmitt HJ, Welte T. Burden of pneumococcal community-acquired pneumonia in adults across Europe: A literature review. Respir Med. 2018;137:6–13. 10.1016/j.rmed.2018.02.007. Epub 2018 Feb 19. PMID: 29605214.
Torres A, Peetermans WE, Viegi G, Blasi F. Risk factors for community-acquired pneumonia in adults in Europe: a literature review. Thorax. 2013;68(11):1057–65. 10.1136/thoraxjnl-2013-204282. PMID: 24130229; PMCID: PMC3812874.
Froes F, Diniz A, Mesquita M, Serrado M, Nunes B. Hospital admissions of adults with community-acquired pneumonia in Portugal between 2000 and 2009. Eur Respir J. 2013;41(5):1141–6. 10.1183/09031936.00216711. Epub 2012 Aug 9. PMID: 22878871.
Teixeira-Lopes F, Cysneiros A, Dias A, Durão V, Costa C, Paula F, Serrado M, Nunes B, Diniz A, Froes F. Intra-hospital mortality for community-acquired pneumonia in mainland Portugal between 2000 and 2009. Pulmonol 2019 Mar-Apr;25(2):66–70. doi: 10.1016/j.pulmoe.2018.06.004. Epub 2018 Jul 17. PMID: 30026065.
Hespanhol V, Bárbara C. Pneumonia mortality, comorbidities matter? Pulmonology. 2020 May-Jun;26(3):123–129. 10.1016/j.pulmoe.2019.10.003. Epub 2019 Nov 29. PMID: 31787563.
Pessoa E, Bárbara C, Viegas L, Costa A, Rosa M, Nogueira P. Factors associated with in-hospital mortality from community-acquired pneumonia in Portugal: 2000–2014. BMC Pulm Med. 2020;20(1):18. 10.1186/s12890-019-1045-x. PMID: 31964385; PMCID: PMC6974967.
European Commission OECD. Health at a Glance: Europe 2018 State of Health in the EU Cycle [Internet]. 2018. https://doi.org/10.1787/health_glance_eur-2018-en
Eurostat. Respiratory diseases statistics [Internet]. 2022. p. ISSN 2443–8219. https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Respiratory_diseases_statistics#Deaths_from_diseases_of_the_respiratory_system
Virgolino A, Antunes F, Santos O, Costa A et al. Margarida Gaspar de Matos, Cristina Bárbara, Manuel Bicho, Cátia Caneiras, Raquel Sabino, Maria Sofia Núncio,. 2020. Towards a Global Perspective of Environmental Health: Defining the Research Grounds of an Institute of Environmental Health Sustainability 12, no. 21: 8963. https://doi.org/10.3390/su12218963
Forum of International Respiratory Societies. The Global Impact of Respiratory Disease [Internet]. Third Edit. European Respiratory Society. 2021. Available from: Accessed 22 September, 2021. firsnet.org/images/publications/FIRS_Master_09202021.pdf
Yang X, Lo K. Environmental health research and the COVID-19 pandemic: A turning point towards sustainability. Environ Res. 2021;197:111157. 10.1016/j.envres.2021.111157. Epub 2021 Apr 20. PMID: 33887273; PMCID: PMC8542966.
Instituto Nacional de Estatística. Censos 2011 Resultados Definitivos - Portugal. Science. Lisboa; 2012.
Instituto Nacional de Estatística. Taxa de desemprego [Internet]. 2014. https://smi.ine.pt/Conceito/Detalhes/545#Histórico_
Instituto Nacional de Estatística. Taxa de abandono precoce de educação e formação [Internet]. 2015. https://smi.ine.pt/Indicador/Detalhes/9654?LANG=PT
Aguiar P. Guia Prático de Estatística em Investigação Epidemiológica: SPSS. Lisboa: Climepsi Editores; 2007.
Direção-Geral de Saúde. Programa Nacional para as Doenças Respiratórias: 2012–2016. Lisboa, Portugal: Ministério da Saúde. 2013. https://www.dgs.pt/documentos-e-publicacoes/programa-nacional-para-as-doencas-respiratorias-2012-2016-pdf.aspx
Bousquet J, Pinto JR, Barbara C, da Sousa JC, Fonseca J, Miguel JP, Boavida J, Bom AT, Malva J, Cordeiro CR. Portugal at the cross road of international chronic respiratory programmes. Rev Port Pneumol (2006). 2015 Sep-Oct;21(5):230-2. doi: 10.1016/j.rppnen.2015.07.001. Epub 2015 Aug 12. PMID: 26275595.
Branco MJ, Paixão E, Nunes B. Vacinação antigripal da população portuguesa, em 2009–2010: cobertura e algumas características do acto vacinal. Lisboa, Portugal; 2010. http://hdl.handle.net/10400.18/226
Machado A, Torres AR, Kislaya I, Neto M. Vacinação antigripal da população portuguesa em 2016/2017 e 2017/2018: cobertura e características do ato vacinal [Internet]. Lisboa, Portugal; 2019. http://repositorio.insa.pt/bitstream/10400.18/5700/3/INSA_Relatorio_Vacinacao-antigripal-epocas-2016-2017_2017-2018.pdf
Restivo V, Costantino C, Bono S, Maniglia M, Marchese V, Ventura G, Casuccio A, Tramuto F, Vitale F. Influenza vaccine effectiveness among high-risk groups: A systematic literature review and meta-analysis of case-control and cohort studies. Hum Vaccin Immunother. 2018;14(3):724–35. Epub 2017 Jun 16. PMID: 28481673; PMCID: PMC5890832.
Kislaya I, Rodrigues AP, Sousa-Uva M, Gómez V, Gonçalves P, Froes F, Nunes B. Indirect effect of 7-valent and 13-valent pneumococcal conjugated vaccines on pneumococcal pneumonia hospitalizations in elderly. PLoS ONE. 2019;14(1):e0209428. 10.1371/journal.pone.0209428. PMID: 30650091; PMCID: PMC6334925.
Tsaban G, Ben-Shimol S. Indirect (herd) protection, following pneumococcal conjugated vaccines introduction: A systematic review of the literature. Vaccine. 2017;35(22):2882–91. 10.1016/j.vaccine.2017.04.032. Epub 2017 Apr 24. PMID: 28449971.
Htun TP, Sun Y, Chua HL, Pang J. Clinical features for diagnosis of pneumonia among adults in primary care setting: A systematic and meta-review. Sci Rep. 2019;9(1):7600. 10.1038/s41598-019-44145-y. PMID: 31110214; PMCID: PMC6527561.
Kaplan V, Angus DC, Griffin MF, Clermont G, Scott Watson R, Linde-Zwirble WT. Hospitalized community-acquired pneumonia in the elderly: age- and sex-related patterns of care and outcome in the United States. Am J Respir Crit Care Med. 2002;165(6):766 – 72. 10.1164/ajrccm.165.6.2103038. PMID: 11897642.
Niederman MS, Torres A. Severe community-acquired pneumonia. Eur Respir Rev. 2022;31(166):220123. 10.1183/16000617.0123-2022. PMID: 36517046; PMCID: PMC9879347.
Case A, Paxson C. Sex differences in morbidity and mortality. Demography. 2005;42(2):189–214. 10.1353/dem.2005.0011. PMID: 15986983.
Wang N, Liu BW, Ma CM, Yan Y, Su QW, Yin FZ. Influence of overweight and obesity on the mortality of hospitalized patients with community-acquired pneumonia. World J Clin Cases. 2022;10(1):104–16. 10.12998/wjcc.v10.i1.104. PMID: 35071510; PMCID: PMC8727241.
Nie W, Zhang Y, Jee SH, Jung KJ, Li B, Xiu Q. Obesity survival paradox in pneumonia: a meta-analysis. BMC Med. 2014;12:61. 10.1186/1741-7015-12-61. PMID: 24722122; PMCID: PMC4021571.
LaCroix AZ, Lipson S, Miles TP, White L. Prospective study of pneumonia hospitalizations and mortality of U.S. older people: the role of chronic conditions, health behaviors, and nutritional status. Public Health Rep. 1989 Jul-Aug;104(4):350–60. PMID: 2502806; PMCID: PMC1579936.
Inoue Y, Koizumi A, Wada Y, Iso H, Watanabe Y, Date C, Yamamoto A, Kikuchi S, Inaba Y, Toyoshima H, Tamakoshi A. Risk and protective factors related to mortality from pneumonia among middleaged and elderly community residents: the JACC Study. J Epidemiol. 2007;17(6):194–202. 10.2188/jea.17.194. PMID: 18094518; PMCID: PMC7058467.
Kahlon S, Eurich DT, Padwal RS, Malhotra A, Minhas-Sandhu JK, Marrie TJ, Majumdar SR. Obesity and outcomes in patients hospitalized with pneumonia. Clin Microbiol Infect. 2013;19(8):709–16. 10.1111/j.1469-0691.2012.04003.x. Epub 2012 Sep 11. PMID: 22963453; PMCID: PMC3594436.
Singanayagam A, Singanayagam A, Chalmers JD. Obesity is associated with improved survival in community-acquired pneumonia. Eur Respir J. 2013;42(1):180–7. 10.1183/09031936.00115312. Epub 2012 Dec 6. PMID: 23222873.
Zhou B, Zhao S, Tang M, Chen K, Hua W, Su Y, Chen S, Liang Z, Xu W, Li X, Xue X, Sun X, Zhang S. Overweight and obesity as protective factors against mortality in nonischemic cardiomyopathy patients with an implantable cardioverter defibrillator. Clin Cardiol. 2020;43(12):1435–42. Epub 2020 Sep 16. PMID: 32936479; PMCID: PMC7724223.
Chen J, Wang J, Jiang H, Li MC, He SY, Li XP, Shen D. Lower long-term mortality in obese patients with community-acquired pneumonia: possible role of CRP. Clin (Sao Paulo). 2019;74:e608. 10.6061/clinics/2019/e608. Epub 2019 Jul 10. PMID: 31291389; PMCID: PMC6607938.
Gasparrini A, Guo Y, Hashizume M, Lavigne E, Zanobetti A, Schwartz J, Tobias A, Tong S, Rocklöv J, Forsberg B, Leone M, De Sario M, Bell ML, Guo YL, Wu CF, Kan H, Yi SM, Coelho M, Saldiva PH, Honda Y, Kim H, Armstrong B. Mortality risk attributable to high and low ambient temperature: a multicountry observational study. Lancet. 2015;386(9991):369–75. 10.1016/S0140-6736(14)62114-0. Epub 2015 May 20. PMID: 26003380; PMCID: PMC4521077.
Ji-young Son, Liu JC, Bell ML. Temperature-related mortality: a systematic review and investigation of effect modifiers. Environ Res Lett. 2019;14(073004). 10.1088/1748–9326/ab1cdb.
Bunker A, Wildenhain J, Vandenbergh A, Henschke N, Rocklöv J, Hajat S, Sauerborn R. Effects of Air Temperature on Climate-Sensitive Mortality and Morbidity Outcomes in the Elderly; a Systematic Review and Meta-analysis of Epidemiological Evidence. EBioMedicine. 2016;6:258–68. 10.1016/j.ebiom.2016.02.034. Epub 2016 Feb 23. PMID: 27211569; PMCID: PMC4856745.
Jacobson LDSV, Oliveira BFA, Schneider R, Gasparrini A, Hacon SS. Mortality Risk from Respiratory Diseases Due to Non-Optimal Temperature among Brazilian Elderlies. Int J Environ Res Public Health. 2021;18(11):5550. 10.3390/ijerph18115550. PMID: 34067373; PMCID: PMC8197018.
Alho AM, Oliveira AP, Viegas S, Nogueira P. Effect of heatwaves on daily hospital admissions in Portugal, 2000–18: An observational study. Lancet Planet Health. 2024;8(5):e318–26. https://doi.org/10.1016/S2542-5196(24)00046-9.
Vrbova L, Mamdani M, Moineddin R, Jaakimainen L, Upshur RE. Does socioeconomic status affect mortality subsequent to hospital admission for community acquired pneumonia among older persons? J Negat Results Biomed. 2005;4:4. 10.1186/1477-5751-4-4. PMID: 15819975; PMCID: PMC1090611.
Braveman PA, Cubbin C, Egerter S, Williams DR, Pamuk E. Socioeconomic disparities in health in the United States: what the patterns tell us. Am J Public Health. 2010;100(Suppl 1):S186–96. 10.2105/AJPH.2009.166082. Epub 2010 Feb 10. PMID: 20147693; PMCID: PMC2837459.
Montez JK, Berkman LF. Trends in the educational gradient of mortality among US adults aged 45 to 84 years: bringing regional context into the explanation. Am J Public Health. 2014;104(1):e82–90. 10.2105/AJPH.2013.301526. Epub 2013 Nov 14. PMID: 24228659; PMCID: PMC3865154.
Link BG, Phelan J. Social conditions as fundamental causes of disease. J Health Soc Behav. 1995;Spec No:80–94. PMID: 7560851.
Phelan JC, Link BG, Diez-Roux A, Kawachi I, Levin B. Fundamental causes of social inequalities in mortality: a test of the theory. J Health Soc Behav. 2004;45(3):265 – 85. doi: 10.1177/002214650404500303. Erratum in: J Health Soc Behav. 2005;46(1):v. PMID: 15595507.
Everett BG, Rehkopf DH, Rogers RG. The Nonlinear Relationship between Education and Mortality: An Examination of Cohort, Race/Ethnic, and Gender Differences. Popul Res Policy Rev. 2013;32(6). 10.1007/s11113-013-9299-0. PMID: 24288422; PMCID: PMC3839428.
Burke LG, Frakt AB, Khullar D, Orav EJ, Jha AK. Association Between Teaching Status and Mortality in US Hospitals. JAMA. 2017;317(20):2105–13. 10.1001/jama.2017.5702.

No competing interests reported.

Download PDF

Editorial decision: Revision requested
31 Jul, 2024
Editor assigned by journal
30 Jul, 2024
Submission checks completed at journal
30 Jul, 2024
First submitted to journal
26 Jul, 2024

You are reading this latest preprint version

Exploring seasonality and hospital differentiation as factors in hospital mortality among Community-Acquired Pneumonia patients in Portugal: Beyond traditional factors

Status:

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

1. Background

2. Methods

3. Results

4. Discussion

5. Conclusions

Declarations

Conflicts of Interest

Funding

Author Contribution

Acknowledgement

References

Additional Declarations

Status:

Version 1