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. 2016 Dec 16;95(50):e5598. doi: 10.1097/MD.0000000000005598

Predicting emergency departments visit rates from septicemia in Taiwan using an age–period–cohort model, 1998 to 2012

I-Shiang Tzeng a, Su-Hsun Liu b,c, Yu Ting Chiou d, Chien-Hsiung Huang e, Cheng-Jung Lee f, Cheng-Yu Chien g, Shou-Chien Hsu e, Yi-Ming Weng g, Kuan-Fu Chen e,h,i,∗, Jih-Chang Chen b,g,∗
Editor: Meixia Lu
PMCID: PMC5268041  PMID: 27977595

Supplemental Digital Content is available in the text

Keywords: age–period–cohort predicting, emergency department visit, septicemia

Abstract

The aim of this study was to determine the age–period–cohort (APC) effects on the rate of infection-related emergency department (ED) visits from septicemia for predicting the same in recent periods.

In our study, we investigated the longitudinal trends in septicemia-related visit rates. Using an APC model to decompose the septicemia visit rates into the effects of age, time period, and cohort, and examine whether their effects varied by sex.

The septicemia ED visit rate was classified as the International Classification of Disease Code 038 by primary and secondary diagnosis between 1998 and 2012.

In both males and females, the visit rate of septicemia showed an increase from 2003 through 2012. An increase in septicemia visit rate after 2003 was observed in all age groups. An APC model indicated a reversal increasing period effect, which increased prominently from 2003 to 2012 in both males and females. The age effect showed an increasing trend. The cohort effect tended to show a slight oscillation from 1913 to 1988. With reference to the prediction of the logarithms of the age-specific 5-year visit rates, we observed that the younger cohorts exhibited a slightly increasing trend, as compared to the older cohorts.

The period effect can explain the increase in septicemia visit rates, suggesting the role of screening for septicemia. Furthermore, it is well known that aging is a relevant risk variable for infectious diseases. The present study concludes that the aged population exhibited a strong increasing future trend for septicemia-related ED visit rates.

1. Introduction

The past 150 years has seen a progressive decline in premature deaths associated with infectious diseases such as septicemia. This decline is thought to be the result of several contributing factors, such as technological advances in the fields of medicine (vaccination), engineering (sanitation), and administration (healthcare delivery).[1] These factors would affect all individuals in a given cohort, depending on the given age of the cohort.

According to this concept, life expectancy may be embodied by the maximum survival rate over a long period. However, there are several types of bacteria that potentially threaten our survival.[2] For example, septicemia is a life-threatening infection caused by large bacteria or germs present in the blood.[3] Indeed, the emergency medical service (EMS) system has to bear the brunt of life-threatening infections.[4] However, in case of early septicemia or with the presence of certain host factors, clinical manifestations may be hidden, making it difficult to clinically diagnose the infection. Since 2004, the national campaign was launched to devote toward decreasing the incidence of healthcare-associated infections (HCAI) in the United Kingdom; another campaign was launched during 2007.[5] These campaigns were proposed to improve infection control and awareness in English National Health Service (NHS) hospitals. It is important to require robust methods, such as interrupted time series analysis, used to assess (or evaluate) the impact of national and regional HCAI interventions on rates of meticillin-resistant Staphylococcus aureus and meticillin-sensitive S. aureus bacteremia in NHS acute hospitals in the East Midlands of the United Kingdom. In addition, initial hospitalization for severe sepsis in Taiwan, the age-standardized annual incidence rates of first episodes increased from 135 per 100,000 in 1997 to 217 per 100,000 in 2006, with annual growth percent about 3.9%. Based on direct observations of the long-term trends in septicemia-associated hospitalization from 1997 to 2006 in Taiwan, it is important for physicians to observe a sustained period effect on hospitals’ capacity for managing patients.[6]

In epidemiology, age–period–cohort (APC) model has been used for describing the secular trend in disease incidence or mortality.[7–12] A 3-factor multiplicative model separates effects from APC for the incidence or rates of a particular event. Age effects are associated with the outcome of time, so change in the number of outcome (deaths caused by cancer). Period effects can affect all ages simultaneously over time. Birth cohort effects involve changes across groups with the same birth year who presented the same outcome during the same period. These effects separated by APC model may provide epidemiologists with important insights on identifying health determinants[13]; hypothesis testing[14]; or potential sources of heterogeneities in the literature.[15] However, less effort has been made previously to the outcome-forecasting potential of the APC model.

In our study, we investigated the longitudinal trends in the rate of infection-related emergency department (ED) visits, focusing only on septicemia. We decomposed the septicemia-related ED visit rates to identify the effects of age, time period, and cohort, and examined whether these effects varied by sex.

We investigated septicemia-related visit rates because the capacity of the emergency physicians to provide medical services has not only been linked with patients’ health status, but it has also been associated with the labor provided for critical infection diseases. Thus, in this paper, we examined the longitudinal trends in the septicemia-related visit rate, attempted to predict the potential increase in visit rates due to infections, and aimed to provide generalized etiological insights by utilizing an aggregated report obtained from a claims database of the National Health Insurance (NHI) program in Taiwan.

2. Methods

2.1. Data sources

The NHI system is compulsory and covers all citizens except prisoners. The NHI research database (NHIRD) covers nearly all (99%) inpatient and outpatient claims for its population of approximately 23 million which has been used widely in various studies. The NHIRD provided encryption participated patients’ identification (ID) numbers, sex, date of birth, dates of hospital admission and discharge, medical institutions providing the services that are indexed by the International Classification of Diseases (ICD), 9th Revision, Clinical Modification (ICD-9-CM) Codes. The Health Promotion Administration of the Ministry of Health and Welfare (MOHW) in Taiwan has maintained electronic databases of all annual ED visits according to relevant emergency case files as ambulatory care expenditures by visits (CD), since 1998 to 2012. The duplicated visit with the same diagnostic codes from the same patient each year such as repeated visit or interhospital transfer are excluded specifically to facilitate the calculation of the annual disease burden on each patient. Split-year treatment occasionally has little effect on the trend. The publicly available datasets can be downloaded in the aggregated format of contingency tables consisting of numbers of ED visits by patient (ID number for the key calculation made by households and remove duplication for each year), sex, age (in 1-year intervals), and ICD-9-CM codes for each year.[16] The septicemia ED visit rate was classified as the ICD Code 038 by primary and secondary diagnosis.

There were 14 age groups ranging from 20 to 85+ years (defined as 20–24, 25–29, 30–34, 35–39, 40–44, 45–49, 50–54, 55–59, 60–64, 65–69, 70–74, 75–79, 80–84, and ≥85 years) and 3 time periods (defined as 1998–2002, 2003–2007, and 2008–2012) with 16 cohorts (defined as1913–1917, 1918–1922, 1923–1927, 1928–1932, 1933–1937, 1938–1942, 1943–1947, 1948–1952, 1953–1957, 1958–1962, 1963–1967, 1968–1972, 1973–1977, 1978–1982, 1983–1987, and 1988–1992). From these, we calculated the age-specific and the age-adjusted (using the 2000 World Standard Population)[17] visit rates. The present study was approved by the Institutional Review Board of Chang Gung Memorial Hospital (no. 103-6269B).

2.2. Statistical analysis

We modeled septicemia-related ED visits by using Poisson APC models which decomposes visit rates over time by age, time period, and cohort. In more detail, visit rates per year, per 100,000 persons, were defined as follows: 

2.2.

The inversion formula for practice outcome, number of patient visits to the ED per year, was calculated as follows: 

2.2.

The framework of APC modeling has been applied to describe the secular trend in patient visit.[18] It is reasonable to perform APC model to investigate the factors for visit rates among septicemia-related ED patients, because age is related to visit rates. These results can provide clues for testing the hypotheses of issue.

In this study, a log-linear Poisson model for the rates was fitted to the data for both genders separately by assuming a Poisson distribution for the number of septicemia-related visits. The particular parameter estimates was maximum likelihood estimates, which were used by free R software (version 3.1.0) (Vienna, Austria) [19] for analysis.

Estimates of the parameters of the above APC model were set out to predict the septicemia-related ED visit rate. There are 2 prediction scenarios in this study. In this context, it was unnecessary to extrapolate the age effects. The period effects are extrapolated using a quadratic regression into the time period, 2013 to 2017. The cohort effects were extrapolated using a cubic multiple-regression on the birth cohorts from 1991 to 1995. Finally, we combined the age effects and extrapolated period and cohort effects to obtain the point estimates of visit rate prediction (marked A). For a comparison, using a quadratic regression to extrapolate the observed log age-adjusted visit rates themselves into the time period, 2013 to 2017 (marked B).

To check model fitness, we plot deviance residuals, progressively from the null model, the age model, the age-period model, and then to the APC model.

3. Results

From 1998 to 2012, there were 179,975 septicemia-related ED visits by adults aged 20 years or older, out of which, more than 50% were males (51.6%, Table 1). Middle-aged patients contributed to 12.9% of the sample (N = 23,141) and 83.5% (N = 150,382) of the septicemia-related ED visits, whereas the elderly (age ≥ 65 years) comprised the predominant patient population (N = 173,523, 96.4%, Table 1). This predominance of male and elderly patients was consistent across the 3 observation periods (Table 1).

Table 1.

Case numbers of ED patient visits from septicemia.

3.

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Figure 1 presents the APC effects for males and females. It is evident that the age effects are quite similar for both the sexes. Septicemia-related ED visits increased exponentially with age in both sexes. There was an upturn in the period effects in both sexes, steeper in male than female for recent period. The birth cohort curves had notable downward inflections in both sexes in about 1938, earlier in female than male.

Figure 1.

Figure 1

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The effect of septicemia-associated emergency department visits rates by age, time period, birth cohort for male and female in Taiwan, 1998 to 2012.

Figure 2 displays the deviance residual plots for males (upper panels) and females (lower panels). We found that the systematic patterns existed in residuals, most obviously in the null models (leftmost panels) progressively disappear in the age model (panels in second column) and the age–period model (panels in third column). As for the residual plots of the APC model (rightmost panels), it can be seen that no systematic pattern is observed.

Figure 2.

Figure 2

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Residual plots for the null models, the age models, the age-period models, and the age–period–cohort models.

Figure 3 presents the logarithms of the age-specific 5-year visit rates (for aforementioned 14 age groups) for males and females, respectively. It also presents the 5-year visit rate predicted values based on Scenario A (dash lines). It is evident that the age-specific visit rates continued to increase from 2008 to 2012, and this trend will continue for the next 5-year period of 2013 to 2017 in both genders.

Figure 3.

Figure 3

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Age-specific emergency department visit rates of septicemia-associated in log scale by birth cohort for male and female in Taiwan, 1998 to 2012. Solid line: observations; dotted line: forecasts.

Figure 4 shows the age-adjusted visit rates of age categories 20 to 39, 40 to 64, and ≥65 years, respectively. The predicted values using the APC model have also been shown in Fig. 4, Scenario A. It is clear that the trend increases dramatically in the period 2013 to 2017 for the elderly population in both genders. The extrapolation of the observed log age-adjusted visit rates, the same figure also presents the predictions for Scenario B. The predictions using such a direct and simple method show a continuously increasing trend, for 5 years, for both males and females.

Figure 4.

Figure 4

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Observation and prediction of age-adjusted visit rates from septicemia for stratified males and females in Taiwan (mark [A] age–period–cohort analysis; mark [B] quadratic regression analysis of logarithm of age-adjusted visit rates, 2013–2017).

4. Discussion

The current study investigated the trends in the septicemia-related ED visit rates by simultaneously applying APC perspectives. Indeed, the APC model predictions can provide an advanced warning based on the future trend. APC effects provide a formal clue to guide the analysis through an explicit consideration of these effects with sufficient goodness-of-fit in the APC model.

Urging growth of ED visits rates due to septicemia, results showed that effects on women were stronger than on men in the recent cohort. We found no gender differences among the cohort effects of mid-20th century generations on the variations in septicemia-related ED visit rates. From these results, we speculate that the overall external changes affect all age groups in 1988 to 2012, while imposing different cohort effects on visit rates due to septicemia, while this difference may be provided to explain the causation rather than the effect on the entire ED visit rates.

According to the latest researches, there have been many studies explaining the overall trend of septicemia. In general, there were studies presented an increase trend in (severe) sepsis.[20,21] Although we observed a decline trend in septicemia-related ED visit rates at an early stage (Fig. 3), which reversed itself by increasing. Viewpoint of public health for gender, the incidence of some infectious diseases had differences in male and female. For example, the adult female has higher incidence of urinary tract infection than male.[22] The incidence of bacteremia (but not the case–fatality ratio) was significantly higher in males than in females.[23] Another study also indicated that Gram-positive bacteria caused more sepsis in males than in females.[24] We similarly observed the adult male has higher incidence of septicemia than female in ED (Fig. 1). Thus, our observations are somewhat consistent with previous studies. Of note, to our knowledge, previous studies did not decompose septicemia visit rates into the effects of APC or examine influences in age-stratified groups.

Further, the quality of medical care for critical illnesses has become an issue of great concern.[25] In Taiwan, to improve the quality of care for critically ill patients in the EMS, the MOHW has emphasized the importance of the quality of care for critical illnesses and the efficiency of disposition by initiating the optimizing emergency medicine quality (OEMQ) program of the NHI in 2012.[26] The OEMQ program provides a protocol for critically ill patients to receive standard treatment and provides reward as accreditation as an NHI-privileged hospital when a set of standards has been reached. Septicemia was one of the index diagnoses for upgrading the critical care performance of hospital EDs in the OEMQ program. Overall, this program may increase visit rates due to septicemia in EDs because of the more aggressive disposition decisions, with a higher likelihood for ED physicians to make septicemia-associated diagnoses. Moreover, the impact of the OEMQ program on septicemia-associated ED visit rates should be measured in future.

For predictions of the logarithms of the age-specific 5-year visit rates, a simple quadratic extrapolation revealed that the younger cohorts exhibited slightly increased trends, which was less than that of the older cohorts. However, a simple quadratic extrapolation of the log age-adjusted visit rates may underestimate some important features hidden in the data (i.e., the cohort effects), especially in the elderly population. Based on a direct observation of the recent trends in visit rates from 2008 to 2012 in Taiwan, we can undoubtedly confirm that the trends will continue to increase in the next few years.

Despite these interesting results, some potential limitations of our study should be noted. First, our study is descriptive and we can only infer about the etiologies of the changes observed. The visit rates derived by APC effects are reamenable for an APC prediction. However, it is important to note that there are set assumptions for the prediction method that we used in the present study. As we based our prediction on visit rates across 15 years, we limited our APC prediction to only the next 5 years. Second, APC analysis can be used extensively in epidemiology and social economic fields in populations of developing or recently developed countries, where there is a lack of long-running cohort studies. Third, we do not have information from the aggregated format datasets to adjust confounders, such as comorbidities, education levels, or lifestyle in the APC model. Further studies using individual data are needed to solve this limitation. Fourth, the number of cases of septicemia was comparatively low (Table 1), compared to the incidence rates of severe sepsis reported in another claims data-based studies.[6] We suspected one of the reasons of utilizing the coding of sepsis. Moreover, women may be less likely to develop septicemia. In general, the age standardized rates for septicemia decreased and then increased during the period, as presented in the period effects. The phenomenon can be explained by a more aggressive disposition by ED physicians and a higher awareness about making septicemia diagnoses as well as an overall increase in the number of cases of septicemia-relevant symptoms and signs in the general population, including at-risk patients. In addition, general progress has been made in medical technology and practices to detect bacteremia.[27,28] Lastly, the diagnostic codes were given to the patients at discharge of the ED to either inpatient or outpatient departments. Under the ED crowding condition in Taiwan, many patients would stay in the ED for 2 to 3 days in average.[29] Therefore, the emergency physicians could observe the patients before they give the final discharge diagnosis in the ED. However, the results of blood culture may still be unavailable at the time patients were discharged from ED, which would result in the underestimated prevalence of sepsis in the ED.

5. Conclusion

The capacity of emergency physicians to provide medical services should be planned by age, time period, and cohort effects on the incidence of infectious diseases. Predicting the EDs visit rates from septicemia-related data is an important element in the process of planning for the future. Since 1993, Taiwan became an aging society because the percentage of the population aged 65 years and over reached 7%.[30] It is known that aging is a relevant risk variable for infectious diseases.[31] Thus, the present study concluded that the aged population will contribute to the increase in the infection-related ED visit rates in the future.

Acknowledgments

The authors thank the Department of Statistics, Ministry of Health and Welfare, Taiwan, for helpful comments and suggestions.

Supplementary Material

Supplemental Digital Content
md-95-e5598-s001.xlsx (73.8KB, xlsx)

Footnotes

Abbreviations: APC = age, period, and cohort, CD = ambulatory care expenditures by visits, ED = emergency department, EMS = emergency medical service, HCAI = healthcare-associated infections, ICD-9-CM = International Classification of Diseases, 9th Revision, Clinical Modification, ID = identification, MOHW = Ministry of Health and Welfare, NHI = National Health Insurance, NHIRD = National Health Insurance Research Database, NHS = National Health Service, OEMQ = optimizing emergency medicine quality.

Authors’ contributions: I-ST created the research idea, performed the analysis, wrote the results and discussion, and contributed to the literature review. S-HL assisted in the literature review and supported the analysis and results interpretation. K-FC participated in critical discussion, contributed to analysis, and aided in editing of the manuscript. J-CC participated in the study design and revised the manuscript. YTC helped cope with the NHIRD dataset. C-HH, C-JL, C-YC, S-CH, and Y-MW provided clinical suggestions and helped revise the manuscript. I-ST prepared the manuscript for submission. All authors read and approved the final manuscript.

Ethical approval: Ethical approval is not required since this study is a secondary analysis of septicemia-associated ED visits data available in the public domain.

Informed consent: The septicemia-associated ED visits data available to the public are not individually identifiable, and therefore their analysis would not involve human subjects. Informed consent is not required for this study.

Availability of data and materials: The dataset supporting the conclusions of this article is included within the article (refer to table, Supplemental Content, which records the septicemia-related ED visit rates calculated by mid-year population and visit number of patients).

The authors have no funding and conflicts of interest to disclose.

Funding: This study was supported by the National Science Council and Chang Gung Memorial Hospital in Taiwan (100-2314-B-182A-038-MY3, CMRPG2C0202, CIRPG2E0022, CMRPG2B0124, CMRPG2B0273, CMRPG2B0373, CPRPG2D0012, and CMRPG3E0911). This study was partly supported by a grant from the Ministry of Science and Technology in Taiwan (MOST 105-2325-B-002-027).

Supplemental Digital Content is available for this article.

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Associated Data

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Supplementary Materials

Supplemental Digital Content
md-95-e5598-s001.xlsx (73.8KB, xlsx)

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