However, it is not known how such an increase in hospitalizations has affected the estimates of healthcare costs attributable to UTIs. Furthermore, it is not clear what subpopulations of patients are driving this growth in incidence: the epidemiology of UTIs differs between men and women and younger and older patients. In addition, although the incidence of UTIs appears to be seasonal [ 17—21 ], it is not clear how seasonality affects hospitalizations for UTIs, especially with respect to different populations of patients.
The purpose of this study is to describe the trends and seasonal patterns in the incidence of UTI hospitalizations by age group and sex. In addition, we describe trends in length of stay, inpatient mortality, and healthcare costs for hospitalizations associated with UTIs. Each record represents a single hospitalization that includes diagnoses, procedures, demographic, and other information about the patient.
We excluded records for patients under 18 years of age or that did not include values for month, year, age, and patient sex. Case counts were normalized to incidence rates with the midyear population estimates from the Census Bureau by each specific combination of sex and age category. Age categories were defined as 18—29, 30—39, 40—49, 50—59, 60—69, 70—79, and over 90 years old.
An autoregressive moving average ARMA model with a seasonal autoregressive component of order 1 was used to characterize the data. Based on the ARMA framework, we can estimate the trend effect while simultaneously controlling for the temporal correlation inherent in time-series data.
The year variable accounts for long-term changes over time, and the month variable captures seasonality—annual periodicities as reflected in changes by month. To further examine UTI seasonality, we detrended the series: a linear trend was fit to each of the subseries described above, and the residuals were obtained. The resulting residuals are a series that has a constant mean no trend but retains the seasonal fluctuations.
For each series, we computed the yearly maximum, minimum, and range observed in these residuals. We also computed the mean patient age within each of the age groups used to create the series. We stratified the resulting data by sex. We estimated the effects of increasing age and increasing year by regressing the observed maximum, minimum, and range on the mean age and the year with separate models for men and women.
We used these measures of severity as opposed to an index such as the Charlson or Elixhauser comorbidity coding systems due to concerns about the lack of time invariance of the comorbidity systems.
Specifically, changes over time would have affected cases and controls differently. In contrast, length of stay, mortality, and costs after adjustment for inflation exist on a standard scale that does not vary over time, and any changes would affect cases and controls in the same way. We excluded any of the cases or controls that had a labor and delivery diagnosis code on their record ICD 9 codes: , , , We only considered length of stay in cases in which the patient was discharged alive to avoid truncation due to death.
When possible, we used the all-payer hospital-specific ratio. If the hospital-specific ratio was not reported, we used the group-average all-payer ratio. These ratios are only available for years — After conversion to total costs, we applied the Consumer Price Index for Medical Care [ 22 ] to convert all dollar amounts to constant December dollars.
We estimate changes in length of stay, mortality, and costs using traditional linear regression or logistic regression models, as appropriate.
We regressed each outcome on the patient age an indicator for each decade , sex, number of procedures performed during the stay, month of year, an indicator for UTI as primary diagnosis, the year, and the interaction between year and the UTI indicator. The primary coefficient of interest is on this interaction term: it reflects the slope difference between the linear trend of non-UTI and UTI patients. For the regression parameter estimates, we used heteroskedastic-consistent standard errors.
Because it is possible that some of the change in incidence of UTIs could have been associated with changes in coding or diagnostic approaches, as a sensitivity analysis, we considered trends in incidence for pyelonephritis ICDCM codes of Pyelonephritis is a more severe diagnosis, and regardless of antimicrobial resistance patters of the causative agent, it is more likely to lead to hospitalization than the diagnosis of an UTI.
Thus, we computed monthly incidence series for pyelonephritis and UTIs and estimated the trend using the ARMA framework explained above. For the years to , there were hospital admissions in the NIS. Of these, were for UTIs in adults.
The data required to construct the time series age, sex, admission year, and month were present on of these records. Somewhat smaller samples were used for the severity models due to the additional variables required eg, length of stay, mortality sample sizes are reported with the regressions.
Weighted summary statistics of the sample are included in Table 1. Between and , the number of UTI admissions increased from The majority of the increase in admissions occurred among women Mean length of stay decreased from 5. A plot of total charges over time is shown in Figure 1. Urinary tract infection UTI incidence and total cost of hospitalizations by sex, — Incidence is the number of cases per people in the community by sex, and real total costs are converted to costs using the Healthcare Cost and Utilization Project cost-to-charge ratio and are normalized to constant December dollars.
Solid lines denote the male series, whereas dotted lines represent the female series. Incidence of hospital admissions for UTIs increased in both men and women of all ages Table 2.
Incidence rates accelerated with advancing age: the growth rate for to year-old women was 7. Although UTI incidence was rising for all sex and age groups, the average rate of increase for women was approximately twice the rate of increase in men. For example, the rate of increase for to year-old men was 9. The incidence of UTI hospitalizations is highly seasonal and our seasonality findings are reported in Table 3. Urinary tract infections peak in the summer months and the nadir occurs during the winter.
The incidence of admissions for UTIs exhibits a stronger seasonal effect for women than for men. Seasonality is most pronounced among younger patients, and it diminishes with advancing age.
Among women, for each year of age there was a decrease of 2. Among men, for each year of age there was a decrease of 1. During our study period, the seasonal intensity changed. Among women, the seasonality increased: the incidence of UTIs for women at the beginning of our sample was less seasonal than at the end.
In contrast, among men, the seasonality diminished. Specifically, with each passing year between and , the average seasonal intensity increased by 3. However, in a model adjusting for age grouped by decade , sex, year, month of year, number of procedures, and a primary diagnosis of an UTI, we found that the length of stay decreased faster for patients with a primary diagnosis of an UTI Table 4.
Specifically, a non-UTI patient stayed an average of Inpatient mortality decreased substantially between and Table 4. Costs increased for all patients between and Table 4. In , more than patients were admitted with a primary diagnosis of UTI. The mean real cost per case has increased by The incidence rate for pyelonephritis was 4. Monthly incidence is shown in Figure 2.
Our regression analysis showed the monthly incidence increased by 0. This compares to the Urinary tract infection UTI and pyelonephritis incidence, — Incidence is the number of cases per people by month. The greatest increase in the number of hospitalizations for UTIs occurred among women, which is not surprising.
Although UTIs are most common in younger women, our results demonstrated that most of the increase in UTI hospitalizations occurred among older women eg, patients older than Urinary tract infection hospitalizations also increased for men, especially among older men. These dramatic changes in incidence of UTI hospitalizations that we report highlight the need to re-estimate costs attributable to UTIs.
These increases in incidence were only observed among UTIs. The incidence of pyelonephritis remained relatively flat between and In addition, the rate of growth over time in pyelonephritis incidence was not statistically significantly different from zero 0.
Assuming that patients with pyelonephritis are more likely to be hospitalized than patients with UTIs because of their more severe symptoms, regardless of the resistance of the causative pathogen, we believe that these findings are consistent with our theory that antimicrobial resistance was driving some of the increase in incidence in hospitalizations for patients with a primary diagnosis of an UTI between and The relatively faster growth in incidence for UTIs compared with pyelonephritis is also suggestive of antimicrobial resistance as a driver of the dramatic increase in the incidence of hospitalizations for UTIs rather than changes in coding or changes in diagnostic practices.
Our study period coincided with reports of increases in antimicrobial resistance for agents commonly used to treat UTIs [ 8 , 12—14 , 16 ]. Fifteen years later, Poppy not her real name is still often in pain. The infection was an attack of cystitis, a common acute urinary tract infection UTI. Back in the UK, her condition became chronic.
Most people — and most doctors — think of UTIs as nasty but short-lived. The standard test is for bacteria in a urine sample. The standard treatment is three to five days of antibiotics. A one-off acute attack can be cleared very quickly. But there are thousands of women whose experience is very different. They cannot get well. They have chronic infection that dominates their lives. They spend months and years in and out of GP practices and hospitals, having tests and treatments that sometimes exacerbate the problem.
And to make it so much worse, they feel they are not taken seriously. Male GPs asked why I was crying. Some women have really bad pain. She was unable to work, suffering stress and anxiety. It had an impact on my sex life and that relationship broke down. There were other factors. But that probably played a part. I was getting urine samples sent away that were coming back normal. I saw urologists in the NHS who rolled their eyes and told me there was nothing wrong.
Like many others, she searched for help online because GPs and urologists diagnose conditions that seem to be labels for an unfixable problem — painful bladder syndrome or interstitial cystitis. There is no offer of a cure.
Many of these women — and it is usually women although not solely — converse in Facebook support groups and talk of a clinic at the Whittington hospital in north London and a consultant — Prof James Malone-Lee, who has pioneered a different approach.
Malone-Lee recently retired from the NHS, but his research, his private practice and the handful of doctors who have adopted his thinking, continue the work. Malone-Lee put it even more strongly. Some in the medical establishment are anxious about his treatment, because he gives women months or even years of antibiotics. But a published review of outcomes for women treated over 10 years at the Luts lower urinary tract symptoms clinic at Hornsey central health centre showed that they got better, just one had a serious side-effect and that there were no instances of antimicrobial resistance.
That one case of a serious side-effect resulted in the clinic being closed down for some months. A research paper subsequently found the other patients got worse during the closure. Henderson is typical of the women treated at the clinic. Would I ever get back to a normal life? Figure: Resistance to key antibiotics among Escherichia coli or coliform positive urine specimens.
Source: Public Health England Fingertips portal. In , a third of Escherichia coli or coliform positive urine specimens in the community in England were resistant to trimethoprim. However, an increased emphasis on nitrofurantoin as first-line treatment in recent years has seen resistance to trimethoprim decrease. Resistance to nitrofurantoin has also seen a slight decline.
But experts argue that, longer term, to really make a significant difference to AMR, an overhaul in the way that UTIs are diagnosed and treated is needed. Some alternatives are getting closer to commercial development.
A second test, currently in development, will test this sample against a panel of 15 antibiotics and tell you which will be the most effective in just 15 minutes. We need a test that is both sensitive and specific, affordable and able to integrate into the workflow of GP surgeries, hospitals and pharmacies.
With the recent CARB-X grant, the plan is to expand these trials to 2, patients, which Dubey estimates will take up to four months. In as little as six months, he is hoping the product will be available commercially, albeit in India to begin with. November N Engl J Med ; 8 : JAMA ; 23 : PMID: Am J Clin Pathol ; 3 September JAC-Antimicrobial Resistance Access provided by. Stuck in the s: why UTI diagnosis badly needs an update Urinary tract infections are the second most common reason for prescribing an antibiotic and, with antimicrobial stewardship a priority for the government, the stakes for accurate diagnosis are being raised ever higher.
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