Hansen et al., 2008 [12] |
Hospital admissions data for the Adelaide metropolitan area, admissions for renal disease, acute renal failure, and renal dialysis |
Adelaide, South Australia, Jan 1995 to Dec 2006 |
Case series approach, Poisson regression models |
2796 |
Mean temperature |
Exclusion of the cool season and analysis conducted within years adjusted for long-term trends |
3 to 8 |
Admissions for renal disease and acute renal failure |
Pincus et al., 2010 [26] |
Patients with renal colic who presented over a 7 y period to the emergency department of a single inner-city Melbourne hospital |
Melbourne, Australia, Jan 1999 to Dec 2005 |
Non-parametric analyses |
3070 |
Mean temperature |
No exclusion criteria |
- |
Presentation of renal colic |
Tawatsupa et al., 2012 [27] |
Data were derived from baseline (2005) and follow-up (2009) self-report questionnaires from a large national Thai Cohort Study |
Bangkok, Thailand, 2005 to 2009 |
Logistic regression model |
405 |
Heat stress |
Adjusted age, alcohol consumption,, smoking, body mass index, income, education, job type, and job location |
- |
Incidence of kidney disease |
Lin et al., 2013 [10] |
Taiwan's National Health Insurance Research Database; age-specific (<65 y, 65+ y and all ages); hospital admission records of nephritis, nephrotic syndrome, or nephrosis, in the form of electronic insurance reimbursement claims |
Taipei, TaoHsinMiao, KeeYi, Central Taiwan, YunChiaNan, East Taiwan, and Southern Taiwan, 2000 to 2008 |
Time series, DLNM |
861 763 |
Mean temperature |
Consecutive temperature extremes, daily area-specific averages of PM10,NO2,O3, relative humidity and wind speed, influenza and pneumonia, holiday effects, day of the week, and long-term trends |
0 to 8 |
Kidney disease hospital admissions |
Bobb et al., 2014 [28] |
Medicare inpatient claims data and assembled data from 27.9 million Medicare beneficiaries per year, aged 65 y or older, enrolled in the fee-for service program for at least 1 mo; daily cause-specific hospitalization rates by principal discharge diagnosis codes, grouped into 283 disease categories using a validated approach |
1943 counties in the USA, 1999 to 2010 |
Time series, log-linear mixed-effects regression model |
6850 |
Heat wave |
County-level factors and temporal trends by matching heat wave days to non-heat wave days by county and week and by adjusting for day of the week and study year |
0 to7 |
Hospitalization for renal failure |
Tasian et al., 2014 [22] |
Insured population using the Market Scan Commercial Claims database; kidney stone presentation defined as a surgical procedure, hospital admission, and/or at least 2 emergency room or outpatient clinic visits <180 d apart for a primary diagnosis of nephrolithiasis using ICD-9th Revision and CPT codes |
Atlanta, Chicago, Dallas, Los Angeles, Philadelphia (USA); 2005 to 2011 |
Time series, DLNM |
60 433 |
Mean temperature |
Daily fluctuations in outdoor activities, season, temperature trends, and differences in the annual at-risk population |
0 to 20 |
Kidney stone presentation |
Ordon et al., 2016 [23] |
Linked healthcare databases in Ontario, Canada, which included all residents aged 19+ y who were admitted to an emergency department for renal colic |
Ontario, Canada, Apr 2002 to Dec 2013 |
Time series, DLNM |
423 396 |
Mean temperature |
Seasonal and long-term effects, daily mean relative humidity, a categorical variable for the day of the week, and an indicator variable for statutory holidays to control for potential holiday effects |
0 to21 |
Daily emergency department visits for renal colic |
Yang et al., 2016 [24] |
Daily emergency ambulance dispatches for renal colic from Guangzhou Emergency Center; renal colic was diagnosed on the basis of a clinical history, physical examination, urinalysis, and imaging examination |
Guangzhou, China, Jan 2008 to Dec 2012 |
Time-series, DLNM |
3158 |
Mean temperature, minimum temperature, maximum temperature |
Seasonality, humidity, public holidays, and day of the week |
0 to7 |
Daily emergency ambulance dispatches for renal colic |
Moyce et al., 2017 [29] |
A convenience sample of 300 field workers was recruited from 15 farms in agricultural regions of California’s Central Valley using the recommended definition and stages of injury from the Kidney Disease: Improving Global Outcomes group |
California, USA, during the summer of 2014 |
Logistic regression model |
35 |
Heat strain |
Age, sex, physiological factors, occupational factors |
- |
Acute kidney injury cumulative incidence |
Ogbomo et al., 2017 [30] |
Michigan Inpatient Database, National Climatic Data Center, and USA Environmental Protection Agency O3 data; ICD system, Ninth Revision, Clinical Modification |
Michigan, USA, May to Sep 2000-2009 |
Case-crossover design |
18 073 |
Mean temperature |
Age, race, sex, and health insurance payer |
0 |
Hospitalization for renal diseases |
Lim et al., 2017 [25] |
Hospital admission data were collected from the database of the Health Insurance Review and Assessment Service; The diagnosis of acute kidney injury was based on the primary and secondary disease codes of the ICD, 10th Revision |
Seoul, Korea, 2007 to 2014 |
Time-series, piece-wise linear regression models |
24 800 |
Mean temperature |
Day of the week, daily mean relative humidity, daily mean air pressure, and time trend |
0 |
Acute kidney injury admissions |