Association of Summer Temperatures With Hospital Admissions for Renal Diseases in New York State: A Case-Crossover Study

Fletcher, Barbara A.; Lin, Shao; Fitzgerald, Edward F.; Hwang, Syni‐An

doi:10.1093/aje/kwr417

Cited by 114 publications

(105 citation statements)

References 30 publications

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“…2 Heat cramps 992. 3 Heat exhaustion from water depletion 992. 4 Heat exhaustion from salt depletion 992.…”

Section: Icd-9 Codementioning

confidence: 99%

“…The case-crossover design documents exposures immediately prior to the event of interest (called the hazard period) and compares them to exposures from a period during which the event of interest did not occur (called the referent periods). The case-crossover design has previously been applied in studies investigating the association between morbidity and temperature [3,10,12]. Because each case acts as its own control, individual characteristics, such as sex, age, and race, are exactly matched; therefore, the time-stratified case-crossover design inherently controls for confounding effects.…”

Section: Case-crossover Modelmentioning

confidence: 99%

“…Further, these missing data can change analytical and public health conclusions from these studies. Previously, public health researchers have either focused on times of known extreme heat events, eliminating the need for a data-driven extreme heat definition; studied one locale or city-specific heat waves, which typically results in exposure data having similar quality or patterns of missingness across heat waves; or have used only 10-20 years of weather data to define extreme heat, a shorter duration than that used in climate science [1][2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…Fletcher et al [3] performed a time-stratified case-crossover analysis to determine an association between temperatures in July and August, during 1991-2004, with hospital admissions for renal diseases in New York State. Basu et al [2] also used a time-stratified case-crossover model to determine associations between high ambient temperature and preterm births in May to September from 1999-2006, in 16 counties in California.…”

Section: Introductionmentioning

confidence: 99%

“…These studies generally exclude cases described as having external causes of injury [10], i.e., car accident; however, associations may be difficult to interpret. Other studies use specific groupings of International Classification of Disease (ICD) codes or specific symptoms to focus their studies on illnesses of interest such as exertional heat-related illness, diabetes, cardiovascular diseases, pulmonary diseases, kidney illnesses, and preterm delivery [1][2][3]5,6,11]. These more focused studies typically have motivating biological mechanisms or processes to inform interpretations.…”

Section: Introductionmentioning

confidence: 99%

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Effect of Missing Data on Estimation of the Impact of Heat Waves: Methodological Concerns for Public Health Practice

et al. 2017

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(1) Background: To demonstrate the potential effects of missing exposure data and model choice on public health conclusions concerning the impact of heat waves on heat-related morbidity.(2) Methods: Using four different methods to impute missing exposure data, four statistical models (case-crossover, time-series, zero-inflated, and truncated models) are compared. The methods are used to relate heat waves, based on heat index, and heat-related morbidities for Florida from 2005-2012. (3) Results: Truncated models using maximum daily heat index, imputed using spatio-temporal methods, provided the best model fit of regional and statewide heat-related morbidity, outperforming the commonly used case-crossover and time-series analysis methods. (4) Conclusions: The extent of missing exposure data, the method used to impute missing exposure data and the statistical model chosen can influence statistical inference. Further, using a statewide truncated negative binomial model, statistically significant associations between heat-related morbidity and regional heat index effects were identified.

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“…2 Heat cramps 992. 3 Heat exhaustion from water depletion 992. 4 Heat exhaustion from salt depletion 992.…”

Section: Icd-9 Codementioning

confidence: 99%

Section: Case-crossover Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Effect of Missing Data on Estimation of the Impact of Heat Waves: Methodological Concerns for Public Health Practice

et al. 2017

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Assessment of Intraseasonal Variation in Hospitalization Associated With Heat Exposure in Brazil

Zhao

Coelho

et al. 2019

JAMA Netw Open

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Key Points Question During the hot season, is timing of heat exposure associated with varied risk of hospitalization in the Brazilian population? Findings In this time-stratified case-crossover study, the association between heat exposure and hospitalization was greatest in the early hot season, particularly for residents living in the northeast and central west regions of Brazil. Exposure to early heat was associated with the highest risk of hospitalization for children and elderly individuals, and for admissions for endocrine, nutritional, and metabolic diseases. Meaning Preventive strategies to mitigate the association of high temperature with population health should focus on extreme high temperatures occurring early in the hot season.

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Vulnerability to extreme-heat-associated hospitalization in three counties in Michigan, USA, 2000–2009

et al. 2016

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Background With climate change, extreme heat (EH) events are increasing, so it is important to understand who is vulnerable to heat-associated morbidity. We determined the association between EH and hospitalizations for all natural causes, cardiovascular, respiratory, and renal diseases, diabetes mellitus, and acute myocardial infarction in Michigan, USA at different intensities and durations. We assessed confounding by ozone and how individual characteristics and health insurance payer (a proxy for income) modified these associations. Methods We obtained Michigan Inpatient Database, National Climatic Data Center, and U.S. Environmental Protection Agency ozone data for May–September, 2000–2009 for three Michigan counties. We employed a case-crossover design and modeled EH as an indicator for temperature above the 95th, 97th or 99th percentile thresholds for 1, 2, 3 or 4 days. We examined effect modification by patient age, race, sex, and health insurance payer and pooled the county results. Results Among non-whites, the pooled odds ratio for hospitalization on EH (97th-percentile threshold) vs. non-EH days for renal diseases was 1.37 (95% CI = 1.13–1.66), which increased with increasing EH intensity, but was null among whites (OR = 1.00, 95% CI = 0.81, 1.25). We observed a null association between EH and cardiovascular hospitalization. EH (99th-percentile threshold) was associated with myocardial infarction hospitalizations. Confounding by ozone was minimal. Conclusions EH was associated with hospitalizations for renal disease among non-whites. This information on vulnerability to heat-associated morbidity helps characterize the public health burden of EH and target interventions including patient education.

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Association of Summer Temperatures With Hospital Admissions for Renal Diseases in New York State: A Case-Crossover Study

Cited by 114 publications

References 30 publications

Effect of Missing Data on Estimation of the Impact of Heat Waves: Methodological Concerns for Public Health Practice

Effect of Missing Data on Estimation of the Impact of Heat Waves: Methodological Concerns for Public Health Practice

Assessment of Intraseasonal Variation in Hospitalization Associated With Heat Exposure in Brazil

Vulnerability to extreme-heat-associated hospitalization in three counties in Michigan, USA, 2000–2009

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