A variety of modeling decisions are made when estimating the impact of weather on mortality, including the shape of the exposure–response curve, lag structure, and temperature metric. These choices affect results and comparability across studies. Earlier work investigated several exposure–response forms, such as the temperature of lowest mortality risk (minimum mortality temperature) and constant linear relationships above (heat slope) and below (cold slope) the minimum mortality temperature,6
generating a V-shaped exposure–response curve. Although useful, these methods do not fully capture the nonlinear association and are problematic for comparing across cities, for example, a comparison of a heat slope calculated for higher than 80°F versus a slope calculated for higher than 90°F. Other studies estimated constant slopes above and below city-specific threshold hot and cold temperatures.10,11,16
When comparing communities with disparate climates, this method forces a V-shaped model that may not reflect actual temperature-mortality relationships in each community.
Our spline approach allows estimation of nonlinear relationships without forcing constant slopes for specific temperature ranges or similarities among communities. Similar methods were applied in a study of 7 US cities.2
For most communities the difference in mortality risk per unit temperature decrease was fairly consistent across mild cold temperatures; however, the heat effect per unit temperature increase rose significantly at higher temperatures. Furthermore, some communities did not have a unique minimum mortality temperature. Slope approximations based on specified temperatures (eg, our absolute estimates) are useful to summarize and compare temperature-response relationships, but interpretation of results should consider that such methods reflect only a portion of the nonlinear relationship. Although the heat and cold effects generated in this study summarize only part of the temperature–mortality relationship, these methods were particularly appropriate for studying such a large range of climates. Complex nonlinear functions such as those used here and in previous studies may provide a more complete assessment of temperature and mortality risk.8,15
Previous studies of weather and mortality have used a variety of temperature measurements. Several studies have recommended apparent temperature or humidex24
because these measures incorporate humidity; others suggested minimum temperature.11
We applied minimum, maximum, and mean daily temperature, and mean daily apparent temperature, and identified heat and cold effects for all metrics. Apparent temperature effects were nearly identical to those of mean daily temperature adjusted for humidity. Heat effects were highest for mean daily temperature. A multicity European study similarly found that mean daily temperature was consistently the strongest predictor of mortality from heat and heat waves,26
compared with daily minimum, maximum, and apparent temperature. Although some differences in estimates may occur, our findings indicate that the various temperature metrics are likely to produce similar results.
We investigated lag times from same day to 28 days previous. Earlier heat-mortality studies identified risk from recent exposure (ie, same day and a few days previous).5–8,11,16
Most studies applied lags of 1 or 2 days, although some used up to 3 days.30
We found the strongest heat-related mortality association for same- and previous-day exposure. The short lag required to capture the effects of heat on mortality suggests a rapid physical response. Some of the effects observed could be the result of short-term mortality displacement, and further study is warranted.
For cold-related mortality, most US studies applied 2- to 5-day lags,1,5,6,11
whereas other researchers found cold effects after 1 or more weeks for some communities.16,31
Findings indicate that longer lags are required to capture cold’s impact on mortality and that using identical lag structures for cold and heat effects is not appropriate. A limitation of longer lag structures is the introduction of more measurement error due to increased time between the exposure and event. Heat and cold effects were similar in magnitude for absolute and relative estimates, which contrasts with earlier US studies finding larger heat effects than cold effects.5,11
We hypothesize that previous studies underestimated cold-related effects through use of shorter lags. Results agree with a European study finding mortality effects occurring days to weeks after cold exposure.16
Findings suggest that cold temperatures more indirectly affect mortality than heat. Infectious diseases, which are more common in industrialized countries during colder weather (when people spend more time indoors and in proximity) could account for much of the cold-related effect. Although we found that heat effects were impacted by shorter exposures and cold effects were affected by longer exposures, the specific lag structures used here (Tlag0–25
) are intended to be representative, not to reflect the only or the exact lag measurements appropriate for temperature-mortality studies.
We took several approaches to comparing temperature effects across communities, including estimates based on each community’s temperature distribution, allowing comparison despite the wide climatic range. Studies of 2 Euro-pean,16
and 50 US5
cities similarly estimated mortality risk for community-specific temperature quantiles. We also calculated the effect of a change in absolute temperature, from a relatively mild (60°F) to hot (80°F) or cold (40°F) temperatures. These 2 types of effect estimates have different interpretations with respect to acclimatization, an important consideration for climate-change studies and public health policy. Acclimatization can occur through physical adaptation, housing characteristics, or behavioral patterns (eg, staying indoors, clothing). With a high degree of acclimatization to weather, results would be similar across communities for relative temperature effects and different for absolute effects. Without a high degree of acclimatization, communities would have similar absolute effects and dissimilar relative effects. Although both absolute and relative temperature effect estimates showed variation across communities, absolute estimates exhibited larger variation, which implies some degree of acclimatization to weather conditions because a given temperature has different impact depending on location. Previous studies have also found some evidence of acclimatization.5,6
Heat effects were generally lower in communities with higher long-term temperatures. This supports the hypothesis that communities and individuals adapt, to some extent, to weather even during temperatures that are extremely warm for that area. Conversely, absolute cold effects were markedly higher in communities with higher temperatures, as observed previously in other areas.32,33
However, a similar association was not observed for relative cold effects. This indicates that those in colder cities seem to acclimatize to some degree, so they are less affected by temperatures of 40°F, but not to the extent of lessening effects at temperatures extremely cold for the community.
Most previous heat wave studies have analyzed specific extreme events (eg, Chicago 199534
or European 200312,13
heat waves). Fewer studies have considered more frequent, less severe heat waves. Our findings suggest that sustained periods of extreme heat present an elevated risk over single days of high temperatures, even if the heat wave period is as short as 2 days, and that duration and intensity of the heat wave affect mortality risk. Future studies might consider separate effects by heat wave duration, intensity, or time of occurrence during the summer.
We identified spatial heterogeneity in heat and cold effects, consistent with other US studies1,2,5–8,10,11
with larger cold effects in the southern US and smaller effects in the north.6,11
Similar to previous research,7,11
we found negligible or null effects for heat in many southeastern communities. Results emphasize the need for multicity studies because results from 1 location may not be applicable elsewhere.
Heat effects were slightly lowered when models included O3
. Cold effects were essentially unchanged. In previous studies, temperature–mortality results were also robust to particles2,16,26,30
Earlier research found ozone-mortality associations were robust to control for temperature.20
Findings of previous studies and our work imply separate and substantial mortality effects from temperature and from air pollution; however, some studies suggest possible interaction between temperature and air pollution.3,36
Observed associations between weather and noncardiorespiratory deaths indicate that weather affects mortality beyond cardiorespiratory responses. Estimates were somewhat higher for cardiovascular and respiratory deaths, especially respiratory, compared with total deaths, consistent with earlier studies.5,37
We found higher susceptibility for older populations; however, other age categories were also subject to temperature–mortality risk. Results from stratifying effect estimates by age were consistent with earlier results based on community-level and individual-level data.11,34,37,38
We found differences in susceptibility related to socioeconomic factors and urbanicity. Previous studies also found community-level socioeconomic factors to explain some variability in communities’ heat effects.6,10
This may reflect baseline health and nutrition status, access to health care, and ability to respond to extreme conditions (eg, AC). Susceptibility by urbanicity may relate to urban-heat-island effects or housing conditions. We found that heat has a lower mortality impact when communities have more central AC, as observed in smaller US studies.7,10,14
This adaptation strategy likely explains some of the regional variation in heat effects. Earlier work1,7
found that heat-related mortality decreased significantly in the southeastern US as AC prevalence increased. Over time, the number of cities without a heat effect has increased,7
especially where AC has reached almost universal prevalence. Heat wave effects, however, were not strongly associated with AC. It is possible that the protection afforded by AC is sufficient to reduce effects of high temperatures, but not to prevent more extreme heat wave effects. Heat effects were higher in communities with higher income. Although this relationship might seem surprising, a recent national study showed that median income is negatively associated with mortality in adults 65 years or younger but not with older individuals, which represents the majority of heat- and cold-related mortality.39
Socioeconomic factors were less important in explaining cold effects; however, communities with a higher percentage of black/African Americans had higher cold effects, although the relationship was uncertain. Earlier research also found insignificant impact of socioeconomic factors on cold effects,5,6
although 1 study found higher cold effects for communities with less education or a lower percentage of population identifying as black.2
A study of cold effects on the elderly found significant associations with poverty, income inequality, and deprivation rate.40
The lack of association in our study might relate to the use of community-level variables. Further investigation using individual-level data is needed. Such data could also improve exposure estimates, especially for longer lag structures.
These findings on the impact of weather on mortality have implications for policymakers and future scientific work. The identified susceptible subpopulations signify the need for targeted heat-mortality prevention efforts. The heterogeneous results across communities indicate the value of multicity research and indicate that approaches to prevent weather-related mortality might be most effective if they are community specific. Results on acclimatization and heat waves are of particular importance to research estimating weather-related mortality impacts from climate change.