This paper was completed over the course of the fall 2016 semester in the Quantitative Methods Analysis class, which is taught by Dr. Mitchell. I selected one topic, namely the potential health outcomes on population due to climate change in Arkansas. On this project, I wanted to assess the specific implications of climate change for human health, because this interest has grown rapidly over the past few years. In 2008, World Health Organization (WHO) Member States held an international conference and approved a World Health Assembly (WHA) resolution recognizing the importance of climate change for human health, and calling for stronger commitment by Member States to address climate change–related health threats (WHA 2008).
The Member States drafted five research priorities that should be reinforced, which was an unusual and important recommendation for such a resolution to contain. The recommended priorities were: (a) health vulnerability to climate change and the scale and nature thereof; (b) health protection strategies and measures relating to climate change and their effectiveness, including cost-effectiveness; (c) health impacts of potential adaptation and mitigation measures in other sectors, such as marine life, water resources, land use, and transport in particular, where these could have positive benefits for health protection; (d) decision support and other tools, such as surveillance and monitoring, for assessing vulnerability and health impacts and targeting measures appropriately; and (e) the assessment of the likely financial costs and other resources necessary for health protection from climate change (WHA 2008).
To assess the extent to which recently published research on climate change and health parallels to the research priority capacities identified by the Member States, I started a systematic review of original quantitative research on climate change and human health. My second objective was to assess trends in the publication of climate change and health research in recent years, and the proportion of these publications that were original research rather than reviews, or other papers not based on original data. Finally, I wanted to apply such research on different counties of the state of Arkansas.
With this artifact, I want to highlight my skills in quantitative methods analysis. Explicitly, I hope to show that I am capable of analyzing an existing quantitative data that can lead to a specific policy, and after extensive research and comparisons with policies in other environment, propose viable policy recommendations for changing the status quo. In doing so, I did a quantitative method analysis on data collected for several policy alternatives. The analysis was used to validate which policy is the most beneficial for the people of Arkansas.
TABLE OF CONTENTS
Climate change effects…………………………………………………………………………………….………7
Projected change for Arkansas………………………………………………………………………………………………….….9
Arkansas demographic and health population………………………………………………………………………………………………..11
Discussion & Conclusion…………………………………………………………………………………………………….16
Climate change, as an environmental health risk affecting at the global scale, causes a unique and involuntary exposure to the world, and therefore represents possibly the largest health inequity of our time. The World Health Organization (WHO, 2004) concurs that climate change is among the greatest health risks of the 21st Century. In 2010, Arkansas cited climate change as an emerging threat to species and habitats within the Arkansas Wildlife Action Plan (AWAP).
According to the Environment Protection Agency (EPA)’s report in 2016, our planet is warming that is why our climate has been changing. Around the world, people have increased the amount of carbon dioxide in the air by forty percent since the late 1700s (EPA, 2016). Other heat-trapping greenhouse gases are also increasing. These gases have warmed the surface and lower atmosphere of our planet about one degree (F) during the last 50 years (EPA, 2016). The collaboration between the EPA and different states’ departments of environment settled that evaporation increases as the atmosphere warms, which increases humidity, average rainfall, and the frequency of heavy rainstorms in many places—but contributes to drought in others.
In Arkansas, average temperatures have fluctuated more than most other states, with average highs and lows changing each year. Since 2000, major disasters have been declared 23 times in Arkansas due to flooding, severe weather, and even hurricanes. As global temperatures continue to rise, Arkansas is expected to experience an increase in public health dangers, more frequent and intense flooding, and additional stress to the state’s water resources (Lau, Collen L., et al., 2010).
Hot days can be unhealthy—even dangerous. High air temperatures can cause heat stroke and dehydration, and affect people’s cardiovascular and nervous systems. Certain people are especially vulnerable, including children, the elderly, the sick, and the poor. Warmer temperatures can also increase the formation of ground-level ozone, a key component of smog. The ozone has a variety of health effects, aggravates lung diseases such as asthma, and increases the risk of premature death from heart or lung disease. EPA and the Arkansas Department of Environmental Quality have been working to reduce ozone concentrations. As the climate changes, continued progress toward clean air will become more difficult.
This paper will provide a general overview of the major paths through which climate change affects health and several methods to adapting to these challenges. In addition, it will provide a synopsis of projected changes to Arkansas’s climate, the discussion of anticipated health impacts to Arkansas’s population. However, it is important to mention that not all health risks due to climate change will manifest everywhere, and in the interests of prioritization, it is important to conduct location-specific adaptation assessments (Ebi and Burton, 2008).
- Litterature Review
Climate may influence health through, for instance, rising temperatures, extreme weather events that cost lives directly, and through the exacerbation of food shortages, and transmission and spread of infectious disease, (e.g. vector- and water-borne diseases), not excluding environmental determinants of health, including air and water pollution. Climate change may also affect health through the knock-on effects of human migration and socioeconomic disruption (Patz et al. 2005; McMichael et al. 2004; Ebi et al. 2006; IPCC 2007; O’Neill and Ebi 2009).
In 1896, the Swedish scientist Svante Arrhenius suggested that human activity could substantially warm the earth by adding CO2 to the atmosphere. Thomas Chamberlin subsequently independently confirmed his predictions (Weart, 2004). At that time, however, such effect on human beings was thought to be caused insignificantly by other influences on global climate, such as sunspots and ocean circulation. However, these observations went unappreciated until recently. Now, human health is recognized as one of the most important impact margins of climate change, and thus is a global research priority (IPCC, 2007). A vast literature – almost exclusively in public health and epidemiology – has emerged to document the excess morbidity and mortality associated with exposure to extreme temperatures, as well as the associated risk factors (IPCC, 2007 and National Institute for Environmental Health Science (NIEHS), 2010).
The establishment of the IPCC in 1988 is measured as an essential step by the world community to address this issue, and has made a huge difference to the evolution of a shared understanding of climate change and to the stimulus for more and better research and modelling of this phenomenon. Thus, the IPCC reported that societies can answer to climate change by adapting to its effects and by reducing greenhouse gas emissions (mitigation), thereby decreasing the rate and magnitude of change (IPCC, 2007). The capacity to adapt and mitigate depends on socioeconomic and environmental circumstances, and the availability of information and technology. Less information is available about the costs and effectiveness of adaptation measures than about mitigation measures.
A critical aspect in considering the human health threats posed by climate change is the degree to which ‘adaptation’ is possible. Adaptation, according to the IPCC, is defined as “adjustment in natural or human systems in response to actual or expected climatic stimuli or their effects, which moderates harm or exploits beneficial opportunities” (IPCC 2007). In order to narrow the scope of the analysis, I emphasis exclusively on health impacts and adaptation driven by exposure to extreme temperatures. It is important to note that the set of changes to the global climate system incontestably goes far beyond rising temperatures (i.e. rising sea‐levels, droughts, storms). The Southern Climate Impacts Planning Program (SCIPP) is a climate hazards research program whose mission is to help Arkansas residents increase their resiliency and level of preparedness for weather extremes now and in the future. However, the increased incidence of extreme temperature events and the prospects of increased heat‐related morbidity and mortality are by far the most studied outcomes in empirical research.
The main points that arise from this literature review are that despite the wide variety of data sets and settings most studies find that temperature extremes lead to significant reductions in health, largely measured with excess mortality. There is also some evidence of heterogeneity in the response across sub-populations and geographical areas, although that evidence is not as conclusive. There is broad proof of a dynamic relationship between temperature exposure and health, where heat impacts on mortality are immediate, and to some extent reflect the influence of harvesting or forward displacement. On the other hand, cold temperature exposure leads to mortality effects that have a tendency to to accumulate over time, indicative of delayed effects.
- Health outcomes:
The overwhelming majority of studies emphasis on mortality or hospitalization rates as the health outcome analyzed (Mapes, D.L. et al. 2003; Konstman, V. et al. 1996; CDC 1996; Schaufelberger, M. et al. 2004). Every cause‐specific mortality for causes that are believed to be controlled by temperature, example, cardiovascular disease and respiratory disease, are generally the main outcomes, as opposed to mortality directly coded as “heat‐related” (Ahmed, A. et al., 2006). This is because only a few deaths among adults are directly coded as being caused by heat on death certificates. Premature mortality is clearly a key societal health outcome (Knowlton, K. et al. 2007 & Jacobson, M. Z. 2008). Nonetheless, the strong focus on these outcomes as opposed to others (e.g., frequency of chronic conditions) also reflects the fact that vital statistics data on age and cause‐specific mortality and administrative records from hospital admissions are available from a wide range of states over relatively long periods of time.
A notable organizational difference happens between the studies in economics and the studies in public health. Economic studies generally estimate models for annual or monthly mortality rates using panel data methods, in particular fixed effects models (Lin, S. et al. 2012; Vu, T. et al. 2002; & McAlister, F. M. et al. 2001). These fixed effects are used to control for permanent time‐invariant differences in health across geographic areas, while also controlling for seasonality and trends in health over time. On the other hand, studies in public health typically are based on models for city‐level daily mortality counts (Pope III, C. A. et al. 1992; Schwartz, J. 1994; & Curriero, F. C. 2002). These are analyzed in a Poisson regression framework and allow for geographical and time-based heterogeneity (Mihaylova, B. et al., 2011).
Other health outcomes considered include measures of infant neonatal health (Deschenes, Greenstone, and Guryan, 2009). These are specifically important given that exposure to extreme weather events during the perinatal and postnatal periods may lead to significant long‐term reductions in health and quality of life (Kinney, D. K. et al. 2008). Therefore, the key implication of this is that the available empirical estimates can only characterize a partial measure of the health‐related welfare loss, because climate change may affect other health outcomes (e.g., morbidity rates, chronic respiratory conditions, and quality of life) and those have not been extensively studied. A key route for a prospect research is to expand upon these health outcomes.
A key challenge for studies of the impact of climate change (e.g., extreme temperatures and massive precipitations) on health that seeks to advise about substantive changes in life expectancy is to develop estimates that are based on the long term impact of such shocks on life expectancy. This research cannot be obtained from studies that correlate day‐to‐day changes in temperature with day‐to‐day changes in mortality in presence of delayed effects and/or harvesting. “Harvesting” or short‐term mortality displacement refers to the temporal advancement of death among persons who are already ill or at high risk of dying (Schwartz, J., 2000). On the other hand, delayed effects refer to the case when the effect of temperature or floods shocks on health takes several days or weeks to manifest themselves. The solution to this issue is to propose studies that examine intermediate and long‐term effects, rather than only short‐term effects, either through appropriate time aggregation of the data seen in Deschenes and Greenstone (2011) or through the use of distributed lag models, as in Braga et al. (2001) and Deschenes and Moretti (2009).
- Temperature exposure:
Heat (and cold) –related mortality is the result of excessive temperature‐related stress experienced by the human body. The body’s heat regulatory function enables us to cope with exposure to high and low temperatures, but this coping increases the stress on many functions, primarily the cardiovascular circulation. Most studies therefore focus on ambient temperature, measuring a single or group of weather stations near cities or county centers, as an indicator of heat stress. This invariably leads to some measurement error since the actual heat stress experienced by the population is not systematically recorded. Further, adaptations such as air conditioning also modify the relationship between ambient temperatures and heat stress.
The majority of studies focus on daily average temperature, and some also use daily minimum and maximum temperatures to capture differences in daytime and nighttime exposure. Additionally, some studies control for measures of relative humidity or dew temperature, or calculate measures of apparent temperature such as the heat index. Generally, these additional considerations do not lead to meaningful changes in the model estimates (compared to models that only control for temperatures).
The key modeling issue with temperature is the fact that nonlinearities and threshold effects need to be accounted for. Credible studies of the effect of temperature on mortality generally detect significant effects only at the upper and lower extremes of the temperature distribution. Empirically, this is accomplished by modeling temperature through splines, threshold indicators, or temperature‐day bins. This latter approach discretizes the daily temperature distribution in a set of ranges or ‘bins’ and then allow each temperature range (up to a reference category) to have a potentially differential impact on the health outcome. The temperature‐day bins approach is used in most economics paper in this literature, whereas in public health the spline approach is most common.
- Climate change effects:
Once an estimate of the temperature‐health relationship is obtained the question becomes what to make of that information? There is a marked difference between the public health and economics literature in that regard. The literature in public health–epidemiology primarily uses this information for broad public health recommendations such as the creation or evaluation of early warning systems and outreach systems. However, these papers generally provide little detail about the implications of their results for predicting the health impacts of climate change.
In contrast, several papers in economics, Deschenes and Greenstone (2011), Deschenes, Greenstone, and Guryan (2009), Barreca (2012), combine the estimated temperature‐mortality relationships with end‐of‐century (i.e., 2070–2099) daily climate change predictions from state of the art climate models and “business‐as‐usual” scenarios. Under a series of assumptions, this allows the calculation of partial estimates of the health‐related social costs of climate change. These are partial estimates because mortality rates are the only health outcome analyzed in these studies. As mentioned in the introduction, the health impacts of climate change are likely to be significantly broader. To date, these calculations have been produced only for the United States, although work in progress by Burgess et al. (2011) is implementing a similar approach for India. Clearly more work is needed to empirically assess the likely impacts of climate change on health in other countries.
The predictions in Deschenes and Greenstone (2011), based on the error‐corrected Hadley 3 A1FI climate model and scenario, suggest that climate change will lead to approximately 63,000 additional deaths annually in the United States at the end of the century, or a net 3 percent increase in the annual mortality rate. This estimates accounts for the increase in heat‐related mortality and the decrease in cold‐related mortality associated with climate. Barecca (2012) reports similar estimates. To put the 3% increase estimate in some context, it is useful to compare it to the observed improvements in longevity in the United States over the last 30 years. During this period, the age‐adjusted mortality rate declined by approximately 1 percent per year. Thus, even if the end of century mortality predictions are taken literally, the increase in mortality predicted to occur under climate change is roughly equivalent to losing just three years of typical improvement in longevity over the rest of the 21st century.
Another approach to characterize the predicted impacts of climate change on mortality is to report the present discounted value (PDV) of the stream expected monetized losses associated with the predicted increase in mortality. This approach requires daily climate model predictions for all years of the 21st century and all climate model grid points (as opposed a single average prediction for the 2070‐ 2099 period). In addition, the mortality estimates need to be transformed in years of life loss using life tables and age‐specific estimates of the temperature‐mortality relationship. Years of life loss estimates can then be monetized using an estimate for the value of statistical life (VSL). Therefore, these monetized calculations entail strong data requirements. To date, only Deschenes and Greenstone (2011) have produced such calculations.
Deschenes and Greenstone (2011) use two different sets of assumptions regarding the VSL. One specification assumes a fixed VSL of $100,000 per life‐year while the other allows for real per capita income grows of 2 percent per year and an elasticity of the value of a life‐year with respect to income of 1.6, which is leads to increases in the VSL (or value of life‐years) over time (Costa and Kahn 2004). Using a discount rate of 3 percent yields a PDV of the U.S. mortality cost of climate change of $1.0 to $5.5 trillion. By comparison, the corresponding PDV of the adaptive residential energy costs of climate change varies from $0.5 to $3.0 trillion, depending on the assumed rate of growth for energy price (0 or 5 percent annually). This simple decomposition of social cost of climate change associated with mortality as the single health outcome and residential energy consumption as the single adaptation highlights that adaptation is economically important: it accounts for about one third of this partial social cost.
Finally, it is important to recognize that these projections of health impacts at the end of the century requires a number of strong assumptions, including that the climate change predictions are correct, that relative prices will remain constant or evolve deterministically according to a projection, the same technologies will prevail, and the demographics of the US population (e.g., age structure) and their geographic distribution will remain unchanged. These assumptions are strong, but their benefit is that they allow for a transparent analysis based on the available historical data.
In particular, the assumption of stability of medical and other technologies and the fact that all studies based on historical data are necessarily identified by data about the past and by short‐run variation in temperature (as opposed to long‐run variation in temperature) generally leads to an overstatement of human health costs of climate change since the set of possible health‐preserving adaptations will necessarily be more limited in the short‐run than in the long‐run. Absent random assignment of climates across populations, no research design based on real‐world data can fully address this point. Therefore all empirical studies of the prospective effect of climate change on health suffer from this limitation, and should be interpreted accordingly.
C.1. Projected Changes for Arkansas
The Nature Conservancy’s climate wizard is a widely accepted, interactive web tool that incorporates data from IPCC climate models and can be used to assess how climate has changed over time and to project what future changes are likely to occur in a given area. It uses a non-parametric quantile-rank approach that maps out the 0 (minimum), 20, 40, 50 (median), 60, 80, and 100th (maximum) percentiles. Girvetz et al. (2009) have displayed maps produced by the Climate Wizard for changes in mean temperature and precipitation for Arkansas using an ensemble of GCMs and the 3 more widely accepted emissions scenarios for 50 years into the future.
Analyses of climate data from as long ago as 1880, show that the Earth’s surface temperature has increased by more than 1.4°F over the past 100 years, with much of the increase taking place over the past 35 years (National Research Council, 2012). Warming temperatures are often attributed to an increase in greenhouse gas emissions, particularly carbon dioxide, which increased 80% between 1970 and 2004 (IPCC 2007).
Historical average temperature for Arkansas ranged from 58 to 63 degrees between 1895 and 2013 (NOAA Climatic Data Center). Average annual temperature by mid-century (2050) is expected to increase under each emissions scenario.
In regards to climate change, it is important to distinguish between climate and weather. Weather is a set of the meteorological conditions for a given point in time in one particular place, while climate is the average, long-term (30 years ±) meteorological conditions and patterns for a geographic area (Brandt et al., 2014). Climate change is defined as a change in the state of the climate that can be identified (e.g., usage of statistical tests) by changes in the mean and/or the variability of its properties, that persists for an extended period, and that is attributed to either natural variability or human-related activities (IPCC 2007).
Climate change is predicted to increase global average temperatures, as well as the number and intensity of heat waves (IPCC, 2007). In addition, climate change is expected to yield changes in precipitation patterns, with more pronounced extremes such as flooding or drought expected. Climate variability and climate change are however not the only drivers of water availability but is determined by other drivers such as population growth and industrialization (IPCC, 2007). Tropical cyclones (typhoons and hurricanes) are assessed to become more intense with larger peak wind speed and heavier precipitation (IPCC, 2007).
The average annual precipitation for Arkansas from 1951 to 2006 was 49.4 inches (NOAA Climatic Data Center). During this timeframe, the average increased by a rate of 0.101% per year. Global predictions for precipitation changes into the future point to an overall decrease. This may be because the Southeast is located in the transition zone between projected wetter conditions to the north and drier conditions to the southwest. The average change in precipitation for Arkansas by mid-century is predicted to be +1.65%, – 0.79%, and +1.74% under the 3 scenarios. Under each scenario, the southern portion of the state would see the greatest decrease in precipitation. Kunkel et al. (2013) and Carter et al. (2013) concluded that though there is uncertainty among the scenarios in projected precipitation amounts, rising temperatures would account for an increased rate of evapotranspiration, and a decrease in available water. Further, climate change models project that precipitation will be produced in fewer and heavier rainfall events. If so, this could lead to a decrease in aquifer recharge because more rainfall would be lost to runoff and could result in an increase in both drought and flooding events. Thus, Carter et al. (2014) forecast that the southeast region is thus predicted to see a significant reduction in water availability.
C.2. Arkansas demographic and Health Population
Understanding the health problems facing Arkansas is significant by looking at the people and how they live. There are close to three million people living in Arkansas (CIA, 2015). Children under the age of eighteen make up twenty-four percent of the population. Individuals over 65 make up 14 percent. There are slightly more females than males in Arkansas. Since women live longer than men, there are increasingly more females than males in the older age groups (AR Health Report, 2015). Arkansas has 75 counties. The number of people in each county has changed over the years. Arkansas is a very rural state compared to the U.S. as a whole. Almost 45 percent of the people in Arkansas live in rural areas, compared to only 19 percent of people in the U.S (AR Health Report, 2015).
There are approximately 2,245,229 whites 76 percent in Arkansas, which makes whites the largest racial group. There are around 450,000 blacks in Arkansas, which makes blacks the second largest group. Blacks are 15 percent of the population. The main minority ethnic group in Arkansas is the Latino group. There are 186,000 Latinos in Arkansas, which is about six percent of the population (Suburban stats, 2016). The education level in Arkansas is lower than the U.S. average for both high school and bachelor’s level degrees or higher. Only 83 percent of Arkansans 25 years and over have finished high school or an equivalency exam (e.g., GED). In the U.S., 85 percent of adults 25 and over have high school diploma.
The average family income in Arkansas is $50,000 per year. This amount is lower than the average family income in the U.S., which is $64,000 (Kaiser Family Foundation, 2016). Family income takes into account every person in the family who works, so it may take into account more than one worker. Arkansas’s poverty rate is very high. At 18 percent, it is the fifth highest in the U.S. This means there are 545,000 people in Arkansas who are living in poverty. The counties in southeast Arkansas have the highest poverty rates. The counties with the lowest poverty rates are in central and northwest Arkansas (Talk poverty: Arkansas Report, 2014).
Approximately 490,000 people in Arkansas live with a disability, not including those who live in nursing homes. This is 17 percent of the total population, which is much higher than the U.S. rate of 12 percent. Many people with disabilities also live in poverty (Arkansas Dept. of Health Report, 2014). Overall, Arkansas is ranked very low in terms of overall health in the country. The state of Arkansas is ranked 48th out of 50 states. Arkansas ranks low for many reasons, which will be described in this report analysis.
- Research Methods
The overall research focus is aimed at analyzing if annual mean temperature changes of the year 2012 and 2015 are statistically significant. And, if the significance in temperature is apparent, then this research will try to analyze if there is a correlation between the temperature change and health factors and outcomes, such as: percentage of physical inactivity (HV1), percentage of smokers (HV2), number physically unhealthy days (HV3), percentage of food insecurity (HV4), and percentage of population who has access to healthy food (HV5), in Arkansas state counties.
The data was collected from the County Health Rankings & Roadmaps and the NOAA (National Centers for Environmental Information) web sites. The data was an Excel file that contains the ranks and scores for each county in Arkansas and the underlying data details for the measures used in calculating the 2012 and 2015 County Health Rankings. In addition, the file contains additional measures that are reported on the County Health Rankings web site for the chosen state, in this case, State of Arkansas.
The one sample t-test is a statistical procedure used to determine whether a process with a specific mean could have generated a sample of observations. I ran one sample t-test to see if the mean in temperature changes between year 2012 and year 2015 was significant enough. The alternative hypothesis assumes that some difference exists between the temperature mean of year 2012 and the comparison temperature mean of year 2015; whereas the null hypothesis assumes that, no difference exists between the annual mean temperatures by county. The observations are independent of one another. The sample data is numeric and continuous.
I also ran the correlation technique to show whether and how strongly those pairs of variables, mentioned above, are related to one another. Correlation can tell us just how much of the variation in peoples’ percentage of physical inactivity (HV1) is related to their number of physically unhealthy days (HV3). Although this correlation is obvious, the data may contain unsuspected correlations. I may also suspect there are correlations, but do not know which are the strongest. An intelligent correlation analysis can lead to a greater understanding of this data.
Most counties are ranked at the bottom of the county health-ranking list in terms of health outcomes and health factors: physical inactivity (HV1), percentage of smokers (HV2), and number physically unhealthy days (HV3), the three major health outcomes that I identified as needing immediate attention in Arkansas, are exclusively registering high number in these counties. For example, Phillips county, located far East, has been ranked 75th in terms of its mortality rate and its social & economic factors from 2012 to 2016, consecutively. In addition, neighboring counties are ranked just above Phillips County. This means that most of these counties are expected to have poor quality of care, short life expectancy, and low health literacy.
Finally, I ran a regression analysis to investigate the relationship between the indicated variables. I seek to ascertain the causal effect of one variable upon another—the effect of change in temperature between 2012 and 2015 upon the number of physically unhealthy days (HV3), for example, or the effect of changes in temperature upon the percentage of food insecurity (HV4). To explore such issues, I assembled data on the underlying variables of interest and employed regression to estimate the quantitative effect of the causal variables upon the variable that they influence. I assessed the ‘statistical significance’ of the estimated relationships, that is, the degree of confidence that the true relationship is close to the estimated relationship.
From Table 2, the one sample t-test statistics demonstrates that the mean in temperature changes between year 2012 and year 2015 was statistically significant. The mean of all the temperature observations made in 2015 was higher from the mean of all the temperature observed in 2012 in Arkansas counties. This is can be used as proof that climate change is significant in Arkansas. Temperatures are changing. The null hypothesis was rejected.
From Table 4, the results were obvious. There was correlation between variables and it was statistically significant between some variables:
- Annual mean Temperature in 2015 was moderately correlated to the percentage of food insecurity (HV4). The correlation was statistically significant at the 0.01 level.
- The percentage of physical inactivity (HV1) was moderately correlated to the percentage of smokers (HV2), number of physically unhealthy days (HV3), and the percentage of food insecurity (HV4). The correlation is significant.
- HV2 is correlated to HV1 and HV3. The correlation is significant.
- HV3 is correlated to HV1 and HV2.
- HV4 is correlated to Temperature of 2015, HV1, and HV5.
- HV5 is only correlated to HV4.
From table 8, I learned that Beta Coeff. B = -17.483, which means that for every unit increase in temperature of year 2015, we would expect a decrease of 17.483 in percentage of food insecurity in overall Arkansas counties.
A growing number of studies present evidence for change in frequency of weather extremes over recent decades. Many health outcomes are sensitive to isolated extreme events, such as extreme temperatures and heavy precipitations. The health surveillance data are available for several decades up to the present day. Hence, it is possible to determine whether any observed changes in disease may be related to changes in climate, but the interpretation is complex. This complexity arises from competing explanations due to changes in important health determinants over time, as well as changes in the way in which diagnoses may be recorded.
This research study tries to accomplish similar mission. It concentrates on all counties region of the state of Arkansas with the hope of providing an insight on major health issues in relation to climate change factors, such as extreme temperatures and extreme levels of precipitation, that are dealt with the population living in these counties, considered mostly as rural counties. In addition, it provides policy recommendations on how to improve the quality of care in these counties, adaptation to climate change, and surveillance; because the overall balance of effects on health is likely to be negative and populations in low-income counties are likely to be predominantly vulnerable to adverse effects. In Arkansas, 45 percent of the population resides in rural areas.
For Arkansas, these deficiencies in quality of care represent neither the failure of professional compassion nor necessarily a lack of resources. Rather, they result from gaps in knowledge, inappropriate applications of available technology or the inability of the state healthcare organizations to adapt and change. Local health care systems may have failed to align practitioner incentives and objectives, to measure clinical practice, or to link quality improvement to better health outcomes.
Arkansas health systems deliver health actions activities to improve or maintain health. These actions take place in the context of, and are influenced by political, cultural, social, and institutional factors. Demographic and socioeconomic makeup, including genetics and personal resources, affect the health status of individuals seeking care. Access to the health care system is required to obtain the care that maintains or improves population health in Arkansas, but simple access is not enough; the system’s capacities must be applied skillfully.
The main conclusion that arises from this research project is that, despite the wide variety of data sets and settings of other studies, it finds that also in Arkansas temperature extremes lead to significant reductions in health, generally measured with excess morbidity and mortality. Policy makers should accept that human health is one of the most important impact margins of climate change, and consequently makes it a global research priority. The mechanisms through which climate change is likely to affect human health are the same throughout the state. Climate change can affect human health through changes in the prevalence of extreme and destructive weather events, through changes in the average and the variability of temperature, through its effect on the disease environment, through droughts, and more.
- POLICY RECOMMENDATIONS
The current state of knowledge about the impact of climate change on human health is such that some specific measures for health protection can now be recommended. Although, the future of climate change is uncertain, failure to invest in prevention and adaptation mechanisms may leave the state of Arkansas poorly prepared to cope with adverse changes and increase the probability of severe consequences. The population growth in low and middle-income counties will pose major challenges for greenhouse gas emissions in the future, if economic growth is based on fossil fuel use. Whilst the 1997 Kyoto protocol and the Paris 2015 COP 21 (Conference of the Parties) are important political initiative to engage countries in developing policies to reduce greenhouse gas emissions to modest targets.
The recommendations stemming from the international colloquium are highly relevant to the assessment of the public health policy for the state of Arkansas. The state policy makers need a conceptual framework that focuses on human well-being while also recognizing associated intrinsic values. Then, policy makers may approach development and health at various levels, which include specific health risk factors, background or habitat change, and institutional (economic and behavioral) levels. The focus should be particularly on the linkages between environment services and human health. For sound public health policy, Arkansas must shift away from dealing primarily with specific risk factors and look upstream to underlying environment determinants of disease and eventually the human behavior and established institutions that are detrimental to sustainable population health.
Arkansas policy makers should draft a policy that encourages providing information based on good scientific evidences to local communities about the links between environmental changes and the public health, including factors that can contribute to specific infectious disease outbreaks. The focus should be on training local professionals in environmental and health science issues through a new trans-discipline linking health and the environment. Hence, local health and environment professionals, who are in the best position to understand local priorities, should make the choices within each region for initial research areas and sites.
Vulnerable Arkansas counties or communities affected by climate change factors should act as centers of integrated analysis of disease emergence; incorporate perspectives and expertise from a variety of natural, social, and health sciences. Such policy would fund research activities that could range from organization of pathogens and vectors to classifying best practices for influencing changes in human behavior to reduce environment and health risks. Then, this research would equip local professionals with the ability to recommend policy at the state level toward maintaining environment function and promoting sustainable public health for future generations.
While considering issues of climate change and health outcomes, the Arkansas population needs to be attentive to entire environments rather than simply their local surroundings. Although the people may not live within a certain environment, its health may indirectly affect their own. The reason behind this analysis is that if these complex relationships are disrupted, there may be unforeseen impacts on human health.
|TABLE 1: Descriptive Statistics|
|Valid N (listwise)||54|
|TABLE 2: One-Sample Statistics|
|N||Mean||Std. Deviation||Std. Error Mean|
|TABLE 3: One-Sample Test|
|Test Value = 0|
|t||df||Sig. (2-tailed)||Mean Difference||95% Confidence Interval of the Difference|
|TABLE 4: Correlations|
|**. Correlation is significant at the 0.01 level (2-tailed).|
|*. Correlation is significant at the 0.05 level (2-tailed).|
|TABLE 5. Variables Entered/Removeda|
|Model||Variables Entered||Variables Removed||Method|
|1||Temp15||.||Stepwise (Criteria: Probability-of-F-to-enter <= .050, Probability-of-F-to-remove >= .100).|
|a. Dependent Variable: HV4|
|TABLE 6. Model Summary|
|Model||R||R Square||Adjusted R Square||Std. Error of the Estimate|
|a. Predictors: (Constant), Temp15|
|TABLE 7. ANOVAa|
|Model||Sum of Squares||df||Mean Square||F||Sig.|
|a. Dependent Variable: HV4|
|b. Predictors: (Constant), Temp15
|TABLE 8. Coefficients|
|Model||Unstandardized Coefficients||Standardized Coefficients||t||Sig.|
|a. Dependent Variable: HV4|
Arkansas Natural Resources Commission (ANRC)
Association of State Wetland Managers (ASWM)
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