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  <journal-id journal-id-type="publisher-id">46</journal-id>
  <journal-id journal-id-type="short-title">gssr</journal-id>
  <journal-id journal-id-type="doi">10.31703/gssr</journal-id>
  <journal-title-group>
    <journal-title>Global Social Sciences Review</journal-title>
    <abbrev-journal-title abbrev-type="publisher">gssr</abbrev-journal-title>
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  <issn publication-format="print">2520-0348</issn>
  <issn publication-format="electronic">2616-793X</issn>
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  <publisher>
    <publisher-name>Humanity Publications</publisher-name>
    <publisher-loc>Pakistan</publisher-loc>
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<article-meta>
  <article-id pub-id-type="publisher-id">390594</article-id>
  <article-id pub-id-type="doi">10.31703/gssr.2018(III-IV).21</article-id>
  <article-id pub-id-type="other" specific-use="submission-id">1063</article-id>
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  <title-group>
    <article-title xml:lang="en">Changing Climate Patterns and Women Health: An Empirical Analysis of District Rawalpindi Pakistan</article-title>
  </title-group>
<contrib-group>
  <contrib contrib-type="author" seq="1" corresp="yes">
    <name>
      <surname>Ajaz</surname>
      <given-names>Tahseen Ajaz</given-names>
    </name>
    <role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Conceptualization" vocab-term-identifier="https://credit.niso.org/contributor-roles/conceptualization/">Conceptualization</role>
    <role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Writing – original draft" vocab-term-identifier="https://credit.niso.org/contributor-roles/writing-original-draft/">Writing – original draft</role>
    <xref ref-type="aff" rid="aff1"/>
    <xref ref-type="corresp" rid="cor1"/>
  </contrib>
  <contrib contrib-type="author" seq="2">
    <name>
      <surname>Majeed</surname>
      <given-names>Muhammad Tariq</given-names>
    </name>
    <role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Writing – review &amp; editing" vocab-term-identifier="https://credit.niso.org/contributor-roles/writing-review-editing/">Writing – review &amp; editing</role>
    <xref ref-type="aff" rid="aff2"/>
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  <aff id="aff1">
    <label>1</label>
    <institution-wrap>
      <institution>Department of Economics, Quaid-i-Azam University</institution>
    </institution-wrap>
    <named-content content-type="author-role">PhD Scholar</named-content>
    <addr-line>Islamabad</addr-line>
    <country>Pakistan</country>
  </aff>
  <aff id="aff2">
    <label>2</label>
    <institution-wrap>
      <institution>Department of Economics, Quaid-i-Azam University</institution>
    </institution-wrap>
    <named-content content-type="author-role">Assistant Professor</named-content>
    <addr-line>Islamabad</addr-line>
    <country>Pakistan</country>
  </aff>
</contrib-group>
<author-notes>
  <corresp id="cor1">Corresponding Author: Tahseen Ajaz, PhD Scholar, Department of Economics, Quaid-i-Azam University, Islamabad, Pakistan.. Contact: 0</corresp>
<fn fn-type="COI-statement" id="fn-coi">
  <p>The authors declare that they have no conflicts of interest.</p>
</fn>
<fn fn-type="ethics-statement" id="fn-ethics">
  <p>This study did not require formal ethics approval.</p>
</fn>
<fn fn-type="data-availability-statement" id="fn-data">
  <p>Data sharing is not applicable to this article.</p>
</fn>
</author-notes>
<pub-date pub-type="epub" date-type="pub" publication-format="electronic">
  <day>31</day>
  <month>12</month>
  <year>2018</year>
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<pub-date pub-type="collection">
  <month>12</month>
  <year>2018</year>
</pub-date>
<pub-date date-type="pub" publication-format="print">
  <day>16</day>
  <month>02</month>
  <year>2022</year>
</pub-date>
  <volume>3</volume>
  <issue>4</issue>
  <season>Fall</season>
  <fpage>320</fpage>
  <lpage>342</lpage>
  <history>
    <date date-type="accepted">
      <day>16</day>
      <month>02</month>
      <year>2022</year>
    </date>
  </history>
<funding-group>
  <funding-statement>
<p>The authors received no specific funding for this work.</p>
  </funding-statement>
</funding-group>
<permissions>
  <copyright-year>2018</copyright-year>
  <copyright-holder>Humanity Publications</copyright-holder>
  <license license-type="open-access" xml:lang="en" xlink:href="https://creativecommons.org/licenses/by/4.0/">
    <license-p>This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License.</license-p>
  </license>
</permissions>
<self-uri content-type="text/html" xlink:href="https://gssrjournal.com/article/Changing-Climate-Patterns-and-Women-Health:-An-Empirical-Analysis-of-District-Rawalpindi-Pakistan"/>
<self-uri content-type="pdf" xlink:href="https://gssrjournal.com/pdf/gssr/R0E2sIzjmf.pdf"/>
<supplementary-material id="suppl-pdf" content-type="pdf" xlink:href="https://gssrjournal.com/pdf/gssr/R0E2sIzjmf.pdf">
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</supplementary-material>
  <abstract>
    <p>Climate change, the greatest environmental challenge of current era, affects human health badly. Natural hazards such as storms, droughts, excessive rains, floods, droughts and increasing temperature always threaten human health. South Asian rural women bear more household responsibilities than men in terms of fetching water and burning biomass fuel for cooking and heating. To obtain these resources women have to go out and are more exposed to outdoor environment and the increased exposure make them more amenable to the effects of changing climatic and weather patterns. The objective of this study is to document women health impacts under climate change in District Rawalpindi, Pakistan. We find that climate change increases the incidence of diseases which affect physical health. In developing countries, extreme weather patterns disproportionally affect vulnerable population like women, children and others bear burden of illness. Pakistan also faces heat waves fluctuation during summer and extreme rainfall pattern which have severe effect on overall health of individuals. We conclude that climatic changes (increasing heat intensity, dry spells, unusual rains and others) affect women health badly. The state has to improve our climate by offering effective policies. This may include reforestation, plantation in and outside homes and environmental friendly policies like renewable energy that is a shift from coal and oil investing energy projects. Increase of green areas within urban localities is also needed.</p>
  </abstract>
<kwd-group kwd-group-type="author-keywords">
  <kwd>Climate Change</kwd>
  <kwd>Weather Conditions</kwd>
  <kwd>Physical health</kwd>
  <kwd>Womens Health</kwd>
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</front>
<body>
<sec id="sec-1">
  <title>Introduction</title>
<p>Climate change is the greatest environmental challenge of current era which affects human health badly. Global climate and weather conditions are changing drastically. History is witness to extreme weather conditions negatively influencing human health (Haines, 2008; Patil &amp; Deepa, 2007; Kovats &amp; Akhtar, 2008; Campbell-Lendrum, Bertollini, Neira, Ebi, &amp; McMichael, 2009). In 1988, infectious diseases for instance malaria, dengue fever, leptospirosis soaring, and cholera spread from heavy rains that continued for 3 days in Central America (Epstein, Mills, Frith, Linden, Thomas, &amp; Weireter, 2005). Approximately, 2.5 million people were affected by the floods triggered by heavy rains of four days after cyclone hit the provinces of Baluchistan and Sindh in the South West region of Pakistan during June 2008. Many areas have been cut off due to rising water. Malaria rose from rain and cyclones in Mozambique also (Epstein, Mills, Frith, Linden, Thomas, &amp; Weireter, 2005). Likewise, in 2003, heat waves killed 10,000 people in Europe and more than 10,000 in France (Kovats &amp; Hajat, 2008). All credit goes to physical environment and changing global climate.</p><p>Climate change is affecting local weather with the incidence of stagnant air events and recurrent heat waves that distresses human health (Ahern, Kovats, Wilkinson, Few, &amp; Matthies, 2005). As, natural hazards always act as a threat to human health for instance, storms, droughts, too much rains, floods, dry spells, increasing temperature and landslides. The after effects of such events become worse due to lack of human knowledge to absorb their consequences. Deforestation and biodiversity loss are the main human actions which make these natural hazards worse for health on earth and global echo system (Barton &amp; Grant, 2006). According to Intergovernmental Panel on Climate Change (IPCC, 2007), humans are contributing in rising concentration of greenhouse gases through transport, agriculture, land use and power generation (McMichael, Friel, Nyong &amp; Corvalan, 2008; Haines, 2008).</p><p>In developing countries, every 19th person whereas, in OECD countries, 1 out of 1500 face health issues caused by climate change (IPCC, 2014). In LDCs, people living in rural areas are more vulnerable as they rely on natural resources to earn their living. In these areas, women are more susceptible than men to climatic changes since majority of the world population comprise women which belong to poor class and have to survive on natural resources which are at threat to changing climate (McKulka, 2009). In South Asia, rural women bear more HH responsibilities than men in terms of fetching and securing water and burning biomass fuel for cooking and heating. To obtain these resources women have to go out and are much exposed to outdoor environment, this increased exposure leads them to face changing climatic and weather patterns. According to Sierra Magazine Staff (2000) majority (more than 66%) of world’s poor are women. Women are disproportionately vulnerable as they face curtailed access to education, basic health services, employment opportunities, salaries inequality and direct violence (Heise, Raikes, Watts, &amp; Zwi, 1994).</p><p>Sustainable Development Goals (SDGs) strive to alleviate environmental health risks to vulnerable groups of LDCs. WHO Global Health figures of Pakistan show that 200/100,000 deaths are due to environmental factors. The objective of this study is to document women health impacts of climate change of District Rawalpindi, Pakistan. Current study intends to analyze the issues related to physical environment (climatic conditions) and its links with physical health of ever married women in District Rawalpindi, Pakistan. This study tries to answer the question: Do changing climatic conditions (adequate/inadequate) have any association with women’s physical health status?</p><p><break/></p><p>Hypotheses</p><p><break/></p><p>Environmental factor plays a vital role in individual’s health specifically on women. The literature on the links of the climatic changes with women’s health status envisages diverse effects. Therefore, it is the dire need to estimate and document effect of climate change on women’s health.</p><p>Climate change, most important aspect of physical environment, is one of the emerging threats to global health. Climate change is likely to reduce cold extremes while increases frequency, intensity, and intervals of heat waves. Climate change might damage human health in many ways, comprising adverse variations in food production (Rosenzweig, 2011), concentrations of outdoor air pollutants (Bernard, Samet, Grambsch,  Ebi &amp; Romieu, 2001; Knowlton, Rosenthal, Hogrefe, Lynn, Gaffin, Goldberg &amp; Kinney, 2004; Haines &amp; Patz, 2004), thermal stress (Martens, 1998), malaria (Loevinsohn, 1994; Tanser, Sharp, &amp; Le Sueur, 2003), aeroallergens (Beggs, 2004), waterborne diseases (Casman, Fischhoff, Small, Dowlatabadi, Rose,  &amp; Morgan, 2001; Charron, Thomas, Waltner-Toews, Aramini, Edge, Kent &amp; Wilson, 2004), dengue fever (Hales, De Wet, Maindonald &amp; Woodward, 2002), extreme events (Knutson, Tuleya &amp; Kurihara, 1998; Ikeda, Yoshitani &amp; Terakawa, 2005) and other diseases (Reiter, 1998; Patz, Campbell-Lendrum, Holloway &amp; Foley, 2005). The hypothesis to be tested is:</p><p>H1a: Changing climatic pattern is negatively associated with women health status</p><p>Since last few decades, health economists pay much of their attention towards maternal and child health care only. But in recent decades women health gains attention worldwide in broader concept of biological, gender and social determinants. Exploration of women health is in transition phase. In this regard, there is a dire need to investigate how women health affects through physical environment. The exposure of interest in this study lies on physical environment spectrum where we discuss theoretically and analyse empirically the challenges faced by women health due to environmental variability and changing weather pattern. To our knowledge, this is the first study of its kind which analyses empirically the relationship between women health and climate change.</p><p>This paper is arranged as follows: Section 2 entails previous literature. Section 3 emphasizes on methods and data analysis. Section 4 gives detailed picture of empirical results along with robustness while Section 5 concludes the study.</p>
</sec>
<sec id="sec-2">
  <title>Literature Review</title>
<p>Climate change is one of the emerging threats to worldwide health. Several studies</p><p>have examined the short and long run problems related to climate change on health, literature claims that higher mortality rates are associated with intensive heat and cold waves (Lee, Steer &amp; Filippi, 2006; Deschenes &amp; Moretti, 2009; Gosling, Lowe, McGregor, Pelling &amp; Malamud, 2009; Deschenes &amp; Greenstone, 2011; Barreca, 2012; Li,  Horton &amp; Kinney, 2013; Gasparrini, Guo, Hashizume, Lavigne, Zanobetti, Schwartz,  &amp; Leone, 2015).</p><p>Many widespread human diseases including mortality, morbidity, malnutrition and infectious diseases are related to climate fluctuations: extreme heat, cold and storms (Patz, Epstein, Burke &amp; Balbus, 1996; Kovats, Campbell-Lendrum, McMichel, Woodward &amp; Cox, 2001; Stott, Stone &amp; Allen, 2004).  It is projected that global average temperature will rise between 1.40C - 5.80C by the end of this century which in turn will raise sea level. Extremes of the floods and droughts are anticipated to upsurge with warmer temperatures. The most vulnerable areas are regions around the Pacific and Indian oceans, Sub-Saharan Africa. Above facts paint a grim picture of environment about the future.</p><p>Haines, Kovats, Campbell-Lendrum &amp; Corvalán (2006) argue that climate is changing due to accumulation of greenhouse gases in atmosphere that result from burning of fossil fuels. Chemical reactions and volatile organic compounds that formulate ozone are highly sensitive to climate change (Sillman &amp; Samson, 1995; Constable, Guenther, Schimel &amp; Monson, 1999; Aw &amp; Kleeman, 2003; Seinfeld &amp; Pandis, 2016). Ozone concentration has adverse effects on health such as emergency hospital admissions, asthma and lungs issues (Lippmann, 1989; Dockery &amp; Pope, 1994; Thurston &amp; Ito, 1999; Bell, Goldberg, Hogrefe, Kinney, Knowlton, Lynn &amp; Patz, 2007).</p><p>Climate change influence health outcomes through several ways, for instance, heat waves, droughts and floods, vector-borne diseases, malnutrition and risk of disasters. According to Dr. Asif Zafar Bhatti in a report on “Risks in flood areas of Pakistan”, women and children are more vulnerable in floods. They have to face starvation, water borne diseases and lack of medical facilities that affect them badly. The girls, children and women are in real danger during flood times due to human trafficking, sexual abuse and lack of shelter. During 2010 flood in Pakistan, many 34,000 women including paramedical staff (lady health worker, LHW, LHVs, midwives and nurses) were missing and were victim of sexual abuse, violence, trafficking and injured (these are unauthorized figures according to a report by Tahira Abdullah). People who have experienced flood suffer from common psychological disorders. In addition, it also affects vector disease distribution (e.g. incidence of dengue, malaria and diarrheal diseases). Climate change influence goods and services (necessary for human health) by influencing ecosystem and biodiversity. Climatic impact is more vulnerable for young mothers who spend most of their time outside home comparative to those working mothers who spend time in ACs.</p><p>Women’s health risk need attention to prevent harmful effects of changing physical environment which is the result of increasing temperatures inside and outside homes and release of chemicals and toxins in air. Although physical environment is easily accessible to all, yet its consequences are expensive in terms of health. Despite a huge strand of literature focused on climatic conditions and related effects of physical environment on health of individuals but less attention has been paid on the most important sector of the economy that is women. Women of lower income group are generally more vulnerable as they have to perform their HH tasks and also have to perform outdoor activities even in unacceptable weather conditions also. Literature has paid less attention towards physical health as a consequence of physical environment. In case of Pakistan, we find no single study analyzing the association of physical environment and women health empirically. To address this gap, we are focusing ever married women’s health to check the vulnerability of physical environmental interactions.</p>
</sec>
<sec id="sec-3">
  <title>Research Methods and Data Analysis</title>
<p>Erbsland, Ried &amp; Ulrich (1994) follow
Grossman health production function and model the effect of physical
environment on health demand and health care. Environment affects health directly
through exposure to risk factors like physical, chemical and biological
factors.</p><p>To
capture environmental effects, this study introduces climatic changes
conditions which depreciate human stock of health. We are following Erbsland,
Ried &amp; Ulrich (1994) empirical model to analyze the effect of climatic
changes conditions on women health. The model takes following form:</p><p><bold>)</bold></p><p>Where,
   stands for self-rated physical health of
women, while   is climate change. On the other hand, vector    includes demographic and socioeconomic
factors.</p><p>Statistical Analysis</p><p>To analyze the data, this section describes the data
source, data collection methods, definition and construction of variables. This
study is going to use primary data (survey data) to investigate the
relationship between environment and health outcomes of married women.
Dependent variable is health status while main variable of interest is
indicators of climate change. On the other hand, demographic and socio-economic
variables are also used in the analysis. Summary analysis of the data is given
in Table 1.</p><table-wrap id="table1"><label>Table 1</label><caption><title>Summary Analysis of Variables</title></caption><table><tbody><tr><td> <p><bold>Variables</bold></p> </td><td> <p><bold>Observations</bold></p> </td><td> <p><bold>Mean</bold></p> </td><td> <p><bold>Std. Dev.</bold></p> </td><td> <p><bold>Min</bold></p> </td><td> <p><bold>Max</bold></p> </td></tr><tr><td colspan="6"> <p><bold>Dependent Variables</bold></p> </td></tr><tr><td valign="bottom"> <p>Physical Health</p> </td><td valign="bottom"> <p>504</p> </td><td valign="bottom"> <p>0.7143</p> </td><td valign="bottom"> <p>0.4522</p> </td><td valign="bottom"> <p>0</p> </td><td valign="bottom"> <p>1</p> </td></tr><tr><td colspan="6"> <p><bold>Independent Variables</bold></p> </td></tr><tr><td valign="bottom"> <p>Climate
  Change</p> </td><td valign="bottom"> <p>504</p> </td><td valign="bottom"> <p>0.8393</p> </td><td valign="bottom"> <p>0.3676</p> </td><td valign="bottom"> <p>0</p> </td><td valign="bottom"> <p>1</p> </td></tr><tr><td colspan="6"> <p><bold>Covariates</bold></p> </td></tr><tr><td valign="bottom"> <p>HH Income</p> </td><td valign="bottom"> <p>504</p> </td><td valign="bottom"> <p>3.6766</p> </td><td valign="bottom"> <p>1.2375</p> </td><td valign="bottom"> <p>1</p> </td><td valign="bottom"> <p>7</p> </td></tr><tr><td valign="bottom"> <p>Age</p> </td><td valign="bottom"> <p>504</p> </td><td valign="bottom"> <p>2.4960</p> </td><td valign="bottom"> <p>0.9542</p> </td><td valign="bottom"> <p>1</p> </td><td valign="bottom"> <p>4</p> </td></tr><tr><td valign="bottom"> <p>Employment</p> </td><td valign="bottom"> <p>504</p> </td><td valign="bottom"> <p>2.2857</p> </td><td valign="bottom"> <p>1.1770</p> </td><td valign="bottom"> <p>1</p> </td><td valign="bottom"> <p>4</p> </td></tr><tr><td valign="bottom"> <p>Gender HH Head</p> </td><td valign="bottom"> <p>504</p> </td><td valign="bottom"> <p>0.8928</p> </td><td valign="bottom"> <p>0.3096</p> </td><td valign="bottom"> <p>0</p> </td><td valign="bottom"> <p>1</p> </td></tr><tr><td valign="bottom"> <p>Life Events</p> </td><td valign="bottom"> <p>504</p> </td><td valign="bottom"> <p>4.3909</p> </td><td valign="bottom"> <p>0.8623</p> </td><td valign="bottom"> <p>1</p> </td><td valign="bottom"> <p>5</p> </td></tr><tr><td valign="bottom"> <p>Weather Change</p> </td><td valign="bottom"> <p>504</p> </td><td valign="bottom"> <p>3.1488</p> </td><td valign="bottom"> <p>2.1733</p> </td><td valign="bottom"> <p>1</p> </td><td valign="bottom"> <p>8</p> </td></tr><tr><td valign="bottom"> <p>Women Empowerment</p> </td><td valign="bottom"> <p>504</p> </td><td valign="bottom"> <p>1.9643</p> </td><td valign="bottom"> <p>0.7181</p> </td><td valign="bottom"> <p>1</p> </td><td valign="bottom"> <p>4</p> </td></tr><tr><td valign="bottom"> <p>Disturbance
  (pollution)</p> </td><td valign="bottom"> <p>504</p> </td><td valign="bottom"> <p>3.1746</p> </td><td valign="bottom"> <p>0 .9548</p> </td><td valign="bottom"> <p>1</p> </td><td valign="bottom"> <p>4</p> </td></tr><tr><td valign="bottom"> <p>Ventilation</p> </td><td valign="bottom"> <p>504</p> </td><td valign="bottom"> <p>0 .9047</p> </td><td valign="bottom"> <p>0 2938</p> </td><td valign="bottom"> <p>0</p> </td><td valign="bottom"> <p>1</p> </td></tr><tr><td valign="bottom"> <p>Women
  are susceptible to climate change</p> </td><td valign="bottom"> <p>504</p> </td><td valign="bottom"> <p>1.0912</p> </td><td valign="bottom"> <p>0 5706</p> </td><td valign="bottom"> <p>0</p> </td><td valign="bottom"> <p>2</p> </td></tr><tr><td valign="bottom"> <p>Land
  Area</p> </td><td valign="bottom"> <p>504</p> </td><td valign="bottom"> <p>9.1171</p> </td><td valign="bottom"> <p>6.0518</p> </td><td valign="bottom"> <p>2</p> </td><td valign="bottom"> <p>30</p> </td></tr></tbody></table></table-wrap> <p><bold>Data Sources and
Construction of Variables</bold></p><p>In
this study, the data is being collected from the primary source (directly from
the respondents), as it is original, reliable and relevant to the topic of the recent
research. As the study is a field-based research so, survey was conducted in
the selected areas of study. And for data collection from the field, closed
ended (structured) questionnaire was designed to evaluate the perceptions of
selected sample. The area of study is
District Rawalpindi and it contains seven tehsils. The sub-divided tehsils are:
Taxila, Kahuta, Kalar Sayedan, Gujar Khan Murree and Kotli Sattiyan, Rawalpindi.
Rawalpindi city is the district capital and has an area of 5,286 km<sup>2</sup>.
At first, its area was 6,192 km<sup>2</sup> till 60s when Islamabad Capital
Zone was engraved out of the district. Total population of Rawalpindi District
is 5,405,633 (2017 estimates). It is situated on the Southern slopes of the
North-Western extremities of the Himalayas, with large mountain territories
with rich valleys navigated by Mountain Rivers.</p><p>The selection of these areas was
done purposively as there is vast heterogeneity in physical environment as well
as in social environment and also has cultural diversity. District Rawalpindi has a disparate
environment including, alpine, temperate and tropical climate of rainy hot
summers and cool dry winters. The environmental location of area around
people has great influence on health, mood,
psychological and behavioral activities, respiration, stress, and sensory
perception.</p><p>The study specifies to married
women’s health. Therefore, sample size consists of 504 respondents that consist
of married, literate and illiterate women of different areas of the cities. The
sample size is selected randomly from the population. All data is analyzed
using Stata 14. There was missing data for non-respondents, so it is excluded
from the analysis. Out of 700 questionnaires, 504 are reported fully, the
reason for non-participation of women in survey is mainly due to less awareness
about environmental issues and its impact on health. Health is dependent
variable in this study. We measure physical health of the respondent by asking
the question: What is your physical health status? The anchors used to answer
this question are excellent, very good, good, fair and poor. While for
estimations, we recode our dependent variables as 0 to 1 following existing survey-based
studies on health (Carlson, 2004; Nyqvist, Finnäs, Jakobsson &amp; Koskinen,
2008; Ferlander &amp;
Mäkinen,
2009). We combined positive outcomes of self-rated physical health (excellent, very
good and good) into ‘good’ and assign them ‘1’, on the other hand, negative
outcomes (fair and poor) are taken as ‘poor’ and a value of ‘0’ is assigned to
them.</p><p>Our focused variable of
the study is climate change. Global atmospheric seasonal changes with
increasing accumulation of greenhouse gases refer to climate change. Women have
more acute issues due to climate changes. According to Bruce and Susan (Climate
Reality Leaders and Health Experts), there are strong evidences that in
adolescent girls the chances of asthma are more due to climatic changes while
in elder women the probability of lung cancer and heart diseases is more. Different
studies investigate the association between climate change and health related
to high temperature and increased mortality (Lee, Steer &amp; Filippi, 2006;
Deschens &amp;
Greenstone, 2011; Barreca, Clay, Deschenes, Greenstone &amp;
Shapiro, 2016). So, to capture the effect of changing climate pattern the
respondents are asked the question: Do you feel that changing weather pattern
is affecting your health?</p><p>Graphical Representation of Analysis</p><p>This
analysis investigates the prevalence of health effects of climate change on
married women, aged between 16 to 65 years and more, of District Rawalpindi.</p><p><break/></p><p>Graphical analysis of dependent (physical health) and independent variables (climate change) in Figure1 explains that climate change is negatively related to physical health (Haines, 2008; Kovats &amp; Akhtar, 2008; Campbell-Lendrum, Bertollini, Neira, Ebi, &amp; McMichael, 2009). Figure 2 depicts that 71% report good health while 29% report that they are in poor health condition. Figure 3 explains that 84% women facing health issues with climate change, on the other hand only 16% women have no issue with climate change. Similarly, Figure 4 represents that which weather pattern affects women health more. The pie chat shows that 32.94% women report that they have health issues with too much rains. While 29% women report that they have health problems with high temperature. Whereas, 12 % of women health issues are responsible for drought and 7% are responsible for dry spells.</p><p><break/></p>
</sec>
<sec id="sec-4">
  <title>Descriptive Analysis</title>
<p>Descriptive analysis of the data reveals that changing
climatic and weather conditions deteriorate health of the women. Association
between health status of women and climatic conditions are explored through
cross tabulation and logistic regressions. Climate changing conditions along
with covariates are independent variables. Demographic data: age, husband age,
and age gap with husband, socio-economic data: HH income, education, employment
status, life events, women empowerment and environmental control variables data
is also obtained. These variables are entered into descriptive analysis and in
regression models as covariates in statistical analysis.</p><table-wrap id="table2"><label>Table
2</label><caption><title>Frequency Distribution (n = 504)</title></caption><table><tbody><tr><td> <p><bold>Characteristics</bold></p> </td><td> <p><bold>Frequency </bold></p> </td><td> <p><bold>%age </bold></p> </td><td> <p><bold>Characteristics</bold></p> </td><td> <p><bold>Frequency </bold></p> </td><td> <p><bold>%age </bold></p> </td></tr><tr><td colspan="3"> <p><bold>Dependent Variable</bold></p> </td><td colspan="3"> <p><bold>Independent Variable</bold></p> </td></tr><tr><td colspan="3"> <p><bold>Physical Health</bold></p> </td><td colspan="3"> <p><bold>Changing Pattern of Weather affect
  your Health</bold></p> </td></tr><tr><td> <p>Good
  health</p> </td><td valign="bottom"> <p>360</p> </td><td valign="bottom"> <p>71.43</p> </td><td> <p>Yes</p> </td><td valign="bottom"> <p>423</p> </td><td valign="bottom"> <p>83.93</p> </td></tr><tr><td> <p>Poor
  health</p> </td><td valign="bottom"> <p>144</p> </td><td valign="bottom"> <p>28.57</p> </td><td valign="bottom"> <p>No</p> </td><td valign="bottom"> <p>43</p> </td><td valign="bottom"> <p>8.53</p> </td></tr><tr><td>  </td><td valign="bottom">  </td><td valign="bottom">  </td><td valign="bottom"> <p>Don’t Know</p> </td><td valign="bottom"> <p>38</p> </td><td valign="bottom"> <p>7.54</p> </td></tr><tr><td colspan="6"> <p><bold>Demographic Variables</bold></p> </td></tr><tr><td> <p><bold><italic>HH Income</italic></bold></p> </td><td colspan="2">  </td><td valign="bottom"> <p><bold><italic>Marital Status</italic></bold></p> </td><td valign="bottom">  </td><td valign="bottom">  </td></tr><tr><td valign="bottom"> <p>No Income</p> </td><td valign="bottom"> <p>5</p> </td><td valign="bottom"> <p>0.99</p> </td><td valign="bottom"> <p>Married</p> </td><td valign="bottom"> <p>461</p> </td><td valign="bottom"> <p>91.47</p> </td></tr><tr><td valign="bottom"> <p>&gt;10000</p> </td><td valign="bottom"> <p>61</p> </td><td valign="bottom"> <p>12.10</p> </td><td valign="bottom"> <p>Widowed</p> </td><td valign="bottom"> <p>0</p> </td><td valign="bottom"> <p>0</p> </td></tr><tr><td valign="bottom"> <p>11000-25000</p> </td><td valign="bottom"> <p>199</p> </td><td valign="bottom"> <p>39.48</p> </td><td> <p>Divorced</p> </td><td> <p>22</p> </td><td> <p>4.37</p> </td></tr><tr><td valign="bottom"> <p>26000-50000</p> </td><td valign="bottom"> <p>125</p> </td><td valign="bottom"> <p>24.80</p> </td><td valign="bottom"> <p>Separated</p> </td><td valign="bottom"> <p>21</p> </td><td valign="bottom"> <p>4.17</p> </td></tr><tr><td valign="bottom"> <p>51000-75000</p> </td><td valign="bottom"> <p>77</p> </td><td valign="bottom"> <p>15.28</p> </td><td colspan="3"> <p><bold>Socio-Economic Variables</bold></p> </td></tr><tr><td valign="bottom"> <p>76000-100000</p> </td><td valign="bottom"> <p>15</p> </td><td valign="bottom"> <p>2.98</p> </td><td colspan="3" valign="top"> <p><bold><italic>Life
  Events (Enough food for three meals a day)</italic></bold></p> </td></tr><tr><td valign="bottom"> <p>&lt;100000</p> </td><td valign="bottom"> <p>22</p> </td><td valign="bottom"> <p>4.37</p> </td><td valign="top"> <p>Dissatisfied</p> </td><td valign="top"> <p>5</p> </td><td valign="top"> <p>0.99</p> </td></tr><tr><td> <p><bold>Gender of HH</bold></p> </td><td colspan="2">  </td><td valign="top"> <p>Uncertain</p> </td><td valign="top"> <p>20</p> </td><td valign="top"> <p>3.97</p> </td></tr><tr><td valign="bottom"> <p>Male</p> </td><td valign="bottom"> <p>450</p> </td><td valign="bottom"> <p>89.29</p> </td><td> <p>Neutral</p> </td><td> <p>37</p> </td><td> <p>7.34</p> </td></tr><tr><td valign="bottom"> <p>Female</p> </td><td valign="bottom"> <p>54</p> </td><td valign="bottom"> <p>10.71</p> </td><td valign="top"> <p>Satisfied</p> </td><td valign="top"> <p>153</p> </td><td valign="top"> <p>30.36</p> </td></tr><tr><td> <p><bold><italic>Age</italic></bold></p> </td><td colspan="2">  </td><td valign="top"> <p>Very Satisfied</p> </td><td valign="top"> <p>289</p> </td><td valign="top"> <p>57.34</p> </td></tr><tr><td valign="bottom"> <p>18-25</p> </td><td valign="bottom"> <p>73</p> </td><td valign="bottom"> <p>14.48</p> </td><td colspan="3" valign="top"> <p><bold><italic>Women Empowerment 2 (Own health
  care decision)</italic></bold></p> </td></tr><tr><td valign="bottom"> <p>26-35</p> </td><td valign="bottom"> <p>201</p> </td><td valign="bottom"> <p>39.88</p> </td><td valign="top"> <p>Wife Dominance</p> </td><td valign="top"> <p>201</p> </td><td valign="top"> <p>39.98</p> </td></tr><tr><td valign="bottom"> <p>36-50</p> </td><td valign="bottom"> <p>137</p> </td><td valign="bottom"> <p>27.18</p> </td><td valign="top"> <p>Husband Dominance</p> </td><td valign="top"> <p>264</p> </td><td valign="top"> <p>60.13</p> </td></tr><tr><td valign="bottom"> <p>&lt;50</p> </td><td valign="bottom"> <p>93</p> </td><td valign="bottom"> <p>18.45</p> </td><td colspan="3" valign="top">  </td></tr><tr><td colspan="3"> <p><bold>Environmental Variables</bold></p> </td><td colspan="3" valign="top"> <p><bold><italic>Employment Status of Respondents</italic></bold></p> </td></tr><tr><td colspan="3"> <p><bold><italic>Housing Ventilation</italic></bold></p> </td><td valign="top"> <p>Government employee</p> </td><td valign="top"> <p>195</p> </td><td valign="top"> <p>38.69</p> </td></tr><tr><td valign="bottom"> <p>Yes</p> </td><td valign="bottom"> <p>456</p> </td><td valign="bottom"> <p>90.48</p> </td><td valign="top"> <p>Private employee</p> </td><td valign="top"> <p>72</p> </td><td valign="top"> <p>14.29</p> </td></tr><tr><td valign="bottom"> <p>No</p> </td><td valign="bottom"> <p>48</p> </td><td valign="bottom"> <p>9.52</p> </td><td valign="top"> <p>Self employed</p> </td><td valign="top"> <p>135</p> </td><td valign="top"> <p>26.79</p> </td></tr><tr><td colspan="3" valign="bottom"> <p><bold><italic>Disturbance from Surroundings</italic></bold></p> </td><td valign="top"> <p>Housewife</p> </td><td valign="top"> <p>102</p> </td><td valign="top"> <p>20.24</p> </td></tr><tr><td valign="bottom"> <p>a)       
  <bold><italic>a)</italic></bold><italic> Smoke</italic></p> </td><td valign="bottom">  </td><td valign="bottom">  </td><td colspan="3" valign="top"> <p><bold><italic>Level of Education (Husband)</italic></bold></p> </td></tr><tr><td valign="bottom"> <p>A lot</p> </td><td valign="bottom"> <p>267</p> </td><td valign="bottom"> <p>52.98</p> </td><td valign="top"> <p>Illiterate</p> </td><td valign="top"> <p>17</p> </td><td valign="top"> <p>3.37</p> </td></tr><tr><td valign="bottom"> <p>A little</p> </td><td valign="bottom"> <p>125</p> </td><td valign="bottom"> <p>24.80</p> </td><td valign="top"> <p>Primary</p> </td><td valign="top"> <p>4</p> </td><td valign="top"> <p>0.79</p> </td></tr><tr><td valign="bottom"> <p>Not very much</p> </td><td valign="bottom"> <p>69</p> </td><td valign="bottom"> <p>13.69</p> </td><td valign="top"> <p>Middle</p> </td><td valign="top"> <p>91</p> </td><td valign="top"> <p>18.06</p> </td></tr><tr><td valign="bottom"> <p>Not at all</p> </td><td valign="bottom"> <p>43</p> </td><td valign="bottom"> <p>8.53</p> </td><td valign="top"> <p>Matric</p> </td><td valign="top"> <p>137</p> </td><td valign="top"> <p>27.18</p> </td></tr><tr><td valign="top"> <p><bold><italic>b)</italic></bold><italic> Noise</italic></p> </td><td valign="top">  </td><td valign="top">  </td><td valign="top"> <p>Intermediate</p> </td><td valign="top"> <p>95</p> </td><td valign="top"> <p>18.85</p> </td></tr><tr><td valign="bottom"> <p>A lot</p> </td><td valign="top"> <p>243</p> </td><td valign="top"> <p>48.21</p> </td><td valign="top"> <p>BA/BSc</p> </td><td valign="top"> <p>95</p> </td><td valign="top"> <p>18.85</p> </td></tr><tr><td valign="bottom"> <p>A little</p> </td><td valign="top"> <p>172</p> </td><td valign="top"> <p>34.13</p> </td><td valign="top"> <p>MA/MSc</p> </td><td valign="top"> <p>45</p> </td><td valign="top"> <p>8.93</p> </td></tr><tr><td valign="bottom"> <p>Not very much</p> </td><td valign="top"> <p>74</p> </td><td valign="top"> <p>14.68</p> </td><td valign="top"> <p>M. Phil</p> </td><td valign="top"> <p>4</p> </td><td valign="top"> <p>0.79</p> </td></tr><tr><td valign="bottom"> <p>Not at all</p> </td><td valign="top"> <p>15</p> </td><td valign="top"> <p>2.98</p> </td><td valign="top"> <p>PhD</p> </td><td valign="top"> <p>16</p> </td><td valign="top"> <p>3.17</p> </td></tr><tr><td valign="bottom"> <p><italic>c)      
  </italic><bold><italic>c)</italic></bold><italic> Pollution</italic></p> </td><td valign="bottom">  </td><td valign="bottom">  </td><td colspan="3" valign="top"> <p><bold>Environmental Variable</bold>s</p> </td></tr><tr><td valign="bottom"> <p>A lot</p> </td><td valign="bottom"> <p>266</p> </td><td valign="bottom"> <p>52.78</p> </td><td colspan="3" valign="top"> <p><bold><italic>Weather Conditions</italic></bold></p> </td></tr><tr><td valign="bottom"> <p>A little</p> </td><td valign="bottom"> <p>75</p> </td><td valign="bottom"> <p>14.88</p> </td><td valign="top"> <p>Too much rains</p> </td><td valign="top"> <p>166</p> </td><td valign="top"> <p>32.94</p> </td></tr><tr><td valign="bottom"> <p>Not very much</p> </td><td valign="bottom"> <p>148</p> </td><td valign="bottom"> <p>29.37</p> </td><td valign="top"> <p>Low Temperature</p> </td><td valign="top"> <p>50</p> </td><td valign="top"> <p>9.92</p> </td></tr><tr><td valign="bottom"> <p>Not at all</p> </td><td valign="bottom"> <p>15</p> </td><td valign="bottom"> <p>2.98</p> </td><td valign="top"> <p>High Temperature</p> </td><td valign="top"> <p>144</p> </td><td valign="top"> <p>28.57</p> </td></tr><tr><td valign="top"> <p><bold><italic>d)</italic></bold><italic> Smell</italic></p> </td><td valign="top">  </td><td valign="top">  </td><td valign="top"> <p>Floods</p> </td><td valign="top"> <p>2</p> </td><td valign="top"> <p>0.40</p> </td></tr><tr><td valign="bottom"> <p>A lot</p> </td><td valign="top"> <p>263</p> </td><td valign="top"> <p>52.18</p> </td><td valign="top"> <p>Drought</p> </td><td valign="top"> <p>60</p> </td><td valign="top"> <p>11.90</p> </td></tr><tr><td valign="bottom"> <p>A little</p> </td><td valign="top"> <p>50</p> </td><td valign="top"> <p>9.92</p> </td><td valign="top"> <p>Short &amp; Intense Rains</p> </td><td valign="top"> <p>28</p> </td><td valign="top"> <p>5.56</p> </td></tr><tr><td valign="bottom"> <p>Not very much</p> </td><td valign="top"> <p>101</p> </td><td valign="top"> <p>20.04</p> </td><td valign="top"> <p>Delay in Rains</p> </td><td valign="top"> <p>19</p> </td><td valign="top"> <p>3.77</p> </td></tr><tr><td valign="bottom"> <p>Not at all</p> </td><td valign="top"> <p>90</p> </td><td valign="top"> <p>17.86</p> </td><td valign="top"> <p>Dry Spells</p> </td><td valign="top"> <p>35</p> </td><td valign="top"> <p>6.94</p> </td></tr></tbody></table></table-wrap> <p>The
youngest women participant is of 16 years of age at the time of marriage and
the eldest one at the time of marriage is of 33 years. Descriptive analysis
shows that 71% women report good physical health (see Table 2). In our analysis
84% of women report that they have health problem with changing weather
pattern, 53% of which are more vulnerable to bad effects of changing climatic
conditions. From these weather patterns, too much rain and high temperature
(33% and 29%) affects more women (see Table 2). This may be due to the fact
that District Rawalpindi has two extreme weather condition areas (Rainy areas
&amp; high temperature areas), 67% women report that they are more vulnerable
to these weather changes as compared to men. Women who are more exposed
(employed) to outdoor environment are at greater risk (84%) of air pollution as
compare to those women having less exposure (16%) to outdoor environment (house
wife). Frequency distribution of variables is discussed in Table 2.</p>
</sec>
<sec id="sec-5">
  <title>Results</title>
<p>To
check the empirical association between climate change and women’s health
status. This study uses logistic regression for physical health this study uses
logistics regression. Odd ratios and marginal effects are used to get better
picture of our results as coefficient of logistic regression cannot be
interpreted. Odd ratio gives us direction of variables and marginal effects
explain the magnitude of the model.</p><table-wrap id="table3"><label>Table
3</label><caption><title>Climate Change and Women Health</title></caption><table><tbody><tr><td> <p><bold>Variables</bold></p> </td><td> <p><bold>Odds
  Ratios</bold></p> </td><td valign="top"> <p><bold>Marginal
  Effects</bold></p> </td><td colspan="2"></td></tr><tr><td> <p>Climate Change</p> </td><td valign="top"> <p>0.5823*</p> <p>(0.1921)</p> </td><td valign="bottom"> <p>-0.0955*</p> <p>(0.050)</p> </td><td colspan="2"></td></tr><tr><td> <p>Disturbance
  (pollution)</p> </td><td valign="top"> <p>1.1273</p> <p>(0.1299)</p> </td><td valign="bottom"> <p>0.0232</p> <p>(0.022)</p> </td><td colspan="2"></td></tr><tr><td> <p>Ventilation</p> </td><td valign="top"> <p>0.7377</p> <p>(0.3866)</p> </td><td valign="bottom"> <p>-0 0573</p> <p>(0.080)</p> </td><td colspan="2"></td></tr><tr><td colspan="3" valign="bottom"> <p><italic>Table
  Continued on Next Page</italic></p> </td><td colspan="2"></td></tr><tr><td colspan="3" valign="top"> <p><italic>Table
  Continued from Previous Page</italic></p> </td><td colspan="2"></td></tr><tr><td> <p>Land area of house</p> </td><td valign="top"> <p>0.9989</p> <p>(0.0192)</p> </td><td valign="bottom"> <p>-0.0002</p> <p>(0.004)</p> </td><td colspan="2"></td></tr><tr><td> <p>Age</p> </td><td valign="top"> <p>0.5191***</p> <p>(0.0161)</p> </td><td valign="bottom"> <p>-0.128***</p> <p>(0.022)</p> </td><td colspan="2"></td></tr><tr><td> <p>HH Income</p> </td><td valign="top"> <p>1.0830</p> <p>(0.1015)</p> </td><td valign="bottom"> <p>-0.00495</p> <p>(0.034)</p> </td><td colspan="2"></td></tr><tr><td> <p>Employment</p> </td><td valign="top"> <p>0.9380*</p> <p>(0.086)</p> </td><td valign="bottom"> <p>-0.0393*</p> <p>(0.026)</p> </td><td colspan="2"></td></tr><tr><td> <p>Women Empowerment</p> </td><td valign="top"> <p>0.
  6715**</p> <p>(0.0885)</p> </td><td valign="bottom"> <p>-0.0788**</p> <p>(0.0255)</p> </td><td colspan="2"></td></tr><tr><td> <p>Life Events</p> </td><td valign="top"> <p>0.7349*</p> <p>(0.1032)</p> </td><td valign="bottom"> <p>-0.0566*</p> <p>(0.0266)</p> </td><td colspan="2"></td></tr><tr><td> <p>HH Gender</p> </td><td valign="top"> <p>1.2838</p> <p>(0.4321)</p> </td><td valign="bottom"> <p>0.0575</p> <p>(0.0715)</p> </td><td colspan="2"></td></tr><tr><td> <p>Observations</p> </td><td valign="top"> <p>504</p> </td><td valign="top"> <p>504</p> </td><td colspan="2"></td></tr><tr><td> <p><bold>Pseudo
  R<sup>2</sup></bold></p> </td><td> <p>0.0801</p> </td><td> <p>0.0801</p> </td><td colspan="2"></td></tr><tr><td colspan="3"> <p><bold>Wald
  Test</bold></p> </td><td colspan="2"></td></tr><tr><td> <p>chi2 (10)</p> </td><td colspan="2" valign="top"> <p>42.86</p> </td><td colspan="2"></td></tr><tr><td> <p>Prob &gt; chi2</p> </td><td colspan="2" valign="top"> <p>0.0000</p> </td><td colspan="2"></td></tr><tr><td colspan="5"> <p><bold>Goodness-of-Fit
  Test</bold></p> </td></tr><tr><td> <p>Chi-square</p> </td><td colspan="3" valign="top"> <p>9.92</p> </td><td></td></tr><tr><td> <p>DF</p> </td><td colspan="3" valign="top"> <p>6</p> </td><td></td></tr><tr><td> <p>Pr &gt; ChiSq</p> </td><td colspan="3" valign="top"> <p>0.1279</p> </td><td></td></tr><tr><td></td><td></td><td></td><td></td><td></td></tr></tbody></table></table-wrap> <p><italic>*10%, ** 5%,
***1%. Constants are not reported</italic></p><p>We also use Wald Test to check the
credibility of our covariates in models. In Wald Test we check that set of
parameters are simultaneously equal to zero. The null hypothesis of Wald Test
is rejected at 0.05% level of significance which suggests that the inclusion of
variables is significant (Prob &gt; chi2 = 0.0000).  We also check our model specification through
Hosmer-Lemeshow goodness of fit test. The null hypothesis of Hosmer-Lemeshow
Test states that model is fitted correctly. Higher the value of (Prob &gt;
chi2) best the fitted model is. In this study, almost all the models depict the
higher probability which shows our model specification is best.</p><p>The results of odd ratios in Table
3 show that climate change negatively affects women’s health outcomes. Odds of
being physically unhealthy will increase by 42% with increase in intensity of
changing climatic patterns. Our results are justified with prior expectation
that climate change increases the incidence of diseases which affect physical
health (Patz &amp; Kvots, 2002; Frumkin, Hess, Luber, Malilay &amp; McGeehin,
2008; Costello, A., Abbas, M., Allen, Ball, Bell, Bellamy &amp; Lee, 2009). In
developing countries, extreme weather patterns disproportionally affect
vulnerable population and bear burden of illness (McGeehin &amp; Mirabelli,
2001; Patz &amp; Kovats 2002; Haines, 2008).</p><p>The results of covariates in Table
3 imply that the pollution has insignificant and negative effect on physical health.
Pollution increases the chances of asthma and respiratory diseases. Breathing
in polluted air and long-term exposure in such environment is associated with
stroke, depression, diabetes, and shorter life-span. Ventilation and land area
has negative and insignificant effect on health. Dampness and mould are a cause
of inadequate ventilation in a house which causes physical health issues such
as asthma, cardio diseases and allergies (Packer, Stewart-Brown &amp; Fowle,
1994; Tiesler, Thiering, Tischer, Lehmann, Schaaf, von Berg &amp; Heinrich, 2015).
21% unhealthy women reported improper ventilation in their homes while 15%
pointed out the incidence of bad health. Poor ventilation is sometimes due to
small houses constructed on a very limited land area.</p><p>Age has inverse and significant
relationship with women health. According to WHO (2007), older women are at a
greater risk of chronic diseases. As a person becomes aged his health level
affects due to lower immune system. Whereas HH income has positive and
insignificant association with health. Income fulfils nutrition intakes, more
access to consumption of higher quality of goods and services, better housing,
and medical care services which have favourable effect on health outcome (Filmer
&amp; Pritchett, 1999; Fayissa &amp; Gutema, 2008; Rajkumar &amp; Swaroop,
2008; Babones, 2008; Cingolani, Thomsson &amp; de Crombrugghe, 2015). In the
same manner, employment also has negative and significant impact on physical
health of women, as women have dual burden both at workplace as well as at home
which weakens women and adversely affect their health.</p><p>In case of women empowerment, women
having less decision power have inverse association with physical health. Less
empowered women have negative health consequences, such as deprived health
outcome, increased burden of HH chores, no easy access to medical and education
facilities and disparities in provision of HH resources (Velkoff &amp; Adlakha
1998; Wallerstein, 2002). This shows that less empowered women with increased
husband dominance has less decision-making power which affect their health
outcome badly. Our results show that 40% women are less empowered due to
decreased decision power in their life while 53% of husbands dominate their
wives in decision making process.</p><p>While in case of life events, sad
events make women suffer in terms of physical health. Similarly, HH head
sex/gender has positive effect on physical health outcome. Optimist HH head,
either male or female, adds positively in the lives of his family people in
terms of taking decisions for them and can make their lives improved and
satisfied but pessimist person can increase their stress and have bad effect on
their minds.</p><p>Estimates of marginal effects
portray that with a unit change in climate decreases women’s physical health by
9 percentage points. Without any question, changing climate disproportionately
affects women’s health (WHO, 2014). The results indicate that weather changes
become more extreme which affect biological system badly in all continents
especially in developing countries (Epstein, 2005, 2007). The climatic changes
and weather patterns not only affect biological system but also have
deteriorating effect on individuals’ health through ripple effect of global
warming (Patil &amp; Deepa, 2007; Kvotes &amp; Akhtar, 2008; Hiscock,
Asikainen, Tuomisto, Jantunen, Pärjälä &amp; Sabel, 2017).</p><p><bold>Robustness</bold></p><p>We
also perform sensitivity analysis to check whether our results are robust to
other determinants of health by adding variables one by one. We include medical
facilities, greenery around homes, water quality, sanitation quality and
overcrowding as potential determinants of health. Overcrowding has negative and
significant effect on physical health outcome. People living with crowded
environment are at a higher risk of illness, the major diseases are respiratory
infections, hepatitis, tuberculosis and trachoma and others (Baker, M., McNicholas, Garrett, Jones, Stewart,
Koberstein, &amp; Lennon, 2000). Lack of medical facilities negatively
affect health outcomes of women. Whereas, improved water and sanitation quality
enhances health outcome, as safe and clean water, hand washing facilities,
proper toilets and hygiene practices are crucial for improved individuals’
health outcomes (Moe &amp; Rheingans, 2006). The indicator of greenery has
positive and significant effect on physical health. Living in natural beauty
improves our quality of life and provides pollution free environment (Pretty,
Peacock, Sellens, &amp; Griffin, 2005). Overall results of sensitivity analysis
suggest that findings are robust to inclusion of other determinants of health.</p><table-wrap id="table4"><label>Table
4</label><caption><title>Climate Change and Women Health (Sensitivity)</title></caption><table><tbody><tr><td> <p><bold>Variables</bold></p> </td><td> <p><bold>Odds Ratios</bold></p> </td><td> <p><bold>Marginal Effects</bold></p> </td></tr><tr><td> <p><bold>Climate Change</bold></p> </td><td valign="top"> <p>0.5504*</p> <p>(0.1848)</p> </td><td valign="bottom"> <p>-0.1013*</p> <p>(0.0502)</p> </td></tr><tr><td> <p>Overcrowding</p> </td><td valign="top"> <p>0.7902**</p> <p>(0.0916)</p> </td><td valign="bottom"> <p>-0.0445**</p> <p>(0.02176)</p> </td></tr><tr><td> <p><bold>Climate Change</bold></p> </td><td valign="top"> <p>0.5718*</p> <p>(01892)</p> </td><td valign="bottom"> <p>-0.0962*</p> <p>(0.0508)</p> </td></tr><tr><td> <p>Medical Facilities</p> </td><td valign="top"> <p>0.6307*</p> <p>(0.1813)</p> </td><td valign="bottom"> <p>-0.0822*</p> <p>(0.0478)</p> </td></tr><tr><td colspan="3" valign="bottom"> <p><italic>Table
  Continued on Next Page</italic></p> </td></tr><tr><td colspan="3"> <p><italic>Table
  Continued from Previous Page</italic></p> </td></tr><tr><td> <p><bold>Climate Change</bold></p> </td><td valign="top"> <p>0.5117**</p> <p>(0.1709)</p> </td><td valign="bottom"> <p>-0.1106**</p> <p>(0.0477)</p> </td></tr><tr><td> <p>Satisfaction with
  water quality</p> </td><td valign="top"> <p>2.4622***</p> <p>(0.6352)</p> </td><td valign="bottom"> <p>0.1722***</p> <p>(0.0492)</p> </td></tr><tr><td> <p><bold>Climate Change</bold></p> </td><td valign="top"> <p>0.6332*</p> <p>(0.2070)</p> </td><td valign="bottom"> <p>-0.0802*</p> <p>(0.0523)</p> </td></tr><tr><td> <p>Sanitation (Sharing
  Toilet)</p> </td><td valign="top"> <p>0.9742</p> <p>(0.0331)</p> </td><td valign="bottom"> <p>-0.0049</p> <p>(0.0064)</p> </td></tr><tr><td> <p><bold>Climate Change</bold></p> </td><td valign="top"> <p>0.6333*</p> <p>(0.2077)</p> </td><td valign="bottom"> <p>-0.0801*</p> <p>(0.0525)</p> </td></tr><tr><td> <p>Greenery around Living
  areas</p> </td><td valign="top"> <p>0.8701
  (0.2077)</p> </td><td> <p>-0.0263 (0.0450)</p> </td></tr><tr><td> <p>Observations</p> </td><td> <p>504</p> </td><td> <p>504</p> </td></tr></tbody></table></table-wrap> <p>*10%,
** 5%, ***1%. Control variables are not reported here</p>
</sec>
<sec id="sec-6">
  <title>Discussion and Concluding Remarks</title>
<p>Research on health consequences of climate change comprising variability in indoor and outdoor climate and changing weather patterns entail in-depth understanding. This study determines how changing patterns of physical environment affect health status by analyzing survey participants of married women in district Rawalpindi.</p><p>Due to climate variability many people suffer from weather and temperature related events, air pollution, water-borne diseases, vector-borne disease and food-borne diseases having imminent effect on their health (Patz &amp; Kvots, 2002; Costello, A., Abbas, M., Allen, Ball, Bell, Bellamy &amp; Lee, 2009). Some climate changing effects on health: increasing morbidity, mortality, malaria, dengue fever, cholera, respiratory issues and other infectious diseases are the result of droughts, heat waves, rains, storms and floods. Similarly, Pakistan also faces heat waves fluctuation during summer (April to September) and extreme rainfall pattern which have sever effect on overall health of individuals (Fareed et al., 2016). And the effect of these climatic conditions is felt by women more acutely. According to Bruce and Susan, “There is evidence of how climate change is associated with an increase in asthma in adolescent girls, a higher risk of acquiring lung cancer and heart disease in mid-life, and heart attacks, strokes, and dementia in older women.”</p><p>Corresponding to main districts of Pakistan, Rawalpindi District not only lacks vegetation but also experiences high heat waves in some areas of the district that are known as “Urban Heat Island”. Urban spread with lack of vegetation causes rise in day and night temperature that is attributed to changing climate patterns. In June 2018, during the summer span in Rawalpindi, night temperature was on average 250C. However, Murree and Kotli Sattian are green neighbor of Rawalpindi and other cities of the District under study experience a bit different climate where on average temperature is moderate during the day time while nights are bit cold in summer even. The results show that climate variability badly affect physical health of women. This may be due to the fact that climate change affects health through infectious diseases, unavailability of food production on land, shortage of water, by contributing biodiversity loss and also loss in ecosystem on which human depends. Heat waves record-breaking estimates of District Rawalpindi show that in April 2006 on average 30.20C, in May 2013 it was 45.50C, likewise in June 2007 it was 460C, July 2012 it was 45.50C and in August and September 2017 was 33.70C was recorded. Previous estimates of record-breaking rainfall over 335 mm in 2001, 219 mm in 2010 and 298 mm in 2014, likewise in Murree 373 mm in 2010, 262 mm 2014. These two weather changing patterns are also evident in Table 2 above which shows that (166/504) women are affected by heavy rain fall while (144/504) feel that high temperature is a reason of their bad health.</p><p>The results also assert that climate change and changing weather pattern has adverse effect women health. It is also observed that in developing countries increased warming, high intensity of heatwaves disproportionately affects the vulnerable population who bear burden of physical illness (heat stress, heat stroke, mental disorders and heat exhaustion) (McGeehin &amp; Mirabelli, 2001; Patz &amp; Kovats 2002; Haines, 2008). These changing climatic patterns in rural areas are worse for women as they have to face double duties at home and in field with spouse to share their burden. On the other hand, in urban areas mostly women are working women (teachers, nurses, private and government employed) and have double burden of workload at home and at work place. This increased exposure of outside environment affects their health badly with changing climatic patterns.</p><p><break/></p><p>Policy Implication</p><p><break/></p><p>Our results show that climatic changes (increasing heat intensity, dry spells, unusual rains and others) affect women health badly. We have to improve our climate by offering different policies designed by state. This may include deforestation, plantation in and outside homes and environmental friendly policies like renewable energy that is a shift from coal and oil investing energy projects. Efforts should also be put to increase green areas within urban locality.</p><p>So, government should also be prepared to manage sudden environmental changes and take steps to create awareness in people to safeguards themselves from disastrous effects of changing climate. Awareness, preparedness and information cells can be managed through media to provide safe guards. Rising awareness among individuals is an essential way to reduce health associated risks of climate change.</p>
</sec>
</body>
<back>
<fn-group content-type="conflict-of-interest">
  <title>Conflict of Interest</title>
  <fn fn-type="conflict">
<p>The authors declare that they have no conflicts of interest.</p>
  </fn>
</fn-group>
<fn-group content-type="ethics-statement">
  <title>Ethics Statement</title>
  <fn fn-type="ethics">
<p>This study did not require formal ethics approval.</p>
  </fn>
</fn-group>
<fn-group content-type="data-availability">
  <title>Data Availability</title>
  <fn fn-type="data-availability-statement">
<p>Data sharing is not applicable to this article.</p>
  </fn>
</fn-group>
<app-group>
  <app id="app-suppl">
    <title>Supplementary Materials</title>
<supplementary-material id="suppl-pdf" content-type="pdf" xlink:href="https://gssrjournal.com/pdf/gssr/R0E2sIzjmf.pdf">
  <label>PDF</label>
  <caption>
    <title>Full Text PDF</title>
  </caption>
</supplementary-material>
  </app>
</app-group>
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