<?xml version="1.0" encoding="UTF-8"?><!DOCTYPE article  PUBLIC "-//NLM//DTD Journal Publishing DTD v3.0 20080202//EN" "http://dtd.nlm.nih.gov/publishing/3.0/journalpublishing3.dtd"><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" dtd-version="3.0" xml:lang="en" article-type="research article"><front><journal-meta><journal-id journal-id-type="publisher-id">JSS</journal-id><journal-title-group><journal-title>Open Journal of Social Sciences</journal-title></journal-title-group><issn pub-type="epub">2327-5952</issn><publisher><publisher-name>Scientific Research Publishing</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.4236/jss.2016.45033</article-id><article-id pub-id-type="publisher-id">JSS-67655</article-id><article-categories><subj-group subj-group-type="heading"><subject>Articles</subject></subj-group><subj-group subj-group-type="Discipline-v2"><subject>Business&amp;Economics</subject><subject> Social Sciences&amp;Humanities</subject></subj-group></article-categories><title-group><article-title>
 
 
  The Impact of Dietary Patterns on Health Expenditure in Rural Hainan of China
 
</article-title></title-group><contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Wen</surname><given-names>Yu</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Shiwei</surname><given-names>Xu</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Mei</surname><given-names>Li</given-names></name><xref ref-type="aff" rid="aff2"><sup>2</sup></xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Zhemin</surname><given-names>Li</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Yu</surname><given-names>Wang</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Abdul-Gafar</surname><given-names>Ahmed</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib></contrib-group><aff id="aff1"><addr-line>Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, China</addr-line></aff><aff id="aff2"><addr-line>Division of Community Health and Humanities, Faculty of Medicine, Memorial University of Newfoundland, St. John’s, NL Canada</addr-line></aff><pub-date pub-type="epub"><day>31</day><month>05</month><year>2016</year></pub-date><volume>04</volume><issue>05</issue><fpage>300</fpage><lpage>304</lpage><history><date date-type="received"><day>21</day>	<month>May</month>	<year>2016</year></date><date date-type="rev-recd"><day>accepted</day>	<month>28</month>	<year>May</year>	</date><date date-type="accepted"><day>31</day>	<month>May</month>	<year>2016</year></date></history><permissions><copyright-statement>&#169; Copyright  2014 by authors and Scientific Research Publishing Inc. </copyright-statement><copyright-year>2014</copyright-year><license><license-p>This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/</license-p></license></permissions><abstract><p>
 
 
   The dietary patterns determine the nutrition structure and suitable nutrition benefits for health. In order to measure the relationship between nutrition and health, this article focuses on the relationship between Dietary patterns and health expenditure. We collected 547 household samples from six counties in rural Hainan Province of China. The authors applied Seemingly Unrelated Regression (SUR) model to estimate the empirical model. The results are: (1) some animal food have significant effect to health expenditure; (2) based on the current analysis, consuming more pork and eggs, health expenditure would significantly increase; while consuming more chicken and fish, health expenditure would significantly decrease. We could conclude that appropriate dietary pattern benefits health; livestock meat consuming, primarily pork, may cause more health care expenditure, while fish-dominant diet could reduce health expenditure 
 
</p></abstract><kwd-group><kwd>Dietary Pattern</kwd><kwd> Health Expenditure</kwd><kwd> Seemingly Unrelated Regression (SUR)</kwd><kwd> Suggestion</kwd></kwd-group></article-meta></front><body><sec id="s1"><title>1. Introduction</title><p>Food and nutrition are the two aspects of the same thing. Food consumption group determines the nutrition structure. The effect of nutrition intake on health is indisputable. The authors exemplify the relationship between diet and diseases in <xref ref-type="table" rid="table1">Table 1</xref>.</p><table-wrap id="table1" ><label><xref ref-type="table" rid="table1">Table 1</xref></label><caption><title> The literature summary on the disease associated with diet and nutrition</title></caption><table><tbody><thead><tr><th align="center" valign="middle" ></th><th align="center" valign="middle" >Author</th><th align="center" valign="middle" >Data source</th><th align="center" valign="middle" >Diet and nutrition</th><th align="center" valign="middle" >Diseases</th></tr></thead><tr><td align="center" valign="middle" >1</td><td align="center" valign="middle" >Nurmatov U., etc. (2011) [<xref ref-type="bibr" rid="scirp.67655-ref1">1</xref>]</td><td align="center" valign="middle" >Literature summary</td><td align="center" valign="middle" >Fruit and vegetable</td><td align="center" valign="middle" >Fewer intakes of fruit and vegetable are related to asthma and allergy for children</td></tr><tr><td align="center" valign="middle" >2</td><td align="center" valign="middle" >Wang Cannan, etc. (2001) [<xref ref-type="bibr" rid="scirp.67655-ref2">2</xref>]</td><td align="center" valign="middle" >Survey on resident’s Food consumption and nutrition-related disease in Xuzhou City</td><td align="center" valign="middle" >Energy nutrition intake decreases, animal protein and fat intake increase</td><td align="center" valign="middle" >mortality rate of Diabetes, colon cancer, breast cancer (female), cerebrovascular disease, cardiovascular disease increase</td></tr><tr><td align="center" valign="middle" >3</td><td align="center" valign="middle" >Xu Zhanzhong etc. (2008) [<xref ref-type="bibr" rid="scirp.67655-ref3">3</xref>]</td><td align="center" valign="middle" >time serial data on food consumption in Xuzhou City, Jiangsu Province; And random 41,786 sample survey on diseases related nutrition to the population with age of 35 years old</td><td align="center" valign="middle" >Comparing with the idea diet pattern, milk, beans, aquatics, vegetable, fruit taken less, and pork, fat and oil taken more.t intake</td><td align="center" valign="middle" >Tobacco, alcohol, Obesity will promote hypertension, stroke, diabetes, bronchial asthma, chronic bronchitis, and fruit consumption and sports activities will protect the health</td></tr><tr><td align="center" valign="middle" >4</td><td align="center" valign="middle" >Wang Cannan, etc. (2001) [<xref ref-type="bibr" rid="scirp.67655-ref4">4</xref>]</td><td align="center" valign="middle" >food consumption random sampling survey and monitoring of disease for urban and rural residents in Jiangsu Province</td><td align="center" valign="middle" >Grains consumption reduced, animal production increased; Carbohydrate nutrients intake decreases, fat and protein intake increases</td><td align="center" valign="middle" >the resistance of infectious disease increases; mortality of diabetes, hypertension, coronary heart disease, colon cancer, breast cancer (female) has a rapid growth;</td></tr><tr><td align="center" valign="middle" >5</td><td align="center" valign="middle" >Mo Baoqing, etc. (2007) [<xref ref-type="bibr" rid="scirp.67655-ref5">5</xref>]</td><td align="center" valign="middle" >Urban dietary survey on 240 households in Jiangsu Province</td><td align="center" valign="middle" >grains intake decreases, while the intake of beans, meat, poultry, milk, aquatics, oil and fat increase; in nutrition, intake of protein, lipids, riboflavin, retinol equivalent, calcium, vitamin C increase</td><td align="center" valign="middle" >Less detection rate of deficiency disease, and higher detection rate of over-nutrition related diseases (children simple obesity, hypertension, hyperlipidemia, diabetes)</td></tr></tbody></table></table-wrap><p>Fewer intakes of fruit and vegetable are related to asthma and allergy for children, which means that the intake of fruit and vegetable benefits to the patient with asthma and allergy [<xref ref-type="bibr" rid="scirp.67655-ref1">1</xref>];</p><p>The unbalanced nutrition, especially more fat intake, are related to some diseases. For example, some chronic disease are associated with dietary fat, such as lard contains saturated fats [<xref ref-type="bibr" rid="scirp.67655-ref6">6</xref>].</p><p>The researchers often link the disease with dietary patterns. More pork, fat and oil intake is associated with hypertension, stroke, diabetes, bronchial asthma, chronic bronchitis [<xref ref-type="bibr" rid="scirp.67655-ref3">3</xref>]; while the intake of beans, meat, poultry, milk, aquatics, oil and fat increase, in the role of nutrition, intake of protein, lipids, riboflavin, retinol equivalent, calcium, vitamin C increase, there is less detection rate of deficiency disease, but higher detection rate of over-nutrition related to diseases, including children obesity, hypertension, hyperlipidemia, diabetes [<xref ref-type="bibr" rid="scirp.67655-ref5">5</xref>]; the similar study also proves that les grains consumption but more animal production, from the degree of nutrition, less carbohydrate intake and more fat and protein intake will lead to the resistance increasing for infectious disease, and also lead to the increasing mortality of diabetes, hypertension, coronary heart disease, colon cancer, breast cancer (female) [<xref ref-type="bibr" rid="scirp.67655-ref4">4</xref>].</p><p>Prolonged intake of fat has side effects, such as the chronic disease of cardio-cerebrovascular diseases, thus reduce or interfere fat consumption. So the unhealthy diet structure will possibly cause nutrition imbalance; then thus make persons to deal with the disease and led to increased health expenditure.</p><p>In order to measure the relationship between nutrition intake and health, this article focuses on examining the impact of food structure on health care expenditure.</p><p>The healthy food structure could be got from empirical analysis, and the authors try to explain the reason from the perspective of nutrition intake. Positive nutrition suggestion is expected to be accepted by relative nutrition management department and governmental sectors. The expected result is that government could take relative measurement to help household establish and maintain a healthy food habitat for further health promotion.</p></sec><sec id="s2"><title>2. Material and Method</title><sec id="s2_1"><title>2.1. Data</title><p>All data is from face to face questionnaire survey co-implemented by Agricultural Information Institute, Chinese Academy of Agricultural Sciences (AII-CAAS) and Science Information Institute, Chinese Academy of Tropical Agricultural Sciences (SII-CATAS) in the end of 2013 in Hainan Province, China (<xref ref-type="fig" rid="fig1">Figure 1</xref>),</p><p>Hainan province is located at the extreme south of China ranging in 18˚10' - 20˚10'N and 108˚37' - 111˚03'E. It is an island covering maritime area of about 2 million square kilometres and land area of about 35.4 thousand square kilometres, ranking the province as the 28<sup>th</sup> compare to other provinces in China.</p><p>Six counties were selected among which include Lingao and Danzhou (North-west of the province), Chengmai and Qiongzhong (Central), and Wenchang and Wanning (South-east). Three towns was selected from each county among which three villages were selected and lastly, a total of 547 farmers were randomly select and interviewed; considering the integrity of the food consumption data, 304 effective questionnaires are used in this article.</p></sec><sec id="s2_2"><title>2.2. Seemingly Unrelated Regression</title><p>The authors apply Seemingly Unrelated Regression (SUR) to estimate the empirical model [<xref ref-type="bibr" rid="scirp.67655-ref7">7</xref>]. In the model, we make the hypothesis that the most adverse nutritional factor of health is animal products regarding food intake. In <xref ref-type="table" rid="table2">Table 2</xref>, the authors list the relevant variables in this article. Health expenditure per capita, denoting as pexp_ health, the dependent variable, is RMB803 Yuan averagely in 2013; the social economic variables include housewife education level, household head’s age, family size, denoting as edu, age and size, respectively; the food consumption per capita is also list in <xref ref-type="table" rid="table2">Table 2</xref>, such as staple grains (mainly rice,), red meat (primarily pork), chicken, egg, aquatic productions (fish, shrimp and shells), milk, vegetable, and fruit, denoting as pwei_ grains, pwei_pork, pwei_poul, pwei_egg, pwei_aquatic, pwei_milk, pwei_veg01, pwei_fruit, respectively.</p><p>In <xref ref-type="table" rid="table3">Table 3</xref>, the Logarithm form of variable excluding social economics variables such as edu, age and size, is listed. As is used to estimate the Tobit model and explain the health expenditure in percentage degree, and also validate model results mutually.</p><p>The relative characteristic factors are also considered in the analysis, for example housewife education, household head’s age, family size, denoting as the logarithm style or proportion: lnedu, lnage, lnsize. The logarithm of food consumption also denote as lnpwei_grains, lnpwei_meat, lnpwei_fat, lnpwei_poul, lnpwei_egg, lnpwei_aquatic, lnpwei_milk, lnpwei_veg01, lnpwei_fruit, respectively. And the definition is listed in detail in <xref ref-type="table" rid="table3">Table 3</xref>.</p></sec></sec><sec id="s3"><title>3. Results</title><p>From SUR model 1 in <xref ref-type="table" rid="table2">Table 2</xref> and Model 2 in <xref ref-type="table" rid="table3">Table 3</xref>, we can find the primary results as following:</p><p>(1) Some animal food consumption has significant effect on health expenditure;</p><p>(2) From <xref ref-type="table" rid="table2">Table 2</xref>, the coefficient of pwei_meat and pwei_egg has positive sign, which means consuming each 1 kg of pork and eggs, health expenditure would significantly increase RMB15 and 43 Yuan; Contrary to this, the coefficient of pwei_poul and pwei_aquatic has negative sign, which means consuming each 1 kg of chicken and fish, health expenditure would significantly decrease RMB27 and 10 Yuan.</p><fig id="fig1"  position="float"><label><xref ref-type="fig" rid="fig1">Figure 1</xref></label><caption><title> Map―China, Hainan province</title></caption><graphic mimetype="image"   position="float"  xlink:type="simple"  xlink:href="http://html.scirp.org/file/67655x7.png"/></fig><table-wrap id="table2" ><label><xref ref-type="table" rid="table2">Table 2</xref></label><caption><title> Variable expression and the results from SUR Model 1</title></caption><table><tbody><thead><tr><th align="center" valign="middle" ></th><th align="center" valign="middle" ></th><th align="center" valign="middle" ></th><th align="center" valign="middle" ></th><th align="center" valign="middle"  colspan="4"  >SUR model</th></tr></thead><tr><td align="center" valign="middle" >Variable</td><td align="center" valign="middle" >Definition</td><td align="center" valign="middle" >Unit</td><td align="center" valign="middle" >Mean</td><td align="center" valign="middle" >Coef.</td><td align="center" valign="middle" >Std.Err.</td><td align="center" valign="middle" >T-value</td><td align="center" valign="middle" >P &gt; T</td></tr><tr><td align="center" valign="middle" >(1)</td><td align="center" valign="middle" >(2)</td><td align="center" valign="middle" >(3)</td><td align="center" valign="middle" >(4)</td><td align="center" valign="middle" >(5)</td><td align="center" valign="middle" >(6)</td><td align="center" valign="middle" >(7)</td><td align="center" valign="middle" >(8)</td></tr><tr><td align="center" valign="middle" >pexp_health</td><td align="center" valign="middle" >Health expenditure per capita</td><td align="center" valign="middle" >RMB yuan</td><td align="center" valign="middle" >803</td><td align="center" valign="middle" >-</td><td align="center" valign="middle" >-</td><td align="center" valign="middle" >-</td><td align="center" valign="middle" >-</td></tr><tr><td align="center" valign="middle" >edu</td><td align="center" valign="middle" >Education level for housewife</td><td align="center" valign="middle" >Year</td><td align="center" valign="middle" >7</td><td align="center" valign="middle" >−4.981</td><td align="center" valign="middle" >59.802</td><td align="center" valign="middle" >−0.080</td><td align="center" valign="middle" >0.934</td></tr><tr><td align="center" valign="middle" >age</td><td align="center" valign="middle" >Age for household head</td><td align="center" valign="middle" >Year</td><td align="center" valign="middle" >47</td><td align="center" valign="middle" >0.430</td><td align="center" valign="middle" >18.970</td><td align="center" valign="middle" >0.020</td><td align="center" valign="middle" >0.982</td></tr><tr><td align="center" valign="middle" >size</td><td align="center" valign="middle" >Family size for household</td><td align="center" valign="middle" >Person</td><td align="center" valign="middle" >5</td><td align="center" valign="middle" >−78.643</td><td align="center" valign="middle" >123.884</td><td align="center" valign="middle" >−0.630</td><td align="center" valign="middle" >0.526</td></tr><tr><td align="center" valign="middle" >pwei_grains</td><td align="center" valign="middle" >Grains per capita</td><td align="center" valign="middle" >Kg</td><td align="center" valign="middle" >309</td><td align="center" valign="middle" >−0.334</td><td align="center" valign="middle" >1.072</td><td align="center" valign="middle" >−0.310</td><td align="center" valign="middle" >0.756</td></tr><tr><td align="center" valign="middle" >pwei_meat</td><td align="center" valign="middle" >Pork per capita</td><td align="center" valign="middle" >Kg</td><td align="center" valign="middle" >118</td><td align="center" valign="middle" >15.015<sup>***</sup></td><td align="center" valign="middle" >5.079</td><td align="center" valign="middle" >2.960</td><td align="center" valign="middle" >0.003</td></tr><tr><td align="center" valign="middle" >pwei_fat</td><td align="center" valign="middle" >Fat per capita</td><td align="center" valign="middle" >Kg</td><td align="center" valign="middle" >17</td><td align="center" valign="middle" >−8.981</td><td align="center" valign="middle" >8.935</td><td align="center" valign="middle" >−1.010</td><td align="center" valign="middle" >0.316</td></tr><tr><td align="center" valign="middle" >pwei_poul</td><td align="center" valign="middle" >Chicken per capita</td><td align="center" valign="middle" >Kg</td><td align="center" valign="middle" >32</td><td align="center" valign="middle" >−26.914<sup>***</sup></td><td align="center" valign="middle" >8.132</td><td align="center" valign="middle" >−3.310</td><td align="center" valign="middle" >0.001</td></tr><tr><td align="center" valign="middle" >pwei_egg</td><td align="center" valign="middle" >Egg per capita</td><td align="center" valign="middle" >Kg</td><td align="center" valign="middle" >10</td><td align="center" valign="middle" >42.678<sup>***</sup></td><td align="center" valign="middle" >14.233</td><td align="center" valign="middle" >3.000</td><td align="center" valign="middle" >0.003</td></tr><tr><td align="center" valign="middle" >pwei_aquatic</td><td align="center" valign="middle" >Fish and other aquatics per capita</td><td align="center" valign="middle" >Kg</td><td align="center" valign="middle" >51</td><td align="center" valign="middle" >−10.016<sup>**</sup></td><td align="center" valign="middle" >4.398</td><td align="center" valign="middle" >−2.280</td><td align="center" valign="middle" >0.023</td></tr><tr><td align="center" valign="middle" >pwei_milk</td><td align="center" valign="middle" >Milk per capita</td><td align="center" valign="middle" >Kg</td><td align="center" valign="middle" >4</td><td align="center" valign="middle" >−18.252</td><td align="center" valign="middle" >20.128</td><td align="center" valign="middle" >−0.910</td><td align="center" valign="middle" >0.365</td></tr><tr><td align="center" valign="middle" >pwei_veg01</td><td align="center" valign="middle" >Vegetable per capita</td><td align="center" valign="middle" >Kg</td><td align="center" valign="middle" >59</td><td align="center" valign="middle" >1.685</td><td align="center" valign="middle" >3.142</td><td align="center" valign="middle" >0.540</td><td align="center" valign="middle" >0.592</td></tr><tr><td align="center" valign="middle" >pwei_fruit</td><td align="center" valign="middle" >Fruit per capita</td><td align="center" valign="middle" >Kg</td><td align="center" valign="middle" >60</td><td align="center" valign="middle" >3.988</td><td align="center" valign="middle" >4.000</td><td align="center" valign="middle" >1.000</td><td align="center" valign="middle" >0.320</td></tr><tr><td align="center" valign="middle" >_cons</td><td align="center" valign="middle" >Constant in model</td><td align="center" valign="middle" >-</td><td align="center" valign="middle" >-</td><td align="center" valign="middle" >215.667</td><td align="center" valign="middle" >1403.919</td><td align="center" valign="middle" >0.150</td><td align="center" valign="middle" >0.878</td></tr></tbody></table></table-wrap><p>Note: <sup>*</sup>10% level significant; <sup>**</sup>5% level significant; <sup>***</sup>1% level significant.</p><table-wrap id="table3" ><label><xref ref-type="table" rid="table3">Table 3</xref></label><caption><title> Variable expression and the results from SUR Model 2</title></caption><table><tbody><thead><tr><th align="center" valign="middle" >Variable</th><th align="center" valign="middle" >Definition</th><th align="center" valign="middle" >Mean</th><th align="center" valign="middle" >Coef.</th><th align="center" valign="middle" >Std.Err.</th><th align="center" valign="middle" >T-value</th><th align="center" valign="middle" >P&gt;T</th></tr></thead><tr><td align="center" valign="middle" >(1)</td><td align="center" valign="middle" >(2)</td><td align="center" valign="middle" >(3)</td><td align="center" valign="middle" >(4)</td><td align="center" valign="middle" >(5)</td><td align="center" valign="middle" >(6)</td><td align="center" valign="middle" >(7)</td></tr><tr><td align="center" valign="middle" >lnpexp_health</td><td align="center" valign="middle" >Logarithm of health expenditure per capita</td><td align="center" valign="middle" >5.34</td><td align="center" valign="middle" >-</td><td align="center" valign="middle" >-</td><td align="center" valign="middle" >-</td><td align="center" valign="middle" >-</td></tr><tr><td align="center" valign="middle" >lnedu</td><td align="center" valign="middle" >Logarithm of education level for housewife</td><td align="center" valign="middle" >1.44</td><td align="center" valign="middle" >−0.087</td><td align="center" valign="middle" >0.056</td><td align="center" valign="middle" >−1.550</td><td align="center" valign="middle" >0.123</td></tr><tr><td align="center" valign="middle" >lnage</td><td align="center" valign="middle" >Logarithm of age for household head</td><td align="center" valign="middle" >3.81</td><td align="center" valign="middle" >0.231</td><td align="center" valign="middle" >0.239</td><td align="center" valign="middle" >0.970</td><td align="center" valign="middle" >0.335</td></tr><tr><td align="center" valign="middle" >lnsize</td><td align="center" valign="middle" >Logarithm of family size for household</td><td align="center" valign="middle" >1.61</td><td align="center" valign="middle" >0.189</td><td align="center" valign="middle" >0.260</td><td align="center" valign="middle" >0.730</td><td align="center" valign="middle" >0.467</td></tr><tr><td align="center" valign="middle" >lnpwei_grains</td><td align="center" valign="middle" >Logarithm of grains per capita</td><td align="center" valign="middle" >5.55</td><td align="center" valign="middle" >0.158</td><td align="center" valign="middle" >0.147</td><td align="center" valign="middle" >1.070</td><td align="center" valign="middle" >0.284</td></tr><tr><td align="center" valign="middle" >lnpwei_meat</td><td align="center" valign="middle" >Logarithm of pork per capita</td><td align="center" valign="middle" >4.59</td><td align="center" valign="middle" >0.331<sup>*</sup></td><td align="center" valign="middle" >0.182</td><td align="center" valign="middle" >1.810</td><td align="center" valign="middle" >0.071</td></tr><tr><td align="center" valign="middle" >lnpwei_fat</td><td align="center" valign="middle" >Logarithm of fat per capita</td><td align="center" valign="middle" >1.73</td><td align="center" valign="middle" >−0.010</td><td align="center" valign="middle" >0.052</td><td align="center" valign="middle" >−0.200</td><td align="center" valign="middle" >0.841</td></tr><tr><td align="center" valign="middle" >lnpwei_poul</td><td align="center" valign="middle" >Logarithm of chicken per capita</td><td align="center" valign="middle" >2.82</td><td align="center" valign="middle" >−0.036</td><td align="center" valign="middle" >0.072</td><td align="center" valign="middle" >−0.500</td><td align="center" valign="middle" >0.619</td></tr><tr><td align="center" valign="middle" >lnpwei_egg</td><td align="center" valign="middle" >Logarithm of egg per capita</td><td align="center" valign="middle" >1.48</td><td align="center" valign="middle" >−0.005</td><td align="center" valign="middle" >0.077</td><td align="center" valign="middle" >−0.060</td><td align="center" valign="middle" >0.952</td></tr><tr><td align="center" valign="middle" >lnpwei_aquatic</td><td align="center" valign="middle" >Logarithm of fish and other aquatics per capita</td><td align="center" valign="middle" >3.34</td><td align="center" valign="middle" >−0.152<sup>**</sup></td><td align="center" valign="middle" >0.076</td><td align="center" valign="middle" >−2.010</td><td align="center" valign="middle" >0.045</td></tr><tr><td align="center" valign="middle" >lnpwei_milk</td><td align="center" valign="middle" >Logarithm of milk per capita</td><td align="center" valign="middle" >0.48</td><td align="center" valign="middle" >0.058</td><td align="center" valign="middle" >0.080</td><td align="center" valign="middle" >0.730</td><td align="center" valign="middle" >0.468</td></tr><tr><td align="center" valign="middle" >lnpwei_veg01</td><td align="center" valign="middle" >Logarithm of vegetable per capita</td><td align="center" valign="middle" >3.59</td><td align="center" valign="middle" >0.140</td><td align="center" valign="middle" >0.087</td><td align="center" valign="middle" >1.610</td><td align="center" valign="middle" >0.107</td></tr><tr><td align="center" valign="middle" >lnpwei_fruit</td><td align="center" valign="middle" >Logarithm of fruit per capita</td><td align="center" valign="middle" >3.65</td><td align="center" valign="middle" >0.223<sup>*</sup></td><td align="center" valign="middle" >0.090</td><td align="center" valign="middle" >2.500</td><td align="center" valign="middle" >0.013</td></tr><tr><td align="center" valign="middle" >_cons</td><td align="center" valign="middle" >Constant in model</td><td align="center" valign="middle" >-</td><td align="center" valign="middle" >1.403</td><td align="center" valign="middle" >1.560</td><td align="center" valign="middle" >0.900</td><td align="center" valign="middle" >0.369</td></tr></tbody></table></table-wrap><p>Note: <sup>*</sup>10% level significant; <sup>**</sup>5% level significant; <sup>***</sup>1% level significant</p><p>(3) From <xref ref-type="table" rid="table3">Table 3</xref>, the coefficient of lnpwei_meat and lnpwei_fruit has positive sign, which means consuming each 1% of pork and fruit more, health expenditure would significantly increase 0.33% and 0.15%; Contrastly, the coefficient of lnpwei_aquatic has negative sign, which means consuming each 1% of aquatic productions (mainly fish, also including less shrimp and shells), the health expenditure would decrease 0.15% significantly.</p><p>(4) Anyway, from both SUR model, the variables’ sign show interesting results: housewife with higher education has the tendency for health expenditure decreasing, and the household head with older age tends to have health expenditure increasing, although these factors are not significant.</p></sec><sec id="s4"><title>4. Conclusion and Discussion</title><p>Firstly, healthy dietary pattern benefits health. For example, based on the current research, less pork and more fish, for pork contains saturated fats, which is demonstrated to be associated with obesity and chronic diseases, but fish with highly unsaturated fatty acids has positive effect for physical health.</p><p>Secondly, the household characteristics influence the overall family health, housewives with higher education level pay more attention to nutrition intake and health, but household head with older age tends less health care;</p><p>Third, families should be encouraged to take the Dietary patterns, and have healthy food for health promotion.</p><p>Forth, animal fat is an important factor which influences family health, for it contains more saturated fats, sometimes, it is difficult to separate from pork, that is why the pwei_fat in <xref ref-type="table" rid="table2">Table 2</xref> and lnpwei_fat in <xref ref-type="table" rid="table3">Table 3</xref> are not significant, for the fat and pork has strongly alternative.</p><p>Fifth, in fact, it is better to study the different Dietary patterns in the role of nutrition, for the suitable and balanced nutrition will be helpful with health status.</p></sec><sec id="s5"><title>Acknowledgements</title><p>This article was supported by the CAAS Science and Technology Innovation Project (number: CAAS-ASTIP-</p><p>2016-AII), founded by Chinese Academy of Agricultural Sciences and Technology.</p></sec><sec id="s6"><title>Cite this paper</title><p>Wen Yu,Shiwei Xu,Mei Li,Zhemin Li,Yu Wang,Abdul-Gafar Ahmed, (2016) The Impact of Dietary Patterns on Health Expenditure in Rural Hainan of China. Open Journal of Social Sciences,04,300-304. doi: 10.4236/jss.2016.45033</p></sec><sec id="s7"><title>NOTES</title></sec></body><back><ref-list><title>References</title><ref id="scirp.67655-ref1"><label>1</label><mixed-citation publication-type="other" xlink:type="simple">Wooldridge, J.M. (2009) Introductory Econometrics—A Modern Approach (4th), Chapter 17, Limited Dependent Variable Models and Sample Selection Corrections. Tsinghua University Press, Beijing, 540-546.</mixed-citation></ref><ref id="scirp.67655-ref2"><label>2</label><mixed-citation publication-type="other" xlink:type="simple">Weisburger, J.H. (1997) Dietary Fat and Risk of Chronic Disease: Mechanistic Insights from Experimental Studies. Journal of the American Dietetic Association, 97, S16-S23. http://dx.doi.org/10.1016/S0002-8223(97)00725-6</mixed-citation></ref><ref id="scirp.67655-ref3"><label>3</label><mixed-citation publication-type="other" xlink:type="simple">Nurmatov, U., Devereux, G., Sheikh, A. and Liu, G.L. 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