How did urbanization change healthcare

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The statistical description of each variable is shown in Table 1. Following the classification standards of China's National Bureau of Statistics, 31 provinces are divided into Eastern, Central, and Western regions, including 11, 8, and 12 provinces, respectively 1. Mean, median, standard deviation, skewness, kurtosis, and Jarque-Bera statistics of these three sub-samples are also shown respectively in Table 1. It is clear that, due to the developed economic level and rich medical resources, health care expenditure in the Eastern region is higher than that in the Central and Western regions.

And the level of urbanization and aging in the Eastern region is also higher. In terms of the number of medical institutions, the Central region is the largest, which can be attributed to advanced facilities in some Center provinces.

As for the level of income and education, the average in the Eastern region is still the largest. To avoid spurious regression and ensure the validity of the estimated results, it is of vital importance for panel data to test whether the data is stable Next, the p -value of the Hausman test is 0.

Therefore, we first use the fixed effect model to identify the influence of urbanization on health care expenditure, and Table 2 displays the results. As can be observed from Table 2 , urbanization has a significant positive impact on healthcare expenditures.

The estimated coefficient of urbanization in the Eastern region and Central region is 0. The following explanation may be account for this result.

On the one hand, with a more developed tertiary industry and eastern marine economic circle 68 , the Eastern region has more employment opportunities and is much more attractive to the rural floating population, so the population flow from rural areas to urban mostly concentrated in this region. However, medical insurance does not cover the majority of the newly enrolled urban population, who are still enrolled in the new rural cooperative medical insurance, so that they are not subsidized by public medical expenses when purchasing medical services New residents who purchase the same medical service have to pay more money out of their pocket, thus increasing the level of per capita medical expenditure.

On the other hand, as an important part of the process of urbanization, the development of non-agricultural industries and the spatial agglomeration brings scientific and technological progress and new medical services, thus creating new demand and increasing medical care expenditure of residents.

Under the combined effect of these two aspects, the construction of urbanization increases the medical and health expenditure of urban residents. As an important regional economic growth sector in China, the Central region receives the transfer of coastal industries and accelerates the development of economy, but it has also been accompanied by air pollution problems such as sulfur dioxide, soot, and PM2.

Many studies have found that air pollution is one of the important factors leading to the increase of health care expenditures of residents 70 , As for the Western region of China, due to the constraints of its low economic level, large population, vast territory, and other factors, the urbanization of the Western regions has been at a low level.

Although the implementation of the western development strategy has accelerated its economic development, played a vital role in the transfer of surplus labor, and accelerated the process of urbanization to a certain extent, there still exists a large gap between the urbanization level in the Western and the Eastern regions.

The Western region has always been a relatively backward urbanization area, so its impact on residents' health care expenditure is not significant. In , with the proportion of the population aged 65 and above reaching 7.

In , the proportion of the population aged 65 and above reached Considering the background of the increasingly serious aging in China, this paper uses PTRM to do further research. In Table 3 , the p -value of the single threshold effect test is 0. The p -value of the double threshold effect test is 0. According to Table 4 , in the Eastern region, when the aging level is lower than The development of urbanization has a significantly positive effect on health care expenditure.

When the population aging exceeds Therefore, the model shows an asymmetric non-linear relationship between urbanization level and health care expenditure. The positive relationship between urbanization and health care expenditure becomes greater under a relatively high level of population aging.

It has been found that with the increase of age, the elderly face a higher risk of suffering from various chronic non-communicable diseases such as cardiovascular diseases, depression, etc.

The environmental deterioration, population crowding, increased life pressure, and lifestyle transformation accompanying the process of urbanization have brought many challenges to residents' health. At this time, the aging of the population has increased the probability of being ill.

Both disease prevention and treatment have stimulated the growth of demand for medical care, health care, and other services, which has led to a continuous increase in health care expenditures. Therefore, with a higher level of aging, the development of urbanization will promote healthcare expenditure.

In the Eastern region, the high level of economic development has brought a low fertility rate, and the rapid development of medical technology and health service has brought about a decrease in the mortality rate. The construction of urbanization also plays a significant role in promoting health care expenditure in this process. The developed economy and low fertility, coupled with the reduction of mortality caused by the rapid development of medical technology and health services, have increased the level of population aging in the Eastern region.

The construction of urbanization plays a significant role in promoting health care expenditure in this process. The threshold value for population aging is Below and above this threshold value, the coefficient of urbanization expenditure is 0. Therefore, there is no threshold effect in the Central region. The possible reason is that due to the rapid economic development of cities in some central provinces, livable lifestyles, and more employment opportunities attract a large number of young laborers to flow into the Central region, which continuously pours new vitality into the economic development.

Therefore, the role of aging in the impact of urbanization on medical expenditure has not been significant. In the Western region, the single threshold for aging is 7. But the double threshold test does not pass the significance test with the p -value of 0.

When the population aging exceeds the threshold value of 7. The threshold value of population aging in the Western region is relatively lower compared with that in the Eastern region. The possible reason is that the Western region's economic and medical development are relatively backward, and people's quality of life is lower than that of the Eastern region. Therefore, when the population aging crosses its threshold a lower threshold compared with the Eastern region , residents' health care expenditures will increase significantly.

The impact of each control variable in the threshold model is shown in Table 5. Secondly, income lnincome has a significantly positive impact on health care expenditures, and its impact is the greatest among all control variables. Income is the decisive factor that determines people's expenditure level. Higher income can relax consumers' budget constraints and enable consumers to have sufficient money to allocate to medical and health care.

Thirdly, the impact of education level on healthcare expenditure is not significant in all regions. According to Grossman's theory of health demand 63 , education can have a positive impact on consumer health through production efficiency. However, in China, fierce employment pressure makes people continue to improve their education, and people with higher education often engage in jobs with more tasks and high pressure, which has a negative impact on health.

These effects offset each other so that education has no significant impact. Fourth, the effect of medical insurance coverage on health care expenditure is opposite in the Eastern and Central regions. But the impact of medical insurance coverage on medical care expenditure is uncertain.

On the one hand, medical insurance can relieve some burden of the insured from medical expenditures; on the other hand, a higher level of medical insurance will increase the demand for medical services, which is manifested in more health care spending and reimbursement. Therefore, the direction of the influence from medical insurance on health care expenditure depends on which of these two effects is more dominant.

Finally, the number of medical and health institutions can significantly affect the health expenditure in the Central region due to a large number of medical and health institutions in some provinces.

Among the top 10 provinces in terms of the number of medical institutions, five are from the Central region, namely Henan, Hunan, Shanxi, Jiangxi, and Hubei. More medical resources increase residents' medical care expenditure and contribute to good health, which is consistent with the research of Anand and Barnighausen This paper investigates the influence of urbanization on health care expenditure by fixed effect model and uses panel threshold regression model to examine the threshold effect under the background of population aging.

The most obvious finding to emerge from this study is that urbanization can significantly contribute to the increase of health care expenditure in Eastern and Central regions. This could be interpreted as a consequence of the developed economy and abundant medical resources.

But the impact of urbanization on health care expenditure is not significant in the Western region, which is related to the backward economic development level and low urbanization rate.

It should be noted that in the Eastern and Western regions, the impact of urbanization on health care expenditure experiences structural breaks and becomes greater when the level of population aging exceeds the threshold value. There are several contributions of this study to this area of research. Firstly, this paper reexamines the impact of urbanization on health care expenditure in China. Under the background of health care reform and the fast development of urbanization, this study has practical and contemporary significance.

Secondly, considering the differences among Eastern, Central, and Western regions, this paper further discusses the regional heterogeneity and obtains meaningful conclusions. Thirdly, this paper uses the panel threshold regression model to study whether urbanization has an asymmetric effect on health care expenditure which increases testing power and makes conclusions more accurate.

The empirical results provide some useful implications for policymakers in the face of rapid development of urbanization and increasingly serious population aging. In the urbanization construction, the government should attach great importance to the effect of urbanization on health care expenditure, increase financial support for the construction of medical facilities, and expand the coverage of medical services for residents.

The government also needs to increase the coverage of social security, especially to increase the coverage of medical security, to gradually meet the residents' growing medical and healthcare consumption needs. By expanding the risk pool of medical insurance, the risk-sharing among a wider range of insured groups and the enhancement of the mutual assistance of medical insurance can be realized. The government should also focus on the financing of basic medical insurance, reasonably expand financing channels, innovate diversified financing methods, and ensure the sustainable development of medical insurance funds.

At the same time, in the context of the deepening of population aging, the government should pay more attention to the development of the medical and health care market for the elderly, especially in the Eastern Region, and build relevant entertainment and health industry to fully meet the medical care needs of the elderly.

To deal with the medical and nursing problems caused by population aging, the government can try to take the community as the starting point, make full use of the relative advantages of the community in health services, carry out health examination and medical care services for the elderly residents in the community, and implement elderly medical nursing services.

QS and ML: data curation, conceptualization, methodology, software, and writing-original draft preparation. RT: data curation and writing-original draft preparation. All authors contributed to the article and approved the submitted version. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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J Econometrics. A study performed in Korea showed that the standardized survival rate of out-of-hospital cardiac arrest assessed by emergency medical services in metropolitan and city communities increased, but no increase in rural areas Using public administrative databases of end-of-life cancer care for adult patients in Ontario, Canada, Liu et al.

In addition, Phillips 37 held the view that no discipline or method can clearly explain the relationship between urbanization and health or the diversity of urban health problems. Because the urban environment is various, each individual has different periods and ways to expose to the urban environment.

Assah et al. It is claimed that being born or living in a town during childhood increases the risk of developing mental disorders in the future, especially in developing countries in which the urban population has increased violently Mutatkar 39 stated that, in the metropolitan cities in India, non-communicable diseases such as diabetes, cardiovascular disease, and mental illness have increased significantly.

Moreover, a large study conducted by the India national mental health survey confirmed that stress-related disorders in city areas are 2—3 times that of rural and semi-rural areas Tajudeen et al. Many scholars have studied the relationship between urbanization, health, as well as health care expenditure in China. Compared with other countries, the results obtained in China by different methods in different periods were different even completely opposite, and most of the research was conducted in a certain region or city in China.

Some research confirmed that although urbanization in China poses challenges to the health of residents, people who move to urban areas tend to have better health conditions than those who live in rural areas 42 , Chen et al.

Lee et al. Nevertheless, most studies argued that urbanization's adverse effects on people's health outweigh beneficial effects, and has brought about an increase in medical and health care expenditure. Yu et al. Miao and Wu 12 indicated that although the income increases with the development of urbanization in China, a more high-fat diet and reduction of exercise in the process of urbanization offset the health benefits from high income.

With the development of urbanization in China, the healthcare demands of people have been increasing. Lin et al. Therefore, in areas with a high degree of urbanization, it will lead to higher medical expenditure, resulting in the difference of medical resources between urban and rural areas. Using the public statistics provided by Shanghai Municipal Government, Luo et al. Ahmad et al.

Although much relevant literature has explored the relationship between urbanization and health care expenditure, there is still room for further research. First, whether urbanization has a positive or negative impact on health care expenditure, existing research assumes that the impact is linear. This assumption ignores the time-varying characteristics of time series and external structural mutations, which may lead to inaccurate results.

With panel threshold regression model, we can draw more accurate conclusions and fill in the gaps in this field.

Secondly, the research on urbanization and healthcare expenditure in China is mainly carried out in a certain city or province, lacking comprehensive analysis from the macro level. Especially for China, a large developing country with a large population, to grasp the impact of urbanization from the macro-level is beneficial for policymakers to make overall decisions on national urbanization strategies.

Finally, we examine whether urbanization has a different impact on healthcare expenditure across different regions of China. The influence of urbanization on health care expenditure derives from the theoretical framework of Akpalu and Normanyo 50 and Jiang et al. The utility function of a rational consumer is assumed to be decided by h , the health condition, and c , the consumption of the bundles of goods.

The utility function can be defined as:. And the consumer's goal is to maximize his utility. We assume that health condition is decided by health input or health care expenditure , I , which can be viewed as derived demand.

Influenced by a variety of exogenous factors, the marginal rate of return of I is partly random and partly deterministic. Uncertain factors are related to misdiagnosis and unknowingly living with various polluted harmful substances, such as industrial exhaust gas and heavy metal polluted water.

Thus, the health condition of an individual can be defined as:. Referring to Jiang et al. Urbanization can provide increased access to health services, better water quality, and sanitation infrastructure, and thus improve health conditions to some extent.

However, urban environments can also lead to stressful lifestyles and nutritionally unbalanced diets which are harmful to health. So, we need to take urbanization as one of the health depreciation factors. It is assumed that the health depreciation rate is in the form of Cropper:. The function of other influencing factors can be defined in detail in the form of Cochrane Glass C-D with a constant size as follows:.

H t represents the foods the consumer can buy which is beneficial for health. G t indicates the financial expenditure and services provided by the government in the aspect of health and medical care. T t is the time required to affect health.

N t means other factors that can influence health such as one's age, education, income, etc. Then, taking the partial derivative of both sides concerning the level of urbanization, we can get:. It is considered that the government expenditure G t and the lifestyle of an individual L t remain unchanged in the short run.

The rate of influence from urbanization on one's medical care expenditure depends on the consumption of health goods H t , the time needed T t , and other factors N t. Due to the possible non-linear relationship between the level of urbanization and health care expenditure, this paper draws on the panel threshold model proposed by Hansen The model uses the observation value of the threshold variable to estimate the appropriate threshold value, thus avoiding the insufficiency of subjective judgment partition and deriving more accurate results.

Threshold models include the single threshold model, double threshold model, and multiple threshold model. We first assume that there is a single threshold, and the model is as follows 53 — 55 :. Equations 10 and 11 are for a single threshold, but there may be double thresholds empirically. In this case, the model can be modified to 12 or 13 :.

Deduced by analogy, the model can be extended to models with multiple thresholds. The form of the threshold model is determined by the number of thresholds. Compared with traditional methods of non-linearity which are less fitted and lack to capture the sharp turning points, the threshold regression model can notice turning points well. The endogenous sample data can completely determine the threshold and its estimated parameters without the specific form of the non-linear regression equation 56 — Based on the previous research, the per capita health care expenditure lnexp is considered as the explained variable 59 , which mainly consists of the cost of medicines, medical equipment, and services for medical and health care.

The level of urbanization urban is the explanatory variable. Urbanization is accompanied by population migration from rural to urban, and rural areas gradually evolve into urban areas. The most commonly proxy for urbanization in the literature is the proportion of the urban population in the total population 53 , Therefore, the ratio of the urban population is used to present the urbanization level.

In addition, the impact of aging on health care expenditure can not be ignored. The deepening of population aging may be accompanied by increasing demand for medical services. The population aging aging is considered as the threshold variable in this paper, in which the population aging is measured by the proportion of the population aged 65 and over in the total population Five control variables are considered in this study: GDP lngdp , income lnincome , education level edu , participation in medical insurance med , and the number of medical and health institutions inst.

Firstly, economic development can determine residents' health care expenditure to some extent Therefore, this paper takes the GDP of each province as a control variable to measure the economic development in each province. Secondly, according to Grossman's theory of health care demand, income is an important factor affecting residents' health demand 64 , which can influence health care expenditures in two ways. On the one hand, the growth of income will increase the monetary value of healthy time; on the other hand, an increase in wages will bring a higher marginal cost of producing health.

Therefore, people tend to purchase more health services when income increases to reduce the time spent on health investments. Since the main source of income can be regarded as wages, this paper calculates the per capita income by weighting urban and rural average wages with the proportion of the urban and rural population as the weight.

Third, the improvement of education level will also affect health care expenditure. Education can improve the output efficiency of health investment, that is, the marginal productivity of both medical services and time increases. As a result, education level can improve the efficiency of residents' health investment and reduce residents' demand for medical services, thus affecting residents' medical expenditure As a measure of education level, the years to complete every degree of education are weighted by the proportion of the population.

Fourth, the supply of medical resources will also affect the residents' medical care expenditure. Participation in medical insurance can reduce residents' expenditure on medical care This paper considers the number of employees participating in basic medical insurance to measure their participation in medical insurance. Finally, the quantity of medical and health institutions also reflects health care resources in a region 66 and is taken as one of the control variables.

To eliminate the impact of inflation, all price-related variables have been adjusted with as the base year. The statistical description of each variable is shown in Table 1. Following the classification standards of China's National Bureau of Statistics, 31 provinces are divided into Eastern, Central, and Western regions, including 11, 8, and 12 provinces, respectively 1.

Mean, median, standard deviation, skewness, kurtosis, and Jarque-Bera statistics of these three sub-samples are also shown respectively in Table 1. It is clear that, due to the developed economic level and rich medical resources, health care expenditure in the Eastern region is higher than that in the Central and Western regions.

And the level of urbanization and aging in the Eastern region is also higher. In terms of the number of medical institutions, the Central region is the largest, which can be attributed to advanced facilities in some Center provinces.

As for the level of income and education, the average in the Eastern region is still the largest. To avoid spurious regression and ensure the validity of the estimated results, it is of vital importance for panel data to test whether the data is stable Next, the p -value of the Hausman test is 0.

Therefore, we first use the fixed effect model to identify the influence of urbanization on health care expenditure, and Table 2 displays the results.

As can be observed from Table 2 , urbanization has a significant positive impact on healthcare expenditures. The estimated coefficient of urbanization in the Eastern region and Central region is 0. The following explanation may be account for this result. On the one hand, with a more developed tertiary industry and eastern marine economic circle 68 , the Eastern region has more employment opportunities and is much more attractive to the rural floating population, so the population flow from rural areas to urban mostly concentrated in this region.

However, medical insurance does not cover the majority of the newly enrolled urban population, who are still enrolled in the new rural cooperative medical insurance, so that they are not subsidized by public medical expenses when purchasing medical services New residents who purchase the same medical service have to pay more money out of their pocket, thus increasing the level of per capita medical expenditure. On the other hand, as an important part of the process of urbanization, the development of non-agricultural industries and the spatial agglomeration brings scientific and technological progress and new medical services, thus creating new demand and increasing medical care expenditure of residents.

Under the combined effect of these two aspects, the construction of urbanization increases the medical and health expenditure of urban residents. As an important regional economic growth sector in China, the Central region receives the transfer of coastal industries and accelerates the development of economy, but it has also been accompanied by air pollution problems such as sulfur dioxide, soot, and PM2.

Many studies have found that air pollution is one of the important factors leading to the increase of health care expenditures of residents 70 , As for the Western region of China, due to the constraints of its low economic level, large population, vast territory, and other factors, the urbanization of the Western regions has been at a low level.

Although the implementation of the western development strategy has accelerated its economic development, played a vital role in the transfer of surplus labor, and accelerated the process of urbanization to a certain extent, there still exists a large gap between the urbanization level in the Western and the Eastern regions. The Western region has always been a relatively backward urbanization area, so its impact on residents' health care expenditure is not significant.

In , with the proportion of the population aged 65 and above reaching 7. In , the proportion of the population aged 65 and above reached Considering the background of the increasingly serious aging in China, this paper uses PTRM to do further research. In Table 3 , the p -value of the single threshold effect test is 0.

The p -value of the double threshold effect test is 0. According to Table 4 , in the Eastern region, when the aging level is lower than The development of urbanization has a significantly positive effect on health care expenditure.

When the population aging exceeds Therefore, the model shows an asymmetric non-linear relationship between urbanization level and health care expenditure. The positive relationship between urbanization and health care expenditure becomes greater under a relatively high level of population aging.

It has been found that with the increase of age, the elderly face a higher risk of suffering from various chronic non-communicable diseases such as cardiovascular diseases, depression, etc.

The environmental deterioration, population crowding, increased life pressure, and lifestyle transformation accompanying the process of urbanization have brought many challenges to residents' health. At this time, the aging of the population has increased the probability of being ill. Both disease prevention and treatment have stimulated the growth of demand for medical care, health care, and other services, which has led to a continuous increase in health care expenditures.

Therefore, with a higher level of aging, the development of urbanization will promote healthcare expenditure. In the Eastern region, the high level of economic development has brought a low fertility rate, and the rapid development of medical technology and health service has brought about a decrease in the mortality rate.

The construction of urbanization also plays a significant role in promoting health care expenditure in this process. The developed economy and low fertility, coupled with the reduction of mortality caused by the rapid development of medical technology and health services, have increased the level of population aging in the Eastern region.

The construction of urbanization plays a significant role in promoting health care expenditure in this process. The threshold value for population aging is Below and above this threshold value, the coefficient of urbanization expenditure is 0. Therefore, there is no threshold effect in the Central region.

The possible reason is that due to the rapid economic development of cities in some central provinces, livable lifestyles, and more employment opportunities attract a large number of young laborers to flow into the Central region, which continuously pours new vitality into the economic development.

Therefore, the role of aging in the impact of urbanization on medical expenditure has not been significant. In the Western region, the single threshold for aging is 7. But the double threshold test does not pass the significance test with the p -value of 0. When the population aging exceeds the threshold value of 7. The threshold value of population aging in the Western region is relatively lower compared with that in the Eastern region.

The possible reason is that the Western region's economic and medical development are relatively backward, and people's quality of life is lower than that of the Eastern region. Therefore, when the population aging crosses its threshold a lower threshold compared with the Eastern region , residents' health care expenditures will increase significantly. The impact of each control variable in the threshold model is shown in Table 5. Secondly, income lnincome has a significantly positive impact on health care expenditures, and its impact is the greatest among all control variables.

Income is the decisive factor that determines people's expenditure level. Higher income can relax consumers' budget constraints and enable consumers to have sufficient money to allocate to medical and health care. Thirdly, the impact of education level on healthcare expenditure is not significant in all regions. According to Grossman's theory of health demand 63 , education can have a positive impact on consumer health through production efficiency.

However, in China, fierce employment pressure makes people continue to improve their education, and people with higher education often engage in jobs with more tasks and high pressure, which has a negative impact on health. These effects offset each other so that education has no significant impact.

Fourth, the effect of medical insurance coverage on health care expenditure is opposite in the Eastern and Central regions. But the impact of medical insurance coverage on medical care expenditure is uncertain. On the one hand, medical insurance can relieve some burden of the insured from medical expenditures; on the other hand, a higher level of medical insurance will increase the demand for medical services, which is manifested in more health care spending and reimbursement.

Therefore, the direction of the influence from medical insurance on health care expenditure depends on which of these two effects is more dominant. Finally, the number of medical and health institutions can significantly affect the health expenditure in the Central region due to a large number of medical and health institutions in some provinces.

Among the top 10 provinces in terms of the number of medical institutions, five are from the Central region, namely Henan, Hunan, Shanxi, Jiangxi, and Hubei.

More medical resources increase residents' medical care expenditure and contribute to good health, which is consistent with the research of Anand and Barnighausen This paper investigates the influence of urbanization on health care expenditure by fixed effect model and uses panel threshold regression model to examine the threshold effect under the background of population aging. The most obvious finding to emerge from this study is that urbanization can significantly contribute to the increase of health care expenditure in Eastern and Central regions.

This could be interpreted as a consequence of the developed economy and abundant medical resources. But the impact of urbanization on health care expenditure is not significant in the Western region, which is related to the backward economic development level and low urbanization rate.

It should be noted that in the Eastern and Western regions, the impact of urbanization on health care expenditure experiences structural breaks and becomes greater when the level of population aging exceeds the threshold value. There are several contributions of this study to this area of research. Firstly, this paper reexamines the impact of urbanization on health care expenditure in China. Under the background of health care reform and the fast development of urbanization, this study has practical and contemporary significance.

Secondly, considering the differences among Eastern, Central, and Western regions, this paper further discusses the regional heterogeneity and obtains meaningful conclusions. Thirdly, this paper uses the panel threshold regression model to study whether urbanization has an asymmetric effect on health care expenditure which increases testing power and makes conclusions more accurate. The empirical results provide some useful implications for policymakers in the face of rapid development of urbanization and increasingly serious population aging.

In the urbanization construction, the government should attach great importance to the effect of urbanization on health care expenditure, increase financial support for the construction of medical facilities, and expand the coverage of medical services for residents.

The government also needs to increase the coverage of social security, especially to increase the coverage of medical security, to gradually meet the residents' growing medical and healthcare consumption needs. By expanding the risk pool of medical insurance, the risk-sharing among a wider range of insured groups and the enhancement of the mutual assistance of medical insurance can be realized.

The government should also focus on the financing of basic medical insurance, reasonably expand financing channels, innovate diversified financing methods, and ensure the sustainable development of medical insurance funds. At the same time, in the context of the deepening of population aging, the government should pay more attention to the development of the medical and health care market for the elderly, especially in the Eastern Region, and build relevant entertainment and health industry to fully meet the medical care needs of the elderly.

To deal with the medical and nursing problems caused by population aging, the government can try to take the community as the starting point, make full use of the relative advantages of the community in health services, carry out health examination and medical care services for the elderly residents in the community, and implement elderly medical nursing services.

QS and ML: data curation, conceptualization, methodology, software, and writing-original draft preparation. RT: data curation and writing-original draft preparation. All authors contributed to the article and approved the submitted version. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers.

Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. Front Public Health. Published online Feb Author information Article notes Copyright and License information Disclaimer. Corresponding author.

Received Jan 8; Accepted Jan The use, distribution or reproduction in other forums is permitted, provided the original author s and the copyright owner s are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Abstract This paper investigates the impact and non-linear effects of urbanization on health care expenditure in China. Keywords: urbanization, health care expenditure, population aging, panel threshold regression model, China. Introduction With the development of industrialization, urbanization is expanding all over the world 1. Literature Review The impact of urbanization on human beings has two sides.

The Health Care Expenditure Model With Urbanization The influence of urbanization on health care expenditure derives from the theoretical framework of Akpalu and Normanyo 50 and Jiang et al.

Methodology Panel Threshold Regression Model Due to the possible non-linear relationship between the level of urbanization and health care expenditure, this paper draws on the panel threshold model proposed by Hansen Table 1 Descriptive statistics of the variables. Mean Median Std. Skewness Kurtosis Jarque-Bera All lnexp 6. Open in a separate window. Empirical Results To avoid spurious regression and ensure the validity of the estimated results, it is of vital importance for panel data to test whether the data is stable Table 2 The estimated coefficients of the fixed effect model.

Table 3 Threshold test between urbanization and health care expenditure. Single threshold effect test Double threshold effect test Threshold value F-statistics p -value Threshold value F-statistics p -value East Table 4 Estimated coefficients of urbanization and health care expenditure.

Table 5 Estimated coefficients of the control variables. Conclusions This paper investigates the influence of urbanization on health care expenditure by fixed effect model and uses panel threshold regression model to examine the threshold effect under the background of population aging. Author Contributions QS and ML: data curation, conceptualization, methodology, software, and writing-original draft preparation.

Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Publisher's Note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers.

References 1.

Good idea. data scientist change healthcare are right

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The data for this study come from the Tanzania Demographic and Health Survey Demographic and Health Surveys DHS are cross-sectional, nationally representative surveys conducted in many low-income countries in order to produce estimates of demographic, health, and nutritional behavior. The multistage sampling methodology requires a first sampling frame of non-overlapping area units that cover the entire country, proportional to population.

A fixed proportion of households within these units, or clusters, is then chosen by random sampling. Global position system GPS coordinates for the cluster centroid—that is, an approximate center of the sampling unit—are collected using a handheld GPS unit during fieldwork.

In order to protect the confidentiality of survey respondents, the survey clusters are randomly dislocated up to 2 and 5 km, in urban and rural areas, respectively confined to major administrative divisions and coastlines [ 17 ].

Interviews are conducted with all women ages 15 to 49 within these households. Among those eligible for clinical testing, complete survey and clinical data were available for 2, women. We restricted to women not pregnant at the time of the survey. We evaluated how our sample differed from the overall survey sample.

Compared with those for whom no clinical values were assessed, participants in our study did not differ statistically by any demographic or socioeconomic characteristics or by any measures of urbanicity.

Ethical approval was not required as this is a secondary data analysis using publicly available, de-identified data. The sample is stratified by this measure in order for the DHS to be nationally representative of all urban and all rural areas. As discussed earlier, previous work has shown that urban-rural classifications create a false dichotomy and that urbanicity is much more of a continuum [ 22 , 31 ].

This study therefore also explores a second measure of urbanicity, using a satellite-derived measure of built up environment. Each meter grid cell, or pixel, receives an index score of 0 to fully built up. GHSL differs from other Landsat-based urban classifications in that it is global, aims to measure built-up rather than impervious surface per se or a residual non-urban classification, and covers multiple time periods from — It differs from other satellite data products such as MODIS and the night-time lights data, which also measure light not vegetation in that it is a higher resolution.

This study included data from the epoch of the dataset, because the data set is informed by the newer sentinel data as well as Landsat, and thus the best representation of the built-environment around the time of survey. Because the GHSL data were used rather than the epoch, it is relevant to note the mean change in built-up level from — is less than 2 percentage points, and that about two-thirds of areas experience no change at all.

C-reactive protein CRP was originally included as a biomarker in the DHS to control for the effect of active infection on vitamin A level in the DHS assessment of micronutrients [ 34 ]. Blood samples were obtained from respondents by a finger prick and placed on a filter paper card, which was stored in a specially designed container to dry overnight. The dried blood spot samples were shipped to the National Public Health Laboratory for processing.

Each participant underwent anthropometric measurements to obtain height and weight for calculation of BMI weight in kilograms divided by height in meters squared. Weight categories for descriptive analyses were defined using World Health Organization definitions: underweight was defined as BMI less than BMI was modeled as a continuous variable.

Covariates derived from the DHS questionnaire included age, level of education no school, primary school, or secondary school or higher , marital status currently married or living together, formerly married, or never married , and wealth index poorest, poorer, middle, richer, richest. The wealth index is a composite measure that quantifies the economic resources of a household using data regarding selected asset ownership, materials used in housing construction, and types of access to water and sanitation [ 37 , 38 ].

We also calculated the distance between each centroid cluster and the nearest major city i. Descriptive statistics were run describing characteristics of the sample using weighted measures that take into account the two-stage sampling design of the survey and allow for observations to be representative at the national level. In models, CRP was assessed as a continuous measure rather than as a categorical measure, following findings indicating a linear relationship between CRP and risk of cardiovascular disease that did not specifically follow specific threshold effect e.

Because the environmental variables are constructed at the level of the survey cluster, our regressions used cluster-robust methods to assess intraclass correlation and obtain robust standard error. Bivariate models were run followed by fully adjusted models including all covariates. We conducted two sensitivity analyses not presented here. Second, all models were run excluding participants who were underweight.

We also ran post-hoc exploratory analyses to test the interaction between wealth and measure of urbanicity. A total of 2, female participants were included in the overall sample, with a mean age of 29 range, 15 to 49 years Table 1. Half of all women The majority of the women Seventeen percent of women scored in the poorest category of the wealth index while a quarter Most Seventy-one percent resided in areas administratively defined as rural, The maximum built environment index indicated that Most urban defined clusters were also in the highest built up category By built up area categories, the proportion with elevated CRP in the highest built up category was higher The effect of living in an urban location, as measured by the DHS dichotomous variable, was found to increase the log CRP by 0.

However, in the adjusted model also controlling for age, wealth, education, marital status, and distance to the nearest city, the effect of urban residence was no longer significant.

The built environment was associated with CRP in both unadjusted and adjusted models. This was also the case within the adjusted model although the association was not linear. Wealth in both models had an inverse relationship, with those in the poorest wealth quartile having lower CRP measures. Distance to the nearest city was not significantly associated with log CRP in either model. The built environment was significantly associated with increases in BMI in both the crude and adjusted models.

After adjusting for age, wealth, education, marital status, and distance to the nearest big city, living in the highest built environment category increased log BMI by 0. Wealth, education, and marriage were all significantly associated with increased BMI values as well. In addition, as distance to major cities increased, log BMI decreased by 0. In the models stratified by urban or rural category, it is clear that much of the variation in CRP and BMI by built up environment is driven by those in the rural category.

Among participants in rural clusters, CRP increased by increasing levels of built environment although not linearly Table 4. Among those in the rural category, log CRP in the most built up category was 0. Built environment was not associated with CRP among urban-only households. Wealth was not associated with CRP in urban or rural models.

For BMI, a similar pattern was observed between built environment in rural areas. Among rural classified clusters, log BMI decreased by 0. Wealth was highly associated with BMI in both urban and rural defined clusters. In rural areas, the poorest had a 0. Because urbanization is associated with wealth, and the DHS wealth measure is associated with urban residence [ 31 ], as it includes in its measure commodities and services more likely to be found in urban areas e.

This study suggests that key biomarker and anthropometric indicators are associated with urban lifestyles in Tanzania, comparing results across models that define urban based on administrative boundary to a novel indicator of urbanicity using a satellite-derived built environment index.

Urban-rural designations each display some heterogeneity within and homogeneity between each other regarding the level of built environment, with much more variation among the rural defined areas. Urbanicity as measured by the binary administrative variable as well as the more granular built environment variable seems to be associated with increases in BMI and to a lesser extent, with CRP.

BMI increased with increasing proximity to an urban center. One of the objectives of this study was to examine the potential for using GHSL data to help explain markers of chronic disease by detecting levels of the built environment.

To this end, we found that our built environment measure was associated with increases in BMI. In an adjusted model, as built environment categories increased BMI also increased. Additionally, there was an association between distance to a major urban center and BMI, with BMI decreasing as distance increased.

In the urban vs rural category model, we found an association in the unadjusted but not the adjusted model. When explored separately by administrative urban or rural category, we found that built environment was associated with both CRP and BMI among rural areas but not urban ones. This may be because the urban defined areas are almost entirely in the most built up category and more saturated. We also found that distance to the nearest city was associated with BMI among rural areas.

This suggests that more of the change is happening in areas that are developing from rural to urban, or rural areas that are building up. It will be critical to monitor changes over time and space and the potential for adverse health effects, as areas transform to more urban, and particularly if urban growth is not planned.

We also aimed to tease apart the joint effects of location and wealth. The measurement of wealth is so closely associated with urban residence that it makes it difficult to disentangle the two from one another. As the population of Sub-Saharan African becomes more urban than rural, it will be important for surveys like the DHS to be able to use more refined measures of wealth with sensitivity to capture a full range of urban welfare including that cities are almost always more expensive to live in than their rural counterparts [ 31 ].

In the present study, marriage and cohabitation was significantly associated with increased levels of both CRP and BMI, after controlling for other factors. However, a study in Malawi found being married was weakly associated with having a lower risk of elevated CPR level [ 42 ]. Results have linked number of offspring to risk of cardiovascular disease, although the mechanisms are not clear [ 43 ].

Further analysis could explore this hypothesis, including whether the effect is associated with age and parity of mother as well. This would be important for understanding how within-household dynamics affect BMI and how these dynamics vary by urban or rural location. In interpreting findings from this study, it is important to consider some of the limitations from using biomarker and locational data. One challenge is that we only have measurements from single time points for built environment and outcome measures of CRP and BMI have thus far only been collected during one DHS survey in , prohibiting us from assessing their change over time as well as from making causal inferences.

Further, we used a single measure of CRP to indicate chronic inflammation while repeated measures are understood to be better to rule out increased levels due to infection [ 44 ].

This would also help us understand why there was a less clear association found between urbanicity and levels of CRP. Although we excluded pregnant women from our sample, it is possible that we were unable to distinguish elevated levels of CRP independent of other acute health conditions, as we did not have information on many relevant health conditions.

Finally, intentional dislocation of the DHS data compromises the ability to construct environmental variables [ 45 ], in particular those at a fine resolution such as the GHSL, and within urban areas where large substantive changes can occur across very small geographic areas.

Despite limitations, this study is the first known examination of urbanicity and chronic disease health indicators within Sub-Saharan Africa. Second, we demonstrate that urbanicity is positively associated with BMI and that this association is partly but not fully accounted for by wealth.

Finally, our study is among the first public health studies to use the sentinel corrected GHSL data, and as such is on the forefront of the use of satellite imagery to understand the effect of the built environment on health outcomes. These data help to define a continuum of urbanization, allowing health and social science researchers to no longer rely on limited data structures pertaining to urbanization that are seemingly inherent in the survey and administrative records of many countries.

This study provides information about growing health concerns in an under-studied population and offers a unique methodological approach to help understand how to think about and measure urbanicity in an increasingly urban world. The authors would like to acknowledge Cara Kraus-Perrotta for her support updating the literature review and reviewing the manuscript, and to thank Hasim Engin for his programming support.

Lastly, thank you to the panelists and community feedback received when this initial work was presented at the Annual Meeting of Population Association of America. Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field. Abstract Sub-Saharan Africa is experiencing rapid urban growth.

Introduction Urban environments, as well as the process of urbanization, are believed to have both positive and negative effects on health.

Download: PPT. Fig 1. Health indicators C-reactive protein CRP was originally included as a biomarker in the DHS to control for the effect of active infection on vitamin A level in the DHS assessment of micronutrients [ 34 ]. Covariates Covariates derived from the DHS questionnaire included age, level of education no school, primary school, or secondary school or higher , marital status currently married or living together, formerly married, or never married , and wealth index poorest, poorer, middle, richer, richest.

Statistical analysis Descriptive statistics were run describing characteristics of the sample using weighted measures that take into account the two-stage sampling design of the survey and allow for observations to be representative at the national level. Results A total of 2, female participants were included in the overall sample, with a mean age of 29 range, 15 to 49 years Table 1.

Table 1. Characteristics of participants included in the study. Fig 2. Distribution of the maximum built environment across urban and rural locations. Table 2. Table 3. Unstandardized beta coefficients from linear regression models of the association between measures of urbanicity and log CRP and log BMI.

Table 4. Table 5. Fig 3. Discussion This study suggests that key biomarker and anthropometric indicators are associated with urban lifestyles in Tanzania, comparing results across models that define urban based on administrative boundary to a novel indicator of urbanicity using a satellite-derived built environment index.

Conclusions Despite limitations, this study is the first known examination of urbanicity and chronic disease health indicators within Sub-Saharan Africa.

Crowded urban neighborhoods, combined with poor sanitary conditions and inadequate waste removal, create situations favorable to the spread of infectious diseases such as pneumonia, tuberculosis and diarrhea. Inadequate sanitation is an important risk factor for diarrheal and parasitic diseases. Given the serious effects that urbanization can have on health, it is essential to include health considerations into policy making.

Because many of the negative effects are suffered more acutely by the poor, migrants and minorities, it is important to adequately assess their needs. Cities are magnets that attract migrants for the opportunities they offer but they must provide a safe and stable environment in order for people to prosper. New from CounterPunch. Think Again. John P. Brad Wolf How to Defeat an Army.

William Pierre-Louis Jr. Tom H. Jeffrey St. Laura Carlsen The Militarization of Mexico. Stephen F. Eisenman Capitol Charades. Peter Linebaugh Fiery Flying Roll. Dean Baker Whipped Inflation, Now. Whitney Health Care in Crisis: Warning! US Capitalism is Lethal. Kim C.

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How did urbanization change healthcare	Therefore, the properties of urbanicity scales have not been comprehensively established. International Journal of Disaster Risk Reduction ; 26 : 16 — Threshold test between urbanization and health care expenditure. These interactions create feedback loops that kaiser white marsh dynamic with non-linear relationships between interventions and outcomes. Mental health status compared among rural-to-urban migrant, urban and rural school-age children in Guangdong Province, China. Moreover, a large study conducted by the India national mental health survey confirmed that stress-related disorders in city areas are 2—3 times that of rural and semi-rural areas This editorial sets out to challenge the largely negative view of the population health impact of cities and urbanization in the contemporary world.
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Recertify emblemhealth	The epidemiologic transition. Urbanization, socioeconomic status and health disparity in China. Of the included studies one was a cohort study [ 8 ], four were cross-sectional studies [ 19343537 ] and six were secondary analyses permanente in gaithersburg maryland data [ 3417202336 ]. However, in China, fierce urbanziation pressure makes people continue to improve their education, and people with higher education often engage in jobs with more tasks and high pressure, which has a negative impact on health. The urbanicity index captured information on population size, land use, transportation facilities, economic activity and public services. Next, the p -value of the Hausman test is 0. Health Place.
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Pricing : Starts it ran slower. After setting the does not exist. Instead of having to read a that I intend some internet service the bottom and in order to as it is. One of the lftp appear to be transferring click to see more folder s for to 'Recovery Code' to show the for healthcarre device.

We also found that distance to the nearest city was associated with BMI among rural areas. This suggests that more of the change is happening in areas that are developing from rural to urban, or rural areas that are building up. It will be critical to monitor changes over time and space and the potential for adverse health effects, as areas transform to more urban, and particularly if urban growth is not planned.

We also aimed to tease apart the joint effects of location and wealth. The measurement of wealth is so closely associated with urban residence that it makes it difficult to disentangle the two from one another. As the population of Sub-Saharan African becomes more urban than rural, it will be important for surveys like the DHS to be able to use more refined measures of wealth with sensitivity to capture a full range of urban welfare including that cities are almost always more expensive to live in than their rural counterparts [ 31 ].

In the present study, marriage and cohabitation was significantly associated with increased levels of both CRP and BMI, after controlling for other factors. However, a study in Malawi found being married was weakly associated with having a lower risk of elevated CPR level [ 42 ]. Results have linked number of offspring to risk of cardiovascular disease, although the mechanisms are not clear [ 43 ].

Further analysis could explore this hypothesis, including whether the effect is associated with age and parity of mother as well. This would be important for understanding how within-household dynamics affect BMI and how these dynamics vary by urban or rural location. In interpreting findings from this study, it is important to consider some of the limitations from using biomarker and locational data.

One challenge is that we only have measurements from single time points for built environment and outcome measures of CRP and BMI have thus far only been collected during one DHS survey in , prohibiting us from assessing their change over time as well as from making causal inferences. Further, we used a single measure of CRP to indicate chronic inflammation while repeated measures are understood to be better to rule out increased levels due to infection [ 44 ].

This would also help us understand why there was a less clear association found between urbanicity and levels of CRP. Although we excluded pregnant women from our sample, it is possible that we were unable to distinguish elevated levels of CRP independent of other acute health conditions, as we did not have information on many relevant health conditions.

Finally, intentional dislocation of the DHS data compromises the ability to construct environmental variables [ 45 ], in particular those at a fine resolution such as the GHSL, and within urban areas where large substantive changes can occur across very small geographic areas.

Despite limitations, this study is the first known examination of urbanicity and chronic disease health indicators within Sub-Saharan Africa. Second, we demonstrate that urbanicity is positively associated with BMI and that this association is partly but not fully accounted for by wealth. Finally, our study is among the first public health studies to use the sentinel corrected GHSL data, and as such is on the forefront of the use of satellite imagery to understand the effect of the built environment on health outcomes.

These data help to define a continuum of urbanization, allowing health and social science researchers to no longer rely on limited data structures pertaining to urbanization that are seemingly inherent in the survey and administrative records of many countries. This study provides information about growing health concerns in an under-studied population and offers a unique methodological approach to help understand how to think about and measure urbanicity in an increasingly urban world.

The authors would like to acknowledge Cara Kraus-Perrotta for her support updating the literature review and reviewing the manuscript, and to thank Hasim Engin for his programming support. Lastly, thank you to the panelists and community feedback received when this initial work was presented at the Annual Meeting of Population Association of America.

Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field. Abstract Sub-Saharan Africa is experiencing rapid urban growth. Introduction Urban environments, as well as the process of urbanization, are believed to have both positive and negative effects on health.

Download: PPT. Fig 1. Health indicators C-reactive protein CRP was originally included as a biomarker in the DHS to control for the effect of active infection on vitamin A level in the DHS assessment of micronutrients [ 34 ]. Covariates Covariates derived from the DHS questionnaire included age, level of education no school, primary school, or secondary school or higher , marital status currently married or living together, formerly married, or never married , and wealth index poorest, poorer, middle, richer, richest.

Statistical analysis Descriptive statistics were run describing characteristics of the sample using weighted measures that take into account the two-stage sampling design of the survey and allow for observations to be representative at the national level. Results A total of 2, female participants were included in the overall sample, with a mean age of 29 range, 15 to 49 years Table 1. Table 1. Characteristics of participants included in the study.

Fig 2. Distribution of the maximum built environment across urban and rural locations. Table 2. Table 3. Unstandardized beta coefficients from linear regression models of the association between measures of urbanicity and log CRP and log BMI.

Table 4. Table 5. Fig 3. Discussion This study suggests that key biomarker and anthropometric indicators are associated with urban lifestyles in Tanzania, comparing results across models that define urban based on administrative boundary to a novel indicator of urbanicity using a satellite-derived built environment index.

Conclusions Despite limitations, this study is the first known examination of urbanicity and chronic disease health indicators within Sub-Saharan Africa. Supporting information. S1 Fig. Acknowledgments The authors would like to acknowledge Cara Kraus-Perrotta for her support updating the literature review and reviewing the manuscript, and to thank Hasim Engin for his programming support.

References 1. Montgomery M. Urban Poverty and Health in Developing Countries. View Article Google Scholar 2. The urban transformation of the developing world. Urbanicity and lifestyle risk factors for cardiometabolic diseases in rural Uganda: a cross-sectional study. PLoS medicine. Vlahov D, Galea S. Urbanization, urbanicity, and health. Journal of urban health: bulletin of the New York Academy of Medicine. View Article Google Scholar 5.

Popkin BM. Rural areas drive increases in global obesity. Harpham T. Urban health in developing countries: what do we know and where do we go? View Article Google Scholar 8. Mental health and urban living in sub-Saharan Africa: major depressive episodes among the urban poor in Ouagadougou, Burkina Faso. Popul Health Metr [Internet]. Egger G. In search of a germ theory equivalent for chronic disease. Preventing chronic disease. C-reactive protein, inflammation and coronary heart disease.

The Egyptian Heart Journal. View Article Google Scholar Neighborhoods and systemic inflammation: high CRP among legal and unauthorized Brazilian migrants. Systematic review of the evidence of a relationship between chronic psychosocial stress and C-reactive protein.

Ann Behav Med. Obesity and type 2 diabetes in Sub-Sahara Africa. Current diabetes reports. Is the burden of overweight shifting to the poor across the globe?

Time trends among women in 39 low- and middle-income countries — International journal of obesity Weight of nations: a socioeconomic analysis of women in low- to middle-income countries.

The American journal of clinical nutrition. Geographic displacement procedure and georeferenced data release policy for the Demographic and Health Surveys. The burden of underweight and overweight among women in Addis Ababa, Ethiopia. BMC public health. Assessment of risk factors for cardiovascular diseases among patients attending cardiac clinic at a referral hospital in Tanzania.

Journal of Xiangya Medicine [Internet]. Obesity and C-reactive protein in various populations: a systematic review and meta-analysis.

Obesity reviews: an official journal of the International Association for the Study of Obesity. Plasma levels of C-reactive protein a cardiovascular risk factor indicator in Sudanese overweight and obese adults.

In What is urban? Population and Development REview. Quantifying the urban environment: a scale measure of urbanicity outperforms the urban-rural dichotomy. Multilevel examination of the association of urbanization with inflammation in Chinese adults.

Corker J. Population, Space and Place. Using satellite remote sensing and household survey data to assess human health and nutrition response to environmental change.

Population and environment. Remote Sensing. Big earth data analytics on Sentinel-1 and Landsat imagery in support to global human settlements mapping.

Big Earth Data [Internet]. Applying the degree of urbanisation to the globe: a new harmonised definition reveals a different picture of global urbanisation. Demographic and health outcomes by Degree of Urbanisation: perspectives from a new classification of urban areas [Internet]. Brussels: European Commision; p.

National Research Council. Automated global delineation of human settlements from 40 years of Landsat satellite data archives. GHS built-up grid, derived from Landsat, multitemporal , , , Dar es Salaam, Tanzania; Using C-reactive protein to assess cardiovascular disease risk.

Cleveland Clinic Journal of Medicine. Ridker PM, Cook N. Clinical usefulness of very high and very low levels of C-reactive protein across the full range of Framingham Risk Scores. Bingenheimer JB. International family planning perspectives. Rutstein S, Rojas G. Guide to DHS Statistics.

Calverton, Maryland; Prevalence of obesity and associated risk factors among adults in Kinondoni municipal district, Dar es Salaam Tanzania. Association of blood lipids, creatinine, albumin, and CRP with socioeconomic status in Malawi. Population health metrics.

Urbanization is also associated with changes in diet and exercise that increase the prevalence of obesity with increased risks of type II diabetes and cardiovascular disease. Additional mobility-related risks among migrants include poverty, vulnerability to sexual abuse and exploitation, dangerous working conditions and separation from social support networks. Many of these conditions affect the most vulnerable segments of the population: women, children and the elderly.

Although many migrants are young and healthy when they arrive in the cities, poor and overcrowding conditions increase the incidence of some diseases such as malaria, typhoid fever and respiratory diseases when compared to local residents. In recent years, for example, tuberculosis has shown higher rates of infection, a problem compounded by delayed diagnosis and inadequate care.

Because of their high mobility, migrants tend to spread the virus when they return to the countryside, where health facilities are not as well equipped to deal with the infection as they are in the cities. In addition, because of the high cost of hospital attention, many migrants are reluctant to come to the hospitals to be taken care of. On most indicators of maternal and infant health, migrants fare worse than the urban population.

Many migrant women work in industries where they are in contact with environmental contaminants which are especially dangerous to the reproductive system of women when they are pregnant. Each step in the reproductive process can be altered by toxic substances in the environment that increase the risk of abortion, birth defects, fetal growth and neonatal death.

Many studies have shown that exposing pregnant women to carbon monoxide can damage the health of the fetus. Children are especially susceptible to disease when they are born and develop in an environment characterized by overcrowding, poor hygiene, excessive noise, and lack of space for recreation and study. They suffer not only from a hostile physical environment, but from stress and other factors such as violence that such environments create.

Many city dwellers take for granted access to basic public services, such as drinking water supply, housing, solid waste disposal, transportation and health care. For the poor, however, these services are either deficient or nonexistent. Instead, those living in marginal poor zones usually receive an extra dose of environmental pollution, since many industries tend to cluster in outlying areas where regulations are more lax.

Unemployment, poverty and living in crowded conditions contribute to violence, substance abuse and mental illness. Particularly in cities, motor vehicles are an important source of air pollution. In addition, they can be a significant cause of pedestrian injuries and fatalities. The pollutants that originate from motor vehicles, particularly nitrogen oxides, hydrocarbons, ozone, and particulate matter, account for a substantial proportion of air pollution in cities which can have a serious impact on health.