Infant Mortality Pregnancy
Infant mortality refers to death within the first year of life to babies born alive. A live birth is defined as the ‘‘complete expulsion or extraction from its mother of a product of conception, irrespective of the duration of pregnancy, which after such separation breathes or shows any other evidence of life'' and death as ‘‘the permanent disappearance of life any time after live birth has taken place'' (United Nations 2001: 13). The infant mortality rate is conventionally measured as the number of deaths to infants below one year of age in a given year, per 1,000 live births in the same year.
Motivation:
The city of Memphis, the 15th largest city in the USA is ranked 8th in terms of African-American population (61% of African-American population) in USA (CDC, 2005). Memphis is ranked 13th in number of crimes committed in cities in the USA (Morgan Quitno Press 2006). Of key importance to this study is that Memphis ranks first in terms of IMR among the 60 largest cities in the USA (CDC, 2002). These factors and recent theories regarding the impact of domestic violence and air pollution on IMR become especially germane in the study of infant mortality at Shelby County, TN.
Problem Statement:
The United States is a highly developed nation with progress booming nearly in all spheres of life; - nevertheless, the infant mortality rate (IMR) is alarmingly high in some places. The IMR in Shelby County, Tennessee during the time period 1999 to 2003 had been consistently above 12 infant deaths per 1000 births, reaching its highest rate in the year 2003 of 15 infant deaths per 1000 births. This means if 1000 babies were born in Shelby County 15 would die in the first year of life. A comparison of IMR among the Nation, State and County for approximately two decades (1980 - 2003) shows Shelby County's IMR is not at all declining while the State's IMR and Nation's IMR are declining. It is clear from fig-1 that Shelby County's IMR is twofold higher than the Nation's IMR. When Shelby Counties IMR is compared with most populous counties in Tennessee for one decade it is also very relevant that Shelby County always stood high in terms of IMR.
| Figure 1: Comparison Among Nation, State and County |
This gives a view of a probable systematic problem existing in Shelby County, TN.
| Figure 2: Comparison Among Most Populous Counties in Tennessee |
Identifying covariates to model infant mortality in Shelby County TN:
Literature on infant mortality can be separated into two distinct categories of predictors: those belonging to the individual-level and those belonging to the group-level. Individual level variables found to be predictors for infant mortality include race (Hummer, 1993; Wise, 1993), maternal age (Singh and Yu 1996), maternal education (Caldwell 1986), marital status (Cramer 1987, Hummer et.al 1993 & Frisbie et.al 1997), sex (Frisbie et.al 1998), maternal smoking habits (Frisbie et.al 1997, Hummer et.al 1999), low birth weight (Pollack et.al 1992, McCormick 1985), preterm birth (Gibson et.al., 2000) and exposure to particulate matter with diameter less than 2.5 nanometer (PM2.5) (Environmental Protection Agency 2001). Group level variables found to be predictors are poverty (Gortmaker 1979), health care facilities (Alexander et.al 1999), lower socioeconomic status neighborhoods (Guest et.al 1998), unemployment (Pampel and Pillai 1986),violent crimes (Collins and David), There are many other plausible unknown individual-level and group-level factors that could led to disproportionate rates of infant mortality among certain racial and ethnic populations.
Methodological Issues:
In general, two theoretical frameworks have been used by researchers to provide guidance and structure in research on infant mortality. The first has been termed the social model, and the second is often referred to as the medical model (e.g., Cramer 1995). ‘‘Social models stress the power of social variables to determine infant survival and the importance of structural change in overcoming disparate outcomes. Medical models stress pathways of frank pathophysiology and their potential interruption through clinical interventions'' (Wise 1993).
There is a necessity to develop and validate a method which better integrates the social models and medical models. Therein lies a key methodological contribution of this dissertation. For example, most of the research on infant mortality had assumed that group-level variables such as poverty in a neighborhood have a direct relationship with IMR controlling for individual risk factors such as low birth weight and -maternal age. Group-level variables are better envisioned as a contextual effect. That means group-level predictors have an affect on individual-level predictors, which, in turn have direct effect on IMR.
The data collected for most studies of the key predictors of IMR is often aggregated to certain geographic units, such as zip codes or Census block groups. Depending on how the boundaries of the aggregation units are drawn, and on the scale of aggregation, analyses can produce different results, which are often termed the Modifiable Areal Unit Problem (MAUP). In addition, some studies look only at the effects of aggregated variables on aggregate outcomes, which lead to the ecological fallacy.
The methodological approaches taken in this research are of two fold. Spatial analyses specifically cluster analysis (Kulldorf, 1995) incorporating time and space has been used to determine the most vulnerable areas for IMR in Shelby County. Further investigation has been done such as Intrinsic Conditional Autoregressive Modeling (ICAR) to determine the vulnerable areas controlling for only group level predictors this will be discussed further in the next two chapters.
Statistical analysis, specifically Generalized Hierarchical Linear Modeling (GHLM), was used for two fold purposes. First, GHLM has been used to integrate social models and medical models of infant mortality. Second earlier research used traditional multiple regression models and logistic regression models to estimate relationships between an outcome variable such as infant mortality and one or more independent variables such as socio-economic conditions, race and low birth weight. A multiple regression model only shows an average relationship between the outcome and predictor variables assuming that residuals are independent and are not correlated. However, in geographical data cases are nested within neighborhoods such as zip codes and census tracts which violate these assumptions of multiple regressions.
Proposed Research:
This research tried to describe the local spatial variations and identified the individual level (low gestational period, maternal smoking) and group level (poverty, domestic violence) influencing risk factors of infant mortality in Shelby County, TN with the usage of Geographical Information System (GIS).
This research integrated diverse forms of data into a GIS for statistical analysis, specifically Generalized Hierarchical Linear Modeling (GHLM). A GIS database was used to assemble linked birth-death records during the period 1999-2003 from the Shelby County Health Department, US census data from the year 2000, PM2.5 data during 1998-2003 from the Environmental Pollution Agency (EPA) and domestic violence data during 2001-2006 from the Shelby County Police Department. With the help of this spatial database a GHLM was set up to explain the relationship between infant mortality and its individual-level and group-level risk factors.
Research Questions:
This research included a detailed case study of infant mortality in Shelby County, TN. The following research questions are addressed:
- Is there significant clustering of infant mortality in Shelby County, TN during the period 1999-2003?
- What role does domestic violence and air pollution (PM2.5 exposure) play in defining where high infant mortality rates occur in Shelby County, TN?
- Are there any significant cross-level interactional effects on infant mortality in Shelby County, TN?
These findings will encourage us to explore the role of air pollution and domestic violence on IMR by giving us detailed explanations of socio-economic differences and their effect on infant mortality. The hypothesis to be tested is that exposure of mothers to PM2.5 and domestic violence in a neighborhood leave their infants more vulnerable to the risk of infant mortality. The validity of the hypothesis of different vulnerability and the hypothesis of different exposure in terms of air pollution and domestic violence has an effect on infant mortality will be tested.
The goals of this research are:
- Document and reveal as accurately as possible the spatial patterns and the underlying relationships between social determinants, environmental determinants and infant mortality.
- Apply a set of useful spatial analytical methods and integrate the results with useful non-spatial analytical methods to integrate the social and medical models.
- Identify the major maternal or individual-level risk factors and group-level factors for infant mortality in Shelby County, TN.
- Reveal the strengths and weaknesses of the various methods.
Significance and Contributions of the Research:
The integration of social models and medical models of infant mortality with the application of spatial analytical methods and multilevel analysis, as proposed in this dissertation, would provide a convenient strategy for a thorough evaluation of infant mortality in Shelby County, TN.
A set of spatial analytical methods, including individual level cluster analysis, group level cluster analysis and multilevel statistical analysis such as GHLM were applied. GIS was mainly used for data management and organization of data into individual layers of data on characteristics of individuals and layers of data on characteristics of zipcodes. Based on the results which showed an interaction of environmental determinants and social determinants this research should help to inform policy makers about how these factors interact, and it can help them target the most at risk individuals (based on individual and group level characteristics) for intervention. These issues have only recently begun to receive attention from the Centers for Disease Control (CDC).
The important aspect of this research lies in its potential to explain relationships between social and individual risk factors contributing for IMR in Shelby County, Tennessee. A medical-geographic analysis broadens the questions we may ask of diverse racial nature, and pointing out gaps in geography of health, environmental policy and crime, particularly domestic violence issues in Memphis, Tennessee.
This research explores the integrative capability of GIS in medical geography. The GIS technology integrates domestic violence data collected from the Police Department of Shelby County, TN, the linked birth death certificates collected from the Shelby County, TN health Department and PM2.5 data collected from the EPA for the analysis which will provide useful information on the relationship between maternal individual risk factors, domestic violence and PM2.5 exposure and infant mortality.
Moreover, the findings from this dissertation will increase knowledge about how social factors influence the individual factors that, in turn, affect risk for infant mortality. The study reveals the strengths and weaknesses of GHLM in this context and when it can be useful. At the same time, the study helps in identifying the data gaps in the medical geography research such that improvements can be made in future monitoring and assessment can become easier and more effective. Accordingly, the results from this study in regards to the interaction of group-level phenomena with individual social and health characteristics may be used to contribute to group level and individual level intervention plans in policymaking decisions.
Organization of the Dissertation:
This dissertation comprises five chapters, including this introductory chapter which examine the problem statement for this study, addresses the research questions and hypothesis.
Chapter two gives an overview of models that were studied for infant mortality. A thorough literature review is given of methodological approaches adopted by researchers.
Chapter three starts with an overview of the study area, Shelby County, demographics and detailed rationale of infant mortality during the year 1999-2003. Describes the data and the method used for obtaining suitable solutions for the questions asked in this dissertation.
Chapter four discusses the results and strong hold in the spatial and statistical analysis.
Chapter five summarizes the findings and addresses the contribution of this study to the current literature and will conclude by giving an account of some policy implications and limitations of this study.
References:
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Cramer, J. C. 1995. Racial and ethnic differences in birth weight: The role of income and financial assistance. Demography 32:231-247.
Wise, P. H. 1993. Confronting racial disparities in infant mortality: Reconciling science and politics. In D. Racial differences in preterm delivery: Developing a new research paradigm. Edited by D. Rowley and H. Tosteson, 7-16. Supplement to Vol. 9, American Journal of Preventive Medicine.
Hummer, R. A. 1993. Racial differentials in infant mortality in the U.S.: an examination of social and health determinants. Social Forces 72:529554.
WISE, P. H. 1993. Confronting racial disparities in infant mortality: reconciling science and politics. American Journal of Preventive Medicine 9(suppl.):7-16.
Alexander, G.R., M.E.Tompkins, M.C. Allen and T.C Hulsey. 1999. Trends and Racial Differences in Birth Weight and Related Survival. Maternal and Child Health Journal 3: 71-79.
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Frisbie, W. P., M. Biegler, P. B. de Turk, D. Forbes, and S. G. Pullum. 1997. Racial and ethnic differences in
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Pampel, F. C., Jr., and V. K. Pillai. 1986. Patterns and determinants of infant mortality in developed nations,
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Environmental Protection Agency. 2001. Air quality criteria for particulate matters, volume ii.
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CDC : http://www.cdc.gov/mmwr/preview/mmwrhtml/mm5115a4.htm
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