Concepts & Measures Flashcards
Hummer, Rogers, & Eberstein. (1998). Sociodemographic differentials in adult mortality: A review of analytic approaches.
Gist: Review of sociodemographic differentials in adult mortality with a focus on different methodologies.
1) Problem: Research on sociodemographic differentials in mortality has been primarily descriptive.
2) Social demography concedes that overall human mortality has improved, but differentials exist for subgroups.
3) Strictly demographic approach: Simple comparisons across subpopulations; central goal is accurate documentation of mortality patterns within and between demographic groups (usually by age, sex, and race/ethnicity but also sometimes including SES or marital factors). Examples: “Hispanic paradox” and “racial mortality crossover”. Limitations: Findings not explanatory; implications of findings vague; based on cross-sectional data.
4) Broader sociodemographic approach: Use of multiple socioeconomic indicators (education, income, employment, income accumulation) for multidimensional approach. Other social factors considered include: marital status, marital transitions, social support, nativity, religion. Limitations: Findings not explanatory; growing concern about influences of health factors and early life conditions on mortality.
5) Unanswered questions: (a) What are the pathways? (e.g., proximate determinants approach); (b) Incorporating time into causal models of differential mortality (3 ways: cohort studies, longitudinal, retrospective); (c) What are the relevant outcome measures? (simple survival vs. cause of death, characterized by different sets of proximate determinants); (d) What is the influence of macro-level factors? (aggregate or multilevel approaches).
Drevenstedt et al (2008). The rise and fall of excess male infant mortality.
Gist: Describing & explaining excess male infant mortality in U.S.
1) Male infants have higher mortality from most causes of death, but the sex differential varies by cause. Males more likely to be born prematurely & suffer from respiratory conditions.
2) Females have more vigorous immune responses and greater resistance to infection, translating to lower mortality from infections and respiratory conditions.
3) Declines in infection, increase in hospital births, medical advancements (C-sections & NICUs), and better nutrition have contributed to lower infant mortality.
4) 1900-1964, infant deaths due to infections dropped 4-fold while infant deaths due to perinatal conditions doubled. This shift worsened the male disadvantage.
5) 1970 and later, improvements in obstetric practices & neonatal care reduced excess male infant mortality. Includes hospital births, C-sections, NICUs.
6) Explanations for sex differences in infant mortality dominated by biological rather than lifestyle factors.
7) Association between prematurity and later lung dysfunction & cardiovascular risk implies a subgroup of rescued male babies experience earlier onset of later-life diseases.
Mesle & Vallin. (2011). Historical trends in mortality.
Gist: Trends in mortality over time, focusing on diversity within & between MDCs and LDCs using the idea of health transitions.
Different types of models/theories: (a) Omran’s epidemiologic transition; (b) Olshansky’s 4th stage of epidemiologic transition (cardiovascular revolution); (c) Olshansky’s “age of re-emergence of infections & parasitic diseases” or Omran’s “age of aspired QoL with paradoxical longevity & persistent inequities” to explain AIDS; (d) Omran’s addition of 6th future stage of “health for all” echoing WHO slogan; (e) “health transition”.
1) First divergence/convergence - Pandemic receding: (a) Similar to Omran’s 1st stage; (b) from mid-18th century to mid-1960s, decrease of infectious disease & increase in chronic disease; (c) decline of infectious disease played large part of mortality decrease between age 0-50 & decline in respiratory disease for very young (0-5) & old (50+).
2) Expanding diversity of LDCs (some followed MDCs’ pattern but others slowed or stopped. Early progress often stopped due to world economic crisis & structural adjustment programs in 1980s. Counties in Africa severely affected by AIDS (infant & child mortality decline but increased adult mortality due to AIDS).
3) Second wave of divergence-convergence - Cardiovascular Revolution: Divergence had West/East divide in early 1990s. Different than Omran’s 3rd stage (except in Russia).
4) Potential 3rd wave: Females further along than males for health transition. Between MDCs, some showed progress in other mortality causes (Japan, France) while others didn’t (U.S., Netherlands). Could be due to differences in types or numbers of causes that lead to mortality between countries. Could be due to some countries’ focus on reducing mortality at very old ages.
Rogers. (2005). Adult mortality
Gist: Discuss data & methods commonly used in this research & review major influences on adult mortality.
1) Methods include: (a) crude death rate, age-standardized mortality rates, life tables, hazard models (more recent).
2) Terms: (a) Life expectancy - Average # of additional years a group of individuals can expect to live at a given exact age; (b) Life span: Maximum 3 of years a person can live.
3) Data sources: (a) Official data - death certificates & census data; (b) linked mortality files (NHIS, NHANES); (c) National Longitudinal Mortality Study, Health & Retirement Study, etc.
4) Framework constitutes demographic characteristics, distal causes, and proximate factors that lead to differences in adult mortality.
5) Demographics: (a) Age - Most important factor impacting mortality (usually displayed w/pop. pyramid); (b) Sex - Female advantage, due to SES, health & risk behavior patterns, marriage; (c) Race/ethnicity - Life expectancy lower for Native Americans & blacks, Hispanics similar to whites (except for Puerto Ricans), Asian Americans the highest (esp. Chinese, Japanese), patterns change (black-white mortality crossover among oldest old, epidemiologic paradox - immigration).
6) Distal causes: (a) SES - Exposure to factors & ability to attenuate strength of health risks individuals face, education most widely used b/c completed relatively early in life, multidimensional nature; (b) Social relations - marital status, family composition, social support, social control, religious involvement; (c) Geographical factors - Southern states suffer higher mortality rates, neighborhood/contextual effects; (d) Human-made & environmental hazards - tech risks, terrorist acts, natural disasters.
7) Proximate factors: (a) Health behaviors - Cig. smoking single preventable determinant of mortality in developed nations; (b) Health conditions - Obesity/BMI.
8) Mortality outcomes: (a) Re-emergence of infectious disease? HIV, tuberculosis, hep C, influenza, septicemia; (b) Taeuber Paradox - Preventing or curing one disease provides an opportunity for death to occur from other diseases.
Geronimus. (1987). On teenage childbearing and neonatal mortality in the United States.
Gist: Goal of reducing neonatal mortality shouldn’t focus solely on age-specific interventions (i.e., teenage pregnancy) but should instead focus on distal factors that influence neonatal mortality for all ages.
1) Preventing teenage childbearing will reduce infant mortality VS. teenage childbearing is a social response to disadvantage & this same disadvantage influences neonatal mortality.
2) Neonatal mortality: # deaths up to 28 days after live birth.
3) Biological & social aspects of early fertility & neonatal mortality are intertwined.
4) Social science assumed that teenage childbearing represented developmental biological properties that increased neonatal risk, specifically reproductive immaturity.
5) In reality, biomedical research finds that there is a developmental superiority of teenagers for childbearing & risk actually increases with advanced age.
6) Instead, same risk factors for neonatal mortality are also risk factors for early childbearing: SES, race/ethnicity, nutritional status, health conditions, low medical services, etc.; policy needs to focus on these distal causes that influence neonatal mortality across the entire reproductive age.
Frisbie et al., (2005). Infant mortality.
Gist: Definition of terms & explaining how biological mechanisms work through SES & sociodemographic factors to “get in the body”.
1) Three birth outcomes: (a) birth weight, (b) gestational age, (c) maturity. Gestational age may be a determinant of birth weight, the latter being a proximate effect on infant survival.
2) Two general models: (a) social model, (b) medical model. Race/ethnicity most commonly seen as a social category, but some researchers link the greater likelihood of LBW among black infants as genetic.
3) Terms: (a) Infant mortality - Death within 1st year of life for infants born alive; (b) Infant mortality rate - number of infant deaths under 1 year in a given year per 1,000 live births; (c) Infant death rate - based on deaths per 1,000 within a cohort rather than a specific time period; (d) Neonatal mortality - deaths to infants less than 28 days; (e) Postneonatal mortality - deaths to infants larger or equal to 28 days to 12 months; (f) Perinatal mortality - combines late fetal deaths (28 weeks or later gestation) with deaths to infants less than 1 week after live birth; (g) Fetal mortality - death prior to complete expulsion/extraction of fetus (stillbirth & abortion).
4) Measurements: (a) low birth weight - under 2,500 g; (b) premature - less than 37 weeks gestation; (c) immaturity - intrauterine-growth-retarded (IUGR) or small-for-gestational-age (SGA) & measured anthropometrically or relative (e.g., percentiles).
5) Methodological issues: (a) distributional (e.g., normal birth & gestation vs. small births have different patterns); (b) population specific standards (heterogeneity by race/ethnicity and gender).
6) Infant mortality transition attributed to lowered infections, etc.
7) Both macrolevel (e.g., neighborhood poverty) and microlevel (e.g., 3 categories) explain infant mortality differences.
8) Background factors: (a) race/ethnicity; (b) SES (maternal education, etc.); (c) demographic (maternal age, parity, marital status, etc.); (d) biological/biomedical (maternal morbidity, pregnancy history, etc.).
9) Prenatal intervening factors: (a) biomedical (prenatal care, maternal weight gain, etc.); (b) behavioral (smoking, nutrition, etc.); (c) psychosocial (stress, wantedness of pregnancy).
10) Postpartum/proximate factors: (a) demographic (sex of infant, plurality); (b) biomedical (birth outcomes, ambient smoke, breastfeeding, etc.).
