Choose at least 3 distinct data sources (e.g., ARIC, HRS, death certificate data, NHS, etc), and give an example of a research question (e.g., a hypothesis about the effect of a specific exposure on a specific outcome) you consider the study exceptionally strong to address. For each, provide an example of a research question you consider the design very weak to address. Explain why the data source is strong or weak for each question. Do not just discuss the questions addressed in the readings, think of new questions, preferably things you might be interested in. This is not supposed to be a commentary related to the substantive questions in the readings: the goal is to focus on the pros and cons of various data sources. For hypotheses each study would not be well equipped to address, if possible describe another study that could address the hypothesis.
California Health Interview Survey (CHIS): This is the state’s data for public health surveillance and tracking changes in health insurance coverage and eligibility for healthcare programs. It asks questions about a broad range of health conditions and behaviors, mental health, health insurance, healthcare use and access.
Strong question: How has the prevalence of asthma/asthma symptoms changed over time in different areas of the state as local air quality changes?
Alternative strong: Looking forward into the future, how would the passing of laws affecting health care coverage and uptake affect health (looking at disparate outcomes of diabetes, cancer, asthma, mental health, etc.)?
Weak question: Investigating disparities in SES status and presence and control of diabetes.
There is a very low survey response rate to the random digit landline and cell phone dialing. Tracking changes over time is more likely to be successful than trying to ascertain absolute prevalence measures of diseases. There is a strong selection bias of selection into the survey, and those who feel they have something to say, or who have more time on their hands are more likely to participate, and these are a fairly selective subsample of the population. Those who are very sick are unlikely to participate, but those who are well and busy working and with families are also less likely to participate. Elderly and unemployed individuals have higher participation rates. Furthermore, these surveys do not include individuals in nursing homes, a population with a high risk for diabetes. As elderly individuals are also some of the poorer segments of the population, this can lead to an underestimate of the effect.
Better dataset- Kaiser Permanente patient data would not be subject to the same concerns of selection bias.
NHAMCS (National Ambulatory Health Care Data):
Strong question: Are the number of sports related concussions/other injuries increasing or decreasing in youth (also in men and women)? – this information tends to be coded well and is relatively consistent over time
Weak question: What is the prevalence of asthma?
NHAMCS does not measure individuals, only occurrences, this leaves people open to being double counted. Thus, if a person visits an emergency multiple times, their data would be counted more than others. Thus this dataset is extremely powerful in measuring the causes of uses of ambulatory care and changes in episodic disease occurences, but is completely inappropriate for chronic disease measurements in the general population.
Better dataset - NHIS
National Vital Statistics System: mortality data that is collected is comparable across a wide-swath of demographic and geographic regions. They also go back a long time in history collecting information on those who pass, age, illness, etc.
Strong question: Understanding the distribution of the burden of breast cancer mortality, trying to understand if certain populations or states or regions of the country have a higher than average fatalities from the disease. This could help to identify areas where studies should be directed to investigate disparities in breast cancer management and inform areas for further intervention.
Weak question: Finding out if there is excess burden of breast cancer in certain populations or if there are disparities in the successful management of breast cancer. Only deaths are recorded, so those who did not die from the disease will not be recorded in this database. Thus, since it is unknown what the full denominator was for the full population of breast cancer patients, it is not possible to determine whether disparities are present in management or in incidence of breast cancer.
Better dataset: statewide cancer registries, which include breast cancer patients. This would allow for investigation into disparities in incidence and trying to determine causes.