EMPIRICAL FRAMEWORK
5.3. Variable Definitions and Descriptive Statistics
5.3.1. Outcome Variables: Health, Drug Prescription and Drug Purchase
Health Outcome
Defining and measuring health stock is challenging because of the multifaceted dimensions of health and measurement errors. Schultz (2001) notes that there is no consensus on how to
conceptualize and measure an individual’s health status. Health scientists have used health indicators such as self-assessed health, activities of daily living (ADL) indexes, anthropometric measures (body mass index, height), self-reported morbidity measures, morbidity measures from administrative files, and mortality. The RLMS gathers several potential candidates for health status. Data collected directly from individuals provides information on self-assessed health, the extent of functional limitations, several chronic diseases and health problems incurred in the previous month. Weight and height and memory are measured by the interviewer. Mortality, defined as death since the previous survey round, is reported by the main household survey respondent.
This study uses self-assessed health as a proxy of health stock. Self-assessed health is a subjective and potentially noisy measure – it is unclear against what benchmark various elderly compare their health, and perception of average health can also be influenced by conditioning experiences of the individual. On the other hand, it describes the overall health of the individual and reflects the individual’s perception of how important various aspects of her health-related wellbeing are. Deaton and Paxson (1998) use it as a single measure of health in their study of income and health relationship. They note that it reflects information individuals have about their health that is
unobserved by physicians, and that it correlates with mortality. In the sample used in this study, elderly women with bad self-assessed health had a higher prevalence of chronic diseases and more problems with activities of daily living (Table 5.4).
Table 5.4: Summary Statistics: Illness Prevalence and ADL problems in Self-assessed Health Groups
Good or Average Health
Bad Health Ever diagnosed with diabetes or increased sugar
in the blood
0.07 0.17
Ever experienced pain in your rib cage 0.65 0.84
Ever diagnosed with a myocardial infarction 0.03 0.09
Ever diagnosed with stroke (blood hemorrhage in
the brain) 0.02 0.07
Diagnosed with anemia in the last 12 months 0.03 0.06
Problems with Activities of Daily Living
(Scale 1-5; 1 =not at all difficult; 5 =cannot do it):
Walk about a kilometer 2.22 3.66
Walk about 200 meters 1.61 2.70
Walk across the room 1.23 2.07
Sit for two hours 1.62 2.36
Stand up after sitting for an extended period 1.86 2.70
Get up from a bed unassisted 1.24 1.96
Climb one flight of stairs without resting 1.65 2.87 Lift and carry abt 5 kg (a sack of vegetables) 1.91 3.16
Squat, crouch or kneel 2.64 3.87
Take a shower or bath unassisted 1.26 2.23
Eat unassisted 1.01 1.26
Dress unassisted 1.07 1.60
Comb your hair unassisted 1.05 1.40
Use the toilet unassisted 1.04 1.50
Note: Values across all periods are recorded.
The RLMS asks the respondents to evaluate their health in the following way: “Tell me, please, how would you evaluate your health?” The respondent chooses between (1) very good, (2) good, (3) average, not good but not bad, (4) bad, (5) very bad. Very few elderly women think highly of their health: across all time periods in this study, only 0.2 percent considered their health to be very good and an additional 3.5 percent good. About 51 percent of the elderly women assessed their health as average, 35 percent bad, and 10 percent very bad. Per-period distribution of self-assessed health is reported in Table 5.5.
In this study I use a dichotomous self-assessed health outcome variable, in which I merge categories (1), (2) and (3) to represent “good” or “average” health, and I merge categories (4) and (5) to represent “bad” health.24 There has been an upward trend in the sample in the reported self-
assessed health (Table 5.5). In 2002, 58.3 percent of women evaluated their health as good or average, up from 53.5 percent of women in 1995.
Drug Prescription
Drug prescription is a dichotomous variable constructed from the survey question “Tell me, please, in the last 30 days did a physician or some other specialist at a medical institution – hospital, polyclinic – write a prescription or advise you to take some kind of medicine?” The variable name ‘drug prescription’ is used throughout this study, but it stands both for drug prescription and drug recommendation. At the time of the study, many drugs typically prescribed in the US needed to be only ‘recommended’ by Russian doctors (e.g. scribbled on a piece of paper), including antibiotics, but the prescription was not required for filling the drug in the pharmacy. An official doctor prescription was required by pharmacists only for dispensing of opiates and drugs eligible for government discounts.
24
I considered employing alternative measures of health stock in the empirical model, such as measures of functional status and chronic disease. Per Schultz (2001) measures of limitations in performing activities of daily living (ADL) appear to approximate health states among elderly and reduce bias in measuring health of people with lower socio-economic status
.
The ADL scale that is used to assess the difficulty of performing everyday tasks, such as bathing, dressing, using the toilet, eating and transferring, was introduced by Katz (1963). I created a similar index using the RLMS data and the Russian context that incorporates responses on help needed with bathing, dressing, eating, using toilet, walking across a room, and climbing stairs. I then used the index to create an ADL dummy that singles out physically impaired elderly from the rest of the elderly. In each of the surveyed years, close to 20 percent of the sample found it at least somewhat difficult to do one or more of these activities. I chose not to use this measure in this study because it was not collected in 2001. I also created but did not use a chronic disease dummy that equals one if the individual was ever diagnosed with diabetes, increased sugar in blood, myocardial infarction, or a stroke (blood hemorrhage in the brain). The RLMS includes information on the year of the diagnosis, which leads to another potential definition based on the diagnosis in the last calendar year. The last potential health stock measure, used by Jensen and Kaspar (2003), is a dummy measuring chest pain, which indicates potential heart problems. The dummy equals to one if the persons has experienced strong pains in the chest, lasting half an hour or more, in the last 12 months.On average, about 30 percent of women in the sample were prescribed or recommended drugs in years 1995 to 2002 (Table 5.5).
Table 5.5: Summary Statistics: Outcome Variables
1994 1995 1996 1998 2000 2001 2002