An empirical model to estimate the demand for primary care in urban settings

Objective. To measure the demand for primary care and its associated factors by building and estimating a demand model of primary care in urban settings.^ Data source. Secondary data from 2005 California Health Interview Survey (CHIS 2005), a population-based random-digit dial telephone survey, conducted by the UCLA Center for Health Policy Research in collaboration with the California Department of Health Services, and the Public Health Institute between July 2005 and April 2006.^ Study design. A literature review was done to specify the demand model by identifying relevant predictors and indicators. CHIS 2005 data was utilized for demand estimation.^ Analytical methods. The probit regression was used to estimate the use/non-use equation and the negative binomial regression was applied to the utilization equation with the non-negative integer dependent variable.^ Results. The model included two equations in which the use/non-use equation explained the probability of making a doctor visit in the past twelve months, and the utilization equation estimated the demand for primary conditional on at least one visit. Among independent variables, wage rate and income did not affect the primary care demand whereas age had a negative effect on demand. People with college and graduate educational level were associated with 1.03 (p < 0.05) and 1.58 (p < 0.01) more visits, respectively, compared to those with no formal education. Insurance was significantly and positively related to the demand for primary care (p < 0.01). Need for care variables exhibited positive effects on demand (p < 0.01). Existence of chronic disease was associated with 0.63 more visits, disability status was associated with 1.05 more visits, and people with poor health status had 4.24 more visits than those with excellent health status. ^ Conclusions. The average probability of visiting doctors in the past twelve months was 85% and the average number of visits was 3.45. The study emphasized the importance of need variables in explaining healthcare utilization, as well as the impact of insurance, employment and education on demand. The two-equation model of decision-making, and the probit and negative binomial regression methods, was a useful approach to demand estimation for primary care in urban settings.^

ECONOMIC ANALYSIS OF THE DEMAND FOR PRESCRIPTION MEDICINE IN THE UNITED STATES

The purpose of this study was to understand the role of principle economic, sociodemographic and health status factors in determining the likelihood and volume of prescription drug use. Econometric demand regression models were developed for this purpose. Ten explanatory variables were examined: family income, coinsurance rate, age, sex, race, household head education level, size of family, health status, number of medical visits, and type of provider seen during medical visits. The economic factors (family income and coinsurance) were given special emphasis in this study.^ The National Medical Care Utilization and Expenditure Survey (NMCUES) was the data source. The sample represented the civilian, noninstitutionalized residents of the United States in 1980. The sample method used in the survey was a stratified four-stage, area probability design. The sample was comprised of 6,600 households (17,123 individuals). The weighted sample provided the population estimates used in the analysis. Five repeated interviews were conducted with each household. The household survey provided detailed information on the United States health status, pattern of health care utilization, charges for services received, and methods of payments for 1980.^ The study provided evidence that economic factors influenced the use of prescription drugs, but the use was not highly responsive to family income and coinsurance for the levels examined. The elasticities for family income ranged from -.0002 to -.013 and coinsurance ranged from -.174 to -.108. Income has a greater influence on the likelihood of prescription drug use, and coinsurance rates had an impact on the amount spent on prescription drugs. The coinsurance effect was not examined for the likelihood of drug use due to limitations in the measurement of coinsurance. Health status appeared to overwhelm any effects which may be attributed to family income or coinsurance. The likelihood of prescription drug use was highly dependent on visits to medical providers. The volume of prescription drug use was highly dependent on the health status, age, and whether or not the individual saw a general practitioner. ^

PRETERM BIRTH AND OCCUPATIONAL PHYSICAL ACTIVITY: A CASE-CONTROL STUDY OF THE RELATIONSHIP BETWEEN LENGTH OF GESTATION AND THE PHYSICAL DEMAND OF U.S. ARMY MILITARY OCCUPATIONAL SPECIALTIES (UNITED STATES)

A case-control study has been conducted examining the relationship between preterm birth and occupational physical activity among U.S. Army enlisted gravidas from 1981 to 1984. The study includes 604 cases (37 or less weeks gestation) and 6,070 controls (greater than 37 weeks gestation) treated at U.S. Army medical treatment facilities worldwide. Occupational physical activity was measured using existing physical demand ratings of military occupational specialties.^ A statistically significant trend of preterm birth with increasing physical demand level was found (p = 0.0056). The relative risk point estimates for the two highest physical demand categories were statistically significant, RR's = 1.69 (p = 0.02) and 1.75 (p = 0.01), respectively. Six of eleven additional variables were also statistically significant predictors of preterm birth: age (less than 20), race (non-white), marital status (single, never married), paygrade (E1 - E3), length of military service (less than 2 years), and aptitude score (less than 100).^ Multivariate analyses using the logistic model resulted in three statistically significant risk factors for preterm birth: occupational physical demand; lower paygrade; and non-white race. Controlling for race and paygrade, the two highest physical demand categories were again statistically significant with relative risk point estimates of 1.56 and 1.70, respectively. The population attributable risk for military occupational physical demand was 26%, adjusted for paygrade and race; 17.5% of the preterm births were attributable to the two highest physical demand categories. ^

PROJECTION OF HEALTH DEMAND IN TERMS OF HOSPITAL ADMISSIONS FOR SELECTED DIAGNOSES IN HEALTH SYSTEMS AGENCY SERVICE AREAS

The National Health Planning and Resources Development Act of 1974 (Public Law 93-641) requires that health systems agencies (HSAs) plan for their health service areas by the use of existing data to the maximum extent practicable. Health planning is based on the identificaton of health needs; however, HSAs are, at present, identifying health needs in their service areas in some approximate terms. This lack of specificity has greatly reduced the effectiveness of health planning. The intent of this study is, therefore, to explore the feasibility of predicting community levels of hospitalized morbidity by diagnosis by the use of existing data so as to allow health planners to plan for the services associated with specific diagnoses.^ The specific objectives of this study are (a) to obtain by means of multiple regression analysis a prediction equation for hospital admission by diagnosis, i.e., select the variables that are related to demand for hospital admissions; (b) to examine how pertinent the variables selected are; and (c) to see if each equation obtained predicts well for health service areas.^ The existing data on hospital admissions by diagnosis are those collected from the National Hospital Discharge Surveys, and are available in a form aggregated to the nine census divisions. When the equations established with such data are applied to local health service areas for prediction, the application is subject to the criticism of the theory of ecological fallacy. Since HSAs have to rely on the availability of existing data, it is imperative to examine whether or not the theory of ecological fallacy holds true in this case.^ The results of the study show that the equations established are highly significant and the independent variables in the equations explain the variation in the demand for hospital admission well. The predictability of these equations is good when they are applied to areas at the same ecological level but become poor, predominantly due to ecological fallacy, when they are applied to health service areas.^ It is concluded that HSAs can not predict hospital admissions by diagnosis without primary data collection as discouraged by Public Law 93-641. ^

AN ECONOMIC ANALYSIS OF THE DEMAND FOR CARE IN A FREE-STANDING EMERGENCY CENTER

Free-standing emergency centers (FECs) represent a new approach to the delivery of health care which are competing for patients with more conventional forms of ambulatory care in many parts of the U.S. Currently, little is known about these centers and their patient populations. The purpose of this study, therefore, was to describe the patients who visited two commonly-owned FECs, and determine the reasons for their visits. An economic model of the demand for FEC care was developed to test its ability to predict the economic and sociodemographic factors of use. Demand analysis of other forms of ambulatory services, such as a regular source of care (RSOC), was also conducted to examine the issues of substitution and complementarity.^ A systematic random sample was chosen from all private patients who used the clinics between July 1 and December 31, 1981. Data were obtained by means of a telephone interview and from clinic records. Five hundred fifty-one patients participated in the study.^ The typical FEC patient was a 26 year old white male with a minimum of a high school education, and a family income exceeding $25,000 a year. He had lived in the area for at least twenty years, and was a professional or a clerical worker. The patients made an average of 1.26 visits to the FECs in 1981. The majority of the visits involved a medical complaint; injuries and preventive care were the next most common reasons for visits.^ The analytic results revealed that time played a relatively important role in the demand for FEC care. As waiting time at the patients' regular source of care increased, the demand for FEC care increased, indicating that the clinic serves as a substitute for the patients' usual means of care. Age and education were inversely related to the demand for FEC care, while those with a RSOC frequented the clinics less than those lacking such a source.^ The patients used the familiar forms of ambulatory care, such as a private physician or an emergency room in a more typical fashion. These visits were directly related to the age and education of the patients, existence of a regular source of care, and disability days, which is a measure of health status. ^