| { | |
| "original_study": { | |
| "claim": { | |
| "hypothesis": "Among the sample of respondents in poor general health who were found to be hypertensive during a screening, the probability of being undiagnosed decreases with education.", | |
| "hypothesis_location": "the abstract, it is also discussed in sections: 1. Introduction, 2. Theoretical framework.", | |
| "statement": "In terms of disease detection, more educated respondents have a higher probability of being diagnosed, but only conditional on being in poor general health (marginal effect for Years of Education=-0.00867, SE=0.00420, significant at 5% level).", | |
| "statement_location": "Table 2, row: Years of Education, column: Respondents in poor health.", | |
| "study_type": "Observational" | |
| }, | |
| "data": { | |
| "source": "Indonesian Family Life Survey (IFLS) fielded by the RAND corporation in collaboration with the Center for Population and Policy Studies of the University of Gadjah Mada and Survey METER.", | |
| "wave_or_subset": "fourth wave, the data were collected between November 2007 and April 2008.", | |
| "sample_size": "4209", | |
| "unit_of_analysis": "hypertensive adults (over 45 years of age in 2007)", | |
| "access_details": "There are no details provided except for the information that the IFLS is a publicly available data set.", | |
| "notes": "Blood pressure was measured three times by trained nurses. The first reading was dropped and average of last two used to construct the hypertension variable. Under-diagnosed individuals are those hypertensives at survey but never diagnosed by a doctor. Respondents’ health variablw was constructed using self-reported health status. It was on a 1–4 scale and later dichotomized into ‘good’ vs. ‘poor’ groups. Per capita expenditures (PCE) was used as proxy for household income." | |
| }, | |
| "method": { | |
| "description": "The authors analyzed data from the fourth wave of the IFLS, focusing on adults over 45 who were found hypertensive during clinical screenings. They examined how education, time preferences, and other socio-economic factors influence the likelihood of being hypertensive but undiagnosed.", | |
| "steps": "1. The authors got access to the publicly available Indonesian Family Life Survey dataset.\n2. Then they restricted the sample to hypertensive adults aged 45 and older. \n3. Then, individuals who reported never having been diagnosed with hypertension by a doctor were coded these as under-diagnosed. \n4. The sample was split into those in good health (very healthy or somewhat healthy) and those in poor health (unhealthy or somewhat unhealthy).\n5. Adding explanatory variables including years of education, time and risk preference parameters, age, age squared, per capita household expenditures, distance to nearest health center, and sex, the authors estimated separate probit models for the two subsamples (good vs. poor general health), using the binary under-diagnosis variable as the dependent variable. \n6. Finally, marginal effects for all explanatory variables were computed and reported.", | |
| "models": "probit regression", | |
| "outcome_variable": "being hypertensive but not previously diagnosed (binary yes = 1, no = 0)", | |
| "independent_variables": "education (measured in years of formal education), age, age squared (to allow for possible non-linear effects), individual risk and time preferences, the distance from the closest health center (to proxy for the ease of access to medical care), household per capita expenditures (PCE), and a sex dummy", | |
| "control_variables": "not stated", | |
| "tools_software": "not stated" | |
| }, | |
| "results": { | |
| "summary": "More educated respondents were more likely to be diagnosed with the disease, but only among those in poor general health (marginal effect for Years of Education=-0.00867, SE=0.00420, significantat 5% level).", | |
| "numerical_results": [ | |
| { | |
| "outcome_name": "Hypertension under-diagnosis", | |
| "value": "-0.00867", | |
| "unit": "NA", | |
| "effect_size": "not stated", | |
| "confidence_interval": { | |
| "lower": "not stated", | |
| "upper": "not stated", | |
| "level": "not stated" | |
| }, | |
| "p_value": "0.05", | |
| "statistical_significance": "true", | |
| "direction": "negative" | |
| } | |
| ] | |
| }, | |
| "metadata": { | |
| "original_paper_id": "http://dx.doi.org/10.1016/j.socscimed.2015.11.051", | |
| "original_paper_title": "Education, individual time preferences, and asymptomatic disease\ndetection.", | |
| "original_paper_code": "not stated", | |
| "original_paper_data": "not stated" | |
| } | |
| } | |
| } |