Comparing estimates of household expenditures between pictorial diaries and surveys in three low- and middle-income countries
Bibliographic record
Abstract
In most low- and middle-income countries (LMICs), household out-of-pocket (OOP) health spending constitutes a major source of healthcare financing. Household surveys are commonly used to monitor OOP health spending, but are prone to recall bias and unable to capture seasonal variation, and may underestimate expenditure-particularly among households with long-term chronic health conditions. Household expenditure diaries have been developed as an alternative to overcome the limitations of surveys, and pictorial diaries have been proposed where literacy levels may render traditional diary approaches inappropriate. This study compares estimates for general household and chronic healthcare expenditure in South Africa, Tanzania and Zimbabwe derived using survey and pictorial diary approaches. We selected a random sub-sample of 900 households across urban and rural communities participating in the Prospective Urban and Rural Epidemiology study. For a range of general and health-specific categories, OOP expenditure estimates use cross-sectional survey data collected via standardised questionnaire, and data from these same households collected via two-week pictorial diaries repeated four times over 2016-2019. In all countries, average monthly per capita expenditure on food, non-food/non-health items, health, and consequently, total household expenditure reported by pictorial diaries was consistently higher than that reported by surveys (each p<0.001). Differences were greatest for health expenditure. The share of total household expenditure allocated to health also differed by method, accounting for 2% in each country when using survey data, and from 8-20% when using diary data. Our findings suggest that the choice of data collection method may have significant implications for estimating OOP health spending and the burden it places on households. Despite several practical challenges to their implementation, pictorial diaries offer a method to assess potential bias in surveys or triangulate data from multiple sources. We offer some practical guidance when considering the use of pictorial diaries for estimating household expenditure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".