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\begin{document} |
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\section*{Does Patient Demand Contribute to the Overuse of Prescription Drugs? (Lopez Sautmann, Schaner)} |
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\section*{Appendix Figures and Tables} |
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\begin{figure}[ht!] |
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\centering |
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\caption{Misallocation of Treatment by Clinic Malaria Test Status (Home Tested Subsample)} |
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\label{misallocation} |
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\begin{minipage}{.9\linewidth} |
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\begin{center} |
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\includegraphics[width=14cm]{{"\fig/B1_misallocation"}.pdf} |
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\end{center} |
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\footnotesize |
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\emph{Notes:} Sample limited to 1,070 patients who had a valid home-based RDT result. 506 patients received no malaria test, 314 received an RDT only, 207 received microscope test only, and 43 received both types of tests. The black bar graphs the share of each subgroup that received a positive home-based RDT result. The dark grey bar graphs the share of the sample that either received a positive home-based test and an antimalarial prescription or had a negative test and no antimalarial prescription. The medium grey bar graphs the share of each group that had a positive home test and an antimalarial prescription; the light grey bar graphs the share of each group that had a negative test and no antimalarial prescription. |
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\end{minipage} |
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\end{figure} |
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\begin{figure}[ht!] |
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\caption{Distribution of Predicted Malaria Risk by Treatment Group} |
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\label{dist_pred_pos} |
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\begin{minipage}{.93\linewidth} |
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\includegraphics[width=15cm]{{"\fig/B2_dist_pred_pos"}.pdf} |
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\footnotesize |
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\emph{Notes:} Kernel density estimates. Vertical dashed lines indicate 25th, 50th, and 75th percentiles of overall distribution respectively. |
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\end{minipage} |
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\end{figure} |
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\begin{landscape} |
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\begin{figure}[ht!] |
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\caption{Treatment Outcomes by Predicted Malaria Risk in Control Group -- By Home RDT Test Status} |
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\label{outcomeXpredpos_control_new_byHome} |
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\begin{minipage}{1\linewidth} |
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\includegraphics[width=23.3cm]{{"\fig/B3_outcomeXpredpos_control_home"}.pdf} |
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\footnotesize |
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\emph{Notes:} Results from local linear regressions. Regressions are run on the full sample, but graphs omit results for top and bottom 2.5 percent of malaria risk distribution to avoid influence of outliers. Vertical dashed lines indicate 25th, 50th, and 75th percentiles of predicted malaria risk respectively. |
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\end{minipage} |
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\end{figure} |
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\end{landscape} |
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\begin{figure}[ht!] |
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\caption{Voucher Treatment Effects by Clinic Observation Day} |
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\label{vouchersXbins} |
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\begin{minipage}{.93\linewidth} |
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\begin{center} |
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\includegraphics[width=13cm]{{"\fig/B4_vouchersXbins"}.pdf} |
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\end{center} |
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\footnotesize |
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\emph{Notes:} Graphs show point estimates of a linear regression model where PV and DV dummies are interacted with dummies for patient observation day bins, along with 95 percent confidence intervals. Robust standard errors clustered at the clinic level in parentheses. All regressions control for clinic visit date fixed effects and dummies for days 1-2 and days 3-4. We use double selection lasso to choose additional controls. See notes to Table \ref{mal_tmt_overall_new} for a list of potential controls. |
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\end{minipage} |
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\end{figure} |
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\clearpage |
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\input{"\tab/B1_clinic_census.tex"} |
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\input{"\tab/B2_means_doctor.tex"} |
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\input{"\tab/B3_mal_tmt_pat_info_lso.tex"} |
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\input{"\tab/B4_mal_tmt_doc_info_lso.tex"} |
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\input{"\tab/B5_selection.tex"} |
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\input{"\tab/B6_diff_test_consent.tex"} |
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\input{"\tab/B7_mal_prob_probit.tex"} |
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\input{"\tab/B8_mal_tmt_overall_nc.tex"} |
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\input{"\tab/B9_mal_tmt_het_nc.tex"} |
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\input{"\tab/B10_mal_tmt_overall_nl.tex"} |
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\input{"\tab/B11_mal_tmt_het_nl.tex"} |
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\input{"\tab/B12_vouchers_tmt_lso.tex"} |
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\input{"\tab/B13_stockpiling_lso.tex"} |
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\begin{landscape} |
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\input{"\tab/B14_mal_testing_info_RX_lso.tex"} |
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\end{landscape} |
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\input{"\tab/B15_cost_overall_lso.tex"} |
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\input{"\tab/B16_expected_match_lso.tex"} |
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\end{document} |
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