Mira esto por favor:
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\begin{document}
\begin{tablenotes}[flushleft]\small
\item[a] capital and large cities; \item[b] continuous variable, so we refer to the number of total cases in figure X to see if the regression is fitted with it.
\note 2003: DHS, 2010:DHS, 2014:PMA round 1, 2015:PMA round 2, 2016::PMA round 3, 2016-17:PMA round 4
\source Authors' calculations ...
\end{tablenotes}
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\begin{table}[p]
\begin{threeparttable}
% \resizebox{\textwidth}{!}{
\centering
%\caption{my tavle}\label{tab:scoring}
\setlength\tabcolsep{0pt}
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\captionof{subtable}{Part 2}
\hspace*{-3cm}
\begin{tabular*}{\linewidth}{@{\extracolsep{\fill}}
%l *{18}{S[table-format=1.3]} @{}}
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\toprule
& \multicolumn{6}{s}{\textcolor{blue}{ \textbf{Boucle de mouhoun}}} & \multicolumn{6}{s}{ \textcolor{blue}{ \textbf{Cascades}}} & \multicolumn{6}{s}{\textcolor{blue}{ \textbf{Centre (- ouagadougou)}}} \\
\cmidrule(){2-7}
\cmidrule(){8-13}
\cmidrule(){14-19}
& {\thead[b]{2003}}
& {\thead[b]{2010}}
& {\thead[b]{2014}}
& {\thead[b]{2015}}
& {\thead[b]{2016}}
& {\thead[b]{2016-17}}
& {\thead[b]{2003}}
& {\thead[b]{2010}}
& {\thead[b]{2014}}
& {\thead[b]{2015}}
& {\thead[b]{2016}}
& {\thead[b]{2016-17}}
& {\thead[b]{2003}}
& {\thead[b]{2010}}
& {\thead[b]{2014}}
& {\thead[b]{2015}}
& {\thead[b]{2016}}
& {\thead[b]{2016-17}}
\\
\midrule
\textcolor{og}{\textbf{Household wealth }} \\
\textbf{quintiles for DHS}\\
\textbf{tertiles\tnote{a} for PMA} \\
lowest tertile \makecell[l]{poorest \\ poorer} & \makecell{131\\143 }\\
middle tertile \makecell[l]{middle } \\
richest tertile \makecell[l]{ richer \\richest } \\
%lowest tertile \makecell[l]{poorest \\ poorer} & \makecell[l]{131 \\143} \\
% middle tertile \makecell[l]{middle } & \\
% richest tertile \makecell[l]{ richer \\richest } & \\
\textcolor{og}{\textbf{Dejure place of residence}} \\
metropolitan\tnote{a} & 0 & 0 & 0 & 0 & 0 & 0& 0 & 0 & 0 & 0 & 0 & 0& 0 & 0 & 0 & 0 & 0 & 0\\
urban& 180 & 264 & 27 & 30 & 67 & 78&364 & 387 & 44 & 39 & 92 & 92&0 & 0 & 0 & 0 & 0 & 0\\
rural &725 & 1082 & 115 & 119 & 190 & 215 & 531 & 698 & 41 & 62 & 90 & 87 & 333 & 372 & 38 & 30 & 38 & 36\\
\textcolor{og}{\textbf{Schooling}} \\
no education &725 & 1030 & 82 & 84 & 140 & 151&648 & 783 & 54 & 67 & 110 & 115 &283 & 267 & 32 & 22 & 31 & 27\\
primary & 119 & 197 & 36 & 56 & 103 & 120 &151 & 172 & 9 & 29 & 62 & 54 &38 & 73 & 2 & 7 & 5 & 7\\
secondary or above & 61 & 118 & 21 & 7 & 10 & 16& 96 & 130 & 21 & 4 & 8 & 6&12 & 32 & 0 & 0 & 0 & 0\\
\textcolor{og}{\textbf{Parity}}\tnote{b} \\
\text{ 0} $\cdots$ \text{ 20}\\
\bottomrule
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\begin{tablenotes}[flushleft]\small
\item[a] the categories are detailed enough and doesn't over-crumble frequencies; \item[b] continuous variable, so we refer to the number of total cases in figure X to see if the regression is fitted with it.
\note 2003: DHS, 2010:DHS, 2014:PMA round 1, 2015:PMA round 2, 2016::PMA round 3, 2016-17:PMA round 4
\source Authors' calculations ...
\end{tablenotes}
\end{threeparttable}
\end{table}
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\end{document}