How to design very narrow, sharp lowpass filters - Only DC needed?
Your questions still leave me wondering as to what you're actually designing. For software implementation on modern x86 CPUs, CICs make almost no sense, but they are extremely elegant in hardware.

These filter definitions are ridiculous if you're planning to use a FIR – a transition width of 1mHz means that a minimum phase equiripple filter [1, (5.75), p. 142] would have 

$$
N_e' \approx \frac 16 \log_{10}\left(\frac{1}{10\delta_1\delta_2^2}\right)\frac{f_s}{\Delta f}
$$
taps, with $f_s$ the sampling rate, $\Delta f$ the transition width, and $\delta_{1,2}$ the passband ripple and the stopband attenuation, respectively.

Now, your use case; I'm guessing from your the graphs from your question from 1 year ago, and also bearing in mind that you'd need incredible numerical accuracy to make use of > 80dB attenuation:

$$
\begin{align*}
f_s &amp;= 6.4\cdot10^4\,\text{Hz,}\\
\Delta f &amp;= 10^{-3}\,\text{Hz,}\\
\delta_2 &amp;= -90\,\text{dB}\\
&amp;= 10^{-9}\\
\delta_1 &amp;= \delta_1\,\text{, everything else being wasteful either on suppression or precision side,}\\\\
N_e' &amp; \approx \frac 16 \log_{10}\left(\frac{1}{10\cdot10^{-27}}\right)\frac{6.4\cdot10^4}{10^{-3}}\\
&amp;=\frac16\cdot 26\cdot6.4\cdot10^7\\
&amp;\approx 2.7 \cdot 10^8\,\text.
\end{align*}
$$

Obviously, Bellanger's approximation wasn't meant for such extreme cases, but the order of magnitude alone highlights the impossibility. Of course, you'd implement this as a decimating polyphase FIR, which means that you'd only need to apply
$$\frac{N_e'}{\frac{f_{s,input}}{f_{s,output}}}=N_e' \frac{f_{s,output}}{f_{s,input}}= N_e'\frac{\frac12}{6.4\cdot10^4}\approx2110$$
tapped filter every input sample clock, but the filter length puts impossible constraints on your machine accuracy if you're using floating point, and very hard constraints if you're building this in hardware on the bitwidth of your samples. As you notice, it does make a lot of difference for what we're designing a filter here – Matlab is not hardware :)

Yes, you can split your filter into multiple stages, but that will usually only reduce the computational load by running "parts" of the filter at lower rates, and will not solve the precision/bit width problems.

So we're ditching FIRs here, unless you're willing to reduce the requirements on stopband attenuation or transition width.


On to IIRs:

There's an approximate formula for the order necessary to achieve given specifications, and that is [1, (7.18), p. 184]:

$$
N_{IIR}\approx 1.076\log_{10}\left(\frac{2}{\sqrt{\delta_1}\delta_2}\right)
\log_{10}\left(\frac{f_s}{\Delta f}\frac4\pi\sin\left(2\pi\frac{f_1}{f_s}\right)\right)\text,
$$
$f_1=f_2-\Delta f$ and $f_2$ being your pass- and stopband edge. Since you're lowest output rate is 0.5 Hz, I'm guessing $f_2=0.5\,\text{Hz}$.

Applying the same specifications as above,
$$\begin{align*}
N_{IIR}&amp;\approx 1.076\log_{10}\left(\frac{2}{\sqrt{10^{-9}}10^{-9}}\right)
\log_{10}\left(\frac{6.4\cdot10^4}{10^-3}\frac4\pi\sin\left(2\pi\frac{4.99\cdot10^{-1}}{6.4\cdot 10^{4}}\right)\right)\\
&amp;\approx 1.076\log_{10}\left(2\cdot10^{13.5}\right)
\log_{10}\left(\frac{2.56\cdot10^8}\pi\cdot4.899\cdot10^{-5}\right)\\
&amp;\approx 1.076\log_{10}\left(2\cdot10^{13.5}\right)
\log_{10}\left(3992\right)\\
&amp;\approx 1.076\cdot13.80\cdot3.601\\
&amp;\approx 53.48
\end{align*}
$$

Note that this is but an estimate! Also notice that this is far from a design approach, it just gives us an idea of how complicated the IIR will end up being.

Now, realistically, let's round up to $N_{IIR}=64$, and then do the classical approach of directly calculation: 

We'll consider elliptic filters, which are optimal in the sense that for any given order, and accepting a certain ripple (which is your DC estimate accuracy, here), they have the most narrow transition width [1, p. 183].

Now, designing these taps isn't a trivial feat; scipy gives me something that roughly looks like that:



Notice the enormous dynamic range of each tap set ($\approx 10^{18}$), and of the overall filter ($\approx10^{65}$). That filter is nothing that reaches its accuracy on a double precision IEEE754 float. 

I propose a hybrid approach: take cascaded halfband filters to get down to something like $\frac1{512}$ of the input sample rate, and then apply an elliptic filter.

1 Bellanger, Maurice: Digital Processing of Signals: theory and practice, second edition, Wiley 1988.